computers and information technologies in agricultural

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Alternative Farming Systems Information Center of the National Agricultural LibraryAgricultural Research Service, U.S. Department of Agriculture

ISSN:1052-5378

Computers and Information Technologies inAgricultural Production and Management. Part I.

June 1991-December 1993

Quick Bibliography Series no. QB 97-09Updates QB 90-83 and QB 91-146

550 Citations in English from the AGRICOLA DatabaseSeptember 1997

Compiled By:Karl R. SchneiderReference and User Services BranchNational Agricultural Library, Agricultural Research Service, U. S. Department of AgricultureBeltsville, Maryland 20705-2351

Compiled For:The Alternative Farming Systems Information Center (http://afsic.nal.usda.gov/), Information Centers BranchNational Agricultural Library10301 Baltimore Ave., Room 132Beltsville, Maryland 20705-2351

Go to:About the Quick Bibliography SeriesPart II, QB 97-10How do I search AGRICOLA (http://agricola.nal.usda.gov) to update a Quick Bibliography? Use the searchstrategy and terms located below, plus the extensive AGRICOLA Help site to locate recent literature on yoursubject of interest. Request Library Materials, https://www.nal.usda.gov/nal-services/request-library-materialsNational Agricultural Library Cataloging RecordCompiler's NotesAbout the Alternative Farming Systems Information CenterSearch StrategyAuthor IndexSubject IndexCitation no.: 1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210,

http://pubs.nal.usda.gov/

https://www.nal.usda.gov/afsic/AFSIC_pubs/qb9710.htm

220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440,450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550

National Agricultural Library Cataloging Record:

Schneider, Karl, 1946-Computers and information technologies in agricultural production and management: Part I.(Quick bibliography series ; 97-09)1. Agriculture--Computer programs--Bibliography. 2. Agriculture-- Automation-- Bibliography. 3. Agriculture--Data processing-- Bibliography. 4. Precision farming-- Bibliography. 5. Robotics-- Bibliography. 6. Tissueculture--Bibliography. 7. Plant micropropagation--Bibliography. 8. Forest management-- Bibliography. 9. Soilmanagement--Bibliography. 10. Natural resources--Management--Bibliography. 11. Animals--Diseases--Bibliography. 12. Plant diseases--Bibliography. 13. Animal breeding--Bibliography. 14. Plant breeding--Bibliography. 15. Animal genetics--Bibliography. 16. Plant genetics--Bibliography.aZ5071.N3 no.97-09

Compiler's Notes

This bibliography expands and updates earlier Quick Bibliography (QB) titles. Please see QB 91-146 and QB90-83 for related earlier records from AGRICOLA. A complex strategy was used, and is included here for yourreference. Assistance from Kate Hayes, of NAL's Technology Transfer Information Center is gratefullyacknowledged.

A great number of subject records were retrieved in searches for this update, because of the long time-spancovered. To accommodate print document size limits, the 1997 update has two Parts with the same title. Part I,QB 97-09, contains records for items added to AGRICOLA from June 1991 through December 1993. Part II,QB 97-10, includes AGRICOLA records added from January 1994 through June 1997.

The extensive search utilized to locate all relevant technology applications records retrieved many items notsuitable for this publication. Several hundred inappropriate records were removed to leave only those focused onpractical use of the various technologies in production related areas. Broad classes of items omitted includerecords treating laboratory applications of sensors and other information technologies, broad scale water-resource management, food products and forest products industries' technology applications, biotechnology andbiochemistry reports, and documents produced by the "Conservation Technology Information Center," coveringBMP's (Best Management Practices) not directly employing specific information technology resources.

Included publications cover subjects ranging from precision farming to robotics to automated tissue culture andmicro- propagation operations. Plant and animal disease management, forest, soil and natural resourcesmanagement (including controlled burning and forest fires) are among subjects covered by records cited here.Various types of sensors, ranging from ion-selective electrodes to ultrasound to various satellite based systemsare used in works listed. Several items treating computer use in plant and animal breeding and applied geneticsand embryo transfer are included. The tendency to err toward inclusion of many documents describing researchapplications of production related technologies is admitted. The author was hoping to provide awareness for thereader of options and possibilities at hand. Computerized training systems in production and management arealso present in this list, to show the availability of such management training tools. The included Search Strategygives the details of terms and concepts utilized in the original search.

Your comments and suggestions are welcome, to aid in improving and refining any updates or supplements tothis publication. Send comments to me, Karl Schneider. Mail to: Reference Section, Room 100, NAL-ARS-USDA, 10301 Baltimore Avenue, Beltsville, MD 20705. Electronic mail may be addressed to:[email protected].



mailto:[email protected]

Thank you for your time and interest!

Go to: Author Index | Subject Index | Top of DocumentCitation no.: 1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210,220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440,450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550

Alternative Farming Systems Information Center (AFSIC)

This publication was compiled for the Alternative Farming Systems Information Center. AFSIC is one of severalInformation Centers at the National Agricultural Library (NAL) that provide in-depth coverage of specificsubject areas relating to the food and agricultural sciences. AFSIC focuses on alternative farming systems, e.g.,sustainable, low-input, regenerative, biodynamic, organic, that maintain agricultural productivity andprofitability, while protecting natural resources. Support for AFSIC comes to NAL from the U.S. Department ofAgriculture's (USDA) Sustainable Agriculture Research and Education (SARE) program, which is under thejurisdiction of the Cooperative State Research, Education, and Extension Service (CSREES).

This publication is available in hardcopy, or electronically on computer diskette, or via AFSIC's Internet WebSite: http://afsic.nal.usda.gov. Please send comments and corrections regarding this publication to the author.Send requests for additional copies to:

Alternative Farming Systems Information CenterJane Potter Gates, CoordinatorNational Agricultural Library, ARS, USDA10301 Baltimore Ave., Room 304Beltsville, MD 20705-2351telephone: 301-504-6559; fax: 301-504-6409WWW: http://afsic.nal.usda.gov


Search Strategy

Set Description

1 COMPUT? or MICROCOMPUTER? or SOFTWARE

2 INFORMATION near1 TECHNOLOG*

3 (EXPERT near1 SYSTEM*) or (ARTIFICIAL near1 INTELLIGENCE) or AI and #1

4 ROBOT or ROBOTS or ROBOTIC or ROBOTICS

5 SENSOR? not SENSORY or ((STEER* or GUIDANCE) near2 (MECHANISM? or CONTROL* orAUTOMAT*)) or (GIS or GPS) and #1

6 THERMAL INFRARED or TIR or THERMOGRAPHY

http://afsic.nal.usda.gov/

http://afsic.nal.usda.gov/

7 MTADS

8 ULTRASONIC or ULTRASOUND

9 ACOUSTIC near3 RESONATOR?

10 CAPACIFLECT*

11 TOWED near1 ARRA*

12 ELECTROMAGNETIC near1 INDUC*

13 ION near1 SELECTIVE near1 ELECTRODE?

14 THERMAL near1 (IMAG* or MASS)

15 ((SITE near1 SPECIFIC) or PRECISION) near1 (FARMING or AGRICULTURE)

16 (YIELD? near1 MAP*) or (VARIABLE near1 RATE?)

17 (LASER? or INFRARED or (COMPUTER near1 VISION) or SONIC or MICROWAVE? or OPTICAL) not(OPTICAL near1 DIS*)

18 PRODUCTION or PRODUCER? or PRODUCING or PRODUCTIVITY or YIELD? or (F1* in CC) or(L1* in CC) or (K1* in CC)

19 (MANAG* or (DECISION near1 SUPPORT)) in TI,DE,ID,CC

20 FARM? or RANCH or RANCHES or HERD? or FLOCK? or SOIL? or RANGE or PASTURE? or GRAZ*or CROP? or GREENHOUSE? or PEST? or DISEASE? or FOREST? or TIMBER

21 #1 or #2 or #3 or #4 or #5 or #6 or #7 or #8 or #9 or #10 or #11 or #12 or #13 or #14

22 la=english

23 #18 or #19

24 #23 and #21

25 (#24 or #17) and #23

26 #25 or #15 or (#16 and #23)

27 #26 and #22

28 ud >9106

29 #29 not (t* in cc)

Computers and Information Technologies inAgricultural Production and Management, Part I.

1.NAL Call No.: QH541.5.F6F67Accelerating development of habitat attributes: planning to programatic.Nelson, D. A. Proc-For-Veg-Manage-Conf p.24-32. (1992)Meeting held on January 14-16, 1992, Eureka, California.Descriptors: forest-plantations; forest-management; undergrowth; conifers; understory; herbicides; habitats;wildlife; endangered-species; wildlife- conservation; thinning; computer-simulation; simulation-models;computer-software

2.NAL Call No.: S494.5.D3C652Accurate binary representation of singulated geranium-cutting images.Wallace, L.; Simonton, W. Comput-Electron-Agric v.6(4): p.319-332. (1992 Jan.)Includes references.Descriptors: geranium; image-processors; mechanical-harvesting; robots; threshold-models

3.NAL Call No.: 49-J82Adjusting weight for body condition score in Angus cows.Northcutt, S. L.; Wilson, D. E.; Willham, R. L. J-Anim-Sci v.70(5): p.1342-1345. (1992 May)Includes references.Descriptors: beef-cows; body-weight; body-condition; height; age

Abstract: Weight, height, and body condition score data supplied by the American Angus Association were usedto determine the effect of body condition score on cow weight and to compute condition score adjustmentfactors. Single records on 11,301 cows for weight and 7,769 cows for height were collected at or near weaning,at which time a subjective condition score (9- point scale) was taken. Limited information on extreme scores 1and 9 allowed only scores 2 through 8 to be included in the analysis. Cows were grouped into age classescorresponding to 2, 3, 4, 5, 6, 7 to 10, and 11+ yr of age. The mathematical model for a weight record includedeffects of fixed herd, year- month the record was collected, cow age, body condition score, and a randomresidual error term. The model for height excluded the condition score effect. Effects of herd, year-month, andcow age were highly significant (P < .0001) for weight and height. Body condition score was a significantsource of variation in weight (P < .0001) and accounted for 16% of the total variation. Adjustment factors forweight (kilograms) by condition score were +116(score 2), +91(score 3), +69(score 4), +39(score 5), 0(score 6),-40(score 7), and -86(score 8).

4.NAL Call No.: S494.5.D3I5-1990Agricultral integrated management systems AIMS: building better tools for making farm decisions.Brook, R. C.; Fick, R. J.; Harmon, R. J.; Harsh, S. B. Proceedings of the 3rd International Conference onComputers in Agricultural Extension Programs / Fedro S. Zazueta, editor. ; January 31- February 1, 1990,Grosvenor Resort Hotel, Disney World Village, Lake Buenavista, FL. Gainesville, FL : Florida CooperativeExtension Service, University of Florida, [1990]. p. 167- 172.Descriptors: agriculture; decision-making; farm-management; computer-software

5.NAL Call No.: S494.5.D3I5-1990The Agricultural Integrated Management Software (AIMS) crop record database.Harmon, R. J.; Harsh, S. B. Proceedings of the 3rd International Conference on Computers in AgriculturalExtension Programs / Fedro S. Zazueta, editor. ; January 31- February 1, 1990, Grosvenor Resort Hotel, DisneyWorld Village, Lake Buenavista, FL. Gainesville, FL : Florida Cooperative Extension Service, University ofFlorida, [1990]. p. 383-388.Includes references.Descriptors: farm-management; crops; record-keeping; computer-software

6.NAL Call No.: FU S49.S7-226Agricultural needs for computers : available software.Strain, J. R. Gainesville, Fla. : Food and Resource Economics Dept., Institute of Food and Agricultural Sciences,University of Florida, [1982] i, 7 leaves, "December 1982." December 1, 1982"--P. 1.Descriptors: Agriculture-Data-processing; Farm-management-Data- processing

7.NAL Call No.: QA76.76.E95A5Agriculture software support and maintenance: the current problem.Berry, J. AI-Appl v.7(2/3): p.41. (1993)Paper presented at a Symposium of the 1992 Annual Meeting of the Entomological Society of America,December 8, 1992, Baltimore, Maryland.Descriptors: insect-pests; crop-production; insect-pests; computer- software; computer-programming; usa

8.NAL Call No.: Z672.I53Agro informatics and decision support systems in France.Waksman, G. Quar-Bull-Int-Assoc-Agric-Inf-Spec v.37(1/2): p.112-119. (1992)IAALD Symposium on "Advances in Information Technology," September 16-20, 1991, Beltsville, Maryland.Descriptors: expert-systems; decision-making; agriculture; france

9.NAL Call No.: HD1421.A47-1991AGROSTAT-PC. [Version 1.1]. AGROSTAT PC.Food and Agriculture Organization of the United Nations. Rome : Food and Agriculture Organization of theUnited Nations, 1991- computer disksTitle from disk label. v. 1. User manual -- v. 2. Population -- v. 3. Land use -- v. 4. Production -- v. 5. Trade -- v.6. Food balance sheets -- v. 7. Forest products.Descriptors: Agriculture-Statistics-Software

10.NAL Call No.: S612.2.N38-1990AGWATER--irrigation management and planning expert system.Hawkins, T.; Burt, C. M. Visions of the future proceedings of the Third National Irrigation Symposium held inconjunction with the 11th Annual International Irrigation Exposition, October 28-November 1, 1990, PhoenixCivic Plaza, Phoenix, Arizona. St. Joseph, Mich. : American Society of Agricultural Engineers, c1990.. p. 64-68.Descriptors: irrigation; computer-software; water-use-efficiency; california

11.NAL Call No.: 290.9-AM32PAIMS: agricultural integrated management software electronic data collection for making farm decisions.Brook, R. C.; Fick, R. J.; Fehr, B.; Harsh, S. B. PAP-AMER-SOC-AGRIC-ENG. St. Joseph, Mich. : The Society.Summer 1990. (90-3022) 9 p. ill.Paper presented at the 1990 International Summer Meeting sponsored by the American Society of AgriculturalEngineers, June 24-27, Columbus, Ohio.Descriptors: dairy-farming; data-collection; electronics; farm- management

12.NAL Call No.: 60.18-J82Airborne laser measurements of rangeland canopy cover and distribution.Ritchie, J. C.; Everitt, J. H.; Escobar, D. E.; Jackson, T. J.; Davis, M. R. J-Range-Manage v.45(2): p.189-193.(1992 Mar.)Includes references.

Descriptors: rangelands; vegetation; measurement; lasers; remote- sensing; shrubs; spatial-distribution;evapotranspiration; texas

Abstract: Studies were made at 2 rangeland areas in south Texas to measure canopy cover and distribution withan airborne laser profiler. In a comparison of laser and ground measurements of canopy cover on the sameeighteen 30.5-m segments at the Yturria area, laser measurements of canopy cover ranged from 1 to 89% andwere correlated significantly (r2 = 0.89) with ground measurements (1 to 88%) on the same eighteen 30.5- msegments. Comparisons of laser measurements of canopy cover for 500- and 940-m segments with an average ofthree 30.5-m ground measurements of canopy cover made within these segments were also significantlycorrelated (r2 = 0.95). Topography, vegetation height,and spatial distribution of canopy cover for 6- to 7-kmflightlines were also measured with the laser profiler. Airborne laser measurements of land surface features canprovide quick and accurate measurements of canopy cover and distribution for large areas of rangeland.Accurate and timely data on the amount and distribution of plant cover are valuable for understanding vegetationcharacteristics, improving estimates of infiltration, erosion, and evapotranspiration for rangeland areas, andmaking decisions for managing rangeland vegetation.

13.NAL Call No.: 290.9-AM32TAlgorithms for microcomputer control of the environment of a production broiler house.Allison, J. M.; White, J. M.; Worley, J. W.; Kay, F. W. Trans-A-S-A-E v.34(1): p.313-320. (1991 Jan.-1991 Feb.)Includes references.Descriptors: poultry-housing; environmental-control; algorithms; computer-software; mathematical-models;relative-humidity; temperature

Abstract: A microcomputer-based system to control the environment of a commercial broiler house wasdeveloped and tested. The system was installed in an existing, totally enclosed, commercial production broilerhouse. Environmental control was provided by a microcomputer for the 51 day grow- out period. This articledescribes the algorithms used to control the environment. The environmental conditions produced are comparedto those in an adjacent house with conventional controls.

14.NAL Call No.: S494.5.D3I5-1988Alternative management options (AMO) software.Gibson, J. M.; Hackett, E. I.; Burkhardt, J. W.; Champney, W. O.; Garrett, J. R. Proceedings of the 2ndInternational Conference on Computers in Agricultural Extension Programs Fedro S Zazueta, AB Del Bottcher,eds p.83-88. (of Florida, [1988?].)Conference held February 10-11, 1988 at the Grosvenor Resort Hotel, Disney World Village, Lake Buenavista,Orlando, Florida.Descriptors: animal-husbandry; planning; production-costs; computer- techniques

15.NAL Call No.: Q184.R4Analysis of the POLDER (POLarization and Directionality of Earth's Reflectances) airborne instrumentobservations over land surfaces.Deuze, J. L.; Breon, F. M.; Deschamps, P. Y.; Devaux, C.; Herman, M.; Podaire, A.; Roujean, J. L. Remote-Sensing-Environ v.45(2): p.137-154. (1993 Aug.)Includes references.Descriptors: field-crops; orchards; land; surfaces; reflectance; optical-instruments; measurement; aircraft; france

16.NAL Call No.: 290.9-AM32TAnalyzing conjuctive use reservoir performance for soybean irrigation. II. Model application.Edwards, D. R.; Fryar, E. O.; Ferguson, J. A. Trans-A-S-A-E v.35(1): p.137-142. (1992 Jan.-1992 Feb.)Includes references.

Descriptors: glycine-max; cost-benefit-analysis; groundwater; irrigation; reservoirs; simulation-models; water-management; arkansas

Abstract: A previous article described the Arkansas Offstream Reservoir Analysis (ARORA) model, whichsimulates operational characteristics of farm reservoirs used with groundwater to provide irrigation to soybean.Since the model is also structured to compute costs and incomes for specified situations, it may be used as a toolin comparing the relative benefits of various reservoir capacities and thus in identifying the best capacity for aparticular scenario. This article describes how ARORA was applied to assess performance of reservoirs insituations with differing groundwater status, soil, and economic parameters. An analysis of the results shows thateach of these variables can influence the performance of reservoirs as assessed on the basis of present worth ofnet incomes. The results also indicate that based on thesame criterion, reservoirs are currently feasible undercertain conditions, particularly when groundwater availability is limited.

17.NAL Call No.: 290.9-AM32TAnalyzing conjunctive use reservoir performance for soybean irrigation. I. Development of a simulationmodel.Edwards, D. R.; Ferguson, J. A.; Fryar, E. O. Trans-A-S-A-E v.35(1): p.129-135. (1992 Jan.-1992 Feb.)Includes references.Descriptors: glycine-max; groundwater; irrigation; reservoirs; simulation-models; water-management;computer-software; arkansas; fortran

Abstract: A mathematical model was developed to simulate the operational characteristics of farm reservoirsused with groundwater for irrigation of soybean in Arkansas. The model, referred to as the Arkansas OffstreamReservoir Analysis (ARORA) model, simulates reservoir and soil water balances, aquifer response to pumping,and soybean yield. Computations of incomes and expenses are performed to enable objective assessment ofreservoir performance under various circumstances. The model also contains an optimization subroutine whichcan help the user identify the reservoir capacity which is best, on the basis of present worth of simulated netincomes, under the specified conditions.

18.NAL Call No.: 99.9-F7662JAnalyzing timber harvesting systems using STALs-3.Koger, J. For-Prod-J v.42(4): p.25-30. (1992 Apr.)Includes references.Descriptors: timbers; harvesting; computer-software; computer- simulation; skidding,-trucking,-and-landing-simulation-stals

Abstract: STALS-3 is a simplified computerized timber harvesting program that utilizes production by function,queuing theory, and simulation techniques to analyze skidding, loading, and trucking interactions at a "hot"landing. Production by function estimates hourly production rates, unit costs of production, and equipmenthourly cost ratios for skidding, loading, and trucking. Queuing theory is used to determine the probability of atruck being available for loading, hourly production, and the optimum number of trucks. The simulation portionof the model determines equipment delays and harvesting costs. The program is written in MicrosoftQuickBASIC and is designed to run on personal computers (IBM and compatible).

19.NAL Call No.: 410.9-P94Anesthetic requirement of isoflurane is reduced in spontaneously hypertensive and Wistar-Kyoto rats.Cole, D. J.; Marcantonio, S.; Drummond, J. C. Lab-Anim-Sci v.40(5): p.506-509. (1990 Sept.)Includes references.Descriptors: rats; anesthetics; anesthesia; hypertension

Abstract: The isoflurane requirement to keep 50% of rats (Rattus norvegicus) unresponsive to noxious stimuli(MAC) was determined in age matched Sprague-Dawley (SD, n = 8), Spontaneously Hypertensive (SHR, n = 8)

and Wistar- Kyoto (WKY, n = 8) strains. Following induction and orotracheal intubation, each rat receivedisoflurane (1.65% end-tidal) for 120 minutes. Physiologic parameters were similar except for expecteddifferences in mean arterial pressure (148 +/- 13mmHg-SHR group, 101 +/- 10mmHg-SD group and 94 +/-12mmHg- WKY group [mean +/- standard deviation]). Anesthetic equilibration was verified by infrared analysisof end-tidal gases. MAC was then determined in each rat by the tail clamp method and a group MAC calculated.

20.NAL Call No.: 280.8-J822An animated instructional module for teaching production economics with 3-D graphics.Debertin, D. L. Am-J-Agric-Econ v.75(2): p.485-491. (1993 May)Includes references.Descriptors: production-economics; microcomputers; teaching-methods; graphic-arts; computer-software

Abstract: An animated instructional module is described for illustrating key production economics concepts. Themodule uses three- dimensional production surface, and two-dimensional contour maps. Two graphics programsare used together to construct diagrams that two dimensional neither program could produce alone. Modulesequences are based on the "classical" two- factor, one-output model, using a production function consistent withtextbook diagrams. Although primarily for upper-division undergraduate or beginning graduate productioneconomics courses, the instructional module provides a useful instructional supplement for advanced students. Afree disk copy of the module for use on a personal computer is available from the author.

Go to: Author Index | Subject Index | Top of Document

21.NAL Call No.: GE5.A66-1993Application of advanced information technologies : effective management of natural resources :proceedings of the 18-19 June 1993 Conference, Spokane, Washington.Heatwole, C. D.; American Society of Agricultural Engineers. Information and Technologies Division. St.Joseph, Mich. : American Society of Agricultural Engineers, c1993. xi, 500 p. : ill., maps, Includesbibliographical references.Descriptors: Natural-resources-Management-Congresses; Information- technology-Congresses

22.NAL Call No.: 280.8-J822Application of computer graphics to undergraduate instruction in agricultural economics.Debertin, D. L.; Jones, L. D. Am-J-Agric-Econ v.73(1): p.25-35. ill. (1991 Feb.)Includes references.Descriptors: agricultural-economics; college-curriculum; computer- assisted-instruction; computer-graphics;microcomputers; teaching-methods; innovation-adoption; evaluation; university-research; kentucky; university-of- kentucky

Abstract: This article outlines are experience in building a freshman- level course in agricultural economicsemploying computer graphics imaging. Lecture material is displayed with a computer connected to a large-screen projector producing high-resolution graphics. The complete course consists of approximately 1,200computer-generated text, chart, or graphics images. An evaluation of the new method was conducted. Resultsindicate that most students prefer lectures that employ computer graphics to those that use a chalkboard or anoverhead projector. Evidence supports the hypothesis that students perform better on exams when theinnovations described in this paper are adopted.

23.NAL Call No.: 80-AC82Application of computervision systems in horticulture.Hack, G. R. Acta-Hortic (304): p.49-54. (1992 Mar.)

Paper presented at the "First International Workshop on Sensors in Horticulture", January 29-31, 1991,Noordwijkerhout, The Netherlands.Descriptors: crop-production; computer-techniques; computer-hardware; computer-software

24.NAL Call No.: 292.9-AM34Application of crop yield functions in reservoir operation.Dariane, A. B.; Hughes, T. C. Water-Resour-Bull v.27(4): p.649-656. (1991 July-1991 Aug.)Includes references.Descriptors: irrigation-water; water-reservoirs; crop-yield; evapotranspiration; water-management;mathematical-models; utah; irrigation- reservoirs

Abstract: A model is developed for real-time operation of an irrigation reservoir with the objective ofmaximizing the value of multiple crop yields during a growing season. The model employs monthly additive andproduct forms or crop yield functions for dry matter and grain crops, respectively. The resulting nonlinearoptimization model uses a log transform to reduce nonlinearities in the model. An application of the proposedmodel is compared to a common operating rule used in simulation models. The proposed model results werebetter in terms of net benefits from crop yields. The model uses GAMS (General Algebraic Modeling System)language. It requires an IBM-compatible microcomputer and is suitable for use by a reservoir manager.

25.NAL Call No.: S494.5.D3I5-1988Application of hand-held microcomputer for work studies in milking parlours.Ordolff, D. W. Proceedings of the 2nd International Conference on Computers in Agricultural ExtensionPrograms Fedro S Zazueta, AB Del Bottcher, eds p.133-138. (of Florida, [1988?].)Conference held February 10-11, 1988 at the Grosvenor Resort Hotel, Disney World Village, Lake Buenavista,Orlando, Florida.Descriptors: microcomputers; data-collection; milking-parlors

26.NAL Call No.: SD143.S64Application of RM-FORPLAN in a corporate environment.Morrow, R.; Kuhn, J. Proc-Soc-Am-For-Natl-Conv p.350-355. (1991)Meeting held Aug 4-7, 1991, San Francisco, California.Descriptors: forest-management; simulation-models; companies; computers; computer-software; planning; data-collection; usa

27.NAL Call No.: HD1.A3The application of SIMOPT2: RICE to evaluate profit and yield-risk in upland-rice production.Alcoilja, E. C.; Ritchie, J. T. Agric-Syst v.33(4): p.315-326. (1990)Includes references.Descriptors: upland-rice; crop-production; crop-yield; risk; profits; computer-software; flow-charts; farmers'-attitudes; innovation-adoption; optimization- methods; philippines; simulation-dualcriteria-optimization-technique-for-upland-rice-production-computer-software

28.NAL Call No.: 290.9-AM32PApplication of the creams hydrology component for runoff predication in Quebec.Enright, P.; Madramootoo, C. A. PAP-AMER-SOC-AGRIC-ENG. St. Joseph, Mich. : The Society. Winter 1990.(90-2514) 18 p.Paper presented at the "1990 International Winter Meeting," December 18-21, 1990, Chicago, Illinois.Descriptors: runoff; surface-water; hydrology; computer-software; quebec; chemicals,-runoff-and-erosion-from-agricultural-management

29.NAL Call No.: S494.5.D3C68-1992Application of thematic mapping in an agricultural information management program.Meij, H. K.; Lanyon, L. E.; McNall, A. D. Computers in agricultural extension programs proceedings of the 4thinternational conference, 28-31 January 1992, Orlando, Florida / sponspored by the Florida CooperativeExtension Service, University of Florida. St. Joseph, Mich. : American Society of Agricultural Engineers,c1992.. p. 7-12.Includes references.Descriptors: farm-management; thematic-mapper; computer-software; pennsylvania

30.NAL Call No.: 49-J82Application of ultrasound for feeding and finishing animals: a review.Houghton, P. L.; Turlington, L. M. J-Anim-Sci v.70(3): p.930-941. (1992 Mar.)Literature review.Descriptors: pigs; cattle; sheep; ultrasound; ultrasonic-fat-meters; carcass-composition; fat-thickness; accuracy;backfat; longissimus-dorsi; age- differences; literature-reviews

Abstract: The ability to evaluate carcass traits in live animals is of value to research, educational, and industrypersonnel. Ultrasonic technology has been tested since the early 1950s and continues to be under investigation asa means of accomplishing this task. The accuracy of ultrasound in predicting carcass traits is variable and isdependent on species, ultrasonic instrumentation, and(or) the skill of the technician. Based on this review, theranges of correlation coefficients (r) for carcass traits as predicted by ultrasound to the respective carcassmeasurement are as follows: swine (fat .20 to .94; longissimus muscle .27 to .93), sheep (fat .42 to .95;longissimus muscle .36 to .79) and beef (fat .45 to .96; longissimus muscle .20 to .94; marbling .20 to .9 1).Although these correlation coefficients give an indication of the accuracy of ultrasound, it should be noted thatthese statistics do not reflect population variation or bias. Applications of ultrasound in swine finishing programsinclude the successful prediction of market weight carcass characteristics and the prediction of percentage oflean cuts before slaughter. In contrast, the application of ultrasound in lamb finishing programs has met withlimited success. Most data indicate that weight and(or) visual estimations of fat are at least as accurate asultrasound predictions of carcass composition. In beef finishing programs, ultrasound has, at times, been usedsuccessfully to predict fat and muscle traits before slaughter and beef carcass chemical composition. The abilityto predict marbling, however, remains unclear and requires further investigation. Ultrasound has also been usedin beef finishing programs to predict days on feed to a constant body compositional end point. Whensummarized, these data indicate that a single ultrasonic measurement of fat can be helpful in predicting days onfeed in yearling cattle. When used alone, however, a single backfat measurement does not provide adequateaccuracy. Therefore, factors such as age, sex, breed type, weight, and hip height are needed to help predict dayson feed more accurate.

31.NAL Call No.: QA76.76.E95A5Approaches and tools to ease the maintenance of knowledge-based systems.Foster, M. A. AI-Appl v.7(2/3): p.49-53. (1993)Paper presented at a Symposium of the 1992 Annual Meeting of the Entomological Society of America,December 8, 1992, Baltimore, Maryland.Descriptors: insect-pests; crop-production; computer-software; knowledge; systems; machinery; learning;techniques; usa

32.NAL Call No.: S494.5.D3I5-1988AQUADEC: Aquacultural decision support software package and an application.Adams, C. M.; Zimet, D. J. Proceedings of the 2nd International Conference on Computers in AgriculturalExtension Programs Fedro S Zazueta p.560-565. (of Florida, [1988?].)

Meeting held February 10-11, 1988 at Lake Buenavista, Orlando, Florida.Descriptors: computer-software; aquaculture

33.NAL Call No.: S494.5.D3C652An artificial-intelligence-based software for designing crop management plans.Rellier, J. P.; Chedru, S. Comput-Electron-Agric v.6(4): p.273-294. (1992 Jan.)Includes references.Descriptors: winter-wheat; crop-management; computer-software; farm- planning; knowledge; flow-charts

34.NAL Call No.: 421-J822Assessing needs for computer pest management software in Nebraska agriculture.Wright, R. J. J-Econ-Entomol v.85(4): p.1218-1221. (1992 Aug.)Includes references.Descriptors: insect-control; pest-management; plant-pests; surveys; computer-software; nebraska

Abstract: A mail survey was conducted to assess current computer hardware use and perceived needs ofpotential users for software related to crop pest management in Nebraska. Surveys were sent to University ofNebraska-Lincoln agricultural extension agents, agribusiness personnel (including independent cropconsultants), and crop producers identified by extension agents as computer users. There were no differencesbetween the groups in several aspects of computer hardware use (percentage computer use, percentage IBM-compatible computer, amount of RAM memory, percentage with hard drive, hard drive size, or monitor graphicscapability). Responses were similar among the three groups in several areas that are important to crop pestmanagement (pest identification, pest biology, treatment decision making, control options, and pesticideselection), and a majority of each group expressed the need for additional sources of such information aboutinsects, diseases, and weeds. However, agents mentioned vertebrate pest management information as a needmore often than the other two groups. Also, majorities of each group expressed an interest in using computersoftware, if available, to obtain information in these areas. Appropriate software to address these needs shouldfind an audience among all three groups.

35.NAL Call No.: S481.R4Assessing pest impact on crop yields at the micro and macro- levels.Teng, P. S. Res-Ext-Ser-Coll-Trop-Agric-Hum-Resour-Univ-Hawaii-Coop-Ext- Serv (134): p.87-90. (1991 Dec.)Proceedings of the 1989 ADAP Crop Protection Conference, held May 18-19, 1989, Honolulu, Hawaii.Descriptors: pest-management; economic-impact; statistical-analysis; computer-software; crop-yield; crop-losses; hawaii

36.NAL Call No.: S494.5.D3C652An autocalibrating model for simulating and measuring net canopy photosynthesis using a standardgreenhouse climate computer.Ehler, N. Comput-Electron-Agric v.6(1): p.1-20. (1991 July)Includes references.Descriptors: dendranthema; computer-analysis; canopy; photosynthesis; carbon-dioxide; measurement;algorithms; mathematical-models; calibration; calculation; environmental-control; microclimate; computers;greenhouses; simulation-models; climatic-factors; computer-software; mass-balance-models; dendranthema-grandiflora

37.NAL Call No.: HD62.5.A98-1991Automate your business plan. Version 4.0. Anatomy of a business plan.Koelsch, J.; Pinson, L.; Jinnett, J.; Analytical Software Partners. Tustin, CA : Analytical Software Partners : Outof Your Mind... and into the Marketplace, c1991. 2 computer disks user's manual.

Title from disk label.Descriptors: New-business-enterprises-Planning-Software; Small- business-Planning-Software; Home-based-businesses-Planning-Software

Abstract: Productivity software for small business.

38.NAL Call No.: 290.9-P972Automated construction data management system.McCullouch, B. Eng-Ext-Ser-Purdue-Univ (162): p.54-57. (1991)Proceedings of the 77th Annual Road School, March 12-14, 1991, West Lafayette, Indiana.Descriptors: road-construction; computer-software; computer-hardware; computer-analysis; management; cost-benefit-analysis; research-projects

39.NAL Call No.: 80-AC82Automated inspection of plants.Hirvonen, J.; Hamalainen, J.; Murmann, K. Acta-Hortic (304): p.137-142. (1992 Mar.)Paper presented at the "First International Workshop on Sensors in Horticulture", January 29-31, 1991,Noordwijkerhout, The Netherlands.Descriptors: crop-production; greenhouse-culture; automatic-control; computer-techniques; computer-hardware;computer-vision

40.NAL Call No.: SB121.I57-1992Automated micropropagation and the application of a laser beam for cutting.Holdgate, D. P.; Zandvoort, E. A. Transplant production systems proceedings of the International Symposium onTransplant Production Systems, Yokokama, Japan, 21-26 July 1992 / edited by K Kurata and T Kozai. Dordrecht: Kluwer Academic Publishers, 1992.. p. 297-311.Includes references.Descriptors: micropropagation; automation; computer-techniques; robots


41.NAL Call No.: S671.A33An automated wool harvesting system.McInnes, M. B. Agric-Eng-Aust v.19(2): p.21-25. (1990)Descriptors: sheep; shearing; automation; robots; australia

42.NAL Call No.: 290.9-AM32TAutomatic geranium stock processing in a robotic workcell.Simonton, W. Trans-A-S-A-E v.33(6): p.2075-2080. ill. (1990 Nov.-1990 Dec.)Includes references.Descriptors: geranium; cuttings; computer-techniques; mechanization; propagation; robots

Abstract: A robotic workcell has been developed for processing geranium cuttings used for vegetativepropagation. Six unit operations including retrieving the cuttings from a conveyor, trimming to size, removingselect leaves,grading, and inserting the finished product into a plug tray cell were incorporated into the workcellconfiguration. Machine vision was used to characterize the branching structure of each individual cutting anddetermine appropriate processing and grading strategies. Fixtures placed in the workcell assisted in theprocessing while the robotic arm handled the cutting and yielded an average cycle time of 6.5 s. Evaluation of

the workcell on two varieties demonstrated good overall performance and results corresponded favorably tocuttings manually processed.

43.NAL Call No.: 290.9-AM32TAutomatic plant feature identification on geranium cuttings using machine vision.Simonton, W.; Pease, J. Trans-A-S-A-E v.33(6): p.2067-2073. ill. (1990 Nov.-1990 Dec.)Includes references.Descriptors: geranium; cuttings; grading; identification; mechanization; propagation; robots

Abstract: A machine vision technique was developed which analyzes a two-dimensional binary image of asingulated geranium cutting and identifies the branching stem structure, including main stem and petioles. Theanalysis technique was based on creation of a directed graph data structure which contains information requiredto rapidly perform plant part identification. Size, shape, and location data were utilized to classify objects asparticular plant features. Evaluation of the image analysis technique indicated good characterization of thebinary structure of geranium cuttings in a timely manner as required for use in a robotic workcell. Identificationof the main stem, petioles, growth tip, and geometry of the interconnections of the plant parts was successfullyperformed. Overlapping sections (e.g., petiole crossings) and occlusions (e.g., leaves over stem segments)contributed to identification errors.

44.NAL Call No.: TP248.25.A96T68-1990Automation of plant tissue culture process.Miwa, Y. Automation in biotechnology a collection of contributions presented at the Fourth Toyota Conference,Aichi, Japan, 21-24 October 1990 / edited by Isao Karube. Amsterdam : Elsevier c1991.. p. 217-233.Includes references.Descriptors: plants; tissue-culture; micropropagation; transplanting; seedlings; lilium; bulbs; automation; robots

Abstract: The present authors have developed a position detector and a growth state discriminator for a seedlingduring the previously conducted fundamental research on an automatizing plant tissue culture process.Furthermore, we have developed a micro robot for transplanting young seedling into culture medium. In thisstudy, we will discuss a fully automated lily's bulb tissue culturing system that was developed as a trial ofautomation of biotechnology performed in a flower production. This system was built with integratingsubsystems which are developed to perform automatically in each process of supplying a bulb, cutting its root,separating the bulbscales, transplanting bulbscales one by one, recognizing the shape of each bulbscale, andplanting the bulbscale into culture medium. Individual subsystem was designed to cope with irregularity in sizeand shape of a bulb or bulbscale. At present, neither a virus contamination nor a detrimental effect to a genetictrait by the mechanical stressing due to the system was recognized in a trial test. It is also found that acompletion of the present system was within one minute. It is, therefore, thought that the system can be used in apractical stage. Meanwhile, the culture robot of a miniature capsule enclosed structure for the home and/orpersonal purposes was made for a trial performance, extending our techniques towards a developing theaforementioned system. Furthermore, a protoplast positioning system using a dielectrophoresis effect in mediumchamber having electrodes will be described. This system was designed to be applicable to a cell fusion andgene injection.

45.NAL Call No.: TC401.W27Automation of the design of agricultural water management projects.Feyen, J.; Liu, F. Water-Resour-Manag v.5(2): p.95-119. (1991)Includes references.Descriptors: drainage-systems; irrigation-systems; design; automation; computer-software

46.NAL Call No.: HD1401.C2B.E.A.R. Plus: a computerized farm management and extension tool for financial planning and risk

analysis.Brown, J.; Turvey, C. G.; Lowry, C. Can-Farm-Econ v.23(1): p.35-40. (1991)Includes references.Descriptors: farm-management; computer-software; financial-planning; risk; projections; innovation-adoption;flow-charts; microcomputers; extension; budgeting-enterprises-and-analyzing-risk-plus-financial-statements

47.NAL Call No.: 44.8-J822Bases and experiences of expressing the protein content of milk-- France.Grappin, R. J-Dairy-Sci v.75(11): p.3221-3227. (1992 Nov.)Includes references.Descriptors: milk; milk-protein-percentage; protein-content; nonprotein-nitrogen; milk-protein; milk-payments;analytical-methods; urea; nitrogen- content; accuracy; seasonal-fluctuations; crude-protein; france

Abstract: In the early 1960s, France started to analyze routinely protein for the DHIA program using the amidoblack method, and, since 1969, milk producers have been paid on the basis of fat and protein. Progressively,infrared analyzers have replaced the dye binding method. Because of the rather large variability of NPN contentand its role on the accuracy of both amido black and infrared methods, analysis in 1974 was changed from CP totrue protein for both economical (NPN has little value) and analytical reasons (better accuracy and centralizedcalibration). Examples are given to illustrate the seasonal and between-herd variability of the proportion of NPNin total N for which urea is the most important and variable NPN compound. Since 1976, the fat:protein priceratio of additional grams to the basic price changed from 74:26 to 34:66. Several studies have shown that thecalibration of infrared instruments in true protein instead of CP provides better accuracy in protein testing with ahigh correlation (r = -.80) between errors and the percentage of NPN in total N. However, a recent study hasshown than urea interferes significantly with the infrared signal. Because protein now has a much higher valuethan fat, a better definition of protein is extremely important for the dairy industry. After 15 yr of experience,both dairy industry and farmers are quite satisfied, however, using a different reference, yet the problem of thecomparison of protein results between France and the other countries remains, especially for breeding programs.

48.NAL Call No.: SF601.B6Beef cattle economics decision-aid software.McGrann, J. M.; Rupp, G. P. Agri-Practice v.13(9): p.15-19, 22. (1992 Oct.)Includes references.Descriptors: beef-cattle; decision-making; computer-software; farm- management; beef-production; farm-budgeting; marketing; texas

49.NAL Call No.: S494.5.D3I5-1988BEEFpro: an integrated Cow/Calf program for microcomputers.Simms, D. D. Proceedings of the 2nd International Conference on Computers in Agricultural ExtensionPrograms Fedro S Zazueta, AB Del Bottcher, eds p.95- 100. (of Florida, [1988?].)Conference held February 10-11, 1988 at the Grosvenor Resort Hotel, Disney World Village, Lake Buenavista,Orlando, Florida.Descriptors: animal-husbandry; computer-software; computer-techniques; kansas

50.NAL Call No.: 49-J82Bioeconomic evaluation of embryo transfer in beef production systems. I. Description of a biologicalmodel for steer production.Ruvuna, F.; Taylor, J. F.; Walter, J. P.; Turner, J. W.; Thallman, R. M. J- Anim-Sci v.70(4): p.1077-1083. (1992Apr.)Includes references.Descriptors: beef-cattle; embryo-transfer; steers; genotypes; equations; growth-rate; dystocia; computer-software; simulation-models

Abstract: Concepts used to derive a deterministic model for evaluating embryo transfer for commercial steerproduction taking into consideration genetic merit for growth and mature size, herd feed supply, and recipientmaternal environment are discussed. Genetic potential of an embryo is used to derive optimal growth rates thatcan be sustained by available herd feed per animal per day. Equations are provided for various measures ofperformance as functions of the feed, genotype of the embryo, and recipient maternal contribution. To assess thevalue of a particular line of embryos, interactions between genotype and nutrient environment are quantified, sothat the benefits of embryos of high genetic merit are evaluated objectively. Product quality, and weight arepredicted from the model to provide a framework that will allow commercial beef producers to determinemarketing strategies likely to result in optimal return.

51.NAL Call No.: 4-AM34PBotanical composition of tropical grass-legume pastures estimated with near-infrared reflectancespectroscopy.Pitman, W. D.; Piacitelli, C. K.; Aiken, G. E.; Barton, F. E. I. Agron- J v.83(1): p.103-107. (1991 Jan.-1991 Feb.)Includes references.Descriptors: paspalum-notatum; aeschynomene-americana; macroptilium- lathyroides; mixed-pastures;botanical-composition; measurement; sampling; infrared-spectroscopy; equations; estimation; computer-software; cal; best; reg70

Abstract: Quantifying pasture composition requires either laborious or subjective approaches. Evaluations ofnear-infrared reflectance spectroscopy (NIRS) to determine botanical composition of mixed pasture swards haveshown potential. In this study, characterization of botanical composition of pastures comprised primarily ofbahiagrass (Paspalum notatum Flugge), aeschynomene (Aeschymomene americana L.) and phasey bean[Macroptilium lathyriodes (L.) Urb.] by NIRS was evaluated. Three approaches (hand-composited samples,single- component samples, and actual pasture samples) were compared for equation development. Theoreticalpotential of NIRS is illustrated by high coefficients of determination (0.98- 0.99) and low standard errors (1.4-2.9%) of equations for the above species from hand-composited samples. Equations developed from the threeapproaches were evaluated for estimation of the botanical composition of a separate group of pasture samples.Equations developed from hand- composited samples from a single source of each component were notacceptable for estimating composition of pasture samples despite the excellent calibration statistics. Single-component samples approached adequate results only for composite total grass and total legume groups, eventhough the pasture sample composition appeared to be well represented in the calibration sample set. Equationsfrom pasture samples provided useful estimates of sample means, although some individual samples were poorlyestimated. Thus, botanical composition of these pastures may be estimated using equations from actual pasturesamples, and estimates of total grass and total legume may be obtained from use of single-component samples,which provides further labor reductions. A comparison of original software and updated software packagesCAL, BEST, REG70, and partial least squares principal component regression showed none to be consistentlysuperior.

52.NAL Call No.: S79.S73Budget generator user's manual.Cameron, D. M.; Parvin, D. W. Jr. Staff-Pap-Ser-Miss-State-Univ-Dep-Agric- Econ-Miss-Agric-For-Exp-Stn.Mississippi State, Miss. : The Station. [1979?]. (43) 42 p.Descriptors: farm-budgeting; computer-software; farm-management; mississippi

53.NAL Call No.: 1.962-C5T71Calculating filled and empty cells based on number of seeds sown per call: a microcomputer application.Wenny, D. L. Tree-plant-notes. Washington, D.C. : U.S. Department of Agriculture, Forest Service. Spring 1993.v. 44 (2) p. 49-52.Includes references.Descriptors: forest-nurseries; seeds; sowing; computer-software; microcomputers

54.NAL Call No.: 100-Or3M-no.874CALFWNTR. CALFWNTR computer software.Riggs, W. W.; Griffith, D.; Oregon State University. Extension Service. Corvallis, Or. : Oregon State UniversityExtension Service, [1991] 11 p. : ill., "CALFWNTR is a microcompuiter [sic] program designed to helpproducers compare the economics of alternative production and marketing strategies ..."Descriptors: Calves-Computer-programs; Calves-Marketing-Computer- programs

55.NAL Call No.: QP33.J681Calorimetric investigations of the different castes of honey bees, Apis mellifera carnica.Fahrenholz, L.; Lamprecht, I.; Schricker, B. J-Comp-Physiol-B-Biochem-Syst- Environ-Physiol v.162(2): p.119-130. (1992)Includes references.Descriptors: apis-mellifera-carnica; caste; energy-metabolism; heatproduction; age-differences; environmental-temperature; calorimetry

Abstract: Honey bees of different age and castes were investigated calorimetrically at 20, 25 and 30 degrees C.Experiments were completed by endoscopic observation of the insects in the visible and the near infrared rangeand by acoustical monitoring and subsequent frequency analysis of various locomotor activities. Directcalorimetric results of this paper are compared with data of indirect calorimetry from the literature using arespiratory quotient of 1.00 and 21.13 J consumed. Agreements between both methods are generally good. Theresults show that weight-specific heat production rates increase with age of worker bees by a factor of 5.6 at 30degrees C, 3.7 at 25 degrees C and 40.0 at 20 degrees C. In groups of foragers the heat production decreaseswith growing group size to around 6% of the value for an isolated bee. The presence of a fertile queen or ofbrood reduces the heat output of a small worker group significantly. Adult drones exhibit a much highermetabolic rate (up to 19.7-fold at 20 degrees C) than juveniles with strong fluctuations in the power-time curves.Fertile queens show a less pronounced heat production rate than virgin queens (54% at 30 degrees C, 87% at 25degrees C and 77% at 20 degrees C). Calorimetric unrest is much higher for young than for adult queens. Heatproduction is very low in both uncapped and capped brood and less than 30% of that of a newly emergedworker. In most cases temperature showed a significant influence on the metabolic level, although its sign wasnot homogeneous between the castes or even within them. Locomotor activities are easily recorded by theacoustic frequency spectrum (0-7.5 khz) and in good agreement with endoscopic observations and calorimetrictraces.

56.NAL Call No.: Q184.R4Candidate high spectral resolution infrared indices for crop cover.Malthus, T. J.; Andrieu, B.; Danson, F. M.; Jaggard, K. W.; Steven, M. D. Remote-sens-environ v.46(2): p.204-212. (1993 Nov.)Includes references.Descriptors: beta-vulgaris; sugarbeet; crops; canopy; sowing-date; plant-density; beet-yellows-closterovirus;stand-characteristics; color; reflectance; soil; surfaces; spectral-data; radiometers; landsat; thematic- mapper; uk;soil-brightness

57.NAL Call No.: 80-AC82The cash system of on-farm decision tools for horticultural enterprises.Hall, F. R.; Lemon, J. R. Acta-Hortic (276): p.323-346. (1990 July)Paper presented at the "Second International Symposium on Computer Modelling in Fruit Research and OrchardManagement," September 5- 8, 1989, Logan, Utah.Descriptors: farm-management; horticulture; support-systems; computer- software; computer-advisory-service-for-horticulture

Abstract: The Computer Advisory Service for Horticulture (CASH) project was initiated in 1985 at The OhioState University upon receipt of a grant from the Kellogg Foundation. The DSS component of the CASH projectseeks to identify and target generic decision making tasks inherent in farm and pest management which apply tovarious agricultural commodities. Such tasks often can be implemented via sophisticated application of spread-sheet software. The CASH/DSS software attempts to organize and present the decision making process pergeneric task in a manner that facilitates grower implementation and analysis of the end result. Simply stated, theCASH system of decision tools is designed to present an alternative view of how information may be betterutilized at the farm level. Specifically, the initial tools allow an identification of the key factors which influencenet economic return and the extent to which they influence the "bottom line." Initial thrusts are for simple butrobust management tools, "what if" programs, which as grower expertise grows, new ideas and expanded "whatif" programs are incorporated. An important point is that growers already have more information available tothem than they are currently utilizing for crop management decisions. The greatest potential for helping growersimprove decision making appears to be through payoff matrix and decision tree concepts. Growers can be taughtto utilize risk management concepts for decision making in uncertain environments. This will require (1)identification of key factors influencing net return, (2) learning how to manipulate return, and (3) learning howto assess the "objective" probability of key variables of insects and disease and their impact on cash return. Thearray of CASH DSS tools now available provides a beginning for accomplishing these decision tasks. This paperprovides an overview of the DSS tools produced by the CASH project.

58.NAL Call No.: S494.5.D3I5-1988Cashblu: cost and return analysis for blueberries.Mizelle, W. O. Jr.; Westberry, G. O.; Krewer, G. W.; Stanaland, R. D. Proceedings of the 2nd InternationalConference on Computers in Agricultural Extension Programs Fedro S Zazueta, AB Del Bottcher, eds p.384-387. (of Florida, [1988?].)Conference held February 10-11, 1988 at the Grosvenor Resort Hotel, Disney World Village, Lake Buenavista,Orlando, Florida.Descriptors: computer-software; cost-analysis; blueberries; georgia

59.NAL Call No.: SB387.R4F67-1990Changes in infrared use for fire management.Warren, J. R. Protecting natural resources with remote sensing the Third Forest Service Remote SensingApplications Conference held at the University of Arizona and the Doubletree Inn, Tucson, Arizona, April 9-13,1990. Bethesda, Md. : American Society of Photogrammetry and Remote Sensing, c1990.. p. 259- 274.Includes references.Descriptors: fire-detection; fire-control; infrared-photography

60.NAL Call No.: SF207.S68CHAPS summary for South Dakota, 1990.Boggs, D. L. S-D-Beef-Rep-Anim-Range-Sci-Dep-Agric-Exp-Stn-Coop-Ext-Serv-S- D-State-Univ (91-17): p.68-70. (1991 Sept.)Descriptors: beef-cows; herds; performance-appraisals; computer- software; south-dakota; cow-herd-appraisal-of-performance-software


61.NAL Call No.: 290.9-AM32PCharacterizing corn growth and development using computer vision.Tarbell, K.; Reid, J. F. PAP-AMER-SOC-AGRIC-ENG (89-7509): p.1-20. (1989 Winter)Paper presented at the 1989 International Winter Meeting sponsored by the American Society of Agricultural

Engineers, December 12-15, 1989, New Orleans.Descriptors: zea-mays; growth; computer-analysis

62.NAL Call No.: aSD11.A48Classification and prediction of successional plant communities using a pathway model.Keane, R. E. Gen-Tech-Rep-INT-U-S-Dep-Agric-For-Serv-Intermt-Res-Stn. Ogden, Utah : The Station. Feb.1989. (257) p. 56-62.Paper presented at the Symposium on "Land Classifications Based on Vegetation: Applications for ResourceManagement," November 17-19, 1987, Moscow, Idaho.Descriptors: plant-succession; plant-communities; classification; computer-software; land-management; models;montana; forsum-computer-software

63.NAL Call No.: 59.8-C33Classification of hard red wheat by near-infrared diffuse reflectance spectroscopy.Delwiche, S. R.; Norris, K. H. Cereal-Chem v.70(1): p.29-35. (1993 Jan.-1993 Feb.)Includes references.Descriptors: winter-wheat; wheat; classification; infrared- spectroscopy; red-spring-wheat

Abstract: Various forms of discriminant analysis models have been developed and tested for distinguishing twoclasses of wheat - hard red winter and hard red spring. Near-infrared diffuse reflectance (NIR) spectroscopy wasused to measure the intrinsic properties of ground samples of hard red winter and spring wheats grown duringthe 1987, 1988, 1989, and 1990 crop years, of which 100 samples from each of the first three years formed thecalibration set for each model. Discriminant functions were developed by using the following parameters: NIR-predicted protein content (adjusted to 12% moisture), NIR- predicted hardness, NIR protein and NIR hardness,and the scores from principal component analysis (PCA) of full-range (1,100-2,498 nm) NIR spectra. Eachfunction was tested on 1,325 samples (excluded from training of the models) from the 1987-1989 crop years andon 678 samples from the 1990 crop year, all of known class. Model performance, expressed as the percent ofmisclassified samples for each year and class, was poorest for the one-parameter models, which often hadmisclassification rates in excess of 25%. A five-factor PCA model was the most accurate, with an averagemisclassification rate of 5% for 1987, 1988, and 1989 samples. However, the misclassification rate of the PCAmodel rose to 8% for the 1990 samples, suggesting that model accuracy is reduced when samples grown duringyears excluded from calibration, such as from a new year's crop, are classified. Examination of the principalcomponent factors indicates that hardness, protein level, and the interaction of water with protein and otherconstituents within wheat are responsible for correct NIR-based classification.

64.NAL Call No.: SB193.F59Clips knowledge engineering software for detecting and managing alfalfa weevils.Rhykerd, L. M.; Engel, B. A.; Wilson, M. C.; Rhykerd, R. L.; Rhykerd, C. L. Proc-Forage-Grassl-Conf p.124-127. (1991)Meeting held April 1-4, 1991, Columbia, Missouri.Descriptors: medicago-sativa; hypera-postica; computer-software; decision-making; pest-management

65.NAL Call No.: S494.5.D3C652Colour segmentation based on a light reflection model to locate citrus fruits for robotic harvesting.Pla, F.; Juste, F.; Ferri, F.; Vicens, M. Comput-Electron-Agric v.9(1): p.53-70. (1993 Aug.)In the special issue: Computer vision / edited by J.A. Marchant and F.E. Sistler.Descriptors: citrus; robots; mechanical-harvesting; color; light; reflection; mathematical-models

66.NAL Call No.: 41.8-M69Combining your clients' herd performance and business records.

Hughes, H. Vet-Med v.87(9): p.941, 944, 948-950. (1992 Sept.)Descriptors: beef-herds; beef-production; performance; profitability; information-systems; computer-software;records; cow-herd-appraisal-and- performance-system; integrated-resource-management-beef-cow; calf-herd-analyzer

67.NAL Call No.: HC79.E5E5Comments on selecting a geographic information system for environmental management.Woodcock, C. E.; Sham, C. H.; Shaw, B. Environ-Manage v.14(3): p.307- 315. (1990 May-1990 June)Includes references.Descriptors: national-parks; environmental-management; government- organizations; evaluation; selection-criteria; computer-software; innovation- adoption; u; s; -department-of-interior,-national-park-service

68.NAL Call No.: 49-J82Commercial adaptation of ultrasonography to predict pork carcass composition from live animal andcarcass measurements.Gresham, J. D.; McPeake, S. R.; Bernard, J. K.; Henderson, H. H. J-Anim- Sci v.70(3): p.631-639. (1992 Mar.)Includes references.Descriptors: pigs; carcass-composition; carcass-weight; sex- differences; ultrasonic-fat-meters; fat-thickness;prediction; equations

Abstract: Live animal and carcass data were collected from market barrows and gilts (n = 120) slaughtered at aregional commercial slaughter facility to develop and test prediction equations to estimate carcass compositionfrom live animal and carcass ultrasonic measurements. Data from 60 animals were used to develop theseequations. Best results were obtained in predicting weight and percentage of boneless cuts (ham, loin, andshoulder) and less accuracy was obtained for predicting weight and ratio of trimmed, bone-in cuts. Independentvariables analyzed for the live models were live weight, sex, ultrasonic fat at first rib, last rib, and last lumbarvertebra, and muscle depth at last rib. Independent variables for the carcass models included hot carcass weight,sex of carcass, and carcass ultrasonic measurements for fat at the first rib, last rib, last lumbar vertebra, andmuscle depth at last rib. Equations were tested against an independent set of experimental animals (n = 60).Equations for predicting weight of lean cuts, boneless lean cuts, fat- standardized lean, and percentage of fat-standardized lean were most accurate from both live animal and carcass measurements with R2 values between.75 and .88. The results from this study, under commercial conditions, suggest that although live animal orcarcass weight and sex were the greatest contributors to variation in carcass composition, ultrasonography canbe a noninvasive means of differentiating value, especially for fat-standardized lean and weight of boneless cuts.

69.NAL Call No.: S561.6.I8I57Company leaves mark in software field.Integrated-Farm-Manage-Notes. Ames, Iowa : Integrated Farm Management Demonstration Prog., Ext.Communications, IA State Univ. Winter 1990. (6) p. 4- 5.Descriptors: farm-management; computer-software; educational-programs; iowa

70.NAL Call No.: 44.8-J822Comparative responses of lactating cows to total mixed rations or computerized individual concentratesfeeding.Maltz, E.; Devir, S.; Kroll, O.; Zur, B.; Spahr, S. L.; Shanks, R. D. J- Dairy-Sci v.75(6): p.1588-1603. (1992June)Includes references.Descriptors: dairy-cows; lactation-number; cattle-feeding; individual- feeding; milk-yield; body-weight;concentrates; feed-supplements; self-feeding; computer-software; computers; feed-intake; dry-matter; costs;israel

Abstract: A trial was conducted in a commercial dairy herd in which the concentrate part of the ration was fedindividually to a group of cows through computerized self-feeders. Performance results were compared withthose of a group fed TMR of 65 to 67% concentrates. Rationing of individual concentrates was according toparity, milk yield, milk yield potential, BW changes, and bunk feedstuffs. Mean intake of concentrates per cowwas about 1 kg/d lower in the individually supplemented cows. This was partly compensated for by a higherintake of bunk feedstuffs. Overall daily milk yield per cow was similar to those receiving a TMR in first paritycows, higher in second parity cows, and lower in third and greater parity cows. The higher performance of thesecond parity cows was achieved in all milk yield potential classes, and the lower yield in subsequent lactationswas due to lower performance in low and high potential classes. The individually supplemented cows gainedless BW than those in the TMR group. Milk yield per unit of BW was better than yield as a variable to refineindividual cow supplementation strategy for allocation of concentrates. Results also suggest that the samecriteria used for supplementation of concentrates can be beneficial to cows' assignments and movements amongdifferent TMR groups. Computerized dispensing of concentrates, when applied properly, can economize onconsumption of concentrates when grouping and feeding different TMR are impossible.

71.NAL Call No.: 44.8-J822Compares: a computerized milking parlor evaluation system.Chang, W.; Barry, M. C.; Jones, L. R.; Merrill, W. G. J-Dairy-Sci v.75(9): p.2578-2586. (1992 Sept.)Includes references.Descriptors: milking-parlors; work-study; microcomputers; computer- analysis; computer-software

Abstract: A microcomputer-based system, Compares (Computerized Milking Parlor Evaluation System), wasdeveloped by the authors to evaluate the efficiency of milking parlor operations. The system utilizes a hand-heldmicrocomputer to collect on-site milking parlor operation information, which is down-loaded to an IBM-compatible microcomputer to generate an analysis and summary report. The Compares system is capable ofmonitoring the activities of multiple operators in a milking parlor using a single hand-held microcomputer.Reports can be generated within minutes after the information is recorded, thus providing an immediate analysisof the milking system being examined. The Compares system excludes the human error that is possible in othermanual recording procedures that can occur from transferring or calculating data, thereby reducing time andeffort required to collect parlor operation information and enabling extensive data collection. Thus, this systemprovides a convenient tool for studying milking parlor operations.

72.NAL Call No.: A99.9-F7625UComparison of a degree-day computer and a recording thermograph in a forest environment.Wickman, B. E. PNW-Res-Note-U-S-Dep-Agric-For-Serv-Pac-Northwest-For-Range- Exp-Stn. Portland, Or. :The Station. Oct 1985. (427) 6 p.Includes references.Descriptors: phenology; plant-ecology; insect-control; temperatures; recording-instruments; computers; forests

73.NAL Call No.: 290.9-AM32TComparison of machine vision with human measurement of seed dimensions.Churchill, D. B.; Bilsland, D. M.; Cooper, T. M. Trans-A-S-A-E v.35(1): p.61-64. ill. (1992 Jan.-1992 Feb.)Literature review.Descriptors: dactylis-glomerata; festuca-arundinacea; lolium-perenne; seeds; dimensions; measurement;machinery; microcomputers; vision; imagery; literature-reviews

Abstract: Length, width, and thickness of tall fescue (Festuca arundinacea), orchardgrass (Dactylis glomerata),and perennial ryegrass seeds (Lolium perenne) were measured by a machine vision system and by four humanoperators using a microscope with a reticle. Statistical analysis showed that the consistency of machine visionmeasurements was greater than that of the human measurements and required about one-third of the time.Overall accuracy of machine vision system measurements appears to be sufficient to be the basis for selection ofscreen opening and indent pocket sizes used in seed conditioning operations.

74.NAL Call No.: SD112.F67Comparison of production thinning with waste thinning using STANDPAK.West, G. G. FRI-Bull-For-Res-Inst-N-Z-For-Serv (151): p.156-170. (1990)Paper presented at the "Symposium on New Approaches to Spacing and Thinning in Plantation Forestry, " heldApril 10-14, 1989, Rotorua, New Zealand.Descriptors: pinus-radiata; models; computer-software; thinning; thinning-regimes

75.NAL Call No.: 49-J82Comparison of real-time ultrasound and other live measures to carcass measures as predictors of beef cowenergy stores.Bullock, K. D.; Bertrand, J. K.; Benyshek, L. L.; Williams, S. E.; Lust, D. G. J-Anim-Sci v.69(10): p.3908-3916.(1991 Oct.)Includes references.Descriptors: beef-cows; fat-thickness; plane-of-nutrition; body- weight; ultrasonic-fat-meters; body-condition;body-measurements; equations; prediction; carcass-composition; body-fat; body-protein

Abstract: Thirty-nine mature cows were divided into three condition groups on the basis of their subcutaneousfat thickness as determined by real- time ultrasound. A representative animal from each group was measured andslaughtered. The remaining cows with each group were stratified evenly into two groups with one group fed togain weight and the other to lose weight. Several ultrasound and other live measures were taken every 4 wk andtwo animals per subgroup were randomly slaughtered. Carcass data were collected and one side of each carcasswas boned, ground, mixed, and subsampled for fat and protein determination. Four regression equations weregenerated to predict percentage of fat (FAT), percentage of protein (PROT), total fat (TOTFAT), total protein(TOTPROT), total calories (CAL), CAL per live weight (CAL/WT), yield grade (YG), and marbling (MARB).The first equation used all live measures (SUB), the second equation used only objective live measures (OBJ),the third equation incorporated traditional live measures (EAS), and the fourth equation used only carcass data(CAR). Adjusted R-squares of the most appropriate equation using the SUB, OBJ, EAS, and CARmeasurements were 82,.73,.82, and .82 for FAT; 82,.57,.6 1, and .66 for PROT; 89,.87,.86, and .85 for TOTFAT;.95,.95,.93, and .74 for TOTPROT; 93,.92,.91, and .90 for CAL; .83,.78,.83, and .82 for CAL/WT; .86, .86, .78,and .93 for YG; and 75,.70,.74, and .74 for MARB, respectively. It seems that condition score or ultrasound withother objective live measures is as accurate in predicting cow composition as carcass measures.

76.NAL Call No.: 325.28-P56Comparison of spatial variability in visible and near-infrared spectral images.Chavez, P. S. Jr. Photogramm-Eng-Remote-Sensing v.58(7): p.957-964. (1992 July)Includes references.Descriptors: mapping; vegetation; crops; forests; swamps; rivers; deforestation; landsat; spatial-variation;satellite-imagery; spectral-data; thematic- mapper; paraguay; kenya; france; thailand; arizona; california;satellite-positioning-and-tracking

77.NAL Call No.: SB319.2.F6F56Comparisons between densitometric measurements, image analysis, and photointerpretation readings ofaerial color infrared photogrophs of citrus trees.Blazquez, C. H. Proc-Annu-Meet-Fla-State-Hortic-Soc. [S.l.] : The Society. 1988 (pub. May 1989). v. 101 p. 66-69. ill.Includes references.Descriptors: citrus-jambhiri; crop-management; aerial-photography; color; imagery; spectral-data; stress; vigor;florida

78.NAL Call No.: 280.8-J822

Computer adoption decisions--implications for research and extension: the case of Texas rice producers.Jarvis, A. M. Am-J-Agric-Econ v.72(5): p.1388-1394. (1990 Dec.)Paper presented at the annual meeting of the American Agricultural Economics Association held August 5-8,1990, Vancouver, British Columbia, Canada.Descriptors: rice; farm-management; decision-making; microcomputers; innovation-adoption; probabilistic-models; case-studies; texas; logit-model

Abstract: This study identifies the characteristics of Texas rice producers who have adopted computers relativeto nonadopters. Primary survey data obtained in the spring of 1990 was examined using logit analysis to identifyhow each characteristic influences the probability of computer adoption. Thirty-seven percent of the respondentsuse computers in their business. The results indicate that as farm size and business complexity increase so doesthe probability of computer adoption. Some evidence that the adoption of computer technology differs fromproduction technology surfaced. Producers' decisions to adopt a computer are associated with the actions of theirpeers and family. Encouraging computer user groups and computer training courses for producers couldencourage the adoption of computer technology.

79.NAL Call No.: 80-AC82A computer aid for decision-making in apple pest management.Haley, S.; Currans, K. G.; Croft, B. A. Acta-Hortic (276): p.27-34. (1990 July)Paper presented at the "Second International Symposium on Computer Modelling in Fruit Research and OrchardManagement," September 5- 8, 1989, Logan, Utah.Descriptors: apples; pest-management; expert-systems; north-america

Abstract: Our computer program is designed to help tree fruit pest managers make decisions on management ofthree major apple pests in western North America, codling moth, San Jose scale and phytophagous mites. Theprogram operates on an IBM-compatible microcomputer and uses commercial expert system, databasemanagement and spreadsheet software. The system has three major components: DIAGNOSE, IDENTIFY andMANAGE. DIAGNOSE identifies pests from the injury they cause on buds, fruit, leaves or bark. IDENTIFYdetermines names of arthropod pests and their common natural enemies found on trees or fruit or in pheromonetraps. MANAGE, the largest module, calculates the net benefit of a pesticide application. Submodels predictcrop value, pest damage, control efficacy and control costs. Pest damage predictions are based on empiricalmodels for codling moth and mites and on an expert estimate for scale. Efficacies of pesticides are estimated byexperienced researchers. The program predicts the combined value at harvest of damage from accumulatedpopulations of those pests selected by the user. Then a list of appropriate pesticides is presented. Next, the netbenefit of an application of the user's choice of pesticide is calculated. Finally, the user may graphically compareside effects of the pesticide selected with those of alternative pesticides. Side effects include toxicities to otherpests, applicator hazard, bee toxicity, toxicity to western predator mite and risk of resistance development.

80.NAL Call No.: HC59.7.A1W6Computer-aided management advice for loan programs run by Indonesian village women.Nystuen, J. D.; Zinn, F. D.; Sulistyo, D.; Darmasetiawan, R. World-Dev v.19(12): p.1753-1766. (1991 Dec.)Includes references.Descriptors: rural-women; loans; information-systems; microcomputers; resource-management; family-planning; villages; development-projects; indonesia


81.NAL Call No.: S494.5.D3C652Computer-aided management of plant tissue culture production.Humphries, S.; Simonton, W.; Thai, C. N. Comput-Electron-Agric v.6(1): p.33-49. (1991 July)

Includes references.Descriptors: plant-tissues; micropropagation; industry; agriculturalproduction; tissue-culture; enterprises;supply-response; fluctuations; demand; agricultural-adjustment; computer-software; mathematical-models;algorithms; management; processing; harvesting; contamination; labor- intensity; space- utilization; labor-allocation; lumped-parameter-discrete-time-models

82.NAL Call No.: SD421.37.C6-1991Computer applications for prescribed fire and air quality management in the Pacific Northwest.Peterson, J. L.; Ottmar, R. D. Proceedings of the 11th Conference on Fire and Forest Meteorology, April 16-19,1991, Missoula, Montana / sponsored by the Society of American Foresters and American Meteorological Soc ;editors, PL Andrews and DF Potts. Bethesda, Md. : Society of American Foresters, c1991.. p. 455-459.This record corrects IND 92025717 which was entered incorrectly under call number SD143.S64.Descriptors: prescribed-burning; emission; air-quality; computer- techniques; computer-software

83.NAL Call No.: 80-AC82Computer assisted selection of locations in South-East Asia for the continous cropping of apples andpeaches.Edwards, G. R.; Sinclair, E. R.; Chapman, K. R. Acta-Hortic. Wageningen : International Society forHorticultural Science. Dec 1990. v. 279 p. 61- 66.Paper presented at the "Third International Workshop on Temperate Zone Fruits in the Tropics and Subtropics,"December 12-16, 1988, Chiang Mai, Thailand.Descriptors: malus; prunus-persica; crop-production; temperate-tree- fruits; computer-software; tropics; site-selection; south-east-asia

84.NAL Call No.: SB476.G7Computer-based tree inventories.Jaenson, R. Grounds-maint v.28(6): p.44, 46, 70-71. (1993 June)Descriptors: forest-inventories; computer-software; landscaping; forest-management; databases; usa

85.NAL Call No.: 80-AC82A computer model for peach orchard replacement.Bauer, L. L.; Bishop, G. D.; Rathwell, P. J. Acta-Hortic (276): p.295- 299. (1990 July)Paper presented at the "Second International Symposium on Computer Modelling in Fruit Research and OrchardManagement," September 5- 8, 1989, Logan, Utah.Descriptors: peaches; orchards; replacement; computer-software; computer-techniques

Abstract: A computer program is illustrated that aids in the decision of determining the economically optimumtime to replace a peach orchard. The decision maker inputs expected yields, annual costs, prices, and harvestcost for the replacement orchard along with the expected current annual income for the existing orchard and adiscount rate. The model uses the information for the replacement orchard to calculate the expected net incomefor each year and the discount rate is used to determine the net present value each year. These annual estimatesare accumulated through a particular year to determine total net present value of income up to and including thatyear. This is done for each year in the expected life of the replacement orchard. Since these are estimates of totalincome, they cannot be compared to an annual income. To accomplish this, the accumulated net present valueestimate for each year is amortized to determine the necessary annual income that will equal the accumulated netpresent value for that year. The largest amortized value is selected to represent the expected average, or annual,income from the replacement orchard. If this is larger than the income expected from the existing orchard, thedecision should be to replace.

86.NAL Call No.: 99.9-F7662J

Computer optimization of hardwood parts yield using gang-rip-first procedures.Hoff, K. G.; Adams, E. L.; Walker, E. S. For-Prod-J v.42(3): p.57-59. (1992 Mar.)Descriptors: hardwoods; lumber; rip-sawing; computer-software; microcomputers

Abstract: A microcomputer program, GR-1ST (gang-rip-first), is available for determining optimum gang-rip-first procedures in processing hardwood lumber. GR-1ST 1ST provides 1) parts yield information per board, 2)plots of each board plus the resulting saw cuts and parts produced; and 3) summary information for all partsproduced during program execution.

87.NAL Call No.: 41.8-M69A computer program for appraising and increasing productivity in beef cattle.Ringwall, K. A.; Berg, P. M.; Boggs, D. L. Vet-Med v.87(7): p.706, 708- 709, 712-714, 716-717. (1992 July)Descriptors: beef-cattle; beef-production; productivity; computer- software; beef-herds; growth; reproductive-performance; cow-herd-appraisal-and- performance-system-ii; chaps-ii

88.NAL Call No.: S494.5.D3I5-1988Computer program helps to manage small resorts for a profit.Eix, J. R. Proceedings of the 2nd International Conference on Computers in Agricultural Extension ProgramsFedro S Zazueta, AB Del Bottcher, eds p.422-427. (of Florida, [1988?].)Conference held February 10-11, 1988 at the Grosvenor Resort Hotel, Disney World Village, Lake Buenavista,Orlando, Florida.Descriptors: resorts; management; computer-software; minnesota

89.NAL Call No.: S494.5.D3C68-1992Computer software development for greenhouse design and management.Fang, W.; Ting, K. C.; Giacomelli, G. A. Computers in agricultural extension programs proceedings of the 4thinternational conference, 28-31 January 1992, Orlando, Florida / sponspored by the Florida CooperativeExtension Service, University of Florida. St. Joseph, Mich. : American Society of Agricultural Engineers,c1992.. p. 274-279.Includes references.Descriptors: greenhouses; design; management; computer-software

90.NAL Call No.: aSD11.A42Computer vision: a nursery management tool.Rigney, M. P.; Kranzler, G. A. Gen-Tech-Rep-RM-Rocky-Mt-For-Range-Exp-Stn-U- S-Dep-Agric-For-Serv(200): p.189-194. (1990 Dec.)Includes references.Descriptors: forest-nurseries; computers; image-processors

91.NAL Call No.: S494.5.D3C652A computer-vision algorithm for automatic guidance of microplant harvesting.McFarlane, N. J. B. Comput-Electron-Agric v.6(2): p.95-106. (1991 Oct.)Includes references.Descriptors: chrysanthemum; mechanical-harvesting; imagery; algorithms; stems; computer-techniques; robots

92.NAL Call No.: 290.9-AM32TComputer vision sensing of stress cracks in corn kernels.Reid, J. F.; Kim, C.; Paulsen, M. R. Trans-A-S-A-E v.34(5): p.2236- 2244. (1991 Sept.-1991 Oct.)Literature review.

Descriptors: zea-mays; maize; kernels; cracking; crop-damage; computer-analysis; optical-properties; stress-grading; detection; literature- reviews

Abstract: Maintaining high quality of corn is important to both corn producers and buyers. Stress crack detectionremains one of the most important tasks in corn quality inspection. Such a measure of quality would be helpfulin assessing not only the end-use values of the corn, but also the drying method used and the amount of expectedbreakage due to subsequent handling procedures. A computer vision system was developed for automaticdetection of corn stress cracks which simulates the processes that the human visual system uses to perceive thestress cracks from the corn kernel in the conventional candling method. The stress crack detection systemconsisted of four consecutive stages, windowing, edge detection, feature representation, and classification. A setof performance criteria was developed to evaluate the stress crack detection system and used to compare theperformance of different configurations on several corn varieties. Evaluation results showed that the systemconfigured with the circular band operator, the Duda road operator, and the Hough transform performed best,with success rates of 78.2% and failure rates of 8.2% of the classification made by one human expert. Theperformance measures of the system with this configuration were equal or superior to that of other humaninspectors. The accuracy of the system was 91.8% when the system was used to distinguish only stress cracked-kernels from sound kernels.

93.NAL Call No.: S75.F87A computer with a green thumb.DePolo, J. Futures-Mich-State-Univ-Agric-Exp-Stn v.8(4): p.11-12. (1991 Winter)Descriptors: flowers; greenhouse-culture; computer-software; michigan

94.NAL Call No.: aSD11.U56Computerized algorithms for partial cuts.Ernst, R. L.; Stout, S. L. Gen-Tech-Rep-NE-U-S-Dep-Agric-For-Serv-Northeast- For-Exp-Stn (148): p.132-147.(1991 Mar.)Paper present at the 8th Central Hardwood Forest Conference, March 4-6, 1991, University Park, Pennsylvania.Descriptors: forest-management; thinning; computer-simulation; algorithms; computer-software; fiber-computer-software; ne-twigs-computer- software; oaksim-computer-software; silvah-computer-software; yields-ms- computer-software

95.NAL Call No.: 280.8-J822Computerized analysis of individual sow-herd performance.Huirne, R. B. M.; Dijkhuizen, A. A.; Renkema, J. A.; Beek, P. v. Am-J-Agric- Econ v.74(2): p.388-399. (1992May)Includes references.Descriptors: pig-farming; farm-management; expert-systems; simulation- models; validity; decision-making;comparisons; weighting; computerized-herd- evaluation-system-for-sows-chess-computer-software

Abstract: A computer model, CHESS, was developed to allow systematic and objective analysis of individualswine breeding farms. The model works by identifying deviations from standards, weighting the deviations,analyzing the weighted deviations, and finally, evaluating individual farm performance based on the results.CHESS consists of one decision support system and three expert systems. A field test to validate the CHESSmodel resulted in a test disagreement between CHESS and human experts of about 4% only.

96.NAL Call No.: S494.5.D3C652A computerized data management and decision support system for gypsy moth management in suburbanparks.Thorpe, K. W.; Ridgeway, R. L.; Webb, R. E. Comput-Electron-Agric v.6(4): p.333-345. (1992 Jan.)Includes references.

Descriptors: lymantria-dispar; pest-management; decision-making; computer-software; urban-parks; district-of-columbia; maryland; gypsy-moth- management-decision-support-system-gymsys-expert-system; montgomery-county,- maryland

97.NAL Call No.: SB1.H6Computerized data management for almond breeding programs.Dicenta, F.; Garcia, J. E. HortScience v.27(3): p.270. (1992 Mar.)Includes references.Descriptors: prunus-dulcis; plant-breeding; computer-software; microcomputers; databases

98.NAL Call No.: QH540.N3Computers in consulting engineering.Howard, C. D. D. NATO-ASI-Ser-Ser-G-Ecol-Sci. Berlin, W. Ger. : Springer- Verlag. 1991. v. 26 p. 267-282.In the series analytic: Decision support systems: Water resources planning / edited by D.P. Loucks and J.R. daCosta. Proceedings of the NATO Advanced Research Workshop on Computer-Aided Support Systems for WaterResources, Research and Management, September 24-28, 1990, Ericeira, Portugal.Descriptors: water-resources; water-management; decision-making; computer-simulation; simulation-models;computer-software; computer-hardware; engineering; consultants

99.NAL Call No.: HD2346.U5R8Computers save time, improve efficiency.Tinsley, W. A. Rural-Enterp v.6(4): p.34-35. (1992 Summer)Descriptors: microcomputers; time; efficiency; farm-management

100.NAL Call No.: aS622.S6Computers to be core of conservation assistance.Shaw, R. R. Soil-Water-Conserv-U-S-Dep-Agric-Soil-Conserv-Serv v.13(2): p.4-6. (1992 July-1992 Aug.)Descriptors: soil-conservation; land-management; computer-techniques; computer-software; databases;automation; usda


101.NAL Call No.: HD1401.A47Concept and implementation of an integrated decision support system (IDSS) for capital-intensivefarming.Wagner, P.; Kuhlmann, F. Agric-Econ-J-Int-Assoc-Agric-Econ v.5(3): p.287-310. (1991 July)In the special issue : Multidisciplinary problem-solving and subject-matter work / edited by G.L. Johnson.Descriptors: intensive-farming; capital; computer-software; models; decision-making; information; farm-management

Abstract: During the evolutionary process of developing software for management tasks, the need for integrationbecame more and more obvious. This paper discusses how integrated information processing can beaccomplished to support the managerial functions. Based on the concepts of control theory principal schemes ofcomparison possibilities and deviation analysis are shown. The philosophy behind the design of an integrateddecision support system (IDSS), the on-farm implementation, and the integration problems of hardware andsoftware are discussed. The applied IDSS consists of several planning and controlling models. These models andthe linkages between them are described in detail.

102.NAL Call No.: S494.5.D3C652Constant velocity air inlet controller.Gates, R. S.; Overhults, D. G.; Walcott, B. L.; Shearer, S. A. Comput- Electron-Agric v.6(2): p.175-190. (1991Oct.)Includes references.Descriptors: air-flow; velocity; animal-housing; ventilation; temperature; controllers; sensors; computer-software; algorithms; flow-charts

103.NAL Call No.: SD143.N6Construction of variable-density empirical yield equations from forest management inventory data.Walters, D. K.; Ek, A. R.; Czysz, D. North-J-Appl-For v.7(3): p.110- 113. (1990 Sept.)Includes references.Descriptors: forest-inventories; yields; equations; mathematical- models

Abstract: Using simple concepts, forest management inventory data, and microcomputers for analysis,methodology is described whereby a forest owner or manager can construct yield equations quickly andeconomically. Models such as these should adequately explain the average yield trends in the data and can beadjusted, through the use of ratios, to specific stand information. Steps and cautions in choice of model form,data aggregation, and fitting procedure are discussed and illustrated. Assumptions and procedures for modelimplementation are also described.

104.NAL Call No.: aSD11.A46-no.304CONSUME. Version 1.01.Ottmar, R. D.; Pacific Northwest Research Station (Portland, Or. Seattle, WA : PNW Research Station, [1993] 2computer disks 1 user's guide.Title from disk label.Descriptors: Prescribed-burning-Software

Abstract: CONSUME is a computer program that calculates woody fuel and duff consumption for resourcemanagers who prescribe fire for management of forest resources.

105.NAL Call No.: QA76.76.E95A5Continuous improvement of software support processes.Lambert, J. R. AI-Appl v.7(2/3): p.45-48. (1993)Paper presented at a Symposium of the 1992 Annual Meeting of the Entomological Society of America,December 8, 1992, Baltimore, Maryland.Descriptors: insect-pests; gossypium; crop-production; computer- software; systems; expert-systems;improvement; decision-making; usa

106.NAL Call No.: 290.9-AM32PCost and return estimator (CARE) a tool for alternative agriculture.Christensen, D. A.; Langemeier, D. L. PAP-AMER-SOC-AGRIC-ENG. St. Joseph, Mich. : The Society. Winter1990. (90-1565) 10 p.Paper presented at the "1990 International Winter Meeting sponsored by the American Society of AgriculturalEngineers," December 18-21, Chicago, Illinois.Descriptors: alternative-farming; budgets; cost-benefit-analysis; crop-management; computer-software;nebraska; care-software

107.NAL Call No.: HD1751.C45

Cost effective software encourages financial management.Stokes, K. W. Choices-Mag-Food-Farm-Resour-Issues v.7(1): p.27. (1992)Includes references.Descriptors: computer-software; microcomputers; record-keeping; farm- management; extension

108.NAL Call No.: QA76.76.E95A5Costs involved in the support and maintenance of the Penn State Apple Orchard Consultants.McClure, J. AI-Appl v.7(2/3): p.54-55. (1993)Paper presented at a Symposium of the 1992 Annual Meeting of the Entomological Society of America,December 8, 1992, Baltimore, Maryland.Descriptors: malus-pumila; orchards; crop-management; support-systems; technical-training; computer-software; expert-systems; costs; pennsylvania

109.NAL Call No.: SB249.N6Cotton fruiting patterns as affected by nitrogen rate and Pix-- preliminary evaluation with COTMAP.Welch, R. A.; Ebelhar, M. W. Proc-Beltwide-Cotton-Conf. Memphis, Tenn. : National Cotton Council ofAmerica. 1991. v. 2 p. 905.Paper presented at the "Cotton Soil Management and Plant Nutrition Conference," 1991, San Antonio, Texas.Descriptors: gossypium-hirsutum; crop-production; crop-yield; crop- quality; fertilizers; computer-software;mississippi

110.NAL Call No.: S494.5.D3I5-1990COWBASE: a beef cow-calf records program.Kunkle, W. E.; Sand, R. S.; Buhl, F. Proceedings of the 3rd International Conference on Computers inAgricultural Extension Programs / Fedro S. Zazueta, editor. ; January 31- February 1, 1990, Grosvenor ResortHotel, Disney World Village, Lake Buenavista, FL. Gainesville, FL : Florida Cooperative Extension Service,University of Florida, [1990]. p. 459-465.Descriptors: beef-herds; record-keeping; computer-software

111.NAL Call No.: S494.5.D3I5-1990COWBOSS: a microcomputer record keeping system for cow/calf herds.Berry, S. L.; Ahmadi, A.; Johnson, H. A.; Riet, W. J. v.; Farley, J. L. Proceedings of the 3rd InternationalConference on Computers in Agricultural Extension Programs / Fedro S. Zazueta, editor. ; January 31-February 1, 1990, Grosvenor Resort Hotel, Disney World Village, Lake Buenavista, FL. Gainesville, FL :Florida Cooperative Extension Service, University of Florida, [1990]. p. 423-429. ill.Includes references.Descriptors: animal-husbandry; cows; record-keeping; computers; systems

112.NAL Call No.: S494.5.D3C68-1992COWREC--a simplified beef cow-calf record keeping system.Sutton, R. W.; Bishop, G. D. Computers in agricultural extension programs proceedings of the 4th internationalconference, 28-31 January 1992, Orlando, Florida / sponspored by the Florida Cooperative Extension Service,University of Florida. St. Joseph, Mich. : American Society of Agricultural Engineers, c1992.. p. 86-90.Includes references.Descriptors: calf-production; record-keeping; computer-software

113.NAL Call No.: S671.A66Crop water stress index of ornamental plants.Sammis, T. W.; Jernigan, D. Appl-Eng-Agric v.8(2): p.191-195. (1992 Mar.)

Includes references.Descriptors: ornamental-plants; species; water-requirements; evapotranspiration; canopy; water-stress; mexico

Abstract: Water requirements were determined for eighteen species of ornamental plants produced under non-limiting water conditions at Las Cruces, New Mexico. Baseline equations were determined from regressionanalysis of canopy-air temperature differential versus air vapor pressure deficit. Canopy temperature wasmeasured using an infrared thermometer. Air temperature and air vapor pressure deficit were measured using anAssmann psychrometer. Regressions for Salt Cedar, Sycamore, Ash, and Aleppo Pine had statistically equivalentslopes and intercepts (P < 0.05); all others were unique in their responses. Canopy and aerodynamic resistancewere calculated from the baseline equations and the noontime and daily transpiration rates were calculated.Daily transpiration ranged from 12.5 mm d-1 (0.49 in. d-1) (alfalfa) to 3 mm d-1 (0.12 in. d-1) (Barberry).Relative transpiration was calculated using alfalfa as a standard. Redbud exhibited a relative transpiration of0.78 and Mulberry showed a relative transpiration of 0.42.

114.NAL Call No.: HD1.A3CROPLOT--an expert system for determining the suitability of crops to plots.Nevo, A.; Amir, I. Agric-Syst v.37(3): p.225-241. (1991)Includes references.Descriptors: farm-planning; land-use-planning; expert-systems; decision-making; crop-management; crop-production; microcomputers; validity

115.NAL Call No.: 44.8-J822The current state of human-computer interface technologies for use in dairy herd management.Jones, L. R. J-Dairy-Sci v.75(11): p.3246-3256. (1992 Nov.)Includes references.Descriptors: information-systems; data-banks; microcomputers; graphs; computer-software; dairy-farming

Abstract: The current state of three human-computer interface areas was reviewed, and potential dairy herdmanagement applications were proposed. Alternative input devices (e.g., touch-sensitive screens and speechrecognition) can provide more intuitive communication with computers. Several user interface designs havebeen developed that narrow the dichotomy between ease of use and ease of learning. Information technologiescan provide dairy herd managers with more complete and immediate access to management information fordecision making: 1) natural language interfaces, which allow users to query a structured database to retrieveinformation; 2) full text retrieval systems, which retrieve pertinent passages from a collection of documents; and3) hypertext, which is a means of linking related passages of text so that they can be browsed in a logical,nonlinear fashion. The third area of human-computer interface concerns methods of integrating decision supportsystems into a management workstation that could contain independent systems, systems integrated through auser interface manager, or systems integrated through an intelligent dialogue manager. Advances in human-computer interfaces, if incorporated into dairy management software, should significantly increase the use ofcomputers for dairy management and improve the decisions made by dairy herd managers.

116.NAL Call No.: 290.9-AM32PCustomized design and layout of swine nursery facilities.Helmink, K. J.; Riskowski, G. L.; Christianson, L. L. PAP-AMER-SOC-AGRIC- ENG. St. Joseph, Mich. : TheSociety. Winter 1989. (89-4552) 15 p.Paper presented at the "1989 International Winter Meeting sponsored by The American Society of AgriculturalEngineers," December 12-15, 1989, New Orleans, Louisiana.Descriptors: piglets; pig-housing; structural-design; computer- software

117.NAL Call No.: 1.98-AG84Cutting energy costs for irrigation.

Senft, D. Agric-Res-U-S-Dep-Agric-Res-Serv v.39(5): p.14-15. (1991 May)Descriptors: irrigation; computer-software; computer-techniques; irrigation-systems; energy-conservation;energy-cost-of-production

118.NAL Call No.: SF601.C66Dairy herd reproductive health management: evaluating dairy herd reproductive performance. II.Etherington, W. G.; Marsh, W. E.; Fetrow, J.; Weaver, L. D.; Seguin, B. E.; Rawson, C. L. Compend-Contin-Educ-Pract-Vet v.13(9): p.1491-1503. (1991 Sept.)Includes references.Descriptors: dairy-cows; heifers; calving-rate; growth; liveweight; age-at-first-calving; culling; conception-rate;dairy-herds; information- services; computer-software

119.NAL Call No.: 44.8-J822Dairybase: an electronic individual animal inventory and herd management system.Spahr, S. L.; Dill, D. E.; Leverich, J. B.; McCoy, G. C.; Sagi, R. J-dairy- sci v.76(7): p.1914-1927. (1993 July)Includes references.Descriptors: dairy-cows; record-keeping; farm-management; computer- software; databases; algorithms

Abstract: A microcomputer application program developed with database management system technology isdescribed for management of animal inventory, reproduction, genetic improvement, feeding, milk production,and health records of dairy cattle. An inventory of cattle, frozen semen, frozen embryos, and nutrient content offeeds is maintained in integrated databases using a relational database management system. Knowledge- basedmanagement information is encoded into the application program to enhance management. The program utilizeselectronic transfer of milk production data from electronic milk meters and has the capability to minimizemanual entry of other data by electronic updating of the database. Use of the program in a 300-cow herdenhanced the detail of data available for management of individual cows and provided an improved method forplanning herd management events, monitoring the current status of individual cows, and custom interfacing herdrecords with new or emerging electronic communication and animal sensor technology.

120.NAL Call No.: 58.9-IN7Data logging for agriculture processing in Malawi.Temple, S. Agric-Eng v.46(4): p.105-107, 130. (1991 Winter)Descriptors: tea; processing; tobacco; production; data-collection; monitoring; temperature; environment;computer-software; malawi


121.NAL Call No.: S494.5.D3C652Database management system for monitoring and warning of codling moth (Cydia pomonella) and carrotfly (Psila rosae).Murali, N. S.; Percy Smith, A. Comput-Electron-Agric v.6(3): p.267-272. (1991 Dec.)Includes references.Descriptors: cydia-pomonella; psila-rosae; databases; monitoring; microcomputers; pest-control; computer-software

122.NAL Call No.: TD420.A1P7DBAPE--a database and model parameter analysis system for agricultural soils to support water qualitymanagement.

Imhoff, J. C.; Carsel, R. F.; Kittle, J. L. Jr.; Hummel, P. R. Water-Sci- Technol-J-Int-Assoc-Water-Pollut-Res-Control v.24(6): p.331-337. (1991)In the series analytic: Watermatex '91 / edited by T.O. Barnwell, P.J. Ossenbruggen and M.B. Beck. Proceedingsof the "Second International Conference on Systems Analysis in Water Quality Management," June 3-6, 1991,Durham, New Hampshire.Descriptors: soil-properties; water-quality; management; agricultural- soils; computer-software; subsurface-runoff; models; databases

123.NAL Call No.: S494.5.B563C87Decision support for integrated greenhouse production systems.Ting, K. C.; Fang, W.; Giacomelli, G. A. Curr-Plant-Sci-Biotechnol- Agric (12): p.293-298. (1991)In the series analytic: Horticulture -- New Technologies and Applications / edited by J. Prakash and R. L. M.Pierik. Proceedings of an International Seminar on New Frontiers in Horticulture, November 25-28, 1990,Bangalore, India.Descriptors: horticultural-crops; greenhouse-culture; crop-production; decision-making; computer-software

124.NAL Call No.: SB599.J69Decision support software for implementation of Russian wheat aphid economic injury levels andthresholds.Legg, D. E.; Wangberg, J. K.; Kumar, R. J-agric-entomol v.10(3): p.205- 213. (1993 July)Includes references.Descriptors: diuraphis-noxia; insect-pests; economic-thresholds; crop- damage; yield-losses; computer-software;models

125.NAL Call No.: S494.5.D3I5-1990Decision support software to elicit risk aversion preferences.Alderfer, R. D.; Harsh, S. B. Proceedings of the 3rd International Conference on Computers in AgriculturalExtension Programs / Fedro S. Zazueta, editor. ; January 31- February 1, 1990, Grosvenor Resort Hotel, DisneyWorld Village, Lake Buenavista, FL. Gainesville, FL : Florida Cooperative Extension Service, University ofFlorida, [1990]. p. 242-246.Includes references.Descriptors: expert-systems; risk; attitudes

126.NAL Call No.: SB1.H6A decision support system for apple thinning in Colorado.Rogoyski, M. K.; Renquist, A. R. HortScience v.27(8): p.915-917. (1992 Aug.)Includes references.Descriptors: malus-pumila; thinning; fruit; decision-making; computer- software; chemical-pruning; colorado;defruiting

Abstract: A decision support system has been developed to help Colorado fruit growers with apple (Malusdomestica Borkh.) thinning. This system can also be used as a teaching aid and as a tool for generating researchhypotheses. The system determines if fruit thinning is needed by identifying catastrophic events that wouldeliminate the need for thinning. The major function of this decision support system is determination of treeresponsiveness to chemical thinning agents. This is accomplished through analysis of the user's answers toquestions related to the physiological status of the trees, environmental data, bearing history, and the applevariety in question. On the basis of the above analysis, two sets of recommendations are presented: generalrecommendations based on the variety selected, and specific ones for that variety based on growth stage and treeresponsiveness to thinners. The user also is provided with the rationale for the recommendations.

127.NAL Call No.: 275.29-OK41CA decision support system for eastern redcedar control.Engle, D. M.; Bernardo, D. J.; Hunter, T. D.; Stritzke, J. F.; Bidwell, T. G. Circ-E-Okla-State-Univ-Coop-Ext-Serv (905): p.16. (1992 Feb.)In the series analytic: Range research highlights, 1983-1991 / edited by T.G. Bidwell, D. Titus and D. Cassels.Descriptors: juniperus-virginiana; brush-control; range-management; computer-software; cost-benefit-analysis;oklahoma

128.NAL Call No.: SB599.C8Decision support system for economic analysis of grasshopper treatment operations in the African Sahel.Coop, L. B.; Croft, B. A.; Murphy, C. F.; Miller, S. F. Crop-Prot v.10(6): p.485-495. (1991 Dec.)Includes references.Descriptors: oedaleus-senegalensis; insect-control; decision-making; cost-benefit-analysis; chemical-control;computer-software; computer-simulation; simulation-models; prediction; economic-thresholds; crop-growth-stage; crop- yield; crop-losses; timing; insecticides; loss-prevention; millets; ghlsim

129.NAL Call No.: QA76.76.E95A5A decision support system for management of Russian wheat aphid in the western United States.Berry, J.; Lanier, W.; Belote, D. AI-Appl v.7(1): p.49-52. (1993)Descriptors: aphidoidea; computer-software; support-systems; management; identification; western-states-of-usa; diuraphis-noxia; management- modules; identification-modules

130.NAL Call No.: 290.9-AM32PA decision support system for planning agroforestry systems.Garcia Ceca, J. L.; Gebremedhin, K. G.; Lassoie, J. P. PAP-AMER-SOC-AGRIC- ENG. St. Joseph, Mich. : TheSociety. Summer 1989. (89-7073) 16 p.Paper presented at the 1989 International Summer meeting, June 25-28, 1989, Quebec, PQ, Canada.Descriptors: agroforestry-systems; planning; computer-software

131.NAL Call No.: HD1773.A2N6A decision support system for sustainable farming.Ikerd, J. E. Northeast-J-Agric-Resour-Econ v. 20(1): p.109-113. (1991 Apr.)Paper submitted in response to call for papers on the theme "The Effects of Agricultural Production onEnvironmental Quality."Descriptors: farm-management; sustainability; farm-planning; computer- software; resource-management;microcomputers; decision-making; sustaining-and- managing-resources-for-tomorrow-farm-resource-management-system-smart-frms- computer-software

132.NAL Call No.: T174.3.J68A decision support system model for technology transfer.Roland, R. J. J-Technol-Transfer v.7(1): p.73-93. (1982 Fall)Includes references.Descriptors: technology-transfer; decision-making; computer-software; information-systems; models

Abstract: Technology transfer is the process by which technology originating at one institutional setting isadapted for use in another. A major impediment to the implementation of new technologies to assist withmangerial decision-making problems is a lack of communication between the technology and managementcommunities. Development of a tool designed to bridge the technology transfer gap was the goal of thisresearch. The result is a prototype software package which may be used on an interactive computer terminal by a

manager for assistance in designing a decision support system (DSS). The four primary research tasks were: 1.Develop a conceptual model of the DSS design process. 2. Select and adapt, or create, appropriate software tomechanize the model. 3. Develop a knowledge base to describe the interactiveness of various organizationvariables and managerial decision-making needs. 4. Collect and analyze interview data and implement resultantproduction rules on the model.

133.NAL Call No.: S494.5.D3C652A decision support system to aid weed control in sugar beet.Edwards Jones, G.; Mumford, J. D.; Norton, G. A.; Turner, R.; Proctor, G. H.; May, M. J. Comput-Electron-Agric v.7(1): p.35-46. (1992 Apr.)Includes references.Descriptors: sugarbeet; weed-control; herbicides; decision-making; expert-systems; flow-charts; computer-software; uk; hypertext

134.NAL Call No.: 41.8-V641A decision support system using milk progesterone tests to improve fertility in commercial dairy herds.Williams, M. E.; Esslemont, R. J. Vet-Rec-J-Br-Vet-Assoc v.132(20): p.503-506. (1993 May)Includes references.Descriptors: dairy-cows; female-fertility; decision-making; computer- software; artificial-insemination; milk;progesterone; ovulation; detection; profitability; moira; management-of-insemination-through-routine-analysis

135.NAL Call No.: 1-AG84TEDemonstration and validation of crop grain yield simulation by EPIC.Kiniry, J. R.; Spanel, D. A.; Williams, J. R.; Jones, C. A. Tech-Bull-U-S- Dep-Agric (1768): p.220-234. (1990Sept.)In the series analytic: EPIC-Erosion/Productivity Impact Calculator. 1. Model Documentation / edited by A.N.Sharpley and J.R. Williams.Descriptors: grain-crops; crop-yield; irrigation; erosion; computer- simulation; computer-software; simulation-models

136.NAL Call No.: 100-T31MA description of the Texas Agricultural Weather Advisory Program software.Dugas, W. A.; Heuer, M. L. Misc-Publ-MP-Tex-Agric-Exp-Stn. College Station, Tex. : The Station. Mar 1985.(1574) 86 p.Descriptors: crop-production; decision-making; weather-data; information- systems; information-processing;computer-hardware; computer-software; growth- models; soil-water-balance; probability-analysis; texas

137.NAL Call No.: S494.5.D3I5-1988The design and production of extension software.Bomash, W. M. Proceedings of the 2nd International Conference on Computers in Agricultural ExtensionPrograms Fedro S Zazueta p.652-657. (of Florida, [1988?].)Meeting held February 10-11, 1988 at Lake Buenavista, Orlando, Florida.Descriptors: extension; computer-software; design

138.NAL Call No.: 290.9-AM32TDesign of an agricultural robot for harvesting melons.Edan, Y.; Miles, G. E. Trans-A-S-A-E v.36(2): p.593-603. (1993 Mar.- 1993 Apr.)Includes references.Descriptors: cucumis-melo; harvesting; robots; automation; computer- software; mathematical-models

Abstract: The performance of an agricultural robot has been evaluated through simulation to determine designparameters for a robotic melon harvester. Animated, visual simulation provided a powerful tool to initiate theevaluation of alternative designs. To quantify the many, closely-related design parameters, numerical simulationtools were developed and applied. Simulations using measured cantaloupe locations revealed the effect of designparameters (configuration, number of arms, and actuator speeds) on the average cycle time. Simulation resultspredicted that a Cartesian robot would perform faster than a cylindrical robot for the melon harvesting task.Activating two arms in tandem was the fastest configuration evaluated. Additional sets of melon locations werestochastically generated from distributions of the field data to determine performance for planting distancesbetween 25 and 125 cm. The fastest cycle time was achieved for an experimental cultural practice that consistedof one plant on each half row in an alternating sequence with 125 cm planting distance. The performance of therobotic melon harvester was found to be highly dependent on the picking time, actuator speeds and plantingdistance.

139.NAL Call No.: 58.9-IN7Design principles for automatic milking systems.Mottram, T. T. Agric-Eng v.46(2): p.39-42. (1991 Summer)Includes references.Descriptors: dairy-farming; milking-machines; automation; design; robots

140.NAL Call No.: 381-J8223Detection of fungal contamination in corn: potential of FTIR-PAS and - DRS.Greene, R. V.; Gordon, S. H.; Jackson, M. A.; Bennett, G. A. J-agric-food- chem v.40(7): p.1144-1149. (1992July)Includes references.Descriptors: zea-mays; maize; kernels; contamination; aspergillus- flavus; gibberella-fujikuroi; chemical-analysis; detection; analytical-methods

Abstract: Evaluation of agricultural grains, such as corn, suffers from a lack of techniques that can analyze solidmaterials. Two techniques, photoacoustic spectroscopy (PAS) and diffuse reflectance spectroscopy (DRS), werecoupled to a Fourier transform infrared (FTIR) spectrometer to provide information about the mid-infraredabsorption spectra of corn. Spectra generated from corn that was infected with Fusarium moniliforme orAspergillus flavus, two mycotoxin producers, were dramatically different from those of uninfected corn. ForF.moniliforme, enhanced spectral differences were associated with elevated culture toxicity. Preliminary studies toappraise the sensitivity of the methodology were conducted utilizing DRS. These indicated that spectra of corncontaminated at the 3% level (dry weight basis) with F. moniliforme were distinguishable from spectralvariations associated with compositional divergence of different corn varieties. PAS was a more sensitivetechnique for detecting such fungal contaminations. Unfortunately, from a practical standpoint, PAS canpresently analyze only one intact kernel at a time.


141.NAL Call No.: QA76.76.E95A5Determination of greenhouse climate setpoints by SERRISTE: the approach and its object-orientedimplementation.Martin Clouaire, R.; Kovats, K.; Cros, M. J. AI-Appl v.7(1): p.1-15. (1993)Includes references.Descriptors: lycopersicon-esculentum; greenhouse-crops; winter; time; environmental-temperature; humidity;computer-software

142.NAL Call No.: SF55.A78A7Determination of longissimus muscle area in pig with ultrasonic linear electronic scanner.Irie, M. Asian-Australasian-J-Anim-Sci v.5(2): p.229-235. (1992 June)Includes references.Descriptors: pigs; ultrasonic-devices; loins; longissimus-dorsi; measurement

143.NAL Call No.: Q184.R4Determination of vegetation canopy parameters from optical measurements.Kuusk, A. Remote-Sensing-Environ v.37(3): p.207-218. (1991 Sept.)Includes references.Descriptors: hordeum-vulgare; trifolium-pratense; canopy; reflectance; spectral-data; growth-period; models;measurement; estonian-ssr

144.NAL Call No.: 80-AC82Determining seedling characteristics using computer vision and its application to an expert system forgrading seedlings.Sase, S.; Nara, M.; Okuya, T.; Sueyoshi, K. Acta-Hortic v.2(319): p.683-688. (1992 Oct.)Paper presented at the International Symposium on Transplant Production Systems- -Biological, Engineeringand Socioeconomics Aspects, July 21-26, 1992, Yokohama, Japan.Descriptors: lactuca-sativa; seedlings; grading; automation; computer- techniques; vision; expert-systems

145.NAL Call No.: S494.5.D3I5-1990Developing countries: A simple software for farm management.Carpineti, C. Proceedings of the 3rd International Conference on Computers in Agricultural ExtensionPrograms / Fedro S. Zazueta, editor. ; January 31- February 1, 1990, Grosvenor Resort Hotel, Disney WorldVillage, Lake Buenavista, FL. Gainesville, FL : Florida Cooperative Extension Service, University of Florida,[1990]. p. 373-378.Descriptors: farm-management; computer-software; developing-countries

146.NAL Call No.: S494.5.D3C652Development and application of computer vision systems for use in livestock production.Stuyft, E. v. d.; Schofield, C. P.; Randall, J. M.; Wambacq, P.; Goedseels, V. Comput-Electron-Agric v.6(3):p.243-265. (1991 Dec.)Includes references.Descriptors: pigs; livestock; animal-production; computer-techniques; feasibility; imagery

147.NAL Call No.: 464.8-AN72Development, implementation, and adoption of expert systems in plant pathology.Travis, J. W.; Latin, R. X. Annu-Rev-Phytopathol. Palo Alto, Calif. : Annual Reviews, Inc. 1991. v. 29 p. 343-360.Literature review.Descriptors: plant-pathology; plant-protection; integrated-pest- management; decision-making; computer-software; expert-systems; literature- reviews; disease-models; artificial-intelligence; knowledge-based-systems;plant; ds; pomme; grapes; counsellor; white-pine-blisterust; apple-pest-and- disease- diagnosis; calex; peaches;penn-state-apple-orchard-consultant; muskmelon-disorder-management-system

148.NAL Call No.: S494.5.D3C68-1992Development of a computer program (UTILIS) for correct pig slurry management.

Balsari, P.; Calvo, A.; Airoldi, G. Computers in agricultural extension programs proceedings of the 4thinternational conference, 28-31 January 1992, Orlando, Florida / sponspored by the Florida CooperativeExtension Service, University of Florida. St. Joseph, Mich. : American Society of Agricultural Engineers,c1992.. p. 559-564.Includes references.Descriptors: pig-slurry; waste-disposal; computer-software

149.NAL Call No.: QH540.J6Development of a database and model parameter analysis system for agricultural soils.Carsel, R. F.; Imhoff, J. C.; Kittle, J. L. Jr.; Hummel, P. R. J-Environ- Qual v.20(3): p.642-647. (1991 July-1991Sept.)Includes references.Descriptors: water-quality; water-management; databases; computer- software; water-flow

Abstract: An interactive computer program was developed for obtaining soils data for geographic analyses andestimation of soil water retention data for simplistic and classical water flow models. The soils data basecontains 8080 soil series identified from the USDA-SCS. The data are organized in sequential files that containtextural, morphological crop support, and geographical location (at a county level) and density (ha/county). Thecomputer program allows the exploration of the database, clarifying the impact of data on modeled processes,screening geographically based data to identify potential sites for model application or testing, and developinginitial guidance on alternative water quality management strategies. The program allows the display of data inthe form of generated reports and production of geographic maps and plots of soil water functional relationships.Indirect methods are used in the program for estimating soil water retention characteristics using texturalinformation from the soil data base. Estimates of variability can be developed within a soil series or amongseries by using reported ranges for textural information on each series contained in the soil database.

150.NAL Call No.: S494.5.D3I5-1988Development of a microcomputer-based expert system for apple scab management.Cooley, D.; Cohen, P.; Ward, K. Proceedings of the 2nd International Conference on Computers in AgriculturalExtension Programs Fedro S Zazueta, AB Del Bottcher, eds p.230-233. (of Florida, [1988?].)Conference held February 10-11, 1988 at the Grosvenor Resort Hotel, Disney World Village, Lake Buenavista,Orlando, Florida.Descriptors: venturia-inaequalis; integrated-pest-management; expert- systems; massachusetts

151.NAL Call No.: 80-AC82Development of an expert system using image database for diagnosing plant protection.Hoshi, T.; Abe, T.; Nuki, K. Acta-Hortic v.2(319): p.635-640. (1992 Oct.)Paper presented at the International Symposium on Transplant Production Systems- -Biological, Engineeringand Socioeconomics Aspects, July 21-26, 1992, Yokohama, Japan.Descriptors: lawns-and-turf; diagnosis; plant-diseases; plant-pests; functional-disorders; expert-systems;imagery; databases; microcomputers

152.NAL Call No.: 44.8-J822Development of an integrated knowledge-based system for management support on dairy farms.Hogeveen, H.; Noordhuizen Stassen, E. N.; Schreinemakers, J. F.; Brand, A. J-Dairy-Sci v.74(12): p.4377-4384.(1991 Dec.)Includes references.Descriptors: dairy-farming; information-processing; computer-software; information-systems; knowledge

Abstract: A knowledge-based system is an advanced computer program that can solve problems requiring theuse of expertise and experience. This feature makes it very suitable for use in dairy farm management. A

knowledge- based system contains a knowledge base, an inference engine, and a user interface. In secondgeneration knowledge-based systems, the knowledge base is based upon a model in which declarativeknowledge is stored. One of the possibilities for a model is a causal model. Causal models and other knowledgerepresentation schemes can be used in an integrated knowledge-based system for management support on dairyfarms. Such a knowledge-based system can contain three modules: 1) a health module, which must, for example,be able to detect and diagnose on-line (subclinical) diseases, such as mastitis, in an early stage; 2) a productionmodule, which must help to reduce and prevent losses from diseases and managerial deficiencies; and 3) afinancial module, which must be able to detect suboptimal financial results and search for the reasons causingthose results. Tools and methods that can be used to build such a large integral knowledge-based system arediscussed.

153.NAL Call No.: 80-AC82Development of automated seedling production and transplanting system robotics.Sakaue, O. Acta-Hortic v.2(319): p.557-562. (1992 Oct.)Paper presented at the International Symposium on Transplant Production Systems- -Biological, Engineeringand Socioeconomics Aspects, July 21-26, 1992, Yokohama, Japan.Descriptors: vegetables; seedlings; production; transplanting; mechanization; automation; robots; construction;design; performance; japan

154.NAL Call No.: S494.5.D3I5-1990Development of software for beef ranchers in California.Drake, D. J.; Finazzo, J.; Ostergard, M. M. Proceedings of the 3rd International Conference on Computers inAgricultural Extension Programs / Fedro S. Zazueta, editor. ; January 31- February 1, 1990, Grosvenor ResortHotel, Disney World Village, Lake Buenavista, FL. Gainesville, FL : Florida Cooperative Extension Service,University of Florida, [1990]. p. 436-440.Includes references.Descriptors: beef-production; management; finance; educational- programs; computer-software; cooperative-extension-service; california

155.NAL Call No.: S671.A66Development of tillage system selection software for corn/soybean production.Meyer, C. R.; Parsons, S. D.; Griffith, D. R.; Mannering, J. V.; Steinhardt, G. C. Appl-Eng-Agric v.7(3): p.367-373. (1991 May)Includes references.Descriptors: zea-mays; glycine-max; production; tillage; computer- software; expert-systems; tillage-expert-system; optimize-production

Abstract: Development of a regionally-specific expert system to estimate corn/soybean production on anindividual-field and whole-farm basis is described. Rules and equations to project yield as a function of tillagesystem, crop rotation, latitude, soil series, and soybean row spacing and maturity group were derived frominterviews with three experts. The resulting knowledge was encoded into computer logic written entirely in C-language. Although very small, the program retains the functionality of expert systems developed in shells. On-line explanations are available to explain why each input is requested. Help screens offer expanded explanationof each question. Conclusions are displayed as they are reached. Management suggestions are offered whereappropriate, including recommending a conservation tillage system, flagging highly erodible fields, indicatingerosion control measures, suggesting that a field be tilled as two separate fields, and warning against farmingsteep slopes in row crops. The program goes beyond the features offered by some shells, permitting the user toback up in the program, to execute UNIX or DOS commands from within the program, and to store a partial runin a disk file to be resumed later. The program has been released as Public Domain software, with over 300copies currently in use.

156.NAL Call No.: 80-AC82Different ways of obtaining technological parameters for computer assisted soil and crop management inthe production of field vegetables in the GDR.Frohlich, H.; Klaring, P. Acta-Hortic (260): p.295-312. (1989 Sept.)Paper presented at the "International Symposium on Growth and Yield Control in Vegetable Production," /edited by G. Vogel, May 22-25, 1989, Berlin, German Democratic Republic.Descriptors: vegetables; field-experimentation; crop-production; crop- management; soil-management;technology; data-collection; computer-software; data-processing; crop-yield; statistical-data; models; german-democratic- republic

157.NAL Call No.: 99.8-F7623Digital forest management: Canfor's experience.Winkle, P. For-Chron v.67(6): p.630-634. (1991 Dec.)Descriptors: forest-management; information-systems; geographic- information-systems

Abstract: Canfor's Englewood Division acquired a GIS two years age. Within this period, we have developed theframework necessary to digitally manage our 200,000 hectare Tree Farm License. This paper focuses mainly onthe role GIS played in our Management and Working Plan. The plan is produced every five years to documentand justify our forest land management techniques. It addresses issues of current and long-term wood supply; the200 year horizon, silviculture regimes, and habitat requirements. GIS was used in conjunction with a forestestate model to test numerous management scenarios. Important issues included the decision to load 'dirty' data,the acquisition of contour data, networking data, raster/vector processing, restructuring for feature codes, andbecoming a 'beta' test site for GIS software. In addition, we discuss our objectives for 1991 relating to training,wildlife habitat, ambrosia control, a cruise prediction system, coordinate geometry and other goals.

158.NAL Call No.: 80-AC82Direct inserting seeder for culture media.Tanaka, F. Acta-Hortic v.2(319): p.551-556. (1992 Oct.)Paper presented at the International Symposium on Transplant Production Systems- -Biological, Engineeringand Socioeconomics Aspects, July 21-26, 1992, Yokohama, Japan.Descriptors: leafy-vegetables; precision-drilling; drills; culture- media; mechanization; robots; construction;design; performance; japan

159.NAL Call No.: 290.9-AM32PDIRTE-1: model development and formulation.Koger, J.; Stokes, B. J.; Sirois, D. L. PAP-AMER-SOC-AGRIC-ENG. St. Joseph, Mich. : The Society. Winter1989. (89-7549) 55 p.Paper presented at the "1989 International Winter Meeting sponsored by the American Society of AgriculturalEngineers," December 12-15, 1989, New Orleans, Louisiana.Descriptors: forest-management; harvesting; equipment; microcomputers

160.NAL Call No.: QA76.76.E95A5DRYPLAN: a computer-based decision-support system for sustainable land- use planning.Biggins, J. G. AI-Appl-Nat-Resour-Manage v.5(3): p.57-59. (1991)Includes references.Descriptors: land-use; sustainability; soil-degradation; erosion; land-management; soil-conservation; planning;expert-systems; computer-software; australia


161.NAL Call No.: 44.8-J822DXMAS: an expert system program providing management advice to dairy operators.Schmisseur, E.; Gamroth, M. J. J-dairy-sci v.76(7): p.2039-2049. (1993 July)Includes references.Descriptors: expert-systems; dairy-farming; farm-management; decision- making; information-systems; culling;cattle-manure; replacement; crops; financial-planning

Abstract: An expert system, or knowledge-based, microcomputer program, DXMAS, was designed anddeveloped to diagnose dairy management problems of dairy farmers of Tillamook County, Oregon and, asappropriate, to advance potential farm reorganization and expansion options. The program provokesmanagement action by projecting lost income opportunities attributed to major management problems andmissed reorganization and expansion opportunities. The DXMAS program analyzes annual economic andproduction performance data provided by dairy operators and has demonstrated the ability, in field testing ofnine different dairy operations, to emulate dairy management experts in the diagnoses of 95 individual dairymanagement problems. In those field tests, the DXMAS program identified a variety of management problemsand estimated annual lost income opportunities ranging from $25 to $450 per milk cow. Field testing suggestedthat the DXMAS program can provide a wide range of expert management advice to dairy operators.

162.NAL Call No.: S544.3.N7A4EASY-MACS: a computer-based system for apple pest management.Nyrop, J.; McInnis, P.; Reissig, H.; Agnello, A.; Rosenberg, D.; Wilcox, W.; Kovich, J. Agfocus-Publ-Cornell-Coop-Ext-Orange-Cty p.15-16. (1990 Mar.)Descriptors: malus-pumila; pest-management; computer-software

163.NAL Call No.: 281.8-AG835An economic analysis of beef stocking rates.Griffin, W. N.; Ortmann, G. F. Agrekon v.29(2): p.102-107. (1990 June)Includes references.Descriptors: beef-cattle; stocking-rate; animal-production; profits; returns; pastures; microcomputers; computer-software; mathematical-models; costs; south-africa

164.NAL Call No.: 421-J822Economic injury levels for management of stalk borer (Lepidoptera: Noctuidae) in corn.Davis, P. M.; Pedigo, L. P. J-Econ-Entomol v.84(1): p.290-293. (1991 Feb.)Includes references.Descriptors: zea-mays; crop-damage; papaipema-nebris; pest-management; simulation-models; computer-software; basic-software

Abstract: A computer program was developed to predict yield in corn (Zea mays L.) infested by stalk borer,Papaipema nebris (Guenee), on the basis of injury profiles for each leaf stage and regression models forpredicting yield of individual plants. Yield losses caused by stalk borer declined as corn was attacked later indevelopment. Once the stalk begins to elongate (6-leaf stage), the ability of the stand to tolerate stalk borerinjury sharply increases. However, yield loss in 6- and 7-leaf corn was much greater under drought stress thanwhen moisture was adequate. Yield losses for selected leaf stages were comparable to those reported for blackcutworm, Agrotis ipsilon (Hufnagel), and European corn borer, Ostrinia nubilalis (Hubner). Predictions fromthis model were used to calculate economic injury levels for corn attacked at leaf stages 1-7 under adequatemoisture and drought conditions. A management program, which incorporates larval sampling in noncrop areasand prediction of movement on the basis of degree-day accumulations, is presented.

165.NAL Call No.: SD143.S64Economic issues in new perspectives: view from the Northwest.Weigand, J. F. Proc-Soc-Am-For-Natl-Conv p.576-577. (1991)Meeting held Aug 4-7, 1991, San Francisco, California.Descriptors: pseudotsuga-menziesii; resource-management; economic- impact; environmental-management;national-forests; models; computer-software; habitats; wildlife; oregon

166.NAL Call No.: S494.5.D3I5-1988Economics and accounting, problems with current farm management software.Griffith, D. A. Proceedings of the 2nd International Conference on Computers in Agricultural ExtensionPrograms Fedro S Zazueta, AB Del Bottcher, eds p.317-322. (of Florida, [1988?].)Conference held February 10-11, 1988 at the Grosvenor Resort Hotel, Disney World Village, Lake Buenavista,Orlando, Florida.Descriptors: farm-management; computer-software

167.NAL Call No.: S494.5.D3C68-1992The economics of integrating poultry and aquaculture production: a dynamic simulation approach.Gempesaw, C. M. I.; Bacon, J. R.; Wirth, F. F. Computers in agricultural extension programs proceedings of the4th international conference, 28-31 January 1992, Orlando, Florida / sponspored by the Florida CooperativeExtension Service, University of Florida. St. Joseph, Mich. : American Society of Agricultural Engineers,c1992.. p. 111-116.Includes references.Descriptors: poultry; aquaculture; animal-production; integrated- systems; economic-viability; computer-software; delaware; aquasim

168.NAL Call No.: 44.8-J822The economics of naturally occurring twinning in dairy cattle.Beerepoot, G. M. M.; Dykhuizen, A. A.; Nielen, M.; Schukken, Y. H. J-Dairy- Sci v.75(4): p.1044-1051. (1992Apr.)Includes references.Descriptors: dairy-cows; twinning; dutch-black-pied; computer- software; simulation-models; costs; cost-benefit-analysis; losses; calves; birth-weight; netherlands

Abstract: To determine the additional costs and returns of twin calvings in dairy cattle, an economic model wasdeveloped on the personal computer. Data used in the model were recorded on 33 farms over 6.5 yr and included381 twin calvings. For missing information, assumptions were made from the literature. Additional calf returnsturned out to be $2.96. Total additional cost were $71.47, consisting of $100.92 for milk reduction, $39.51 forincreased premature culling, $19.25 for increased occurrence of abortion, $5.69 for increased therapy, and $6.09for increased calving interval. Total losses were on average $171.47 - $62.69 = $108.51 per twin birth. Realisticchanges in input variables could not change this negative outcome to a positive result. Therefore, it wasconcluded that it is not profitable to select to increase the number of twins in dairy cattle.

169.NAL Call No.: S671.A66Economics of swath manipulation during field curing of alfalfa.Rotz, C. A.; Savoie, P. Appl-Eng-Agric v.7(3): p.316-323. (1991 May)Includes references.Descriptors: alfalfa; curing; tedding; swath-turners; computer- software; models; economics

Abstract: The economic values of tedding and swath inversion operations in alfalfa hay production wereevaluated with DAFOSYM, a comprehensive model of crop growth, harvest, storage, and feeding on a dairy

farm. Twenty-six year simulations determined the long-term performance and economics of the two processesfor a variety of management strategies on a representative dairy farm in Michigan. Tedding reduced the averagefield curing time about 13 h in first cutting and 6 h on later cuttings while swath inversion reduced the averagecuring time by 1 to 6 h. Mechanical losses caused by tedding were greater than the average rain-induced lossavoided by using the process. With little improvement in the quantity and quality of hay produced, additionalmachinery and labor costs of tedding decreased farm income. Swath inversion caused less loss, but costs werehigher giving a similar range in the loss of farm income. Simulation of the systems on the same farm in QuebecCanada with two cuttings of alfalfa gave similar results. The economic value of swath manipulation treatmentswas not highly dependent upon any of the major model parameters assumed in the analysis.

170.NAL Call No.: aHV4701.A952Education, computer software and animal welfare.Peterson, N. S. Anim-Welfare-Inf-Cent-Newsl v.2(2): p.3, 7. (1991 Apr.- 1991 June)Includes references.Descriptors: animal-welfare; animal-testing-alternatives; computer- software; educational-technology

171.NAL Call No.: 4-AM34PEffect of maize maturity on radiation-use efficiency.Major, D. J.; Beasley, B. W.; Hamilton, R. I. Agron-J v.83(5): p.895- 903. (1991 Sept.-1991 Oct.)Includes references.Descriptors: zea-mays; hybrids; solar-radiation; use-efficiency; photosynthesis; stand-density; canopy;reflectance; models; leaf-area-index; leaf-angle; distribution; transmittance; crop-yield; maturity; alberta; early-maturing-hybrids; photosynthetically-active-radiation

Abstract: Maize (Zea mays L.) production has expanded into short- season regions but it is not known whetherthe radiation-use efficiency (RUE, g MJ- 1) of new early hybrids differs from those grown in traditional maize-growing regions. In this study, spectral reflectance measurements were used to derive estimates ofphotosynthetically active radiation (PAR) absorbed to compare the RUE of 10 maize hybrids that varied inadaptation from Iowa 110-d relative maturity to the earliest 60-d relative maturity hybrids commerciallyavailable. Reflectance measurements were made radiometrically in the visible and near-infrared regions of theelectromagnetic spectrum at approximately weekly intervals in 1985, 1986, and 1988. The 10 maize hybridswere grown at three densities at Lethbridge, Alberta, on an irrigated silty clay loam soil (Typic Haploboroll).The Scattering by Arbitrarily Inclined Leaves (SAIL) model of canopy reflectance was inverted to produce dailyestimates of the fraction of absorbed PAR, p. Multiplying p by daily PAR irradiance gave daily estimates ofabsorbed PAR (APAR, MJ m-2), which were summed for the season. Radiation use efficiency was obtained bydividing whole-plant yield at harvest by seasonal (emergence to harvest) APAR. Averaged over years, hybrids,and densities, RUE was 2.3 g MJ-1. Radiation-use efficiency increased with population density across hybridsregardless of maturity. Seasonal RUE was lower than reported in the literature but there is evidence that chillinginjury due to low night temperatures at the high elevation and semi-arid location of the study reducedphotosynthesis. The results suggest that spectral reflectance can be used effectively by breeders to identifyhybrids that are more efficient users of PAR and that maize hybrids resistant to chilling injury may be needed athigh latitudes.

172.NAL Call No.: 49-J82Effects of forage and protein source on feedlot performance and carcass traits of Holstein and crossbredbeef steers.Comerford, J. W.; House, R. B.; Harpster, H. W.; Henning, W. R.; Cooper, J. B. J-Anim-Sci v.70(4): p.1022-1031. (1992 Apr.)Includes references.Descriptors: steers; holstein-friesian; beef-cattle; maize-silage; crossbreds; alfalfa-haylage; soybean-oilmeal;fish-meal; carcass-composition; carcass- quality; carcass-weight; feed-conversion-efficiency

Abstract: Fifty-eight Holstein and 58 crossbred beef steers were individually fed one of four isonitrogenous dietsto evaluate the effects of forage source (corn silage and alfalfa haylage) and protein source (soybean meal andfish meal) on feedlot performance. Phase I diets (up to 354 kg of BW) were 40% forage and 60% concentratesand were fed for 70 to 136 d (depending on diet and breed group). Phase 2 diets (354 kg of BW until slaughter)were 20% forage and 80% concentrates and were fed for 127 to 150 d (depending on diet and breed group).Slaughter end points were .6 cm of 12th rib fat for Holsteins and 1.0 cm of rib fat for crossbreds using real-timeultrasonic estimates. The steers were fed for a maximum of 330 d each year. Forage source was a significantcomponent of variation for most growth, efficiency, and carcass traits. Holstein and crossbred steers fed alfalfahaylage had significantly lower average daily gain, feed efficiency, dressing percentage, and empty body fat andrequired more days on feed to reach slaughter end points, but had higher total feed energy intake available forproduction. Steers fed corn silage diets had significantly greater energetic efficiency (P < .05) than those fedalfalfa haylage, due to increased use of ME to produce fat in the carcass. Protein type did not influence gain,feed or energetic efficiency, energy intake, or most carcass traits. A significant protein system X forage sourceinteraction among the four diets was detected for crossbred steers fed corn silage and fish meal, for which therewas significantly greater feed conversion with lower energy intake above maintenance, possibly due to betterfiber digestion and(or) amino acid flow to the lower tract. Alfalfa haylage plus soybean meal diets decreased (P< .05) the percentage of Holsteins grading USDA Choice or higher. These results indicate that corn silage,because of greater energy concentration, was a more desirable forage in feedlot diets composed ofless than orequal to 40% forage and that protein type (soybean meal and fish meal) in growing diets is not an importantfactor in feedlot performance or carcass traits of Holstein or crossbred steers that are fed these diets.

173.NAL Call No.: 80-AM329The electronic orchidist.Am-Orchid-Soc-Bull v.62(3): p.266-270. (1993 Mar.)Descriptors: orchidaceae; cultivation; computer-software

174.NAL Call No.: 290.9-AM32TAn end-effector for robotic removal of citrus from the tree.Pool, T. A.; Harrell, R. C. Trans-A-S-A-E v.34(2): p.373-378. (1991 Mar.-1991 Apr.)Includes references.Descriptors: citrus; harvesting; mechanical-damage; mechanical- harvesting; performance; robots; testing;florida

Abstract: The design of a robotic end-effector for picking citrus fruit is presented and its performance evaluated.The end-effector utilized a rotating-lip mechanism to capture a fruit. Incorporated into the end-effector were acolor CCD camera and an ultrasonic transducer for determining the location of a fruit in three dimensions. End-effector performance was assessed by quantifying its capture envelope, fruit removal success rate, and damageinflicted to fruit and tree. Capture envelop was determined with laboratory tests while success and damage rateswere quantified through field trials. The end-effector successfully removed fruit in 69% of the pick attempts andcaused damage on 37% of the pick attempts. It was concluded that the rotating-lip approach to citrus removalwas appropriate but refinement of the end-effector was needed to improve its sucess rate and to reduce damagerates.

175.NAL Call No.: S494.5.E547Energy input-output simulation of crop production.Muller, R. E. Energy-World-Agric. Amsterdam : Elsevier. 1992. v. 5 p. 89- 116.In the series analytic: Analysis of Agricultural Energy Systems / edited by R.M. Peart and R.C. Brook.Descriptors: crop-production; energy-consumption; input-output- analysis; computer-software; usa

176.NAL Call No.: 58.9-IN7Engineering opportunities in the environment.

O'Callaghan, J. R. Agric-Eng v.46(3): p.80-83. (1991 Autumn)Includes references.Descriptors: agricultural-engineering; farming; farm-equipment; machinery; fertilizer-distributors; straw-disposal; harvesting; computer- software

177.NAL Call No.: aSD388.A1U52An engineering survey method for use with the Laser Technology, Inc., tree laser device.Moll, J. Eng-Field-Notes-U-S-Dep-Agric-For-Serv-Eng-Staff. Washington, D.C. : The Staff. Nov/Dec 1992. v. 24p. 31-38.Includes references.Descriptors: forests; roads; surveys; lasers

178.NAL Call No.: S494.5.D3I5-1988Enhancing extension agribusiness management programming using the management edge.Torok, S. J. Proceedings of the 2nd International Conference on Computers in Agricultural Extension ProgramsFedro S Zazueta, AB Del Bottcher, eds p.356-361. (of Florida, [1988?].)Conference held February 10-11, 1988 at the Grosvenor Resort Hotel, Disney World Village, Lake Buenavista,Orlando, Florida.Descriptors: agribusiness; management; computer-software; expert- systems

179.NAL Call No.: SB121.I57-1992Environmental and hormonal effects in micropropagation.Read, P. E. Transplant production systems proceedings of the International Symposium on TransplantProduction Systems, Yokokama, Japan, 21-26 July 1992 / edited by K Kurata and T Kozai. Dordrecht : KluwerAcademic Publishers, 1992.. p. 231-246.Includes references.Descriptors: micropropagation; automation; robots

180.NAL Call No.: TP248.25.A96T68-1990Environmental control and automation in micropropagation.Kozai, T. Automation in biotechnology a collection of contributions presented at the Fourth Toyota Conference,Aichi, Japan, 21-24 October 1990 / edited by Isao Karube. Amsterdam : Elsevier c1991.. p. 279-304.Includes references.Descriptors: plants; micropropagation; automation; tissue-culture; explants; carbon-dioxide-enrichment;photosynthesis; light-intensity; environmental- control; robots; plantlets

Abstract: Reasons for the high production cost of micropropagated plantlets are discussed. CO2 concentrationsin the culture vessel and photosynthetic characteristics of plantlets in vitro (in tissue culture vessels) aredescribed. The effect of CO2 enrichment under high photosynthetic photon flux conditions on the growth ofplantlets in vitro (in tissue culture vessels) is shown. Four prototype robotic/automated micropropagationsystems recently developed in Japan are introduced. Both may contribute to a reduction in cost ofmicropropagated plants in the future.


181.NAL Call No.: 290.9-AM32PEnvironmentally sound agricultural production systems through site- specific farming.Engel, B. A.; Gaultney, L. D. PAP-AMER-SOC-AGRIC-ENG. St. Joseph, Mich. : The Society. Winter 1990. (90-

2566) 7 p.Paper presented at the "1990 International Winter Meeting", December 18-21, 1990, Chicago, Illinois.Descriptors: agricultural-production; environmental-protection; information-systems; environmental-impact;geographic-information-systems

182.NAL Call No.: HD1.A3EPIC: an operational model for evaluation of agricultural sustainability.Jones, C. A.; Dyke, P. T.; Williams, J. R.; Kiniry, J. R.; Benson, V. W.; Griggs, R. H. Agric-Syst v.37(4): p.341-350. (1991)Includes references.Descriptors: sustainability; wind-erosion; water-erosion; erosion- control; weather-data; hydrology; cycling;soil-temperature; tillage; growth; soilmanagement; crop-management; costs; returns; productivity; climatic-change; simulation-models; computer-software; evaluation; usa; erosion- productivity-impact-calculator

183.NAL Call No.: 281.8-AG835Erratum: An economic analysis of beef stocking rates.Griffin, W. N.; Ortmann, G. F. Agrekon v.29(3): p.ii. (1990 Sept.)Descriptors: beef-cattle; stocking-rate; animal-production; profits; returns; pastures; microcomputers; computer-software; mathematical-models; costs; south-africa

184.NAL Call No.: Q184.R4Estimating vegetation amount from visible and near infrared reflectances.Price, J. C. Remote-Sensing-Environ. New York, N.Y. : Elsevier Science Publishing. July 1992. v.41 (1) p. 29-34.Includes references.Descriptors: vegetation; canopy; measurement; spatial-variation; soil; reflectance; landsat; leaf-area-index;agricultural-land; equations; vegetation- cover; satellite-positioning-and-tracking; leaf-vegetation-index

185.NAL Call No.: SF207.B442Estimation of backfat thickness in beef cattle by ultrasound.Gauck, D. M.; Davis, M. E. Ohio-Beef-Cattle-Res-Ind-Rep (90-2): p.170- 176. (1990 Mar.)Includes references.Descriptors: beef-cattle; ultrasound; backfat; prediction; slaughter; measurement; carcass-yield; ohio

186.NAL Call No.: 41.8-M69Evaluating individual and overall herd data for beef cattle clients.Ringwall, K. A.; Berg, P. M.; Boggs, D. L. Vet-Med v.87(8): p.849-854. (1992 Aug.)Descriptors: beef-herds; performance; computer-software; record- keeping; records; evaluation; chaps-ii

187.NAL Call No.: S590.C63Evaluating SOY-DRIS for predicting manganese deficiency and sufficiency.Shuman, L. M.; Wilson, D. O.; Hallmark, W. B. Commun-Soil-Sci-Plant- Anal v.23(9/10): p.1019-1029. (1992)Includes references.Descriptors: glycine-max; foliar-diagnosis; dris; mineral- deficiencies; mineral-excess; fertilizer-requirement-determination; manganese; accuracy; computer-software; georgia; sufficiency-range-method-srm

188.NAL Call No.: S671.A66Evaluating timber sale bids using optimal bucking technology.Olsen, E. D.; Pilkerton, S. J.; Garland, J. J. Appl-Eng-Agric v.7(1): p.131-136. (1991 Jan.)

Includes references.Descriptors: timber-trade; harvesting; forest-management; computer- software; valuation; stand-characteristics;cruise; buck

Abstract: This study documented and field tested a method of using optimal bucking procedures to aid incruising and stand value appraisals. The CRUISE/BUCK method can estimate the type of logs which should becut from a stand and evaluate the potential revenue if different sets of mills are chosen as the purchasers. Thistype of pre-harvest analysis can aid managers in how to "merchandize" the stand. Alternative methods ofcollecting diameter measurements were compared.

189.NAL Call No.: 49-J82Evaluation of alternative techniques to determine pork carcass value.Akridge, J. T.; Brorsen, B. W.; Whipker, L. D.; Forrest, J. C.; Kuei, C. H.; Schinckel, A. P. J-Anim-Sci v.70(1):p.18-28. (1992 Jan.)Includes references.Descriptors: pigs; carcass-grading; carcass-composition; meat-yield; carcass-quality; ultrasound; ultrasonic-fat-meters; economic-evaluation; optical- instruments; electromagnetic-radiation; scanning; market-prices; bonuses;discounts; backfat

Abstract: Three techniques for estimating the value of pork carcasses were evaluated: an optical probe, a real-time ultrasound scanner, and an electromagnetic scanner (EMSCAN). The ability of these techniques to predictcarcass value was compared to the predictive ability of actual measures of backfat depth and longissimus musclearea taken with a ruler and a dot grid. Results indicated the EMSCAN model was the best predictor of carcassvalue. However, the optical probe, ultrasound, and the ruler/dot grid all provided information not contained inthe EMSCAN model. The choice among ultrasound, the optical probe, and the ruler/dot grid depends on how thecarcass will be used. There is no significant difference between ultrasound and the ruler/dot grid or the opticalprobe and the ruler/dot grid if the carcass is to be marketed in wholesale primal form, but the ruler/dot grid issuperior if the ham and loin are to be sold as lean, boneless products. A model combining the EMSCAN andoptical probe readings provided more accurate value predictions than either technique alone. A carcass valuematrix for use in pricing pork carcasses was developed using readings from the optical probe. Carcass use has asubstantial impact on value differences between fat and lean pigs.

190.NAL Call No.: 44.8-J822Evaluation of bulls for nonreturn rates within artificial insemination organizations.Schaeffer, L. R. J-Dairy-Sci v.76(3): p.837-842. (1993 Mar.)Includes references.Descriptors: dairy-bulls; ai-bulls; male-fertility; pregnancy-rate; computer-software; evaluation; sires;repeatability; canada

Abstract: Programs have been written for use on microcomputers to utilize breeding receipt data collected by AIorganizations to evaluate dairy bulls for nonreturn rates. The statistical model of analysis allows the user to haveup to four fixed factors, as well as herd or herd-year effects, technician effects, and service sire effects. As anexample, 137,874 AI in 1991 from one organization were analyzed. Data included information from 3609 herds,80 technicians, and 464 sires. Although AI organizations traditionally compute 60- to 90-d nonreturn rates, theseprograms have caused organizations to consider using shorter period nonreturn rates in order to evaluate bullssooner. Evidence from other work indicated that the evaluations of bulls from this analysis were more highlycorrelated with physiological characteristics of ejaculates than simple nonreturn rates.

191.NAL Call No.: SF380.I52Evaluation of cropping strategies in game ranching using a livestock productivity model.Baptist, R.; Sommerlatte, M. Small-Ruminant-Res v.5(3): p.195-203. (1991 Aug.)Includes references.

Descriptors: alcelaphus-buselaphus; computer-software; simulation- models; productivity; culling; game-farming

192.NAL Call No.: S530.A4An evaluation of on-farm microcomputer use.Quinlan, D. J-Agric-Educ v.31(1): p.7-11. (1990 Spring)Includes references.Descriptors: microcomputers; farm-management; usage; farm-surveys; educational-programs; evaluation; iowa;tama-county,-iowa

193.NAL Call No.: 80-AC82Evaluation of the performance of ion-selective electrodes in an automatead NFT system.Heinen, M.; Harmanny, K. Acta-Hortic (304): p.273-280. (1992 Mar.)Paper presented at the "First International Workshop on Sensors in Horticulture", January 29-31, 1991,Noordwijkerhout, The Netherlands.Descriptors: crop-production; greenhouse-culture; nutrient-film- techniques; nutrient-solutions; temperature;hysteresis; monitoring; sensors; electrodes

194.NAL Call No.: 49-AN55An evaluation of two ultrasonic instruments for the prediction of carcass lean grade in growing pigs.Krieter, J.; Kalm, E. Anim-Prod v.52(pt.2): p.361-366. (1991 Apr.)Includes references.Descriptors: pigs; ultrasonic-fat-meters; body-composition; fat- percentage; liveweight; prediction;methodology; costs

195.NAL Call No.: 49-J82Evaluation of ultrasonic estimates of carcass fat thickness and longissimus muscle area in beef cattle.Perkins, T. L.; Green, R. D.; Hamlin, K. E. J-Anim-Sci v.70(4): p.1002- 1010. (1992 Apr.)Includes references.Descriptors: beef-cattle; steers; ultrasonic-fat-meters; backfat; prediction; accuracy; fat-thickness; longissimus-dorsi; area; carcass- composition; measurement; errors

Abstract: Yearling crossbred feedlot steers (n = 495) and heifers (n = 151) were ultrasonically measured at the12-13th rib interface 24 h before slaughter to evaluate the accuracy of ultrasonic measurements of fat thickness(BFU) and longissimus muscle area (LMAU) for prediction of actual carcass measures. Isonification was withan Aloka 210DX ultrasound unit equipped with a 12.5-cm, 3.0-MHz, linear array transducer by two technicians.Carcass fat thickness (BFC) and longissimus muscle area (LMAC) were measured 48 h postmortem. Differencesbetween ultrasonic and actual carcass measures were expressed in actual (BFDIFF and LMADIFF) and inabsolute (BFDIFF and LMADIFF) terms for backfat and longissimus muscle area, respectively. When expressedas percentages of the actual carcass measures, the average absolute differences indicated error rates of 20.6% forbackfat and 9.4% for longissimus muscle area. Average actual differences (BFDIFF and LMADIFF) indicatedthat underprediction occurred more often than overprediction for both measures. The BFU was within .25 cm ofBFC 70% of the time, and LMAU was within 6.5 cm2 of LMAC 53% of the time. Ultrasound measurementsBFU and LMAU more accurately predicted BFC and LMAC in thinner and more lightly muscled cattle,respectively. Simple correlation coefficients between ultrasonic and carcass measures were .75 (P < .01) for BFand .60 (P < .01) for LMA. Analyses of variance of absolute differences between ultrasonic and carcassmeasures indicated no significant differences to exist between technicians. Predictive accuracy of ultrasonicmeasures did not change as the level of experience of technicians increased during the study. This researchindicates that ultrasonic measurements of backfat and longissimus muscle area using these techniques takenbefore slaughter may be relatively accurate predictors of final carcass fat thickness and longissimus muscle areain beef cattle.

196.NAL Call No.: 49-J82Evaluation of ultrasound for prediction of carcass fat thickness and longissimus muscle area in feedlotsteers.Smith, M. T.; Oltjen, J. W.; Dolezal, H. G.; Gill, D. R.; Behrens, B. D. J- Anim-Sci v.70(1): p.29-37. (1992 Jan.)Includes references.Descriptors: steers; ultrasound; ultrasonic-fat-meters; fat-thickness; live-estimation; longissimus-dorsi; carcass-composition; muscles; accuracy

Abstract: Four hundred fifty-two yearling steers from two experiments were measured for subcutaneous fatthickness and longissimus muscle area between the 12th and 13th ribs using realtime linear array ultrasoundequipment. Ultrasonic predictions were compared to corresponding carcass measurements to determine accuracyof ultrasound measurements. In Exp. 1, 74% of the ultrasonic estimates of fat thickness were within 2.54 mm ofcarcass values (r = .81) and muscle area was predicted within 6.45 cm(2) for 47% of all carcasses (r = .43).Although similar correlation coefficients between ultrasonic and carcass fat thickness were obtained in Exp. 2 (r= .82.), estimates were more biased; only 62% of ultrasound estimates were within 2.54 mm of carcassmeasurements. Improvement in longissimus muscle area estimates was noted in Exp. 2, in which 54% ofultrasonic estimates were within 6.45 cm(2) of carcass values (r = .63). The extremes for each trait proved mostdifficult to predict; fat thickness was underestimated on fatter cattle and muscle area was underpredicted onmore heavily muscled steers. Ultrasonic measurements of fat thickness are precise and accurate in determiningcarcass fat thickness, but muscle area estimates are inconsistent and warrant further investigation.

197.NAL Call No.: HC79.I55K44-19991Every manager's guide to information technology : a glossary of key terms and concepts for today'sbusiness leader.Keen, P. G. W. Boston, Mass. : Harvard Business School Press, c1991. viii, 170 p., Includes index.Descriptors: Information-technology-Dictionaries

198.NAL Call No.: SB599.U6-[no.]-44Experiments on hand-held radiometry and IR-thermography of winter wheat in field plot experiments.Nilsson, H. E. Uppsala : Sveriges lantbruksuniversitet, 1987. 48 p. : ill., Summary in Swedish. Bibliography: p.13-15.

199.NAL Call No.: S494.5.D3C652Expert result analyzer for a field operations simulator.Lal, H.; Peart, R. M.; Shoup, W. D.; Jones, J. W. Comput-Electron-Agric v.6(2): p.123-141. (1991 Oct.)Includes references.Descriptors: crop-production; farm-management; expert-systems; computer-simulation; simulation-models;farmsys-computer-software

200.NAL Call No.: 44.8-J822Expert system for evaluation of reproductive performance and management.Domecq, J. J.; Nebel, R. L.; McGilliard, M. L.; Pasquino, A. T. J-Dairy- Sci v.74(10): p.3446-3453. (1991 Oct.)Includes references.Descriptors: dairy-cows; dairy-herds; dairy-performance; reproductive- efficiency; expert-systems; conception-rate

Abstract: A microcomputer expert system for dairy herd reproductive management was developed using anexpert system shell and Turbo Pascal. The expert system initially examines the broad areas of days open, days tofirst breeding, detection of estrus, and conception rate to determine whether a problem exists. Interpretationsranging from "excellent" to "severe" were established for each trait. The system then selects an area for

evaluation that has the largest negative influence on days open. Once an area has been selected for furtherevaluation, the expert system utilizes information from the user and DHI reports developed by the Dairy RecordsProcessing Center in Raleigh, NC. These reports identify problems with conception categorized by production,parity, service number, days in milk, breed, and service sire. In addition, questions are presented by the expertsystem to isolate problems of accuracy of data, use of natural service, semen handling, AI technique, detectionof estrus, signs of estrus, and other management areas. Recommendations and suggestions are given. Tencommercial herds having a conception rate less than 40% were evaluated by the expert system and by anextension reproduction specialist who supplied information for the system. Of 100 areas investigated, the expertsystem and extension specialist identified 47 as potential problem areas, agreeing on 85% of them. Mostdiscrepancies resulted from the specialist applying a less restrictive standard when values were close to apreselected threshold.


201.NAL Call No.: SB1.H6An expert system for integrated production management in muskmelon.Sullivan, G. H.; Ooms, W. J.; Wilcox, G. E.; Sanders, D. C. HortScience v.27(4): p.305-307. (1992 Apr.)Includes references.Descriptors: cucumis-melo; crop-production; expert-systems; integrated-systems; decision-making

Abstract: A management expert system that enables producers to fully assess the integrated resourcerequirements, management risks, and profit potential for growing muskmelon was developed. The expert systemenvironment Guru was used as the development software.

202.NAL Call No.: SF85.A1R32An expert system for prescribed burning of rangelands.Wright, H. A.; Burns, J. R.; Chang, H.; Blair, K. Rangelands v.14(5): p.286-292. (1992 Oct.)Includes references.Descriptors: rangelands; prescribed-burning; computer-software; field- tests; grasslands; weather; planning;range-management; texas

203.NAL Call No.: S494.5.D3I5-1990Extension experience with B.E.A.R.PLUS: financial planning software which incorporates risk.Brown, J. D.; Turvey, C. G.; Pfeiffer, W. C.; Anderson, J. A. Proceedings of the 3rd International Conference onComputers in Agricultural Extension Programs / Fedro S. Zazueta, editor. ; January 31- February 1, 1990,Grosvenor Resort Hotel, Disney World Village, Lake Buenavista, FL. Gainesville, FL : Florida CooperativeExtension Service, University of Florida, [1990]. p. 709- 714.Descriptors: farm-management; financial-planning; risk; analysis; computer- software; budgeting-enterprises-and-analysing-risk-plus-financial-statements

204.NAL Call No.: S494.5.D3I5-1988Extension mail management system in Umatilla County.Prothero, G. L. Proceedings of the 2nd International Conference on Computers in Agricultural ExtensionPrograms Fedro S Zazueta p.862-867. (of Florida, [1988?].)Meeting held February 10-11, 1988 at Lake Buenavista, Orlando, Florida.Descriptors: computer-software; computer-techniques; extension; oregon

205.NAL Call No.: SB249.N6

Extension of crop insurance evaluation system to cotton growers.Lovell, A. C.; Allen, G.; Richardson, J. W.; Zimmel, P.; Cochran, M. J.; Coats, R. E.; Windham, T. E. Proc-Beltwide-Cotton-Conf. Memphis, Tenn. : National Cotton Council of America. 1991. v. 1 p. 430-435.Paper presented at the "Cotton Economics and Marketing Conference," 1991, San Antonio, Texas.Descriptors: gossypium-hirsutum; crop-production; crop-yield; crop- insurance; computer-techniques;computer-software; cirman,-crop-insurance-risk- management-analyzer

206.NAL Call No.: S671.A66Factors affecting performance of sliding-needles gripper during robotic transplanting of seedlings.Yang, Y.; Ting, K. C.; Giacomelli, G. A. Appl-Eng-Agric v.7(4): p.493- 498. (1991 July)Includes references.Descriptors: ornamental-plants; seedlings; transplanting; robots; automation

Abstract: Transplanting tests with commercially grown seedling plugs were conducted using a Sliding-Needleswith Sensor (SNS) gripper operated by a SCARA type robot. A total of 11 plug trays, with 600 cells each, weretested. Many mechanical and horticultural factors were found to affect the percentage of successfultransplanting, which were analyzed to understand their influence on the effectiveness of the gripper. Themechanical factors were 1) the angles of gripper needles; 2) plug extraction acceleration; and 3) the sensorsensitivity. The horticultural factors included 1) empty cells on the plug trays; 2) plant species; 3) rootconnections; 4) adhesion between roots and cell walls; 5) root zone moisture; and 6) the number of seedlings inone cell.

207.NAL Call No.: S494.5.D3I5-1988Farm L.P., A microcomputerized farm level systems analysis program.Novak, J. L.; McIntosh, C. Proceedings of the 2nd International Conference on Computers in AgriculturalExtension Programs Fedro S Zazueta, AB Del Bottcher, eds p.58-63. (of Florida, [1988?].)Conference held February 10-11, 1988 at the Grosvenor Resort Hotel, Disney World Village, Lake Buenavista,Orlando, Florida.Descriptors: farm-management; systems-analysis; linear-programming; microcomputers

208.NAL Call No.: 280.8-J822Farm labor legislation: a computer program to assist growers.Alwang, J.; Wooddall Gainey, D.; Johnson, T. G. Am-J-Agric-Econ v.73(4): p.1027-1035. (1991 Nov.)Includes references.Descriptors: farm-workers; hired-labor; labor-legislation; computer- software; law; growers; virginia; migrant-labor-law-computer-software

Abstract: Labor recruitment and management is critical to agricultural production. Dependence on hired labor isgrowing, even in states and regions where farm labor needs were traditionally met by family members.Employment in agriculture is governed by a large number of complex federal and state regulations. Acomputerized information system designed to facilitate compliance with these regulations is described. A usersurvey shows that the system is widely and effectively used.

209.NAL Call No.: S494.5.D3C68-1992FARMCARE, a case study of the evaluation of long term conservation plans.Haagensen, A. M. Computers in agricultural extension programs proceedings of the 4th internationalconference, 28-31 January 1992, Orlando, Florida / sponspored by the Florida Cooperative Extension Service,University of Florida. St. Joseph, Mich. : American Society of Agricultural Engineers, c1992.. p. 364- 369.Descriptors: farm-management; farm-planning; cost-benefit-analysis; computer- software; australia

210.NAL Call No.: SB317.5.H68Farmer's bookshelf: a computerized hypermedia information system for crops.Kobayashi, K. D.; Bittenbender, H. C. HortTechnology v.1(1): p.118-120. (1991 Oct.-1991 Dec.)Includes references.Descriptors: information-systems; computer-software; crop-production; information-technology; hypertext

211.NAL Call No.: S494.5.D3C68-1992FarmPlan 2.0--a linear programming model for cash grain and beef farms.Aakre, D.; Olson, F.; Egeberg, R.; Swenson, A.; Hughes, H.; Rice, D. Computers in agricultural extensionprograms proceedings of the 4th international conference, 28-31 January 1992, Orlando, Florida / sponsporedby the Florida Cooperative Extension Service, University of Florida. St. Joseph, Mich. : American Society ofAgricultural Engineers, c1992.. p. 19-24.Descriptors: farm-management; decision-making; linear-programming; computer- software

212.NAL Call No.: HD1.A3FARMSYS--a whole-farm machinery management decision support system.Lal, H.; Jones, J. W.; Peart, R. M.; Shoup, W. D. Agric-Syst v.38(3): p.257-273. (1992)Includes references.Descriptors: farm-machinery; farm-management; decision-making; computer-software; simulation-models;testing; evaluation; labor; weather-data; capacity; information; tractors; farming-systems; expert-systems;prolog- programming-in-logic-computer-software

213.NAL Call No.: S494.5.D3C652Feature extraction of spherical objects in image analysis: an application to robotic citrus harvesting.Pla, F.; Juste, F.; Ferri, F. Comput-Electron-Agric v.8(1): p.57-72. (1993 Feb.)Includes references.Descriptors: citrus; mechanical-harvesting; imagery; robots; mathematical-models; digital-images

214.NAL Call No.: 100-Or3M-no.873FEEDLOT. FEEDLOT computer software.Riggs, W. W.; Torrell, L. A.; Oregon State University. Extension Service. Corvallis, Or. : Oregon StateUniversity, Extension Service, [1991] 9 p. : ill., "FEEDLOT is a microcomputer program designed to helpproducers compare the economics of alternative production and marketing strategies."Descriptors: Feedlots-Computer-programs

215.NAL Call No.: S494.5.D3I5-1990Field Crops Insect Management software.Landis, D. A.; Harsh, S. B.; Black, J. R.; Brook, R. C.; Harmon, R. J. Proceedings of the 3rd InternationalConference on Computers in Agricultural Extension Programs / Fedro S. Zazueta, editor. ; January 31-February 1, 1990, Grosvenor Resort Hotel, Disney World Village, Lake Buenavista, FL. Gainesville, FL :Florida Cooperative Extension Service, University of Florida, [1990]. p. 389-394. ill.Includes references.Descriptors: field-crops; insect-control; computer-software

216.NAL Call No.: SD409.N48A field-oriented competition index for young jack pine plantations and a computerized decision tool forvegetation management.Morris, D. M.; Forslund, R. R. New-For v.5(2): p.93-107. (1991)

Includes references.Descriptors: pinus-banksiana; forest-plantations; plant-competition; shade; boreal-forests; vegetation-management; computer-software; microprocessors; decision-making; shade-index

Abstract: A field-oriented competition index (Shade Index) was developed using a series of mensurationalmeasurements on competitors surrounding individual jack pine seedlings. This index and accompanyingsoftware were developed for a hand-held microprocessor, allowing for on-site evaluations. The Shade indexestimates the percent occupancy of competitor crowns overtopping individual jack pine seedlings within a 1.4 mradius of the subject tree. Using 360 crop tree-centred plots situated on six four-year-old plantations, theaccuracy of the index was tested against a more complex competition index (Total Canopy Cover) obtained fromvertical hemispherical photographs. Both of these indices attempt to quantify the amount of light beingintercepted by competitors. A relationship was found to exist between these two indices with Pearson correlationcoefficients ranging from 0.82 to 0.90. Linear regression models of seedling diameter regressed against theShade index for the different site/stock type combinations are presented. All models were significant at greaterthan p = 0.0001, with coefficients of determination ranging from 0.42 to 0.71. This index was incorporated intosoftware for a hand-held microprocessor to allow onsite evaluation. These evaluations have the potential to beused to set tending priorities or assess vegetation control measures.

217.NAL Call No.: S494.5.D3I5-1988Finding your agricultural advantage.Levins, R. A.; Johnson, D. M. Proceedings of the 2nd International Conference on Computers in AgriculturalExtension Programs Fedro S Zazueta, AB Del Bottcher, eds p.362-367. (of Florida, [1988?].)Conference held February 10-11, 1988 at the Grosvenor Resort Hotel, Disney World Village, Lake Buenavista,Orlando, Florida.Descriptors: crop-production; profitability; computer-software; computer-techniques

218.NAL Call No.: 290.9-AM32PFinite element analysis and optimization of a robot gripper design.Edan, Y.; Haghighi, K.; Stroshine, R. L.; Cardenas Weber, M. PAP-AMER-SOC- AGRIC-ENG. St. Joseph, Mich.: The Society. Winter 1989. (89-7537) 14 p.Paper presented at the "1989 International Winter Meeting sponsored by The American Society of AgriculturalEngineers," December 12-15, 1989, New Orleans, Louisiana.Descriptors: melons; robots; finite-element-analysis; optimization

219.NAL Call No.: aSD11.A48The fire effects information system: a tool for shrub information management.Bradley, A. F. Gen-Tech-Rep-INT-U-S-Dep-Agric-For-Serv-Intermt-Res-Stn (276): p.263-266. (1990 Nov.)Paper presented at the Symposium on "Cheatgrass invasion, shrub die-off, and other aspects of shrub biologyand management," April 5-7, 1989, Las Vegas, Nevada.Descriptors: shrubs; fire-ecology; computer-software; arid-regions; wildfires; databases; usa

220.NAL Call No.: S494.5.D3I5-1988Five year planning by dairy farmers using FINPACK.Unger, R.; Bennett, M. Proceedings of the 2nd International Conference on Computers in AgriculturalExtension Programs Fedro S Zazueta, AB Del Bottcher, eds p.308-312. (of Florida, [1988?].)Conference held February 10-11, 1988 at the Grosvenor Resort Hotel, Disney World Village, Lake Buenavista,Orlando, Florida.Descriptors: animal-husbandry; planning; computer-techniques; computer-software; missouri


221.NAL Call No.: S75.F87Floriculture on line.Penner, K. Futures-Mich-State-Univ-Agric-Exp-Stn v.9(3): p.19-20. (1991 Fall)Descriptors: floriculture; farm-management; computer-software; michigan

222.NAL Call No.: SD13.C35Focal point seed zones: site-specific seed zone delineation using geographic information systems.Parker, W. H. Can-J-For-Res-J-Can-Rech-For v.22(2): p.267-271. (1992 Feb.)Includes references.Descriptors: conifers; seed-sources; information-systems; provenance; pinus-banksiana; north-america; ontario

Abstract: A new site-specific approach to defining seed zones in North American conifers is described. Usingfocal point seed zones, an individual site to be reforested becomes the focal point, and a unique seed zone isestablished for that site as needed. This approach depends upon (i) obtaining good comparative data in adaptivecharacteristics from throughout the range to be regenerated based upon a series of short-term growth tests in acommon garden and (or) greenhouse and (ii) graphic analysis of multivariate summary scores by geographicinformation systems software to delimit boundaries of unique seed zones for any location to be reforested. Asample focal point seed zone is delineated for jack pine (Pinus banksiana Lamb.) reforestation of a site innorthern Ontario. This approach has considerable potential to help prevent decreased growth and yield due to theplanting of maladapted seed.

223.NAL Call No.: SF601.B6Forage analyses for dietary diagnosis and management.Anderson, B.; Rice, D.; Kubik, D.; Rasby, R. Agri-Practice v.12(3): p.29-32. (1991 May-1991 June)Includes references.Descriptors: cattle-feeding; forage; infrared-spectroscopy; testing; crude-protein; digestibility; production-costs;feed-supplements; near-infrared- reflectance-spectroscopy

224.NAL Call No.: S494.5.D3C68-1992Forage manager: an integrated approach to forage management evaluation and decision-making.Panciera, M. T.; Bruce, L. B.; Gavlak, R. G. Computers in agricultural extension programs proceedings of the4th international conference, 28-31 January 1992, Orlando, Florida / sponspored by the Florida CooperativeExtension Service, University of Florida. St. Joseph, Mich. : American Society of Agricultural Engineers,c1992.. p. 43-54.Includes references.Descriptors: fodder-crops; management; decision-making; computer- software

225.NAL Call No.: 99.8-F7623The Forest Management Decision Support System project.Bulger, D.; Hunt, H. For-Chron v.67(6): p.622-628. (1991 Dec.)Includes references.Descriptors: forest-management; decision-making; computer-techniques; computer-software; ontario

Abstract: The focus of a decision support system is much different from Management Information Systems(MIS) and data-based "decision support systems". Decision support systems, as defined by the authors, focus ondecisions and decision makers, and on information. Technology is treated as a tool and data as the raw material.In many traditional systems the focus is on the technology, and the data is the "information", while decisionmakers are, to some extent, externalized. The purpose of the Forest Management Decision Support System(FMDSS) project is to develop a set of software tools for creating forest management decision support systems.

This set of tools will be used to implement a prototype forest management decision support system for thePlonski forest, near Kirkland Lake, Ontario. There are three critical ingredients in building the FMDSS, theseare: (1) knowledge of the decision making process, (2) knowledge of the forest, and (3) the functionality ofunderlying support technology. The growing maturity of the underlying technology provides a tremendousopportunity to develop decision support tools. However, a significant obstacle to building FMDSS has been thediffuse nature of knowledge about forest management decision making processes, and about the forestecosystem itself. Often this knowledge is spread widely among foresters, technicians, policy makers, andscientists, or is in a form that is not easily amenable to the decision support process. This has created a heavyburden on the project team to gather and collate the knowledge so that it could be incorporated into the functionand design of the system. It will be difficult to gauge the success of this exercise until users obtain the softwareand begin to experiment with its use.

226.NAL Call No.: SD143.N6FORSOM: a spreadsheet-based forest planning model.Leefers, L. A.; Robinson, J. W. North-J-Appl-For v.7(1): p.46-47. (1990 Mar.)Includes references.Descriptors: forest-management; planning; computer-software; simulation-models; optimization; forest-simulation-optimization-model

227.NAL Call No.: 64.8-C883Fractional integrated stomatal opening to control water stress in the field.Fiscus, E. L.; Mahbub Ul Alam, A. N. M.; Hirasawa, T. Crop-Sci v.31(4): p.1001-1008. (1991 July-1991 Aug.)Includes references.Descriptors: zea-mays; water-stress; mass-flow; porometers; automatic- irrigation-systems; leaf-water-potential;leaf-conductance; grain; crop-yield; canopy; temperature; kernels; weight; yield-components; correlated-traits;stomatal-resistance; colorado; crop-water-stress-index

Abstract: The usefulness of totally automated irrigation control systems is well established. Mass-flowporometers can be used as the sensing and feedback elements to implement such a system for the experimentalcontrol of water stress in the field. This study was conducted to determine if consistent relationships could beestablished between the mass-flow readings and other water-related physiological parameters. A range of stressconditions were imposed on plots of corn (Zea mays L.) by the system during the 1986 and 1987 field seasons inGreeley, CO. Midday leaf xylem water potential, leaf diffusive conductance, and year-end grain yields weremeasured during both years. In 1987, additional measurements were made of the infrared canopy temperaturefor calculating the Crop Water Stress Index (CWSI), and individual kernel weights and numbers, to determinethe components of the grain yield predictions observed in 1986. Reductions in the number of kernels producedper unit land area were associated with stress-induced delays of silking relative to pollen shed. Additional yieldreductions in some treatments were attributable to reduced weight per kernel. Significant correlations werefound between the mass-flow sensors and grain yield and CWSI. The relationship between grain yield andstomatal conductance was consistent over both years, suggesting that the cumulative mean conductance may beuseful as a yield predictor.

228.NAL Call No.: HD1.A3A framework for crop growth simulation model applications.Thornton, P. K.; Dent, J. B.; Bacsi, Z. Agric-Syst v.37(4): p.327-340. (1991)Includes references.Descriptors: maize; wheat; soybeans; peanuts; technology-transfer; computer-software; growth-models;simulation-models; farm-inputs; weather-data; soil; field-size; varieties; fertilizers; irrigation; timing;establishment; international-benchmark-sites-network-for-agrotechnology-transfer-project; decision-support-system-for-agrotechnology-transfer-dssat- computer-software

229.NAL Call No.: Q184.R4Functional patterns in an annual grassland during an AVIRIS overflight.Gamon, J. A.; Field, C. B.; Roberts, D. A.; Ustin, S. L.; Valentini, R. Remote-Sensing-Environ v.44(2/3): p.239-253. (1993 May-1993 June)Includes references.Descriptors: grasslands; annuals; image-processors; vegetation; spatial-distribution; plant-physiology;productivity; canopy; remote-sensing; california; airborne-visible; infrared-imaging-spectrometer

230.NAL Call No.: S544.3.N9C46GARDPLAN.Smith, R. C.; Egeberg, R.; Askew, R. G.; Franklund, D. NDSU-Ext-Serv-Publ- North-Dakota-State-Univ. Fargo: The University. Mar 1986. (H-893) 4 p.Includes references.Descriptors: gardening; vegetables; varieties; decision-making; computer-software; databases; north-dakota

231.NAL Call No.: S494.5.D3I5-1990"GEDE--GUEPARD"--an optimization software for crop production systems.Goth, C. Proceedings of the 3rd International Conference on Computers in Agricultural Extension Programs /Fedro S. Zazueta, editor. ; January 31- February 1, 1990, Grosvenor Resort Hotel, Disney World Village, LakeBuenavista, FL. Gainesville, FL : Florida Cooperative Extension Service, University of Florida, [1990]. p. 379-382.Descriptors: crop-production; cropping-systems; computer-software

232.NAL Call No.: 49-AN55Genetic components of growth and ultrasonic fat depth traits in Meishan and Large White pigs and theirreciprocal crosses.Haley, C. S.; d'Agaro, E.; Ellis, M. Anim-Prod v.54(pt.1): p.105-115. (1992 Feb.)Includes references.Descriptors: pigs; large-white; pig-breeds; crossbreeding; crossbred- progeny; unrestricted-feeding; growth-rate;age-differences; fat-thickness; heterosis; genotypes; sex-differences; feed-intake; feed-conversion; genetic-effects; litter-size; scotland

233.NAL Call No.: 49-J82Genetic improvement programs in livestock: swine testing and genetic evaluation system (STAGES).Stewart, T. S.; Lofgren, D. L.; Harris, D. L.; Einstein, M. E.; Schinckel, A. P. J-Anim-Sci v.69(9): p.3882-3890.(1991 Sept.)Includes references.Descriptors: pigs; genetic-improvement; maternal-effects; performance- recording; best-linear-unbiased-prediction; contemporary-comparisons; information-services; computer-software; growth; reproduction

Abstract: Genetic evaluations for the U.S. swine industry are conducted by the eight purebred associations of theNational Association of Swine Records. Within-herd evaluations of the growth traits (days to 105 kg [market]and backfat depth) were first reported in 1986. Analyses of the maternal traits (litter size at birth and weaning,and litter 21-d weight) were inaugurated in 1987. Expected progeny differences (EPD) are. reported for all traitsand for general, paternal, and maternal bioeconomic indexes. A sow productivity index combining only maternaltraits is available. All records are adjusted according to National Swine Improvement Federation (NSIF)guidelines for effects such as number of pigs transferred at crossfostering and age at recorded observation priorto the BLUP evaluation. Within-herd analyses of individual contemporary groups are conducted immediately onreceipt of performance records at each breed association office. All parents in the herd and the young pigs in thecurrent group are evaluated. A report is returned to the breeder for use in herd selection and the EPD are placed

in the pedigree file. The genetic base of each herd is defined as the first n tested pigs or litters, where n is thenumber of pigs registered annually within the herd. Change in mean EPD between groups is indicative ofgenetic trend. Periodic across-herd analyses are used to update interim within-herd analyses and a national siresummary is published.

234.NAL Call No.: 23-AU783Genetic parameters for liveweight and ultrasonic fat depth in Australian meat and dual-purpose sheepbreeds.Brash, L. D.; Fogarty, N. M.; Gilmour, A. R.; Luff, A. F. Aust-J-Agric- Res v.43(4): p.831-841. (1992)Includes references.Descriptors: sheep-breeds; liveweight; dual-purpose-breeds; fat- thickness; genetic-correlation; heritability;inbreeding; ultrasonic-fat-meters; new-south- wales

235.NAL Call No.: 23-AU783Genetic variation in liveweight and ultrasonic fat depth in Australian Poll Dorset sheep.Atkins, K. D.; Murray, J. I.; Gilmour, A. R.; Luff, A. L. Aust-J-Agric- Res v.42(4): p.629-640. (1991)Includes references.Descriptors: sheep-breeds; fat-thickness; genetic-correlation; genetic-improvement; genetic-variation;heritability; liveweight; phenotypic- correlation; ultrasonics; new-south-wales; australian-poll-dorset-breed

236.NAL Call No.: A99.9-F7632UGENGYM: a variable density stand table projection system calibrated for mixed conifer and ponderosapine stands in the southwest.Edminster, C. B.; Mowrer, H. T.; Mathiasen, R. L.; Schuler, T. M.; Olsen, W. K.; Hawksworth, F. G. Res-Pap-RM-U-S-Dep-Agric-For-Serv-Rocky-Mt-For-Range-Exp- Stn. Fort Collins, Colo. : The Station. Aug 1991. (297)32 p.Includes references.Descriptors: mixed-forests; coniferous-forests; pinus-ponderosa; growth-models; yields; models; computer-software; arizona; new-mexico; colorado; generalized-growth-and-yield-model-gengym

237.NAL Call No.: QE48.8.Y37-1990Geostatistics for waste management : a user's manual for the GEOPACK (version 1.0) geostatisticalsoftware system. User's manual for the GEOPACK (version 1.0) geostatistical software system.Yates, S. R.; Yates, M. V. M. V.; Walters, D. M.; Robert S. Kerr Environmental Research Laboratory. Ada, Okla.: Robert S. Kerr Environmental Research Laboratory, Office of Research and Development, U.S. EnvironmentalProtection Agency, [1990] vi, 70 p. : ill., "U.S. Salinity Laboratory."Descriptors: Geology-Statistical-methods-Software

238.NAL Call No.: S494.5.E547GETOH--a computer program for evaluation of on-farm alcohol production.Ogilvie, J. R. Energy-World-Agric. Amsterdam : Elsevier. 1992. v. 5 p. 281- 317.In the series analytic: Analysis of Agricultural Energy Systems / edited by R.M. Peart and R.C. Brook.Descriptors: ethanol-production; computer-software; energy- requirements; fuels; liquids; costs; processing;energy-consumption

239.NAL Call No.: aSD11.A48GIS database design for industrial forest management.Murphy, D. L. Gen-Tech-Rep-INT-U-S-Dep-Agric-For-Serv-Intermt-Res-Stn. Ogden, Utah : The Station. Feb.1989. (257) p. 114-119.

Paper presented at the Symposium on "Land Classifications Based on Vegetation: Applications for ResourceManagement," November 17-19, 1987, Moscow, Idaho.Descriptors: forest-management; databases; computer-software; geographic-information-system-computer-software

240.NAL Call No.: SF951.E62Grass management and anlaysis.Hintz, H. F. Equine-Pract v.12(10): p.5-6. (1990 Nov.-1990 Dec.)Includes references.Descriptors: grasses; endophytes; spectroscopy; nutrient-uptake; grassland-management; near-infrared-reflectance-spectroscopy


241.NAL Call No.: 100-Or3M-no.872GRASSFAT. GRASSFAT computer software.Riggs, W.; Griffith, D.; Oregon State University. Extension Service. Corvallis, Or. : Oregon State UniversityExtension Service, [1991]. 11 p. : ill., "GRASSFAT is a microcompuiter [sic] program designed to helpproducers compare the economics of alternative production and marketing strategies ..."Descriptors: Cattle-Computer-programs; Cattle-Marketing-Computer- programs

242.NAL Call No.: S1.N32Grow the MAX with the minimum: program shows profit, environment go hand in hand.Erb, K.; Cramer, C. New-Farm v.14(5): p.12. (1992 July-1992 Aug.)Descriptors: sustainability; computer-software; cost-benefit-analysis; farm-inputs; erosion; losses-from-soil-systems; costs; crop-yield

243.NAL Call No.: 80-AC82Growth prediction of lettuce plants by image processing.Shibata, T.; Iwao, K.; Takano, T. Acta-Hortic v.2(319): p.689-694. (1992 Oct.)Paper presented at the International Symposium on Transplant Production Systems- -Biological, Engineeringand Socioeconomics Aspects, July 21-26, 1992, Yokohama, Japan.Descriptors: lactuca-sativa; growth; measurement; weight; crop-yield; prediction; computer-techniques; crop-yield; prediction; computer-techniques; microcomputers; image-processors; leaf-area; top-fresh-weight

244.NAL Call No.: 281.9-C81Ae-no.91-2A guide to processing dairy farm business summaries in county and regional extension offices for MicroDFBS V 2.5, IBM PC, XT and IBM- compatible microcomputers. Micro DFBS.Putnam, L. D.; Knoblauch, W. A.; Smith, S. F.; New York State College of Agriculture and Life Sciences. Dept.of Agricultural Economics. Ithaca, N.Y. : Dept. of Agricultural Economics, New York State College ofAgriculture and Life Sciences, Cornell University, [1991] 94 p., Cover title.Descriptors: Dairy-farms-New-York-State-Management-Computer-programs

245.NAL Call No.: 290.9-AM32TGX: a smalltalk-based platform for greenhouse environment control. I. Modeling and managing thephysical system.Gauthier, L. Trans-A-S-A-E v.35(6): p.2003-2009. ill. (1992 Nov.-1992 Dec.)

Includes references.Descriptors: greenhouses; environmental-control; computer-programming; computer-software

Abstract: Protected cultivation allows the production of crops under very diverse conditions. The afferenttechnologies, however, can be complex and costly. Hence, the effectiveness of greenhouse systems, both fromeconomical and technical points of view, depends more and more upon the availability of sophisticated, on-lineand automated decision-making systems capable of dynamically optimizing the underlying processes. Suchsystems have to be able to intervene in a number of areas such as crop protection, climate control, crop nutrition,and operational and strategic planning. In other words, the automated control system that takes charge, in part orin whole, of a greenhouse production system needs to draw upon a considerable body of knowledge in order tobe effective. In this article, a conceptual and operational framework (GX) supporting the representation andmanagement of real and virtual entities used or found in greenhouses is described. It was designed to supportvarious types of digital process controllers as well as the creation and deployment of knowledge-based controlstrategies. GX makes use of the object-oriented paradigm as expressed in the Smalltalk programming system. Ithas been used as a simulation platform and for the real time monitoring and control of a greenhouse range. TheGX programming environment is extensible. It can accommodate a wide variety of situations and provides asemantically rich environment for the design and operation of knowledge-based greenhouse control systems.

246.NAL Call No.: S671.A66Hardware for microcomputer control of the environment of a production broiler house.Allison, J. M.; White, J. M. Appl-Eng-Agric v.7(1): p.119-123. (1991 Jan.)Includes references.Descriptors: poultry-housing; broilers; broiler-production; controlled-atmospheres; microcomputers;environmental-control

Abstract: A microcomputer-based system to control the environment of a totally enclosed broiler house wasdeveloped and tested. The system was installed in an existing producer owned broiler house. Environmentalcontrol was provided for the seven-week grow-out of the flock. This article describes the microcomputerhardware and modifications to the structure and existing hardware required to make the control system reliable,functional, and to assure continued operation if the microcomputer or electronic interfaces should fail.

247.NAL Call No.: 290.9-AM32PHarvest planning using cruise/buck.Olsen, E. D.; Pilkerton, S.; Garland, J. J. PAP-AMER-SOC-AGRIC-ENG. St. Joseph, Mich. : The Society.Summer 1989. (89-7070) 12 p.Paper presented at the 1989 International Summer meeting, June 25-28, 1989, Quebec, PQ, Canada.Descriptors: forestry-engineering; logging; microcomputers; computer- aided-cruising

248.NAL Call No.: 80-AC82A hazelnut pest management expert system.Drapek, R. J.; Calkin, J. A.; Fisher, G. C. Acta-Hortic (276): p.21-25. (1990 July)Paper presented at the "Second International Symposium on Computer Modelling in Fruit Research and OrchardManagement," September 5- 8, 1989, Logan, Utah.Descriptors: corylus-avellana; insect-control; plant-disease-control; expert-systems

Abstract: An expert system (HAZLPEST) was developed for insect and disease pest management on hazelnuts(Corylus avellana L.). This program is divided into four distinct sub-programs: insect identification, insectmonitoring and control, insecticide selection, and disease management. Discussions are included on problemsarising from the inclusion of pesticide information in agricultural software, the cost effectiveness of pestmanagement expert system production, and on problems associated with distributing computer software to userswith a wide range of hardware capabilities.

249.NAL Call No.: 44.8-J822Heat stress and milk production in the South Carolina coastal plains.Linvill, D. E.; Pardue, F. E. J-Dairy-Sci v.75(9): p.2598-2604. (1992 Sept.)Includes references.Descriptors: dairy-cows; heat-stress; milk-production; computer- software; computer-simulation; milk-yield;south-carolina

Abstract: A model developed for the South Carolina coastal plains relates hours with temperature-humidityindex values above 74 and 80 to summer season daily milk production. When tested on an independentproduction data set for 1985, the root mean square model error was less than 1.3 kg/d per cow. The model can beused to develop expected summer season dairy production climatologies. Realtime milk production forecastsobtained using daily predicted maximum and minimum temperatures can be used in herd management to reduceeffects of heat stress on productivity.

250.NAL Call No.: 4-AM34PHERB: decision model for postemergence weed control in soybean.Wilkerson, G. G.; Modena, S. A.; Coble, H. D. Agron-J v.83(2): p.413- 417. (1991 Mar.-1991 Apr.)Includes references.Descriptors: glycine-max; weed-control; chemical-control; decision- making; economic-thresholds; herbicides;computer-software; microcomputers; crop- yield; yield-losses; crop-weed-competition; returns

Abstract: To efficiently use postemergence herbicides, decision makers must determine when weed populationsexceed economic treatment thresholds. An interactive microcomputer program named HERB has beendeveloped to help evaluate potential crop damage from multi-species weed complexes in soybean [Glycine max(L.) Merr.] and determine the appropriate course of action. Seventy-six weed species were rated on a scale ofzero to 10 according to their competitiveness with soybean. Potential yield loss is estimated from these rankings,the number of weeds of each species present in the field, and expected weed-free yield. The recommendationwhether to apply a herbicide and, if so, which one, is based on herbicide cost and efficacies under differentconditions and expected soybean selling price. Alternative herbicide choices are ranked according to expectednet return. HERB is intended to provide the latest research information in an organized and easily usable format.The approach should be applicable to other crops and pests.

251.NAL Call No.: S494.5.D3C652HERBICIDE ADVISOR: a decision support system to optimise atrazine and chlorsulfuron activity andcrop safety.Ferris, I. G.; Frecker, T. C.; Haigh, B. M.; Durrant, S. Comput-Electron- Agric v.6(4): p.295-317. (1992 Jan.)Includes references.Descriptors: atrazine; chlorsulfuron; computer-software; simulation- models; weather-forecasting; expert-systems; efficiency; safety; flow-charts; databases; extension; decision-making; australia; weather-generator

252.NAL Call No.: S544.3.M9E23Herd performance evaluation templates.Griffith, D.; Brownson, R. EB-Mont-State-Univ-Ext-Serv. Bozeman, Mont. : The Service. July 1988. (31) 27 p.Descriptors: beef-cows; reproductive-performance; computer-analysis; computer- software

253.NAL Call No.: SF955.E6Hoof and distal limb surface temperature in the normal pony under constant and changing ambienttemperatures.Mogg, K. C.; Pollitt, C. C. Equine-Vet-J v.24(2): p.134-139. (1992 Mar.)

Includes references.Descriptors: horses; limbs; hooves; temperature; sensors; metacarpus; thermography; limb-bones

254.NAL Call No.: T174.3.J68Human safety risk in automated information systems.Chick, M. J. J-Technol-Transfer v.10(2): p.43-52. (1986 Spring)Includes references.Descriptors: information-systems; safety; risk; public-health; computer-techniques; technology-transfer

Abstract: This article discusses the complex problems involved in minimizing risks when applying automatedinformation systems to functions that can affect human safety and lives, and limitations on the way technologicalrisk is assessed in todays environment. It calls for policies at the highest levels and research on managementapproaches to providing a focus for evaluating and solving automated information system problems causingfailure and for applying the automated systems in a manner that will minimize the potential for harm toindividuals. The author also believes it to be very important that problems presented are disclosed to informationmanagers that may be part of the decision-making on what and how much to automate, and also those involvedin other technologies and functions that use automated information at their core. Automated information systems(computers and telecommunications) have changed our everyday life. Because of fast changing technology andcreative software development, beneficial computer applications in business, education, scientific applications,and personal use now prevail. With automated information systems, our society has increased productivity,saved money, and has made possible many things previously considered impossible. In general, society hasbenefitted from increased automation of information.

255.NAL Call No.: 99.8-F768HW-BUCK game improves hardwood bucking skills.Pickens, J. B.; Lyon, G. W.; Lee, A.; Frayer, W. E. J-For v.91(8): p.42-45. (1993 Aug.)Includes references.Descriptors: hardwoods; log-breakdown-methods; optimization; computer- software; computer-simulation

256.NAL Call No.: QA76.76.E95A5IDEA: intelligent data retrieval in English for Agriculture.Jones, L. R.; Spahr, S. L. AI-Appl-Nat-Resour-Manage v.5(1): p.56-66. (1991)Includes references.Descriptors: dairy-farming; information-retrieval; microcomputers; databases; languages; information-storage;dictionaries; semantic-aproach

257.NAL Call No.: 80-AC82IMAG decision-support system for tree nurseries.Lookeren Campagne, P. v.; Annevelink, E. Acta-Hortic (295): p.209-221. (1991 May)Paper presented at the "23rd International Horticultural Congress on Horticultural Economics and Marketing,"August 27-September 1, 1990, Florence, Italy.Descriptors: forest-nurseries; computer-software; decision-making; boomcompas-computer-software; institute-of-agricultural-engineering

258.NAL Call No.: S494.5.D3C652Image-guidance for robotic harvesting of micropropagated plants.McFarlane, N. J. B. Comput-Electron-Agric v.8(1): p.43-56. (1993 Feb.)Includes references.Descriptors: micropropagation; robots; mechanical-harvesting; imagery; computers; algorithms

259.NAL Call No.: SD143.S64Implementing and integrating multi-resource models in geographic information systems.Arthaud, G. J. Proc-Soc-Am-For-Natl-Conv p.597-598. (1991)Meeting held Aug 4-7, 1991, San Francisco, California.Descriptors: resource-management; models; geographical-distribution; computer-software; wildlife; habitats;recreation; multiple-land-use; logging; minnesota

260.NAL Call No.: 1.962-C5T71Improved container sowing with an electronically controlled optical seeder.Wenny, D. L.; Edson, J. L. Tree-Plant-Notes-U-S-Dep-Agric-For-Serv v.42(3): p.4-8. (1991 Summer)Includes references.Descriptors: pot-culture; container-grown-plants; sowing; machinery; electrical-control; conifers; forest-nurseries; seeding-machinery


261.NAL Call No.: SB1.H6Improved pesticide rate conversions.Fitzpatrick, G. E.; Verkade, S. D. HortScience v.26(3): p.313. (1991 Mar.)Includes references.Descriptors: pesticides; application-rates; planting-stock; microcomputers; computer-software; container-grown-plants; programmable- calculators

262.NAL Call No.: S494.5.D3I5-1988Improving financial management competency and computer skills of extension professionals.Piernot, B. L. Proceedings of the 2nd International Conference on Computers in Agricultural ExtensionPrograms Fedro S Zazueta p.754-758. (of Florida, [1988?].)Meeting held February 10-11, 1988 at Lake Buenavista, Orlando, Florida.Descriptors: financial-planning; training; extension-agents; microcomputers; skills; texas

263.NAL Call No.: S494.5.D3C652Incorporating non-numeric and subjective information in investment decision models for farmers.Monke, J. D.; Sherrick, B. J.; Sonka, S. T. Comput-Electron-Agric v.7(3): p.203-217. (1992 Sept.)Includes references.Descriptors: farmers; investment; information-needs; decision-making; computer-software; microcomputers;stochastic-processes; data-analysis; farm- management; spreadsheets; decision-aids

264.NAL Call No.: S494.5.D3C652An indexed-hash algorithm for an agrometeorological data management system.Wang, Y. B.; Mack, T. P. Comput-Electron-Agric v.8(2): p.105-115. (1993 Mar.)Includes references.Descriptors: meteorology; algorithms; microcomputers; databases; comparisons

265.NAL Call No.: S671.A33An industry view of engineering research needs for livestock.Blackshaw, J. K. Agric-Eng-Aust v.19(1): p.14-15. (1990)

Descriptors: livestock; handling; agricultural-engineering; research; sheep; shearing; pig-housing; transport;ultrasonic-devices; australia

266.NAL Call No.: 44.8-J824Influence of milk fat higher in unsaturated fatty acids on the acccuracy of milk fat analyses by the mid-infrared spectroscopic method.Stegeman, G. A.; Baer, R. J.; Schingoethe, D. J.; Casper, D. P. J-Food- Prot v.54(11): p.890-893. (1991 Nov.)Includes references.Descriptors: milk-fat; food-composition; unsaturated-fatty-acids; acids; laboratory-methods; accuracy

Abstract: An experiment was conducted to investigate the reliability of milk fat measurement by the mid-infrared spectroscopic method when analyzing milk fat containing greater than normal amounts of unsaturatedfatty acids. Sixteen mid-lactation Holstein cows were divided into four treatments including a control (C),control with bovine somatotropin (C+), bovine somatotropin and added dietary fat from sunflower seeds (Sun+),or bovine somatotropin and added dietary fat from safflower seeds (Saff+). Milks were sampled weekly for 16weeks (n = 256). Unsaturated fatty acid percentages in milk fat were 25.0, 28.4. 39.6, and 37.9 for C, C+, Sun+,and Saff+ treatments, respectively. Milk fat percentages measured by the Mojonnier fat extraction and mid-infrared spectroscopic methods were 2.99, 2.97; 3.06, 3.01; 2.73, 2.56: and 2.86, 2.74 for C, C+. Sun+, andSaff+ treatments, respectively. Results indicate the mid- infrared spectroscopic method underestimates the fatcontent in milk which is higher in unsaturated fatty acids. Dairy producers feeding diets with added fat fromunsaturated fat sources may be underpaid for milk fat content when the milk is analyzed by the mid-infraredspectroscopic method. A possible remedy for this problem may be to have milk plants calibrate the mid- infraredspectroscopic instrument with milk samples containing higher than normal amounts of unsaturated fatty acids inmilk fat.

267.NAL Call No.: QH540.J6An information management technology program for ex ante nutrient loss reduction from farms.Lemberg, B.; McSweeney, W. T.; Lanyon, L. E. J-Environ-Qual v.21(4): p.574-578. (1992 Oct.-1992 Dec.)Includes references.Descriptors: dairy-farms; fertilizers; fertilizer-requirement- determination; nutrients; losses-from-soil; use-efficiency; farm-management; environmental- impact; economic-impact; information-systems; computer-software; water-resources; environmental-protection

Abstract: Reducing nutrient losses from farms to the environment can be done before or after the nutrients havebeen applied to the fields. If effective best management practices can be implemented before nutrients areapplied (ex ante), difficult and uncertain remedial management practices can be avoided. The relativeenvironmental and economic consequences of an information management technology program were comparedunder two contrasting water resource protection perspectives by linear programming simulation of a dairy farm.The information program was based on measuring the amount of materials transferred to and from the fields ascrops and manure, and the sampling and analyses of those materials. Potential N losses to the environment werereduced substantially and costs of the information management program were generally more than offset by thesavings in fertilizer expenditures compared to the outcome when no credit was given to manure nutrients in thefertilization of farm crops. Exacting requirements for nutrient utilization under a restrictive water resourceprotection perspective resulted in only a fraction of the total manure produced being spread on the farm fields,however. The negative economic impart of this limitation was potentially much greater than the costs toimplement the information management technology program. Standards for both the extent of the informationrequired to adequately meet the environmental expectations and the acceptable range of the expectations must beestablished if the management practice is to be feasible and successful.

268.NAL Call No.: 280.8-J822Information technology, coordination, and competitiveness in the food and agribusiness sector.Streeter, D. H.; Sonka, S. T.; Hudson, M. A. Am-J-Agric-Econ v.73(5): p.1465-1475. (1991 Dec.)

Paper presented at the annual meetings of the American Agricultural Economics Association, August 4-7, 1991,Manhattan, Kansas. Discussions by M.L. Cook and M.E. Bredahl, p. 1472-1473 and D.A. Mefford, p. 1474-1475.Descriptors: agribusiness; food-industry; information; technology; market-competition; coordination;consumers; production; marketing; agricultural- economists

269.NAL Call No.: aSD11.U56Informs-TX overview.Williams, S. B. Gen-Tech-Rep-NE-U-S-Dep-Agric-For-Serv-Northeast-For-Exp- Stn (175): p.85-92. (1993 June)Paper presented at a workshop on "Spatial Analysis and Forest Pest Management," Apr 27-30, 1992, MountainLakes, Virginia.Descriptors: forest-resources; resource-management; integrated- systems; geographical-information-systems;databases; computer-software; integrated-forest-resource-management-system

270.NAL Call No.: S612.2.N38-1990Infrared telemetry and tensiometers--a closed loop irrigation management tool.Feuer, L. Visions of the future proceedings of the Third National Irrigation Symposium held in conjunction withthe 11th Annual International Irrigation Exposition, October 28-November 1, 1990, Phoenix Civic Plaza,Phoenix, Arizona. St. Joseph, Mich. : American Society of Agricultural Engineers, c1990.. p. 583- 588.Includes references.Descriptors: irrigation-requirements; irrigation-scheduling; telemetry; tensiometers; water-management

271.NAL Call No.: SD1.S34Infrared thermography as a means of assessing seedling quality.Orlander, G.; Egnell, G.; Forsen, S. Scand-J-For-Res v.4(2): p.215-222. ill. (1989)Includes references.Descriptors: pinus-sylvestris; picea-abies; seedlings; transpiration; temperatures; infrared-radiation; heat-production; measurement

272.NAL Call No.: 41.8-C163Infrared thermography of pigs with known genotypes for stress susceptibility in relation to pork quality.Schaefer, A. L.; Jones, S. D. M.; Murray, A. C.; Sather, A. P.; Tong, A. K. W. Can-J-Anim-Sci v.69(2): p.491-495. ill. (1989 June)Includes references.Descriptors: pigs; body-temperature; measurement; pork; meat-quality; stress; susceptibility; genotypes

273.NAL Call No.: S494.5.D3C652An integrated computer instructional approach to improve dairy cattle estrus detection.Johnson, P. J.; Oltenacu, P. A.; Ferguson, J. D. Comput-Electron-Agric v.7(1): p.61-70. (1992 Apr.)Includes references.Descriptors: dairy-cattle; estrus; computer-simulation; computer- assisted-instruction; learning-ability;computer-software; flow-charts; animal- husbandry; farm-management

274.NAL Call No.: S494.5.D3I5-1988Integrated county information management in a multi-vendor environment.Dyche, J. R. Jr.; Smith, G. E. Proceedings of the 2nd International Conference on Computers in AgriculturalExtension Programs Fedro S Zazueta p.890-895. (of Florida, [1988?].)Meeting held February 10-11, 1988 at Lake Buenavista, Orlando, Florida.Descriptors: microcomputers; information-systems; integration; extension; indiana

275.NAL Call No.: SD143.S64An integrated hierarchical planning system for Navajo Nation forest lands.Wood, D. B.; Covington, W. W. Proc-Soc-Am-For-Natl-Conv p.369-373. (1991)Meeting held Aug 4-7, 1991, San Francisco, California.Descriptors: forest-management; planning; models; american-indians; resource-management; computer-software; arizona

276.NAL Call No.: S544.3.N9C46Integrated reproductive management. II. Economics of beef cattle production practices.Eide, W.; Wohlgemuth, K.; Toman, N. NDSU-Ext-Serv-Publ-North-Dakota-State- Univ. Fargo, N.D. : TheUniversity. Oct 1982. (AS-772) 5 p.Descriptors: beef-cattle; beef-production; cost-benefit-analysis; statistics; computer-software

277.NAL Call No.: SD143.S64Integrated resource management on the Hoopa Valley Indian Reservation: a case study in collaborationand self-determination.Harris, R. R. Proc-Soc-Am-For-Natl-Conv p.578-579. (1991)Meeting held Aug 4-7, 1991, San Francisco, California.Descriptors: resource-management; planning; models; computer-software; american-indians; forest-management; california

278.NAL Call No.: 290.9-AM32PIntegrated software for managing potato production decisions.Stevenson, W. R.; Curwen, D.; Binning, L.; Wyman, J.; Koenig, J.; Rice, G.; Schmidt, R.; Zajda, J. PAP-AMER-SOC-AGRIC-ENG. St. Joseph, Mich. : The Society. Winter 1990. (90-7560) 5 p.Paper presented at the "1990 International Winter Meeting sponsored by the American Society of AgriculturalEngineers," December 18-21, 1990, Chicago, Illinois.Descriptors: solanum-tuberosum; crop-management; crop-production; expert-systems; pcm-potato-crop-management-software

279.NAL Call No.: S539.5.J68An integrated systems approach to potato crop management.Connell, T. R.; Koenig, J. P.; Stevenson, W. R.; Kelling, K. A.; Curwen, D.; Wyman, J. A.; Binning, L. K. J-Prod-Agric v.4(4): p.453-460. (1991 Oct.-1991 Dec.)Includes references.Descriptors: solanum-tuberosum; cultivars; crop-management; integrated-systems; integrated-pest-management;integrated-control; weed- control; insect- control; plant-disease-control; irrigation; ammonium-nitrate;pesticides; productivity; production-costs; returns; crop-yield; environmental- impact; emergence; irrigation-scheduling; computer-software; computer-analysis; monitoring; petioles; plant-analysis; nitrate; agricultural-chemicals; environmental-factors; wisconsin

280.NAL Call No.: 99.8-F7632Integration of geographic information systems with a diagnostic wind field model for fire managemnent.Zack, J. A.; Minnich, R. A. For-Sci v.37(2): p.560-573. (1991 June)Includes references.Descriptors: wildfires; forest-fires; fire-behavior; wind; models; information-systems; krissy-model

Abstract: The past 10 years have seen an increased interest in diagnostic wind modeling efforts in the fields ofair pollution research and wind energy engineering. Applications relating wind to forest fire behavior are also

beginning to capitalize on computer-generated outputs from wind models. Most wind model outputs have beenconsidered useful only as intermediate data files loaded into specialized software packages for furtherprocessing. Output data are used to generate various output products without being passed into sophisticatedmathematical models. With the developed technology of geographic information systems (GIS), new mapproducts can be created. If designed properly, these maps can pass information more efficiently to both thedecision maker and the GIS for further analysis. The methods used to create and edit topographic andmeteorological databases, display the results of the KRISSY diagnostic wind field model, and perform analyseson the topography and estimated wind field are described.


281.NAL Call No.: QA76.76.E95A5Integration of simulation models and an expert system for management of rangeland grasshoppers.Berry, J. S.; Kemp, W. P.; Onsager, J. A. AI-Appl-Nat-Resour-Manage v.5(1): p.1-14. (1991)Includes references.Descriptors: rangelands; insect-pests; acrididae; insect-control; information-services; decision-making;microcomputers; expert-systems; simulation- models; integration; support-systems; western-states-of-usa

282.NAL Call No.: 325.28-P56Interactive boundary delineation of agricultural lands using graphics workstations.Cheng, T. D.; Angelici, G. L.; Slye, R. E.; Ma, M. Photogramm-Eng-Remote- Sensing v.58(10): p.1439-1443.(1992 Oct.)Includes references.Descriptors: agricultural-land; crops; land-use; statistical-data; usda; aerial-photography; domestic-production;crop-production; livestock- farming; computer-software; image-processors; landsat; automation; missouri;national-agricultural-statistics-service; sampling-units; computer-assisted- stratification-and-sampling-procedures

283.NAL Call No.: aSD11.U56The interactive impact of forest site and stand attributes and logging technology on stand management.LeDoux, C. B.; Baumgras, J. E. Gen-Tech-Rep-NE-U-S-Dep-Agric-For-Serv- Northeast-For-Exp-Stn (148):p.148-156. (1991 Mar.)Paper present at the 8th Central Hardwood Forest Conference, March 4-6, 1991, University Park, Pennsylvania.Descriptors: forest-management; computer-simulation; simulation- models; harvesting; site-factors; hardwoods;manage-computer-software

284.NAL Call No.: TC801.I66Introducing monitoring and evaluation into main system management--a low investment approach.Bird, J. D. Irrig-Drain-Syst-Int-J v.5(1): p.43-60. (1991 Feb.)Includes references.Descriptors: irrigation-systems; performance-appraisals; irrigation- water; water-management; water-distribution; microcomputers; monitoring; sri- lanka

285.NAL Call No.: Q184.R4Inversion of surface parameters from passive microwave measurements over a soybean field.Wigneron, J. P.; Kerr, Y.; Chanzy, A.; Jin, Y. Q. Remote-Sensing- Environ v.46(1): p.61-72. (1993 Oct.)Includes references.

Descriptors: glycine-max; crops; biomass; monitoring; soil-water; microwave-radiation; emission; remote-sensing; models; france

286.NAL Call No.: SB599.U6-[no.]-43IR-thermography of canopy temperatures of wheat and barley at different nitrogen fertilization andirrigation.Nilsson, H. E. Uppsala : Sveriges lantbruksuniversitet, 1987. 49 p. : ill., Summary in Swedish. Bibliography: p.8.

287.NAL Call No.: S544.3.N6N62Irrigation of peanuts.Sneed, R. E. AG-N-C-Agric-Ext-Serv-N-C-State-Univ (331,rev.): p.100- 112. (1991 Dec.)In the series analytic: 1992 Peanuts.Descriptors: arachis-hypogaea; irrigation; irrigation-equipment; field-tests; irrigation-systems; irrigation-requirements; pesticides; law; state-government; computer-software; north-carolina

288.NAL Call No.: SB1.H6Irrigation regimes affect leaf yield and water use by turnip and mustard.Smittle, D.; Dickens, W. L.; Stansell, J. R.; Simonne, E. HortScience v.27(4): p.308-310. (1992 Apr.)Includes references.Descriptors: brassica-campestris; brassica-juncea; irrigation- scheduling; water-use; models; evapotranspiration;soil-water-content; soil- water-regimes; crop-yield; leaves; pan-evaporation

Abstract: Turnip (Brassica rapa L.) and mustard (Brassica juncea L.) were grown in drainage lysimeters undercontrolled soil water regimes during 2 years. Irrigation regimes consisted of water applications when the soilwater tension at a 10-cm depth exceeded 25, 50, or 75 kPa throughout growth of the two crops on two soil typesduring spring and fall production seasons. Leaf yield and water use were highest when irrigation was applied at25 kPa soil water tension. Regression equations are presented to describe the relationships of daily panevaporation and water use to plant age, and to compute daily evapotranspiration : pan evaporation ratios (cropfactors) during spring and fall production seasons.

289.NAL Call No.: 290.9-AM32TIrrigation scheduling of spring wheat using infrared thermometry.Stegman, E. C.; Soderlund, M. Trans-A-S-A-E v.35(1): p.143-152. (1992 Jan.-1992 Feb.)Includes references.Descriptors: triticum; cultivars; irrigation-scheduling; infrared- imagery; thermometers; water-management;north-dakota; marshall-cultivar; wheaton-cultivar

Abstract: Irrigation scheduling for spring wheat requires information on different irrigation timing methods.Irrigation timing based on allowable root zone available water depletion and selected crop water stress index(CWSI) thresholds were evaluated in terms of their effect on spring wheat yield. A field study was conducted atOakes, North Dakota in 1987 and 1988 on a Maddock sandy loam soil with two varieties of spring wheat(Marshall and Wheaton) using a split plot randomized lock design. Irrigation was metered to each plot usingtrickle irrigation tubing. Neutron soil water measurements along with a water balance model were used to timeirrigations that were based on different allowed root zone depletions. Infrared thermometer sensors (IRT) wereused to measure in situ canopy temperatures and along with measured climatic information were used to timeirrigations using the CWSI approach. Additionally, crop phenological stages and final grain yield weremeasured. The non-stressed water baselines necessary for the CWSI differed between the two seasons but weresimilar to those from previous studies. The CWSI methods were feasible from the Feekes scale S4 (beginningpseudo-stem) to S11.2 (mealy ripe). Minimal yield reductions were observed using the CWSI method forthresholds less than 0.4-0.5 during this period. Minimal yield reductions were observed by maintaining the root

zone allowable depletion below 50%. The grain yield-evapotranspiration (ET) relationship was linear in bothyears but with different slopes and intercepts. When analyzed on a relative basis to maximum ET (ET(m)), asingle relationship fit both years' data with a yield sensitivity factor of 1.58. Irrigations timed at CWSI = 0.5reduced seasonal water application by 18% relative to treatments irrigated at CWSI = 0.2.

290.NAL Call No.: SB121.I57-1992Issues in robotic system design for transplant production systems.Simonton, W. Transplant production systems proceedings of the International Symposium on TransplantProduction Systems, Yokokama, Japan, 21-26 July 1992 / edited by K Kurata and T Kozai. Dordrecht : KluwerAcademic Publishers, 1992.. p. 103-115.Includes references.Descriptors: transplanting; robots; automation

291.NAL Call No.: 251.8-R32Joint adoption of microcomputer technologies: an analysis of farmers' decisions.Huffman, W. E.; Mercier, S. Rev-Econ-Stat v.73(3): p.541-545. (1991 Aug.)Includes references.Descriptors: microcomputers; farm-management; innovation-adoption; decision-making; econometric-models;farm-helper-services; iowa

292.NAL Call No.: S494.5.D3C652Kalman filter and an example of its use to detect changes in poultry production responses.Roush, W. B.; Tomiyama, K.; Garnaoui, K. H.; D'Alfonso, T. H.; Cravener, T. L. Comput-Electron-Agric v.6(4):p.347-356. (1992 Jan.)Includes references.Descriptors: hens; feed-intake; monitoring; algorithms; change; production; computer-software; noise

293.NAL Call No.: 80-AC82Kiwifruit nutrition management service: A mathematical model and database for commercialconsultancy.Buwalda, J. G.; Smith, G. S. Acta-Hortic (276): p.79-86. (1990 July)Paper presented at the "Second International Symposium on Computer Modelling in Fruit Research and OrchardManagement," September 5- 8, 1989, Logan, Utah.Descriptors: kiwifruits; actinidia-deliciosa; databases; nutrition- information; support-systems; new-zealand

Abstract: A mathematical model linked to a database, known as the Kiwifruit Nutrition Management Service(KNMS), has been developed for delivery of nutrition management advice to the kiwifruit industry in NewZealand. The model summarises nutrient fluxes within the orchard ecosystem and hence derives a budget offertiliser requirements. Monitoring of the orchard nutrient status, with leaf and soil analysis, provides furtherrefinement of the fertiliser programme, as well as early detection of nutrient disorders. Fertiliser quantitiesrequired to correct any disorders are similarly calculated according to a budget. This paper summarises thestructure and operation of this system for delivering nutrition management advice. A database stores relevantorchard data and orchard-specific recommendations. The fertiliser budget accounts for uptake, efficiency offertiliser recovery, cycling within the orchard, and any previous disorders. The major advantage of this newmethod for nutrition management is orchard- specificity. The information gathered over time within the databaseprovides an increasingly complete description of nutrient dynamics within individual orchards, so thatrecommendations become increasingly precise. The database also becomes the focal point for identifyinglimitations to yield. This database is now becoming an increasingly valuable research resource, for examiningnutrient dynamics within individual orchards and general nutrition relationships for kiwifruit. In the first year ofoperation, the KNMS database helped define a relationship between soil sulphur content and vine potassiumstatus. The KNMS database is now developing as an integral component of additional consultancy packages.

The KNMS is operated on a microcomputer (IBM- compatible), employing the database management systemSIR ("Scientific Information Retrieval"). Data is submitted by growers through consultants to the scientistsmaintaining the KNMS database, and recommendations are computed and returned via the consultants.

294.NAL Call No.: S494.5.D3I5-1988A knowledge-based, decision-support system for grading carcass beef.Chen, Y. R.; Robinson, S. A. Proceedings of the 2nd International Conference on Computers in AgriculturalExtension Programs Fedro S Zazueta, AB Del Bottcher, eds p.107-112. (of Florida, [1988?].)Conference held February 10-11, 1988 at the Grosvenor Resort Hotel, Disney World Village, Lake Buenavista,Orlando, Florida.Descriptors: carcass-grading; beef; support-systems; computer-software

295.NAL Call No.: S671.A66Knowledge-based system for environmental design of stream modifications.Shields, F. D. Jr.; Aziz, N. M. Appl-Eng-Agric v.8(4): p.553-562. (1992 July)Includes references.Descriptors: watershed-management; streams; modification; expert- systems; erosion-control; flood-control

Abstract: A knowledge-based, microcomputer software package was developed for preliminary selection ofenvironmental features for use with streambank protection projects, straightened and enlarged channels, andflood control levees. The system contains a module for each of the three major alteration types: bank protection,levees, and channels. Each module queries the user for information regarding environmental factors to beprotected and a description of the project setting, with the internal logic configured to minimize the number ofquestions asked. System output consists of a list of environmental design features suitable for the specificlocation and descriptive information. Help screens explain why certain questions are asked, define terms, andsuggest responses or sources of information. At the conclusion of a consultation, additional help screens may bedisplayed that provide a discussion of each recommended feature, a list of existing projects that incorporate thefeature, and a bibliography. The streambank protection module screens a master list of 20 methods based on thedominant erosion mechanisms operative at the project site, and the channel module performs a rough channelstability assessment using regime equations. The latest version of the software aids in feature selection, but doesnot design channel alterations. However, the software interfaces with routines that perform basic hydrauliccomputations (e.g., composite roughness, normal depth, riprap size) for steady flow in order to allow users toquickly evaluate feasibility of in-channel environmental features. A survey of users indicated that the packagehas been used by entry- level and experienced professionals to perform a limited range of specialized tasks.Seventy-four percent of the users described the software as a useful instrument for planning and preliminarydesign.

296.NAL Call No.: 290.9-AM32TA knowledge-based system for insecticide management for rice crops.Gupta, C. P.; Suryanto, H. Trans-A-S-A-E v.36(2): p.585-591. (1993 Mar.-1993 Apr.)Includes references.Descriptors: oryza-sativa; insect-control; insecticides; sprayers; computer-software; droplet-size; mathematical-models; tropical-asia; basic- computer-program

Abstract: A knowledge-based system was developed using an expert system shell to help farmers in insecticidemanagement for rice crops for tropical Asian countries. It has 72 rules for recommending insecticides and anexternal program written in BASIC for selecting sprayers. Insecticides are recommended based on the type ofinsect, symptoms, economic threshold, cost, and the effectiveness of chemical. An attempt was made to face thissystem with a real problem of rice leaf folder. Field experiments have been performed to evaluate the program'srecommendations for controlling the rice leaf folder. The program should be expanded for other major riceinsects before it is used by farmers. An external program for sprayer selection has been developed. Sprayer

selection is based on droplet size, deposition efficiency, capacity, and operating cost. Laboratory and fieldexperiments using manually carried sprayers were to provide data required by the user.

297.NAL Call No.: S494.5.D3C652Knowledge engineering approaches in developing expert simulation systems.Batchelor, W. D.; McClendon, R. W.; Wetzstein, M. E. Comput-Electron- Agric v.7(2): p.97-107. (1992 July)Includes references.Descriptors: soybeans; insect-pests; pest-management; expert-systems; crops; growth; simulation-models;knowledge; engineering; comparisons; evaluation; validity; case-studies; smartsoy-computer-software; soygro-simulation-models

298.NAL Call No.: SB476.G7Landscape software.Rogers, M. Grounds-Maint v.27(7): p.42, 44, 48, 65. (1992 July)Descriptors: landscape-gardening; landscape; design; landscaping; computer-software

299.NAL Call No.: 80-AC82Laser photoacoustics: a novel method for ethylene determination in plant physiological studies.Woltering, E. J.; Harren, F.; Bicanic, D. D. Acta-Hortic (261): p.201- 208. (1989 Dec.)Paper presented at the "Fourth International Symposium on Postharvest Physiology of Ornamental Plants," /edited by S. Mayak, March 20-25, 1988, Herzliya, Israel.Descriptors: cymbidium; oncidium; phalaenopsis; epidendrum; cut- flowers; emasculation; effects; ethylene-production; analytical-methods; plant- physiology

300.NAL Call No.: 382-P56Latitudinal and seasonal variation in calculated ultraviolet-B irradiance for rice-growing regions of Asia.Bachelet, D.; Barnes, P. W.; Brown, D.; Brown, M. Photochem-Photobiol v.54(3): p.411-422. (1991 Sept.)Includes references.Descriptors: oryza-sativa; agricultural-geography; latitude; seasonal- variation; ultraviolet-radiation; clouds;asia; cloud-cover

Abstract: Ultraviolet-B (UV-B, 280-320 nm) irradiance was calculated for more than 1200 sites in Asia tocharacterize the spatial and temporal variation in the present UV-B climate for rice-growing regions. Theanalytical model of Green et al. (Photochem. Photobiol. 31, 59-65, 1980) was used to compute UV-B irradiancefor clear skies using satellite-observed ozone column thickness and local elevation data. Ground-basedobservations of cloud cover were then used to approximate the average effect of cloud cover on UV-B irradianceusing the approach of Johnson et al. (Photochem. Photobiol. 23, 179- 188, 1976). Over the geographic range ofrice cultivation, the maximum daily effective UV-B irradiance (UV- BBE), When weighted according to ageneral plant action spectrum, was found to vary approx. 2.5-fold under both clear and cloudy sky conditions.Under clear skies, the timing of maximum solar UV-BBE changed with latitude and varied from February-March near the equator to July-August at temperate locations. Cloud cover was found to alter the season ofmaximum UV-BBE in many tropical regions, due to the pronounced monsoonal climate, but had little effect onUV-B seasonality at higher latitudes. Under a climate resulting from a doubling of atmospheric carbon dioxide,estimated UV-B using predicted cloud cover was found to change by up to 17% from present conditions inThailand. Both latitudinal and seasonal variation in solar UV-B radiation may be important aspects of the UV-Bclimate for rice as cultivars differ in sensitivity to UV-B and are grown under diverse conditions and locations.


301.NAL Call No.: 44.8-J822LEARNREPRO: a computer-assisted training program for teaching dairy reproduction management.Johnson, P. J.; Oltenacu, P. A.; Blake, R. W. J-Dairy-Sci v.75(8): p.2288-2293. (1992 Aug.)Includes references.Descriptors: dairy-herds; dairy-education; reproductive-performance; records; computer-assisted-instruction

Abstract: LEARNREPRO consists of three computer software packages that were developed to improve dairyreproductive problem-solving skills by college students, professionals, and farmers. Tutorial drill and practice,and simulation approaches are utilized. The module REPRO-MEASURES covers six common DHI measures ofherd reproductive performance. The USE-OF-RECORDS module integrates information from the previousmodule while presenting additional problem-solving strategies needed to analyze DHI reproductive records. TheESTRUS-DETECTION module addresses effective estrus detection strategies. User surveys have demonstratedthe need for more use of this instructional approach. Educators are encouraged to use these modules and also todevelop additional computer teaching programs to facilitate the development of problem-solving skills.

302.NAL Call No.: SD143.S64The legacy forest approach: application of geographical decision support for integrated resourcemanagement.Covington, W. W.; Wood, D. B. Proc-Soc-Am-For-Natl-Conv p.374-378. (1991)Meeting held Aug 4-7, 1991, San Francisco, California.Descriptors: forest-management; logging; planning; models; computer- software; resource-management;arizona; environmental-preservation

303.NAL Call No.: S494.5.D3C68-1992Leveraging farmer exposure to farm management software through agricultural input dealers.Jacobsen, R. M.; Dahl, B. L.; Larson, L. D.; Watt, D. J. Computers in agricultural extension programsproceedings of the 4th international conference, 28-31 January 1992, Orlando, Florida / sponspored by theFlorida Cooperative Extension Service, University of Florida. St. Joseph, Mich. : American Society ofAgricultural Engineers, c1992.. p. 736-739.Includes references.Descriptors: farm-management; computer-software; support-systems; extension; usa

304.NAL Call No.: DISS-F1991032Linking X-band radar backscattering and optical reflectance with crop growth models.Bouman, B. A. M. Wageningen [Netherlands] : Landbouwuniversiteit te Wageningen, [1991] 169 p. : ill.,Summary in English and Dutch.Descriptors: Agriculture-Remote-sensing

305.NAL Call No.: 49-J82Live animal measurement of carcass traits: estimation of genetic parameters of beef cattle.Robinson, D. L.; Hammond, K.; McDonald, C. A. J-Anim-Sci v.71(5): p.1128-1135. (1993 May)Includes references.Descriptors: beef-cattle; sires; subcutaneous-fat; longissimus-dorsi; body-composition; equations; heritability;phenotypic-correlation; environmental- factors; genetic-correlation; ultrasonic-fat-meters

Abstract: Ultrasound measurements by trained and accredited sonographers on 9,232 Angus, Hereford, andPolled Hereford cattle at an average age of 450 d were used to estimate genetic and environmental (co)variancesfor weight at scanning (Wt), longissimus muscle area (LMA), longissimus muscle area adjusted to a constantweight of 400 kg (LMAawt), and fat depths at the rump and 12/13 rib sites. Estimated kilograms (ESMkg) andpercentage of saleable meat yield (ESM%) were also calculated and analyzed. Subjective muscle scores,

available for 2,488 animals, were also included in the analysis. Estimated heritabilities were 46% for Wt, 21%for LMA and LMAawt, 37% for rump fat, 30% for rib fat, 15% for muscle score, 44% for ESMkg, and 36% forESM%. The two measurements, LMA and LMAawt, had high genetic (.82) and environmental (.91)correlations. The two fat depths were also highly correlated (.86 genetic; .67 environmental). Weight at scanningwas moderately correlated with LMA (.45 genetic; .41 environmental). Differences between breeds could not bedetected, but some variation in parameter estimates between data sets of the same breed was observed.Environmental correlations between fat depths or muscle score and Wt were approximately .3; geneticcorrelations were .07 to .12. Subjective muscle score had marginally higher genetic correlations with LMA thanwith LMAawt (.22 vs .08) but similar environmental correlations (.31 vs .27). Results show that carcass traitsmeasured by ultrasound and predictions of meat yield have genetic variability, are moderately heritable, and thatgenetic progress based on genetic evaluation by mixed-model analysis can be made.

306.NAL Call No.: 290.9-AM32PLow-cost, portable multi-purpose monitoring/control system.Kay, F.; Czarick, M.; Tyson, B. PAP-AMER-SOC-AGRIC-ENG (89-3024): p.95- 103. (1989 Summer)Paper presented at the International Summer Meeting of the American Society of Agricultural Engineers and theCanadian Society of Agricultural Engineering, June 25-28, 1989, Quebec, PQ, Canada.Descriptors: poultry; monitoring; environment; microcomputers

307.NAL Call No.: S494.5.D3C652Machine vision based analysis and harvest of somatic embryos.Harrell, R. C.; Hood, C. F.; Molto, E.; Munilla, R.; Bieniek, M.; Cantliffe, D. J. Comput-Electron-Agric v.9(1):p.13-23. (1993 Aug.)In the special issue: Computer vision / edited by J.A. Marchant and F.E. Sistler.Descriptors: somatic-embryogenesis; mechanical-harvesting; maturity

308.NAL Call No.: S596.7.M62-1991Maize phasic development.Kiniry, J. R. Modeling plant and soil systems / John Hanks and JT Ritchie, co-editors. Madison, Wis. : AmericanSociety of Agronomy, 1991.. p. 55- 70.Includes references.Descriptors: zea-mays; crop-yield; mathematical-models; phenology; photoperiod; temperature; computer-software; computer-simulation

309.NAL Call No.: SB950.A1V4A management information system for the control of pest animals and plants in Victoria, Australia.Backholer, J. R.; Lane, D. W. A.; Ward, E. A. Proc-Vertebr-Pest-Conf (14th): p.25-27. (1990 July)Meeting held March 6-8, 1990, Sacramento, California.Descriptors: weed-control; vertebrate-pests; pest-management; information-systems; microcomputers; victoria;pest-management-information- system

310.NAL Call No.: S494.5.E547Management strategies for low-temperature maize drying.VanEE, G. R.; Kline, G. L. Energy-World-Agric. Amsterdam : Elsevier. 1992. v. 5 p. 117-155.In the series analytic: Analysis of Agricultural Energy Systems / edited by R.M. Peart and R.C. Brook.Descriptors: maize; grain-drying; drying-temperature; feasibility- studies; crop-production; harvesters;simulation-models; computer-software; usa; faldry-simulation-models

311.NAL Call No.: 49-J82

Management systems for Holstein steers that utilize alfalfa silage and improve carcass value.Ainslie, S. J.; Fox, D. G.; Perry, T. C. J-Anim-Sci v.70(9): p.2643- 2651. (1992 Sept.)Includes references.Descriptors: steers; holstein-friesian; alfalfa-silage; grazing- experiments; cattle-feeding; zeranol; trenbolone;estradiol; concentrates; diet; liveweight- gain; feed-intake; dry-matter; feed-conversion-efficiency; carcass-composition; meat-cuts; metabolizable-energy

Abstract: Two trials were conducted to evaluate the effect of two- phase feeding systems using alfalfa silage orpasture on the performance and carcass characteristics of Holstein steers. During the growing phase (98 d) ofTrial 1, steers received alfalfa silage at either 40, 22, or 7% of the DMI. During the growing phase of Trial 2,steers received alfalfa silage at either 39 or 8% of their DMI (140 d) or grazed an orchardgrass/ryegrass pasture(175 d). During the finishing phase, all steers received a 90% concentrate diet until they reached a small degreeof marbling at the 12th rib as predicted by ultrasonic attenuation. In Trial 1, one-half were initially implantedwith zeranol and reimplanted with trenbolone acetate and estradiol (TBA+E) after 98 d. In Trial 2, one-half wereimplanted twice with TBA+E at a 120-d interval. Trial 1 average daily gains (kilograms) for the 40, 22, and 7%alfalfa silage treatments were 1.14, 1.25, and 1.38 in Period 1 (all different from each other at P < .05); 1.31,1,34, and 1.19 in Period 2; and 1.25, 1.25, and 1.26 overall. Trial 2 average daily gains (kilograms) for the 39, 8,and pasture treatments were 1.50, 1.71, and 92 for Period 1 (all different from each other at P < .05); .93, .75,1.11 for Period 2 (all different from each other at P < .05); and 1.16, 1.17, and 1.03 overall (pasture different at P< .05). No consistent effects of diet or implant on carcass characteristics were observed. When cattle wereimplanted with TBA+E, daily gain and feed efficiency improved by l6% (P < .10) and 11% (P < .10),respectively.

312.NAL Call No.: S494.5.D3C68-1992Managing feeder calf sales with PC-File+.Osborne, R. R.; Osborne, P. I. Computers in agricultural extension programs proceedings of the 4thinternational conference, 28-31 January 1992, Orlando, Florida / sponspored by the Florida CooperativeExtension Service, University of Florida. St. Joseph, Mich. : American Society of Agricultural Engineers,c1992.. p. 91-94.Includes references.Descriptors: calves; marketing; computer-techniques; computer- software; west-virginia

313.NAL Call No.: 56.8-J822Managing the land: a technology perspective.Shaw, R. R. J-Soil-Water-Conserv v.46(6): p.406-408. (1991 Nov.-1991 Dec.)Descriptors: land-management; technology-transfer; remote-sensing; information-systems; computer-software;usda; water-management; soil- conservation; geographic-information-systems; u; s; -soil-conservation-service

314.NAL Call No.: SF391.P55MANEX: a computer program for recording and management of experimental data from a pig researchfarm.Buron, G.; Lechevallier, M.; Gatel, F. Pig-News-Inf v.11(4): p.527-532. (1990 Dec.)Includes references.Descriptors: pigs; computer-software; data-collection; information- storage; farrowing; body-weight; feed-intake; growth-rate

315.NAL Call No.: 275.29-OK41CMarketing.Ward, C. E. Circ-E-Okla-State-Univ-Coop-Ext-Serv (826,rev.): p.51-57. (1991 Apr.)In series analytic: Alfalfa integrated management in Oklahoma.

Descriptors: medicago-sativa; hay; marketing; computer-software; quality-standards; prices; statistics;oklahoma; haymarket

316.NAL Call No.: SB1.H6Mathematical indices for comparing small fruit crops for harvest time and trait similarity.Khanizadeh, S.; Fanous, M. A. HortScience v.27(4): p.346-348. (1992 Apr.)Includes references.Descriptors: small-fruits; fragaria-ananassa; harvesting-date; ripening; maturation-period; genotypes; cultivars;earliness; evaluation; comparisons; mathematical-models; computer-software

Abstract: Three mathematical indices were developed to estimate: 1) potential for early dollar return or earlyripening (IE), 2) concentrated cropping (IC), and 3) deviation similarity of a genotype to known cultivars (ID).Early ripening genotypes with high yield early in the season will have larger IE values than late genotypes withlower yield early in the season. Genotypes with few harvests will have larger IC values than those requiringseveral harvests. The ID index helps to identify and group genotypes with similar characteristics. These indicescondense numerous values or arrays of traits into single index values, thereby simplifying genotypecomparisons.

317.NAL Call No.: QL391.N4J62Maximizing the potential of cropping systems for nematode management.Noe, J. P.; Sasser, J. N.; Imbriani, J. L. J-Nematol v.23(3): p.353- 361. (1991 July)Includes references.Descriptors: gossypium-hirsutum; glycine-max; hoplolaimus-columbus; nematode-control; rotation; cropping-systems; population-density; yield-losses

Abstract: Quantitative techniques were used to analyze and determine optimal potential profitability of 3-yearrotations of cotton, Gossypium hirsutum cv. Coker 315, and soybean, Glycine max cv. Centennial, withincreasing population densities of Hoplolaimus columbus. Data collected from naturally infested on-farmresearch plots were combined with economic information to construct a microcomputer spreadsheet analysis ofthe cropping system. Nonlinear mathematical functions were fitted to field data to represent damage functionsand population dynamic curves. Maximum yield losses due to H. columbus were estimated to be 20 on cottonand 42% on soybean. Maximum at harvest population densities were calculated to be 182/100 cm3 soil forcotton and 149/100 cm3 soil for soybean. Projected net incomes ranged from a $17.74/ha net loss for thesoybean-cotton-soybean sequence to a net profit of $46.80/ha for the cotton- soybean-cotton sequence. Therelative profitability of various rotations changed as nematode densities increased, indicating economicthresholds for recommending alternative crop sequences. The utility and power of quantitative optimization wasdemonstrated for comparisons of rotations under different economic assumptions and with other managementalternatives.

318.NAL Call No.: SF55.A78A7The measurement of fat thickness in live cattle with an ultrasonic device as a predictor of carcasscomposition.Mitsuhashi, T.; Mitsumoto, M.; Yamashita, Y.; Ozawa, S. Asian-Australasian- J-Anim-Sci v.3(4): p.263-267.(1990 Dec.)Includes references.Descriptors: beef-cattle; japanese-black; ultrasonic-fat-meters; carcass-composition; prediction; japan

319.NAL Call No.: 58.8-J82A measurement technique for yield mapping of corn silage.Vansichen, R.; Baerdemaeker, J. d. J-Agric-Eng-Res v.55(1): p.1-10. (1993 May)Includes references.

Descriptors: zea-mays; maize-silage; mechanical-harvesting; crop- yield; measurement; harvesters; mapping;recording-instruments; yield-map; spatial-yield-variation

Abstract: Yield mapping may form an important part of an in-field site-specific crop production system, both forthe spatial analysis of production efficiency and for the determination of spatially optimized application rates forfertilizer and sowing of seed. The objective of the work reported here, was to apply the principle of yieldmapping to whole crop harvesting corn silage. The yield measurement is based on the continuous recording ofthe material flowrate through harvesting machine, the machine driving speed and the machine location in thefield. For the flowrate measurement, the shaft of the material blower and the drive shaft of the base unitpowering the cutterhead, feedrolls and front attachement, were instrumented with strain gauge torquetransducers. Within the calibration range, the signals of these sensors showed a linear relationship to theflowrate. The harvester was also equipped with a speed radar and a data acquisition system based on a personalcomputer. The location tracking of the machine was done by integrating the machine speed and manualrecording of the machine path in the field. The construction of a yield map, from the recorded signals, includedseveral digital signal processing operations. The resulting map of a 1.2 ha corn silage field showed spatial yieldvariation from 1.2 to 4.8 kg/m2 with average of 3.2 kg/m2 and standard deviation of 0.64 kg/m2.

320.NAL Call No.: S544.3.O5O5Measuring carcass traits in live hogs.Luce, W. G. OSU-Ext-Facts-Coop-Ext-Serv-Okla-State-Univ. Stillwater, Okla. : The Service. Apr 1991.(3662,rev.) 2 p.Descriptors: pigs; carcass-grading; live-estimation; backfat; probes; ultrasonic-fat-meters


321.NAL Call No.: S494.5.D3C68-1992Megabucks: a computerized educational tool for farm management analysis.Sell, R. S.; Stearns, L. D.; Dahl, B. L.; Larson, L. D.; Watt, D. L. Computers in agricultural extension programsproceedings of the 4th international conference, 28-31 January 1992, Orlando, Florida / sponspored by theFlorida Cooperative Extension Service, University of Florida. St. Joseph, Mich. : American Society ofAgricultural Engineers, c1992.. p. 231-234.Includes references.Descriptors: farm-management; analysis; teaching-materials; computer- software

322.NAL Call No.: 290.9-AM32TMelon material properties and finite element analysis of melon compression with application to robotgripping.Cardenas Weber, M.; Stroshine, R. L.; Haghighi, K.; Edan, Y. Trans-A-S-A- E v.34(3): p.920-929. (1991 May-1991 June)Literature review.Descriptors: cucumis-melo; handling; loading; robots; melons; elasticity; finite-element-analysis; physical-properties; stress-analysis; simulation- models; literature-reviews

Abstract: The moduli of elasticity of the inner, middle, and outer portions of melon flesh (Cucumis meloreticulatus L. cv. 'Superstar') were determined using flat plate compression of cylindrical samples. The modulusof elasticity of the melon rind was determined using a tensile loading test. These values were used in a FiniteElement Analysis (FEA) of melon compression. The FEA predictions for deformations resulting from flat platloading of half a melon were within 11% (average error) of the values measured in an Instron compression testat a strain rate of 25.4 mm/min. After this verification procedure, the FEA was used to predict the internalstresses and the deformations of melons handled by two types of robot grippers: a parallel plate gripper and a v-v

notch gripper. The maximum equivalent stresses of 66 kPa (9.6 psi) and 28.2 kPa (4.1 psi) for the parallel plateand v-v-notch gripper, respectively, were located near the surface of the melon at the point of load application.These are well below the experimentally determined ultimate strength of the melon tissue, which was 132 kPa(19.1 psi) for the outer melon flesh. Effect on bruising could not be predicted because failure criteria for bruisingwere not available.

323.NAL Call No.: S494.5.D3C652A message system to integrate diverse programs and databases in a farm decision support system.Parsons, D. J.; Randle, D. G. Comput-Electron-Agric v.8(2): p.117-127. (1993 Mar.)Includes references.Descriptors: dairy-farms; farm-management; farm-planning; databases; decision-making; computer-software

324.NAL Call No.: 450-AN7A method for analysing plant architecture as it relates to fruit quality using three-dimensional computergraphics.Smith, G. S.; Curtis, J. P.; Edwards, C. M. Ann-Bot v.70(3): p.265-269. (1992 Sept.)Includes references.Descriptors: actinidia-deliciosa; plant-morphology; fruits; spatial- distribution; computer-analysis; canopy;computer-graphics; crop-quality; computer- software; mapit-software

Abstract: A method was developed for the spatial analysis of plant architecture as it relates to the within-plantvariation in the physical, chemical, and postharvest characteristics of the fruit. Computer graphics were used toreconstruct the architectural framework and spatial arrangement of the fruit in the canopy of kiwifruit vines(Actinidia deliciosa) trained on two different support structures. An infra-red beam theodolite was used to obtainthe spatial coordinates of the vines components. The data files generated by the theodolite were in turn used withsoftware specifically written for the project (MAPIT - Microcomputer Aided Plant Imaging Technology) toprovide a 3- dimensional reconstruction of the original vines. Each fruit was colour coded so that extremes intheir attributes could be easily identified and accurately located in the canopy of the vine. Patterns were clearlydiscernible for both the pergola and T-bar trained vines. The heavier fruit were located at the apical ends of thecanes, while greater soluble solids concentrations were associated with the smaller fruit located closer to thecordon. These patterns were consistent for all of the vines examined. The use of the theodolite coupled with thecomputer graphics described in this paper provides a rapid and objective means of accurately describing plantarchitecture.

325.NAL Call No.: S481.R4A method for creating custom-made standard area diagrams to assess crop pest damage.Wall, G. C.; Wall, P. L. Res-Ext-Ser-Coll-Trop-Agric-Hum-Resour-Univ-Hawaii- Coop-Ext-Serv (134): p.26-28.(1991 Dec.)Proceedings of the 1989 ADAP Crop Protection Conference, held May 18-19, 1989, Honolulu, Hawaii.Descriptors: capsicum; xanthomonas-campestris-pv -vesicatoria; manihot-esculenta; xanthomonas-campestris-pv-manihotis; crop-damage; computer- hardware; computer-software; guam

326.NAL Call No.: 99.9-F7662JA method for determining the cost of manufacturing individual logs into lumber.Howard, A. F. For-Prod-J v.43(1): p.67-71. (1993 Jan.)Includes references.Descriptors: logs; sawmilling; variable-costs; fixed-costs

Abstract: A method is proposed for determining sawmill variable costs for individual logs and computing totalfixed costs of manufacturing lumber. The methodology is based on standard accounting practices and theprinciples of production economics. Processing time functions are derived for each machine center, a detailed

cost analysis is completed for the mill, and the data are combined to estimate log variable costs and mill fixedcosts. The methodology was applied to a case study mill in the interior of British Columbia to compute thevariable costs for a range of log diameters typically processed at the mill. Application of the procedures can helpin the maximization of profits at sawmills by insuring that only logs with positive contributions to profits aresawn.

327.NAL Call No.: S544.3.M9E23Methods and procedures for machinery management and enterprise budgeting.Griffith, D. EB-Mont-State-Univ-Ext-Serv. Bozeman, Mont. : The Service. Aug 1989. (52) 37 p.Includes references.Descriptors: farm-machinery; farm-budgeting; computer-software; farm- management

328.NAL Call No.: S494.5.D3I5-1988Microcomputer aided land productivity assessment.Robert, P. C.; Anderson, J. L. Proceedings of the 2nd International Conference on Computers in AgriculturalExtension Programs Fedro S Zazueta, AB Del Bottcher, eds p.64-69. (of Florida, [1988?].)Conference held February 10-11, 1988 at the Grosvenor Resort Hotel, Disney World Village, Lake Buenavista,Orlando, Florida.Descriptors: land-productivity; assessment; microcomputers; computer- techniques

329.NAL Call No.: 290.9-AM32PA microcomputer-based double crop machinery management model.Allison, J. M. Jr.; McClendon, R. W.; Wetzstein, M. E. PAP-AMER-SOC-AGRIC- ENG. St. Joseph, Mich. : TheSociety. Summer 1989. (89-1019) 19 p.Paper presented at the 1989 International Summer Meeting, June 25-28, 1989, Quebec, PQ, Canada.Descriptors: farm-machinery; double-cropping; computer-simulation

330.NAL Call No.: 4-AM34PMicrocomputer-based experiment management system. II. Data analysis.Loussaert, D. Agron-J v.84(2): p.256-259. (1992 Mar.-1992 Apr.)Includes references.Descriptors: experiments; research; computer-analysis; data-analysis; computer-software; microcomputers;analysis-of-covariance; analysis-of-variance; regression-analysis; student's-test; least-squares

Abstract: Microcomputer-based statistical analysis can provide a convenient, inexpensive means of dataanalysis. Computer software has been developed to complement a general experiment management system. Thesoftware will perform analysis of variance, with or without covariant analysis, of data arranged in variousexperimental designs. Regression analysis, best fit regression and multiple regression analysis can also beconducted with this software. Data can be input as a continuation of complementary experiment managementsoftware or entered through a text file. The treatment means can be grouped in any combination of treatments ortreatment interactions desired and comparisons may be made using Least Significant Difference comparisons, aStudent's t-probability matrix, or specific treatment means may be pooled to make specific comparisons. Thissystem provides a convenient means of doing lower level statistical comparisons using an IBM/compatiblecomputer.

331.NAL Call No.: S494.5.D3C68-1992A microcomputer based farm accounting & business analysis program.Vogelsmeier, B.; Hein, N.; Ehlmann, G. Computers in agricultural extension programs proceedings of the 4thinternational conference, 28-31 January 1992, Orlando, Florida / sponspored by the Florida CooperativeExtension Service, University of Florida. St. Joseph, Mich. : American Society of Agricultural Engineers,

c1992.. p. 37-42.Includes references.Descriptors: farm-accounting; farm-enterprises; analysis; computer- software; missouri; management-information-records-mir

332.NAL Call No.: 41.8-V641A microcomputer model for predicting output from beef suckler herds.Menzies, F. D. Vet-Rec-J-Br-Vet-Assoc v.130(1): p.9-12. (1992 Jan.)Includes references.Descriptors: beef-herds; beef-production; prediction; simulation- models; computer-simulation

333.NAL Call No.: S494.5.D3I5-1988Microcomputer models as teaching aids in extension: reseed-the economics of alfalfa reestablishment.Hesterman, O. B.; Hilker, J. H.; Black, J. R.; Durling, J. C. Proceedings of the 2nd International Conference onComputers in Agricultural Extension Programs Fedro S Zazueta, AB Del Bottcher, eds p.378-383. (of Florida,[1988?].)Conference held February 10-11, 1988 at the Grosvenor Resort Hotel, Disney World Village, Lake Buenavista,Orlando, Florida.Descriptors: computer-assisted-instruction; alfalfa; crop- establishment

334.NAL Call No.: aSD11.U57Microcomputer software for predicting growth of southern timber stands.Farrar, R. M. Jr. Gen-Tech-Rep-SO-U-S-Dep-Agric-For-Serv-South-For-Exp-Stn. New Orleans, La. : TheStation. May 1992. (89) 19 p.Includes references.Descriptors: pinus; populus; growth; yields; prediction; computer- software; microcomputers; computer-simulation

335.NAL Call No.: S565.7.E74-1990Microcomputers on the farm : getting started. 2nd ed.Erickson, D. E. 1.; Hinton, R. A.; Szoke, R. D. 1. Ames : Iowa State University Press, 1990. viii, 99 p. : ill.,Includes index.Descriptors: Farm-management-Data-processing; Microcomputers

336.NAL Call No.: S590.S68MicroLEIS: a microcomputer-based Mediterranean land evaluation information system.Rosa, D. d. l.; Moreno, J. A.; Garcia, L. V.; Almorza, J. Soil-Use- Manage v.8(2): p.89-96. (1992 June)Includes references.Descriptors: land-evaluation; information-systems; computer- techniques; microcomputers; mediterranean-climate

Abstract: A computer-based land evaluation information system (MicroLEIS) was developed for optimal use ofagricultural and forestry land systems under Mediterranean conditions. Through an interactive procedure severalland capability, suitability and yield prediction methods may be applied. The system addresses land evaluation atreconnaissance, semi-detailed and detailed scales in an interrelated manner. Biophysical land evaluation methodsare incorporated using empirical, scale-appropriate models, which range from purely qualitative(reconnaissance) through semi- quantitative (semi-detailed) to quantitative (detailed). This software is helpfulfor teaching, research and development, predicting appropriate agroforestry land uses. Its use is illustrated by anexample. MicroLEIS runs on IBM PC, XT, AT, or a compatible microcomputer with at least 128 kilobytes of

RAM and a PC-DOS or MS-DOS version 2.0 or later operating system. The software package on double or highdensity diskettes can be obtained from the first author.

337.NAL Call No.: SF955.E6Microwave thermography: a non-invasive technique for investigation of injury of the superficial digitalflexor tendon in the horse.Marr, C. M. Equine-Vet-J v.24(4): p.269-273. (1992 July)Includes references.Descriptors: horses; tendons; trauma; thermography; microwave- treatment; ultrasonography

338.NAL Call No.: Q184.R4Microwave vegetation indexes for detecting biomass and water conditions of agricultural crops.Paloscia, S.; Pampaloni, P. Remote-Sensing-Environ v.40(1): p.15-26. (1992 Apr.)Includes references.Descriptors: remote-sensing; crops; canopy; microwave-radiation; emission; temperature; vegetation; indexes;radiometers; plants; biophysics; moisture-content; surfaces; biomass; detection; polarization; models; thermal-infrared-imagery; measurement; leaf-area-index; normalized-temperatures; polarization-indexes

339.NAL Call No.: S539.5.J68Milk per acre spreadsheet for combining yield and quality into a single term.Undersander, D. J.; Howard, W. T.; Shaver, R. D. J-prod-agric v.6(2): p.231-235. (1993 Apr.-1993 June)Includes references.Descriptors: dairy-cows; milk-yield; milk-production; forage; crop- yield; crop-quality; animal-production;prices; computer-techniques; computer- analysis; computer-software; cost-benefit-analysis; milk-production-costs; milk90

340.NAL Call No.: 472-N42Milking automation for all its worth.Blankesteijn, H.; Clery, D. New-Sci v.133(1806): p.27. (1992 Feb.)Descriptors: milking-machines; robots; proloin


341.NAL Call No.: S27.A3A mobile workstation for use in an integrated pest management program on the Russian wheat aphid.Legg, D. E.; Bennett, L. E. Great-Plains-Agric-Counc-Publ (142): p.66- 69. (1992)Proceedings of the Fifth Russian Wheat Aphid Conference, January 26-28, 1992, Fort Worth, Texas.Descriptors: diuraphis-noxia; integrated-pest-management; computer- hardware; computer-software

342.NAL Call No.: Q184.R4A model for backscattering characteristics of tall prairie grass canopies at microwave frequencies.Bakhtiari, S.; Zoughi, R. Remote-Sensing-Environ v.36(2): p.137-147. (1991 May)Includes references.Descriptors: prairies; grasslands; canopy; models; microwave- radiation; frequency; remote-sensing; attenuation

343.NAL Call No.: 80-AC82

A model for the diurnal course of air temperature: pomological applications.Rojas Martinez, R.; Hernandez Herrera, A.; Garza Gutierrez, R. Acta- Hortic (276): p.209-213. (1990 July)Paper presented at the "Second International Symposium on Computer Modelling in Fruit Research and OrchardManagement," September 5- 8, 1989, Logan, Utah.Descriptors: pome-fruits; air-temperature; growth; mathematical- models; mexico

Abstract: A refinement of the Parton and Logan (1981) model for the diurnal course of air temperature wasvalidated under subtropical Mexican conditions. The refined model was run against 757 non-consecutive dailytemperature curves taken from thermograph records. The two equations of the model had average coefficients ofdetermination (r(2) of 0.96 and 0.90, respectively. Both the standard error of temperature estimation and theabsolute error of temperature estimation averaged less than 1 degrees C. When the refinement of the Parton andLogan model was used to calculate Chill Units under subtropical Mexican conditions and compared against theapproach used by Richardson, et al. it gave a mean percentage error of 5% compared with an error of 24% forthe Richardson method.

344.NAL Call No.: SB191.M2C44-1986Model inputs.Ritchie, J. T.; Kiniry, J. R.; Jones, C. A.; Dyke, P. T. CERES-Maize a simulation model of maize growth anddevelopment / edited by CA Jones and JR Kiniry with contributions by PT Dyke [et al]. 1st ed. : College Station: Texas A&M University Press, 1986.. p. 37-48.Descriptors: zea-mays; soil-water-balance; simulation-models; soil-water- content; roots; genetics; computer-software

345.NAL Call No.: 290.9-AM32PModeling nutrients in runoff from potato fields using creams.Wiyo, K.; Madramootoo, C. A.; Enright, P.; Bastien, C. PAP-AMER-SOC-AGRIC- ENG. St. Joseph, Mich. : TheSociety. Winter 1990. (90-2505) 17 p.Paper presented at the "1990 International Winter Meeting," December 18-21, 1990, Chicago, Illinois.Descriptors: water-quality; leaching; subsurface-drainage; computer- software; quebec; chemicals,-runoff-and-erosion-from-agricultural-management

346.NAL Call No.: SD143.S64Modeling the interaction of silvicultural practices, wood quality, and product value in Douglas-fir.Briggs, D. G.; Fight, R. D. Proc-Soc-Am-For-Natl-Conv p.87-91. (1991)Meeting held Aug 4-7, 1991, San Francisco, California.Descriptors: pseudotsuga-menziesii; wood-properties; models; wood- products; computer-software; forest-management; usa

347.NAL Call No.: SB599.B73Modelling and economics.Mckinion, J. M. Monograph-Br-Crop-Prot-Counc (43): p.205-215. (1989)In the series analytic: Progress and prospects in insect control / edited by N.R. McFarlane. Proceedings of aninternational conference, September 18-20, 1989, Reading, United Kingdom.Descriptors: gossypium; crop-management; expert-systems; simulation- models; usa; comax-software

348.NAL Call No.: HD1.A3Modifications to the simulation model POTATO for use in New York.Ewing, E. E.; Heym, W. D.; Batutis, E. J.; Snyder, R. G.; Khedher, M. B.; Sandlan, K. P.; Turner, A. D. Agric-Syst v.33(2): p.173-192. (1990)Includes references.

Descriptors: solanum-tuberosum; cultivars; simulation-models; growth- models; modification; growth-analysis;biomass-production; photosynthesis; loam- soils; weather-data; tubers; shoots; leaf-area-index; stems; weight;physiological-age; computer-software; calibration; new-york; idaho; russet- burbank-potato; katahdin-potato;freeville,-new-york; aberdeen,-idaho; dry- weights

349.NAL Call No.: aSD11.A42Monitoring cold hardiness of tree seedlings by infrared thermography.Laacke, R. J.; Weatherspoon, C. P.; Tinus, R. W. Gen-Tech-Rep-RM-Rocky-Mt- For-Range-Exp-Stn-U-S-Dep-Agric-For-Serv (137): p.97-102. (1986 Dec.)Paper presented at a Meeting of the Combined Western Forest Nursery Council and Intermountain NurseryAssociation, August 12-15, 1986, Tumwater, Washington. Includes references.Descriptors: pinus-ponderosa; picea-engelmannii; pseudotsuga- menziesii; seedlings; cold-resistance; infrared-imagery; foliage; temperature; monitoring; arizona

350.NAL Call No.: 80-AC82MOPIS: a strategic planning model for fruit farm.Caggiati, P.; Gallerani, V.; Zanni, G. Acta-Hortic (276): p.315-322. (1990 July)Paper presented at the "Second International Symposium on Computer Modelling in Fruit Research and OrchardManagement," September 5- 8, 1989, Logan, Utah.Descriptors: fruit; orchards; farm-planning; computer-software; simulation-models

Abstract: MOPIS (Strategic Planning Model) is designed for medium- and long-term planning in fruit farms. Itis a decision support system (DSS) geared to help growers in defining more intelligently and accuratelymanagement, economic and financial strategies. Based on the principle of simulation, it relies on the feedbackmechanism, i.e. decisions are made in relation to the expected results predicated on those decisions. MOPISforecasts the consequences of selected options thereby enabling via trial and error runs of satisfactory responseto a given set of conditions. It has all the DSS features: analytical comprehensiveness, flexibility of use and thecapability of performing sensitivity analysis by changing the variables with the highest uncertainty levels.MOPIS is essentially a budget model the potential capability of which has been enormously upgraded, throughcomputerization by its links to a data bank containing standard management and economic information. It isstructured to determine the farm resources available at the initial planning stage as well as the management,economic, financial and marketing decisions. The consequent results are subjected to feasibility evaluation basedon available resources and economic benefit and the decisions can then be modified until satisfactory results areattained. When the grower deems that a sufficient number of strategies has been weighed, the most viable plansare compared so as to choose the one most responsive to the specific goals.

351.NAL Call No.: 80-AC82Multifru: multiple-criteria decision making in orchard management.Alvisi, F.; Malagoli, C.; Regazzi, D. Acta-Hortic (313): p.233-240. (1992 Oct.)Paper presented at the Third International Symposium on Computer Modelling in Fruit Research and OrchardManagement, February 11-14, 1992, Palmerston North, New Zealand.Descriptors: fruit-crops; orchards; crop-management; decision-making; growers; computer-software; italy

352.NAL Call No.: HC79.E5E5Multiple-resource modeling as a tool for conservation: its applicability in Mexico.Bojorquez Tapia, L. A.; Efolliott, P. F.; Guertin, D. P. Environ-Manage v.14(3): p.317-324. (1990 May-1990June)Includes references.Descriptors: pinus-ponderosa; resource-conservation; environmental- legislation; computer-software;simulation-models; land-use-planning; resource- management; flow-charts; mexico; 1988-general-law-of-ecological-equilibrium-and- environmental-protection; microsim-computer-sofware

353.NAL Call No.: 99.8-AU74Multiple-use planning: an application of FORPLAN to an Australian forest.McKenney, D. W. Aust-For v.53(2): p.113-123. (1990)Includes references.Descriptors: forest-management; planning; multiple-use; models; optimization; computer-software; forplan-forest-planning

354.NAL Call No.: Q184.R4Multisite analyses of spectral-biophysical data for sorghum.Richardson, A. J.; Weigand, C. L.; Wanjura, D. F.; Dusek, D.; Steiner, J. L. Remote-Sensing-Environ. New York,N.Y. : Elsevier Science Publishing. July 1992. v.41 (1) p. 71-82.Includes references.Descriptors: sorghum-bicolor; leaf-area-index; spectral-data; biophysics; solar-radiation; reflectance; equations;texas; normalized- difference-vegetation-index; perpendicular-vegetation-index; near-infrared-to- red-ration-vegetation-index; transformed-soil- adjusted-vegetation-index

355.NAL Call No.: GB746.W33Multiwave laser biomonitoring of the aquatic environment.Babichenko, S. M.; Lapimaa, Yu. Yu.; Poryvkina, L. V. Water-Resour v.18(6): p.638-643. (1992 Sept.)Translated from: Vodnye Resursy, v. 18 (6), 1991, p. 162-168. (GB746.V55).Descriptors: phytoplankton; algae; lasers; monitoring; aquatic- environment; remote-sensing; spectrometers;spectral-data; satellite-imagery; species; composition; biological-production; ecological-balance

356.NAL Call No.: 49-J82The National Sheep Improvement Program: a review.Wilson, D. E.; Morrical, D. G. J-Anim-Sci v.69(9): p.3872-3881. (1991 Sept.)Literature review.Descriptors: sheep; genetic-improvement; performance-recording; rams; best-linear-unbiased-prediction;selection-criteria; computer-software; usa

Abstract: A nationally organized sheep improvement program for sheep producers in the United States wasimplemented in 1987 under the name of the National Sheep Improvement Program (NSIP). This programcompleted a 3-yr Phase I project on February 16, 1990, that involved the definition of a uniform set ofperformance guidelines, development of an NSIP records processing center with associated performancerecording materials and computer software, and the enrollment of both purebred and commercial flocks.Organizers of the NSIP have defined 12 traits of economic importance to the U.S. sheep industry for geneticevaluation: number of lambs born, total ewe productivity, six growth traits, and four wool traits. Geneticevaluations are currently being conducted on a within-flock basis and will move to an across-flock, within-breedbasis when sufficient genetic ties between flocks are established. The genetic evaluations use BLUP proceduresand provide genetic merit values in the form of expected progeny differences for every animal in a flock.

357.NAL Call No.: aSB130.T57-1992Near IR and Color imaging for bruise detection on Golden Delicious apples.Throop, J. A.; Aneshansley, D. J.; Upchurch, B. L. [1992?] 1 v. : ill., Caption title.Descriptors: Plant-products-Postharvest-physiology; Apple-Postharvest- technology; Infrared-technology

358.NAL Call No.: HD1773.A3N6New applications for three-dimensional computer graphics in production economics.Debertin, D. L.; Pagoulatos, A.; Bradford, G. L. Rev-Agric-Econ v.13(1): p.141-154. (1991 Jan.)

Includes references.Descriptors: production-economics; production-functions; computer- graphics; computer-software;optimization; cost-analysis; constrainedoptimization

Abstract: This paper illustrates the usefulness of high-resolution computer graphics to illustrate constrainedoptimization problems in production economics. A third degree polynomial production function reveals that forsufficiently small input levels, concave isoquants could occur within the region enclosed by the ridge lines.Another feature is the ability to reveal the function that is maximized or minimized in the constrainedoptimization problem and to see the linkages between the shape of the isoquants (product transformation andisocost curves) and the shape of the function being maximized in the constrained optimization problem. Thesetechniques also permit a better understanding of the product-space counterparts to the factor space productionsurface. We also show that empirical analyses can benefit from computer graphics, particularly analysesemploying flexible functional forms.

359.NAL Call No.: TP669.I57New era dawning for eastern Europe's oilseeds, fats and oils industries.Int-News-Fats-Oils-Relat-Mater v.1(8): p.670-672, 676-677. (1990 Aug.)Descriptors: fats; oils-and-fats-industry; computer-software; models; imports; exports; international-trade;production; eastern-europe

360.NAL Call No.: S494.5.D3I5-1988A new Italian accounting software.Carpineti, C. Proceedings of the 2nd International Conference on Computers in Agricultural ExtensionPrograms Fedro S Zazueta p.770-774. (of Florida, [1988?].)Meeting held February 10-11, 1988 at Lake Buenavista, Orlando, Florida.Descriptors: farm-management; farm-accounting; computer-software; italy


361.NAL Call No.: 41.8-V641A new method for bovine embryo production: a potential alternative to superovulation.Kruip, T. A. M.; Pieterse, M. C.; Beneden, T. H. v.; Vos, P. L. A. M.; Wurth, Y. A.; Taverne, M. A. M. Vet-Rec-J-Br-Vet-Assoc v.128(9): p.208-210. ill. (1991 Mar.)Includes references.Descriptors: cows; embryos; collection; oocytes; maturity; ultrasound; embryo-culture; immature-oocytes

362.NAL Call No.: HC79.E5E5A new method for predicting vegetation distributions using decision tree analysis in a geographicinformation system.Moore, D. M.; Lees, B. G.; Davey, S. M. Environ-Manage v.15(1): p.59- 71. maps. (1991 Jan.-1991 Feb.)Includes references.Descriptors: state-forests; forest-resources; forest-management; mapping; vegetation; environmental-assessment; decision-making; models; computer- software; new-south-wales; forest-communities; kiola-state-forest,- new-south-wales; south-brooman-state-forest,-new-south-wales

363.NAL Call No.: 442.8-AN72New technology for cropping systems.Milbourn, G. Ann-Appl-Biol v.120(2): p.189-195. (1992 Apr.)

Address of the President of the Association of Applied Biologists at a meeting held September 17-18, 1991,University of York.Descriptors: crop-production; biotechnology; genetic-engineering; expert-systems; imagery; remote-sensing; uk;non-food-crop-production; robotics

364.NAL Call No.: 1-F766FIA new way to keep track of fire employees.Mac Millen, K. ed. Fire-Manage-Notes-U-S-Dep-Agric-For-Serv v.52(1): p.34-36. (1991)Descriptors: fire-fighting; personnel; computer-software; forest- fires; montana; redcard-manager

365.NAL Call No.: 80-AC82Nondestructive plant mass determination by computer image analysis and microwaves.Ernst, D.; Kuhn, W. Acta-Hortic (260): p.329-341. (1989 Sept.)Paper presented at the "International Symposium on Growth and Yield Control in Vegetable Production," /edited by G. Vogel, May 22-25, 1989, Berlin, German Democratic Republic.Descriptors: ficus-benjamina; dry-matter-accumulation; analytical- methods; computer-analysis; infrared-imagery; microwave-radiation; absorption

366.NAL Call No.: SD397.H3H37The Northeast Decision Model.Twery, M. J. Proc-Annu-Hardwood-Symp-Hardwood-Res-Counc p.127-130. (1992)Paper presented at a meeting on "The future of multiple user forstry in eastern hardwood forests," June 1-3,1992, Cashiers, North Carolina.Descriptors: forest-management; decision-making; computer-software; northeastern-states-of-usa

367.NAL Call No.: 340.8-AG8A note on the influence of a windbreak on plant temperature.Thofelt, L.; Rufelt, H.; Brattemo, P. A. Agric-Forest-Meteorol v.32(1): p.1-11. ill. (1984 July)Includes references.Descriptors: ribes-nigrum; windbreaks; temperatures; meteorological- factors; thermography

368.NAL Call No.: S539.5.J68NPK$PLUS: a computer program to examine agronomic and economic value of alternative fertilizerrates.Johnson, G. V.; Nofziger, D. L. J-Prod-Agric v.5(4): p.415-420. (1992 Oct.-1992 Dec.)Includes references.Descriptors: fertilizers; lime; application-rates; decision-making; computer-software; economic-analysis; crop-management

369.NAL Call No.: 290.9-AM32TAn object-oriented field operations simulator in PROLOG.Lal, H.; Peart, R. M.; Jones, J. W.; Shoup, W. D. Trans-A-S-A-E v.34(3): p.1031-1039. (1991 May-1991 June)Includes references.Descriptors: farm-management; crop-production; farm-machinery; farm- workers; multiple-cropping; resource-management; simulation-models; weather; computer-software; field-experimentation; florida

Abstract: This article describes the structure, logic, and programming technique of an agricultural simulationmodel in Logic Programming (PROLOG) with object-oriented data structures. The model simulates fieldoperations of multicrop production systems by estimating work based upon the available farm resources

(machinery and labor) and weather on a daily basis. The conventional approach to simulation in procedurallanguages makes it difficult to capture the human decision patters responsible for the system's behavior. Simpleapproximations and averages are often used, instead. The new simulation approach facilitated representing andmanipulating qualitative knowledge (heuristics) such as the manager's preferences in allocating the availableresources (machinery and labor) to different operations, in addition to quantitative and procedural computationsessential for simulating the system's behavior. The testing procedures for verifying the performance of thesimulator and the quality of the reports produced are discussed along with the results.

370.NAL Call No.: HD1401.A47On-farm computers for farm management in Sweden: potentials and problems.Ohlmer, B. Agric-Econ-J-Int-Assoc-Agric-Econ v.5(3): p.279-286. (1991 July)In the special issue : Multidisciplinary problem-solving and subject-matter work / edited by G.L. Johnson.Descriptors: microcomputers; farm-management; uses; comparisons; sweden

Abstract: The potential uses of on-farm computers in management and the problems in these uses are analyzed.The analysis is based on a study of present uses of on-farm computers in Sweden. The results are compared withexperiences from other countries. On-farm computer owners use almost the same management methods asbefore the computer investment. The main difference is that they used to hire service organizations to do someof the management tasks and now they are doing it by themselves with the aid of the computer. Thus, the on-farm computer owners have to have the same knowledge level as the service agents and advisers. The use of on-farm computers has so far affected the processing and storage of data for farm management purposes. Apotential next step is communication of data from external computer systems at suppliers, customers, advisersand other farmers as well as automated data capture within the farm. One hindrance for this development is thelack of standardization of data and concept definitions. If this potential was realized the marginal costs of dataand information would decrease. It would be profitable to use more information in the farm management, i.e. todevelop the farm management functions. When farmers develop their management methods they will need stillmore knowledge. Service agents and advisers would have to change from doing management tasks for farmersto teaching farmers how to do these tasks and supporting farmers in the interpretation and analysis ofinformation.

371.NAL Call No.: 290.9-AM32POn-farm testing of peanut and soybean models in north Florida.Boote, K. J.; Bennett, J. M.; Jones, J. W.; Jowers, H. E. PAP-AMER-SOC- AGRIC-ENG. St. Joseph, Mich. : TheSociety. Summer 1989. (89-4040) 54 p.Paper written for presentation at the 1989 International Summer Meeting American Society of AgriculturalEngineering and the Canadian Society of Agricultural Engineering, June 25-28, 1989, Quebec Canada.Descriptors: arachis-hypogaea; glycine-max; crop-production; simulation-models; computer-software; florida;pnutgro; soygro

372.NAL Call No.: 381-J8224On-line optimization of biotechnological processes. I. Application to open algal pond.Guterman, H.; Ben Yaakov, S. Biotechnol-Bioeng v.35(4): p.417-426. (1990 Feb.)Includes references.Descriptors: algae; biotechnology; production; ponds; mathematical- models

Abstract: A new on-line optimization and control procedure applicable to biotechnological systems for which aprecise mathematical model is unavailable has been developed and tested. The proposed approach is based on anonline search for optimum operating conditions by an automatic system using a modified simplex algorithm towhich several features have been added to permit real time operation. The simplex algorithm is the upper levelof a hierarchical software package in which the other levels are cost evaluation, control, data acquisition, andsignal processing. The optimization method was tested in a laboratory minipond for the cultivation of Spirulinaplatensis. The controlled parameters were light intensity, optical density, pH, and temperature. The proposed

optimization method can be applied to other biological processes provided that the pertinent variables can bemeasured and controlled and the cost function can be defined mathematically.

373.NAL Call No.: SD13.R4ONTWIGS: a forest growth and yield projection system adapted for Ontario.Payandeh, B.; Huynh, L. N. Inf-Rep-O-X-Can-For-Serv-Great-Lakes-For-Cent. Saulte Ste. Marie, Ont. : TheCentre. 1991. (412) 15 p.Includes references.Descriptors: forest-trees; growth; yields; projections; growth-models; models; computer-software; ontario;elstwigs

374.NAL Call No.: S494.5.D3C652An open information system for the swine production and marketing industry: its scope, topology andtelecommunication strategy.Groeneveld, E.; Lacher, P. Comput-Electron-Agric v.7(2): p.163-185. (1992 July)Includes references.Descriptors: pigs; meat-and-livestock-industry; animal-production; information-needs; information-systems;computer-software; marketing; record- keeping; classification; telecommunications; animal-husbandry

375.NAL Call No.: 58.8-J82Operational planning in horticultural: optimal space allocation in pot- plant nurseries using heuristictechniques.Annevelink, E. J-Agric-Eng-Res v.51(3): p.167-177. (1992 Mar.)Includes references.Descriptors: nurseries; greenhouses; pot-plants; space-requirements; optimization; production; planning;decision-making; support-systems; labor; utilization; automation; microcomputers; layout; linear-programming;dynamic- programming; algorithms; netherlands; space-allocation-planning; operationallevel

Abstract: At IMAG, in Wageningen, a Decision Support System (DSS) for glasshouse nurseries has beendeveloped, called the IMAG Production Planning system (IPP). This system focuses first on a tactical planninglevel and enables the grower to design a production plan, which optimizes space and labour utilization in hisgreenhouse. This tactical production plan, however, still has to be translated to an operational level. One of theproblems involved here is determining the exact location of the planned crops in the greenhouse in each periodof the plan. A space allocation plan gives these exact locations. The choice of the locations can influence theamount of internal transport and labour (for example during the spacing operation) that will be required forrealizing the production plan. Other space allocation criteria are the specifications prescribed by the productionprocess of the crop. An automated, highly interactive system for space allocation planning on the operationallevel is being developed for use on a personal computer. This enables the grower to design and easily change agraphical allocation plan that consists of a space- time diagram and, for each period, a layout of the compartmentwith the allocated crops. Construction of a space allocation plan is a complex mathematical problem because ofthe large number of crops with variable space requirements during their production and because of the manyfactors that influence the quality of the space allocation plan. A number of traditional operational researchtechniques, such as linear programming and dynamic programming, were found to be inadequate to solve theproblem, mainly because of the enormous calculation time required. Research has therefore concentrated onfinding new heuristic techniques, that will deliver a good (but not necessarily optimal) space allocation plan.One of the heuristic techniques that seems promising is the Genetic Algorithm.

376.NAL Call No.: aSD11.A42-no.219Operations guide for FORPLAN on microcomputers (release 13).Kent, B. M.; Rocky Mountain Forest and Range Experiment Station (Fort Collins, C. Fort Collins, Colo. : U.S.Dept. of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, [1992] 67 p.,

"September 1992."Descriptors: FORPLAN-Computer-program; Forest-management-United- States-Computer-programs; Forests-and-forestry-United-States-Computer- programs

377.NAL Call No.: S671.A33Opportunities for agricultural engineering research in harvesting and processing.Brown, W. T. Agric-Eng-Aust v.19(1): p.22-23. (1990)Descriptors: crops; harvesting; crop-production; sheep; shearing; robots; agricultural-engineering; research;value-added; australia

378.NAL Call No.: HC79.E5E5Opportunity costs of implementing forest plans.Fox, B.; Keller, M. A.; Schlosberg, A. J.; Vlahovich, J. E. Environ- Manage v.14(4): p.509. (1990 July-1990Aug.)Descriptors: forest-management; opportunity-costs; planning; decision- making; computer-software;ecosystems; arizona; national-forest-management-act- 1976; teams-computer-software; decision-support-systems

379.NAL Call No.: 80-AC82An optical leaf wetness sensor.Griffioen, H.; Kornet, J. G.; Schurer, K. Acta-Hortic (304): p.127-135. (1992 Mar.)Paper presented at the "First International Workshop on Sensors in Horticulture", January 29-31, 1991,Noordwijkerhout, The Netherlands.Descriptors: pseudotsuga-menziesii; propagation; crop-management; sensors; leaves; forests; moisture-content;mathematical-models; equations

380.NAL Call No.: S494.5.D3I5-1990Optimal allocation of various quality feeds for dairy herd.Wachenheim, C. J.; Erickson, R. W.; Borton, L. R.; Harsh, S. B. Proceedings of the 3rd International Conferenceon Computers in Agricultural Extension Programs / Fedro S. Zazueta, editor. ; January 31- February 1, 1990,Grosvenor Resort Hotel, Disney World Village, Lake Buenavista, FL. Gainesville, FL : Florida CooperativeExtension Service, University of Florida, [1990]. p. 565- 570.Includes references.Descriptors: dairy-herds; feeds; costs; computer-software; dairy-herd- feed-management-program


381.NAL Call No.: 290.9-AM32POptimized design of water management systems.Prasher, S. O.; Barrington, S. F. PAP-AMER-SOC-AGRIC-ENG. St. Joseph, Mich. : The Society. Summer 1989.(89-2143) 13 p.Paper presented at the 1989 International Summer Meeting, June 25-28, 1989, Quebec, PQ, Canada.Descriptors: water-management; design; computer-software; cost- benefit-analysis

382.NAL Call No.: 290.9-AM32POptimizing resource allocation for greenhouse potted plant production.Fang, W.; Ting, K. C.; Giacomelli, G. A. PAP-AMER-SOC-AGRIC-ENG. St. Joseph, Mich. : The Society. Winter

1989. (89-7540) 16 p.Paper presented at the "1989 International Winter Meeting sponsored by the American Society of AgriculturalEngineers," December 12-15, 1989, New Orleans, Louisiana.Descriptors: greenhouses; operations-research; computer-software

383.NAL Call No.: 290.9-AM32TOptimizing resource allocation for greenhouse potted plant production.Fang, W.; Ting, K. C.; Giacomelli, G. A. Trans-A-S-A-E v.33(4): p.1377- 1382. (1990 July-1990 Aug.)Includes references.Descriptors: crop-production; pot-plants; greenhouse-culture; resource-allocation; computer-software

Abstract: A procedure for studying the profitability of greenhouse potted plant production systems subject toresource constraints was developed. The constrained condition and resources were the crop production schedule,greenhouse space, labor, and budget. A database containing the information for determining the requiredresources and operating costs for growing various crops was established. The database also provides theestimated revenue from sales of the crops, on a per pot basis. An algorithm was developed to determine first thefeasibility of a given production plan and then determine the quantities of crops to be grown in order to yield anoptimum profit. The result of this algorithm may serve to optimize allocation of resources for year-roundproduction. The algorithm along with the crop database was incorporated into a user-friendly micro-computerprogram.

384.NAL Call No.: 80-AC82Orchard 2000: towards a decision support system for New Zealand's orchard industries.Atkins, T. A.; Laurenson, M. R.; Mills, T. M.; Ogilvie, D. K. Acta- Hortic (313): p.173-182. (1992 Oct.)Paper presented at the Third International Symposium on Computer Modelling in Fruit Research and OrchardManagement, February 11-14, 1992, Palmerston North, New Zealand.Descriptors: malus-pumila; actinidia-deliciosa; orchards; commercial- farming; management; technology;innovation-adoption; information-technology; growers; information-systems; support-systems; decision-making;new-zealand

385.NAL Call No.: 58.8-J82Orientation independent machine vision classification of plant parts.Simonton, W.; Pease, J. J-Agric-Eng-Res v.54(3): p.231-243. (1993 Mar.)Includes references.Descriptors: machinery; vision; ornamental-plants; cuttings; classification; image-processors

Abstract: Machine vision can be a vital tool for measuring key process parameters in many agriculturalproduction systems, including commercial nurseries and greenhouses. One important role of machine vision inthese systems may be to identify plant features and properties which allow for robotic processing, automatedgrading, and other tasks. A technique for identifying key features of ornamental cuttings was developed whichwas orientation independent, provided complete classification and interconnection of the major plant parts (e.g.main stem, petioles, and leaf blades), and relied exclusively on the morphological content of a binary image. Thetechnique segmented a plant image into objects which could then be classified according to geometric data.Results indicated the technique was effective on geranium cutting images regardless of position or orientation. Ahigh average percentage of total image pixels (96.6%) were classified correctly in 80 sample images tested.However, sources of error for certain misclassifications revealed limitations of this technique. Branching or fork-based segmentation can leave gaps in the geometric data required for satisfactory classification. Also, plant partswhich overlap and/or occlude other parts cause conflicting geometric data which can yield classification errors.The use of spectral information may be required to extend the technique and improve robustness.

386.NAL Call No.: 49-J82

An overview of beef cattle improvement programs in the United States.Middleton, B. K.; Gibb, J. B. J-Anim-Sci v.69(9): p.3861-3871. (1991 Sept.)Literature review.Descriptors: beef-cattle; genetic-improvement; beef-breeds; breeding- programs; breeders'-associations;performance-recording; computer-software; performance-testing; usa

Abstract: A periodic review of beef improvement programs is useful as a benchmark and as an opportunity toreevaluate industry direction. The history of improvement programs is reviewed with particular emphasis onrecording organizations, program financing, and technological progress. The various breed associations havebecome the primary suppliers of performance programs, which are largely funded through registration income.Current practices are described from the aspects of traits recorded and delivery systems to collect, analyze, anddistribute the data. The unique or innovative features of several breed programs are highlighted, and conspicuousindustry gaps are noted. Finally, a survey is made of the organizational, technical, and educational challengesfacing beef improvement. Although increased participation in genetic improvement programs is expected,substantial efforts are needed to serve adequately the needs of a changing beef cattle industry.

387.NAL Call No.: HD1407.C6An overview of NEMPIS: National Economic Milk Policy Impact Simulator.Kaiser, H. M. Cornell-Agric-Econ-Staff-Pap-Dep-Agric-Econ-Cornell-Univ- Agric-Exp-Stn. Ithaca, N.Y. : TheStation. Feb 1992. (92-02) 26 p.Includes references.Descriptors: agricultural-policy; dairy-technology; retail-prices; computer-software; simulation-models;economic-impact; usa

388.NAL Call No.: 290.9-AM32TParameter adjustment to a crop model using a sensor-based decision support system.Thomson, S. J.; Peart, R. M.; Mishoe, J. W. Trans-A-S-A-E v.36(1): p.205-213. (1993 Jan.-1993 Feb.)Includes references.Descriptors: arachis-hypogaea; crop-management; decision-making; expert-systems; growth-models; sensors;simulation-models; soil-water; comax- software; modvex-software

Abstract: A knowledge-based system was developed to adjust input parameters to the soil-water and rootingcomponents of PNUTGRO, a process- oriented peanut growth model. The system was developed to provide abetter representation of temporal water status in the root zone of a growing crop. Soil water sensors providedinput to adjust appropriate parameters based on interpretation of their readings. These interpretations wereprogrammed using human expertise combined with data from peanuts grown in lysimeters. A separate expertsystem screened sensor readings to insure their validity before using their readings to adjust parameters. Tests ofthe system over one season showed that model-based representations of soil-water status converged on sensor-based representations in the soil water regulation zone as the adjusted input parameters converged on new staticvalues early in the season.

389.NAL Call No.: S671.A66Parametric design with associated costs and production data of swine nurseries.Helmink, K. J.; Christianson, L. L.; Riskowski, G. L. Appl-Eng-Agric v.7(2): p.237-247. (1991 Mar.)Includes references.Descriptors: pig-housing; ventilation; design; costs; parametric- programming; computer-software; illinois-nursery-improvement-software

Abstract: The Illinois Nursery Improvement Software (INIS) is a computerized, parametric design aid for swinenurseries and prenurseries. INIS prepares plan and elevation drawings, specifies equipment and materials andcompares ventilation options. Costs of alternative ventilation systems are calculated. Users can estimate

productivity improvements (feed efficiency, health costs, gain rates, and mortality rates) that will result fromimproved ventilation to compare with ventilation system costs.

390.NAL Call No.: S494.5.D3C68-1992The PASTURE program for determining pasture stocking rates.Swenson, A. L.; Sedivec, K. K. Computers in agricultural extension programs proceedings of the 4thinternational conference, 28-31 January 1992, Orlando, Florida / sponspored by the Florida CooperativeExtension Service, University of Florida. St. Joseph, Mich. : American Society of Agricultural Engineers,c1992.. p. 64-69.Includes references.Descriptors: livestock; stocking-rate; grazing-intensity; computer- software; north-dakota

391.NAL Call No.: HC59.7.A1W6Performance and potential of information technology: An international perspective.Mody, A.; Dahlman, C. World-Dev v.20(12): p.1703-1719. (1992 Dec.)In the special issue: Diffusion of information technology: opportunities and constraints / edited by A. Mody andC. Dahlman.Descriptors: diffusion-of-information; technology; evaluation; productivity; telecommunications

392.NAL Call No.: 47.8-AM33PPerformance of broilers fed rations formulated by stochastic nonlinear programming or linearprogramming with a margin of safety.D'Alfonso, T. H.; Roush, W. B.; Cravener, T. L. Poult-Sci v.72(4): p.620-627. (1993 Apr.)Includes references.Descriptors: broilers; broiler-performance; fowl-feeding; computer- software; stochastic-programming; feed-formulation; linear-programming; meat- and- bone-meal; nutrient-content; variance; production-costs

Abstract: A feeding trial compared the production of broilers fed rations formulated by linear programming(LP), linear programming with a margin of safety (LPMS), and stochastic nonlinear programming (SP). The SPand LPMS programs met requirements at a specified confidence level (CL); however, SP rations were lower incost. Treatments included six rations (four replicates per ration with 15 birds per replicate). Variances ofmethionine, lysine, calcium, and phosphorus were considered. Treatments were: 1) LP; 2) SP and 3) LPMS, bothwith .69 CL on meeting requirements (NRC, 1984) for the specified nutrients; 4) SP and 5) LPMS, with aminoacid CL increased to .90; and 6) SP identical to Treatment 4, but with meat and bone meal restrictions relaxedfrom 5 to 10%. The SP rations utilized both nutritionally variable ingredients (e.g., rendered by- products) andnutritionally consistent ingredients (e.g., amino acid supplements) while costing less than the equivalent LPMSrations. Birds performed the same between equivalent SP and LPMS treatments (P > .05) on the basis of bodyweight and feed conversion. The SP rations were more profitable than the LPMS counterparts.

393.NAL Call No.: SD143.N6Performing a break-even yield analysis using a microcomputer.Blinn, C. R.; Hove, G. P. North-J-Appl-For v.8(1): p.38-41. (1991 Mar.)Includes references.Descriptors: forest-economics; economic-analysis; investment; microcomputers; break-even-point

394.NAL Call No.: QL461.A52Pest management materials databases.Edwards, C. R. Amer-Entomol v.37(2): p.72-73. (1991 Summer)Descriptors: databases; computer-software; extension; insect-pests; management; usa; pmmdb-software

395.NAL Call No.: 100-M668PigCHAMP makes champs of Minnesota producers.Hansen, D. Minn-Sci-Agric-Exp-Stn-Univ-Minn. St. Paul, Minn. : The Station. [1992.] v. 47 (1) p. 1.Descriptors: pigs; meat-production; factors-of-production; computer-software; computer-analysis; minnesota

396.NAL Call No.: S494.5.D3C652Plant grading by vision using neural networks and statistics.Brons, A.; Rabatel, G.; Ros, F.; Sevila, F.; Touzet, C. Comput-Electron- Agric v.9(1): p.25-39. (1993 Aug.)In the special issue: Computer vision / edited by J.A. Marchant and F.E. Sistler.Descriptors: cyclamen; imagery; grading; simulation-models

397.NAL Call No.: Z672.I53Plant it!--CD: a multimedia CD-ROM on ornamental horticulture.Mason, P. R. Quar-Bull-Int-Assoc-Agric-Inf-Spec v.37(1/2): p.23-30. (1992)IAALD Symposium on "Advances in Information Technology," September 16-20, 1991, Beltsville, Maryland.Descriptors: ornamental-plants; horticulture; compact-discs; multimedia-instruction; expert-systems;information-storage; imagery; databases; usa; compact-disk-read-only-memory; memory-text; audio; national-agricultural- library

398.NAL Call No.: 290.9-AM32PPlant production cost accounting/management (PPCAM) system.Power, K. C.; Fitzgerald, J. B.; Meyer, G. E.; Schulte, D. D. PAP-AMER-SOC- AGRIC-ENG. St. Joseph, Mich. :The Society. Winter 1989. (89-7569) 6 p.Paper presented at the 1989 International Winter Meeting, December 12-15, 1989, New Orleans, Louisiana.Descriptors: crop-production; production-costs; computer-software

399.NAL Call No.: SB1.H6Plant production cost-accounting/management system.Power, K. C.; Fitzgerald, J. B.; Meyer, G. E.; Schulte, D. D. HortScience v.26(2): p.201-203. (1991 Feb.)Includes references.Descriptors: ornamental-plants; vegetables; crop-production; cost- analysis; production-costs; computer-software; microcomputers; greenhouse- culture; nurseries; pp-cam

Abstract: A microcomputer program has been developed to keep records on energy, labor costs, product pricing,and revenue predictions for greenhouse and nursery production. The program manages plant production data,potentially enabling the grower to improve production and profits. The grower can use the program to determinehow much it costs to produce individual plants, to ascertain labor costs and where to reallocate employees.Advertising and other indirect costs can be included to determine cost of production on a per-plant or per-square-foot basis.

400.NAL Call No.: S494.5.D3C68-1992PORKPLANNER: a microcomputer record keeping system for pork production.Ahmadi, A.; Farley, J. L.; Berry, S. L. Computers in agricultural extension programs proceedings of the 4thinternational conference, 28-31 January 1992, Orlando, Florida / sponspored by the Florida CooperativeExtension Service, University of Florida. St. Joseph, Mich. : American Society of Agricultural Engineers,c1992.. p. 76-80.Includes references.Descriptors: pigs; animal-production; record-keeping; computer- software; porkplanner


401.NAL Call No.: 99.8-AU74Portable field computers in New Zealand forest management.Gordon, A. D. Aust-For v.54(4): p.219-225. (1991)Includes references.Descriptors: forest-management; computers; portable-instruments; computer-software; computer-techniques;new-zealand

402.NAL Call No.: TL796.A1C3A potential landscape basis for the analysis of NOAA-AVHRR data.Izaurralde, J. A.; Crown, P. H. Can-J-Remote-Sensing v.16(1): p.24-29. (1990 Apr.)Includes references.Descriptors: ground-cover; crops; landscape; land-use; natural- resources; spectral-data; responses; correlated-traits; objectives; discriminant-analysis; infrared-imagery; remote-sensing; alberta; agroecology; target-objects;agroecological-resource-areas

403.NAL Call No.: 41.8-V641Potential of infra-red thermography for the detection of summer seasonal recurrent dermatitis (sweetitch) in horses.Braverman, Y. Vet-Rec-J-Br-Vet-Assoc v.125(14): p.372-374. ill. (1989 Sept.)Includes references.Descriptors: horses; dermatitis; summer; detection; infrared- photography; culicoides-imicola; disease-vectors;israel

404.NAL Call No.: SB379.A9A9Predicting high quality in kiwifruit.Crisosto, C. H. Calif-Grow v.16(9): p.33-34. (1992 Sept.)Descriptors: actinidia-deliciosa; food-quality; harvesting; consumer- preferences; ripening; nondestructive-testing; taste-panels; infrared- spectroscopy; california

405.NAL Call No.: SB112.5.P74Predicting maize phenology.Kiniry, J. R.; Bonhomme, R. Predicting crop phenology / editor, Tom Hodges. Boca Raton : CRC Press, c1991..p. 115-131.Includes references.Descriptors: zea-mays; crop-yield; phenology; mathematical-models; photoperiod; temperature; computer-software; computer-simulation

406.NAL Call No.: 59.8-C333Predicting wheat sprout damage by near-infrared reflectance analysis.Shashikumar, K.; Hazelton, J. L.; Ryu, G. H.; Walker, C. E. Cereal-Foods- World v.38(5): p.364-366. (1993May)Includes references.Descriptors: wheat; preharvest-sprouting; crop-damage

407.NAL Call No.: 382-SO12

Prediction of botanical composition in grassland herbage samples by near infrared reflectancespectroscopy.Garcia Criado, B.; Garcia Ciudad, A.; Perez Corona, M. E. J-Sci-Food- Agric v.57(4): p.507-515. (1991)Includes references.Descriptors: grasslands; botanical-composition; prediction; infrared- spectroscopy

Abstract: Near infrared reflectance spectroscopy (NIRS) was evaluated as a method to predict the botanicalcomposition of seminatural grassland in 'dehesa' systems. Samples of herbaceous biomass were harvested overfour consecutive years, determining in each-by manual separation-the proportion by weight of the followingtaxonomic groups: grasses, legumes and the rest of the families in a single block (others). After reconstructingthe natural samples they were analysed by NIRS. One set of samples (calibration set) was selected for thedevelopment of the equations, assaying different mathematical treatments (log 1/R, first derivative and secondderivative). The ranges of coefficients of multiple determination and standard errors of calibration, respectively,for the various components were: grasses, 0.86 to 0.92 and 6.66 to 9.14; legumes, 0.77 to 0.81 and 6.82 to 7.43;and 'others', 0.85 to 0.88 and 8.17 to 9.54. The remaining samples not included in the development of the NIRSequations (prediction set) were used for the purposes of validating the best equations. Standard errors ofperformance were: grasses, 6.12; legumes, 7.56 and 'others', 7.70.

408.NAL Call No.: 41.8-C163The prediction of pork carcass composition using live animal echographic measurements from theKrautkramer USK7, Ithaca Scanoprobe 731C and Aloka SSD- 210DXII Echo Camera.Sather, A. P.; Newman, J. A.; Jones, S. D. M.; Tong, A. K. W.; Zawadski, S. M.; Colpitts, G. Can-J-Anim-Sciv.71(4): p.1001-1009. (1991 Dec.)Includes references.Descriptors: pigs; carcass-composition; ultrasonic-devices; ultrasonics; probes; body-fat; muscles; depth;prediction; meat-yield; analogue- probes; digital-probes; real-time-ultrasonics

409.NAL Call No.: 41.8-C163The prediction of pork carcass composition using the Hennessy Grading Probe and the Aloka SSD-210DXII Echo Camera.Sather, A. P.; Newman, J. A.; Jones, S. D. M.; Tong, A. K. W.; Zawadski, S. M.; Colpitts, G. Can-J-Anim-Sciv.71(4): p.993-1000. (1991 Dec.)Includes references.Descriptors: pigs; carcass-composition; ultrasonic-devices; probes; body-fat; muscles; dimensions; prediction;meat-yield

410.NAL Call No.: 290.9-AM32TPREFLO: a water management model capable of simulating preferential flow.Workman, S. R.; Skaggs, R. W. Trans-A-S-A-E v.33(6): p.1939-1948. (1990 Nov.-1990 Dec.)Includes references.Descriptors: soil-water-movement; simulation-models; water-flow; water-management; water-table; computer-software; hydraulic-conductivity; runoff; preflo-software

Abstract: Preferential flow through large continuous pores affects the distribution of water in the soil profile byreducing runoff and increasing total infiltration. In this study, a water management model (PREFLO) wasdeveloped which could be used to simulate unsaturated and saturated movement of water in a soil profile inwhich preferential flow might occur. PREFLO is based on a one-dimensional finite difference solution to theRichards equation with a nonuniform grid spacing. Large pores are described on a macroscopic scale withvertical movement of water computed from the equation for flow in a capillary tube. Water moving from thelarge pores into the soil matrix via horizontal infiltration is added to the sink term in the Richards equation.Example simulations indicated that PREFLO can be a useful tool in simulating the timing, frequency, andvolume of preferential flow in a soil profile. Use of hourly rainfall data and the Richards equation to simulate

soil water conditions allowed the PREFLO model to predict preferential flow when infiltration into the soilmatrix was limiting. Simulated water table response in soils that contain preferential flow channels was shown tobe dependent on hydraulic conductivity and the number of large pores. For a simulated rainfall event on a soilwith a hydraulic conductivity of 0.15 cm/h, PREFLO predicted a rapid rise of the water table caused by waterponding in the large pores. Simulations using soils with hydraulic conductivities of 0.5 cm/h and 0.8 cm/hresulted in less rapid movement of the water table. The wetting front was shown to move slowly through the soilprofile for the low conductivity soil which deviates from the drained to equilibrium state assumed inDRAINMOD.

411.NAL Call No.: S494.5.D3I5-1990PRESET--a computer model for forecasting and tracking livestock and farm input prices.RoHrig, C. Proceedings of the 3rd International Conference on Computers in Agricultural Extension Programs /Fedro S. Zazueta, editor. ; January 31- February 1, 1990, Grosvenor Resort Hotel, Disney World Village, LakeBuenavista, FL. Gainesville, FL : Florida Cooperative Extension Service, University of Florida, [1990]. p. 750-755.Includes references.Descriptors: farm-management; livestock-farming; input-output- analysis; computer-software

412.NAL Call No.: QH301.A76Prevalence of pea bacterial blight in UK seeds stocks, 1986- 1990.Roberts, S. J.; Reeves, J. C.; Biddle, A. J.; Taylor, J. D.; Higgins, P. Aspects-Appl-Biol (27): p.327-332. (1991)In the series analytic: Production and protection of legumes / edited by R.J. Froud-Williams, P. Gladders, M.C.Heath, J.F. Jenkyn, C.M. Knott, A. Lane and D. Pink.Descriptors: pisum-sativum; cultivars; pseudomonas-syringae-pv; -pisi; seed-testing; databases;microcomputers; uk

413.NAL Call No.: Z672.I53Problem solving strategies for agricultural expert systems.Pohlmann, J. M. Quar-Bull-Int-Assoc-Agric-Inf-Spec v.37(1/2): p.107- 111. (1992)IAALD Symposium on "Advances in Information Technology," September 16-20, 1991, Beltsville, Maryland.Descriptors: expert-systems; agriculture; problem-solving; crop- production

414.NAL Call No.: 100-T31MProcedural guide for SPBDSS, the Southern Pine Beetle Decision Support System.Saunders, M. C.; Loh, D. K.; Payne, T. L.; Rykiel, E. J. Jr.; Pulley, P. E.; Coulson, R. N.; Sharpe, P. J. H.; Hu, L.C. Misc-Publ-MP-Tex-Agric-Exp-Stn. College Station, Tex. : The Station. July 1985. (1579) 23 p.Includes references.Descriptors: dendroctonus-frontalis; insect-control; decision-making; computer-software; forest-management

415.NAL Call No.: 100-T31MProcedural guide for using the interactive version of the TAMBEETLE model of southern pine beetlepopulation and spot dynamics.Turnbow, R. H.; Coulson, R. N.; Hu, L.; Billings, R. F. Misc-Publ-MP-Tex- Agric-Exp-Stn. College Station, Tex.: The Station. Sept 1982. (1518) 24 p.Includes references.Descriptors: dendroctonus-frontalis; insect-control; computer- software; forest-management; models

416.NAL Call No.: aS494.5.D3P75-1982Proceedings from the Practitioner Workshop on Microcomputers and Agriculture Management in

Developing Countries : June 3-4, 1982, Washington, D.C. Microcomputers and agriculture managementin developing countries.Practitioner Workshop on Microcomputers and Agriculture Management in Developing Countries (1982 :Washington, D. C. Washington, D.C. : U.S. Dept. of Agriculture, Office of International Cooperation andDevelopment, 1982. 27 leaves, "Organized by the Development Project Management Center (DPMC) in theTechnical Assistance Division of the Office of International Cooperation and Development, USDA."Descriptors: Agriculture-Developing-countries-Data-processing- Congresses; Microcomputers-Developing-countries-Congresses

417.NAL Call No.: S494.5.D3C652Processing of living plant images for automatic selection and transfer.He, W. B.; Beck, M. S.; Martin, W. J. Comput-Electron-Agric v.6(2): p.107-122. (1991 Oct.)Includes references.Descriptors: cucumis-sativus; somatic-embryogenesis; micropropagation; algorithms; selection; robots; seed-germination; image-processors

418.NAL Call No.: SB436.J6A program for the basic formula method for tree valuation.Fitzpatrick, G. E.; Verkade, S. D. J-Arboric v.16(11): p.297-299. (1990 Nov.)Includes references.Descriptors: trees; valuation; arboriculture; calculation; computer- software

419.NAL Call No.: SD143.N6A programmable calculator-assisted procedure for marking unevenaged stands.Moser, J. W. Jr.; Raney, J. D. North-J-Appl-For v.7(3): p.140-142. (1990 Sept.)Includes references.Descriptors: forest-management; forest-trees; marking; computer- software; diameter-distribution

420.NAL Call No.: SB435.5.A645Programming for success.Arbor-Age v.12(1): p.22-24. (1992 Jan.)Descriptors: arboriculture; computer-programming; computer-software; computers


421.NAL Call No.: 99.9-F7662JPrototyping an automated lumber processing system.Klinkhachorn, P.; Kothari, R.; Huber, H. A.; McMillin, C. W.; Mukherjee, K.; Barnekov, V. For-Prod-J v.43(2):p.11-18. (1993 Feb.)Includes references.Descriptors: hardwoods; lumber; processing; automation; cutting; lasers; computer-techniques; optimization

Abstract: The Automated Lumber Processing System (ALPS) is a multidisciplinary continuing effort directedtoward increasing the yield obtained from hardwood lumber boards during their process of remanufacture intosecondary products (furniture, etc.). ALPS proposes a nondestructive vision system to scan a board for itsdimension and the location and expanse of surface defects on it. This information is then used to determine anefficient placement of the desired wood parts. Finally, a laser path planning algorithm is used to obtain anefficient path for the Computer Numeric Controlled (CNC) laser to follow to effectively punch out desired parts.

While some individual subsystems of ALPS have been reported separately in previous communications, ourrecent success with the vision system required by ALPS has made the integration of the individual modules ofALPS possible. The vision subsystem and some other subsystems have been prototyped at West VirginiaUniversity. Recent efforts have been directed toward integrating these subsystems with the material-handlingand laser cut-up system at Michigan State University in an attempt to create a fully functional prototype ofALPS.

422.NAL Call No.: S451.O5O8Ranch calculator (RANCALC): for Lotus-123 and compatible spreadsheets). A spreadsheet to aid inplanning for cow/calf and cow/calf-stocker operations.Lusby, K. S.; Walker, O. L. OSU-Curr-Rep-Okla-State-Univ-Coop-Ext-Serv. Stillwater, Okla. : The Service. Aug1991. (3252) 6 p.Includes references.Descriptors: cattle-husbandry; computer-software; statistical- analysis; beef-cattle; oklahoma

423.NAL Call No.: 1.98-AG84RANGETEK, a rancher's best friend.Corliss, J. Agric-Res-U-S-Dep-Agric-Res-Serv v.39(6): p.22. (1991 June)Descriptors: grazing; range-management; computer-software; computer- techniques

424.NAL Call No.: 80-AC82Real-time weather systems in agricultural support: the loop is closed-- or is it.Bingham, G. E.; McCurdy, G. D.; Hill, R. W. Acta-Hortic (313): p.271- 283. (1992 Oct.)Paper presented at the Third International Symposium on Computer Modelling in Fruit Research and OrchardManagement, February 11-14, 1992, Palmerston North, New Zealand.Descriptors: fruit-crops; orchards; crop-management; weather-data; information; information-technology;support-systems; models; utah

425.NAL Call No.: 80-AC82Relations among the water supply, foliage temperature and the yield of tomato.Helyes, L. Acta-Hortic p.115-121. (1990 Aug.)Paper presented at the "Third International Symposium on Processing Tomatoes," November 29-December 2,1989, Avignon, France.Descriptors: lycopersicon-esculentum; irrigation; foliage; temperature; crop-yield; water-supply; hungary

Abstract: In the frame of the research work directed to the development of irrigation order of the vegetable cropssince 1985 we have been examining the effect of different water supplies to the foliage temperature of the crops.Here I demonstrate the results obtained in 1986-1987 years with the "K. Jubileum" variety of tomato(Lycopersicon esculentum L.) from the measurements performed with the infrared remote thermometer. With thehourly (6h-20h) measurings performed during the vegetation period we have determined the daily foliagetemperature dynamics of the tomato stands of different foliage temperature water supplies, and the day sectionof most suitable for characterizing the water supply. In 1986, in 26.5 degrees C average air temperature, theaverage of foliage temperature of the optimum water-supply stand was 25.9 degrees C, while that of theunirrigated stand was 28.3 degrees C. Thus the mean foliage temperature difference between the treatments was2.4 degrees C. The mean foliage temperature differences between the treatments of different water supplymanifested themselves in the yield quantity as well. In the case of optimum water- supplied stand the harvestableyield was 82.5 t/ha, while in case of the unirrigated treatments the harvestable yield was only 24.7 t/ha. Theresults of the year 1987 have justified to draw similar conclusions.

426.NAL Call No.: SB123.P535

Relationship between grain yield and remotely-sensed data in wheat breeding experiments.Ball, S. T.; Konzak, C. F. Plant-Breed-Z-Pflanzenzucht v.110(4): p.277- 282. (1993 May)Includes references.Descriptors: triticum-aestivum; crop-yield; grain; remote-sensing; variety-trials; infrared-photography; aerial-photography; reflectance; genotypes; washington

427.NAL Call No.: 49-J82Relationship of mode of porcine somatotropin administration and dietary fat to the growth performanceand carcass characteristics of finishing pigs.Azain, M. J.; Bullock, K. D.; Kasser, T. R.; Veenhuizen, J. J. J-Anim- Sci v.70(10): p.3086-3095. (1992 Oct.)Includes references.Descriptors: pigs; somatotropin; diet; dietary-fat; sex-differences; feed-conversion-efficiency; insulin-like-growth-factor; backfat; carcass- composition; controlled-release; blood-sugar; ultrasound; leanness

Abstract: Ninety-six pigs were used to investigate the relationship of diet (control vs fat-supplemented withequal energy:protein ratios), porcine somatotropin (pST) administration (nontreated; 2 mg/d, daily injection; and2 mg/d, 6-wk implant), and sex (barrows and gilts) to performance and carcass characteristics. Diet and pSTtreatments were initiated at 87 kg of BW and continued for 38 d. Both the fat-supplemented diet (P < .001) andpST treatment (P < .0001) improved feed efficiency. The effects of diet were accounted for by differences inenergy density of the diets. Across diets, pST improved gain:feed ratio by 29 and 16% in pigs treated by dailyinjection and the implant, respectively; the two modes of delivery resulted in different responses (P < .01).Circulating insulin-like growth factor I (IGF-I) levels, determined from blood samples drawn on d 35, wereincreased 2.5-fold above those of controls in pigs treated by either daily injection or the implant. However, theelevation of glucose and decrease in blood urea nitrogen concentrations in response to pST were of a greatermagnitude in pigs treated by daily injection. Similarly, reductions in backfat thickness and the rate of backfataccretion determined by ultrasound were greater in response to the daily injection of pST than in response to theimplant. Lean meat ratio, calculated from measurements with a Fat-O-Meater probe, was increased by 6 and13% by the implant and daily injection, respectively. It is concluded that although the use of an implant thatdelivers pST on a continuous basis was as effective as the same dose administered as a bolus injection forincreasing IGF-I levels, it was less effective in improving feed efficiency and carcass quality.

428.NAL Call No.: 442.8-J8222Relationship of scrotal surface temperature measured by infrared thermography to subcutaneous anddeep testicular temperature in the ram.Coulter, G. H.; Senger, P. L.; Bailey, D. R. C. J-Reprod-Fertil v.84(2): p.417-423. ill. (1988 Nov.)Includes references.Descriptors: rams; scrotum; testes; temperatures; temperature- relations; measurement

429.NAL Call No.: SF371.R47Relative importance of traits for efficiency of market lamb and wool production in North America.Wang, C. T.; Dickerson, G. E. Sheep-Res-J v.7(1): p.19-23. (1991 Winter)Includes references.Descriptors: sheep; computer-software; simulation-models; selection- criteria; selective-breeding; breeding-value; wool-production; lamb-production; economic-impact; growth-rate; feed-intake; costs; lambing-interval

430.NAL Call No.: SB317.5.A6Remotely piloted aircraft for low altitude aerial surveillance in agriculture.Fouche, P. S.; Booysen, N. W. Appl-Plant-Sci-Toegepaste-Plantwetenskap v.5(2): p.53-59. ill. (1991)Includes references.Descriptors: aircraft; aerial-surveys; crop-management; forest- plantations; infrared-photography; remote-sensing; south-africa

431.NAL Call No.: 80-AC82Request to cultivation method from tomato harvesting robot.Kondo, N.; Shibano, Y.; Mohri, K.; Fujiura, T.; Monta, M. Acta-Hortic v.2(319): p.567-572. (1992 Oct.)Paper presented at the International Symposium on Transplant Production Systems- -Biological, Engineeringand Socioeconomics Aspects, July 21-26, 1992, Yokohama, Japan.Descriptors: lycopersicon-esculentum; mechanical-harvesting; automation; robots; construction; design;performance; japan; manipulators

432.NAL Call No.: TP248.25.A96T68-1990Requirements and technologies for automated plant growth systems on space bases.Tibbitts, T. W.; Bula, R. J.; Morrow, R. C.; Corey, R. B.; Barta, D. J. Automation in biotechnology a collection ofcontributions presented at the Fourth Toyota Conference, Aichi, Japan, 21-24 October 1990 / edited by IsaoKarube. Amsterdam : Elsevier c1991.. p. 325-335.Includes references.Descriptors: plants; culture-techniques; growth-chambers; automation; environmental-control; environmental-factors; space-flight

Abstract: Plant growth systems involving the use of higher plants, and possibly algae, appear to be a necessityfor long duration habitation in space. Use of plants in a bioregenerative life support system would minimize thecost of removing carbon dioxide and providing food, oxygen and pure water to maintain humans in space. Therequirements for such life support systems will involve technologies that are quite different from those ofsystems being used for short duration space missions. The plant growing system, including the supportequipment needed to sustain growth, harvest the crop, process the useful edible product, and recycle the wasteproducts, must be of minimum size and weight to reduce the cost of transport to the space base. The system mustincorporate a high level of automation and robotics so that the astronaut-hours required for plant maintenanceare kept to a minimum, but have provision for astronaut interaction if system malfunction occurs. The systemmust be constructed to sustain growth and productivity of several different plant species simultaneously, toprovide diversity in the diet, and redundancy in case of loss of one or more of the species. The growing areashould be compartmentalized so that the individual units can be isolated for separate maintenance, cleaning andsanitation. All chamber and plant culture equipment must be constructed from materials that can be effectivelysanitized at appropriate intervals. Another important requirement for space bases will be the need to keep powerconsumption at low levels. Effort is being directed toward the development of new technologies for plant growthin space. Progress has been made in the development of an improved plant lighting unit, a nutrient deliverysystem that can supply water and nutrients to plants in microgravity, and a nutrient composition control systemutilizing ion exchange materials for maintaining nutrient concentrations and nutrient balance for plants. Thereare many additional technology needs that will require resolution for the effective operation of a plant growingsystem in space. Technologies need to be developed for effective gaseous exchange between the plant growthunits and the human habitation areas, control of pathogenic microbes in the nutrient media, identifying andcontrolling contaminants that accumulate in the atmosphere and nutrient solution, and for monitoring theproductivity of the plants.

433.NAL Call No.: SD388.F8REVHAUL: A revenue-tracking program for log-hauling contractors.Wong, T. B. Tech-Rep-For-Eng-Res-Inst-Can. Pointe Claire, Quebec : The Institute. Dec 1990. (101) 19 p.Descriptors: logs; transport; trucks; contractors; costs; computer-software

434.NAL Call No.: S37.F72Riceseed.Slaton, N.; Helms, R.; Hall, S. FSA-Coop-Ext-Serv-Univ-Arkansas. Little Rock, Ark. : The Service. Mar 1993.(2017,rev.) 4 p.

In subseries: Computer Technical Series.Descriptors: oryza-sativa; sowing-rates; computer-software; soil- texture; sowing-date; sowing-methods;seedbeds; cultivars

435.NAL Call No.: 290.9-AM32PRobot arm for forest thinning R.A.F.T.Bonicelli, B.; Lucas, L.; Perret, F.; Bonnafous, J. C. PAP-AMER-SOC-AGRIC- ENG. St. Joseph, Mich. : TheSociety. 1989. (89-7056) 9 p.Paper presented at the 1989 International Summer Meeting, June 25-28, 1989, Quebec, PQ, Canada.Descriptors: forestry-practices; thinning; robots

436.NAL Call No.: 290.9-AM32TRobotic end effector for handling greenhouse plant material.Simonton, W. Trans-A-S-A-E v.34(6): p.2615-2621. (1991 Nov.-1991 Dec.)Includes references.Descriptors: greenhouses; automation; mechanization; propagation; sensors

Abstract: An end effector was developed to investigate the mechanics and control of robotic handling andmanipulation of plant material of a type commonly found in commercial greenhouses. The end effector wasshown to handle a wide range of sizes of geranium cuttings with rare indications of damage to the petioles(1.5%) and main stem (2.0%). Several features of the end effector were determined to be important for reliable,non- damaging performance. A two-stage feedback controller which combined position/velocity control withforce control was successful in minimizing cycle time while also minimizing impact velocity and resultantimpact loads on plant material. A machine vision local scene analysis technique provided an automatic methodof obstacle avoidance by the fingers in a plant canopy. Padded fingers with relatively small, curved endsminimized contact area and assisted in decreasing impact forces. In general, results indicate the importance ofsensing and interpretation of the sensor data to assist a robot in accommodating the nonuniformity typical ofplants.

437.NAL Call No.: S671.3.A97-1991Robotic plant handling and processing for agricultural systems.Simonton, W. Automated agriculture for the 21st century proceedings of the 1991 symposium, 16-17 December1991, Chicago, Illinois. St. Joseph, Mich. : American Society of Agricultural Engineers, c1991.. p. 226-235.Includes references.Descriptors: greenhouses; plants; handling; robots

438.NAL Call No.: S715.M44R63-1991Robotic systems for selective harvesting : basic concepts and prototype tests.Miles, G. E.; United States Israel Binational Agricultural Research and Development Fund. Bet Dagan, Israel :BARD, 1991. 176 p. : ill., Final report.Descriptors: Muskmelon-Harvesting-Machinery; Fruit-Harvesting- Machinery

439.NAL Call No.: TP248.25.A96T68-1990Robotic workcell for flexibly automated handling of young transplants.Ting, K. C. Automation in biotechnology a collection of contributions presented at the Fourth ToyotaConference, Aichi, Japan, 21-24 October 1990 / edited by Isao Karube. Amsterdam : Elsevier c1991.. p. 261-278.Includes references.Descriptors: plants; transplanting; automation; robots; plugtransplanting

Abstract: Automated handling of young transplants in the form of plugs has become an important process inmeeting their increasing market demand. The research team at Rutgers University has been studying theimplementation of robots for plug transplanting. The objective is to develop a flexibly automated plugtransplanting workcell which will handle a wide range of plug species and container sizes. In this workcell, theplugs are extracted from one container, and transported and planted into another container. The end-effector usedto manipulate individual plugs is equipped with a capacitive proximity sensor. The function of the sensor is todetect the presence of a plug, after extraction and before planting by the end- effector, to insure that the finishedcontainer is filled with plugs. The source and destination plug containers are transported by two overpassingconveyor belts. The belts are capable of making indexed advancement so that the distance for plug transportationbetween the containers may be minimized. The characteristics and parameters associated with the workcell aresystematically analyzed.

440.NAL Call No.: SB121.I57-1992Robotics and image analysis applied to micropropagation.Brown, F. R. Transplant production systems proceedings of the International Symposium on TransplantProduction Systems, Yokokama, Japan, 21-26 July 1992 / edited by K Kurata and T Kozai. Dordrecht : KluwerAcademic Publishers, 1992.. p. 283-296.Includes references.Descriptors: micropropagation; robots


441.NAL Call No.: S494.5.B563C87Robotics applications to transplanting of plug seedlings.Ting, K. C.; Giacomelli, G. A. Curr-Plant-Sci-Biotechnol-Agric (12): p.307-309. (1991)In the series analytic: Horticulture -- New Technologies and Applications / edited by J. Prakash and R. L. M.Pierik. Proceedings of an International Seminar on New Frontiers in Horticulture, November 25-28, 1990,Bangalore, India.Descriptors: seedlings; transplanting; robots; innovations; horticultural-crops

442.NAL Call No.: 290.9-AM32PRobotics in forest harvesting machines.Courteau, J. PAP-AMER-SOC-AGRIC-ENG. St. Joseph, Mich. : The Society. Summer 1989. (89-0022) 16 p. ill.,maps.Paper presented at the "1989 International Summer Meeting jointly sponsored by the American Society ofAgricultural Engineers and the Canadian Society of Agricultural Engineering," June 25-28, 1989, Quebec, PQ,Canada.Descriptors: forestry-machinery; hydraulics; mechanical-harvesting; mathematical-models; robots; sensors;canada

443.NAL Call No.: NBU SD387-I57-R63-1990Robotics in forestry : forest operations in the age of technology : proceedings of the symposium held onSeptember 7, 1990 at the Ramada Suites Hotel, Vaudreuil, Quebec.Courteau, J.; Robotics in Forestry Symposium 1990 : Vaudreuil, Q. Pointe Claire, Quebec : Forest EngineeringResearch Institute of Canada, c1990. ii, 48 p. : ill., Includes bibliographical references.Descriptors: Forestry-innovations; Logging-Technological-innovations; Robotics

444.NAL Call No.: 80-AC82

Robotization in the production of grafted seedlings.Honami, N.; Taira, T.; Murase, H.; Nishiura, Y.; Yasukuri, Y. Acta- Hortic v.2(319): p.579-584. (1992 Oct.)Paper presented at the International Symposium on Transplant Production Systems- -Biological, Engineeringand Socioeconomics Aspects, July 21-26, 1992, Yokohama, Japan.Descriptors: fruit-vegetables; seedlings; grafting; production; mechanization; robots; construction; design;performance

445.NAL Call No.: 23-AU783Role of computer stimualtion in the application of knowledge to animal industries.Black, J. L.; Davies, G. T.; Fleming, J. F. Aust-J-Agric-Res v.44(3): p.541-555. (1993)In special issue: Quantitative animal nutrition and metabolism.Descriptors: pig-fattening; computer-simulation; computer-software; costs; decision-making; farm-management;feeding; simulation-models; literature- reviews; new-south-wales; auspig-computer-model

446.NAL Call No.: 49-J82The role of instrument grading in a beef value-based marketing system.Cross, H. R.; Whittaker, A. D. J-Anim-Sci v.70(3): p.984-989. (1992 Mar.)Paper presented at a symposium titled "Application of Ultrasound in Animal Science Research", Ames, Iowa.Descriptors: beef; carcasses; grading; ultrasound; marketing- techniques; carcass-composition; instruments

Abstract: A functional value-based marketing system must have a means of identifying the value of individualanimals or carcasses. The U.S. beef industry has had a strong interest in instrument grading for the past 11 yr.With the major shift toward a value-based system of trading (carcass), the beef industry has defined its needs foran instrument to assess value. Ultrasound seems to be the technology with the greatest chance of success. Thispaper outlines the history of instrument grading and industry's progress and plans in this area.

447.NAL Call No.: 100-OK4-3Role of NIRS-based nutritional monitoring systems for grazing and nutritional management of rangelivestock.Stuth, J. W.; Lyons, R. K.; Angerer, J. P.; McKown, C. D. Misc-Publ-Agric- Exp-Stn-Okla-State-Univ p.83-93.(1991)Paper presented at the "Second Grazing Livestock Nutrition Conference," Aug. 2- 3, 1991, Steamboat Springs,Colorado.Descriptors: livestock; nutritive-value; infrared-spectroscopy

448.NAL Call No.: SF5.A8-1990Role of ultrasound for selecting beef cattle.Harada, H.; Moriya, K.; Fukuhara, R. Proceedings, the 5th AAAP Animal Science Congress, May 27-June 1,1990, Taipei, Taiwan, Republic of China. Chunan, Miaoli, Taiwan : The Organization Committee, Fifth AAAPAnimal Science Congress, c1990. v. 3 p. 276.Includes references.Descriptors: beef-cattle; carcass-composition; ultrasound

449.NAL Call No.: TP248.25.A96T68-1990The Ruthner container system.Ruthner, E. Automation in biotechnology a collection of contributions presented at the Fourth ToyotaConference, Aichi, Japan, 21-24 October 1990 / edited by Isao Karube. Amsterdam : Elsevier c1991.. p. 305-323.Includes references.

Descriptors: container-grown-plants; containers; automation; robots; growth-chambers; environmental-control;horticulture

Abstract: An environmental controlled system for the continuous, year round production of fresh, living plantsfor the nutrition of men in the same way as for the purpose of producing and maintaining test plants for differentresearch activities with combination possibilities to modern robotic techniques and computer analysers in amodular containerized size is explained and discussed with respect to economics.

450.NAL Call No.: 1-F766FIRXWINDOW: fire behavior program for prescribed fire planning.Andrews, P. L.; Bradshaw, L. S. Fire-Manage-Notes-U-S-Dep-Agric-For- Serv v.51(3): p.25-29. (1990)Includes references.Descriptors: forest-fires; rangelands; prescribed-burning; computer- software

451.NAL Call No.: 80-AC82SARA: software for the analysis of orchard profitability.Alvisi, F.; Malagoli, C.; Regazzi, D. Acta-Hortic (276): p.305-314. (1990 July)Paper presented at the "Second International Symposium on Computer Modelling in Fruit Research and OrchardManagement," September 5- 8, 1989, Logan, Utah.Descriptors: orchards; profitability; computer-software

Abstract: The setting up of an orchard necessitates a series of choices which can influence the economic results.Prior to investing in a productive process that is expected to continue over the long term, the growers shouldcarefully evaluate which species, which cultivars and which management methods to adopt. Net present value,internal rate of return, cost/return ratio and payback period are among the most frequently used parameters ofeconomic analysis. SARA constitutes a specific software designed to calculate the value of these parametersonce the elements of orchard costs and returns are known. At the same time it is possible to perform sensitivityanalysis on the more probable price fluctuations in relation to production and quantitative variations producedduring the orchard's full cropping period.

452.NAL Call No.: S494.5.D3C68-1992Scheduling beef-forage grazing systems.Thompson, T. L.; Newell, T. R.; Klopfenstein, T. J.; Moser, L. E.; Waller, S. S.; Wilkerson, V. A. Computers inagricultural extension programs proceedings of the 4th international conference, 28-31 January 1992, Orlando,Florida / sponspored by the Florida Cooperative Extension Service, University of Florida. St. Joseph, Mich. :American Society of Agricultural Engineers, c1992.. p. 70-75.Includes references.Descriptors: grassland-management; computer-software; grazing-systems; nebraska; rangeplan

453.NAL Call No.: 275.29-N272EXSelecting a computer system for the farm business.Jose, H. D. EC-Coop-Ext-Serv-Univ-Nebr. Lincoln, Neb. : The Service. 1983. (83-877) 14 p.Descriptors: farm-management; computer-software; computers; glossaries

454.NAL Call No.: S494.5.D3I5-1988Selecting cow/calf recordkeeping software.Holman, K. L. Proceedings of the 2nd International Conference on Computers in Agricultural ExtensionPrograms Fedro S Zazueta, AB Del Bottcher, eds p.116-120. (of Florida, [1988?].)Conference held February 10-11, 1988 at the Grosvenor Resort Hotel, Disney World Village, Lake Buenavista,

Orlando, Florida.Descriptors: animal-husbandry; record-keeping; computer-software

455.NAL Call No.: FICHE-S-72Selection and use of thermography/infrared imaging systems.Colliver, D. G.; Turner, L. W.; Dillon, O. W. Jr. Am-Soc-Agric-Eng- Microfiche-Collect. St. Joseph, Mich. : TheSociety. 1988. (fiche no. 88-3514) 22 p. ill.Paper presented at the 1988 Winter Meeting of the American Society of Agricultural Engineers. Available forpurchase from: The American Society of Agricultural Engineers, Order Dept., 2950 Niles Road, St. Joseph,Michigan 49085. Telephone the Order Dept. at (616) 429-0300 for information and prices. Includes references.Descriptors: remote-sensing; infrared-imagery; thermography; surfaces; temperature; instruments

456.NAL Call No.: 80-AC82Sensing structure in crop and soil.Day, W. Acta-Hortic (304): p.339-344. (1992 Mar.)Paper presented at the "First International Workshop on Sensors in Horticulture", January 29-31, 1991,Noordwijkerhout, The Netherlands.Descriptors: crop-production; lasers; scanning

457.NAL Call No.: A99.9-F7625USensitivity of TRIM projections to management, harvest, yield, and stocking adjustment assumptions.Alexander, S. J. Res-Note-PNW-U-S-Dep-Agric-For-Serv-Pac-Northwest-Res-Stn. Portland, Or. : The Station.Mar 1991. (502) 17 p.Includes references.Descriptors: forest-trees; pseudotsuga-menziesii; supply; projections; timber-trade; yield-forecasting; computer-software; models; age; timber- resource-inventory-model

458.NAL Call No.: 80-AC82A sideward lighting system using diffusive optical fibers for production of vigorous micropropagatedplantlets.Kozai, T.; Kino, S.; Jeong, B. R.; Kinowaki, M.; Ochiai, M.; Hayashi, M.; Mori, K. Acta-Hortic v.1(319): p.237-242. (1992 Oct.)Paper presented at the "International Symposium on Transplant Production Systems: Biological, Engineeringand Socioeconomic Aspects," July 21-26, 1992, Yokohama, Japan.Descriptors: solanum-tuberosum; micropropagation; explants; fluorescent-lamps; lighting; systems; diffusion;fibers; optical-properties; acclimatization; lighting-direction; photosynthetically-active-radiation

459.NAL Call No.: S539.5.J68A simple, microcomputer model of rangeland forage growth for management decision support.Berry, J. S.; Hanson, J. D. J-Prod-Agric v.4(4): p.491-499. (1991 Oct.- 1991 Dec.)Includes references.Descriptors: acrididae; range-management; economic-analysis; integrated-pest-management; decision-making;mathematical-models; simulation- models; computer-simulation; forage; seasonal-growth; maturation;temperature; rain; soil-water-content; soil-water-regimes; water-holding-capacity; soil- water-potential;rangelands; validity; temporal-variation; herbivores; grazing; computer-software; montana

460.NAL Call No.: 100-AL1HA simple ultrasound instrument is effective in predicting body composition of live pigs.Chiba, L. I. Highlights-Agric-Res-Ala-Agric-Exp-Stn v.39(4): p.6. (1992 Winter)

Descriptors: pigs; body-composition; ultrasonic-fat-meters; assessment; liveweight; carcass-composition;prediction


461.NAL Call No.: 80-AC82A simplified dry matter production model for apple using automatic programming simulation software.Lakso, A. N.; Johnson, R. S. Acta-Hortic (276): p.141-148. (1990 July)Paper presented at the "Second International Symposium on Computer Modelling in Fruit Research and OrchardManagement," September 5- 8, 1989, Logan, Utah.Descriptors: apples; crop-yield; dry-matter; computer-simulation

Abstract: Previous research on modelling of dry matter production and yield of apple trees has resulted incomplex models that have remained incomplete or inadequately described and tested. The complexity of boththe physiological processes and the programming needed to describe them often limits the usefulness of thesemodels primarily to their originators. To overcome some of these limitations, a simplified dry matter productionmodel has been developed with the user-friendly "Stella" dynamic simulation automatic programming languageon a Macintosh. This language can be used effectively by researchers not trained in programming, thus greatlyexpanding the potential use and testing of the model by other researchers. Compared to earlier models the modeldescribed here is simplified in several ways. The most important is that the basic time step is one day rather thanone minute or hour. Using the daily integral eliminates the complexity of the diurnal changes in radiationgeometry. The daily photosynthesis (P) integral is calculated as described by Charles- Edwards from maximumP rate, photochemical efficiency, daily integral of light, daylength, canopy extinction coefficient and leaf areaindex (or fraction of available light intercepted). Leaf area development is based on degree-day accumulation, aconstant leaf area production/degree-day, the total shoot numbers and the fraction of shoots growing at any time.Respiration is driven by temperature. Wood and leaf respiration are based on surface areas; fruit respiration onweight. Estimates of dry matter/carbon ratio are varied over the season depending on estimates of costs ofsynthesis of dry matter components. Evaluation of the assumptions and limitations is continuing, although initialtesting with data from the literature has been promising. Validation testing by growth analysis in field trees hasbegun.

462.NAL Call No.: 290.9-AM32TSimulation for determining greenhouse temperature setpoints.Jones, P.; Jones, J. W.; Hwang, Y. Trans-A-S-A-E v.33(5): p.1722-1728. ill. (1990 Sept.-1990 Oct.)Includes references.Descriptors: greenhouses; air-temperature; heat-regulation; simulation-models; computer-software;lycopersicon-esculentum; crop-yield; economic- analysis; profitability; florida; north-carolina; microsoft-fortran

Abstract: Two separately developed simulation models were linked and used to evaluate different environmentalcontrol strategies in Florida tomato production greenhouses. POLY-2 is a model of a double poly, quonset-stylegreenhouse typical of Florida. It is a dynamic model that realistically simulates environmental control equipmentactions. TOMGRO is a dynamic crop model that simulates 1) growth, 2) development, and 3) quantity andtiming of yield of tomatoes. Both models are based on independent empirical data sets used for calibration andvalidation, respectively. The two models were linked by incorporating POLY-2 into TOMGRO as a sub-routine.Historical weather data for Tallahassee, Florida and Raleigh, North Carolina are used by the POLY-2 subroutineto simulate greenhouse environmental conditions which are used in turn by TOMGRO to simulate developmentand growth of the tomato crop. During simulation runs POLY-2 keeps track of heating fuel requirements andTOMGRO keeps track of tomato yield. Simulations over a range of setpoints showed that the optimal setpointdepends directly on the price of fuel, the value of the tomatoes, and location.

463.NAL Call No.: QA76.9.C65S95-1989Simulation models in agriculture: from cellular level to field scale.Stockle, C. O. Proceedings of the 1989 Summer Computer Simulation Conference July 24-27, 1989, the StoufferAustin Hotel, Austin, Texas / edited by Joe K Clema ; conference sponsor, the Society for Computer Simulation.San Diego, CA : The Society, c1989.. p. 639-644.Includes references.Descriptors: crop-production; agriculture; simulation-models; computer-simulation; agricultural-research

Abstract: Computer simulation models are becoming a common tool in agricultural research and teaching, andtheir range of applications is extending now to agricultural planning, policy making, technology transfer and on-farm management. A whole array of computer simulation models have been or are under development, frommass transfer at the single plant cell level to transport processes of eroded soil particles or pollutants at awatershed level; from growth of individual cells to growth of multicrops in the field. The use of computersimulation in agriculture has had a rapid increase with the fast expansion of microcomputers' availability. A briefreview of available models and a discussion of benefits and problems of the simulation boom will be presented.

464.NAL Call No.: QD415.A1J62Simulation of ethanol production processes based on enzymatic hydrolysis of lignocellulosic materialsusing ASPEN PLUS.Galbe, M.; Zacchi, G. Appl-Biochem-Biotechnol. Totowa, N.J. : Humana Press. Spring 1992. v. 34/35 p. 93-104.Paper presented at the "Thirteenth Symposium on Biotechnology for Fuels and Chemicals," May 6-10, 1991,Colorado Springs, Colorado.Descriptors: wood; lignocellulose; saccharification; hydrolysis; cellulase; hexoses; pentoses; fermentation;ethanol-production; computer- simulation; computer-software; waste-water; simulation-models

465.NAL Call No.: 23-R88SIRATAC: death and rebirth.Ralph, W. Rural-Res-CSIRO-Q (147): p.8-12. ill. (1990 Winter)Includes references.Descriptors: gossypium; helicoverpa-armigera; helicoverpa-punctigera; insect-control; insecticide-resistance;computer-software; crop-management; crop- yield; australia; new-south-wales

466.NAL Call No.: QH540.I84Software implementation of a decision support system for land use planning.Wadsworth, R. A. ITE-symp (27): p.88-91. (1992)In the series analytic: Land use change: The causes and consequences / edited by M.C. Whitby.Descriptors: land-use-planning; land-use; decision-making; expert- systems; computer-analysis; computer-software

467.NAL Call No.: 309.9-N216Solar infrared transmitting, par absorbing polyethylene mulch: physical properties and crop response.Loy, J. B. Proc-Natl-Agric-Plast-Congr (23rd): p.165-173. (1991)Meeting held Sept. 29 - Oct. 3, 1991, Mobile, Alabama.Descriptors: polyethylene-film; infrared-radiation; physical- properties; crop-yield; photosynthetically-active-radiation

468.NAL Call No.: 4-AM34PSOYHERB--A computer program for soybean herbicide decision making.Renner, K. A.; Black, J. R. Agron-J v.83(5): p.921-925. (1991 Sept.- 1991 Oct.)

Includes references.Descriptors: glycine-max; herbicides; application-methods; weeds; decision-making; weed-competition;computer-software

Abstract: There has been a rapid increase in the number of herbicides and herbicide mixtures registered for usein soybean [Glycine max (L.) Merr.] production. SOYHERB is a computer program developed to assistCooperative Extension Service personnel, agribusiness, farmers, and teachers in determining herbicide optionsfor soybean production. Tillage practices, atrazine (6-chloro- N-ethyl-N'-(1-methylethyl)-1,3,5-triazine-2,4-diamine) or simazine (6-chloro- N,N'-diethyl-1,3,5-triazine-2,4-diamine) use in a previous corn crop, soil typeand percentage of organic matter, soil pH, projected crop rotation plans, method of herbicide application, andweed species and weed pressure are entered by the user. SOYHERB generates herbicide programs and their costper acre that provide excellent control of all weed species at the weed pressures indicated. Fair (80- 90%) weedcontrol options may also be generated. Additional screens describe control of perennial weeds, a summary ofherbicide premixes, and a table listing the maximum height of broadleaf weeds controlled by postemergenceherbicides. Data can be saved for future reference. A computer capable of running MS-DOS or PC-DOS version2.1 or greater with a minimum of 512K bytes of RAM is required.

469.NAL Call No.: SD112.F67Spacing/thinning requirements for radiata pine in relation to product prices and exchange rates.Whiteside, I. D. FRI-Bull-For-Res-Inst-N-Z-For-Serv (151): p.200-212. (1990)Paper presented at the "Symposium on New Approaches to Spacing and Thinning in Plantation Forestry, " heldApril 10-14, 1989, Rotorua, New Zealand.Descriptors: pinus-radiata; spacing; thinning; computer-software; logs; quality; profitability; prices; stankpak;sawmod

470.NAL Call No.: SD143.S64Spatial disaggregation process: applying strategic planning to the ground.Merzenich, J. P. Proc-Soc-Am-For-Natl-Conv p.580-582. (1991)Meeting held Aug 4-7, 1991, San Francisco, California.Descriptors: forest-management; computer-software; databases; planning; maps; computer-hardware; national-forests; private-ownership; oregon

471.NAL Call No.: 60.19-B773Spatial heterogeneity and other sources of variance in sward height as measured by the sonic and HFROsward sticks.Hutchings, N. J. Grass-Forage-Sci-J-Br-Grassl-Soc v.46(3): p.277-282. (1991 Sept.)Includes references.Descriptors: grass-sward; plant-height; measurement; spatial- variation; sampling; instruments; variance-components

472.NAL Call No.: S494.5.D3I5-1988Spending and consumer expenditures.Scannell, E. Proceedings of the 2nd International Conference on Computers in Agricultural Extension ProgramsFedro S Zazueta p.549-553. (of Florida, [1988?].)Meeting held February 10-11, 1988 at Lake Buenavista, Orlando, Florida.Descriptors: money-management; consumer-expenditure; computer-software

473.NAL Call No.: 4-AM34PStatistical analysis of yield trials with MATMODEL.Gauch, H. G. Jr.; Furnas, R. E. Agron-J v.83(5): p.916-920. (1991 Sept.-1991 Oct.)

Includes references.Descriptors: crop-yield; variety-trials; statistical-analysis; computer-software

Abstract: Yield trials guide agronomic recommendations and breeding selections, but often are limited byinaccuracy, missing data, and the difficulty of understanding complex genotype-environment interactions.Recent studies have shown that a statistical model rather new to agriculturists, the Additive Main Effects andMultiplicative Interaction (AMMI) model can reduce these limitations. This paper introduces MATMODEL, aconvenient program for the required calculations that runs on IBM compatible personal computers with MS-DOS (PC-DOS) 2.1 or higher and on the Apple Macintosh. MATMODEL enables one to (i) increase theaccuracy of yield estimates, (ii) improve selections, (iii) impute missing data, (iv) model and understand thegenotypes, environments, and interaction, particularly with a biplot graph, and (v) design flexible and efficientexperiments. MATMODEL routinely provides yield estimates as accurate as raw treatment means based upontwo to five times as many replications, so MATMODEL offers a remarkably cost-effective option for gainingaccuracy. This option is particularly valuable in light of recent trends towards testing genotypes in moreenvironments with fewer replications.

474.NAL Call No.: HD1.A3A stochastic model simulating the dairy herd on a PC.Sorensen, J. T.; Kristensen, E. S.; Thysen, I. Agric-Syst v.39(2): p.177-200. (1992)Includes references.Descriptors: dairy-cows; dairy-herds; animal-production; stochastic- models; simulation-models;microcomputers; cattle-feeding; liveweight-gain; calving; culling; constraints; livestock-numbers; milk-production; quotas; dairy-research; computer-simulation; time-stepping-models

475.NAL Call No.: 290.9-AM32PStochastic modeling of robotic workcell for seedling plug transplanting.Ting, K. C.; Yang, Y.; Fang, W. PAP-AMER-SOC-AGRIC-ENG. St. Joseph, Mich. : The Society. Winter 1990.(90-1539) 12 p.Paper presented at the 1990 International Winter Meeting, December 18-21, 1990, Chicago, Illinois.Descriptors: seedlings; transplanting; robots; stochastic-models

476.NAL Call No.: 290.9-AM32PStormwater management, erosion, & sediment control by computer aided design (SEDCAD): landfillapplication.Schwab, P.; Warner, R. C. PAP-AMER-SOC-AGRIC-ENG. St. Joseph, Mich. : The Society. Summer 1989. (89-2017) 19 p.Paper presented at the "1989 International Summer Meeting" jointly sponsored by the American Society ofAgricultural Engineers and the Canadian Society of Agricultural Engineering, June 25-28, 1989, Quebec,Canada.Descriptors: landfills; runoff-water; erosion-control; sediment; computer-software; structural-design

477.NAL Call No.: SD143.S64Strategies for modeling the effect of silvicultural regime on wood quality in Douglas-fir.Maguire, D. A. Proc-Soc-Am-For-Natl-Conv p.80-85. (1991)Meeting held Aug 4-7, 1991, San Francisco, California.Descriptors: pseudotsuga-menziesii; wood-properties; models; computer- software; forest-plantations; forest-management; wood-products; pacific-states- of-usa

478.NAL Call No.: 80-AC82The study of the grafting robot.

Onoda, A.; Kobayashi, K.; Suzuki, M. Acta-Hortic v.2(319): p.535-540. (1992 Oct.)Paper presented at the International Symposium on Transplant Production Systems- -Biological, Engineeringand Socioeconomics Aspects, July 21-26, 1992, Yokohama, Japan.Descriptors: cucurbit-vegetables; planting-stock; grafting; mechanization; robots; construction; design;performance

479.NAL Call No.: SD143.S64STUMP: a system of timber utilization and mill processing.Yaussy, D. A.; Brisbin, R. L. Proc-Soc-Am-For-Natl-Conv p.613-614. (1990)Paper presented at the meeting on, "Are Forests the Answer," held July 29-Aug 1, 1990, Washington, D.C.Descriptors: timbers; stumpage-value; yields; logs; inventories; computer-software; timber-appraisal

480.NAL Call No.: SB191.M2C44-1986Subroutine structure.Jones, C. A.; Richie, J. T.; Kiniry, J. R.; Godwin, D. C. CERES-Maize a simulation model of maize growth anddevelopment / edited by CA Jones and JR Kiniry with contributions by PT Dyke [et al]. 1st ed. : College Station: Texas A&M University Press, 1986.. p. 49-111.Descriptors: zea-mays; soil-water-balance; computer-software; simulation- models; computer-programming;nitrogen; computer-simulation


481.NAL Call No.: HD1.A3'Summer Pack', a user-friendly simulation software for the management of sheep grazing dry pastures orstubbles.Orsini, J. P. G. Agric-Syst v.33(4): p.361-376. (1990)Includes references.Descriptors: sheep-farming; farm-management; decision-making; computer-software; simulation-models;stocking-rate; sheep-feeding; liveweight- gain; grazing; dry-feeding; pastures; stubble; summer; autumn;western-australia

482.NAL Call No.: 49.9-AU72SummerPack, an interactive computer software for the prediction of liveweight changes of sheep grazingdry pastures or stubbles in the south of Australia.Orsini, J. P. G. Proc-Aust-Soc-Anim-Prod. Sydney : Pergamon Press. 1990. v. 18 p. 535.Meeting held on July 8-12, 1990, Adelaide, South Australia.Descriptors: sheep-farming; computer-software; australia

483.NAL Call No.: SB197.A1T7Sustaining productive pastures in the tropics. 12. Decision support software as an aid to managing pasturesystems.Clewett, J. F.; Cavaye, J. M.; McKeon, G. M.; Partridge, I. J.; Scanlan, J. C. Trop-Grassl v.25(2): p.159-164.(1991 June)Paper presented at the "Fourth Australian Conference on Tropical Pastures," November, 1990, Toowoomba,Queensland, Australia.Descriptors: tropical-grasslands; pastures; grassland-management; sustainability; productivity; beef-production;decision-making; computer- software; computer-simulation; stocking-rate; australia; grassman

484.NAL Call No.: S494.5.D3I5-1988Swine productivity program.Norton, S. D. Proceedings of the 2nd International Conference on Computers in Agricultural ExtensionPrograms Fedro S Zazueta, AB Del Bottcher, eds p.113-115. (of Florida, [1988?].)Conference held February 10-11, 1988 at the Grosvenor Resort Hotel, Disney World Village, Lake Buenavista,Orlando, Florida.Descriptors: pigs; animal-production; record-keeping; computer- software

485.NAL Call No.: S494.5.D3I5-1990Swine simulation for housing, feeding and profitability.Watt, D. L.; Jacobsen, R. M.; Rice, D. G. Proceedings of the 3rd International Conference on Computers inAgricultural Extension Programs / Fedro S. Zazueta, editor. ; January 31- February 1, 1990, Grosvenor ResortHotel, Disney World Village, Lake Buenavista, FL. Gainesville, FL : Florida Cooperative Extension Service,University of Florida, [1990]. p. 506-511.Includes references.Descriptors: pigs; animal-husbandry; computer-software; simulation- models; swinegro

486.NAL Call No.: 99.9-F7662JA system for computer-based design and implementation of time studies.Howard, A. F.; Gasson, R. For-Prod-J v.41(7/8): p.53-55. (1991 July- 1991 Aug.)Includes references.Descriptors: forestry-engineering; work-study; harvesting; computer- techniques; computer-software

487.NAL Call No.: SB249.N6A system for studying automated cotton irrigation.Webb, W. M.; Upchurch, D. R.; Wanjura, D. F. Proc-Beltwide-Cotton-Conf. Memphis, Tenn. : National CottonCouncil of America. 1991. v. 1 p. 445- 448.Paper presented at the "Cotton Engineering-Systems Conference," 1991, San Antonio, Texas.Descriptors: gossypium-hirsutum; crop-production; automatic- irrigation-systems; computer-techniques;computer-software

488.NAL Call No.: QH541.5.F6F67SYSTUM-1: simulating the growth of young conifers under management.Powers, R. F.; Ritchie, M. W.; Ticknor, L. O. Proc-Annu-For-Veg-Manage-Conf. Redding, Calif. TheConference. Aug 1989. (10th) p. 101-115.Meeting held Nov 1-3, 1988, Eureka, CA.Descriptors: conifers; growth; height; diameter; basal-area; computer- software; computer-simulation; california

489.NAL Call No.: SD143.S64Tactical harvest planning.Sessions, J.; Sessions, J. B. Proc-Soc-Am-For-Natl-Conv p.362-368. (1991)Meeting held Aug 4-7, 1991, San Francisco, California.Descriptors: logging; planning; environmental-impact; wildlife; habitats; roads; logging-effects; transport;computer-analysis; computer- software; computer-hardware; usa

490.NAL Call No.: S494.5.D3I5-1988TAMWFARM--A whole farm computerized financial planning program.Gerloff, D. C. Proceedings of the 2nd International Conference on Computers in Agricultural Extension

Programs Fedro S Zazueta, AB Del Bottcher, eds p.303-305. (of Florida, [1988?].)Conference held February 10-11, 1988 at the Grosvenor Resort Hotel, Disney World Village, Lake Buenavista,Orlando, Florida.Descriptors: farm-management; financial-planning; computer-software; texas-aandm-whole-farm-analysis-and-record-management

491.NAL Call No.: 56.8-J822Teaching land management with a microcomputer-based model.Ross, D.; Nash, T.; Harbor, J. J-Soil-Water-Conserv v.47(3): p.226-230. (1992 May-1992 June)Includes references.Descriptors: land-management; soil-conservation; teaching-methods; land-use; computer-assisted-instruction;microcomputers; computer-simulation; simulation-models; universal-soil-loss-equation; water-erosion; runoff;measurement; erosion-control; sediment; geological-sedimentation; gully- erosion; land-types

492.NAL Call No.: SD143.S64Teams: a decision support system for integrated resource management.Covington, W. W.; Dewhurst, S. M.; Wood, D. B. Proc-Soc-Am-For-Natl- Conv p.516-517. (1991)Meeting held Aug 4-7, 1991, San Francisco, California.Descriptors: forest-management; decision-making; computer-software; watershed-management; models;american-indians; arizona

493.NAL Call No.: QP901.A33-v.286Temperature and environmental effects on the testis.Zorgniotti, A. W. 1.; Conference on Temperature and Environmental Factors and the Testis (1989 : New YorkUniversity School of Medicine). New York : Plenum Press, c1991. xi, 335 p. : ill., "Proceedings of a Conferenceon Temperature and Environmental Factors and the Testis, held December 8-9, 1989, at the New YorkUniversity School of Medicine, New York, New York"--T.p. verso. Includes bibliographical references andindex.Descriptors: Testis-Effect-of-heat-on-Congresses; Infertility,-Male- Environmental-aspects-Congresses; Testis-Thermography-Congresses

494.NAL Call No.: S494.5.D3C652Terraces for erosion and runoff: a program simulation (TERPS).Johnson, A. T.; Holly, T. Comput-Electron-Agric v.7(2): p.121-132. (1992 July)Includes references.Descriptors: terraces; erosion-control; computer-software; flow- charts; microcomputers; location-of-production; topography; data-processing

495.NAL Call No.: S494.5.D3I5-1990Texas FARMDAY: Farm Accident Risk Management & Data Acquisition sYstem.Freeman, S. A.; Valco, T. D.; Whittaker, A. D. Proceedings of the 3rd International Conference on Computers inAgricultural Extension Programs / Fedro S. Zazueta, editor. ; January 31- February 1, 1990, Grosvenor ResortHotel, Disney World Village, Lake Buenavista, FL. Gainesville, FL : Florida Cooperative Extension Service,University of Florida, [1990]. p. 21-26.Includes references.Descriptors: farming; occupational-hazards; trauma; risk; educational- programs; computer-software; texas;knowledge-based-system

496.NAL Call No.: TJ810.A1S6

Thermal study of the receiver of a focusing solar collector using infrared thermography.Argiriou, A.; Pasquetti, R.; Papini, F.; Arconada, A.; Audibert, M. Sol- Energy v.43(1): p.45-55. ill. (1989)Includes references.Descriptors: solar-energy; solar-radiation; solar-collectors; construction; infrared-radiation; three-dimensional-models; calculation; operations; thermal-operating-conditions

497.NAL Call No.: S1.S68Thermographic characteristics of silt from soils which have been fertilized over a long period of time.Vodyanitskii, Yu. N.; Gradusov, B. P.; Khlystovskii, A. D. Sov-Agric- Sci (1): p.39-41. (1985)Translated from: Vsesoiuznaia akademiia sel'skokhoziaistvennykh nauk, Doklady, p.24-26. (20 AK1).Descriptors: silt; thermographic-properties; npk-fertilizers

498.NAL Call No.: SF951.J65Thermographic detection of gingering in horses.Turner, T. A.; Scoggins, R. D. J-Equine-Vet-Sci. Wildomar, Calif. : W.E. Jones. 1985. v. 5 (1) p. 8-10.Includes 15 references.Descriptors: horses; anus; sphincters; temperatures; infrared- radiation; infrared-spectrophotometry

499.NAL Call No.: SF910.5.V4Thermographic evaluation of the effect of three bandage productsKoblunk, C. N.; Walter, P. A.; Trent, A. M.; Libbey, C.; Salazar, R. Vet- Comp-Orthop-Traumatol-VCOT.Stuttgart : F.K. SchattauerIncludes references.Descriptors: horses; thermography; blood-circulation; bandages

500.NAL Call No.: SF601.C66Thermography: a review in equine medicine.Turner, T. A.; Purohit, R. C.; Fessler, J. F. Compend-Contin-Educ-Pract- Vet v.8(11): p.855-862. ill. (1986 Nov.)Literature review. Includes references.Descriptors: horses; veterinary-equipment; diagnostic-techniques; thermal-properties


501.NAL Call No.: 41.8-AM3AThermography of the bovine scrotum.Purohit, R. C.; Hudson, R. S.; Riddell, M. G.; Carson, R. L.; Wolfe, D. F.; Walker, D. F. Am-J-Vet-Res v.46(11):p.2388-2392. ill. (1985 Nov.)Includes 14 references.Descriptors: bulls; thermographic-properties; scrotum; testicular- diseases

502.NAL Call No.: S591.55.N4T47-no.55Thermography : principles and applications in the Oost-Gelderland remote sensing study project.Nieuwenhuis, G. J. A. Wageningen : Institute for Land and Water Management Research, [1986?]. leaves 51-58: ill., map (col.), Cover title. "Reprinted from: ITC Journal 1, 1986." Abstract in English, French and Spanish.Bibliography: leaves 57-58.

503.NAL Call No.: 80-C733Thermography reveals heat losses [Energy conservation].Schaupmeyer, C. Am-Veg-Grower-Greenhouse-Grower v.29(11): p.38-39, 42. ill. (1981 Nov.)

504.NAL Call No.: SB610.W39The threshold concept and its application to weed science.Coble, H. D.; Mortensen, D. A. Weed-Technol-J-Weed-Sci-Soc-Am v.6(1): p.191-195. (1992 Jan.-1992 Mar.)Paper presented at the "Symposium on Ecological Perspectives on Utility of Thresholds for Weed Management,"February 5, 1991.Descriptors: weeds; economic-thresholds; weed-biology; crop-weed- competition; crop-yield; yield-losses;weed-control; decision-making; simulation- models; computer-software; action-thresholds; herb

505.NAL Call No.: S671.A66Timber harvester: a microcomputer-based system for automatic selection of timber harvestingequipment.Randhawa, S. U.; Scott, T. M.; Olsen, E. D. Appl-Eng-Agric v.8(1): p.121-127. (1992 Jan.)Includes references.Descriptors: harvesters; timbers; automation; mechanization; simulation-models; computer-techniques

Abstract: The potential gains that could be realized from mechanization and automation of timber harvesting aresignificant. Mechanization increases production output and efficiency, and product quality. However, selectingan appropriate degree of mechanization to avoid under-utilization of expensive resources is a critical decision,and requires that the product mix, and environmental and user constraints be matched against the availabletechnology and required performance criteria. This article describes a microcomputer-based system whichqueries a user on the logging and market conditions. The system then matches these user's needs to a level ofmechanization that would maximize the efficiency of the production operation. The computer accomplishes thisby searching a set of data bases containing information on available technology and its impact on productionefficiency, economics, and the environment. The level of mechanization is determined by specific combinationsof existing machines. The alternatives generated using this methodology may then be analyzed using asimulation model. This tool is intended to aid long-term, strategic level planning by both public agencyengineers and private industry managers and owners. It could also be used for short term tactical planning bycontractors.

506.NAL Call No.: S494.5.D3I5-1990TOBVALUE a computer publication.Dangerfield, C. W. Jr.; Mills, F. D. Jr. Proceedings of the 3rd International Conference on Computers inAgricultural Extension Programs / Fedro S. Zazueta, editor. ; January 31- February 1, 1990, Grosvenor ResortHotel, Disney World Village, Lake Buenavista, FL. Gainesville, FL : Florida Cooperative Extension Service,University of Florida, [1990]. p. 430-436.Includes references.Descriptors: tobacco; market-prices; computer-software; georgia

507.NAL Call No.: 309.9-N216Tomato, melon, and pepper production on degradable and infrared- transmitting mulches in Oregon.Hemphill, D. D. Jr.; Clough, G. H. Proc-Natl-Agric-Plast-Congr (22nd): p.7-12. (1990)Paper presented at the "22nd Congress of National Agricultural Plastics Association," May 21-25, 1990,Montreal, Quebec.Descriptors: plastic-film; mulching; rowcrops; vegetables; oregon

508.NAL Call No.: 80-AC82Tomato plant response to laser bean seed treatment.Szwonek, E.; Felczynska, A. Acta-Hortic (287): p.451-454. (1991 May)Paper presented at the "Second International Symposium on Protected Cultivation of Vegetables in mild winterclimates" October 29- November 13, 1989, Crete, Greece.Descriptors: lycopersicon-esculentum; seed-treatment; lasers; greenhouse-culture; yield-response-functions

Abstract: A greenhouse experiment has been conducted. After seeds had been boosted with laser light they weresown into peat substrate at increased N,P,K,Mg rates. Seed germination attributes, the growth of plants and yieldwere evaluated. Also nutrient concentrations in both substrate and plants were determined.

509.NAL Call No.: aSD11.A48A tool for assessing the impacts of mountain pine beetle and related management strategies.Davis, M. S.; White, W. B. Gen-Tech-Rep-INT-U-S-Dep-Agric-For-Serv-Intermt- Res-Stn (262): p.37-40. (1989May)Paper presented at the symposium on "Management of Lodgepole Pine to Minimize Losses to the Mountain PineBeetle," July 12-14, 1988, Kalispell, Montana.Descriptors: computer-software; simulation-models; forest-resources; forest-management; resource-management

510.NAL Call No.: SB121.I57-1992Transplant production robots in Japan.Kurata, K. Transplant production systems proceedings of the International Symposium on Transplant ProductionSystems, Yokokama, Japan, 21-26 July 1992 / edited by K Kurata and T Kozai. Dordrecht : Kluwer AcademicPublishers, 1992.. p. 313-329.Includes references.Descriptors: transplanting; automation; micropropagation; japan

511.NAL Call No.: 290.9-AM32TTransplant size sensing using dual-wavelength reflectance.Eddington, D. L.; Suggs, C. W.; McClure, W. F.; Miller, T. K. I. Trans-A-S- A-E v.34(3): p.1010-1015. (1991May-1991 June)Includes references.Descriptors: nicotiana; pot-plants; reflectance; spectral-data; sensors; size-graders; seedlings; transplanting

Abstract: A plant sensing system has been developed for detecting and rejecting undersize tobacco transplants.Fiber optic bundles were used to illuminate transplants and receive reflected near-infrared and visible red light.A computer-based sensing system was developed that resulted in 95% successful classification based on a leafarea threshold of 15 CM2.

512.NAL Call No.: QP251.A1T5Transvaginal ultrasound guided follicular aspiration of bovine oocytes.Pieterse, M. C.; Vos, P. L. A. M.; Kruip, T. A. M.; Wurth, Y. A.; Beneden, T. H. v.; Willemse, A. H.; Taverne, M.A. M. Theriogenology v.35(4): p.857- 862. (1991 Apr.)Includes references.Descriptors: cows; oocytes; collection; graafian-follicles; ultrasonography; ultrasonics; estrous-cycle; estrus;vagina; ovum-pick-up

Abstract: A transvaginal ultrasound guided follicular aspiration technique was developed for the repeatedcollection of bovine oocytes from natural cycling cows. In addition, the feasibility of using this method for

collecting immature oocytes for in vitro embryo production was also evaluated. Puncturing of visible folliclesfor ovum pick-up was performed in 21 cows over a three month period. All visible follicles larger than 3 mmwere punctured and aspirated three times during the estrous cycle on Day 3 or 4. Day 9 or 10 and Day 15 or 16.The mean (+/- SEM) estrous cycle length after repeated follicle puncture was 22.2 +/- 0.3 days. The mean totalnumber of punctured follicles per estrous cycle was 12.6 +/- 0.3. The largest (P < 0.05) number of folliclespunctured (5.1 +/- 0.3) for ovum pick-up was on Day 3 or 4 of the estrous cycle. The overall recovery rate of541 punctured follicles was 55%. Most oocytes (P < 0.05) were aspirated from follicles smaller than 10 mm.Following in vitro maturation and fertilization (IVM/IVF), 104 oocytes were transferred to sheep oviducts. Sixdays later, 75 ova/embryos were recovered, after flushing the oviduct of the sheep, of which 24% developed intotransferable morulae and blastocysts. In this study, a reliable nonsurgical, follicular aspiration procedure wasused for the repeated collection of immature oocytes which could be used successfully for in vitro production ofembryos. This procedure offers a competitive alternative to conventional superovulation/embryo collectionprocedures.

513.NAL Call No.: 58.9-IN7'TREEFIT'--an aid to forestry and silviculture.Hartnup, R. Agric-Eng v.46(3): p.88-91. (1991 Autumn)Includes references.Descriptors: silviculture; trees; planting; computer-software; site- selection; uk; england; wales

514.NAL Call No.: TP248.25.A96T68-1990Trends in automation for clonal propagation by tissue culture.Aitken Christie, J. Automation in biotechnology a collection of contributions presented at the Fourth ToyotaConference, Aichi, Japan, 21-24 October 1990 / edited by Isao Karube. Amsterdam : Elsevier c1991.. p. 235-260.Includes references.Descriptors: plants; tissue-culture; micropropagation; clones; automation; robots; seedlings; transplanting

Abstract: Clonal propagation by tissue culture is frequently more expensive than other forms of propagationusing cuttings or seed because it is labour intensive and more specialised. The aim of automation is to reduce thecost per plantlet by reducing labour input. Bulk handling of tissues and plantlets is essential. The main areas ofclonal propagation by tissue culture that have been automated include nutrient media preparation, handling ofcontainers in the laboratory and greenhouse, misting and watering of plantlets ex vitro, and management in thelaboratory and greenhouse by computer. These aspects are more straightforward and have been easter toautomate than the in vitro stages. Where in vitro automation has been attempted, the methods chosen weredependent on the growth and multiplication habits of species and where and if tissues were cut duringsubculture. Various aspects of in vitro automation, including liquid feeding, support systems, hedging,homogenisation, nodule culture, encapsulation and sugar-free micropropagation are discussed. Organogeniccultures are being grown, multiplied, and processed in some cases, on a pre-commercial scale in bioreactors. Forshoot cultures with an upright growth habit, robotic and mechanised systems have been developed for cuttingand planting nodal segments with leaves and/or meristems. Automated and robotic systems have been developedfor handling (grading, trimming and transplanting) small seedlings and cuttings in the greenhouse. Thesesystems could also be applied to plants propagated by tissue culture at the greenhouse stage. Importantconsiderations when evaluating and developing automated systems include the cost, yields and quality of plants,contamination, damage to the tissues and vitrification. Some of the automated systems developed to date will bereviewed with respect to these points.

515.NAL Call No.: S564.7.T87-1989Turbofarm : a cash accounting system for farmers and ranchers. Version 3.00. Turbo farm.Cothern, J. S.; University of California, D. C. E. Davis : UC Davis Cooperative Extension, c1989. 2 computerdisks + 1 user's manual.

Title from title screen.Descriptors: Agriculture-Accounting-Software; Farm-management-Records- and-correspondence-Software;Electronic-spreadsheets-Software

Abstract: A program that makes it possible for farmers to maintain concise financial information about theiroperations, i.e.: farm income and expenses, listings of land types and uses, chemical history use, depreciationinformation, inventory listings, net worth balance sheet, etc.

516.NAL Call No.: HC79.E5E5Two methods to define and compute visual buffer strips in a forested environment.Rasmussen, W. O. Environ-Manage v.16(3): p.189-196. (1992 May-1992 June)Includes references.Descriptors: trees; stems; vegetation; plant-effects; visibility; probability; forest-management; environmental-management; methodology; comparisons- ; overstory-vegetation; line-based-visual-buffer-strip; area-based-visual-buffer-strip; visual-impact

517.NAL Call No.: NBU SF768.2-H67-G58-1986Ultrasonic imaging and reproductive events in the mare.Ginther, O. J. Madison, Wis. : University of Wisconsin-Madison, c1986. xvi, 378 p. : ill., Includes bibliographiesand index.Descriptors: Horses-Reproduction; Mares; Horses-Breeding

518.NAL Call No.: 49-J82Ultrasonic, needle, and carcass measurements for predicting chemical composition of lamb carcasses.Ramsey, C. B.; Kirton, A. H.; Hogg, B.; Dobbie, J. L. J-Anim-Sci v.69(9): p.3655-3664. (1991 Sept.)Includes references.Descriptors: lambs; carcass-composition; evaluation; measurement; ultrasonic-fat-meters; fat-thickness; live-estimation; fat-percentage; protein- percentage

Abstract: Three groups (n = 147) of New Zealand mixed breed lambs averaging 170 d of age and 31.7 kg inweight were killed after a diet of pasture to determine whether the total depth of soft tissues over the 12th rib 11cm from the dorsal midline (GR) could be measured in live lambs with sufficient accuracy to warrant its use as aselection tool for breeding flock replacements. Relationships among live and carcass measurements and carcasschemical composition also were determined. An ultrasonic measurement of GR in the live lambs was a moreaccurate predictor of carcass GR (r = .87) and percentage carcass fat (r = .80) than was a measurement of GRmade with a needle (r = .80 and .67, respectively). Both measurements were sufficiently accurate to permitculling of over-fat lambs from breeding flock replacement prospects. The best single indicator of percentagecarcass fat (r = .87) was a shoulder fat measurement, followed closely by carcass GR (r = .85). Both weresuperior to USDA yield grade for estimating carcass chemical composition in these young, lightweight lambs.These two measurements also were most highly related to percentage carcass protein (r = -.78 and r = -.77,respectively). These results indicate possibilities for improving the method of evaluating the composition of U.S. lamb carcasses.

519.NAL Call No.: 49-J82Ultrasonic prediction of carcass merit in beef cattle: evaluation of technician effects on ultrasonicestimates of carcass fat thickness and longissimus muscle area.Perkins, T. L.; Green, R. D.; Hamlin, K. E.; Shepard, H. H.; Miller, M. F. J-Anim-Sci v.70(9): p.2758-2765.(1992 Sept.)Includes references.Descriptors: beef-cattle; fat-thickness; ultrasonic-fat-meters; technicians; reliability; muscle-tissue; longissimus-dorsi; measurement

Abstract: The objective of this study was to determine technician effects of live animal ultrasonic estimates offat thickness (FTU) and longissimus muscle area (LMAU). Steers (n = 36) representing four breed-types (BrownSwiss, Average Zebu-cross Mexican, Corriente Mexican, and typical British crossbred) of commercial slaughtercattle were isonified to estimate accuracy and repeatability of fat thickness (FT) and longissimus muscle area(LMA) measurements by two experienced technicians. Repeated measures of FTU and LMAU were taken bytechnicians on two consecutive days with an Aloka 500V ultrasound unit equipped with a 3.5-MHz, 172-mmscanning width, linear-array transducer. Ultrasonic estimates of fat thickness and LMAU were taken at the 12thand 13th rib interface 48 h before slaughter; carcass fat thickness (FTC) and longissimus muscle area (LMAC)were measured 48 h postmortem. Means for FTU, FTC, LMAU, and LMAC were .91 +/- .36 cm, .82 +/- .40 cm,70.7 +/- 9.43 cm2, and 72.4 +/- 8.9 cm2, respectively. Ultrasound and carcass measures of FT and LMA weredifferent (P < .01) among breed-types but were not different (P > . 10) between technicians or for technician Xbreed-type interactions. Pooled simple correlation coefficients (P < .01) were .87 and .86 between FTU and FTCand .76 and .82 between LMAU and LMAC for Technicians 1 and 2, respectively. Repeatabilities estimated byintraclass correlation methods were .91 +/- .03 and .81 +/- .06 for images repeated over 2 d and .95 +/- .02 and.83 +/- .05 for images repeated by two technicians for FT and LMA, respectively. Repeatability estimates ofLMA interpretation from videotape were .86 +/- .05 within technician and .76 +/- .07 between technicians.These results indicate equal importance of ultrasonic image retrieval and interpretation by experiencedevaluators when estimating FT and LMA in slaughter cattle.

520.NAL Call No.: S671.A66Ultrasonic tree caliper.Upchurch, B. L.; Anger, W. C.; Vass, G.; Glenn, D. M. Appl-Eng-Agric v.8(5): p.711-714. (1992 Sept.)Includes references.Descriptors: fruit-trees; ultrasonic-devices; transducers; sensors; diameter; measurement; design

Abstract: A unique sensing system utilizing an ultrasonic transducer for measuring tree trunk diameters isdescribed. The transducer with supporting electronic circuitry detected changes as small as 2.5 mm (0.1 in.).Diameters of circular objects were calculated using the time interval for sound waves to travel from thetransducer to the object and back to the sensor. With a 'V' shaped hook to fix the back of the object relative to thesensor, the distance between the transducer and object decreased as the diameter increased and was highlycorrelated (r2 = 0.99) with the actual diameter of objects within a calibration set. The 95% confidence intervalfor the expected error with the unit was +/- 0.013 cm (+/- 0.005 in.) for the calibration set. On actual tree trunks,the ultrasonic caliper had a mean error of -0.05 cm (0.02 in.) with a standard deviation of 0.31 which resulted ina 95% confidence interval exceeding the desired accuracy of the unit. However, the large variance was attributedto the inability to accurately measure the tree diameter at the exact location the ultrasonic measurement wastaken.


521.NAL Call No.: 41.8-M69Ultrasonographic determination of pregnancy in small ruminants.Bretzlaff, K.; Edwards, J.; Forrest, D.; Nuti, L. Vet-Med v.88(1): p.12-19. (1993 Jan.)Includes references.Descriptors: sheep; goats; ultrasound; pregnancy-diagnosis; herd- improvement; culling; pregnancy-complications; ultrasonic-devices; texas

522.NAL Call No.: SB4.P532The use of a computer management system for testing candidate cereal varieties for Distinctness,uniformity and Stability and the award of Plant Breeders Rights.Jarman, R. J.; Hampson, A. G. Plant-Var-Seeds v.4(3): p.161-168. (1991 Dec.)

Includes references.Descriptors: plant-breeding; cereals; patents; breeders'-rights; variety-classification; cultivar-identification;computer-techniques; assessment; computer- software; uk

523.NAL Call No.: S494.5.D3C652Use of estimated breeding value microcomputer programs to improve pork production efficiency.Schinckel, A. P. Comput-Electron-Agric v.6(1): p.63-69. (1991 July)Includes references.Descriptors: pigs; pigmeat; meat-production; genetic-improvement; breeding-value; microcomputers; computer-software; performance; reproductive- traits; growth; backfat; selection-index; boars; data-analysis;contemporary- comparisons; mathematics; on-farm-data-analysis

524.NAL Call No.: S671.A66The use of graphics to present the results of erosion models.Bingner, R. L. Appl-Eng-Agric v.7(2): p.193-197. (1991 Mar.)Includes references.Descriptors: erosion; models; watersheds; computer-software; graphic- arts; runoff; creams; answers

Abstract: This study shows how graphical representations of a watershed system can be used to analyze therunoff and sediment yield. Combining output data from an erosion model, such as runoff, sediment yield, andparticle size distribution of the eroded sediment, onto a single screen on a computer monitor, permits immediateanalysis as a rainfall event occurs. This study shows how the erosion models CREAMS and ANSWERS can bemodified for simulations on small and large watersheds, using graphics to enhance the results.

525.NAL Call No.: 65.9-SO83The use of microcomputers and spreadsheet programmes to aid replant decision making.Tobin, P. D.; Ellis, R. D. Proc-Annu-Congr-S-Afr-Sugar-Technol-Assoc (62nd): p.169-174. (1988)Meeting held on June 6-9, 1988, Durban and Mount Edgecombe, South Africa.Descriptors: saccharum-officinarum; replanting; decision-making; microcomputers; computer-software; cost-analysis; crop-yield

526.NAL Call No.: SF207.B442Use of real-time ultrasound to identify multiple fetuses in beef cattle.Davis, M. E.; Haibel, G. K. Ohio-Beef-Cattle-Res-Ind-Rep (92-1): p.10- 18. (1992 Mar.)Includes references.Descriptors: beef-cows; ultrasound; multiple-births; nutrient- requirements; aberdeen-angus; fetus; ohio

527.NAL Call No.: aSD11.A46A users guide for SAMM: a prototype southeast Alaska multiresource model.Weyermann, D. L.; Fight, R. D.; Garrett, L. D. U-S-D-A-For-Serv-Gen-Tech- Rep-PNW-GTR-Pac-Northwest-Res-Stn. Portland, Or. : The Station. Aug 1991. (274) 49 p.Descriptors: forest-resources; computer-software; timbers; hydrology; wildlife; natural-resources; models;forest-management; alaska

528.NAL Call No.: S494.5.D3I5-1988Uses of portable computers for technology transfer in extension programs for ornamental horticulture.Verkade, S. D.; Fitzpatrick, G. E. Proceedings of the 2nd International Conference on Computers in AgriculturalExtension Programs Fedro S Zazueta p.729-732. (of Florida, [1988?].)Meeting held February 10-11, 1988 at Lake Buenavista, Orlando, Florida.

Descriptors: microcomputers; mobile-equipment; technology-transfer; ornamental-plants; extension; laptop-computers

529.NAL Call No.: 44.8-J822Using advanced computer technologies to increase extension effectiveness.Tomaszewski, M. A. J-Dairy-Sci v.75(11): p.3242-3245. (1992 Nov.)Includes references.Descriptors: microcomputers; information-systems; expert-systems; compact-discs

Abstract: Recent advances in microcomputer-based products provide Extension with the opportunity to reviseand to update historic delivery systems. Decision support systems furnish Extension professionals, consultants,allied industries, and producers with a new resource to solve problems. Use of relational databases on the farm,updated from external databases, provides users with a new option for problem solving. Hypertext and authoringlanguages create new ways to manage more effectively the information available through the database. Decisionsupport systems, using an on-farm database, can be developed to address and to evaluate more specificmanagement problems. Use of tools that can directly access and manipulate producers' external and internal dataincreases the effectiveness of the Extension professional.

530.NAL Call No.: 80-AC82Using computer vision for putting flower bulbs upright.Langers, R. A. Acta-Hortic (304): p.187-198. (1992 Mar.)Paper presented at the "First International Workshop on Sensors in Horticulture", January 29-31, 1991,Noordwijkerhout, The Netherlands.Descriptors: ornamental-plants; crop-production; bulbs; position; sensors; computer-techniques

531.NAL Call No.: 275.28-J82Using computers in farm management education.Powell, M.; Powell, T. A.; Green, J.; Bitney, L. J-Ext. Madison, Wis. : Extension Journal. Winter 1991. v. 29 p.34-35.Includes references.Descriptors: microcomputers; farm-management; agricultural-education; surveys; nebraska

532.NAL Call No.: SB476.G7Using infrared thermometers.Martin, D. L. Grounds-Maint v.26(8): p.54, 56, 58. (1991 Aug.)Descriptors: lawns-and-turf; canopy; temperature; measurement; infrared-radiation; thermometers

533.NAL Call No.: S544.3.C2C3Using reference evapotranspiration (ETo) and crop coefficients to estimate crop evapotranspiration (ETc)for agronomic crops, grasses, and vegetable crops.Snyder, R. L.; Lanini, B. J.; Shaw, D. A.; Priott, W. O. Leafl-Univ-Calif- Coop-Ext-Serv. Berkeley, Calif. : TheService. July 1987. (21427) 12 p.Includes references.Descriptors: crops; evapotranspiration; water-requirements; irrigation-scheduling; computer-software;california; california-irrigation- management-information-system-cimis

534.NAL Call No.: SF951.E62Using ultrasonography in broodmare management. 1.Vogelsang, M. M. Equine-Pract v.14(8): p.17-22. (1992 Sept.)

Includes references.Descriptors: mares; ultrasonography; ultrasonic-diagnosis; reproductive-organs; pregnancy-diagnosis

535.NAL Call No.: SF951.E62Using ultrasonography in broodmare management. 3.Vogelsang, M. M. Equine-Pract v.15(1): p.26, 28-32. (1993 Jan.)Includes references.Descriptors: mares; ultrasonic-diagnosis; reproductive-organs

536.NAL Call No.: SB249.N6The utilization of a geographical information system in a boll weevil field management program.Wiygul, G.; McCoy, J.; Smith, J. W. Proc-Beltwide-Cotton-Prod-Res-Conf p.248-250. (1990)Meeting held January 9-14, 1990, Las Vegas, Nevada.Descriptors: anthonomus-grandis; gossypium-hirsutum; pest-management; trapping; pheromone-traps; winter;geographical-distribution; computer- software; information-systems

537.NAL Call No.: 49-J82Validation of real-time ultrasound technology for predicting fat thicknesses, longissimus muscle areas, andcomposition of Brangus bulls from 4 months to 2 years of age.Waldner, D. N.; Dikeman, M. E.; Schalles, R. R.; Olson, W. G.; Houghton, P. L.; Unruh, J. A.; Corah, L. R. J-Anim-Sci v.70(10): p.3044-3054. (1992 Oct.)Includes references.Descriptors: beef-bulls; ultrasound; ultrasonic-fat-meters; longissimus-dorsi; fat-thickness; carcass-composition;carcass-yield

Abstract: Sixty Brangus bulls were evaluated live using two real-time ultrasound instruments and fourtechnicians to estimate longissiimus muscle area (LMA) and 12th rib fat thickness (FT) every 4 mo beginning at4 and 12 mo of age, respectively, and continuing until 24 mo of age. Ten bulls were slaughtered every 4 mo todetermine actual LMA and FT 9-10-11th rib chemical composition, yield grade (YG) factors, and empty bodyweight EBW3. Live animal traits were used to predict 9-10-11th rib composition, YG, and EBW. Scanned meanFT was accurate (P < .05) at 16 mo and was not different (P = .09) from the actual mean FT (95% of the time theerror in estimation was less than or equal to .33 cm). Scanned mean LMA was accurate (P < .05) at 12 mo (95%of the time the error in estimation was less than or equal to 20.0 cm2). Absolute differences between scannedand actual mean FT and LMA were different (P < .05) from zero for the main effects of month, operator and(or)interpreter, and instrument. Increased level of operator skill did not improve the accuracy of FT or LMAmeasurements, whereas increased level of skill of the interpreter of scans did improve the accuracy of LMAestimations. There was no difference (P > .05) between ultrasound instruments in accuracy of estimating FT orLMA. The most accurate prediction of YG occurred at 12 mo and incorporated LW, hip height (HH), andultrasound LMA (R2 = .95, SD = .14). The most accurate prediction of EBW occurred at 16 mo andincorporated LW, HH, and ultrasound FT (R2 = 99, SD = 6.65 kg), whereas the most accurate equation forcombined slaughter periods incorporated LW, HH, and ultrasound LMA (R2 = .99, SD = 20.71 kg). We concludethat scanning of LMA at 12 mo and of FT at 12 or 16 mo were sufficiently accurate to characterize groups ofbulls; however, some individual measurements were quite inaccurate. Measurements at other months should notbe considered accurate for either individuals or groups of bulls. Yield grade and EBW can be accuratelyestimated from live animal and ultrasound measurements, which may be useful in identifying Brangus cattlewith superior cutability and may eliminate the need for serial slaughter in research projects.

538.NAL Call No.: S671.A66Validation of the CASPR aerial spray efficiency model.Curbishley, T. B.; Teske, M. E.; Barry, J. W. Appl-eng-agric v.9(2): p.199-203. (1993 Mar.)

Includes references.Descriptors: aerial-spraying; forest-management; models; costs; computer-software; estimation

Abstract: This article describes the implementation of the Baltin- Amsden formula, a method for estimating thecost of an aerial spray operation, onto personal computers through the CASPR (Computer Assisted SprayProductivity Routine) program. The CASPR predictions are compared to observed data taken during the 1991Gypsy Moth Eradication Program run by the Utah Department of Agriculture, Division of Plant Industry. Themodel is able to predict the total times of the aerial spray operation to within 23%, on average, and therefore,provides a means of estimating quickly the cost of any aerial spray operation scenario.

539.NAL Call No.: SB193.F59Validation of the grass model and it's potential use in western Oregon pasture management.Ballerstedt, P. Proc-Forage-Grassl-Conf p.272-275. (1990)Paper presented at the "Forage and Grassland Conference," June 6-9, 1990, Blacksburg, Virginia.Descriptors: lolium-perenne; trifolium-repens; grassland-management; grazing; computer-software; oregon;hayval-programs

540.NAL Call No.: S494.5.S86S8Videography: a management tool for sustainable agriculture.Stutte, G. W. J-Sustainable-Agric v.1(3): p.81-93. (1991)Includes references.Descriptors: farming-systems; sustainability; canopy; reflectance; infrared-imagery; spatial-variation; stress;computer-software; image-capture- and-analysis-system


541.NAL Call No.: 80-AC82vPETE: a phenological model built for integration into software systems.Currans, K. G.; Croft, B. A. Acta-Hortic (276): p.35-41. (1990 July)Paper presented at the "Second International Symposium on Computer Modelling in Fruit Research and OrchardManagement," September 5- 8, 1989, Logan, Utah.Descriptors: fruit-trees; integrated-pest-management; computer- software

Abstract: vPETE was conceived as a means to better integrate a phenological model into an expert system forIntegrated Pest Management (IPM) in deciduous tree fruits. We discuss vPETE as an insect phenology modeldriven by degree-days via an operating system technique called a "PIPE" (Ritchie and Thompson 1974). Adistributed delay routine is the basis of vPETE, which configures the life cycle of an organism and expands it fornew generations. vPETE simplifies phenological modeling in larger software systems and is adaptable to manysystems. Operating system techniques used by vPETE are dynamic memory allocation, stream input/output, andmultitasking. All output from vPETE is communicated to a graphical display program or file. No humaninteraction is performed. Versions of vPETE run on parallel computing platforms.

542.NAL Call No.: SB249.N6Water stress effects on cotton lint yield using infrared thermometry to schedule irrigations.Husman, S. H.; Garrot, D. J. Jr. Proc-Beltwide-Cotton-Conf. Memphis, Tenn. : National Cotton Council ofAmerica. 1992. v. 3 p. 1109-1110.Paper presented at the Cotton Soil Management and Plant Nutrition Conference, 1992.Descriptors: gossypium; water-stress; lint; yields; irrigation- scheduling; arizona; waddell,-arizona

543.NAL Call No.: S544.3.N7A4What's the real scoop on sonic wildlife control devices.Curtis, P. D. Agfocus-Publ-Cornell-Coop-Ext-Orange-Cty p.12. (1991 Oct.)Descriptors: wildlife-management; sounds; pest-control; repellents; ultrasonic-devices

544.NAL Call No.: S27.A3Wheat aphid! A simple decision support and educational tool for economic management of the Russianwheat aphid infesting winter.Legg, D. E.; Kumar, R. Great-Plains-Agric-Counc-Publ (142): p.62-65. (1992)Proceedings of the Fifth Russian Wheat Aphid Conference, January 26-28, 1992, Fort Worth, Texas.Descriptors: diuraphis-noxia; integrated-pest-management; computer- programming; computer-software; insect-control

545.NAL Call No.: 290.9-AM32PWhole farm field machine cost program: WFMACH$--features, concerns, and applications.Robb, J. G.; Ellis, D. E.; Smith, J. A. PAP-AMER-SOC-AGRIC-ENG. St. Joseph, Mich. : The Society. Winter1990. (90-1560) 12 p.Paper presented at the "1990 International Winter Meeting sponsored by the American Society of AgriculturalEngineers," December 18-21, Chicago, Illinois.Descriptors: farm-machinery; field-experimentation; costs; farm- management; microcomputers; nebraska

546.NAL Call No.: S494.5.D3I5-1990Whole farm linear programming--from mainframe to PC.Harrison, W.; Peralta, F.; Benson, F. Proceedings of the 3rd International Conference on Computers inAgricultural Extension Programs / Fedro S. Zazueta, editor. ; January 31- February 1, 1990, Grosvenor ResortHotel, Disney World Village, Lake Buenavista, FL. Gainesville, FL : Florida Cooperative Extension Service,University of Florida, [1990]. p. 721-725. ill.Includes references.Descriptors: farm-management; microcomputers; linear-programming; extension; indiana

547.NAL Call No.: Z672.I53Will we manage or be managed by our technologies.Vacin, G. L. Quar-Bull-Int-Assoc-Agric-Inf-Spec v.37(1/2): p.5-9. (1992)IAALD Symposium on "Advances in Information Technology," September 16-20, 1991, Beltsville, Maryland.Descriptors: computers; literacy; information-science; appropriate- technology; technical-progress

548.NAL Call No.: QK725.I43Workability and productivity of robotic plug transplanting workcell.Ting, K. C.; Giacomelli, G. A.; Ling, P. P. In-Vitro-Cell-Dev-Biol- Plant v.28P(1): p.5-10. (1992 Jan.)Paper presented at the Session-in-Depth "Robotics in Tissue Culture," at the 1991 World Congress on Cell andTissue Culture, June 16-20, 1991, Anaheim, California.Descriptors: transplanting; transplanters; automation; robots; greenhouses; horticulture; lifting

549.NAL Call No.: 1.9-P69PA working description of the Penn State apple orchard consultant, an expert system.Travis, J. W.; Rajotte, E.; Bankert, R.; Hickey, K. D.; Hull, L. A.; Eby, V.; Heinemann, P. H.; Crassweller, R.;McClure, J.; Bowser, T. Plant-Dis v.76(6): p.545-554. (1992 June)Includes references.

Descriptors: malus; orchards; crop-production; expert-systems; integrated-pest-management; plant-disease-control; chemical-control; decision- making; diffusion-of-information; information-processing; microcomputers;computer-techniques; innovation-adoption; pennsylvania; disease-potential- modules; insect-threshold-modules;chemical-management-modules

550.NAL Call No.: 290.9-AM32PYield mapping winter wheat for improved crop management.Peterson, C. L.; Hawley, K. N.; Whitcraft, J. C.; Dowding, E. A. PAP-AMER- SOC-AGRIC-ENG. St. Joseph,Mich. : The Society. Summer 1989. (89-7034) 19 p.Paper presented at the 1989 International Summer Meeting, June 25-28, 1989, Quebec, PQ, Canada.Descriptors: winter-wheat; yields; mapping; spatial-variation; idaho; washington; field-navigation

Author Index


Aakre, D. 211Abe, T. 151Adams, C.M. 32Adams, E.L. 86Agnello, A. 162Ahmadi, A. 111, 400Aiken, G.E. 51Ainslie, S.J. 311Airoldi, G. 148Aitken Christie, J. 514Akridge, J.T. 189Alcoilja, E.C. 27Alderfer, R.D. 125Alexander, S.J. 457Allen, G. 205Allison, J.M. 13, 246Allison, J.M. Jr. 329Almorza, J. 336Alvisi, F. 351, 451Alwang, J. 208American Society of Agricultural Engineers. Information and Technologies Division. 21Amir, I. 114Analytical Software Partners. 37Anderson, B. 223Anderson, J.A. 203Anderson, J.L. 328Andrews, P.L. 450Andrieu, B. 56Aneshansley, Daniel J. 357Angelici, G.L. 282Anger, W.C. 520Angerer, J.P. 447

Annevelink, E. 257, 375Arconada, A. 496Argiriou, A. 496Arthaud, G.J. 259Askew, R.G. 230Atkins, K.D. 235Atkins, T.A. 384Audibert, M. 496Azain, M.J. 427Aziz, N.M. 295Babichenko, S.M. 355Bachelet, D. 300Backholer, J.R. 309Bacon, J.R. 167Bacsi, Z. 228Baer, R.J. 266Baerdemaeker, J. de 319Bailey, D.R.C. 428Bakhtiari, S. 342Ball, S.T. 426Ballerstedt, P. 539Balsari, P. 148Bankert, R. 549Baptist, R. 191Barnekov, V. 421Barnes, P.W. 300Barrington, S.F. 381Barry, J.W. 538Barry, M.C. 71Barta, D.J. 432Barton, F.E. II 51Bastien, C. 345Batchelor, W.D. 297Batutis, E.J. 348Bauer, L.L. 85Baumgras, J.E. 283Beasley, B.W. 171Beck, M.S. 417Beek, P. van 95Beerepoot, G.M.M. 168Behrens, B.D. 196Belote, D. 129Ben Yaakov, S. 372Beneden, T.H. van 361, 512Bennett, G.A. 140Bennett, J.M. 371Bennett, L.E. 341Bennett, M. 220Benson, F. 546Benson, V.W. 182Benyshek, L.L. 75Berg, P.M. 87, 186Bernard, J.K. 68Bernardo, D.J. 127

Berry, J. 7, 129Berry, J.S. 281, 459Berry, S.L. 111, 400Bertrand, J.K. 75Bicanic, D.D. 299Biddle, A.J. 412Bidwell, T.G. 127Bieniek, M. 307Biggins, J.G. 160Billings, R.F. 415Bilsland, D.M. 73Bingham, G.E. 424Bingner, R.L. 524Binning, L. 278Binning, L.K. 279Bird, J.D. 284Bishop, G.D. 85, 112Bitney, L. 531Bittenbender, H.C. 210Black, J.L. 445Black, J.R. 215, 333, 468Blackshaw, J.K. 265Blair, K. 202Blake, R.W. 301Blankesteijn, H. 340Blazquez, C.H. 77Blinn, C.R. 393Boggs, D.L. 60, 87, 186Bojorquez Tapia, L.A. 352Bomash, W.M. 137Bonhomme, R. 405Bonicelli, B. 435Bonnafous, J.C. 435Boote, K.J. 371Booysen, N.W. 430Borton, L.R. 380Bouman, Bas A. M. 304Bowser, T. 549Bradford, G.L. 358Bradley, A.F. 219Bradshaw, L.S. 450Brand, A. 152Brash, L.D. 234Brattemo, P.A. 367Braverman, Y. 403Breon, F.M. 15Bretzlaff, K. 521Briggs, D.G. 346Brisbin, R.L. 479Brons, A. 396Brook, R.C. 4, 11, 215Brorsen, B.W. 189Brown, D. 300Brown, F.R. 440

Brown, J. 46Brown, J.D. 203Brown, M. 300Brown, W.T. 377Brownson, R. 252Bruce, L.B. 224Buhl, F. 110Bula, R.J. 432Bulger, D. 225Bullock, K.D. 75, 427Burkhardt, J.W. 14Burns, J.R. 202Buron, G. 314Burt, C.M. 10Buwalda, J.G. 293Caggiati, P. 350Calkin, J.A. 248Calvo, A. 148Cameron, D.M. 52Cantliffe, D.J. 307Cardenas Weber, M. 218, 322Carpineti, C. 145, 360Carsel, R.F. 122, 149Carson, R.L. 501Casper, D.P. 266Cavaye, J.M. 483Champney, W.O. 14Chang, H. 202Chang, W. 71Chanzy, A. 285Chapman, K.R. 83Chavez, P.S. Jr. 76Chedru, S. 33Chen, Y.R. 294Cheng, T.D. 282Chiba, L.I. 460Chick, M.J. 254Christensen, D.A. 106Christianson, L.L. 116, 389Churchill, D.B. 73Clery, D. 340Clewett, J.F. 483Clough, G.H. 507Coats, R.E. 205Coble, H.D. 250, 504Cochran, M.J. 205Cohen, P. 150Cole, D.J. 19Colliver, D.G. 455Colpitts, G. 408, 409Comerford, J.W. 172Conference on Temperature and Environmental Factors and the Testis (1989 : New York University School ofMedicine). 493Connell, T.R. 279

Cooley, D. 150Coop, L.B. 128Cooper, J.B. 172Cooper, T.M. 73Corah, L.R. 537Corey, R.B. 432Corliss, J. 423Cothern, J. Steven. 515Coulson, R.N. 414, 415Coulter, G.H. 428Courteau, J. 442Courteau, Jean. 443Covington, W.W. 275, 302, 492Cramer, C. 242Crassweller, R. 549Cravener, T.L. 292, 392Crisosto, C.H. 404Croft, B.A. 79, 128, 541Cros, M.J. 141Cross, H.R. 446Crown, P.H. 402Curbishley, T.B. 538Currans, K.G. 79, 541Curtis, J.P. 324Curtis, P.D. 543Curwen, D. 278, 279Czarick, M. 306Czysz, D. 103d'Agaro, E. 232D'Alfonso, T.H. 292, 392Dahl, B.L. 303, 321Dahlman, C. 391Dangerfield, C.W. Jr. 506Danson, F.M. 56Dariane, A.B. 24Darmasetiawan, R. 80Davey, S.M. 362Davies, G.T. 445Davis, M.E. 185, 526Davis, M.R. 12Davis, M.S. 509Davis, P.M. 164Day, W. 456Debertin, D.L. 20, 22, 358Delwiche, S.R. 63Dent, J.B. 228DePolo, J. 93Deschamps, P.Y. 15Deuze, J.L. 15Devaux, C. 15Devir, S. 70Dewhurst, S.M. 492Dicenta, F. 97Dickens, W.L. 288

Dickerson, G.E. 429Dijkhuizen, A.A. 95Dikeman, M.E. 537Dill, D.E. 119Dillon, O.W. Jr. 455Dobbie, J.L. 518Dolezal, H.G. 196Domecq, J.J. 200Dowding, E.A. 550Drake, D.J. 154Drapek, R.J. 248Drummond, J.C. 19Dugas, W.A. 136Durling, J.C. 333Durrant, S. 251Dusek, D. 354Dyche, J.R. Jr. 274Dyke, P.T. 182, 344Dykhuizen, A.A. 168Ebelhar, M.W. 109Eby, V. 549Edan, Y. 138, 218, 322Eddington, D.L. 511Edminster, C.B. 236Edson, J.L. 260Edwards, C.M. 324Edwards, C.R. 394Edwards, D.R. 16, 17Edwards, G.R. 83Edwards, J. 521Edwards Jones, G. 133Efolliott, P.F. 352Egeberg, R. 211, 230Egnell, G. 271Ehler, N. 36Ehlmann, G. 331Eide, W. 276Einstein, M.E. 233Eix, J.R. 88Ek, A.R. 103Ellis, D.E. 545Ellis, M. 232Ellis, R.D. 525Engel, B.A. 64, 181Engle, D.M. 127Enright, P. 28, 345Erb, K. 242Erickson, Duane E., 1931 335Erickson, R.W. 380Ernst, D. 365Ernst, R.L. 94Escobar, D.E. 12Esslemont, R.J. 134Etherington, W.G. 118

Everitt, J.H. 12Ewing, E.E. 348Fahrenholz, L. 55Fang, W. 89, 123, 382, 383, 475Fanous, M.A. 316Farley, J.L. 111, 400Farrar, R.M. Jr. 334Fehr, B. 11Felczynska, A. 508Ferguson, J.A. 16, 17Ferguson, J.D. 273Ferri, F. 65, 213Ferris, I.G. 251Fessler, J.F. 500Fetrow, J. 118Feuer, L. 270Feyen, J. 45Fick, R.J. 4, 11Field, C.B. 229Fight, R.D. 346, 527Finazzo, J. 154Fiscus, E.L. 227Fisher, G.C. 248Fitzgerald, J.B. 398, 399Fitzpatrick, G.E. 261, 418, 528Fleming, J.F. 445Fogarty, N.M. 234Food and Agriculture Organization of the United Nations. 9Forrest, D. 521Forrest, J.C. 189Forsen, S. 271Forslund, R.R. 216Foster, M.A. 31Fouche, P.S. 430Fox, B. 378Fox, D.G. 311Franklund, D. 230Frayer, W.E. 255Frecker, T.C. 251Freeman, S.A. 495Frohlich, H. 156Fryar, E.O. 16, 17Fujiura, T. 431Fukuhara, R. 448Furnas, R.E. 473Galbe, M. 464Gallerani, V. 350Gamon, J.A. 229Gamroth, M.J. 161Garcia Ceca, J.L. 130Garcia Ciudad, A. 407Garcia Criado, B. 407Garcia, J.E. 97Garcia, L.V. 336

Garland, J.J. 188, 247Garnaoui, K.H. 292Garrett, J.R. 14Garrett, L.D. 527Garrot, D.J. Jr. 542Garza Gutierrez, R. 343Gasson, R. 486Gatel, F. 314Gates, R.S. 102Gauch, H.G. Jr. 473Gauck, D.M. 185Gaultney, L.D. 181Gauthier, L. 245Gavlak, R.G. 224Gebremedhin, K.G. 130Gempesaw, C.M. II 167Gerloff, D.C. 490Giacomelli, G.A. 89, 123, 206, 382, 383, 441, 548Gibb, J.B. 386Gibson, J.M. 14Gill, D.R. 196Gilmour, A.R. 234, 235Ginther, O. J. 517Glenn, D.M. 520Godwin, D.C. 480Goedseels, V. 146Gordon, A.D. 401Gordon, S.H. 140Goth, C. 231Gradusov, B.P. 497Grappin, R. 47Green, J. 531Green, R.D. 195, 519Greene, R.V. 140Gresham, J.D. 68Griffin, W.N. 163, 183Griffioen, H. 379Griffith, D. 252, 327Griffith, D.A. 166Griffith, D.R. 155Griffith, Duane. 54, 241Griggs, R.H. 182Groeneveld, E. 374Guertin, D.P. 352Gupta, C.P. 296Guterman, H. 372Haagensen, A.M. 209Hack, G.R. 23Hackett, E.I. 14Haghighi, K. 218, 322Haibel, G.K. 526Haigh, B.M. 251Haley, C.S. 232Haley, S. 79

Hall, F.R. 57Hall, S. 434Hallmark, W.B. 187Hamalainen, J. 39Hamilton, R.I. 171Hamlin, K.E. 195, 519Hammond, K. 305Hampson, A.G. 522Hansen, D. 395Hanson, J.D. 459Harada, H. 448Harbor, J. 491Harmanny, K. 193Harmon, R.J. 4, 5, 215Harpster, H.W. 172Harrell, R.C. 174, 307Harren, F. 299Harris, D.L. 233Harris, R.R. 277Harrison, W. 546Harsh, S.B. 4, 5, 11, 125, 215, 380Hartnup, R. 513Hawkins, T. 10Hawksworth, F.G. 236Hawley, K.N. 550Hayashi, M. 458Hazelton, J.L. 406He, W.B. 417Heatwole, Conrad D. 21Hein, N. 331Heinemann, P.H. 549Heinen, M. 193Helmink, K.J. 116, 389Helms, R. 434Helyes, L. 425Hemphill, D.D. Jr. 507Henderson, H.H. 68Henning, W.R. 172Herman, M. 15Hernandez Herrera, A. 343Hesterman, O.B. 333Heuer, M.L. 136Heym, W.D. 348Hickey, K.D. 549Higgins, P. 412Hilker, J.H. 333Hill, R.W. 424Hinton, Royce A. 335Hintz, H.F. 240Hirasawa, T. 227Hirvonen, J. 39Hoff, K.G. 86Hogeveen, H. 152Hogg, B. 518

Holdgate, D.P. 40Holly, T. 494Holman, K.L. 454Honami, N. 444Hood, C.F. 307Hoshi, T. 151Houghton, P.L. 30, 537House, R.B. 172Hove, G.P. 393Howard, A.F. 326, 486Howard, C.D.D. 98Howard, W.T. 339Hu, L. 415Hu, L.C. 414Huber, H.A. 421Hudson, M.A. 268Hudson, R.S. 501Huffman, W.E. 291Hughes, H. 66, 211Hughes, T.C. 24Huirne, R.B.M. 95Hull, L.A. 549Hummel, P.R. 122, 149Humphries, S. 81Hunt, H. 225Hunter, T.D. 127Husman, S.H. 542Hutchings, N.J. 471Huynh, L.N. 373Hwang, Y. 462Ikerd, J.E. 131Imbriani, J.L. 317Imhoff, J.C. 122, 149Irie, M. 142Iwao, K. 243Izaurralde, J.A. 402Jackson, M.A. 140Jackson, T.J. 12Jacobsen, R.M. 303, 485Jaenson, R. 84Jaggard, K.W. 56Jarman, R.J. 522Jarvis, A.M. 78Jeong, B.R. 458Jernigan, D. 113Jin, Y.Q. 285Jinnett, Jerry. 37Johnson, A.T. 494Johnson, D.M. 217Johnson, G.V. 368Johnson, H.A. 111Johnson, P.J. 273, 301Johnson, R.S. 461Johnson, T.G. 208

Jones, C.A. 135, 182, 344, 480Jones, J.W. 199, 212, 369, 371, 462Jones, L.D. 22Jones, L.R. 71, 115, 256Jones, P. 462Jones, S.D.M. 272, 408, 409Jose, H.D. 453Jowers, H.E. 371Juste, F. 65, 213Kaiser, H.M. 387Kalm, E. 194Kasser, T.R. 427Kay, F. 306Kay, F.W. 13Keane, R.E. 62Keen, Peter G. W. 197Keller, M.A. 378Kelling, K.A. 279Kemp, W.P. 281Kent, Brian M. 376Kerr, Y. 285Khanizadeh, S. 316Khedher, M.B. 348Khlystovskii, A.D. 497Kim, C. 92Kiniry, J.R. 135, 182, 308, 344, 405, 480Kino, S. 458Kinowaki, M. 458Kirton, A.H. 518Kittle, J.L. Jr. 122, 149Klaring, P. 156Kline, G.L. 310Klinkhachorn, P. 421Klopfenstein, T.J. 452Knoblauch, Wayne A. 244Kobayashi, K. 478Kobayashi, K.D. 210Koblunk, C.N. 499Koelsch, John. 37Koenig, J. 278Koenig, J.P. 279Koger, J. 18, 159Kondo, N. 431Konzak, C.F. 426Kornet, J.G. 379Kothari, R. 421Kovats, K. 141Kovich, J. 162Kozai, T. 180, 458Kranzler, G.A. 90Krewer, G.W. 58Krieter, J. 194Kristensen, E.S. 474Kroll, O. 70

Kruip, T.A.M. 361, 512Kubik, D. 223Kuei, C.H. 189Kuhlmann, F. 101Kuhn, J. 26Kuhn, W. 365Kumar, R. 124, 544Kunkle, W.E. 110Kurata, K. 510Kuusk, A. 143Laacke, R.J. 349Lacher, P. 374Lakso, A.N. 461Lal, H. 199, 212, 369Lambert, J.R. 105Lamprecht, I. 55Landis, D.A. 215Lane, D.W.A. 309Langemeier, D.L. 106Langers, R.A. 530Lanier, W. 129Lanini, B.J. 533Lanyon, L.E. 29, 267Lapimaa, Yu.Yu. 355Larson, L.D. 303, 321Lassoie, J.P. 130Latin, R.X. 147Laurenson, M.R. 384Lechevallier, M. 314LeDoux, C.B. 283Lee, A. 255Leefers, L.A. 226Lees, B.G. 362Legg, D.E. 124, 341, 544Lemberg, B. 267Lemon, J.R. 57Leverich, J.B. 119Levins, R.A. 217Libbey, C. 499Ling, P.P. 548Linvill, D.E. 249Liu, F. 45Lofgren, D.L. 233Loh, D.K. 414Lookeren Campagne, P. van 257Loussaert, D. 330Lovell, A.C. 205Lowry, C. 46Loy, J.B. 467Lucas, L. 435Luce, W.G. 320Luff, A.F. 234Luff, A.L. 235Lusby, K.S. 422

Lust, D.G. 75Lyon, G.W. 255Lyons, R.K. 447Ma, M. 282Mac Millen, K. ed. 364Mack, T.P. 264Madramootoo, C.A. 28, 345Maguire, D.A. 477Mahbub Ul Alam, A.N.M. 227Major, D.J. 171Malagoli, C. 351, 451Malthus, T.J. 56Maltz, E. 70Mannering, J.V. 155Marcantonio, S. 19Marr, C.M. 337Marsh, W.E. 118Martin Clouaire, R. 141Martin, D.L. 532Martin, W.J. 417Mason, P.R. 397Mathiasen, R.L. 236May, M.J. 133McClendon, R.W. 297, 329McClure, J. 108, 549McClure, W.F. 511McCoy, G.C. 119McCoy, J. 536McCullouch, B. 38McCurdy, G.D. 424McDonald, C.A. 305McFarlane, N.J.B. 91, 258McGilliard, M.L. 200McGrann, J.M. 48McInnes, M.B. 41McInnis, P. 162McIntosh, C. 207McKenney, D.W. 353McKeon, G.M. 483Mckinion, J.M. 347McKown, C.D. 447McMillin, C.W. 421McNall, A.D. 29McPeake, S.R. 68McSweeney, W.T. 267Meij, H.K. 29Menzies, F.D. 332Mercier, S. 291Merrill, W.G. 71Merzenich, J.P. 470Meyer, C.R. 155Meyer, G.E. 398, 399Middleton, B.K. 386Milbourn, G. 363

Miles, G.E. 138Miles, Gaines E. 438Miller, M.F. 519Miller, S.F. 128Miller, T.K. III 511Mills, F.D. Jr. 506Mills, T.M. 384Minnich, R.A. 280Mishoe, J.W. 388Mitsuhashi, T. 318Mitsumoto, M. 318Miwa, Y. 44Mizelle, W.O. Jr. 58Modena, S.A. 250Mody, A. 391Mogg, K.C. 253Mohri, K. 431Moll, J. 177Molto, E. 307Monke, J.D. 263Monta, M. 431Moore, D.M. 362Moreno, J.A. 336Mori, K. 458Moriya, K. 448Morrical, D.G. 356Morris, D.M. 216Morrow, R. 26Morrow, R.C. 432Mortensen, D.A. 504Moser, J.W. Jr. 419Moser, L.E. 452Mottram, T.T. 139Mowrer, H.T. 236Mukherjee, K. 421Muller, R.E. 175Mumford, J.D. 133Munilla, R. 307Murali, N.S. 121Murase, H. 444Murmann, K. 39Murphy, C.F. 128Murphy, D.L. 239Murray, A.C. 272Murray, J.I. 235Nara, M. 144Nash, T. 491Nebel, R.L. 200Nelson, D.A. 1Nevo, A. 114New York State College of Agriculture and Life Sciences. Dept. of Agricultural Economics. 244Newell, T.R. 452Newman, J.A. 408, 409Nielen, M. 168

Nieuwenhuis, G. J. A. 502Nilsson, Hans Eric. 198, 286Nishiura, Y. 444Noe, J.P. 317Nofziger, D.L. 368Noordhuizen Stassen, E.N. 152Norris, K.H. 63Northcutt, S.L. 3Norton, G.A. 133Norton, S.D. 484Novak, J.L. 207Nuki, K. 151Nuti, L. 521Nyrop, J. 162Nystuen, J.D. 80O'Callaghan, J.R. 176Ochiai, M. 458Ogilvie, D.K. 384Ogilvie, J.R. 238Ohlmer, B. 370Okuya, T. 144Olsen, E.D. 188, 247, 505Olsen, W.K. 236Olson, F. 211Olson, W.G. 537Oltenacu, P.A. 273, 301Oltjen, J.W. 196Onoda, A. 478Onsager, J.A. 281Ooms, W.J. 201Ordolff, D.W. 25Oregon State University. Extension Service. 54, 214, 241Orlander, G. 271Orsini, J.P.G. 481, 482Ortmann, G.F. 163, 183Osborne, P.I. 312Osborne, R.R. 312Ostergard, M.M. 154Ottmar, R.D. 82Ottmar, Roger D. 104Overhults, D.G. 102Ozawa, S. 318Pacific Northwest Research Station (Portland, Or.). 104Pagoulatos, A. 358Paloscia, S. 338Pampaloni, P. 338Panciera, M.T. 224Papini, F. 496Pardue, F.E. 249Parker, W.H. 222Parsons, D.J. 323Parsons, S.D. 155Partridge, I.J. 483Parvin, D.W. Jr. 52

Pasquetti, R. 496Pasquino, A.T. 200Paulsen, M.R. 92Payandeh, B. 373Payne, T.L. 414Peart, R.M. 199, 212, 369, 388Pease, J. 43, 385Pedigo, L.P. 164Penner, K. 221Peralta, F. 546Percy Smith, A. 121Perez Corona, M.E. 407Perkins, T.L. 195, 519Perret, F. 435Perry, T.C. 311Peterson, C.L. 550Peterson, J.L. 82Peterson, N.S. 170Pfeiffer, W.C. 203Piacitelli, C.K. 51Pickens, J.B. 255Piernot, B.L. 262Pieterse, M.C. 361, 512Pilkerton, S. 247Pilkerton, S.J. 188Pinson, Linda. 37Pitman, W.D. 51Pla, F. 65, 213Podaire, A. 15Pohlmann, J.M. 413Pollitt, C.C. 253Pool, T.A. 174Poryvkina, L.V. 355Powell, M. 531Powell, T.A. 531Power, K.C. 398, 399Powers, R.F. 488Practitioner Workshop on Microcomputers and Agriculture Management in Developing Countries (1982 :Washington, D.C.). 416Prasher, S.O. 381Price, J.C. 184Priott, W.O. 533Proctor, G.H. 133Prothero, G.L. 204Pulley, P.E. 414Purohit, R.C. 500, 501Putnam, Linda D. 244Quinlan, D. 192Rabatel, G. 396Rajotte, E. 549Ralph, W. 465Ramsey, C.B. 518Randall, J.M. 146Randhawa, S.U. 505

Randle, D.G. 323Raney, J.D. 419Rasby, R. 223Rasmussen, W.O. 516Rathwell, P.J. 85Rawson, C.L. 118Read, P.E. 179Reeves, J.C. 412Regazzi, D. 351, 451Reid, J.F. 61, 92Reissig, H. 162Rellier, J.P. 33Renkema, J.A. 95Renner, K.A. 468Renquist, A.R. 126Rhykerd, C.L. 64Rhykerd, L.M. 64Rhykerd, R.L. 64Rice, D. 211, 223Rice, D.G. 485Rice, G. 278Richardson, A.J. 354Richardson, J.W. 205Richie, J.T. 480Riddell, M.G. 501Ridgeway, R.L. 96Riet, W.J. van 111Riggs, William. 241Riggs, William W. 54, 214Rigney, M.P. 90Ringwall, K.A. 87, 186Riskowski, G.L. 116, 389Ritchie, J.C. 12Ritchie, J.T. 27, 344Ritchie, M.W. 488Robb, J.G. 545Robert, P.C. 328Robert S. Kerr Environmental Research Laboratory. 237Roberts, D.A. 229Roberts, S.J. 412Robinson, D.L. 305Robinson, J.W. 226Robinson, S.A. 294Robotics in Forestry Symposium 1990 : Vaudreuil, Quebec). 443Rocky Mountain Forest and Range Experiment Station (Fort Collins, Colo.). 376Rogers, M. 298Rogoyski, M.K. 126RoHrig, C. 411Rojas Martinez, R. 343Roland, R.J. 132Ros, F. 396Rosa, D. de la 336Rosenberg, D. 162Ross, D. 491

Rotz, C.A. 169Roujean, J.L. 15Roush, W.B. 292, 392Rufelt, H. 367Rupp, G.P. 48Ruthner, E. 449Ruvuna, F. 50Rykiel, E.J. Jr. 414Ryu, G.H. 406Sagi, R. 119Sakaue, O. 153Salazar, R. 499Sammis, T.W. 113Sand, R.S. 110Sanders, D.C. 201Sandlan, K.P. 348Sase, S. 144Sasser, J.N. 317Sather, A.P. 272, 408, 409Saunders, M.C. 414Savoie, P. 169Scanlan, J.C. 483Scannell, E. 472Schaefer, A.L. 272Schaeffer, L.R. 190Schalles, R.R. 537Schaupmeyer, C. 503Schinckel, A.P. 189, 233, 523Schingoethe, D.J. 266Schlosberg, A.J. 378Schmidt, R. 278Schmisseur, E. 161Schofield, C.P. 146Schreinemakers, J.F. 152Schricker, B. 55Schukken, Y.H. 168Schuler, T.M. 236Schulte, D.D. 398, 399Schurer, K. 379Schwab, P. 476Scoggins, R.D. 498Scott, T.M. 505Sedivec, K.K. 390Seguin, B.E. 118Sell, R.S. 321Senft, D. 117Senger, P.L. 428Sessions, J. 489Sessions, J.B. 489Sevila, F. 396Sham, C.H. 67Shanks, R.D. 70Sharpe, P.J.H. 414Shashikumar, K. 406

Shaver, R.D. 339Shaw, B. 67Shaw, D.A. 533Shaw, R.R. 100, 313Shearer, S.A. 102Shepard, H.H. 519Sherrick, B.J. 263Shibano, Y. 431Shibata, T. 243Shields, F.D. Jr. 295Shoup, W.D. 199, 212, 369Shuman, L.M. 187Simms, D.D. 49Simonne, E. 288Simonton, W. 2, 42, 43, 81, 290, 385, 436, 437Sinclair, E.R. 83Sirois, D.L. 159Skaggs, R.W. 410Slaton, N. 434Slye, R.E. 282Smith, G.E. 274Smith, G.S. 293, 324Smith, J.A. 545Smith, J.W. 536Smith, M.T. 196Smith, R.C. 230Smith, Stuart F. 244Smittle, D. 288Sneed, R.E. 287Snyder, R.G. 348Snyder, R.L. 533Soderlund, M. 289Sommerlatte, M. 191Sonka, S.T. 263, 268Sorensen, J.T. 474Spahr, S.L. 70, 119, 256Spanel, D.A. 135Stanaland, R.D. 58Stansell, J.R. 288Stearns, L.D. 321Stegeman, G.A. 266Stegman, E.C. 289Steiner, J.L. 354Steinhardt, G.C. 155Steven, M.D. 56Stevenson, W.R. 278, 279Stewart, T.S. 233Stockle, C.O. 463Stokes, B.J. 159Stokes, K.W. 107Stout, S.L. 94Strain, J. Robert. 6Streeter, D.H. 268Stritzke, J.F. 127

Stroshine, R.L. 218, 322Stuth, J.W. 447Stutte, G.W. 540Stuyft, E. van der 146Sueyoshi, K. 144Suggs, C.W. 511Sulistyo, D. 80Sullivan, G.H. 201Suryanto, H. 296Sutton, R.W. 112Suzuki, M. 478Swenson, A. 211Swenson, A.L. 390Szoke, Ronald D., 1934 335Szwonek, E. 508Taira, T. 444Takano, T. 243Tanaka, F. 158Tarbell, K. 61Taverne, M.A.M. 361, 512Taylor, J.D. 412Taylor, J.F. 50Temple, S. 120Teng, P.S. 35Teske, M.E. 538Thai, C.N. 81Thallman, R.M. 50Thofelt, L. 367Thompson, T.L. 452Thomson, S.J. 388Thornton, P.K. 228Thorpe, K.W. 96Throop, James A. 357Thysen, I. 474Tibbitts, T.W. 432Ticknor, L.O. 488Ting, K.C. 89, 123, 206, 382, 383, 439, 441, 475, 548Tinsley, W.A. 99Tinus, R.W. 349Tobin, P.D. 525Toman, N. 276Tomaszewski, M.A. 529Tomiyama, K. 292Tong, A.K.W. 272, 408, 409Torok, S.J. 178Torrell, L. Allen. 214Touzet, C. 396Travis, J.W. 147, 549Trent, A.M. 499Turlington, L.M. 30Turnbow, R.H. 415Turner, A.D. 348Turner, J.W. 50Turner, L.W. 455

Turner, R. 133Turner, T.A. 498, 500Turvey, C.G. 46, 203Twery, M.J. 366Tyson, B. 306Undersander, D.J. 339Unger, R. 220United States Israel Binational Agricultural Research and Development Fund. 438University of California, Davis. Cooperative Extension. 515Unruh, J.A. 537Upchurch, B.L. 520Upchurch, Bruce L. 357Upchurch, D.R. 487Ustin, S.L. 229Vacin, G.L. 547Valco, T.D. 495Valentini, R. 229VanEE, G.R. 310Vansichen, R. 319Vass, G. 520Veenhuizen, J.J. 427Verkade, S.D. 261, 418, 528Vicens, M. 65Vlahovich, J.E. 378Vodyanitskii, Yu.N. 497Vogelsang, M.M. 534, 535Vogelsmeier, B. 331Vos, P.L.A.M. 361, 512Wachenheim, C.J. 380Wadsworth, R.A. 466Wagner, P. 101Waksman, G. 8Walcott, B.L. 102Waldner, D.N. 537Walker, C.E. 406Walker, D.F. 501Walker, E.S. 86Walker, O.L. 422Wall, G.C. 325Wall, P.L. 325Wallace, L. 2Waller, S.S. 452Walter, J.P. 50Walter, P.A. 499Walters, D.K. 103Walters, David M. 237Wambacq, P. 146Wang, C.T. 429Wang, Y.B. 264Wangberg, J.K. 124Wanjura, D.F. 354, 487Ward, C.E. 315Ward, E.A. 309Ward, K. 150

Warner, R.C. 476Warren, J.R. 59Watt, D.J. 303Watt, D.L. 321, 485Weatherspoon, C.P. 349Weaver, L.D. 118Webb, R.E. 96Webb, W.M. 487Weigand, C.L. 354Weigand, J.F. 165Welch, R.A. 109Wenny, D.L. 53, 260West, G.G. 74Westberry, G.O. 58Wetzstein, M.E. 297, 329Weyermann, D.L. 527Whipker, L.D. 189Whitcraft, J.C. 550White, J.M. 13, 246White, W.B. 509Whiteside, I.D. 469Whittaker, A.D. 446, 495Wickman, B.E. 72Wigneron, J.P. 285Wilcox, G.E. 201Wilcox, W. 162Wilkerson, G.G. 250Wilkerson, V.A. 452Willemse, A.H. 512Willham, R.L. 3Williams, J.R. 135, 182Williams, M.E. 134Williams, S.B. 269Williams, S.E. 75Wilson, D.E. 3, 356Wilson, D.O. 187Wilson, M.C. 64Windham, T.E. 205Winkle, P. 157Wirth, F.F. 167Wiygul, G. 536Wiyo, K. 345Wohlgemuth, K. 276Wolfe, D.F. 501Woltering, E.J. 299Wong, T.B. 433Wood, D.B. 275, 302, 492Woodcock, C.E. 67Wooddall Gainey, D. 208Workman, S.R. 410Worley, J.W. 13Wright, H.A. 202Wright, R.J. 34Wurth, Y.A. 361, 512

Wyman, J. 278Wyman, J.A. 279Yamashita, Y. 318Yang, Y. 206, 475Yasukuri, Y. 444Yates, M. V. (Marylynn V.) 237Yates, S. R. 237Yaussy, D.A. 479Zacchi, G. 464Zack, J.A. 280Zajda, J. 278Zandvoort, E.A. 40Zanni, G. 350Zawadski, S.M. 408, 409Zimet, D.J. 32Zimmel, P. 205Zinn, F.D. 80Zorgniotti, Adrian W., 1925 493Zoughi, R. 342Zur, B. 70

Subject Index


1988-general-law-of-ecological-equilibrium-and-environmental-protection 352aberdeen-angus 526aberdeen,-idaho 348absorption 365acclimatization 458accuracy 30, 47, 187, 195, 196, 266acids 266acrididae 281, 459actinidia-deliciosa 293, 324, 384, 404action-thresholds 504aerial-photography 77, 282, 426aerial-spraying 538aerial-surveys 430aeschynomene-americana 51age 3, 457age-at-first-calving 118age-differences 30, 55, 232agribusiness 178, 268agricultural-adjustment 81agricultural- chemicals 279agricultural-economics 22agricultural- economists 268agricultural-education 531agricultural-engineering 176, 265, 377

agricultural-geography 300agricultural-land 184, 282agricultural-policy 387agricultural-production 81, 181agricultural-research 463agricultural-soils 122agriculture 4, 8, 413, 463Agriculture-Accounting-Software 515Agriculture-Data-processing 6Agriculture-Developing-countries-Data-processing-Congresses 416Agriculture-Remote-sensing 304Agriculture-Statistics-Software 9agroecological-resource-areas 402agroecology 402agroforestry-systems 130ai-bulls 190air-flow 102air-quality 82air-temperature 343, 462airborne-visible 229aircraft 15, 430alaska 527alberta 171, 402alcelaphus-buselaphus 191alfalfa 169, 333alfalfa-haylage 172alfalfa-silage 311algae 355, 372algorithms 13, 36, 81, 91, 94, 102, 119, 258, 264, 292, 375, 417alternative-farming 106american-indians 275, 277, 492ammonium-nitrate 279analogue-probes 408analysis 203, 321, 331analysis-of-covariance 330analysis-of-variance 330analytical-methods 47, 140, 299, 365anesthesia 19anesthetics 19animal-housing 102animal-husbandry 14, 49, 111, 220, 273, 374, 454, 485animal-production 146, 163, 167, 183, 339, 374, 400, 474, 484animal-testing-alternatives 170animal-welfare 170annuals 229answers 524anthonomus-grandis 536anus 498aphidoidea 129apis-mellifera-carnica 55apple-pest-and-disease- diagnosis 147Apple-Postharvest-technology 357apples 79, 461application-methods 468

application-rates 261, 368appropriate-technology 547aquaculture 32, 167aquasim 167aquatic-environment 355arachis-hypogaea 287, 371, 388arboriculture 418, 420area 195area-based-visual-buffer-strip 516arid-regions 219arizona 76, 236, 275, 302, 349, 378, 492, 542arkansas 16, 17artificial-insemination 134artificial-intelligence 147asia 300aspergillus-flavus 140assessment 328, 460, 522atrazine 251attenuation 342attitudes 125audio 397auspig-computer-model 445australia 41, 160, 209, 251, 265, 377, 465, 482, 483australian-poll-dorset-breed 235automatic-control 39automatic-irrigation-systems 227, 487automation 40, 41, 44, 45, 100, 138, 139, 144, 153, 179, 180, 206, 282, 290, 375, 421, 431, 432, 436, 439, 449,505, 510, 514, 548autumn 481backfat 30, 185, 189, 195, 320, 427, 523bandages 499basal-area 488basic-computer-program 296basic-software 164beef 294, 446beef-breeds 386beef-bulls 537beef-cattle 48, 50, 87, 163, 172, 183, 185, 195, 276, 305, 318, 386, 422, 448, 519beef-cows 3, 60, 75, 252, 526beef-herds 66, 87, 110, 186, 332beef-production 48, 66, 87, 154, 276, 332, 483beet-yellows-closterovirus 56best 51best-linear-unbiased-prediction 233, 356beta-vulgaris 56biological-production 355biomass 285, 338biomass-production 348biophysics 338, 354biotechnology 363, 372birth-weight 168blood-circulation 499blood-sugar 427blueberries 58

boars 523body-composition 194, 305, 460body-condition 3, 75body-fat 75, 408, 409body-measurements 75body-protein 75body-temperature 272body-weight 3, 70, 75, 314bonuses 189boomcompas-computer-software 257boreal-forests 216botanical-composition 51, 407brassica-campestris 288brassica-juncea 288break-even-point 393breeders'-associations 386breeders'-rights 522breeding-programs 386breeding-value 429, 523broiler-performance 392broiler-production 246broilers 246, 392brush-control 127buck 188budgeting-enterprises-and-analysing-risk-plus-financial-statements 46, 203budgets 106bulbs 44, 530bulls 501cal 51calculation 36, 418, 496calex 147calf-herd-analyzer 66calf-production 112calibration 36, 348california 10, 76, 154, 229, 277, 404, 488, 533california-irrigation-management-information-system-cimis 533calorimetry 55calves 168, 312Calves-Computer-programs 54Calves-Marketing-Computer-programs 54calving 474calving-rate 118canada 190, 442canopy 36, 56, 113, 143, 171, 184, 227, 229, 324, 338, 342, 532, 540capacity 212capital 101capsicum 325carbon-dioxide 36carbon-dioxide-enrichment 180carcass-composition 30, 68, 75, 172, 189, 195, 196, 311, 318, 408, 409, 427, 446, 448, 460, 518, 537carcass-grading 189, 294, 320carcass- quality 172, 189carcass-weight 68, 172carcass-yield 185, 537

carcasses 446care-software 106case-studies 78, 297caste 55cattle 30Cattle-Computer-programs 241cattle-feeding 70, 223, 311, 474cattle-husbandry 422cattle-manure 161Cattle-Marketing-Computer-programs 241cellulase 464cereals 522change 292chaps-ii 87, 186chemical-analysis 140chemical-control 128, 250, 549chemical-management-modules 549chemical-pruning 126chemicals,-runoff-and-erosion-from-agricultural-management 28, 345chlorsulfuron 251chrysanthemum 91cirman,-crop-insurance-risk-management-analyzer 205citrus 65, 174, 213citrus-jambhiri 77classification 62, 63, 374, 385climatic-change 182climatic-factors 36clones 514cloud-cover 300clouds 300cold-resistance 349collection 361, 512college-curriculum 22color 56, 65, 77colorado 126, 227, 236comax-software 347, 388commercial-farming 384compact-discs 397, 529compact-disk-read-only-memory 397companies 26comparisons 95, 264, 297, 316, 370, 516composition 355computer-advisory-service-for-horticulture 57computer-aided-cruising 247computer-analysis 36, 38, 61, 71, 92, 252, 279, 324, 330, 339, 365, 395, 466, 489computer-assisted-instruction 22, 273, 301, 333, 491computer-assisted-stratification-and-sampling-procedures 282computer-graphics 22, 324, 358computer- hardware 23, 38, 39, 98, 136, 325, 341, 470, 489computer-programming 7, 245, 420, 480, 544computer-simulation 1, 18, 94, 98, 128, 135, 199, 249, 255, 273, 283, 308, 329, 332, 334, 405, 445, 459, 461,463, 464, 474, 480, 483, 488, 491computer-software 1, 4, 5, 7, 10, 13, 17, 18, 20, 23, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 38, 45, 46, 48, 49, 50,51, 52, 53, 57, 58, 60, 62, 64, 66, 67, 69, 70, 71, 74, 81, 82, 83, 84, 85, 86, 87, 88, 89, 93, 94, 96, 97, 98, 100,

101, 102, 105, 106, 107, 108, 109, 110, 112, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 126, 127, 128,129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 141, 145, 147, 148, 149, 152, 154, 155, 156, 160, 162, 163,164, 165, 166, 167, 168, 169, 170, 173, 175, 176, 178, 182, 183, 186, 187, 188, 190, 191, 202, 203, 204, 205,208, 209, 210, 211, 212, 215, 216, 217, 219, 220, 221, 224, 225, 226, 228, 230, 231, 233, 236, 238, 239, 242,245, 249, 250, 251, 252, 255, 257, 259, 261, 263, 267, 269, 273, 275, 276, 277, 279, 282, 287, 292, 294, 296,298, 302, 303, 308, 310, 312, 313, 314, 315, 316, 321, 323, 324, 325, 327, 330, 331, 334, 339, 341, 344, 345,346, 348, 350, 351, 352, 353, 356, 358, 359, 360, 362, 364, 366, 368, 369, 371, 373, 374, 378, 380, 381, 382,383, 386, 387, 389, 390, 392, 394, 395, 398, 399, 400, 401, 405, 410, 411, 414, 415, 418, 419, 420, 422, 423,429, 433, 434, 445, 450, 451, 452, 453, 454, 457, 459, 462, 464, 465, 466, 468, 469, 470, 472, 473, 476, 477,479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 492, 494, 495, 504, 506, 509, 513, 522, 523, 524,525, 527, 533, 536, 538, 539, 540, 541, 544computer-techniques 14, 23, 39, 40, 42, 49, 82, 85, 91, 100, 117, 144, 146, 204, 205, 217, 220, 225, 243, 254,312, 328, 336, 339, 401, 421, 423, 486, 487, 505, 522, 530, 549computer-vision 39computerized-herd-evaluation-system-for-sows-chess-computer-software 95computers 26, 36, 70, 72, 90, 111, 258, 401, 420, 453, 547concentrates 70, 311conception-rate 118, 200coniferous-forests 236conifers 1, 222, 260, 488constrained-optimization 358constraints 474construction 153, 158, 431, 444, 478, 496consultants 98consumer-expenditure 472consumer-preferences 404consumers 268container-grown-plants 260, 261, 449containers 449contamination 81, 140contemporary-comparisons 233, 523contractors 433controlled-atmospheres 246controlled-release 427controllers 102cooperative-extension-service 154coordination 268correlated-traits 227, 402corylus-avellana 248cost-analysis 58, 358, 399, 525cost-benefit-analysis 16, 38, 106, 127, 128, 168, 209, 242, 276, 339, 381costs 70, 108, 163, 168, 182, 183, 194, 238, 242, 380, 389, 429, 433, 445, 538, 545counsellor 147cow-herd-appraisal-and-performance-system 66cow-herd-appraisal-and-performance-system-ii 87cow-herd-appraisal-of-performance-software 60cows 111, 361, 512cracking 92creams 524crop-damage 92, 124, 164, 325, 406crop-establishment 333crop-growth-stage 128crop-insurance 205crop-losses 35, 128

crop-management 33, 77, 106, 108, 114, 156, 182, 278, 279, 347, 351, 368, 379, 388, 424, 430, 465crop-production 7, 23, 27, 31, 39, 83, 105, 109, 114, 123, 136, 156, 175, 193, 199, 201, 205, 210, 217, 231, 278,282, 310, 363, 369, 371, 377, 383, 398, 399, 413, 456, 463, 487, 530, 549crop-quality 109, 324, 339crop-water-stress-index 227crop-weed-competition 250, 504crop-yield 24, 27, 35, 109, 128, 135, 156, 171, 205, 227, 242, 243, 250, 279, 288, 308, 319, 339, 405, 425, 426,461, 462, 465, 467, 473, 504, 525cropping-systems 231, 317crops 5, 56, 76, 161, 282, 285, 297, 338, 377, 402, 533crossbred-progeny 232crossbreds 172crossbreeding 232crude-protein 47, 223cruise 188cucumis-melo 138, 201, 322cucumis-sativus 417cucurbit-vegetables 478culicoides-imicola 403culling 118, 161, 191, 474, 521cultivar-identification 522cultivars 279, 289, 316, 348, 412, 434cultivation 173culture-media 158culture-techniques 432curing 169cut-flowers 299cutting 421cuttings 42, 43, 385cyclamen 396cycling 182cydia-pomonella 121cymbidium 299dactylis-glomerata 73dairy-bulls 190dairy-cattle 273dairy-cows 70, 118, 119, 134, 168, 200, 249, 339, 474dairy-education 301dairy-farming 11, 115, 139, 152, 161, 256dairy-farms 267, 323Dairy-farms-New-York-State-Management-Computer-programs 244dairy-herd-feed-management-program 380dairy-herds 118, 200, 301, 380, 474dairy-performance 200dairy-research 474dairy-technology 387data-analysis 263, 330, 523data-banks 115data-collection 11, 25, 26, 120, 156, 314data-processing 156, 494databases 84, 97, 100, 119, 121, 122, 149, 151, 219, 230, 239, 251, 256, 264, 269, 293, 323, 394, 397, 412, 470decision-aids 263decision-making 4, 8, 48, 64, 78, 95, 96, 98, 101, 105, 114, 123, 126, 128, 131, 132, 133, 134, 136, 147, 161,201, 211, 212, 216, 224, 225, 230, 250, 251, 257, 263, 281, 291, 323, 351, 362, 366, 368, 375, 378, 384, 388,

414, 445, 459, 466, 468, 481, 483, 492, 504, 525, 549decision-support-system-for-agrotechnology-transfer-dssat- computer-software 228decision-support-systems 378deforestation 76defruiting 126delaware 167demand 81dendranthema 36dendranthema-grandiflora 36dendroctonus-frontalis 414, 415-department-of-interior,-national-park-service 67depth 408dermatitis 403design 45, 89, 137, 139, 153, 158, 298, 381, 389, 431, 444, 478, 520detection 92, 134, 140, 338, 403developing-countries 145development-projects 80diagnosis 151diagnostic-techniques 500diameter 488, 520diameter-distribution 419dictionaries 256diet 311, 427dietary-fat 427diffusion 458diffusion-of-information 391, 549digestibility 223digital-images 213digital-probes 408dimensions 73, 409discounts 189discriminant-analysis 402disease-models 147disease-potential-modules 549disease-vectors 403distribution 171district-of-columbia 96diuraphis-noxia 124, 129, 341, 544domestic-production 282double-cropping 329drainage-systems 45drills 158dris 187droplet-size 296dry-feeding 481dry-matter 70, 311, 461dry-matter-accumulation 365dry-weights 348drying-temperature 310ds 147dual-purpose-breeds 234dutch-black-pied 168dynamic-programming 375dystocia 50

earliness 316early-maturing-hybrids 171eastern-europe 359ecological-balance 355econometric-models 291economic-analysis 368, 393, 459, 462economic-evaluation 189economic-impact 35, 165, 267, 387, 429economic-thresholds 124, 128, 250, 504economic-viability 167economics 169ecosystems 378educational-programs 69, 154, 192, 495educational-technology 170effects 299efficiency 99, 251elasticity 322electrical-control 260electrodes 193electromagnetic-radiation 189Electronic-spreadsheets-Software 515electronics 11elstwigs 373emasculation 299embryo-culture 361embryo-transfer 50embryos 361emergence 279emission 82, 285, 338endangered-species 1endophytes 240energy-conservation 117energy-consumption 175, 238energy-cost-of-production 117energy-metabolism 55energy-requirements 238engineering 98, 297england 513enterprises 81environment 120, 306environmental-assessment 362environmental-control 13, 36, 180, 245, 246, 432, 449environmental- factors 279, 305, 432environmental- impact 181, 267, 279, 489environmental-legislation 352environmental-management 67, 165, 516environmental-preservation 302environmental-protection 181, 267environmental-temperature 55, 141epidendrum 299equations 50, 51, 68, 75, 103, 184, 305, 354, 379equipment 159erosion 135, 160, 242, 524erosion-control 182, 295, 476, 491, 494

erosion-productivity-impact-calculator 182errors 195establishment 228estimation 51, 538estonian-ssr 143estradiol 311estrous-cycle 512estrus 273, 512ethanol-production 238, 464ethylene-production 299evaluation 22, 67, 182, 186, 190, 192, 212, 297, 316, 391, 518evapotranspiration 12, 24, 113, 288, 533experiments 330expert-systems 8, 79, 95, 105, 108, 114, 125, 133, 144, 147, 150, 151, 155, 160, 161, 178, 199, 200, 201, 212,248, 251, 278, 281, 295, 297, 347, 363, 388, 397, 413, 466, 529, 549explants 180, 458exports 359extension 46, 107, 137, 204, 251, 274, 303, 394, 528, 546extension-agents 262factors-of-production 395faldry-simulation-models 310family-planning 80farm-accounting 331, 360farm-budgeting 48, 52, 327farm-enterprises 331farm-equipment 176farm-helper-services 291farm-inputs 228, 242farm-machinery 212, 327, 329, 369, 545farm-management 4, 5, 11, 29, 46, 48, 52, 57, 69, 78, 95, 99, 101, 107, 119, 131, 145, 161, 166, 192, 199, 203,207, 209, 211, 212, 221, 263, 267, 273, 291, 303, 321, 323, 327, 360, 369, 370, 411, 445, 453, 481, 490, 531,545, 546Farm-management-Data-processing 6, 335Farm-management-Records-and-correspondence-Software 515farm-planning 33, 114, 131, 209, 323, 350farm-surveys 192farm-workers 208, 369farmers 263farmers'-attitudes 27farming 176, 495farming-systems 212, 540farmsys-computer-software 199farrowing 314fat-percentage 194, 518fat-thickness 30, 68, 75, 195, 196, 232, 234, 235, 518, 519, 537fats 359feasibility 146feasibility-studies 310feed-conversion 232feed-conversion-efficiency 172, 311, 427feed-formulation 392feed-intake 70, 232, 292, 311, 314, 429feed-supplements 70, 223feeding 445

Feedlots-Computer-programs 214feeds 380female-fertility 134fermentation 464fertilizer-distributors 176fertilizer-requirement-determination 187, 267fertilizers 109, 228, 267, 368festuca-arundinacea 73fetus 526fiber-computer-software 94fibers 458ficus-benjamina 365field-crops 15, 215field-experimentation 156, 369, 545field-navigation 550field-size 228field-tests 202, 287finance 154financial-planning 46, 161, 203, 262, 490finite-element-analysis 218, 322fire-behavior 280fire-control 59fire-detection 59fire-ecology 219fire-fighting 364fish-meal 172fixed-costs 326flood-control 295floriculture 221florida 77, 174, 369, 371, 462flow-charts 27, 33, 46, 102, 133, 251, 273, 352, 494flowers 93fluctuations 81fluorescent-lamps 458fodder-crops 224foliage 349, 425foliar-diagnosis 187food-composition 266food-industry 268food-quality 404forage 223, 339, 459forest-communities 362forest-economics 393forest-fires 280, 364, 450forest-inventories 84, 103forest-management 1, 26, 84, 94, 157, 159, 188, 225, 226, 239, 275, 277, 283, 302, 346, 353, 362, 366, 378, 401,414, 415, 419, 470, 477, 492, 509, 516, 527, 538Forest-management-United-States-Computer-programs 376forest-nurseries 53, 90, 257, 260forest-plantations 1, 216, 430, 477forest-resources 269, 362, 509, 527forest-simulation-optimization-model 226forest-trees 373, 419, 457forestry-engineering 247, 486

Forestry-innovations 443forestry-machinery 442forestry-practices 435forests 72, 76, 177, 379Forests-and-forestry-United-States-Computer- programs 376FORPLAN-Computer-program 376forplan-forest-planning 353forsum-computer-software 62fortran 17fowl-feeding 392fragaria-ananassa 316france 8, 15, 47, 76, 285freeville,-new-york 348frequency 342fruit 126, 350fruit-crops 351, 424Fruit-Harvesting-Machinery 438fruit-trees 520, 541fruit-vegetables 444fruits 324fuels 238functional-disorders 151game-farming 191gardening 230generalized-growth-and-yield-model-gengym 236genetic-correlation 234, 235, 305genetic-effects 232genetic-engineering 363genetic-improvement 233, 235, 356, 386, 523genetic-variation 235genetics 344genotypes 50, 232, 272, 316, 426geographic-information-system-computer-software 239geographic-information-systems 157, 181, 313geographical-distribution 259, 536geographical-information-systems 269geological-sedimentation 491Geology-Statistical-methods-Software 237georgia 58, 187, 506geranium 2, 42, 43german-democratic-republic 156ghlsim 128gibberella-fujikuroi 140glossaries 453glycine-max 16, 17, 155, 187, 250, 285, 317, 371, 468goats 521gossypium 105, 347, 465, 542gossypium-hirsutum 109, 205, 317, 487, 536government-organizations 67graafian-follicles 512grading 43, 144, 396, 446grafting 444, 478grain 227, 426grain-crops 135

grain-drying 310grapes 147graphic-arts 20, 524graphs 115grass-sward 471grasses 240grassland-management 240, 452, 483, 539grasslands 202, 229, 342, 407grassman 483grazing 423, 459, 481, 539grazing-experiments 311grazing-intensity 390grazing-systems 452greenhouse-crops 141greenhouse-culture 39, 93, 123, 193, 383, 399, 508greenhouses 36, 89, 245, 375, 382, 436, 437, 462, 548ground-cover 402groundwater 16, 17growers 208, 351, 384growth 61, 87, 118, 182, 233, 243, 297, 334, 343, 373, 488, 523growth-analysis 348growth-chambers 432, 449growth-models 136, 228, 236, 348, 373, 388growth-period 143growth-rate 50, 232, 314, 429guam 325gully- erosion 491gypsy-moth-management-decision-support-system-gymsys-expert-system 96habitats 1, 165, 259, 489handling 265, 322, 437hardwoods 86, 255, 283, 421harvesters 310, 319, 505harvesting 18, 81, 138, 159, 174, 176, 188, 283, 377, 404, 486harvesting-date 316hawaii 35hay 315haymarket 315hayval-programs 539heat-production 55, 271heat-regulation 462heat-stress 249heifers 118height 3, 488helicoverpa-armigera 465helicoverpa-punctigera 465hens 292herb 504herbicides 1, 133, 250, 468herbivores 459herd-improvement 521herds 60heritability 234, 235, 305heterosis 232hexoses 464

hired-labor 208holstein-friesian 172, 311Home-based-businesses-Planning-Software 37hooves 253hoplolaimus-columbus 317hordeum-vulgare 143horses 253, 337, 403, 498, 499, 500Horses-Breeding 517Horses-Reproduction 517horticultural-crops 123, 441horticulture 57, 397, 449, 548humidity 141hungary 425hybrids 171hydraulic-conductivity 410hydraulics 442hydrology 28, 182, 527hydrolysis 464hypera-postica 64hypertension 19hypertext 133, 210hysteresis 193idaho 348, 550identification 43, 129identification-modules 129illinois-nursery-improvement-software 389image-capture-and-analysis-system 540image-processors 2, 90, 229, 243, 282, 385, 417imagery 73, 77, 91, 146, 151, 213, 258, 363, 396, 397immature-oocytes 361imports 359improvement 105inbreeding 234indexes 338indiana 274, 546individual-feeding 70indonesia 80industry 81Infertility,-Male- Environmental-aspects-Congresses 493information 101, 212, 268, 424information-needs 263, 374information-processing 136, 152, 549information-retrieval 256information-science 547information-services 118, 233, 281information-storage 256, 314, 397information-systems 66, 80, 115, 132, 136, 152, 157, 161, 181, 210, 222, 254, 267, 274, 280, 309, 313, 336, 374,384, 529, 536information-technology 210, 384, 424Information-technology-Congresses 21Information-technology-Dictionaries 197infrared-imagery 289, 349, 365, 402, 455, 540infrared-imaging-spectrometer 229infrared-photography 59, 403, 426, 430

infrared-radiation 271, 467, 496, 498, 532infrared-spectrophotometry 498infrared-spectroscopy 51, 63, 223, 404, 407, 447Infrared-technology 357innovation-adoption 22, 27, 46, 67, 78, 291, 384, 549innovations 441input-output-analysis 175, 411insect-control 34, 72, 128, 215, 248, 279, 281, 296, 414, 415, 465, 544insect-pests 7, 31, 105, 124, 281, 297, 394insect-threshold-modules 549insecticide-resistance 465insecticides 128, 296institute-of-agricultural-engineering 257instruments 446, 455, 471insulin-like-growth-factor 427integrated-control 279integrated-forest-resource-management-system 269integrated-pest-management 147, 150, 279, 341, 459, 541, 544, 549integrated-resource-management-beef-cow 66integrated-systems 167, 201, 269, 279integration 274, 281intensive-farming 101international-benchmark-sites-network-for-agrotechnology-transfer-project 228international-trade 359inventories 479investment 263, 393iowa 69, 192, 291irrigation 10, 16, 17, 117, 135, 228, 279, 287, 425irrigation-equipment 287irrigation-requirements 270, 287irrigation-reservoirs 24irrigation-scheduling 270, 279, 288, 289, 533, 542irrigation-systems 45, 117, 284, 287irrigation-water 24, 284israel 70, 403italy 351, 360japan 153, 158, 318, 431, 510japanese-black 318juniperus-virginiana 127kansas 49katahdin-potato 348kentucky 22kenya 76kernels 92, 140, 227kiola-state-forest,-new-south-wales 362kiwifruits 293knowledge 31, 33, 152, 297knowledge-based-system 495knowledge-based-systems 147krissy-model 280labor 212, 375labor-allocation 81labor- intensity 81labor-legislation 208

laboratory-methods 266lactation-number 70lactuca-sativa 144, 243lamb-production 429lambing-interval 429lambs 518land 15land-evaluation 336land-management 62, 100, 160, 313, 491land-productivity 328land-types 491land-use 160, 282, 402, 466, 491land-use-planning 114, 352, 466landfills 476landsat 56, 76, 184, 282landscape 298, 402landscape-gardening 298landscaping 84, 298languages 256laptop-computers 528large-white 232lasers 12, 177, 355, 421, 456, 508latitude 300law 208, 287lawns-and-turf 151, 532layout 375leaching 345leaf-angle 171leaf-area 243leaf-area-index 171, 184, 338, 348, 354leaf-conductance 227leaf-vegetation-index 184leaf-water-potential 227leafy-vegetables 158leanness 427learning 31learning-ability 273least-squares 330leaves 288, 379lifting 548light 65light-intensity 180lighting 458lighting-direction 458lignocellulose 464lilium 44limb-bones 253limbs 253lime 368line-based-visual-buffer-strip 516linear-programming 207, 211, 375, 392, 546lint 542liquids 238literacy 547

literature- reviews 30, 73, 92, 147, 322, 445litter-size 232live-estimation 196, 320, 518livestock 146, 265, 390, 447livestock-farming 282, 411livestock-numbers 474liveweight 118, 194, 234, 235, 460liveweight- gain 311, 474, 481loading 322loam-soils 348loans 80location-of-production 494log-breakdown-methods 255logging 247, 259, 302, 489logging-effects 489Logging-Technological-innovations 443logit-model 78logs 326, 433, 469, 479loins 142lolium-perenne 73, 539longissimus-dorsi 30, 142, 195, 196, 305, 519, 537loss-prevention 128losses 168losses-from-soil 267losses-from-soil-systems 242lumber 86, 421lumped-parameter-discrete-time-models 81lycopersicon-esculentum 141, 425, 431, 462, 508lymantria-dispar 96machinery 31, 73, 176, 260, 385macroptilium-lathyroides 51maize 92, 140, 228, 310maize-silage 172, 319malawi 120male-fertility 190malus 83, 549malus-pumila 108, 126, 162, 384manage-computer-software 283management 38, 81, 88, 89, 122, 129, 154, 178, 224, 384, 394management-information-records-mir 331management-modules 129management-of-insemination-through-routine-analysis 134manganese 187manihot-esculenta 325manipulators 431mapit-software 324mapping 76, 319, 362, 550maps 470Mares 517, 534, 535market-competition 268market-prices 189, 506marketing 48, 268, 312, 315, 374marketing-techniques 446marking 419

marshall-cultivar 289maryland 96mass-balance-models 36mass-flow 227massachusetts 150maternal-effects 233mathematical-models 13, 24, 36, 65, 81, 103, 138, 163, 183, 213, 296, 308, 316, 343, 372, 379, 405, 442, 459mathematics 523maturation 459maturation-period 316maturity 171, 307, 361measurement 12, 15, 36, 51, 73, 142, 143, 184, 185, 195, 243, 271, 272, 319, 338, 428, 471, 491, 518, 519, 520,532meat-and- bone-meal 392meat-and-livestock-industry 374meat-cuts 311meat-production 395, 523meat-quality 272meat-yield 189, 408, 409mechanical-damage 174mechanical-harvesting 2, 65, 91, 174, 213, 258, 307, 319, 431, 442mechanization 42, 43, 153, 158, 436, 444, 478, 505medicago-sativa 64, 315mediterranean-climate 336melons 218, 322memory-text 397metabolizable-energy 311metacarpus 253meteorological-factors 367meteorology 264methodology 194, 516mexico 113, 343, 352michigan 93, 221microclimate 36microcomputers 20, 22, 25, 46, 53, 71, 73, 78, 80, 86, 97, 99, 107, 114, 115, 121, 131, 151, 159, 163, 183, 192,207, 243, 246, 247, 250, 256, 261, 262, 263, 264, 274, 281, 284, 291, 306, 309, 328, 330, 334, 335, 336, 370,375, 393, 399, 412, 474, 491, 494, 523, 525, 528, 529, 531, 545, 546, 549Microcomputers-Developing-countries-Congresses 416microprocessors 216micropropagation 40, 44, 81, 179, 180, 258, 417, 440, 458, 510, 514microsim-computer-sofware 352microsoft-fortran 462microwave-radiation 285, 338, 342, 365microwave-treatment 337migrant-labor-law-computer-software 208milk 47, 134milk-fat 266milk-payments 47milk-production 249, 339, 474milk-production-costs 339milk-protein 47milk-protein-percentage 47milk-yield 70, 249, 339milk90 339

milking-machines 139, 340milking-parlors 25, 71millets 128mineral-deficiencies 187mineral-excess 187minnesota 88, 259, 395mississippi 52, 109missouri 220, 282, 331mixed-forests 236mixed-pastures 51mobile-equipment 528models 62, 74, 101, 122, 124, 132, 143, 156, 165, 169, 171, 236, 259, 275, 277, 280, 285, 288, 302, 338, 342,346, 353, 359, 362, 373, 415, 424, 457, 477, 492, 524, 527, 538modification 295, 348modvex-software 388moira 134moisture-content 338, 379money-management 472monitoring 120, 121, 193, 279, 284, 285, 292, 306, 349, 355montana 62, 364, 459montgomery-county,-maryland 96mulching 507multimedia-instruction 397multiple-births 526multiple-cropping 369multiple-land-use 259multiple-use 353muscle-tissue 519muscles 196, 408, 409muskmelon-disorder-management-system 147Muskmelon-Harvesting-Machinery 438national-agricultural-library 397national-agricultural-statistics-service 282national-forest-management-act-1976 378national-forests 165, 470national-parks 67natural-resources 402, 527Natural-resources-Management-Congresses 21ne-twigs-computer-software 94near-infrared-reflectance-spectroscopy 223, 240near-infrared-to-red-ration-vegetation-index 354nebraska 34, 106, 452, 531, 545nematode-control 317netherlands 168, 375New-business-enterprises-Planning-Software 37new-mexico 236new-south-wales 234, 235, 362, 445, 465new-york 348new-zealand 293, 384, 401nicotiana 511nitrate 279nitrogen 480nitrogen- content 47noise 292

non-food-crop-production 363nondestructive-testing 404nonprotein-nitrogen 47normalized-difference-vegetation-index 354normalized-temperatures 338north-america 79, 222north-carolina 287, 462north-dakota 230, 289, 390northeastern-states-of-usa 366npk-fertilizers 497nurseries 375, 399nutrient-content 392nutrient-film-techniques 193nutrient-requirements 526nutrient-solutions 193nutrient-uptake 240nutrients 267nutrition-information 293nutritive-value 447oaksim-computer-software 94objectives 402occupational-hazards 495oedaleus-senegalensis 128ohio 185, 526oils-and-fats-industry 359oklahoma 127, 315, 422on-farm-data-analysis 523oncidium 299ontario 222, 225, 373oocytes 361, 512operational-level 375operations 496operations-research 382opportunity-costs 378optical-instruments 15, 189optical-properties 92, 458optimization 218, 226, 255, 353, 358, 375, 421optimization- methods 27optimize-production 155orchards 15, 85, 108, 350, 351, 384, 424, 451, 549orchidaceae 173oregon 165, 204, 470, 507, 539ornamental-plants 113, 206, 385, 397, 399, 528, 530oryza-sativa 296, 300, 434overstory-vegetation 516ovulation 134ovum-pick-up 512pacific-states- of-usa 477pan-evaporation 288papaipema-nebris 164paraguay 76parametric-programming 389paspalum-notatum 51pastures 163, 183, 481, 483

patents 522pcm-potato-crop-management-software 278peaches 85, 147peanuts 228penn-state-apple-orchard-consultant 147pennsylvania 29, 108, 549pentoses 464performance 66, 153, 158, 174, 186, 431, 444, 478, 523performance-appraisals 60, 284performance-recording 233, 356, 386performance-testing 386perpendicular-vegetation-index 354personnel 364pest-control 121, 543pest-management 34, 35, 64, 79, 96, 162, 164, 297, 309, 536pest-management-information-system 309pesticides 261, 279, 287petioles 279phalaenopsis 299phenology 72, 308, 405phenotypic-correlation 235, 305pheromone-traps 536philippines 27photoperiod 308, 405photosynthesis 36, 171, 180, 348photosynthetically-active-radiation 171, 458, 467physical-properties 322, 467physiological-age 348phytoplankton 355picea-abies 271picea-engelmannii 349pig-breeds 232pig-farming 95pig-fattening 445pig-housing 116, 265, 389pig-slurry 148piglets 116pigmeat 523pigs 30, 68, 142, 146, 189, 194, 232, 233, 272, 314, 320, 374, 395, 400, 408, 409, 427, 460, 484, 485, 523pinus 334pinus-banksiana 216, 222pinus-ponderosa 236, 349, 352pinus-radiata 74, 469pinus-sylvestris 271-pisi 412pisum-sativum 412plane-of-nutrition 75planning 14, 26, 130, 160, 202, 220, 226, 275, 277, 302, 353, 375, 378, 470, 489plant 147plant-analysis 279plant-breeding 97, 522plant-communities 62plant-competition 216plant-density 56

plant-disease-control 248, 279, 549plant-diseases 151plant-ecology 72plant-effects 516plant-height 471plant-morphology 324plant-pathology 147plant-pests 34, 151plant-physiology 229, 299Plant-products-Postharvest-physiology 357plant-protection 147plant-succession 62plant-tissues 81planting 513planting-stock 261, 478plantlets 180plants 44, 180, 338, 432, 437, 439, 514plastic-film 507plug-transplanting 439pmmdb-software 394pnutgro 371polarization 338polarization-indexes 338polyethylene-film 467pome-fruits 343pomme 147ponds 372population-density 317populus 334pork 272porkplanner 400porometers 227portable-instruments 401position 530pot-culture 260pot-plants 375, 383, 511poultry 167, 306poultry-housing 13, 246pp-cam 399prairies 342precision-drilling 158prediction 68, 75, 128, 185, 194, 195, 243, 318, 332, 334, 407, 408, 409, 460preflo-software 410pregnancy-complications 521pregnancy-diagnosis 521, 534pregnancy-rate 190preharvest-sprouting 406prescribed-burning 82, 202, 450Prescribed-burning-Software 104prices 315, 339, 469private-ownership 470probabilistic-models 78probability 516probability-analysis 136

probes 320, 408, 409problem-solving 413processing 81, 120, 238, 421production 120, 153, 155, 268, 292, 359, 372, 375, 444production-costs 14, 223, 279, 392, 398, 399production-economics 20, 358production-functions 358productivity 87, 182, 191, 229, 279, 391, 483profitability 66, 134, 217, 451, 462, 469profits 27, 163, 183progesterone 134programmable-calculators 261projections 46, 373, 457prolog-programming-in-logic-computer-software 212proloin 340propagation 42, 43, 379, 436protein-content 47protein-percentage 518provenance 222prunus-dulcis 97prunus-persica 83pseudomonas-syringae-pv 412pseudotsuga-menziesii 165, 346, 349, 379, 457, 477psila-rosae 121public-health 254quality 469quality-standards 315quebec 28, 345quotas 474radiometers 56, 338rain 459rams 356, 428range-management 127, 202, 423, 459rangelands 12, 202, 281, 450, 459rangeplan 452rats 19real-time-ultrasonics 408record-keeping 5, 107, 110, 111, 112, 119, 186, 374, 400, 454, 484recording-instruments 72, 319records 66, 186, 301recreation 259red-spring-wheat 63redcard-manager 364reflectance 15, 56, 143, 171, 184, 354, 426, 511, 540reflection 65reg70 51regression-analysis 330relative-humidity 13reliability 519remote-sensing 12, 229, 285, 313, 338, 342, 355, 363, 402, 426, 430, 455repeatability 190repellents 543replacement 85, 161replanting 525

reproduction 233reproductive-efficiency 200reproductive-organs 534, 535reproductive-performance 87, 252, 301reproductive- traits 523research 265, 330, 377research-projects 38reservoirs 16, 17resorts 88resource-allocation 383resource-conservation 352resource-management 80, 131, 165, 259, 269, 275, 277, 302, 352, 369, 509responses 402retail-prices 387returns 163, 182, 183, 250, 279ribes-nigrum 367rice 78rip-sawing 86ripening 316, 404risk 27, 46, 125, 203, 254, 495rivers 76road-construction 38roads 177, 489Robotics 363, 443robots 2, 40, 41, 42, 43, 44, 65, 91, 138, 139, 153, 158, 174, 179, 180, 206, 213, 218, 258, 290, 322, 340, 377,417, 431, 435, 437, 439, 440, 441, 442, 444, 449, 475, 478, 514, 548roots 344rotation 317rowcrops 507runoff 28, 410, 491, 524runoff-water 476rural-women 80russet-burbank-potato 348s 67, 313saccharification 464saccharum-officinarum 525safety 251, 254sampling 51, 471sampling-units 282satellite-imagery 76, 355satellite-positioning-and-tracking 76, 184sawmilling 326sawmod 469scanning 189, 456scotland 232scrotum 428, 501seasonal-fluctuations 47seasonal-growth 459seasonal-variation 300sediment 476, 491seed-germination 417seed-sources 222seed-testing 412seed-treatment 508

seedbeds 434seeding-machinery 260seedlings 44, 144, 153, 206, 271, 349, 441, 444, 475, 511, 514seeds 53, 73selection 417selection-criteria 67, 356, 429selection-index 523selective-breeding 429self-feeding 70semantic-aproach 256sensors 102, 193, 253, 379, 388, 436, 442, 511, 520, 530sex-differences 68, 232, 427shade 216shade-index 216shearing 41, 265, 377sheep 30, 41, 265, 356, 377, 429, 521sheep-breeds 234, 235sheep-farming 481, 482sheep-feeding 481shoots 348shrubs 12, 219silt 497silvah-computer-software 94silviculture 513simulation-dualcriteria-optimization-technique-for-upland-rice-production- computer-software 27simulation-models 1, 16, 17, 26, 36, 50, 95, 98, 128, 135, 164, 168, 182, 191, 199, 212, 226, 228, 251, 281, 283,297, 310, 322, 332, 344, 347, 348, 350, 352, 369, 371, 387, 388, 396, 410, 429, 445, 459, 462, 463, 464, 474,480, 481, 485, 491, 504, 505, 509sires 190, 305site-factors 283site-selection 83, 513size-graders 511skidding,-trucking,-and-landing-simulation-stals 18skills 262slaughter 185Small-business-Planning-Software 37small-fruits 316smartsoy-computer-software 297soil 56, 184, 228soil-brightness 56soil-conservation 100, 160, 313, 491-soil-conservation-service 313soil-degradation 160soil- management 156, 182soil-properties 122soil-temperature 182soil-texture 434soil-water 285, 388soil-water-balance 136, 344, 480soil-water-content 288, 344, 459soil-water-movement 410soil-water-potential 459soil-water-regimes 288, 459solanum-tuberosum 278, 279, 348, 458

solar-collectors 496solar-energy 496solar-radiation 171, 354, 496somatic-embryogenesis 307, 417somatotropin 427sorghum-bicolor 354sounds 543south-africa 163, 183, 430south-brooman-state-forest,-new-south-wales 362south-carolina 249south-dakota 60south-east-asia 83sowing 53, 260sowing-date 56, 434sowing-methods 434sowing-rates 434soybean-oilmeal 172soybeans 228, 297soygro 371soygro-simulation-models 297space-allocation-planning 375space-flight 432space-requirements 375space-utilization 81spacing 469spatial-distribution 12, 229, 324spatial-variation 76, 184, 471, 540, 550spatial-yield-variation 319species 113, 355spectral-data 56, 76, 77, 143, 354, 355, 402, 511spectrometers 355spectroscopy 240sphincters 498sprayers 296spreadsheets 263sri-lanka 284stand-characteristics 56, 188stand-density 171stankpak 469state-forests 362state-government 287statistical-analysis 35, 422, 473statistical-data 156, 282statistics 276, 315steers 50, 172, 195, 196, 311stems 91, 348, 516stochastic-models 474, 475stochastic-processes 263stochastic-programming 392stocking-rate 163, 183, 390, 481, 483stomatal-resistance 227straw-disposal 176streams 295stress 77, 272, 540

stress-analysis 322stress-grading 92structural-design 116, 476stubble 481student's-test 330stumpage-value 479subcutaneous-fat 305subsurface-drainage 345subsurface-runoff 122sufficiency-range-method-srm 187sugarbeet 56, 133summer 403, 481supply 457supply-response 81support-systems 57, 108, 129, 281, 293, 294, 303, 375, 384, 424surface-water 28surfaces 15, 56, 338, 455surveys 34, 177, 531susceptibility 272sustainability 131, 160, 182, 242, 483, 540sustaining-and-managing-resources-for-tomorrow-farm-resource-management-system- smart-frms-computer-software 131swamps 76swath-turners 169sweden 370swinegro 485systems 31, 105, 111, 458systems-analysis 207tama-county,-iowa 192target-objects 402taste-panels 404tea 120teaching-materials 321teaching-methods 20, 22, 491teams-computer-software 378technical-progress 547technical-training 108technicians 519techniques 31technology 156, 268, 384, 391technology-transfer 132, 228, 254, 313, 528tedding 169telecommunications 374, 391telemetry 270temperate-tree-fruits 83temperature 13, 102, 120, 193, 227, 253, 308, 338, 349, 405, 425, 455, 459, 532temperature-relations 428temperatures 72, 271, 367, 428, 498temporal-variation 459tendons 337tensiometers 270terraces 494testes 428testicular-diseases 501

testing 174, 212, 223Testis-Effect-of-heat-on-Congresses 493Testis-Thermography-Congresses 493texas 12, 48, 78, 136, 202, 262, 354, 495, 521texas-aandm-whole-farm-analysis-and-record-management 490thailand 76thematic-mapper 29, 56, 76thermal-infrared-imagery 338thermal-operating-conditions 496thermal-properties 500thermographic-properties 497, 501thermography 253, 337, 367, 455, 499thermometers 289, 532thinning 1, 74, 94, 126, 435, 469thinning-regimes 74three-dimensional-models 496threshold-models 2tillage 155, 182tillage-expert-system 155timber-appraisal 479timber-resource-inventory-model 457timber-trade 188, 457timbers 18, 479, 505, 527time 99, 141time-stepping-models 474timing 128, 228tissue-culture 44, 81, 180, 514tobacco 120, 506top-fresh-weight 243topography 494tractors 212training 262transducers 520transformed-soil- adjusted-vegetation-index 354transmittance 171transpiration 271transplanters 548transplanting 44, 153, 206, 290, 439, 441, 475, 510, 511, 514, 548transport 265, 433, 489trapping 536trauma 337, 495trees 418, 513, 516trenbolone 311trifolium-pratense 143trifolium-repens 539triticum 289triticum-aestivum 426tropical-asia 296tropical-grasslands 483tropics 83trucks 433tubers 348twinning 168uk 56, 133, 363, 412, 513, 522

ultrasonic-devices 142, 265, 408, 409, 520, 521, 543ultrasonic-diagnosis 534, 535ultrasonic-fat-meters 30, 68, 75, 189, 194, 195, 196, 234, 305, 318, 320, 460, 518, 519, 537ultrasonics 235, 408, 512ultrasonography 337, 512, 534ultrasound 30, 185, 189, 196, 361, 427, 446, 448, 521, 526, 537ultraviolet-radiation 300undergrowth 1understory 1universal-soil-loss-equation 491university-of-kentucky 22university-research 22unrestricted-feeding 232unsaturated-fatty-acids 266upland-rice 27urban-parks 96urea 47usa 7, 26, 31, 84, 105, 175, 182, 219, 303, 310, 346, 347, 356, 386, 387, 394, 397, 489usage 192usda 100, 282, 313use-efficiency 171, 267uses 370utah 24, 424utilization 375vagina 512validity 95, 114, 297, 459valuation 188, 418value-added 377variable-costs 326variance 392variance-components 471varieties 228, 230variety-classification 522variety-trials 426, 473vegetables 153, 156, 230, 399, 507vegetation 12, 76, 184, 229, 338, 362, 516vegetation-cover 184vegetation-management 216velocity 102ventilation 102, 389venturia-inaequalis 150vertebrate-pests 309veterinary-equipment 500victoria 309vigor 77villages 80virginia 208visibility 516vision 73, 144, 385visual-impact 516waddell,-arizona 542wales 513washington 426, 550waste-disposal 148

waste-water 464water-distribution 284water-erosion 182, 491water-flow 149, 410water-holding-capacity 459water-management 16, 17, 24, 98, 149, 270, 284, 289, 313, 381, 410water-quality 122, 149, 345water-requirements 113, 533water-reservoirs 24water-resources 98, 267water-stress 113, 227, 542water-supply 425water-table 410water-use 288water-use-efficiency 10watershed-management 295, 492watersheds 524weather 202, 369weather-data 136, 182, 212, 228, 348, 424weather-forecasting 251weather-generator 251weed-biology 504weed-competition 468weed-control 133, 250, 279, 309, 504weeds 468, 504weight 227, 243, 348weighting 95west-virginia 312western-australia 481western-states-of-usa 129, 281wheat 63, 228, 406wheaton-cultivar 289white-pine-blisterust 147wildfires 219, 280wildlife 1, 165, 259, 489, 527wildlife- conservation 1wildlife-management 543wind 280wind-erosion 182windbreaks 367winter 141, 536winter-wheat 33, 63, 550wisconsin 279wood 464wood-products 346, 477wood-properties 346, 477wool-production 429work-study 71, 486xanthomonas-campestris-pv-manihotis 325xanthomonas-campestris-pv-vesicatoria 325yield-components 227yield-forecasting 457yield-losses 124, 250, 317, 504yield-map 319

yield-response-functions 508yields 103, 236, 334, 373, 479, 542, 550yields-ms-computer-software 94zea-mays 61, 92, 140, 155, 164, 171, 227, 308, 319, 344, 405, 480zeranol 311


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