angrau.ac.in1)2013finalfor...2 the journal of research angrau (published quarterly in march, june,...

2

The Journal of Research ANGRAU(Published quarterly in March, June, September and December)

Dr. T. PradeepPrincipal Scientist(Breeding),Maize Research Station,ARI Campus, Rajendranagar,Hyderabad.

Dr. R. SudhakarSenior Scientist (Plant Pathology),Seed Research & Technology Centre,ANGRAU, Rajendranagar, Hyderabad.

Dr. M. V. RamanaSenior Scientist (Agronomy),AICRP on Integrated Farming Systems,Water Technology Centre,College of Agriculture, Rajendranagar,Hyderabad.

Dr. G. Sravan KumarAdditional Controller of Examination &University Head, Department of English,College of Agriculture, Rajendranagar,Hyderabad.

Dr. A. ManiAssociate ProfessorDept. of Agril. Engineering & TechnologyCollege of Agriculture, Rajendranagar,Hyderabad.

Dr. T. RameshAssociate ProfessorDept. of Plant PhysiologyCollege of Agriculture, Rajendranagar,Hyderabad.

Dr. I. Sreenivas RaoProfessor and Head, Dept. of Extension Education,ANGRAU, Rajendranagar, Hyderabad.

Dr. T. NeerajaProfessor, Dept. of Resource Management andConsumer Sciences,College of Home Science,Saifabad, Hyderabad.

Dr. A. Lalitha AI&CC and ANGRAU Press, Rajendranagar, Hyderabad

SUBSCRIPTION TARIFF

Individual (Annual) : Rs. 300/-

Individual (Life) : Rs. 1200/-

ADVISORY BOARD

EDITORIAL COMMITTEE MEMBERS

RESEARCH EDITOR

EDITOR

Dr. P. Gidda ReddyDirector of Extension,Rajendranagar, Hyderabad.

Dr. R. Sudhakar RaoDirector of Research,Rajendranagar, Hyderabad.

Dr. P. Chandrasekhar RaoProf. & University Head,Dept. of Soil Science & Agril. Chemistry& Dean of Agriculture i/cRajendranagar, Hyderabad.

with effect from April, 2012:

Institutional (Annual) : Rs. 1200/-

Printing Charges : Rs. 100/- per pageDDs may be sent to The Managing Editor, Journal of Research ANGRAU, Agricultural Information & Communication Centre

and ANGRAU Press - Agricultural Research Institute, Rajendranagar - Hyderabad - 500 030

Dr. K. VeeranjaneyuluUniversity LibrarianANGRAU, Rajendranagar, Hyderabad.

Dr. K. Anand SinghPrincipal Agricultural Information Officer

AI&CC and ANGRAU Press, Rajendranagar,Hyderabad.

MANAGING EDITORDr. P. Chandrasekhar Rao

Dean of Agriculture i/cANGRAU, Rajendranagar, Hyderabad.

Dr. T.V. SatyanarayanaDean of Agril. Engineering & Technology,Rajendranagar, Hyderabad.

Dr. A. Sharada DeviDean of Home ScienceRajendranagar, Hyderabad.

3

Effect of foliar application of NPK nutrients on growth and yield of chilli 1(Capsicum annuum L.)A.KIRAN KUMAR

Nutrient uptake by rice crop under long term integrated nutrient management in 5rice – rice cropping system in AlfisolsV. MAHESWARA PRASAD and P. PRABHU PRASADINI

Changes in maturity indices during vermicompsoting Vs conventional Composting 14of agricultural wastesCH. S. RAMA LAKSHMI, P.C. RAO, G.PADMAJA, T.SREELATHA,M.MADHAVI, P.V.RAO and A.SIREESHA

Influence of integrated nutrient management on physical properties of Alfisols 20under rice –rice cropping system in southern telangana zoneV. MAHESWARA PRASAD and P. PRABHU PRASADINI

Genetic variability, heritability and character association studies in 30sweet Sorghum [Sorghum bicolor (L.) Moench]VEMANNA IRADDI, T. DAYAKAR REDDY, A. V. UMAKANTH, CH. RANI,D. VISHNU VARDHAN REDDY and M. H. V. BHAVE

PART II : VETERINARY SCIENCE

Estrus synchronization response and fertility rate following treatment 39with Pgf2α And Gnrh in acyclic lactating ongole cowsK.VENKATA RAMANA, K.SADASIVA RAO, K.SUPRIYA and N.RAJANNA

A study on migration pattern of sheep flocks in telangana region of Andhra Pradesh 42N. RAJANNA, M. MAHENDAR and K. VENKATA RAMANA

Utilization of poultry waste an un-conventional protein source in small ruminant rations 47J. NARASIMHA, V.CHINNI PREETHAM and S.T.VIROJI RAO

Postpartum ovarian follicular dynamics and estrus activity in lactating ongole cows 51K. VENKATA RAMANA, K. SADASIVA RAO, K. SUPRIYA and N. RAJANNA

PART III : RESEARCH NOTES

Development and evaluation of fiber enriched khakra 56M. KIRTHY REDDY, UMADEVI, P.S.S SAILAJA and KUNA APARNA

Influence of nitrogen and weed management on growth and yield of 61aerobic rice (Oryza sativa L.)K. SANDHYARANI, M. MALLA REDDY, R. UMA REDDY and P.V. RAO

Effect of seed priming on biochemical changes during seed storage 66of Maize (Zea mays L.) hybridsM. RAM KUMAR, P. S. RAO, V. PADMA and K. V. RADHA KRISHNA

CONTENTSPART I : PLANT SCIENCE

4

An economic analysis of Blackgram in Gulbarga district of Karnataka 70DEEPAK HEGDE, D. V. SUBBA RAO, N. VASUDEV and K. SUPRIYA

Gene action and combining ability studies in Chickpea (Cicer arietinum L.) 74B. REDDY YAMINI, V. JAYALAKSHMI, B. NARENDRA and P. UMAMAHESHWARI

Genetic divergence in Brinjal (Solanum melongena L.) 79BALAJI LOKESH, P.SURYANARAYANA REDDY, R.V.S.K.REDDY and N.SIVARAJ

Relationship between profile of beneficiary farmers and the socio-economic impact 83of irrigated agriculture modernization and water bodies restoration and management(IAMWARM) project in Pudukkottai districtG. ABIRAMI, B.VIJAYABHINANDANA and T. GOPI KRISHNA

Construction of selection indices for F2 population derived from crosses between 88grain Sorghum × sweet Sorghum [Sorghum bicolor (L.) Moench]VEMANNA IRADDI, T. DAYAKAR REDDY, A. V. UMAKANTH, CH. RANI,D. VISHNU VARDHAN REDDY and M. H. V. BHAVE

Evaluation of performance of Dendrobium orchid hybrids 93B. GOPALA RAO, P.T.SRINIVAS and M.H.NAIK

Profile characteristics of Sugarcane farmers in Chittoor district of Andhra Pradesh 96S. RAMALAKSHMI DEVI, P. V. SATYA GOPAL, V.SAILAJA and S.V. PRASAD

A study on diffusion status of System of Rice intensification (SRI) in Andhra Pradesh ```101 K. NIRMALA and R. VASANTHA

Correlation and Path Coefficient analysis for yield and physiological attributes 105In Rice (Oryza sativa L.) hybrids under saline soil conditionsM.SUDHARANI, P.RAGHAVA REDDY, G.HARIPRASAD REDDY and CH.SURENDRA RAJU

Genetic divergence studies for yield and physiological Attributes in 109Groundnut (Arachis hypogaea L.)D. NIRMALA, V. JAYALAKSHMI, B. NARENDRA and P. UMAMAHESHWARI

Influence of methods of irrigation on plant growth, yield, flower quality and 114vase life in Dendrobium orchid hybrid Sonia-17 under shade netB. GOPALA RAO, P.T. SRINIVAS and M.H.NAIK

Study on pesticide residues of selected vegetables grown in north coastal 116zone of Andhra PradeshY. PUNYAVATHI and V. VIJAYALAKSHMI

A study on the knowledge level of farmers on recommended Tea cultivation 121practices in NepalKESHAV KATTEL, R. VASANTHA and M. JAGAN MOHAN REDDY

An economic analysis of value addition to Cotton 124E. RADHIKA, R. VIJAYA KUMARI and D.V. SUBBA RAO

Development of phytosterol enriched flavoured milk 127M. PENCHALA RAJU , ANURAG CHATHURVEDI and KUNA APARNA

Correlation and path analysis for yield and its components in Rice (Oryza sativa L.) 132C.MANIKYA MINNIE, T.DAYAKAR REDDY and CH.SURENDER RAJU

5

Chilli (Capsicum annuum L.) belongs to thefamily, Solanaceae and originated from SouthAmerica (Wikipedia, 2006). Chilli is rich in vitamin Cand pro-vitamin A, particularly the red chilli (Sparkyby,2006). India is the largest producer, consumer andexporter of chilli and contribute to 25% of total world’sproduction. In India, chilli is grown in almost all thestates across the length and breadth of the country.Andhra Pradesh is the largest producer of chilli inIndia, contributes about 30% to the total area underchilli, followed by Karnataka (20%), Maharashtra(15%), Orissa (9%), Tamil Nadu (8%) and other statescontributing 18 % to the total area under chilli(Agrocrops, 2012).

Recently, foliar feeding has been widely usedand accepted as an essential part of crop production,especially on horticultural crops (Pace Gary, 1982).The purpose of foliar feeding is not to replace soilfertilization. Supplying a plant’s major nutrient needs(nitrogen, phosphorus and potassium) is mosteffective and economical via soil application. Foliarfeeding can be an effective management tool tofavorably influence pre-reproductive growth stagesby compensating for environmentally inducedstresses of adverse growing conditions and/or poornutrient availability. Early foliar applications can makean already good crop better, either by stimulatingmore vigorous growth or maximizing the yield

EFFECT OF FOLIAR APPLICATION OF NPK NUTRIENTS ON GROWTH ANDYIELD OF CHILLI (Capsicum annuum L.)

A.KIRAN KUMARFruit Research Station, Dr.Y.S.R.Horticultural University, Sangareddy, Medak-502001

Date of Receipt : 03.07.2012 Date of Acceptance : 02.02.2013

email: [email protected]

ABSTRACT

A field trial was conducted on foliar application of NPK in randomized block design on chilli variety “Prakash(LCA 206)” at Jannareddy Venkatreddy Horticultural Research Station, Malyal, Warangal district of Andhra Pradesh(19.570N and 78.660E) with eleven treatment combinations from foliar sprays of 19:19:19 NPK @ 2.5 g l-1, 5 g l-1, 7.5g l-1and 10 g l-1) and KNO3 @ 5 g l-1 and control. The results revealed that four foliar sprays of 19:19:19 NPK @ 7.5 gl-1 + KNO3 @ 5 g l-1 scheduled at monthly intervals starting one month after transplanting significantly enhanced thefresh (9820 kg ha-1) and dry (3320 kg ha-1) pod yield and resulted in significantly longer fruits (7.6 cm).There was nosignificant difference in plant height, plant spread (E-W and N-S), fruit girth and dry pod recovery percentage.

potential growth stage period. The advantages of foliarfeeding in accomplishing the desired crop responsesare two-fold. In order to enhance the effectivenessof any foliar application certain base solutions shouldbe applied. Nitrogen should be present in any basesolution. N-P, N-S or N-P-S base solutions areinfluential during early stages of growth utilizing 1:2or 1:3 N-P2O5 ratios. N-P-K-S base solutions aresuggested to influence the flowering /fruiting stages,utilizing 2:1:1 N-P2O5-K2O ratio. The main objectiveof the present investigation was to study the effectof foliar applied nutrients i.e., NPK and ultimately tostudy the effect on growth and yield.

MATERIALS AND METHODS

The experiment was laid out in randomizedblock design with three replications on chilli variety“Prakash (LCA - 206)” at Jannareddy VenkatreddyHorticultural Research Station, Malyal, Warangaldistrict of Andhra Pradesh (19.570N and 78.660E) witheleven treatment combinations of foliar sprays (19:19:19 [email protected] g l-1, 5 g l-1, 7.5 g l-1 and 10 g l-1) andKNO3 @ 5g l-1 and control for three years. FYM wasapplied @ 25 t/ ha-1, the NPK fertilizers were uniformlyapplied @ 220-60-80 kg ha-1 in all the experimentalplots. One month old seedlings were transplanted at60 x 60 cm spacing. Four foliar sprays of 19:19:19NPK @ 2.5g l-1, 5g l-1, 7.5 g l-1 and 10 g l-1 (T1 to T4),19:19:19 [email protected] l-1, 5g l-1, 7.5 g l-1 and 10 g l-1 in

J.Res. ANGRAU 41(1) 1-4, 2013

6

Treatments Plant

Height (cm)

Plant Spread

E-W(cm)

Plant Spread N-S(cm)

T1-19:19:19 NPK @ 2.5 g l-1 90.3 57.7 55.1

T2-19:19:19 NPK @ 5.0 g l-1 93.8 56.4 54.6

T3-19:19:19 NPK @ 7.5 g l-1 96.8 56.0 54.9

T4-19:19:19 NPK @ 10.0 g l-1 96.2 56.7 54.4

T5-19:19:19 NPK @ 2.5 g l-1+ KNO3 @5 g l-1 92.7 54.5 53.5

T6-19:19:19 NPK @ 5g/l + KNO3 @ 5 g l-1 89.9 54.2 55.0

T7-19:19:19 NPK @ 7.5 g/l + KNO3 @ 5 g l-1 98.4 57.4 55.2

T8-19:19:19NPK @ 10 g/l + KNO3 @ 5 g l-1 91.1 56.7 55.9

T9-KNO3 @ 5 g l-1f rom one month after transplanting 94.4 57.5 56.3

T10-KNO3 @ 5 g l-1 at the time of flowering 91.6 54.4 52.7

T11-Control 89.9 53.9 52.8

CD @ 5% NS NS NS

SEm ±

2.72 1.39 1.25

Table 1. Pooled analysis data for growth components of chilli as influenced by foliar spray

application of 19:19:19 NPK and KNO3.

combination with KNO3 @ 5g l-1 (T5 to T8 ) and KNO3

@ 5g l-1 (T9) were scheduled at monthly intervalsstarting one month after transplanting. At the time offlower initiation two sprays of KNO3 @ 5g l-1 (T10)was scheduled at monthly intervals and unsprayedcontrol (T11). Intercultural operations and Plantprotection measures were uniform in all theexperimental plots. For recording observations onplant height, plant spread, fresh weight, dry weight,fruit length and fruit girth, five plants in each bed

were selected at random and labelled. The data thusrecorded for three years was pooled and subjectedto statistical analysis (Panse and Sukhatme, 1985).

RESULTS AND DISCUSSION

There was no significant difference in theplant height and plant spread (Table 1), fruit girthand percentage of dry pod recovery (Table 2). Freshpod yield (9820 kg ha-1) and dry pod yield (3320 kgha-1) were significantly maximum with four foliar

KIRAN KUMAR

7

Treatments Fruit

Length (cm)

Fruit Girth (cm)

Fresh pod yield (kg

ha-1)

Dry pod yield (kg

ha-1)

Recovery (%)

T1-19:19:19 NPK @ 2.5 g l-1 7.3 0.9 8380 2650 31.6

T2-19:19:19 NPK @ 5.0 g l-1 7.4 1.0 8380 2800 32.8

T3-19:19:19 NPK @ 7.5 g l-1 7.6 1.0 8870 2910 33.4

T4-19:19:19 NPK @ 10.0 g l-1 7.4 1.0 8330 2850 33.5

T5-19:19:19 NPK @ 2.5 g l-1+ KNO3 @5 g l-1

7.2 1.0 7970 2670 34.2

T6-19:19:19 NPK @ 5g/l + KNO3

@ 5 g l-1 7.5 1.0 8590 2730 31.8

T7-19:19:19 NPK @ 7.5 g/l + KNO3 @ 5 g l-1

7.6 1.0 9820 3320 33.8

T8-19:19:19NPK @ 10 g/l + KNO3 @ 5 g l-1

7.5 1.0 8490 2720 32.0

T9-KNO3 @ 5 g l-1from one month after transplanting

7.5 0.9 8070 2730 33.8

T10-KNO3 @ 5 g l-1 at the time of flowering

7.5 1.0 8310 2690 32.3

T11-Control 7.1 0.9 6450 2240 34.7

CD @ 5% 0.3 NS 1444 480 NS

SEm ±

0.1 0.02 489 160 0.82

Table 2. Pooled analysis data for yield components of chilli as influenced by foliar spray

application of 19:19:19 NPK and KNO3.

sprays of 19:19:19 NPK @ 7.5g l-1 + KNO3 @ 5g l-1

scheduled at monthly intervals starting from onemonth after transplanting (Table 2). These resultswere supported by Lovatt (2005), who reported thatfoliar spray of 1% either 19:19:19 or KNO3 at 45, 60and 75 days after planting increased the crop yieldby about 10% over unsprayed control. These resultsare in line with those of Patil and Biradar (2001), whoapplied NPK 19:19:19 as foliar application and found

significant effect on fruit weight of chilli. Singh et al.(2002) reported that gross and marketable yield ofOnion was highest with basal application of NPK andfoliar application of 1% KNO3 at 30, 45 and 60 daysafter transplanting.

Foliar application of 19:19:19 [email protected] g l-1

+ KNO3 @ 5g l-1 resulted significantly longer fruits(7.6 cm) (Table 2). Similarly, Baloch (2008) reportedsignificant increase in fruit length by foliar application

EFFECT OF FOLIAR APPLICATION OF NPK NUTRIENTS ON GROWTH AND YIELD OF CHILLI

8

of Nitrophen 4%, Nitrogen compound 12%, Iron 2%,Magnesium 2%, Manganese 2%, Boron 2%, Copper4%, Molybdenum 2%, Potash 8%, P2O5 12% andCalcium 8%. Similar studies have also beenconducted by Jiskani (2005), who found that foliarapplication of zinc 3.0 ppm, copper 1.0 ppm and boron0.5 ppm produced the highest number of fruits perplant and increasing frequency of NPK (19:19:19)spraying from three to four times did not increasethe number of chilli fruits per plant. Increased yieldsdue to foliar spray could be attributed to the reasonthat foliar feeding is often effective when roots areunable to absorb sufficient nutrients from the soil and

such a condition could arise from an infertile soil, ahigh degree of soil fixation, losses from leaching,soil temperatures, lack of soil moisture, or restricted,injured or diseased root system. Further, Silberbush(2002) also reported that foliar fertilization is a widelyused practice to correct nutritional deficiencies inplants caused by improper supply of nutrients to roots.

Improvement in yield of chilli was evidentwith increase in NPK 19:19:19 concentration.However, application beyond 7.5 g l-1 water was noteffective and thus 7.5g l-1 water along with KNO3 @5g l-1 was considered to be an optimum concentrationfor commercial production of Chilli.

REFERENCES

Agrocrops. 2012. Crop report 2011/12 Oilseeds &spices pp.6-7

Baloch, Q. B, Chachar ,Q.I and Tareen, M.N. 2008.Effect of foliar application of macro and micronutrients on production of green chillies(Capsicum annuum L.) Journal of AgriculturalTechnology, 4 (2):177-184

Jiskani, M.M. 2005. Foliar fertilizers—fast actingagents. Daily DAWN, the Internet Edition,Monday December 5, 2005.

Lovatt, C.J. 2005. Formulation of foliar phosphorusfertilize for chilli. www.freepatentsonline.com

Gary, P.M. 1982. Foliar fertilization: somephysiological perspectives. Paper presentedto American Chemical Society, 13 th

September, 1982.

Panes, V.G and Sukhatme, G.V. 1985. Statisticalmethods for agricultural workers, IndianCouncil of Agrilcultural Research, New Delhi.

Patil, R and Biradar, R. 2001. Effect of foliarapplication of essential nutrients on chillies.Agricultura Tecnica Santiago 51(3): 256-259.

Silberbush, L.F. 2002. Response of maize to foliarvs. soil application of nitrogen-phosphorus-potassium fertilizers. Journal of PlantNutrition. 25 (11): 2333-2342

Singh, D.K, Pandey, A.K, Pandey, U.B and Bhonde,S.R. 2002. Effect of farm yard manurecombined with foliar application of NPK mixtureand micronutrients on growth, yield and qualityof onion. News letter-National HorticulturalResearch and Development Foundation. 21-22(1): 1-7

Sparkyby, F. 2006. Sparky Boy Enterprises. PlanetNatural.1-6.

Wikipedia.2006. Chillies: history, cultivation andprocessing pp.1-6.

KIRAN KUMAR

9

India is one of the main countries producingRice (Oryza sativa L.) in the world. Rice –Rice is themost predominant cropping system in southerntelangana zone of Andhra Pradesh state.Deterioration of soil fertility and declining productivitydue to indiscriminate application of nutrients throughthe fertilizers with the threat of the decliningproductivity has become major problem. Continuouscropping and long term fertilization are liable tochange the soil properties and crop production,depending upon the type of management practices(Santhy et al, 1998). The micronutrient deficienciesare being recognized in soils intensively cultivatedwith cereals in several parts of the country. This isaggravated by the continuous application of highanalysis fertilizers without replenishing the depletedmicronutrients. Therefore, the incorporation of organicmaterial is emphasized to supply the micronutrientsand thereby maintain the nutrient balance.

Hence, an investigation was made to assessthe soil nutrient supplying capacity under differentINM practices in a long-term fertilization experimentwith continuous rice - rice cropping system.


The present studies were conducted duringtwo consecutive years 2005-06 and 2006-07 atAgricultural College Farm, Rajendranagar,Hyderabad. The experiment was conducted on asandy clay loam soil on which only rice is growncontinuously in both kharif and rabi seasons since

NUTRIENT UPTAKE BY RICE CROP UNDER LONG TERM INTEGRATEDNUTRIENT MANAGEMENT IN RICE – RICE CROPPING SYSTEM IN ALFISOLS

V. MAHESWARA PRASAD and P. PRABHU PRASADINIDAATT Centre, Krishna District, Machilipatnam – 521 002

ABSTRACT

Nutrient uptake by rice crop in different integrated nutrient management treatments at different stages ofcrop growth in rice-rice cropping system was studied in alfisols of Southern Telangana Zone of Andhra Pradesh fortwo consecutive years during 2005-06 and 06-07. The crop fertilized with increased level of nutrients accumulatedmore phytomass at every stage of sampling in kharif or rabi seasons during the two years. The uptake of NPK, Zn, Cu,Fe and Mn was also maximum in response to the application of recommended dose of fertilizers. There was nodefinite increase in the phytomass or uptake of major or minor nutrients consistently throughout the crop growthperiod during the two years by the integrated nutrient supply system compared to the application of recommendeddose of fertilizers.

1988. The experiment was laid out in randomizedblock design with 12 treatments (Table 1) in threereplications. Rice variety RNR 23064 was plantedadopting a spacing of 20 cm x 10 cm in 59.8 m2

sized plot.

Sampling of plants was done by uprootingfive hills per treatment at tillering, panicle initiationand harvesting stage of the crop in each seasonduring the two years of the investigation for thenutrient removal studies. The samples were groundusing Willey mill and extracted with triacid. Theresultant extract was subjected to analysis of N.Pand K as per the procedures outlined in Tandon(1995).

Finally, the uptake of macro nutrients wascalculated by using the following formula; Nutrientuptake (kg ha-1) = nutrient content (%) x dry matter(kg ha-1) divided by 100. Whereas the uptake of micronutrients was calculated by using the formula;Nutrient uptake (g ha-1) = nutrient content (%) x drymatter (g ha-1) divided by 100.


Major Nutrients

a. Nitrogen

The crop grown without the external input ofmanures and fertilizers removed lesser quantities of26, 35, and 63 kg N ha-1 in kharif 2005 at tillering,panicle initiation and harvesting stage of the crops



J.Res. ANGRAU 41(1) 5-13, 2013

10

respectively. In the subsequent rabi, the cropremoved 38, 33 and 65 kg N ha-1 at the respectivestages (Table 2). In kharif 2006, rice removed 24 kgN ha-1 at tillering, 40 kg at panicle initiation and 40 kgN ha-1 at harvesting stage of the crop in T2. In therabi season, it removed 29, 31 and 27 kg ha-1. Theapplication of 50 % recommended dose of fertilizerscontinuously in the kharif and rabi season,significantly enhanced the quantity of N removed bythe crop at every stage of its growth. This trendimproved with increase in the quantity of nutrientsapplied upto the optimum schedule. The integratedsupply of nutrients by substituting 25% N fertilizerwith glyricidia and application of 75 % recommendeddose of fertilizers in kharif significantly increased theuptake of nitrogen at tillering both in kharif and rabiduring two years.

b. Phosphorus

The uptake of this nutrient increased from1.0 kg at tillering to 4.4 kg at panicle initiation andfurther to 8.0 kg ha-1 at harvesting stage of the cropduring kharif 2005 (Table 3). The uptake of the nutrientwas increased by the application of fertilizers indifferent proportions during kharif and rabi season.The adoption of integrated nutrient managementoptions by substituting 50 % N fertilizer with FYM,paddy straw or glyricidia appreciably increased theP uptake compared to the effect of chemical fertilizersapplied at recommended dose. The substitution of25 % N fertilizer with any one of these three organicsources in the kharif season and application of 75 %recommended dose of fertilizers in the rabi seasonalso showed similar results.

c. Potassium

Rice removed more Potassium in responseto the application of different levels of fertilizers inkharif and rabi season and their integration with theorganic source of nutrients (Table 4). The unfertilizedcrop removed 32, 48 and 66 kg ha-1 in kharif and 38,62 and 71 kg K ha-1 in rabi at tillering, panicle initiationand harvesting stage of the crop during 2005-06. Theapplication of 50 % recommended dose of fertilizersin the kharif season increased the uptake significantlyto 38, 77 and 71 kg K ha-1 at the respective stagesof crop growth. The applicat ion of 50 %recommended dose of fertilizers in the rabi season

also increased the uptake significantly to 49, 70, 114kg K ha-1. These trends repeated during the secondyear also. A large quantity of 60, 88 and 84 kg K ha-

1 in kharif and 62, 84 and 158 kg K ha-1 was removedin rabi during the first year (2005-06) by theapplication of recommended dose of fertilizers bothin the kharif and rabi season. The uptake at tilleringwas 61 kg ha-1 in kharif 2006 and 63 kg ha-1 in rabi.The uptake was 61, 82 and 63 kg ha-1 at tillering,panicle initiation and harvesting in kharif while thecrop removed 63, 82 and 100 kg ha-1 K duringcorresponding stages in the rabi seasons during 2006-07 in response to this optimum fertilizer schedule torice-rice crop sequence. The integrated supply ofnutrients by substituting 50 % N fertilizer either withFYM, paddy straw or glyricidia in the kharif seasonand application of recommended dose of fertilizersin the rabi season did not improve the uptake of K byrice compared to the crop supplied with nutrientscontinuously through the recommended dose offertilizers.

The role of any of these organics to substitute25 % N fertilizer in the kharif season and applicationof 75 % recommended dose of fertilizers in the rabiseason was also not distinct to significantly increasethe uptake of K compared to rice grown with fertilizersalone.

B. Micronutrients

a. Zinc

The application of fertilizers with organicsource of nutrients activated the crop to remove largequantity of Zn from the soil. The results showed thata low quantity of 29.2 g Zn was removed at tillering,56.5 g at panicle initiation and 88.3 g ha -1 atharvesting stage of rice in the kharif season whilethe uptake was 44.6, 53.7 and 77.93 g ha-1 from theunfertilized soil during the first year (Table 5). Theapplication of 50 % recommended dose of fertiliserscontinuously in kharif and rabi season to thesequence crops significantly increased the uptakeof this nutrient throughout the crop growth period.The crop was estimated to remove 43.6, 95.1 and135.6 g Zn ha-1 in kharif and 48.6, 85.5 and 160.4 gha-1 in rabi at tillering, panicle initiation and harvestingduring the first year. It removed 28.3, 63.5 and 174.9g Zn ha-1 in kharif and 40.6, 75.74 and 146.1 g ha-1 inthe rabi season during the three corresponding growth

PRASAD and PRASADINI

11

stages of crop in the second year. The uptake of thismicronutrient in general tended to increase with thefertilizer schedule upto the optimum. Rice fertilizedwith the recommended level of nutrients through thefertilisers removed a large quantity of 50.1, 112.1and 210.7 g Zn ha-1 in kharif and 57.2, 104.3 and255.4 g Zn ha-1 in rabi at tillering, panicle initiationand harvesting stages respectively. During the secondyear rice removed 36.1, 92.2 and 250 g Zn ha-1 inkharif season and 46.3, 101.6 and 174.4 g ha-1 in therabi season. The substitution of 50 % N fertilizer inkharif and application of recommended dose offertilizers in rabi significantly increased the uptakeof the micronutrient throughout the crop growingperiod compared to the supply of nutrientscontinuously through the chemical fertilizers onlyduring the second year.

The substitution of 50% N fertilizer withglyricidia in kharif and application of recommendeddose of fertilizers in the rabi season was the bestnutrient management strategy. It enabled the crop toremove significantly more quantity of Zn consistentlyat every stage of crop growth during kharif and rabiduring the two year crop sequence. The substitutionof 25 % N fertilizer with glyricidia in kharif andapplication of 75 % recommended dose of fertilizersin rabi was also superior to the complete dependenceon chemical fertilizers. However, the uptake of Znby this treatment was not significantly different atpanicle initiation in the first year and at harvest inthe second year.

b. Copper

Rice removed 4.90, 10.29 and 19.22 g ha-1

Cu in kharif and 7.57, 9.32 and 14.94 g ha-1 in rabi attillering, panicle initiation and harvesting stage fromthe soil mineralized nutrients without the externalsupply during 2005-06 (Table 6). The uptake was4.14, 8.93 and 23.61 g Cu ha-1 in the kharif seasonwhile it was 5.08, 8.06 and 10.71 g ha-1 in the rabiseason during the second cycle of the crop sequence.The fertilizer application triggered the uptake of thismicronutrient. The crop fertilized with 50 %recommended dose of fertilizers invariable removedsignificantly larger quantity of Cu ha-1 throughout thegrowth period. The dressing of soil with optimum

fertilizer schedule for rice in kharif and rabi seasonmaximized the uptake of Cu compared to relativelyhigh level of farmers schedule. Rice removed 8.86,19.92 and 44.75 g Cu ha-1 in kharif while it removed9.30, 18.30 and 54.37 g Cu ha-1 in rabi during 2005-06. Similarly, maximum uptake of 6.00, 15.26 and52.82 g Cu ha-1 was recorded at tillering, panicleinitiation and harvesting stage in kharif while it was7.56, 17.28 and 38.47 g ha-1 in rabi during 2006-07.The substitution of 50 % N fertilizer in kharif withFYM, paddy straw or glyricidia and the application ofrecommended level of fertilizers in rabi were notsignificantly superior to enhance the uptake of thismicronutrient significantly compared to thesupplement of nutrients entirely through chemicalfertilizers. The substitution of 25 % N fertilizer withany of the three organic sources in kharif andapplication of 75 % recommended dose of fertilizersin the rabi season were also not distinguished assuperior nutrient management practices to fertilizertreatments.

c. Iron

The uptake of nutrients was low by growingrice in sequence on the native soil fertility withoutexternal input of organic or inorganic source ofnutrients in the kharif season. The crop removed156.9, 308.9 and 399.2 g Fe ha-1 at tillering, panicleinitiation and harvesting during kharif 2005 (Table 7).In the subsequent rabi, rice removed 251.3, 302.9and 318.9 g ha-1 Fe. The application of fertilizersincreased the uptake of this nutrient. The uptake wassignificantly more even by the application of 50 %recommended dose of fertilizers continuously in thekharif and rabi season. The crop fertilized with 50 %recommended dose of nutrients removed 152.2,314.2 and 984.3 g Fe ha-1 in kharif and 223.4, 461.1and 769.5 g Fe ha-1 in the rabi season. The uptake ofthis micronutrient further increased with the levelfertilizer application to a maximum upto therecommended dose.

The substitution of 50 or 25 % N fertilizerwith FYM in the kharif season was beneficial. Itincreased the uptake of Fe significantly more attillering stage of the crop both in kharif and rabi

NUTRIENT UPTAKE BY RICE CROP UNDER LONG TERM INM

12

seasons during the two years over the crop grownentirely with chemical fertilizers. But, the substitutionof 50 % N fertilizer with glyricidia was more beneficial.It increased the uptake of Fe by rice in significantlylarger quantities upto panicle initiation both in kharifand rabi season during the two years. Such abeneficial effect of integrated nutrient managementby substituting 25 % N fertilizer with glyricidia wason the other hand persistent only at tillering stage.

d. Manganese

Rice grown with different levels of fertilizersand integrated with organic source of nutrientsremoved larger quantities of this micronutrient thanfrom the unfertilized soil. The crop removed 187.4 gha-1 Mn at tillering, 384.9 g at panicle initiation and137.9 g ha-1 at harvesting stage during kharif 2005-06 (Table 8). The crop grown in the subsequentseason removed 269.8, 328.5 and 324.69 g ha-1 Mn.During the second year rice removed 154.9, 351.9and 474.9 kg Mn ha-1 in kharif. In the rabi season itremoved 192.1, 313.6 and 215.7 g Mn ha-1 at tillering,panicle init iation and harvesting stage. Thecontinuous application of 50 % recommended doseof fertilizers increased the uptake of this micronutrientduring the corresponding crop growth stages in kharifand rabi in both the years. The crop fertilized withthis treatment dose removed 276.0, 629.9 and 574.9g Mn ha-1 at tillering, panicle initiation and harvestingstage in the kharif season, while it removed 287.4,525.1 and 676.8 g ha-1 in rabi 2005-06. The uptakewas 183.1, 374.7 and 784.5 g Mn ha-1 in kharif, whileit was 232.3, 483.3 and 643.7 g ha-1 in the rabi seasonduring the three crop growth stages in the secondyear crop cycle. The contribution of FYM or glyricidiato substitute 50 % N fertilizer was superior tochemical fertilizers alone in enhancing the uptake ofthis micronutrient only at tillering stage during 2005-06.

The available Zn and Cu in the fertilized soilwas on par with the unfertilized soil at different stagesof crop growth during the two year rice-rice croppingsequence. The co-application of 50 per centrecommended dose of NPK through fertilizers and

FYM or glyricidia equivalent 50 per cent N fertilizerin the kharif season followed by the recommendeddose of fertilizers in the rabi season significantlyenhanced the availability of Fe both in the surface 0-15 and lower soil depth of 15-30 cm. The substitutionof 25 per cent N fertilizer with these organic materialsin the kharif season and application of 75 per centrecommended dose of fertilizers in rabi season alsoenriched the soil with more quantity of available Zn.But, this improvement was relatively less consistentat different stages of crop growth than theirsubstitution for 50 % N fertilizer. This trend was alsosimilar for the soil available Cu content. Additionally,its improvement was also recorded by substituting50 or 25 % N fertilizer with paddy straw only in thefirst year. The benefit of increased availability of Fein the soil was recorded at the tillering stage during2005-06 by the substitution of 50 per cent N fertilizerwith FYM in kharif season and application ofrecommended dose of fertilizers in the rabi seasononly in the surface layer. On the other hand, theavailability of Mn was not influenced by theapplication of fertilizers or the conjunctive use ofnutrients through organic and inorganic sources.Rajeev Kumar et al., (1993), Singh et al., (1999)reported that the incorporation of organic sources inthe soil along with the fertilizers increased theavailable micronutrients. The magnificent responsesof integrated nutrient management treatments inleaving behind larger quantities of copper sustainedthe crop requirement in sufficient quantity. Theavailability of Zn increased by the co application ofFYM by substituting 25 or 50% N fertilizer.Nonetheless, the site of present experimentcontinuously cropped with rice-rice croppingsequence for the past 17 years contained much higherquantities of Zn, Cu, Fe, Mn than the critical limit of0.6 mg kg-1 Zn, 0.2 mg kg-1 Cu, 4 mg kg-1 Fe, and 3mg kg-1 Mn even in the unfertilized control. Notcomplacent with the data so achieved, it would be awise step to substitute 50 per cent N fertilizer withFYM or glyricidia to averse the likely depletion ofthese micro but essential elements for crop growthin the years to come


13

Sl. No Kharif Rabi T1 No fertilizers, No organic manures No fertilizers, No organic manures T2 50 Rec. NPK dose through fertilizers 50 Rec. NPK dose through fertilizers T3 50 % Rec. NPK dose through fertilizers 100 % Rec. NPK dose through OM T4 75 % Rec. NPK dose through fertilizers 75 % Rec. NPK dose through fertilizers

T5 100 % Rec. NPK dose through fertilizers 120:60:60 kg ha-1

100 % Rec. NPK dose through fertilizers 120:60:60 kg ha-1

T6 50 % Rec. NPK dose through fertilizers + 50 % N through FYM

100 % Rec. NPK dose through fertilizers

T7 75 % Rec. NPK dose through fertilizers + 25 % N through FYM


T8 50 % Rec. NPK dose through fertilizers + 50 % N through paddy straw


T9 75 % Rec. NPK dose through fertilizers + 25 % N through paddy straw


T10 50 % Rec. NPK dose through fertilizers + 50 % N through glyricidia


T11 75 % Rec. NPK dose through fertilizers + 25 % N through glyricidia


T12 Conventional farmers practice 80:50:20 kg ha-1 NPK

Conventional farmers practice 80:50:20 kg ha-1 NPK

Table 1. Details of the treatments

2005-06 2006-07 Tillering Panicle

initiation Harvesting Tillering Panicle

initiation Harvesting Treatments

Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi T1 26 38 35 33 63 65 24 29 40 31 40 27

T2 47 45 77 73 100 114 31 39 51 63 60 59

T3 32 46 70 80 111 134 29 41 58 81 77 86

T4 49 49 82 84 105 173 35 43 80 78 75 68

T5 55 48 119 110 112 126 42 48 106 96 79 92

T6 39 51 94 104 115 128 39 42 89 95 72 74

T7 53 59 106 107 112 143 41 48 97 94 69 75

T8 30 45 88 81 108 179 37 42 85 82 61 75

T9 48 46 97 83 105 150 30 41 68 93 69 76

T10 59 66 121 119 121 181 45 54 109 118 91 80

T11 62 70 125 121 119 135 50 53 115 117 92 80

T12 42 52 85 79 103 123 35 43 69 82 68 72

SE + 2.25 1.6 9.7 7.7 5.8 10.6 2.4 4.2 4.7 8.7 7.9 9.0

CD at 5 % 4.6 3.4 20.2 16.0 12.0 22 5.0 8.6 9.8 20.2 16.4 18.6

Table 2. Influence of integrated nutrient management practices on N uptake (kg ha-1) in rice-rice croppingsystem


14

Table 3. Influence of integrated nutrient management practices on P uptake (kg ha-1) in rice-rice croppingsystem



initiation Harvesting Treat

ments

Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi T1 1.0 1.6 4.4 4.1 8.0 3.5 1.0 1.2 5.0 3.8 8.8 4.7

T2 1.6 1.7 6.9 6.7 10.0 10.6 1.1 1.4 5.1 5.7 12.9 10.5

T3 1.3 1.8 6.3 7.5 11.9 19.0 1.2 1.7 5.2 7.7 9.6 16.3

T4 1.6 1.8 6.7 7.2 10.7 17.4 1.2 1.5 6.8 6.7 12.1 12.4

T5 3.6 1.9 8.0 7.6 13.3 25.5 1.4 1.6 7.3 7.3 13.1 13.3

T6 2.9 2.0 7.1 7.9 13.7 28.7 1.3 1.7 7.0 7.4 11.9 15.0

T7 3.5 1.9 7.8 7.9 13.6 22.6 1.3 1.6 7.3 7.0 12.0 14.5

T8 3.4 1.8 6.3 7.3 11.6 28.1 1.1 1.7 6.1 7.4 11.1 13.6

T9 3.3 2.1 7.1 7.0 11.3 23.0 1.1 1.6 6.2 7.0 13.2 14.6

T10 3.7 2.2 8.1 8.3 14.9 27.4 1.4 1.7 7.5 8.2 16.7 17.5

T11 3.8 2.3 8.2 8.0 14.9 20.4 1.6 1.7 7.8 7.7 11.9 15.5

T12 3.5 1.8 7.0 6.5 10.6 15.9 1.1 1.4 5.5 6.5 11.3 16.5

SE + 0.14 0.04 0.54 0.91 1.06 2.55 0.02 0.04 0.54 0.63 0.57 1.54

CD at 5 % 0.30 0.08 1.12 1.88 2.20 5.30 0.05 0.09 1.12 1.30 1.18 3.20

Table 4. Influence of integrated nutrient management practices on K uptake (kg ha-1) in rice-rice cropping

system



initiation Harvesting Treatment

Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi T1 32 38 48 62 66 71 34 39 48 42 49 65

T2 38 49 77 70 71 114 41 50 57 62 54 99

T3 34 51 70 82 84 114 38 52 59 86 62 90

T4 50 60 77 80 77 114 51 62 76 75 80 90

T5 60 62 88 84 84 158 61 63 82 82 63 100

T6 59 62 80 88 89 177 60 64 78 85 71 160

T7 49 60 86 86 87 149 51 62 80 80 80 157

T8 58 60 60 80 82 179 60 62 65 82 82 182

T9 60 63 78 76 77 149 62 65 70 80 91 160

T10 61 65 90 90 100 181 65 65 84 90 90 185

T11 60 63 92 88 94 152 61 65 86 88 90 180

T12 49 50 76 72 74 114 50 52 62 72 62 115

SE + 2.1 4.4 1.1 3.3 0.5 10.6 2.3 3.1 1.6 8.8 1.4 1.1

CD at 5 % 4.3 9.2 14.7 6.9 4.2 22.0 4.7 6.5 3.4 18.3 3.0 22.4


15

Table 5. Influence of integrated nutrient management practices on Zn uptake (g ha-1) in Rice-Ricecropping system



initiation Harvesting Treatment


T2 43.6 48.6 95.1 85.5 135.6 160.4 28.3 40.6 63.5 75.74 174.9 146.1

T3 35.1 54.7 86.3 99.3 151.2 221.5 29.3 47.0 64.2 103.48 214.1 169.0

T4 45.60 53.9 96.7 96.9 171.2 220.5 33.2 42.6 84.3 90.60 220.8 145.0

T5 50.1 57.2 112.1 104.3 210.7 255.4 36.1 46.3 92.2 101.6 250.0 174.4

T6 46.3 67.8 114.3 124.2 174.0 307.9 41.0 56.3 106.8 117.0 293.8 197.9

T7 53.5 67.5 129.7 118.6 217.5 292.7 40.9 48.5 107.4 105.3 271.6 119.8

T8 35.7 55.7 80.7 110.3 163.0 251.3 32.7 51.0 68.3 112.5 195.4 179.5

T9 46.8 51.2 106.1 104.0 184.3 241.4 34.0 47.0 69.2 101.4 201.9 183.3

T10 56.57 65.0 128.2 127.8 242.6 312.8 44.0 55.3 109.9 123.8 294.6 206.5

T11 57.97 69.1 124.5 121.1 258.1 307.9 47.2 53.6 112.9 117.2 286.7 193.8

T12 49.3 46.3 90.8 87.1 178.7 199.0 34.2 41.8 72.1 91.8 195.6 151.6

SE + 2.3 1.8 7.4 10.8 12.8 27.0 1.5 2.8 2.3 7.0 19.6 9.8

CD at 5 % 4.8 3.8 15.4 22.4 26.5 56.4 3.2 5.8 4.7 14.5 40.6 20.4

Table 6. Influence of integrated nutrient management practices on Cu uptake (g ha-1) in Rice-Ricecropping system




ments Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi

T1 4.90 7.57 10.29 9.32 19.22 14.94 4.14 5.08 8.93 8.06 23.61 10.71

T2 7.49 8.23 17.07 15.31 29.10 33.43 4.90 6.26 10.37 12.37 37.23 31.35

T3 5.80 8.82 15.82 17.52 33.12 50.12 4.96 7.42 10.64 16.88 46.14 38.05

T4 7.82 9.29 17.48 16.91 76.25 46.30 5.50 6.93 14.14 15.36 47.75 31.79

T5 8.86 9.30 19.92 18.30 44.75 54.37 6.00 7.56 15.26 17.28 52.82 38.47

T6 7.44 11.02 19.56 19.97 38.75 59.38 6.28 8.89 16.57 18.52 55.07 39.97

T7 8.66 9.80 20.52 19.58 41.24 56.68 6.24 8.66 16.67 16.51 53.10 38.68

T8 5.60 8.98 13.73 17.47 32.34 48.38 4.90 8.10 11.53 17.25 39.14 36.55

T9 3.28 8.38 17.41 16.37 34.14 45.06 5.41 7.45 11.26 15.60 42.01 36.38

T10 9.16 10.86 19.44 20.61 50.01 58.68 6.65 8.95 17.11 19.15 58.08 41.07

T11 9.27 11.30 19.36 19.18 46.47 59.74 7.40 8.37 17.06 17.22 57.74 38.86

T12 7.85 8.67 16.10 15.42 37.20 49.27 5.15 6.85 11.97 14.66 41.77 32.53

SE + 0.54 0.46 1.37 1.50 3.30 4.18 0.32 0.55 1.16 0.89 4.10 3.61

CD at 5 % 1.12 0.96 2.84 3.12 6.84 8.68 0.66 1.14 2.42 1.86 8.50 7.50


16

Table 7. Influence of integrated nutrient management practices on uptake of Fe (g ha-1) in rice-ricecropping system



initiation Harvesting Treatments


T2 239.4 277.8 578.2 510.6 658.2 671.0 152.2 223.4 314.2 461.1 984.3 769.5

T3 193.5 308.7 530.9 603.7 765.2 972.9 159.5 260.8 314.2 630.5 1240.3 893.3

T4 251.5 309.9 606.2 606.7 933.8 975.2 178.6 241.8 440.6 566.9 1274.9 746.7

T5 280.0 322.2 690.3 631.6 1152.6 1168.1 202.2 262.9 488.6 599.4 1479.5 977.4

T6 314.4 447.5 738.7 751.0 1055.9 1478.8 260.3 319.9 608.4 728.3 1665.0 1110.6

T7 349.9 393.8 781.5 711.3 1105.3 1297.5 251.1 290.5 593.0 623.5 1577.9 1073.2

T8 231.9 365.5 489.5 666.3 719.0 1193.9 208.3 287.0 412.0 667.9 1125.0 991.3

T9 287.3 330.3 597.8 566.2 707.4 1068.9 209.8 262.8 400.0 590.2 1107.6 908.2

T10 362.4 448.5 773.2 754.8 1099.0 1391.7 275.7 315.2 630.6 770.6 1721.6 1057.2

T11 355.7 457.1 756.0 704.9 1183.0 1382.3 306.5 303.6 618.2 656.8 1598.7 886.0

T12 274.8 320.8 569.5 546.7 798.0 870.5 199.4 241.1 390.8 534.0 1109.2 694.4

SEM + 1.1 8.1 35.0 39.1 46.3 68.7 6.2 12.3 11.0 41.1 120.5 7.7

CD at 5 % 22.6 18.8 72.6 81.2 96.0 142.6 12.8 25.4 22.8 85.2 250.0 160.8

Table 8. Influence of integrated nutrient management practices on uptake of Mn (g ha-1) in rice-ricecropping system




ments Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi

T1 187.4 269.8 384.9 328.5 137.0 324.6 154.9 192.1 351.9 313.6 474.7 215.7

T2 276.0 287.4 629.9 525.1 574.9 676.8 183.1 232.3 374.7 483.3 784.5 643.7

T3 222.8 312.9 618.8 616.2 767.5 940.5 191.7 270.3 381.9 660.8 993.9 746.9

T4 286.9 322.2 695.5 618.7 935.2 964.8 211.2 251.0 536.4 626.6 1099.3 647.2

T5 300.2 346.0 811.4 682.7 1222.4 1170.1 243.3 273.7 584.4 673.3 1343.0 845.1

T6 332.6 438.5 802.5 751.2 1009.2 1432.9 267.7 338.8 608.4 807.4 1516.4 965.6

T7 371.5 400.0 812.0 711.3 961.5 1270.3 261.7 290.3 576.1 713.8 1349.3 914.44

T8 225.7 373.9 519.5 670.3 729.0 1146.9 204.6 311.1 412.0 730.2 1011.6 867.4

T9 276.3 334.3 653.2 614.6 771.12 1060.3 208.7 273.5 399.6 656.2 1017.3 862.6

T10 354.5 436.7 859.7 780.8 1215 1423.6 274.1 334.7 630.6 843.6 1576.6 988.1

T11 355.7 449.6 802.1 736.2 1285.8 1347.4 293.8 322.8 613.9 756.9 1454.1 883.3

T12 266.9 318.8 628.4 572.11 791.1 894.2 202.1 249.4 421.8 545.0 928.4 807.1

SEM + 13.6 7.7 61.0 45.8 90.3 106.5 12.7 29.8 35.7 58.0 66.2 112.8

CD at 5 % 28.3 16.0 126.6 95.0 166.5 220.8 26.4 61.8 74.0 120.4 116.5 234.0


17

REFERENCES

Rajeev Kumar, Singh, K.P and Sarkar, A K 1993.Cumulative effects of cropping and fertilizeruse on the status of micronutrients in soil andcrop. Fertilizer News. 38(11): 13-17.

Santhy, P., Jayashree Sankar, S., Muthuvel, P andSelvi, D. 1998. Long term fertilizer experiments– status of N, P and K fractions in soil, Journalof Indian Society Soil Science 46(3): 395-398.

Singh, N.P., Sachan, R.S., Pandey, P.Cand Bisht,P.S. 1999. Effect of decade long fertilizer andmanure application on soil fertility andproductivity of rice – wheat system in aMollisol. Journal of Indian Society of soilScience 48(1):72-79.

Tandon, H.L.S. 1995, Methods of analysis of soils,plants, water and fertilizers FDCO, New Delhi,pp.143.


18

The recycling of crop residues and organicwastes through composting is the key technologyfor production of organic manures. Vermicompostingoffers a promising for the recycling of organic wastes.Present study is the comparative assessment ofvermicomposting and composting for their maturityindices The study was carried out at RegionalAgricultural Research Stat ion, Anakapalle,Visakhapatnam district during 2009.

The basic raw materials used for compostingand vermicomposting were

1) Sugarcane trash, 2) Weeds (Cyperus rotundus,Cynodon dactylon,Cleome viscosa, Commalina bengalensis and Trianthema portul acastrum)3) Vegetable market waste and 4) Paddy straw. Incase of Earthworm species Eisenia foetida wasused for vermicomposting @ 1 kg per ton of organicresidue and 1 % N as urea and 2 % SSP were usedas chemical additives for conventional composting.Both methods of composting were carried out incement pits with 6 x 2 x 0.6 m size. The compost

CHANGES IN MATURITY INDICES DURING VERMICOMPSOTING VSCONVENTIONAL COMPOSTING OF AGRICULTURAL WASTES

CH. S. RAMA LAKSHMI, P.C. RAO, G.PADMAJA, T.SREELATHA,M.MADHAVI, P.V.RAO and A. SIREESHA

Regional Agricultural Research Station, ANGR Agricultural University, Anakapalle - 531001



ABSTRACT

The present investigation was carried out at Regional Agricultural Research Station, Anakapalle during2009 to monitor the changes in maturity indices i.e total organic carbon, total nitrogen, C/N ratio, humic substancesand humification index during conventional method of composting and vermicomposting of different organic residuesi.e sugarcane trash, weeds, vegetable market waste and paddy straw. The results revealed that the total organiccarbon decreased with the passage of time during vermicomposting and conventional composting in all the organicresidues. However the percent decrease was more in vermicomposting than conventional composting in a particularperiod of time. The total nitrogen content of different vermicomposts and conventional composts increased duringcomposting process, high increase was observed in vermicomposting than conventional composting. Total Nitrogencontent in both the composts was higher in vegetable market waste and lower in paddy straw. C/N ratio decreasedwith the passage of time during vermicomposting and conventional composting in all the organic residues, howeverpaddy straw recorded the highest C/N ratio while vegetable market waste exhibited lowest C/N ratio. The humic andfulvic production increased with incubation in both the composting methods and in all the treatments, yield of humicacid was maximum from the vegetable market waste vermicompost followed by weed vermicompost. Minimum percent of humic substances were recorded with cane trash and rice straw. A well known index for humification is theHA/FA ratio, in both the composts paddy straw compost recorded low ratio and high ratio was recorded in vegetablemarket waste compost. Thus, Vermicomposting offers a promising solution for the recycling of organic wastes intovaluable organic manure with in a short period of time over conventional composting.

samples at 15, 30, 45 and 60 days interval forvermicomposts and 15, 30, 45, 60 and 110 daysinterval for composting composts were collected fromeach treatment for laboratory analysis.

The organic residues used for vermicomposting and conventional composting wereanalysed for their maturity indices by using standardprocedures as pH and was Electrical Conductivity(dSm-1) determined in 1 : 50 organic material (driedand powdered) and water suspension by usingcombined glass electrode pH meter and EC meter(Jackson, 1973). Organic carbon content wasdetermined by using dry combustion method(Jackson, 1973). The total nitrogen content (%) inthe dried compost sample was determined bymicrokjeldahl distillation method after destroying theorganic matter using H2SO4 and H2O2 (Piper, 1966).C/N ratio was calculated from the above parameters.Extraction, fractionation and purification of compostsamples. The humic substances were isolated,extracted and purified by following Tyurin’s method

J.Res. ANGRAU 41(1) 14-19, 2013

19

Fig 1. Changes in C/N ratio duringvermicomposting

Fig 2. Changes in C/N ratio duringconventional composting

Fig 3. Changes in humic acid content(%) during vermicomposting

Fig. 4 Changes in humic acid content(%) during conventional com-posting

LAKSHMI et al

18 A

20

as described by Kononova (1966). Humification indexwas computed from ratio of humic acid to fulvic acid.


The organic residues used in the study wereneutral in reaction with non saline electricalconductivity. Total organic carbon and C/N ratio variedfrom 35.22 (vegetable market waste) to 37.05 %(paddy straw) and 22.29 : 1 (vegetable market waste)to 68.61 : 1 (paddy straw), respectively. Regardingmacro nutrient status of the organic residues, 0.54(paddy straw) to 1.58 (vegetable market waste) % N,0.10 (paddy straw) to 0.81 (vegetable marketwaste) % P and 0.90 (weeds) to 1.10 (cane trash) %K was recorded.

Changes in total organic carbon (TOC)during vermicomposting and composting

The total organic carbon content (%) of allthe organic residues showed decreasing trend duringboth the methods of composting i.e vermicompostingand conventional composting (Table 1 and 2). Thetotal organic carbon content during vermicompostingvaried from 32.28 (vegetable market waste) to 36.89(paddy straw) on 15th day, while on 60th day, it wasvaried from 23.88 (weeds) to 24.62 % (cane trash).In case of conventional composting, the total organiccarbon content ranged between 32.45 (vegetablemarket waste) to 36.50 (paddy straw) on 15th day,while on 110th day, they varied from 23.05 (vegetablemarket waste) to 24.22 (cane trash). Total organiccarbon decreased with the passage of time in all theorganic residues and in both the composting methods.Total organic carbon content decreased with thedecomposition during vermicomposting andcomposting in all the organic residues, might be dueto total organic carbon is lost as carbon dioxidethrough microbial respiration and mineralization oforganic matter causing increase in total nitrogen, partof the carbon in the decomposing residues releasedas CO2 and part was assimilated by the microbialbiomass, microorganisms used the carbon as asource of energy and decomposing the organic matter(Swathi Pattnaik and Vikram Reddy, 2010). Thereduction was higher in vermicomposting as compared

to composting at a particular period of time, whichmay be due to the fact that earthworms have higherassimilating capacity and the earthworms affect theloss of carbon in the form of carbon dioxide throughmineralization of organic carbon (Swathi Pattnaik andVikram Reddy, 2010).

Changes in total nitrogen duringvermicomposting and conventional composting

The changes in total nitrogen content duringvermicomposting from 15 to 60 days were 0.62 to1.14 % (cane trash), 1.30 to 1.88 % (weeds), 1.58 to2.11 % (vegetable market waste) and 0.51 to 1.12 %(paddy straw). In case of conventional compostingfrom 15 to 110 days, it was varied from 0.63 to 0.98% (cane trash), 1.34 to 1.68 % (weeds), 1.61 to 1.81% (vegetable market waste) and 0.53 to 0.96 %(paddy straw). The total nitrogen content increasedduring composting process, however more increasewas observed in vermicomposting than normalcomposting. Irrespective of the composting methods,significantly higher and lower nitrogen content wasrecorded in vegetable market waste and paddy straw,respectively.

The increase in nitrogen content duringvermicomposting was due to decomposition of organicmatter containing proteins and conversion ofammonical nitrogen to nitrate nitrogen. As the organicmatter passes through the gut of the earthworms,the material gets digested by enzyme activity whichresults in breakdown of proteins and nitrogencontaining compounds. Decrease in pH is anotherimportant factor in retention of nitrogen as it is lostas ammonia at high pH values. The increase in totalnitrogen content during conventional composting maybe due to direct manifestation of mass loss due tomineralization of organic fraction (Krishna Murthy etal., 2010). Lower nitrogen values during conventionalcomposting than vermicomposting might be due toloss of nitrogen in the form of ammonia volatilizationduring thermophilic phase. Higher nitrogen values invermicomposting might be due to high degree ofdecomposition and release of nitrogenous productsthrough excreta, urine and mucoproteins (Kitturmathet al. 2007).

CHANGES IN MATURITY INDICES DURING VERMICOMPSOTING VS CONVENTIONAL

21

Changes in C/N ratio duringvermicomposting and conventional composting

The data presented in Table 1 and 2 revealedthat the C/N ratio of cane trash, weeds, vegetablemarket waste and paddy straw at 15 days ofvermicomposting was 57.74:1, 25.69:1, 20.43:1 and72.33:1, respectively. At the end of vermicompostingi.e at 60 days the C/N ratio was further reduced to21.60:1,12.70:1,11.34:1 and 21.57:1 in cane trash,weeds, vegetable market waste and paddy straw,respectively. Where as in conventional compostingthe C/N ratio at 15 days was 57.02:1, 24.87:1, 20.16:1and 68.87:1, respectively and 60 days aftercomposting the reduction was 34.63:1, 16.56:1,14.19:1 and 36.08:1 and at the end of compostingi.e. at 110 days it was further reduced to 24.71:1,13.76:1, 12.73:1 and 24.89:1 in cane trash, weeds,vegetable market waste and paddy straw,respectively. In both the composting methods paddystraw recorded the highest C/N ratio while vegetablemarket waste exhibited lowest C/N ratio, howeverthe percent decrease was more in vermicompostingthan conventional composting in a particular periodof time (Fig.1 & 2).

The decrease in C/N ratio duringvermicomposting was due to respiratory activity ofearthworms and microorganisms and increase innitrogen by mineralization of organic matter andexcretion of nitrogenous wastes. Similar results werereported by Alok Bhardwaj (2010). The reduction incarbon and lowering of C/N ratio in thevermicomposting and conventional composting couldbe achieved either by the respiratory activity ofearthworms and microorganisms or by increase innitrogen by microbial mineralization of organic matterin combination with addition of the worm’s nitrogenouswastes through their excretion. The rate of reductionof C/N ratio was high during vermicomposting thanconventional composting. The duration ofvermicomposting varied from 55 to 60 days forvarious organic residues under study, while it tookalmost 110 days for conventional composting (Auldryet al., 2009). During vermicomposting given optimumconditions of temperature and moisture, earthwormsfeed on organic component of organic residues whichis ground into smaller particles in their gizzard. Later

on the enzyme activity in the intestine brings aboutrapid conversion of cellulose and protenaceousmaterials. This may account for reduced time invermicomposting than conventional composting.

Changes in Humic Acid content (%)during vermicomposting and composting

The humic acid production increased with progressof decomposition in both the composting methods.The increase in humic acid content invermicomposting from 15 to 60 days varied from 7.50to 9.85 % in cane trash, 8.12 to 10.40 % in weeds,9.00 to 10.85 % in vegetable market waste and 7.12to 9.10 % in paddy straw. At the end ofvermicomposting significant increase in humic acidcontent (10.40 %) was recorded in vegetable marketwaste than cane trash and paddy straw, howeververmicomposting of weeds recorded on par result(10.40 %) with vegetable market waste, whilesignificantly lowest humic acid (9.10 %) was recordedin paddy straw. At the end of conventional compostingi.e 110 days high humic acid content of 10.22 %was observed in vegetable market waste, whileminimum content of 9.75 % was recorded in canetrash. Vegetable market waste recorded 20 & 25 %increase of humic acid content from initial to maturityin vermicomposting and conventional composting,respectively.

The humic acid production increased withincubation in both the composting methods and in allthe treatments, Xiaowei et al. (2010) observed thatincreasing levels of humic acid represent high degreeof humification. Humification was found to bedependent on biochemical characteristics andcomposition of raw material. The high humic acidcontent during vermicomposting implies good qualityand maturity of compost. Vegetable market wasterecorded highest humic acid content (10.85 %)followed by weeds (10.40%), cane trash (9.85 %,)and rice straw (9.10 %) during vermicomposting. Thiswas probably due to variation in the amount,composition and differential degradation of lignins(Tejada, 2009) (Fig.3 & 4)

Changes in Fulvic Acid content (%)during vermicomposting and composting

LAKSHMI et al

22

Tabl

e 2

. Cha

nges

in c

onte

nts

of to

tal o

rgan

ic c

arbo

n (T

OC

), to

tal n

itrog

en (T

N) a

nd C

/N ra

tio (c

onve

ntio

nal)

com

post

ing

at d

iffer

ent t

ime

inte

rval

s

Tabl

e 1.

Cha

nges

in c

onte

nts

of to

tal o

rgan

ic c

arbo

n (T

OC

), to

tal n

itrog

en (T

N) a

nd C

/N ra

tio (v

erm

icom

post

ing)

at d

iffer

ent t

ime

inte

rval

s

S.E

.m+

CD

at (

5%)

CD

at

(5%

)

CHANGES IN MATURITY INDICES DURING VERMICOMPOSTING VS CONVENTIONAL

23

Tabl

e 4.

Cha

nges

in h

umic

aci

d (H

A),

fulv

ic a

cid

(FA

) and

hum

ic a

cid/

fulv

ic a

cid

ratio

dur

ing

conv

entio

nal c

ompo

stin

g at

diff

eren

t tim

e in

terv

als

Tabl

e 3.

Cha

nges

in h

umic

aci

d (H

A),

fulv

ic a

cid

(FA

) and

hum

ic a

cid/

fulv

ic a

cid

ratio

dur

ing

verm

icom

post

ing

at d

iffer

ent t

ime

inte

rval

s

LAKSHMI et al

24

During vermicomposting the change in fulvicacid content from 15 to 60 days varied from 2.58 to2.42 % in cane trash, 2.56 to 2.41 % in weeds, 2.61to 2.50 % in vegetable market waste and 2.11 to2.08 % in paddy straw. In conventional compostingthe changes in fulvic acid content from 15 to 110days were 2.18 to 2.16 % in cane trash, 2.28 to 2.22% in weeds, 2.29 to 2.26 % in vegetable market wasteand 2.17 to 2.15 % in paddy straw. Higher fulvic acidcontent was recorded in vermicomposting thanconventional composting. With the progress ofdecomposition fulvic acid did not followed anyparticular trend in both the methods of composting.Krishna Murthy et al. (2010) were of the opinion thatlow fulvic acid and high humic acid percentage werethe indications that the compost has reached anadvanced stage of maturity and also stated that thecompost quality increased with increasing humic acidpercentage.

Changes in humic acid/fulvic acid ratio (HA/FA ratio) during vermicomposting and composting

The humification index (HI), which is the ratio between

the humic acid and fulvic acid, is believed to be a

good maturity and stability index. The changes inhumic acid/fulvic acid ratio during vermicomposting

and conventional composting were presented in Table3 and 4. During vermicomposting the HA/FA ratio

varied from 2.91 (cane trash) to 3.45 (vegetablemarket waste) at 15 days and 3.72 (paddy straw) to

4.34 (vegetable market waste) at 60 days, where as

in conventional composting it was ranged between3.46 to 3.56 at 15 days and 4.23 to 4.55 at 110 days.

In both the composting methods minimum HA/FAratio was recorded in paddy straw and maximum ratio

was recorded in vegetable market waste. The

increase in humic acid to fulvic acid ratio reflectsthe formation of complex molecules (humic acids)

from more simple molecules (fulvic acids). Similarincrease in humic acid/fulvic acid ratio during

incubation of organic residues was observed byXiaowei et al., (2010).

REFERENCES

Alok Bhardwaj. 2010. Management of kitchen wastematerial through vermicomposting. AsianJournal of Experimental Biological Sciences.1 (1): 175-177.

Auldry Chaddy Petrus., Osumanu Haruna Ahmed andAb Majid Nik Muhamad. 2009. Chemicalcharacteristics of compost and humic acidfrom sago waste. American Journal of AppliedScience. 6(11): 1880-1884.

Jackson ML. 1973. Soil Chemical Analysis. PrenticeHall of India Pvt. Ltd., New Delhi PP:1-485.

Kitturmath, M.S., Giraddi, R.S and Basavaraj, B.2007. Nutrient changes during earthworm-Eudrilus eugeniae mediated vermicompostingof Agro-industrial wastes. Karnataka Journalof Agricultural Sciences. 20(3): 653-654.

Kononova, M.M. 1966. Soil organic matter, its nature,origin and role in soil fertility. 2nd Edition,Pergamon Press. Oxford. PP:400-410.

Krishna Murthy, R., Sreenivasan, N and Prakash,S.S. 2010. Chemical and biochemicalproperties of Parthenium and Chormolaena

compost. International Journal of Science andNature. 1(2): 166-171.

Manuel Tejada, Ana Maria Garcia-Martinez and JuanParrado. 2009. Relationships betweenbiological and chemical parameters on thecomposting of a municipal solid waste.Bioresource Technology. 100: 4062-4065.

Piper, C.S. 1966. Soil and Plant Analysis. HansPublishers, Bombay

Swati Pattnaik and Vikram Reddy, M. 2010. Nutrientstatus of vermicompost of urban green wasteprocessed by three earthworm species-Eisenia foetida, Eudrilus eugeniae andPerionyx excavatus. Applied andEnvironmental Soil Science.

Xiaowei, Li., Meiyan Xing., Jian Yang and ZhidongHuang. 2010. Compositional and functionalfeatures of humic acid like fractions fromvermicomposting of sewage sludge andcowdung. Journal of Hazardous material.185(2,3): 740-748.

CHANGES IN MATURITY INDICES DURING VERMICOMPOSTING VS CONVENTIONAL

25

The soil physical properties play animportant role in determining its suitability for cropproduction. Soil should be physically fertile to providea good crop growth medium. This is greatly influencedby several management practices. Soil physicalproperties such as water holding capacity, bulkdensity, total porosity, air-filled porosity, hydraulicconductivity, and soil-depth greatly influence rootdevelopment which in turn influence plant growth andperformance. Rice –rice is the most predominantcropping system in the Andhra Pradesh, particularlyin southern telangana zone. The major problem isthe deterioration of soil physical properties due tocontinuous puddling and impaired soil fertility due toindiscriminate application of nutrients through thefertilizers with the threat of the declining productivity.

The use of locally available organic sourceshas higher potential to improve the soil physicalproperties that is in terms of water holding capacity,soil porosity and bulk density, soil fertility and thereby soil quality as a whole sustain the level of cropproductivity in the Rice –rice cropping system.Hence, an investigation was made to understand theinfluence of integrated nutrient management (INM)on physical properties of Alfisols under rice-ricecropping system.

Date of Receipt :07.12.2012 Date of Acceptance : 31.01.2013


INFLUENCE OF INTEGRATED NUTRIENT MANAGEMENT ON PHYSICALPROPERTIES OF ALFISOLS UNDER RICE –RICE CROPPING SYSTEM IN

SOUTHERN TELANGANA ZONEV. MAHESWARA PRASAD and P. PRABHU PRASADINIDAATT Centre, Krishna District, Machilipatnam - 521002

ABSTRACT

Studies were conducted to understand the influence of integrated nutrient management on physical propertiesof Alfisols under rice-rice cropping system during 2005-06 and 2006-07 at Agricultural College Farm, Rajendranagar,Hyderabad. The bulk density, porosity and water holding capacity did not change significantly by the application ofdifferent levels of fertilizers in both kharif and rabi season compared to the unfertilized soil. The application of 50%recommended dose of 120:60:60 kg ha-1 NPK integrated with 50% N fertilizer equivalent through FYM, paddy strawor glyricidia in kharif season followed by the application of recommended dose of 120:60:60 kg NPK ha-1 throughfertilizers in rabi season significantly reduced the bulk density and porosity in the upper 0-15 cm and increased thewater holding capacity upto 30 cm depth. Similar change in the physical properties was observed due to applicationof 75% recommended dose of fertilizers integrated with 25% N fertilizer equivalent through any one of the threeorganic sources in kharif season and application of 75% recommended dose of fertilizers in rabi. The improvementin the rate of infiltration of water and hydraulic conductivity was recorded only at the transplanting stage and was notconsistent during the two seasons.


The Present studies were conducted in twoconsecutive years 2005-06 and 2006-07 atAgricultural College Farm, Rajendranagar,Hyderabad. The experiment was conducted on asandy clay loam soil on which only rice was growncontinuously in both Kharif and Rabi seasons since1988. The experiments were laid out in randomizedblock design with 12 treatments in three replications.Rice variety; RNR 23064 was planted adopting aspacing of 20 cm x 10 cm in 59.8 m2 sized plot. Thetreatments comprised of control treatment with outfertilizers and organic manures (T1), 50 %Recommended NPK dose through fertilizers (T2), 50% Recommended NPK dose through fertilizers (T3),75 % Recommended NPK dose through fertilizers(T4), 100 % Recommended NPK dose throughfertilizers 120:60:60 kg ha-1(T5), 50 % RecommendedNPK dose through fertilizers + 50 % N through FYM(T6), 75 % Recommended NPK dose throughfertilizers + 25 % N through FYM (T7), 50 %Recommended NPK dose through fertilizers + 50 %N through paddy straw (T8), 75 % RecommendedNPK dose through fertilizers + 25 % N through paddystraw (T9), 50 % Recommended NPK dose throughfertilizers + 50 % N through glyricidia (T10), 75 %Recommended NPK dose through fertilizers + 25 %

J.Res. ANGRAU 41(1) 20-29, 2013

26

N through glyricidia (T11) and Conventional farmerspractice 80:50:20 kg ha-1 NPK (T12) during kharif. Whilethe treatments during rabi were; control treatment without fertilizers and organic manures (T1), 50 %recommended NPK dose through fertilizers (T2), 100% recommended NPK dose through fertilizers (T3),75 % recommended NPK dose through organicmanures (T4), 100 % Recommended NPK dosethrough fertilizers 120:60:60 kg ha-1(T5), 100 %recommended NPK dose through fertilizers (T6), 75% recommended NPK dose through fertilizers (T7),100 % Recommended NPK dose through fertilizers(T8), 75 % Recommended NPK dose throughfertilizers (T9), 100 % Recommended NPK dosethrough fertilizers (T10), 75 % Recommended NPKdose through fertilizers (T11) and Conventional(farmers) practice 80:50:20 kg ha-1 NPK (T12).

Sample Collection

The soil samples were collected with soilauger at random from each treatment plot at 0-15and 15-30 cm depth before transplanting, panicleinitiation and harvesting stages of the crop in eachseason. The soil samples were dried under shade,powdered using wooden mortar and pestle and thenpassed through a 2 mm sieve before taking upanalysis. Soil samples were collected with coresampler of size 5.25 x 6 cm to determine soil bulkdensity by using the method suggested by Black(1965). Porosity was calculated by using the formula:Porosity = [1-BD/PD] x 100; Where, BD = Bulkdensity of soil (Mg m-3); PD = Particle density (Mgm-3) of soil.

Undisturbed soil samples collected incylindrical cores at different stages from 0-15 and15-30 cm depths were used for the determination ofhydraulic conductivity using constant pressure headmethod as per the procedure outlined by Jalota et al.(1998). Infiltration rate was determined in situ, at thetime of sowing, 60 DAS and harvest of the crop withdouble ring infiltrometer as suggested by Bertrand(1965) and described by Jalota et al. (1998) and theinfiltration rate was reported as cm hr-1. The WaterHolding Capacity of soils was estimated by Keens

Cup method (Black, 1965). Grain yield was recordedat the end of each season for two years.


Bulk density

The data on bulk density of soil in the surfacelayer upto 15 cm depth in response to differentnutrient management treatments is furnished in Table1. The trend exhibited a progressive increase in thebulk density with advance in age of the crop fromtransplanting to panicle initiation and at harvest in allthe treatments during the 2005-06 as well as 2006-07. The soil cultivated with rice without the applicationof fertilizer or manures had low bulk density of 1.53g cm-3 at the time of transplanting both during kharifand rabi seasons in 2005-06. No significant differencewas recorded by the application of differentproportions of fertilizers in kharif and rabi seasonscompared to un fertilized soil. But the integratedsupply of nutrients by substituting 50 or 25 % Nfertilizer with FYM in the rainy season dropped downthe bulk density significantly. The substitution of 50or 25 % N through paddy straw or glyricidia in kharifseason and application of recommended or 75 %recommended N P K dose in rabi after the respectivekharif season treatments significantly reduced thebulk density of the soil at transplanting compared tocontrol both during 2005-06 and 2006-07. Thereduction in the bulk density of the soil was alsorecorded at the panicle initiation and at harvesting ofthe crop due to the integrated nutrient managementin kharif season and fertilizer application in rabiseason compared to the fertilized or unfertilized soil.The layer at 15 - 30 cm invariably recorded higherbulk density than the top layer irrespective of thetreatment (Table 2). Unlike in the top layer, thesubstitution of 50 or 25 % N through organic sourceshad no significant influence on this variable at anystage of the crop growth either in kharif or rabi seasonduring 2005-06 or in 2006-07.

The bulk density of the soil was relativelylow at transplanting, but it increased at panicleinitiation and harvesting stage of the crop consequentto the settlement of the soil and trafficking by humanlabour for cultural operations both during kharif andrabi in the two years. As a result, the porosity andwater holding capacity reduced from transplanting toharvesting stage of the crop. This inverse relationship

INFLUENCE OF INM ON PHYSICAL PROPERTIES

27

of bulk density with these two parameters is an oft -cited phenomenon (Tripathi et al., 2003). Chawlaand Chhabra (1991) also reported that the continuousapplication of N fertilizer had no significant influenceon this soil physical parameter. However, the presentstudy confirmed that the co application of organicsource of nutrients by partly replacing the chemicalfertilizers had a subtle advantage to improve the soilphysical properties. The substitution of 50 % Nfertilizer through FYM, paddy straw or glyricidia inkharif and the application of recommended dose offertilizers in rabi or the substitution of 25 % N fertilizerwith any one of the three organic sources in kharifand the application of 75 % recommended dose offertilizers in the rabi season significantly reduced thebulk density at transplanting, panicle initiation andharvesting stage of rice continuously during the fourseasons in the biologically active rooting depth of 0-15 cm. This reduction in bulk density could probablybe assigned to the reason that the addition of organicmatter increased the volume of the soil per unitweight. Such benefit of reduction in bulk density ofthe soil through the incorporation of organic matterhas been well documented by Vasanthi andKumarswamy (1999).

Porosity

The soil was more porous at 0-15 cm soildepth in all the treatments at transplanting than atpanicle initiation stage both in kharif and rabi seasons(Table 3). It reduced further at harvest. The porositywas 42.64 and 41.89 % at transplanting in kharif 2005and 2006, respectively by growing rice without theapplication of manures or fert ilizers. Thecorresponding values were 42.26 and 41.89 % in rabiseason of 2005 and 2006 respectively. Thecontinuous application of recommended dose offertilizers to rice-rice cropping system had nosignificant influence and substitution of 50 % Nthrough the organics i.e. FYM, paddy straw andglyricidia in kharif season and the application ofrecommended dose of NPK through fertilizers in rabiseason significantly increased the porosity of the soilduring both the seasons. The application of 25 % Nthrough the organics in the kharif season and 75 %recommended NPK through fertilizers in the rabiseason also made the soil more porous than theunfertilised soil.

The porosity was significantly more than inunfertilized soils due to the carry over effect ofintegrated nutrient management treatments. Thetrends were persistent at transplanting, panicleinitiation and harvest. The porosity was relatively lowin the lower layer of 15-30 cm soil depth (Table 4).The porosity increased significantly at transplantingby the substitution of 50 % recommended level of Nwith FYM compared to continuous fertilizerapplication at recommended level in the kharif andrabi season during both the years. This improvementalso persisted at panicle initiation and harvestingstage in kharif and rabi seasons during the secondyear. The trends with the other sources of organicnutrients substituted with 25 or 50 % of therecommended level of N were highly irregular.

The porosity of the fertilized soil was similarto the unfertilized soil. But, a magnificentimprovement in the volume fraction of pores wasevident due to the integrated nutrient managementof rice in kharif followed by fertilizer application inrabi season during the two year rice-rice croppingsequence. This trend was obviously due to anincrease in the volume of pore space because of theaddition of organic matter to the total volume of thesoil. But, Katele et al., (1992) observed that theaddition of FYM in an alfisol did not bring a significantchange in this parameter. Bhagat et al. (2003) andTripathi et al (2003) also observed that theincorporation of Lantana camera into the soil reducedthe bulk density and increased the porosity which inturn improved the retention of water.

Infiltration

There was a distinct response of significantimprovement in infiltration of water at transplantingdue to substitution of 25 or 50 % recommended levelof N with FYM compared to the practice ofrecommended level of fertilizer application both inkharif and rabi season during the two years (Table5). The substitution of 25 % recommended level of Nwith glyricidia also established similar trend. Theresponse due to the integration of organic nutrientsthrough paddy straw was irregular. The differencesin infiltration due to fertilizer application and theintegration of organic nutrients were not apparentduring the panicle initiation and harvesting stage ofthe crop in either of the two years. Among the organic


28

sources FYM, had a consistent response to improvethe infiltration of water significantly atleast during thepart of rice growing period. Kumar et al., (1992) alsorecorded significant improvement in infiltration rateof water due to integrated nutrient management ofnutrients in rice-wheat cropping system.

Hydraulic conductivity

The hydraulic conductivity of the soil suppliedwith different levels of fertilizers ranged from 0.25 to0.27 cm h-1 at transplanting in kharif season during2005-06 and from 0.23 to 0.25 cm h-1 in thesubsequent rabi (Table 6). A significant improvementin this physical property of the soil was recorded bothin kharif and rabi due to the substitution of FYM @25 % N fertilizer equivalent in kharif. But thesubstitution of 25 or 50 % recommended level of Nthrough glyricidia significantly improved the hydraulicconductivity both in kharif and rabi seasons duringthe second year. The hydraulic conductivity wassignificantly low in the kharif season in the fertilizertreatments than in integrated nutrient managementtreatments by substituting 25 or 50 % recommendedlevel of NPK through FYM or glyricidia only in thesecond year. Such improvement was not recordedin the rabi season during both the years. No significantvariation in the hydraulic conductivity in the surfacesoil upto 15 cm depth was recorded either in kharif orrabi season at harvest stage of the crop during 2005-06 or 2006-07.

The hydraulic conductivity was invariably lowin the lower (15 – 30 cm) depth than upper layer ofthe soil in all the treatments at different stages ofcrop growth (Table 7). It ranged from 0.20 to 0.22 cmh-1 at the time of transplanting in kharif 2005 and from0.19 to 0.21 cm h-1 during 2006 due to different levelsof fertilizer application. During the subsequent growthphases of panicle initiation and harvesting, hydraulicconductivity was similar in unfertilized, fertilized andintegrated nutrient management treatments duringboth the years.

Water holding capacity

The maximum water holding capacity of thesoil was reduced consistently with advance in age ofthe crop from transplanting to panicle initiation andat harvest in all the treatments (Table 8). The soilsupplied with different levels of nutrients through the

fertilizers held 42.04 - 42.14 % water at transplantingin kharif and 41.26 – 41.41 % in rabi during 2005-06.The water retention of the soil improved significantlyby substituting 25 or 50 % recommended level of Nwith FYM. This effect was long lasting until harvestboth during kharif and rabi in the first year. Paddystraw and glyricidia were also effective sources toretain more moisture at different stages of crop growthduring the two years. The water holding capacity wasrelatively low at 15-30 than 0-15 cm soil depthirrespective of the treatment during both the years(Table 9). Unlike in the top layer, the substitution oforganics at 25 or 50 % fertilizer N equivalent wasdistinct with significantly higher moisture content thanin the fertilized or unfertilized plots from transplantingto panicle initiation stage in the kharif season.

The water holding capacity was also bestimproved by the substitution of 50 or 25 per cent Nfertilizer with FYM in the Kharif season. Theimprovement in this soil physical property was longsustained until harvest due to the cumulativeinfluence of reduced bulk density, increased porosityvis –a- vis an increase in the infiltration and hydraulicconductivity of the soil during the early periods ofcrop growth. The substitution of 50 or 25 per cent Nfertilizer with rice straw or glyricidia also in generalhad a long lasting effect in better water retention forgood crop management. The lower 15-30 cm soil layerwas not influenced by organic matter additions. Animprovement in water holding capacity of the soil bythe combined application of organic and inorganicsource of nutrients was also recorded by Vennila andMuthuvel (1998).

Grain yield

The unfertilized crop produced low grain yieldof 2475 and 2199 kg ha-1 in kharif and 2025 and 1545kg ha-1 in rabi during 2005-06 and 2006-07,respectively (Table 10). The fertilizer applicationbenefited the crop to produce more yield. Theproduction increased to 3739 and 3920 kg ha-1 in twoseasons during 2005-06, while it increased to 2911and 2801 kg ha-1 in kharif and rabi in 2006-07. Thestrategy to apply 50 % recommended dose offertilizers in kharif and recommended dose offertilizers in rabi maintained the proportion ofproduction in consonance with the increase in levelof nutrients added. The production raised enormously


29

to as high as 5405 kg ha-1 in rabi 2005-06 and 4105kg ha-1 during 2006-07 due to the high dose ofrecommended level of fertilizers. The application ofrecommended dose of fertilizers continuously in kharifand rabi seasons invariably produced significantlymore grain yield. High produce of 4676 and 5890 kgha-1 was realized by this treatment in the kharif andrabi seasons during 2005-06. Similarly, maximumgrain yield of 3983 kg ha-1 in kharif and 3801 kg ha-1

in rabi was realized from the optimum fertilizerschedule in the second year.

The substitution of 50 % N fertilizer withpaddy straw in kharif season reduced the grain yieldcompared to the production realized by applicationof recommended dose of fertilizers during both theyears. The substitution of 25 % N fertilizer with paddystraw in kharif season reduced the grain yieldsignificantly only during the second year. The

substitution of 50 % N fertilizer with glyricidia in kharifseason and application of recommended dose offertilizers in rabi season were the best integratednutrient management strategies. The substitution of25 % N fertilizer with glyricidia in kharif season andapplication of 75 % recommended dose of fertilizersin rabi season was also highly productive.

The results of the present investigationshowed that the physical properties of the soil wereinfluenced by different nutrient management practices.These effects were highly distinguished in the upper0-15 cm depth and were less distinct in the lower 15-30 cm depth. This layer wise differentiation wasprobably due to the more weathered andmicrobiologically intensive portion within which theorganic and inorganic sources of nutrients wereincorporated for their reactions than in the lower layer.

Table 1. Influence of integrated nutrient management treatments on bulk density (g cm-3) of soil inrice-rice cropping system at 0-15 cm depth

2005-06 2006-07 Treat ment

Transplanting Panicle

initiation Harvesting Transplanting Panicle

initiation Harvesting

Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi

T1 1.53 1.53 1.56 1.56 1.59 1.60 1.54 1.54 1.57 1.57 1.60 1.61

T2 1.54 1.54 1.57 1.56 1.60 1.60 1.55 1.55 1.58 1.58 1.61 1.62

T3 1.53 1.54 1.57 1.56 1.60 1.61 1.54 1.55 1.58 1.58 1.61 1.61

T4 1.53 1.54 1.57 1.56 1.60 1.61 1.54 1.55 1.58 1.58 1.61 1.61

T5 1.53 1.54 1.57 1.56 1.60 1.61 1.54 1.55 1.58 1.58 1.61 1.62

T6 1.43 1.45 1.45 1.47 1.61 1.49 1.44 1.43 1.46 1.45 1.48 1.47

T7 1.45 1.47 1.47 1.49 1.49 1.51 1.46 1.45 1.48 1.47 1.50 1.53

T8 1.45 1.47 1.47 1.49 1.49 1.51 1.46 1.45 1.48 1.47 1.50 1.52

T9 1.47 1.49 1.49 1.51 1.51 1.53 1.48 1.45 1.50 1.50 1.52 1.53

T10 1.46 1.48 1.49 1.50 1.51 1.52 1.47 1.45 1.50 1.47 1.52 1.54

T11 1.47 1.49 1.48 1.51 1.50 1.53 1.48 1.47 1.49 1.49 1.51 1.53

T12 1.53 1.53 1.50 1.56 1.59 1.59 1.54 1.54 1.57 1.57 1.60 1.61

SEm + 0.03 0.03 0.03 0.02 0.03 0.03 0.02 0.02 0.03 0.02 0.30 0.30

CD at 5 %

0.06 0.05 0.07 0.04 0.05 0.07 0.04 0.05 0.06 0.05 0.07 0.07


30

Table 2 . Influence of integrated nutrient management treatments on bulk density (g cm-3) of soil inrice-rice cropping system at 15-30 cm depth

2005-06 2006-07 Treatment Transplanting Panicle




T1 1.56 1.57 1.59 1.59 1.64 1.63 1.57 1.58 1.60 1.61 1.64 1.65

T2 1.57 1.58 1.60 1.57 1.65 1.61 1.58 1.59 1.61 1.62 1.65 1.66

T3 1.56 1.58 1.59 1.57 1.64 1.61 1.57 1.58 1.60 1.61 1.64 1.65

T4 1.56 1.58 1.59 1.57 1.64 1.61 1.57 1.58 1.60 1.61 1.64 1.66

T5 1.56 1.58 1.59 1.57 1.64 1.61 1.57 1.58 1.60 1.61 1.64 1.65

T6 1.49 1.48 1.51 1.49 1.55 1.52 1.48 1.47 1.50 1.49 1.55 1.54

T7 1.51 1.50 1.53 1.51 1.57 1.54 1.50 1.49 1.52 1.51 1.57 1.56

T8 1.51 1.50 1.53 1.51 1.57 1.54 1.50 1.49 1.52 1.51 1.57 1.56

T9 1.53 1.52 1.55 1.53 1.57 1.56 1.52 1.51 1.52 1.51 1.57 1.55

T10 1.52 1.51 1.54 1.52 1.58 1.55 1.51 1.50 1.53 1.52 1.58 1.57

T11 1.53 1.52 1.55 1.53 1.59 1.56 1.51 1.50 1.53 1.52 1.58 1.58

T12 1.56 1.57 1.59 1.60 1.64 1.64 1.57 1.58 1.60 1.60 1.65 1.66

SEm + 0.04 0.05 0.04 0.06 0.50 0.07 0.08 0.10 0.08 0.06 0.09 0.07

CD at 5 %

NS NS NS NS NS NS NS NS NS NS NS NS

Table 3 . Influence of integrated nutrient management treatments on porosity (%) of soil in rice-

rice cropping system at 0-15 cm depth





T1 42.64 42.26 41.51 41.13 40.00 39.62 41.89 41.89 40.75 40.75 39.62 39.24

T2 42.26 41.89 41.31 41.13 39.62 39.62 41.51 41.51 40.38 40.28 39.24 38.87

T3 42.64 41.89 42.26 41.13 40.75 39.24 41.51 41.89 40.38 40.28 39.24 39.24

T4 42.64 41.89 42.26 41.13 40.75 39.24 41.51 41.89 40.38 40.28 39.24 39.24

T5 42.64 41.89 42.26 41.13 40.75 39.24 41.51 41.51 40.38 40.28 39.24 39.24

T6 44.98 45.28 44.15 44.53 43.02 43.77 45.66 46.04 44.91 40.28 44.15 44.13

T7 44.15 44.53 43.40 43.77 42.26 43.02 44.91 45.28 44.15 44.53 43.40 42.26

T8 44.15 44.53 43.40 43.77 42.26 43.02 44.91 45.28 44.15 44.53 43.40 42.64

T9 43.40 43.77 42.64 43.02 41.51 42.26 44.15 44.15 43.40 43.40 42.64 42.26

T10 43.77 44.15 43.02 43.40 41.89 42.64 44.53 45.28 43.40 44.53 42.64 41.89

T11 43.40 43.77 42.64 43.02 41.51 42.66 44.15 44.53 43.77 44.53 43.02 42.26

T12 42.64 41.89 41.51 41.13 40.00 39.62 41.57 41.89 40.38 40.28 39.24 38.17

SEm+ 0.17 0.79 0.12 0.76 0.26 1.04 1.10 1.36 1.30 1.32 1.37 1.14

CD at 5 % 0.31 1.65 0.25 1.58 0.54 2.16 2.31 2.83 2.69 2.73 2.85 2.37


31

Table 4 . Influence of integrated nutrient management treatments on porosity (%) of soil in rice-ricecropping system at 15-30 cm





T1 41.13 40.75 40.00 10.00 38.11 38.49 40.75 40.38 39.62 39.27 38.11 38.00

T2 40.75 40.38 39.62 40.75 37.74 39.24 40.78 40.00 39.24 38.87 37.74 37.74

T3 41.33 40.38 40.00 40.75 38.11 39.24 40.38 40.38 39.62 39.24 38.11 37.26

T4 41.33 40.38 40.00 40.75 38.11 39.27 40.38 40.38 39.62 39.24 38.00 37.74

T5 41.33 40.38 40.00 40.75 38.11 39.27 40.38 40.38 39.62 39.24 38.11 37.74

T6 43.77 44.18 40.00 43.17 41.51 42.64 44.40 44.53 43.40 43.77 41.51 41.89

T7 43.02 43.40 43.02 43.02 40.75 44.89 43.40 43.77 42.64 43.02 40.75 41.13

T8 43.02 43.80 42.26 43.02 40.75 41.89 43.40 43.77 42.64 43.02 40.75 41.13

T9 42.26 42.64 42.26 42.26 40.75 41.13 42.64 43.02 42.64 43.02 40.75 41.51

T10 42.64 43.02 41.51 42.64 40.38 41.51 43.02 43.40 42.26 42.64 40.38 40.75

T11 42.26 43.64 41.51 42.26 40.00 41.13 43.02 43.40 42.26 42.64 40.38 40.75

T12 41.13 40.38 40.00 40.75 38.11 39.24 40.38 40.300 39.62 39.24 38.11 37.28

SEm + 0.92 1.30 1.43 1.35 1.52 2.00 1.33 1.01 1.52 1.45 1.56 1.46

CD at 5 % 1.87 2.68 2.95 NS 3.16 4.15 2.74 2.11 3.15 3.00 3.22 3.02

Table 5 . Influence of integrated nutrient management treatments on infiltration (mm h-1) of soil inrice-rice cropping system


initiation Harvesting Transplanting Panicle initiation Harvesting


T1 6.56 6.45 6.36 6.25 6.96 6.88 6.35 6.25 6.15 6.00 6.75 6.65

T2 6.58 6.47 6.37 6.26 6.98 6.85 6.37 6.27 6.17 6.06 6.77 6.67

T3 6.59 6.46 6.33 6.27 7.00 6.87 6.35 6.26 6.15 6.06 6.75 6.66

T4 6.58 6.48 6.36 6.28 6.97 6.88 6.37 6.27 6.10 6.07 6.77 6.67

T5 6.56 6.46 6.35 6.26 6.95 6.86 6.36 6.26 6.15 6.06 6.76 6.66

T6 6.75 6.66 6.55 6.46 7.01 6.85 6.56 6.46 6.47 6.26 6.97 6.86

T7 6.95 6.80 6.75 6.60 7.35 6.80 6.72 6.62 6.52 6.44 7.12 7.02

T8 6.61 6.50 6.40 6.30 7.01 6.70 6.40 6.30 6.25 6.10 6.80 6.90

T9 6.70 6.60 6.50 6.45 7.10 6.75 6.55 6.45 6.35 6.25 6.95 6.85

T10 6.62 6.52 6.42 6.35 7.00 6.75 6.42 6.32 6.22 6.14 6.82 6.75

T11 6.72 6.65 6.52 6.30 7.12 6.70 6.55 6.45 6.35 6.26 6.95 6.85

T12 6.55 6.40 6.35 6.20 6.85 6.60 6.30 6.20 6.10 6.00 6.80 6.60

SEm + 0.07 0.09 0.08 0.07 0.17 0.16 0.09 0.09 0.21 0.15 0.31 0.24

CD at 5 % 0.14 0.18 0.17 0.14 NS NS 0.19 0.17 NS NS NS NS


32

Table 6 . Influence of integrated nutrient management treatments on hydraulic conductivity (cm h-1) ofsoil in rice-rice cropping system at 0-15 cm

2005-06 2006-07 Treatment Trans

planting Panicle

initiation Harvesting Trans

planting Panicle


Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi T1 0.26 0.25 0.24 0.22 0.30 0.29 0.24 0.24 0.22 0.22 0.28 0.28 T2 0.25 0.24 0.23 0.21 0.29 0.28 0.23 0.23 0.21 0.21 0.27 0.27 T3 0.25 0.24 0.23 0.21 0.29 0.28 0.23 0.22 0.21 0.20 0.27 0.26 T4 0.26 0.25 0.27 0.22 0.30 0.29 0.24 0.24 0.22 0.22 0.28 0.28 T5 0.27 0.26 0.25 0.23 0.31 0.30 0.25 0.25 0.23 0.23 0.29 0.29 T6 0.29 0.28 0.27 0.25 0.33 0.32 0.27 0.26 0.25 0.24 0.31 0.30 T7 0.31 0.30 0.29 0.27 0.34 0.34 0.29 0.29 0.28 0.27 0.33 0.33 T8 0.28 0.27 0.26 0.27 0.32 0.31 0.26 0.26 0.24 0.24 0.30 0.30 T9 0.29 0.28 0.27 0.25 0.33 0.32 0.27 0.27 0.25 0.25 0.31 0.31 T10 0.29 0.28 0.27 0.25 0.33 0.32 0.28 0.27 0.25 0.25 0.32 0.31 T11 0.30 0.29 0.28 0.26 0.33 0.33 0.28 0.28 0.26 0.26 0.32 0.32 T12 0.26 0.25 0.24 0.22 0.30 0.29 0.24 0.24 0.22 0.22 0.28 0.28

SEm + 0.01 0.01 0.22 0.18 0.19 0.16 0.02 0.08 0.09 0.21 0.15 0.17 CD at 5 % 0.02 0.03 NS NS NS NS 0.021 0.015 0.16 NS NS NS

Table 7 . Influence of integrated nutrient management treatments on hydraulic conductivity (cm h-1) ofsoil in rice-rice cropping system at 15-30 cm

2 0 0 5 -0 6 2 0 0 6 -0 7 T re a tm e n t T ra n s -

p la n tin g P a n ic le

in it ia tio n H a rv e s tin g T ra n s -

p la n tin g P a n ic le

in it ia tio n H a rv e s tin g

K h a rif R a b i K h a rif R a b i K h a rif R a b i K h arif R a b i K h a rif R a b i K h a rif R a b i

T 1 0 .2 1 0 .2 0 0 .1 9 0 .1 8 0 .2 5 0 .2 4 0 .2 0 0 .1 9 0 .1 8 0 .1 7 0 .2 4 0 .2 3

T 2 0 .2 0 0 .1 9 0 .1 8 0 .1 7 0 .2 4 0 .2 3 0 .1 9 0 .1 8 0 .1 8 0 .1 6 0 .2 3 0 .2 2

T 3 0 .2 0 0 .1 9 0 .1 8 0 .1 7 0 .2 4 0 .2 3 0 .1 9 0 .1 8 0 .1 7 0 .1 6 0 .2 3 0 .2 2

T 4 0 .2 1 0 .2 0 0 .1 9 0 .1 8 0 .2 5 0 .2 4 0 .2 1 0 .1 9 0 .1 9 0 .1 7 0 .2 4 0 .2 3

T 5 0 .2 2 0 .2 1 0 .2 0 0 .1 9 0 .2 6 0 .2 5 0 .2 1 0 .2 0 0 .1 9 0 .1 8 0 .2 5 0 .2 4

T 6 0 .2 4 0 .2 3 0 .2 2 0 .2 1 0 .2 8 0 .2 8 0 .2 3 0 .2 1 0 .2 1 0 .1 9 0 .2 8 0 .2 5

T 7 0 .2 6 0 .2 5 0 .2 4 0 .2 3 0 .3 0 0 .2 9 0 .2 5 0 .2 4 0 .2 3 0 .2 2 0 .2 8 0 .2 8

T 8 0 .2 3 0 .2 2 0 .2 1 0 .2 0 0 .2 7 0 .2 6 0 .2 2 0 .2 1 0 .2 0 0 .2 0 0 .2 6 0 .2 5

T 9 0 .2 4 0 .2 3 0 .2 2 0 .2 1 0 .2 8 0 .2 7 0 .2 3 0 .2 2 0 .2 1 0 .2 0 0 .2 7 0 .2 6

T 1 0 0 .2 4 0 .2 3 0 .2 2 0 .2 1 0 .2 8 0 .2 7 0 .2 3 0 .2 2 0 .2 1 0 .2 0 0 .2 7 0 .2 6

T 1 1 0 .2 5 0 .2 4 0 .2 3 0 .2 2 0 .2 9 0 .2 8 0 .2 4 0 .2 3 0 .2 2 0 .2 1 0 .2 8 0 .2 7

T 1 2 0 .2 1 0 .2 0 0 .1 9 0 .1 8 0 .2 5 0 .2 4 0 .2 0 0 .1 9 0 .1 8 0 .1 7 0 .2 4 0 .2 4

S E + 0 .0 1 0 .0 1 0 .0 2 0 .0 3 0 .0 5 0 .0 6 0 .0 2 0 .0 1 0 .0 4 0 .0 3 0 .0 4 0 .0 3

C D a t 5 % 0 .0 3 0 .0 3 N S N S N S N S 0 .0 4 0 .0 2 N S N S N S N S

INFLUENCE of INM on PHYSICAL PROPERTIES

33

Table 8. Influence of integrated nutrient management treatments on water holding capacity (%) of soilin rice-rice cropping system at 0-15 cm





T2 41.76 41.38 40.65 40.31 38.53 38.52 41.26 41.00 39.75 39.52 37.42 38.44

T3 42.14 41.33 41.65 40.31 39.26 38.14 41.59 41.34 39.331 39.50 38.16 38.52

T4 42.14 41.41 41.66 40.25 39.24 38.29 41.54 41.30 39.44 39.50 38.25 38.52

T5 42.10 41.26 41.55 40.28 39.28 38.42 41.63 41.05 39.39 39.50 38.14 38.38

T6 44.48 44.72 43.74 43.83 42.00 42.65 43.81 45.28 42.67 39.50 41.00 38.21

T7 43.65 44.03 42.78 43.06 41.19 42.01 43.16 45.31 41.86 44.00 40.09 43.05

T8 43.25 44.00 42.63 42.29 41.02 42.02 42.72 43.46 41.56 44.00 40.00 43.10

T9 42.95 43.20 41.98 42.81 40.14 41.16 42.44 43.21 40.00 42.95 39.24 41.00

T10 43.17 43.84 42.21 42.91 40.25 41.54 42.64 44.33 41.34 44.05 39.21 43.11

T11 42.90 43.19 41.88 42.35 40.16 41.36 42.69 43.47 40.92 44.00 39.18 42.93

T12 42.00 41.35 40.72 40.74 39.06 38.54 41.37 41.08 39.88 39.76 38.08 38.02

SEm + 0.53 0.90 0.47 0.59 0.72 0.77 0.57 1.04 0.65 0.89 1.01 1.05

CD at 5 % 1.10 1.87 0.98 1.22 1.50 1.86 1.18 2.15 1.36 1.85 2.11 2.18

Table 9. Influence of integrated nutrient management treatments on water holding capacity (%) of soilin rice-rice cropping system at 15-30 cm





T2 40.26 39.83 39.08 40.00 38.16 38.14 39.83 39.19 38.49 37.92 36.67 36.57

T3 40.79 39.80 39.17 40.02 38.21 38.21 39.80 39.45 38.46 37.92 37.15 36.00

T4 40.72 39.81 39.23 40.10 38.34 38.19 39.85 39.48 38.51 37.81 37.18 36.54

T5 40.10 39.85 39.20 40.15 38.19 38.16 39.81 39.45 39.39 37.84 37.20 36.50

T6 42.68 43.64 39.18 42.05 38.05 41.46 43.72 43.42 42.81 42.95 40.44 40.30

T7 42.34 42.86 42.29 42.18 41.05 40.75 42.61 42.67 41.72 42.06 39.86 41.00

T8 42.48 42.85 41.51 42.14 40.44 40.71 42.64 42.47 41.46 42.06 39.79 40.29

T9 41.65 42.05 41.64 41.19 40.26 40.11 41.96 42.21 41.40 42.00 39.81 40.25

T10 42.06 42.56 40.25 41.21 39.08 40.28 42.21 41.20 41.51 41.46 39.25 40.25

T11 41.84 43.10 40.36 41.20 39.17 40.19 42.36 41.20 41.59 41.46 39.18 40.30

T12 40.69 39.43 39.24 40.00 38.30 38.22 39.55 39.19 38.73 38.37 37.10 37.00

SEm + 0.45 0.90 1.02 1.86 1.35 1.20 0.96 1.04 0.91 1.53 1.35 1.42

CD at 5 % 0.94 1.86 2.12 NS NS NS 2.00 2.16 1.87 3.18 NS NS


34

Table 10. Influence of integrated nutrient management treatments on yield in Rice - Rice croppingsystem

Grain yield (kg ha-1) Treatment 2005-06 2006-07

Kharif Rabi Kharif Rabi T1 2475 2025 2199 1545

T2 3739 3920 2911 2801

T3 3676 5405 3111 4150

T4 3891 5200 3601 3397

T5 4676 5890 3983 3801

T6 4140 5795 3782 3889

T7 4649 5665 3856 3582

T8 3856 5375 2444 3815

T9 4411 5230 2980 3546

T10 4973 6070 4052 4480

T11 4977 6915 4251 4244

T12 4371 4745 3244 3333

SEm + 236 91 149 375

CD at 5 % 492 191 311 783

REFERENCES

Bertrand, A.R. 1965. Rate of water intake in the fieldin: Measures of soil analysis by Black, C.A.part I Agronomy 9 : 374-390.

Bhagat, R.M., Bhardwaj, A.K and Pradeep, K.,Sharma. 2003. Long term Effect of ResidueManagement on soil physical properties, wateruse and yield of rice in north – western India,journal of the Indian Society of Soil Science,51 : 111-117.

Black, C.A. 1965. Methods of soil analysis Part,American Society of Agronomy, Wisconsin,USA. Density, water contents and microbialpopulation of soil. Journal of Indian Society ofsoil science 40 :553-555.

Chawla, K.L and Chabra, R. 1991. Physicalproperties of gypsum amended sodic soils asaffected by long term use of fertilizers. Journalof Indian Society of Soil Science 39 :40-46

Jalota, S.K., Ramesh Khera and Ghuman, B.S. 1998.Measures in soil physics Narosa publishinghouse, New Delhi

Katele, P., Leinweber, P and Menning, P. 1992. Onthe influence of soil organic mater on physicalproperties of soil. Agrobiological Research45 : 18-27

Kumar, K., Meelu, O.P., Singh, Y and Singh, B. 1992.Effect of continuous application of organicmanures on the physical properties of soils inrice-wheat cropping system. International RiceResearch news Letter. 17 : 4-16.

Tripathi, R.P., Gaur, M.K and Rawat, M.S. 2003.Puddling Effects on soil physical propertiesand rice performance under shallow water tableconditions of tarai. Journal of the Indian Societyof Soil Science, 51: 118-124

Vasanthi, D and Kumarswamy, K. 1999. Efficacy ofvermicompost to improve soil fertility and riceyield. Journal of the Indian society of soilscience 47 : 268-272

Vennila, R.K and Muthuvel, P. 1998. Effect of longterm fertilization on physical properties of soils.Madras Agricultural Journal 85 : 290-292.


35

The genetic improvement of quantitativecharacters in a crop species depends upon heritabilitypattern of the trait in question, nature and amount ofvariability present in the existing germplasm.Moreover sweet sorghum is also not an exception.Knowledge on the genetic advance that is expectedby applying selection pressure to a segregatingpopulation is useful in designing effective breedingprogramme. Evaluation of these segregatingprogenies helps in estimation of various genetic andnon-genetic components of variance. The study ofvariability provides an opportunity for selecting thedesirable genotypes. Heritability is a fraction ofvariance in phenotypic expression that arises fromgenetic effects. The nature of the selection unitsand sampling errors also influences greatly themagnitude of heritability. The estimates of heritabilityin segregating generations help to know geneticvariance, genotype - environment interaction and theprogress expected from selection.

The fresh stalk yield and sugar yield in sweetsorghum, as in other crops, is a complex quantitativecharacter and its expression depends upon itscomponent characters. The knowledge on the

ABSTRACT

The present investigation on genetic variability, heritability and character association in large F2 populationof sweet sorghum was carried out at Directorate of Sorghum Research, Rajendranagar, Hyderabad. F1 was generatedduring 2010 kharif and second filial generation in the following season. The mean and variance in respect of 14quantitative characters in F2 population indicated wide range of variability for most of the traits. However, variabilityrange was low for the traits like nodes per plant, stem girth, brix per cent and total soluble sugars. The distributionpattern of this F2 population revealed complementary interaction in the inheritance of days to 50% flowering, days tomaturity, plant height, total biomass, fresh stalk yield, grain yield, brix per cent, juice yield, juice extraction per cent,total soluble sugars, sugar yield and bioethanol yield, while inheritance of nodes per plant and stem girth exhibitedduplicate type of epistasis. Most of these characters except days to 50% flowering and days to maturity exhibitedhigh heritability coupled with moderate to high genetic advance as per cent of mean indicating predominance ofadditive gene action in their genetic control. Further, correlation studies in F2 generation revealed significant andpositive correlation of fresh stalk yield with total biomass, grain yield, plant height, nodes per plant, stem girth, daysto 50% flowering and days to maturity, while sugar yield with juice yield, fresh stalk yield, total biomass, grain yield,total soluble sugars, brix per cent, bioethanol yield and juice extraction per cent. These correlated traits can beeffectively utilized in formulating indirect selection schemes. While path analysis studies revealed maximum positivedirect effect of total soluble sugars and juice yield on sugar yield.

GENETIC VARIABILITY, HERITABILITY AND CHARACTER ASSOCIATIONSTUDIES IN SWEET SORGHUM [Sorghum bicolor (L.) Moench]

VEMANNA IRADDI, T. DAYAKAR REDDY, A. V. UMAKANTH, CH. RANI,D. VISHNU VARDHAN REDDY and M. H. V. BHAVE

Department of Genetics and Plant BreedingAcharya N.G. Ranga Agricultural University, Hyderabad – 500 030



relative contribution of different yield components andtheir direct and indirect impact towards sugar yieldis of immense value in selection of superiorgenotypes. Keeping the situations present in theforegoing paragraphs in view, the present investigationwas under taken to study genetic variability,heritability and character association in sweetsorghum.

MATERIALS AND METHODSThe material for this experiment comprised

of F2 population of a cross derived from parents (27B with SSV 84) having low and high sugar contentdeveloped at Directorate of Sorghum Research,Rajendranagar, Hyderabad during kharif 2010. TheF1 plants of this cross were grown during rabi 2010 -11 and selfed to produce the F2 seeds, which wereevaluated during summer 2012. The F2 segregatinggenerations were grown in plots with twenty rows eachin three separate blocks. These plants were sown inplots of twenty rows spaced 45 cm apart with a plantspacing of 15 cm with 2 - 3 seeds per hill in each rowof 4 mt length. Thinning was done to retain onehealthy plant per hill at 15 and 25 days after sowing.All the recommended package of practices wasfollowed to raise a good and healthy crop.

J.Res. ANGRAU 41(1) 30-38, 2013

36

RESULTS AND DISCUSSIONThe descriptive statistics and genetic

variability parameters with respect to fourteenquantitative characters in F2 population of the cross‘27 B × SSV 84’ is presented in Table 1. Thesedescriptive statistics, unravels basic idea of thebreeding material. The characteristics of this F2

population in respect of various quantitativecharacters as indicated by these first and seconddegree statistics are discussed below.

Wide range of variability was present for thetraits such as plant height, total biomass, fresh stalkyield, grain yield, juice yield, juice extraction per cent,sugar yield and bioethanol yield of this cross of sweetsorghum studied as indicated by their respectivemean and variances. Existence of moderatevariability was observed for days to 50% floweringand days to maturity. However, the variability rangewas low for the traits like nodes per plant, stem girth,brix per cent and total soluble sugars.

The study of distribution properties such asco-efficients of skewness (third degree statistic) andkurtosis (fourth degree statistic) provides insight aboutthe nature of gene action and number of genescontrolling the traits, respectively. All the studiesreported nature of genetic control of quantitative traitsin sorghum is based on first degree (gene effectsthrough generation mean analysis) and second degree(components of genetic variances through diallel, line× tester analysis, etc.) statistics. Skewness andkurtosis are greater than first and second degreestatistics which reveal interaction genetic effects. Theskewed distribution of a trait in general suggests thatthe trait is under the control of non-additive geneaction, especially epistasis and influenced byenvironmental variables (Kimberg and Bingham, 1998and Roy, 2000). Positive skewness is associatedwith complementary interaction and negativeskewness is associated with duplicate (additive ×additive) gene interactions predominantly in the samedirections. Complete ambi-directional epistasishowever produces kurtosis while distribution stayssymmetrical around mean. The genes controlling thetrait with skewed distribution tend to be predominantlydominant irrespective of whether they have increasingor decreasing effects on the expression of the trait.

The traits with leptokurtic and platykurticdistribution are controlled by fewer and a large numberof genes, respectively. Kurtosis is negative or closeto zero in the absence of gene interactions and is

positive in the presence of gene interactions. Theinference on the relative number of genes and natureof genetic control of different traits in F2 generationof this sweet sorghum cross is discussed below.

Platykurtic and positively skewed distributionsuggested the involvement of relatively large numberof segregating genes with dominance basedcomplementary type of interaction in the inheritanceof days to 50% flowering, days to maturity, plantheight, total biomass, fresh stalk yield, grain yield,juice yield, juice extraction per cent and sugar yieldin ‘27 B × SSV 84’ cross. Maximizing the geneticgain in respect of these traits with positively skeweddistribution requires intense selection from theexisting variability.

The inheritance of nodes per plant and stemgirth recorded negatively skewed platykurticdistribution which indicates that these traits aregoverned by large number of dominant genes withduplicate type of epistasis. These traits have evolvedwith dominance and dominance based duplicateepistasis which helps to protect the individual plantsfrom deleterious alleles arising from existingvariability (Roy, 2000).

The leptokurtic and positively skeweddistribution for traits such as brix per cent, totalsoluble sugars and bioethanol yield suggested theinvolvement of relatively fewer number of segregatinggenes with dominance based complementaryinteraction in the inheritance of these traits. Toachieve maximum genetic gain in respect of thesetraits needs intense selection.

Estimation of variability parameters in apopulation is a pre-requisite for breeding programmeaimed at improving yield, quality and other importantcharacters under consideration. Unless a majorportion of variation is heritable, attempts to improvecharacters by selection would be futile. Therefore, itis necessary to have information on both PCV andGCV, so that the heritability, which helps the breederto predict the expected genetic advance possible byselection for characters, can be computed. Accordingto Johnson et al. (1955), heritability estimates alongwith genetic gain would be more useful than the formeralone in predicting the effectiveness of selection.Therefore it is essential to consider the predictedgenetic advance along with heritability estimate as atool in selection programme for better efficiency.

GENETIC VARIABILITY, HERITABILITY AND CHARACTER ASSOCIATION STUDIES

37

The range in mean values does not reflectthe total variance in the material studied. Hence,actual variance has to be estimated for the charactersto know the extent of existing variability. Howeverabsolute values of phenotypic and genotypic variancecannot be used for comparing the degree of variabilityin different characters because the characters differin the unit of measurement. Hence, the co-efficientof variation (PCV and GCV) which is calculated byconsidering the respective means have been usedfor the comparisons.

In the present study, the range of variabilitywas quite high for most of the characters studiedexcept days to 50% flowering, days to maturity,nodes per plant, stem girth, brix per cent and totalsoluble sugars, which exhibited low to moderateamount variability. This indicates ample scope forthe improvement of highly variable characters, whichwere generated by segregation and recombination,whereas, it may not be equally effective for acharacter, which exhibited narrow range of variability.

In general, PCV values were relatively higherthan GCV values which is coupled with negligibledif ferences between them, indicates lessenvironmental influence on most of the traits exceptnodes per plant, stem girth, grain yield, brix per cent,total soluble sugars, sugar yield and bioethanol yield.

Days to 50% flowering and days to maturityexhibited lower values of GCV and PCV. This was inconformity with the reports of Rajappa (2009). Highheritability coupled with low genetic advance as percent of mean exhibited by this cross, indicatedpredominant role of non-additive gene action for thesetraits and this result is in accordance with the reportsof Sankarapandian (2002). Patil et al. (1996),Sankarapandian (2002), Unche et al. (2008a) andKachapur and Salimath (2009) also reported highheritability for this trait.

Plant height exhibited high values of GCVand PCV but differences between them was relativelynarrow indicating less influence of environment in theexpression of this trait. The high broad senseheritability (97.42%) coupled with high geneticadvance as per cent of mean (48.37%) indicatedpredominant role of additive gene action in its geneticcontrol. The results of heritability and genetic advancein the present study is in total agreement with thereports of Sankarapandian et al. (1996), Umakanthet al. (2004), Sandeep et al. (2009a) and Rajappa(2009).

Nodes per plant and stem girth registeredmoderate PCV and GCV values with negligibledifference between them indicating less influence ofenvironment on these traits. The results of PCV andGCV were in agreement with earlier report of Rajappa(2009). These traits exhibited high heritability coupledwith moderate and high genetic advance expressedas per cent of mean, respectively indicating role ofadditive gene action in there genetic control. Resultsof the present study are in corroborative with theearlier reports of Sankarapandian et al. (1996),Krishnakumar et al. (2004), Rajappa (2009) andSandeep et al. (2009a). Reliability can be placed onthese traits for selection of segregants owing to itshigh heritability coupled with high genetic advance.

Total biomass per plant recorded highervalues of PCV and GCV with negligible differencebetween them indicating less influence of environmenton the expression of trait of interest. The highestimates of broad sense heritability (97.04%) andgenetic advance expressed as per cent of mean(93.74%) in this cross indicating preponderance ofadditive gene action in the genetic control of this trait.Similar results were earlier reported by Unche et al.(2008a) with respect to heritability and geneticadvance indicating efficiency of simple selection inderiving desirable segregants.

Fresh stalk yield registered higher values ofPCV and GCV with negligible difference betweenthem, indicating less influence of environment on theexpression of this trait. Higher values for this traitwere earlier reported by Sankarapandian et al. (1996)and Rajappa (2009). The broad sense heritability andgenetic advance estimates were also higherindicating usefulness of this trait in selection ofdesirable segregants due to its genetic control byadditive gene action. This is in accordance with theearlier observations made by Sankarapandian et al.(1996), Krishnakumar et al. (2004), Patel et al. (2006),Unche et al. (2008a), Sandeep et al. (2009a) andRajappa (2009).

Grain yield registered higher values of PCVand GCV with conspicuous difference between themindicating high environmental influence. Higher valuesfor this trait were earlier reported by Sankarapandianet al. (1996) and Rajappa (2009). The broad senseheritability and genetic advance estimates were alsohigher indicating usefulness of this trait in selectionof desirable segregants due to its genetic control byadditive gene action.

VEMANNA et al

38

Brix per cent registered has considerableenvironment influence. However, high heritability andmoderate genetic advance expressed as per cent ofmean was recorded revealing the major role ofadditive gene action in genetic control of this trait.These results were in accordance with the reports ofSankarapandian (2002), Sandeep et al. (2009a) andRajappa (2009) for heritability and genetic advance,but contradicts with the results obtained bySankarapandian et al. (1996) and Rajappa (2009) withrespect to GCV and PCV estimates who reportedrelatively higher values. Higher heritability andgenetic advance for this trait indicate effectivenessof simple direct select ion in improvementprogrammes.

Juice yield registered higher PCV and GCVvalues with negligible difference between them,indicating less influence of environment on theexpression of this trait. This also recorded high broadsense heritability (98.27%) coupled with high geneticadvance as per cent of mean (123.06%) indicatingpredominant role of additive gene action in the geneticcontrol of this trait. Higher values for this trait wasearlier reported by Rajappa (2009) for PCV and GCVvalues and by Sankarapandian et al. (1996),Sankarapandian (2002), Kachapur and Salimath(2009) and Rajappa (2009) for heritability and geneticadvance. This trait can be considered as a potentialfor improvement by simple selection owing to highheritability and genetic advance.

Juice extraction per cent exhibited highervalues of PCV and GCV with narrow differencebetween them, indicat ing less influence ofenvironment on the expression of the trait. This iscoupled with high heritability (95.03%) and geneticadvance as per cent of mean (43.80%) indicatingpredominant role of additive gene action. Similarresults with respect to heritability and geneticadvance were reported earlier by Sankarapandian(2002) and Sandeep et al. (2009a).

Total soluble sugars exhibited moderatevalue of PCV and low value of GCV with negligibledifferences between them indicating less influenceof environment on the expression of this trait. Broadsense heritability and genetic advance estimates werealso higher. Moderate variability of this trait coupledwith high heritability and genetic advance indicatehigher scope for further improvement through simpleselection procedures.

Moderate value of PCV and low value of GCVwere registered by bioethanol yield coupled with highbroad sense heritability and moderate geneticadvance expressed as per cent of mean indicatingmajor role of additive gene action in the geneticcontrol of this trait.

Sugar yield registered higher values of PCVand GCV compared to all other traits under studywith considerable difference between them indicatingsubstantial environmental influence on the expressionof this trait, which is reflected in relatively higher broadsense heritability (95.16%) and high genetic advanceexpressed as per cent of mean (123.47%) indicatingmajor role of additive gene action in the geneticcontrol of this trait. Result of the present study is inconformity with the earlier reports of Krishnakumaret al. (2004) and Patel et al. (2006) with respect toheritability and genetic advance. Though heritabilityand genetic advance indicate scope for simple directselection to be effective for this trait, actual gain wouldentirely depend on its intrinsic association with itsattributing traits.

Correlation CoefficientsThe correlation co-efficients among the

selected characters related to fresh stalk yield andsugar yield in F2 population of ‘27 B × SSV 84’ sweetsorghum cross were estimated; results weretabulated in Table 2 and 3 and briefly described inthe following paragraphs.

Association of fresh stalk yield with itscomponent characters Fresh stalk yield per plantwas significantly and positively associated with totalbiomass per plant, grain yield per plant, plant height,nodes per plant, stem girth, days to 50% floweringand days to maturity. Similar trends were evidentfrom the studies of Hapase and Repale (1999), Naharet al. (2002), Krishnakumar et al., (2004), Singh andKhan (2004), Kadian and Mehta (2006), Patel et al.(2006) and Unche et al. (2008b).

Among the fresh stalk yield attributingcharacters, positive and significant association wasnoticeable between days to 50% flowering with daysto maturity, plant height, grain yield per plant, nodesper plant, total biomass per plant and stem girth; plantheight with nodes per plant, total biomass per plant,stem girth and grain yield per plant; nodes per plantwith total biomass per plant, grain yield per plant andstem girth; stem girth with total biomass per plantand grain yield per plant; total biomass per plant with


39

grain yield per plant. These findings were inconfirmation with the findings of Manickam and Das(1994), Ganesh et al. (1995), Verma et al. (1999) andKachapur and Salimath (2009). However, positive andsignificant association of days to flowering and plantheight observed in the present study contradicts withthe report of Manickam and Das (1994) who observednegative significant correlation of days to floweringwith plant height and plant height with stem girth.

Thus, to improve the fresh stalk yield insweet sorghum it is important to select the plantswith relatively higher plant height, total biomass, stemgirth, nodes per plant and days to maturity, as thesetraits had direct relation with fresh stalk yield asindicated by their positive and significant association.Hence, fresh stalk yield can be increased by followingindirect selection using above associated traits insweet sorghum.

Association of sugar yield with itscomponent characters Association between sugaryield was positive and highly significant with juiceyield per plant, fresh stalk yield per plant, totalbiomass per plant, grain yield per plant, total solublesugars, brix per cent, bioethanol yield per plant andjuice extraction per cent. The results of the presentinvestigation were in corroborative with Mallikarjunet al. (1998), Hapase and Repale (1999), Verma etal. (1999), Singh and Khan (2004), Kadian and Mehta(2006) and Unche et al. (2008b).

Association among sugar yield attributingcharacters The association of total biomass withfresh stalk yield per plant, juice yield per plant, grainyield per plant, brix per cent, total soluble sugarsand bioethanol yield per plant; fresh stalk yield perplant with juice yield per plant, grain yield per plant,brix per cent, total soluble sugars and bioethanol yieldper plant; grain yield per plant with juice yield perplant; brix per cent with total soluble sugars,bioethanol yield per plant, juice extraction per centand juice yield per plant; juice yield per plant withjuice extraction per cent, total soluble sugars andbioethanol yield per plant; juice extraction per centwith total soluble sugars and bioethanol yield perplant; total soluble sugar with bioethanol yield perplant were positive and significant. The reports ofGanesh et al. (1995), Singh and Khan (2004), Kadianand Mehta (2006), Kachapur and Salimath (2009),

Unche et al. (2008b) and Sandeep et al. (2010) werein agreement with the above results.

The results on association of sugar yield withits attributing traits indicated importance of juice yield,fresh stalk yield, total biomass, grain yield, totalsoluble sugars, brix per cent, bioethanol yield andjuice extraction per cent in improving sugar yield asthese traits had direct relation with sugar yield.Hence, improvement in these traits automaticallyimprove sugar yield. Thus, the above correlated traitscan be effectively utilized in formulating indirectselection schemes.

Path analysis

The correlation estimates are the sum totalof direct effect and indirect effects of an independentcharacter on a dependent character and it is quiteobvious that the correlation (positive or negative) maybe of small magnitude and non-significant in spite ofits direct effect and/or some of the indirect effectsare operating in the opposite direction. Therefore, pathanalysis is required to partition the correlation valueof independent characters on dependent characterinto direct and indirect effects so as to get a correctpicture of the association of characters. Hence, pathco-efficient analysis was carried out to know thedirect and indirect effects of the componentcharacters on sugar yield and the results arepresented in Table 4.

The results of path analysis of componentcharacters of sugar yield indicated maximum positivedirect effect of total soluble sugars and juice yieldon sugar yield whereas bioethanol yield, fresh stalkyield and total biomass has very high, moderate andlow negative direct effect, respectively on sugar yield.However, all the traits exhibited moderate to highpositive indirect effect via juice yield and total solublesugars. The indirect effect via total biomass and freshstalk yield is negative and low to moderate while juiceextraction per cent is negligible and negative. Theseresults were in accordance with the earlier reposts ofMallikarjun et al. (1998), Hapase and Repale (1999)and Kachapur and Salimath (2009). In general, theresults revealed that the indirect contribution of thecharacters viz., total biomass, fresh stalk yield andjuice extraction per cent via juice yield resulted intheir positive correlation with sugar yield.

VEMANNA et al

40


41

VEMANNA et al

42

REFERENCES

Ganesh, S., Khan, A. K. F., Suresh, M and Senthil,N., 1995. Character association for alcoholyield in sweet sorghum. The MadrasAgricultural Journal. 82: 361-363.

Hapase, R. S and Repale, J. M., 1999. Variability,correlation and path analysis in sugarcane. In:Proceedings of the 61st Annual Convention ofthe Sugar Technologists Association of India,New Delhi, India, 7-9 September: pp. 130-141.

Johnson, H. W, Robinson, H. F and Comstock, R.E., 1955. Estimates of genetic andenvironmental variabi lity in soybean.Agronomy Journal. 47: 314- 318.

Kachapur, R. M and Salimath, P. M., 2009. Geneticstudies on correlation and characterassociation in sweet sorghum [Sorghumbicolor (L.) Moench]. Green Farming. 2: 343-346.

Kadian, S. P and Mehta, A. S., 2006. Correlationand path analysis in sugarcane. Indian Journalof Agricultural Research. 40: 47-51.

Kimbeng, C. A and Bingham, E. T., 1998. Populationimprovement in Lucerne (Medicago sativa L.)components of inbreeding depression aredifferent in original and improved populations.Australian Journal of Experimental Agriculture.38: 831-836.

Krishnakumar, Singh, P. K and Singh, J. R. P., 2004.Genetic variability and character associationin subtropical clones of Sugarcane(Saccharum complex hybrid). Indian Sugar.54: 189-198.

Mallikarjun, H., Khanure, S. K and Kachapur, M. D.,1998. Correlation and path analysis for juicequality parameters in sweet sorghumgenotypes. The Madras Agricultural Journal.85: 207-208.

Manickam, S and Das, L. D. V., 1994. Characterassociation and path analysis in foragesorghum. Mysore Journal of AgriculturalSciences. 28: 116-119.

Nahar, S. M. N., Khaleque, M. A and Miah, M. A.,2002. Correlation, path co-efficient andconstruction of selection index in sugarcane.Pakistan Sugar Journal. 17: 2-10.

Patel, K. C., Patel, A. I., Mali, S. C., Patel, D. U andVashi, R. D., 2006. Variability, correlation andpath analysis in sugarcane (Saccharum spp.).Crop Research. 32: 213-218.

Patil, F. B., Gadekar, D. A and Bhoite, A. G., 1996.Variability studies in forage sorghum. Journalof Maharashtra Agricultural Universities.Z21: 330-332.

Rajappa, P. V., 2009. Morphological and AFLP markerbased genetic diversity in sweet sorghumworking germplasm. M. Sc. (Agri.) Thesissubmitted to University of AgriculturalSciences, Bangalore.

Roy, D., 2000. Analysis of skewness and kurtosis.In: Plant breeding – The Analysis andExploitation of Variation. Narosa PublishingHouse. New Delhi. India. pp 300-304.

Sandeep, R. G., Gururaja Rao, M. R., Chikkalingaiahand Shivanna, H., 2009a. Assessment ofvariability for grain yield, ethanol yield and theiratt ribut ing characters in germplasmaccessions of sweet sorghum [Sorghumbicolor (L.) Moench]. Mysore Journal ofAgricultural Sciences. 43: 472-476.

Sandeep, R. G., Gururaja Rao, M. R., Chikkalingaiahand Shivanna, H., 2010. Association and pathanalysis for ethanol yield in sweet sorghum[Sorghum bicolor (L.) Moench]. Mysore Journalof Agricultural Sciences. 44: 28-30.

Sankarapandian, R., 2002. Variability studies in twogroups of hybrid populations in forage sorghum.Forage Research. 28: 46-48.

Sankarapandian, R., Rajarathinam, S and Muppidathi,N., 1996. Genetic variability, correlation andpath co-efficient analysis of jaggery yield andrelated attributes in sweet sorghum.The Madras Agricultural Journal. 83: 628-631.


43

Singh, S. P and Khan, A. Q., 2004. Inter-relationshipand path analysis in sugarcane (Saccharumspp. Complex). Environment and Ecology. 22:903-911.

Umakanth, A. V., Madhududhana, R andMadhvilatha, K., 2004. Variability, characterassociation and path analysis in rabi sorghum.The Andhra Agricultural Journal. 51: 333-336.

Unche, P. B., Misal, M. B., Borgaonkar, S. B.,Chavan, B. D and Sawant, D. R., 2008b.Correlation studies in sweet sorghum [Sorghum

bicolor (L.) Moench]. International Journal ofPlant Sciences. 3: 69-72.

Unche, P. B., Misal, M. B., Borgaonkar, S. B.,Godhawale, G. V., Chavan, B. D and Sawant,D. R., 2008a. Genetic variability studies insweet sorghum [Sorghum bicolor (L.) Moench].International Journal of Plant Sciences. 3: 16-18.

Verma, P. S., Shri Pal and Karma, N. K., 1999.Genetic variability and correlation studies insugarcane. Indian Sugar. 49: 125-128.

VEMANNA et al

44

Ongole breed of cattle is dual purpose (milkand draught) cattle, due to their adaptability traits,superior production capacity and high diseaseresistance under harsh tropical conditions. However,certain reproductive impediments like long serviceperiod which results in to long calving intervals,nocturnal incidence of estrus with shorter durationare limiting the economic use of this cattle breed.The postpartum ovarian inactivity could be due tosuckling induced inhibition of the LH surge (Gumenet al, 2003 and Naidu et al, 2007).Hence, an attemptwas made to study the follicular dynamics during thepostpartum and to enhance the fertility with PGF2 α and GnRH hormonal protocols in lactating Ongolecows.


A total of fifty postpartum lactating Ongolecows in their 2nd to 5th lactation maintained understandard feeding and management stationed at CattleProject, Live Stock Research Station, Lam Farm,Guntur were included in this study. The cows wererandomly divided in to two groups consisting of 22cows in each treatment group and six cows in control

ESTRUS SYNCHRONIZATION RESPONSE AND FERTILITY RATE FOLLOWINGTREATMENT WITH PGF2α AND GnRH IN ACYCLIC LACTATING ONGOLE COWS

K.VENKATA RAMANA, K.SADASIVA RAO, K.SUPRIYA and N.RAJANNADepartment of Veterinary Gynaecology and Obstetrics, College of Veterinary Science,

Sri Venkateswara Veterinary University, Rajendranagar – 500 030

ABSTRACT

Estrus synchronization response and fertility following Ovsynch and double prostaglandin injection protocolsin post partum Ongole cows was studied. A total of 50 Ongole parous cows above 60 days postpartum were dividedin to two treatment groups consisting of 22 cows in each and 6 cows in control group. The follicular dynamics weremonitored every day by ultrasonography till the ovulation. The emergence of follicular wave was observed on 1.33,3-4 days in Ovsynch and double PG groups, respectively. The size of dominant follicle found to be 12.48 ± 0.57 mmin Ovsynch and control group whereas 10.00 ± 0.78 mm in double PG group. In Ovsynch group estrus response wasnoticed in 100 per cent cows and estrus was recorded after 50 to 80 hours of PGF2á injection with the duration of16.28 ± 2.36 hrs While in double PG the duration of estrus and mean estrous cycle length recorded as 14.20 ± 2.56hrs and 21.50 ± 0.21 days, respectively. The ovulations were noticed after 1.50 ± 0.22 days of 2nd GnRH injectionwith conception rate of 54.54 per cent in Ovsynch. The mean service period found to be 81.18 ± 1.62 days inlactating multiparous Ongole cows. It may be concluded that double injection of prostaglandin has better conceptionrate than Ovsynch protocol and both the treatments reduced the service period around 80 days when compare withcontrol group, there by reduced the calving interval in lactating Ongole cows.



J.Res. ANGRAU 41(1) 39-41, 2013

group. Estrus synchronization was performed byOvsynch protocol (Treatment-I) i.e., day 0 Receptal10 g i.m, day 7 Lutalyse 25 mg i.m and again onday 9 Receptal 10 g i.m. Where as in double injectionof prostaglandin group (Treatment-II) the firstinjection (Lutalyse 25 mg i.m) was given on day ‘0’and 2nd injection was given on day 12 th. Theinseminations were carried out on observed estrusin both the groups. The non returned cows wereexamined for pregnancy per rectally between day 60to 70 post insemination.

Ovarian structures in six cows in eachtreatment groups and control group were monitoreddaily by using a real time B-mode ultrasound scannerwith a trans rectal linear-array transducer from day‘0’ of GnRH injection to till the end of induced estruswith ovulation to assess the fate of first wavedominant follicle and the emergence of subsequentwave. The scanning was also done in control groupand ovarian changes and estrus response wasrecorded and compared with treatment groups duringthe treatment period. The data was analyzed byMinitab®(16) (2012) software.

45


In Ovsynch protocol the first injection ofGnRH is designed to induce ovulation and formationof a new or accessory corpus luteum and a newfollicular wave. In the present investigationultrasound scanning of ovaries was done every dayafter injection of GnRH induced ovulation and till theformation of corpus luteum. However, follicular waveemerge was noticed in all the six cows on day1.33+0.21. The dominant follicle resulted from thiswave emergence grew up to 12.48+0.57 mm by day7.6 with a growth rate of 1.66+0.26 mm per day. Thisis in agreement with the reports of Mishra et al.(2002)and Souza et al. (2006) in Sahiwal cows with GnRH.However, Krishna Mohan et al. (2010) reported muchlesser size (9.17 + 0.27 mm) dominant follicle inSahiwal cows.

All the cows with a corpus luteum which wereadministered with luteolytic dose (25 mg of DinoprostTromethamine) on day 7 were in estrus within2.33+0.21 days. The dominant follicle of all the sixcows was ovulated with the second dose of GnRHon day 9 of the luteal phase. The ovulation recordedwas 100 per cent with GnRH treatment which is inagreement with Gumen et al. (2003). This could bedue to larger size of the dominant follicle measuringmore than 9 mm will potentially ovulate in the GnRHtreatment animals (Adams 1992). Ultrasoundmonitoring of the ovulated cows had shown a corpusluteum of 16.50+0.30 mm on day 10 following theinduced estrus. Out of 22 postpartum lactating cows,8 cows showed estrus after 1st GnRH injection andthe cows exhibited normal estrus except two whichhad weak estrus following PGF2α administration onday 7 of the treatment. Second dose of GnRH wasadministered on day 9 and all the cows inseminatedat the observed estrus. The estrus response recordedin this study was in agreement with Mialot et al.(1998) and Vasconcelos et al. (1999). In the presentstudy the conception rate found to be 54.54+0.36per cent.

The ultrasound scanning of PGF2α treatedcows revealed a dominant follicle of 10.00 + 0.78

mm within 3-4 days after the PGF2α administration,

which is in agreement with Alan et al. (2003) andNaidu et al. (2010) in Nelore cows. Out of 22 cowstreated with double injections of prostaglandins 18exhibited estrus within 3.66+0.21 days. The time ofonset of estrus following PGF2α injection recorded inthis study is corroborating to the findings of Alves etal. (2002) in Gir cows.

In the present study the conception ratefound to be 67.00 +0.26 per cent. Naidu et al. (2006)has reported higher conception rate (90.0%) than thepresent findings in Ongole cows, which might be dueto better synchrony of ovulation and fertilization asthe existing follicles were influenced the next waveof follicles during induction. All the treated postpartumlactating ongole cows in Ovsynch and double PGgroups exhibited 100 and 82.5 per cent of estrus,respectively. The reason for varied estrus responserate between different treatment groups might be dueto the difference in the treatment protocols and dueto, presence of too small dominant follicles (< 9 mm)at the time of second PGF2 injection (Rivera et al.1998).

In the control group the mean time requiredfor conception after calving was recorded as163.60+10.72 (95-397) days, which is less than theprevious studies (Venkateswarulu, 1971; Rao et al,1985; Acharya & Bhat 1990; Ravi Kiran et al. 1995;Rao et al. 2001and Naidu et.al,.,.2010). The serviceperiod of cows in the treatment groups resulted areduction of approximately 85 days compared tocontrol group cows.

Hence, it is concluded that the estrussynchronization with GnRH and PGF2 gives betterresults and would reduce the calving to service perioddrastically and beneficial to the farmers in dairyindustry.

ACKNOWLEDGEMENT

The authors express their sincere gratitude to theSVVU, Tirupati for providing facilities to carry outthe research work at Cattle Project, Live StockResearch Station, Lam Farm, Guntur.A.P.

RAMANA et al

46

REFERENCES

Acharya and Bhat 1990. Productive and reproductivetraits in Ongole cows. ICAR Bulletin.

Adams, G. P. Matteri R. Kastetic, J. P. Ko, J. C .Hand Ginther, O J 1992. Association betweensurges of follicle stimulating hormone and theemergence of follicle wave in heifers. Journalof Reproduction and Fertility 94: 177-178.

Alan Bennett Maia Alexandre Alves Ciro TorresReinaldo Jose Mendes Streets Vicente RibeiroRocha Jr Giovanni Ribeiro e CarvalhoJefferson Ferreira da Fonseca Alberto NetoMarcatti Anderson George of Assisi-2003.Characteristics of follicular dynamics and lutealregression in cows of Gir and Nelore cows aftertreatment with cloprostenol. Journal of AnimalScience. 32(1) 1806-9290.

Alves N G costa E P da Guimaraes J D Silva M RZamperlini B Costa F M J Santos A D FMiranda Neto T 2002. Ovarian activity inHolstein and crossbreed Holstein x Zebu cowsduring two normal estrous cycles.(Portuguese). Revista brasieira de Zootecnia.31(2), 627-634. 35 ref.

Gumen A Guenther J N Wiltbank M C 2003. Follicularsize and response to Ovsynch versusdetection of estrous in anovular and ovularlactating dairy cows. Journal of dairy Science86 (10) 3184-3194.

Krishna Mohan U K Mishra O P Mishra Singh Cand Prakash B S 2010. Follicular developmentpattern in post partum anestrous sahiwal cowsduring Ovsynch protocol. IndianVet. Journal.,87: 448-450.

Mialot J P Laumonnier G Ponsart C FauxpointH Barassin F Ponter A A and Deletang F1999. Postpartum subestrus in dairy cows;comparison of treatment with prostaglandin F2or GnRH + prostaglandin F2 + GnRH.Theriogenology 52 : 901-911.

Mishra O P Khan J R and Awasthi M K 2002Study of follicular dynamics in Sahiwal cows.Indian Journal Animal Reproduction 23: 193.

Naidu G V Babu Rao K 2006 Estrus pattern andconception rate in postpartum lactating Ongole

cows. Indian Journal of Animal Reproduction27 (1) 14 – 17.

Naidu G V Rao A S Rao K B 2007 Progesteroneprofile in postpartum lactating Ongole (Zebu)cows. Indian Journal of Animal Reproduction.28 (1) : 12-14,

Naidu G V Seshagiri A Babu Rao K 2010.Progesterone profile in postpartum lactatingOngole (Zebu) cows. Indian Journal of AnimalReproduction. 31 (1) :79-80.

Rao K B and Venkata Naidu G 2001 Annual Progressreport of the technical programme on Geneticimprovement of Ongole breed throughAssociate Herd Testing Scheme, ANGRAU

Rao A V and Venkataramaiah P 1985 Studies on theeffectiveness of a smaller dose ofprostaglandin F2 in increasing the reproductiveefficiency of Ongole cattle. Indian VeterinaryJournal 67: 528-530

Ravikiran G Rao G N and Jayarama Krishna VSatyanarayana A 1995. Performance of ongoleand crossbred cows under village conditions.Indian Journal of Animal Science 65: 782.

Rivera G M Goni C G Chaves M A Ferrero S B andBo G A 1998. Ovarian follicular wavesynchronization and induction of ovulation inpostpartum beef cows. Theriogenology 49 :1365 -1375.

Souza A F Pinheiro V G Ereno R L and Barros M2006. Synchronization of ovulation inanestrous Nelore cows treated with hormonalprotocol without progesterone or progestagens.Reproduction fertility and development. 18(2)115-116.

Vasconcelos J L M Silcox R W Rosa G J M PursleyJ R and Wiltbank M C 1999. Synchronizationrate, size of the ovulatory follicle andpregnancy rate after synchronization ofovulation beginning on different days of theestrous cycle in lactating dairy cows.Theriogenology 52 : 1067 – 1078.

Venkateswarlu M 1971. Studies on geneticcorrelation and inheritance of economiccharacteristics of Ongole cattle, M.Sc Thesissubmitted to Agra University.

ESTRUS SYNCHRONIZATION RESPONSE AND FERTILITY RATE

47



Sheep is one of the important livestockspecies contributing to the livelihood of resource poorfarmers in rural areas, particularly that are prone todrought. It contributes to the farm households notonly by acting as source of livelihood and nutritionalsecurity, but also as a moving asset, which can beliquidated at a times of crises within short period.Andhra Pradesh state is known for its diversifiedlivestock resources in nine well defined agro climaticzones. Andhra Pradesh state is divided into threegeopolitical regions viz., Coastal Andhra, Telanganaand Rayalaseema. According to 2008 census sheeppopulation in Andhra Pradesh are 255.39 lakhs andranks first in the country. In Telangana region ofAndhra Pradesh, sheep rearing largely depended ongrazing under extensive system of production.Whenever the grazing sources degraded, farmerswere compelled to resort migration. During migrationshepherds night shelter their flocks in farmer’s fieldand get some payment either in cash or kind inexchange for leftover of sheep manure. However,information on migration pattern of sheep flocks underfield condition is scanty. Therefore, knowledge aboutthe migration pattern will help policy makers andplanners in making suitable corrective and remedial

A STUDY ON MIGRATION PATTERN OF SHEEP FLOCKS IN TELANGANAREGION OF ANDHRA PRADESH

N. RAJANNA, M. MAHENDAR and K. VENKATA RAMANADepartment of Livestock Production and Management,

Sri Venkateswara Veterinary University, College of Veterinary Science,Rajendranagar, Hyderabad-30.

ABSTRACT

A survey was carried out to collect information about migration pattern of sheep flocks in Telangana regionduring 2010-2011. A total of 576 farmers were selected by multistage stratified random sampling technique and theinformation was collected from them through personal interview. It was revealed the existence of twenty traditionalmigratory routes in the study area. The mean duration and distance of migration of flocks were 124.3 ± 10.5 daysand 112.2 ± 19.5 km, respectively. The migration mostly started in the mid-January and extended up to July. Theperception of farmers about basis for migration was lack of grazing resources (90.80%), periodical drought (80.90%),traditional occupation (77.78%), fields filled with crops (74.65%), disease problem (64.06%), lack of feeding resources(61.81%), lack of water resources (30.73%) and heavy rains (23.44%) and ranked them from I to VIII. The problemsfaced during migration included attack of diseases (87.85%), lack of shelter for animals (81.60%), theft (74.13%),restriction of entry into other villages (71.18%), lack of veterinary facilities (67.88%) predators (23.26%) and abortionsdue to stress (13.72%). Thus, knowledge about the migration pattern will help policy makers and planners in makingsuitable corrective and remedial measures

measures. Hence it was felt very essential to studythe migration pattern of sheep flocks in Telanganaregion of Andhra Pradesh .


The study was undertaken in Telanganaregion of Andhra Pradesh during 2010-2011.Telangana region was divided into three zones viz.,Northern Telangana Zone (NTZ), Central TelanganaZone (CTZ) and Southern Telangana Zone (CTZ) onthe basis of the agro-climatic conditions. Multistagestratified random sampling technique was applied toselect the villages and sheep farmers. In the firststage two districts from each zone were selectedand in the second stage four mandals from eachdistrict and in the third stage four villages from eachselected mandal were selected based on sheeppopulation. From each village 6 respondentspossessing sheep were selected randomly for thepresent study. Hence, 576 sheep farmers constitutedthe study sample. Data on migration practices,reasons and problems of migration were studied fromthe respondents by face-to-face interview. Based onfarmers responses frequency and percentages werecalculated and accordingly rankings were given.

J.Res. ANGRAU 41(1) 42-46, 2013

48

Table 1. Months, duration and distance of migration of sheep flocks in Telangana region

S.No. Zone Tract No. Months of migration Duration (days) Distance (Km)

1 NTZ I 15 th Feb- 15 th May 60 60

2 NTZ II 5th Feb-5th June 121 25

3 NTZ III 20 th Feb- 20 th June 121 120

4 NTZ IV 15 th Feb- 15 th June 121 55

5 NTZ V 10 th Feb-10 th June 121 80

Mean± SE 108.8± 12.2 68.0 ±15.7

6 STZ VI 5th April- 5 th July 91 70

7 STZ VII 15 th April- 15 th July 94 200

8 STZ VIII 25 th Dec- 25 th July 213 70

9 STZ IX 5th April- 5 th July 91 30

10 STZ X 5th Jan-5th April & 10 th Aug-10 th Sep

122 20

11 STZ XI 15 th Jan- 15 th Aug 212 232

12 STZ XII 10 th April- 10 th July 92 232

13 STZ XIII 15 th Feb- 15 th April 60 150

14 STZ XIV 5th Jan-5th Aug 212 200

15 STZ XV 20 th Feb-20 th June 121 25

16 STZ XVI 10 th Jan-10 th June 151 250

17 STZ XVII 15 th Feb- 15 th June 121 260

Mean± SE 131.6 ±15.4 144.9± 27.51

18 CTZ XVIII 5th Jan- 5 th April 90 25

19 CTZ XIX 20 th Jan- 20 th June 151 50

20 CTZ XX 20 th Feb- 20 th June 121 90

Mean± SE 120.66± 17.66 55.0± 18.92

Overall Mean± SE 124.3 ± 10.05 112.2 ± 19.5


Sheep rearing largely depended on grazingunder extensive system of production. Whenever,the grazing sources degraded farmers werecompelled to resort migration. The migration in locallanguage (Telugu) known as ‘Valasa’ or ‘Mannem’ isbeing performed by farmers who are having largeflock size. The migration started between 5th Feband 20th Feb in I to V tracks and between 5th Jan –20th Feb in XVIII to XX tracks, whereas, year roundmigration was observed in ST zone due to prevailedcropping pattern (Table 1). In majority of tracks sheep

farmers returned from migration to their native trackbetween April and June. Thus initiation linked to theonset of dry season and withering of surfacevegetation while the termination was based on theonset of monsoon. The migrating sheep flockcovered a minimum distance of 25 km and maximumof 260 km with a mean of 112.2 ± 19.5 km. Theduration of migration ranged from 60 to 213 dayswith a mean of 124.3 ± 10.5 days. Kumaravelu (2007)identified 8 major, 10 minor and eleven migratorytracks, respectively in Andhra Pradesh and TamilNadu. Regarding the onset and return of migration

A STUDY ON MIGRATION PATTERN OF SHEEP FLOCKS IN TELANGANA

49

Tabl

e 2.

Rea

sons

for m

igra

tion

of s

heep

floc

k’s

as p

erce

ived

by

shee

p fa

rmer

s in

Tel

anga

na re

gion

RAJANNA et. al.

50

Tabl

e 3.

Pro

blem

s fa

ced

duri

ng s

heep

floc

k’s

mig

ratio

n as

per

ceiv

ed b

y sh

eep

farm

ers

in T

elan

gana

regi

on

A STUDY ON MIGRATION PATTERN OF SHEEP FLOCKS IN TELANGANA

51

REFERENCES

Arora, A. L., Prince, L. L. L and Mishra, A. K. 2007.Performance evaluation of Jaisalmeri sheepin farmer’s flocks. Indian Journal of AnimalSciences: 77 (8)759-762

Dorji, T., Tshering, G., Wangchuk, T., Rege, J. E. Oand Hannote, O. 2003. Indigenous sheepgenetic resources and management in Bhutan.Animal Genetics Resource information Bulletin33: 81-91.

Gopaldass.T 2007. Production performance andmanagement practices of Pugal sheep in thehome tract. Indian Journal AnimalSciences.77(8)763-766.

Kuldeepporwal, Karim, S. A., Sisodia, S. L and Singh,V. K. 2006. Socio-economic survey of sheepfarmers in western Rajasthan. Indian Journalof Small Ruminants 12 (1): 74-81.

Kumaravelu, N. 2007. Analysis of sheep productionsystem in Southern and Northern Zones ofTamilnadu. Ph.D Thesis submitted to Tamil

Nadu Veterinary and Animal SciencesUniversity, Chennai.

Pattanayak, G. R., Patro, B. N., Das, S. K and Nayak,S. 2003. Survey and performance evaluationof Ganjam Sheep. Indian Journal of SmallRuminants 9(1): 47-49.

Rajapandi, S. 2005. Distribution and managementpractices of Coimbatore sheep. Thesissubmitted to Veterinary College and ResearchInstitute, Namakkal, Tamil Nadu.

Suresh, A., Gupta, D. C and Mann, J. S. 2008Constraints in adoption of improvedmanagement practices of sheep farming insemi-arid region of Rajasthan. Indian Journalof Small Ruminants 14 (1): 93-98.

Sushilkumar , Sharma R C, Mishra, A. K and AroraA L 2003. Production performance of sheepand certain management practices in farmer’sflocks of south East Rajasthan. Indian journalof small ruminants, 9(2): 103-105.

RAJANNA et al

the present findings corroborated with Pattanayak etal. (2003), Sushilkumar et al. (2003), Arora et al.(2007) and Gopaldass (2007).

As far as duration and distance coveredRajapandi, (2005) reported Coimbatore sheepmigrated a distance of 100 to 200 km and Kumaravelu(2007) had described the duration (days) rangedfrom 91 to 315 days in southern zone of Tamil Nadustate.

Reason for migration

Majority (90.80%) of sheep farmersperceived lack of grazing resources followed byperiodical drought (80.90%), traditional occupation(77.78), fields filled with crops (74.65%), diseaseproblem (64.06%), lack of feeding resources(61.81%), lack of water resources (30.73%) andheavy rains (23.44%) as a basis of migration andranked them from I to VIII, respectively (Table 2) .Farmers should be encouraged to take up SilviPasture system, controlled grazing and culling ofunproductive animal as a remedial measure for theabove problems. These results are in conformity withfindings of Dorji et. al. (2003) and Saravanakumar et

al. (2003) who reported shortage of water and grazingland and feeding resources, tradition, successivedrought, and disease outbreaks were the reasons ofmigration.

Problems faced during migration

Problems faced by sheep farmers wereranked in the order of attack of diseases (87.85%),lack of shelter for animals (81.60%), theft (74.13%),restriction of entry into other villages (71.18%), lackof veterinary facilities (67.88%) predators (23.26%)and abortions due to stress (13.72%) from I to VII,respectively. During migration, the shepherds alongwith sheep flocks spent most of the time in forests,river belts and remote villages where the veterinaryfacilities were not available in time and up to themark, leading to disease outbreaks. The sheepfarmers allowed animals for penning during nighttimes. Hence the sheep could not receive anyprotection from adverse weather leading to diseasesusceptibility. Lack of care during lambing and fornew born lambs during migration has lead to lambmortality because the lambs also move continuouslywithout any protection from heat resulting in heatstress. These findings were corroborated with theKuldeepporwal et al. (2006) and Suresh et al. (2008).

52

The Poultry population in India is 489 millionand the manure availability is estimated to be 12.1

million tons (Livestock census, 2003). Poultry wasteis an important source of energy as well as un-

conventional non protein nitrogen source for

ruminants. In the present experiment an attempt wasmade to efficiently utilize poultry litter in complete

feed of Sheep and goats and to study its effect onvoluntary feed intake and nutrient digestibility.


The layer poultry litter required for the presentstudy was obtained in a single lot and was sun dried.

Six each of healthy Nellore rams and indigenousbucks weighing 18.90±0.80 and 18.50±0.67

respectively were fed complete feed containing

poultry litter (35%), cotton seed hulls (40%), wheatbran (15%), molasses (8.5%), mineral mixture

(1.0%), and salt (0.5%), Rovimix (vitaminsupplement) was added at the rate of 10 g per 100kg.

of the ration. The complete feed thus prepared hasprotein and energy levels according to ICAR (1985)

recommendations. The experimental feed was offered

to each animal ad libitum (restricting refusals to3.5%) in a digestion and metabolism experiment. The

metabolism trial was conducted on all the twelveanimals. A preliminary period of 21 days was followed

by a five day adjustment period in metabolism cages;

ABSTRACT

A complete feed containing poultry litter (35%) and other feed ingredients were formulated and processedin to mash. The feed was tested on six each of Nellore rams and indigenous bucks in a digestion- cum-metabolismtrial using a completely randomized design to asses the voluntary feed intake and nutrient utilization. The voluntaryfeed intake of DM and the intake per kg DMI was significantly (P<0.01) higher in sheep than in goats. There was nosignificant difference in the digestibility of DM, OM, CP, CF, EE and NFE between species. All the experimentalanimals showed positive N, Ca and P balances. The DCP and TDN intakes were significantly (P<0.01) higher insheep than in goats. Based on the present study it was observed that poultry litter could be used up to 35%level incomplete feeds of small ruminant as an un-conventional protein source without any adverse effect.

UTILIZATION OF POULTRY WASTE AN UN-CONVENTIONAL PROTEIN SOURCEIN SMALL RUMINANT RATIONS

J. NARASIMHA, V.CHINNI PREETHAM AND S.T.VIROJI RAOAll India Co-ordinated Research Project on poultry breeding, College of Veterinary Science,

Sri Venkateswara Veterinary University, Hyderabad-500030

a 5 day collection period was followed. During thetrial the amount of complete ration and water offered

and residues of feed were weighed. Similarly, thequantity of faeces and urine voided were also

recorded. Feed, faeces and urine samples were

analyzed for proximate principles and phosphorus(AOAC 1984) and calcium by the method of (Talapatra

et al., 2005). The data was subjected to statisticalanalysis as per the methods suggested by

(Nageshwer Rao, 1983).


The experimental ration and poultry litter were

analyzed for proximate principles and the results werepresented in (Table 1). The average dry matter intake

(DMI) was significantly (P<0.01) higher in sheep than

in goats (Table 2). Similar results were observed by(Venugopal et al., 1997) who reported significantly

(P<0.01) higher dry matter intake in sheep than ingoats fed complete feed containing poultry droppings.

However, in the present study the higher dry matterintake may be attributed to higher palatability of

experimental ration. Water intake per kg dry matter

intake was significantly (P<0.01) higher in sheep thanin goats (Table 2). These results were in agreement

with (Murthy at al., 1996) who observed higher waterintake in sheep than in goats. Several studies

indicated that water intake in small ruminants depend


Date of Receipt : 16.08.2012 Date of Acceptance :13.12.2012

J.Res. ANGRAU 41(1) 47-50, 2013

53

on various factors like dry matter, season, climatic

condition and type of feed (Taneja, 1969; Mehrothraand Mullick, 1960).

There was no significant difference indigestibility co-efficient of Dry matter, Organic matter,

(Table 2). Similar f indings were reported by(Mallikarjuna, 1989), in sheep and goats fed rations

containing cotton straw, maize cobs for dry matter.

A non-significant difference in digestibilities for CrudeProtein was observed in sheep and goats. Similar

results were reported by (Murthy et al., 1996) whenfed poultry litter and poultry droppings in the pelleted

ration. The lower digestibility of Crude Protein in thepresent experiment may be due to incorporation of

cotton seed hulls at 40 percent level. The cotton seed

hulls contained about 31 percent lint, mostly madeof cellulose and about 14.3 percent lignin. It is quite

likely that proteins in the cotton seed hulls are locatedin the structural component of cell. Structural protein

(Maynard et al., 1981) which may not be available

for the microbes to attack due to high lignin content.Further the fineness of cotton seed hulls during

processing might have also contributed to lowerdigestibility due to faster rate of passage through the

digestive tract (Keys and Smith., 1984). There wasno significant difference in ether extract and crude

fibre digestibility in the two species of sheep and

Proximate principle Experimental ration (%) Poultry litter (%)

Dry matter 91.25 93.45 Organic matter 80.00 63.21 Crude protein 12.18 15.70 Ether extract 1.87 0.87 Crude fibre 30.17 15.08 Total ash 20.00 36.78 Acid insoluble ash 6.19 12.39 Nitrogen free extract 35.78 31.57 Calcium 0.89 4.57 Phosphorous 0.76 3.7

Table 1. Proximate composition of experimental ration and poultry litter (%DM)

goats (Table 2). The findings are in agreement with

those of (Sreedhar et al., 1993).

The non-significant difference in Nitrogen free

extract digestibility between sheep and goats

observed in the present study concur with the results

of (Murthy et al., 1996) who also reported non-

significant difference in Nitrogen free extract

digestibility between sheep and goats fed poultry

litter/poultry droppings based pelleted ration. All

experimental animals were in positive N, Ca, and P

balances indicating that the experimental feed

supplied these nutrients in required quantities to both

species. Sheep retained significantly (P>0.01) higher

N, Ca and P than goats (Table 2). This could be

attributed to higher Dry matter intake in sheep. The

ration met Digestible crude protein (DCP) and Total

digestible nutrients (TDN) requirements as

recommended by ICAR (1985). The results of this

study indicate that complete feed containing poultry

litter up to 35 percent level could be utilized for feeding

of small ruminants. The complete ration formulated

in this study met the nutritive requirement of sheep

and goats. Further it was observed that poultry litter

could be used up to 35 percent level in complete

feeds of small ruminant rations as an un- conventional

protein source without any adverse effect.

NARASIMHA et al

54

Table 2. Digestibility of nutrients in sheep and goats

Nutrient Sheep Goat Level of significance

DMI % BW 5.62±0.23 2.76±0.16 (**)

Water intake L/100kg 13.71±0.55 4.61±0.16 (**)

Per kg DMI 2.56±0.24 1.66±0.13 (**)

Digestibility co-efficients (%)

DM 51.57±0.64 51.53±1.16 NS

OM 56.29±0.15 56.85±0.64 NS

CP 51.39±0.91 51.59±0.89 NS

EE 67.49±1.79 65.41±1.31 NS

CF 51.15±0.35 51.96±0.58 NS

NFE 61.99±0.29 62.54±0.72 NS

N (g/kg) 4.98±0.34 2.56±0.13 (**)

Ca (g/kg) 2.76±0.23 1.59±0.08 (**)

P (g/kg) 2.08±0.14 0.97±0.04 (**)

DCP intake

Per day (g) 67.64±4.64 32.18±2.63 (**)

Per 100kg body weight (kg) 0.35±0.01 0.17±0.13 (**)

TDN intake

Per day (g) 499.26±37.17 240.74±19.20 (**)

Per 100kg body weight (kg) 2.63±0.11 1.03±0.09 (**)

NS- Non significant ; ** Significant (P>0.01)

REFERENCES

AOAC. 2005. Official methods of analysis.Association of official analytical chemist. 18th

Ed. Washington DC. USA.

ICAR. 1985. Nutrient requirement of livestock andpoultry publications and information divisionIndian Council of Agricultural Research, NewDelhi

Keys,J.E and Smith,L.W. 1984. Effect of EnsilingCorn Stover with Alfalfa on Growth, Intake,and Digestion by Yearling Dairy Heifers asCompared with Whole Corn Plant

Silage.Journal of dairy science 67(9) :1971-1975

Livestock census, 2003. Department of animalhusbandry, dairying and fisheries. Ministry ofAgriculture, Government of India. http: //dahd/nic.in/census.htm.

Mallikarjuna, G.1989. Studies on the pattern ofvolatile fatty acids concentration in rumen ofSsheep and goats and their relative levels withrespect to time after feeding. MVSc Thesissubmitted to Acharya N.G.Ranga AgriculturalUniversity, Hyderabad.

UTILIZATION OF POULTRY WASTE AN UN-CONVENTIONAL PROTEIN SOURCE

55

Maynard, L., Lossli,J.K., Horold, F.H and Warner,R.L.1981.Animal nutrition, 7th edition, TataMcGrawHill Publishing company, New Delhi,India

Mehrothra and Mullick, D.N.1960. Indian Journal ofVeterinary Science and Animal Husbandry30:30

Murthy, K.S., M.R. Reddy and G.V.N. Reddy. 1996.Nutritive value of supplements containingpoultry droppings/litter for sheep and goats.Small Ruminant Research. 21(2):71-75.

Nageswera Rao,G. 1983. Statistics for Agriculturalsciences. Oxford and IBH Publishingcompany, New Delhi

Sreedhar,C., Reddy, T.J and Raghavan G.V. 1993.Nutrient availability in goats fed cotton seedhulls and poultry waste based concentrate

mixture. Indian Journal of Animal Nutrition 10(2): 77-81

Talapatra, S.K.,Ray,S.C and Sen, K.C. 1940. Theanalysis of mineral constituents in biologicalmaterials. Indian Journal of Veterinary Scienceand A.H. 10:243

Taneja, G.L. 1969. Variation in body temperature,respiration rate, water intake and body weighton Marwari sheep during the year. IndianVeterinary Journal 46:49

Venugopal Rao D., Naidu M.M and Raghavan G.V.1997. Utilisation of complete feed containingpoultry droppings in sheep and goats. IndianVeterinary Journal 74:858-861

NARASIMHA et al

56

The postpartum period plays a pivotal rolein cattle reproduction. The duration of postpartumanestrus has an important influence on reproductiveperformance. The information available about theresumption of ovarian activity in postpartum lactatingOngole cows is very scanty. During the puerperium,the uterus involutes and the hypothalamo-hypophyseal-ovarian axis resumes cyclical secretionof gonadotropic/gonadal hormones leading to firstpostpartum ovulations and regulates estrous cycles(Peter and Laming 1986). In normal puerperium, theseevents are completed within six weeks after calving.The main objective of this study was to establish thefact of follicular development pattern and theircharacterization in the postpartum Ongole cows.


A total of ten estrous cycles from six cows whichhave shown ovarian activity were selected andexamined with ultrasonography on the follicular wavepattern in normal estrous cycles of postpartumlactating Ongole cows. The ovarian structures werestudied daily by a real time B-mode ultrasound scannerusing a trans-rectal 7.5-MHz transducer (Medison600). The transducer was inserted into the rectum

POSTPARTUM OVARIAN FOLLICULAR DYNAMICS AND ESTRUS ACTIVITY INLACTATING ONGOLE COWS

K. VENKATA RAMANA, K. SADASIVA RAO, K. SUPRIYA and N. RAJANNADepartment of Animal Reproduction Gynaecology & Obstetrics, College of Veterinary Science,

Sri Venkateswara Veterinary University, Rajendranagar – 500 030

ABSTRACT

The objective of this study was to characterize early postpartum follicular dynamics in lactating Ongolecows in relation to their ovarian activity and subsequent reproductive performance using 70 multiparous lactatingOngole cows. The follicles measuring above 6 mm diameter and corpus luteum measuring 8-10 mm were detectedby day 20-25 postpartum period by ultrasonography. The first and second wave of the dominant follicle emerged on1.80 + 0.8 and 12.46 + 0.20 days respectively. The mean growth rate and size was significant (P<0.05) between first,second and third wave of the same estrous cycle. The corpus luteum grew to a mean value of maximum diameter of15.93 + 0.37 and 17.8 + 0.37 mm on the day of 13.1+1.50, 14.6+0.56 and remained up to 14.6+ 1.50 and 15.9+0.45days of the estrous cycle in two and three wave cycles, respectively. About 22.85 per cent (16/70) of the postpartumlactating Ongole cows have shown estrus by day 60 out of seventy postpartum cows. The postpartum onset ofbehavioral estrus was highly variable and varied with a mean value of 109.50 + 4.66. Only 18.75 per cent of cowsexhibited intense and 18.75 per cent cows exhibited weak estrus and the remaining (62.50 per cent) cows showednormal estrus. In the present study 56.25 per cent cows conceived for one insemination and 57.14 per cent cowsrequired two or more inseminations per conception. The conception rate recorded was 46.15 + 0.12 and the serviceperiod was 163.60+10.72 .

and was moved forward to place it on the ovary andthe entire ovary was scanned. The ovaries of eachexperimental cow were examined every other dayfrom day 15 of post partum to day 45, or till it exhibitsbehavioral estrus (natural estrus exhibited cowsovaries were scanned daily). Ovaries were scannedon multiple planes (Ginther et al 2003) to ensurecomplete and accurate study of follicles and corpusluteum. The internal ultrasound caliper was utilizedto measure the length and width of these structuresand the diameter was determined by taking the meanof their length and width (Zeitoun et al. 1996). Thedata was processed with simple t test by Minitabsoft ware.


The follicular wave pattern in the normaloestrous cycle of post partum lactating Ongole cowswas presented in Table (1). Variable length ofanoestrus period following parturition in the cow wasobserved in the study. Cows in good body conditionnormally have 30 days for first ovulation. In thepresent investigation about 22.85 per cent (16/70) ofthe postpartum lactating Ongole cows have shownestrus by day 60 with resumption of ovarian activity


Date of Receipt : 06.12.2012 Date of Acceptance :28.01.2013

J.Res. ANGRAU 41(1) 51-55, 2013

57

with small follicles measuring > 6 mm diameter. Inthe ultrasound monitoring follicles measuring above6 mm diameter and corpus luteum measuring 8-10mm were detected by day 20-25 postpartum period.The resumption of ovarian activity observed in thisinvestigation is close to the findings of Roche et al.(1992) Henao et al. (2000). The follicular size > 6mm reported in this study is in concurrence with theobservations of Krishna mohan et al (2010) andSatheesh kumar et al (2008). However,Rajamahendran et al. (1990) reported first postpartumovulation as early as 10-15 days postpartum in Bosindicus cows.

The follicular growth pattern monitored usingan ultrasound scanner during the standing estrus inten estrus cycles revealed the occurrence of seventwo wave and the other three shown three wavecycles with wave emergence on day 1.80 + 0.80 and12.46+0.50 for two wave cycles, day 1.52 +0.03, 9.60+0.40 and 15.20 + 0.37 for three wave cycles,respectively. The two wave pattern (7/10) of folliculargrowth observed in the present investigation is inclose agreement with the findings of Figueiredo etal. (1997), Mayra et al. 1999), Alan et al. (2003),Borges et al. (2004), Gaur and Purohit (2007) andAkter et al. (2010) who have reported predominantlytwo wave pattern in the estrus cycle in Nelore or Zebucows. On the contrary much shorter intervals werereported by Borges et al.(2001) in zebu crosses,Rhodes et al.(1995) in cross breed heifers and Malhiet al. (2005) in Hereford cows.

The mean diameter of the first and seconddominant follicles (10.23 +0.40 and 12.30+ 0.36 mm)of the two wave recorded in this study were in closeagreement with the findings of Figueiredo et al. (1997)Mayra et al. (1997) Akter et al. (2010) in Nelore cows.Contrary to these findings Gaur and Purohit (2007)observed the lesser average diameter of dominantfollicle size of 8.02 + 0.38 and 9.10 + 0.24 mm inNelore cows for the first and second waves,respectively. However Krishna Mohan et al. (2010)also recorded higher diameter of (14.6 + 0.12 mm)than the present study in normal cyclic Sahiwal cows.

It was observed that the first wave dominant follicleof three wave cycles attained a maximum size of10.70+0.20 mm with a mean growth rate of 0.87+0.03mm per day and in the second wave of the dominant

follicle maximum size was 9.80+0.37 mm with growthrate of 0.77+0.06 per day. The third wave maximumdiameter was 12.80+0.37 mm with a mean growthrate of 1.46+0.90 mm per day, which is significantlybigger and faster in growth rate than the first andsecond waves in the same estrous cycle. However,the growth rate of second and third waves were notsignificant. This could be due to larger duration ofdominant higher progesterone and low pulsatilefrequency of LH secretion (Wolfenson et al. 2004)

The growth rate of the dominant follicle recorded inthe present study were in agreement with the findingsof Figueiredo et al. (1997). The variation in thediameter and growth rate of the dominant folliclereported could be due to inherent characters of thebreeds.

Body score condition (Rhodes et al.1995),negative energy balance, Insulin like Growth factor,inhibin and gonadotrophin hormones systemic levelsRhodes et al. 1997) altered LH pulse frequency andfollicular growth.

In normal two wave cycles, the first wave dominantfollicle did not differ significantly in their size withovulatory dominant follicle but it was small in size.The smaller diameter of first wave dominant folliclemight be due to the fact that the first wave emergedduring the period of higher progesterone productionby the corpus luteum. These findings are in closeagreement with the observations of Figueiredo et al.(1997), Mayra et al. (1999) and Viana et al. (2000) inNelore cows.

In the present study the progesteroneconcentration observed during the development offirst follicular wave was between 1.48 to 2.98 ng/mlwhich could be a contributing factor for the smallersize of the dominant follicle of this first wave (Vinoleset al. 1999).

Perusal of data revealed that, the meanmaximum diameter of the ovulatory follicle was largerthan the diameter of the dominant follicles of previouswaves, which was in agreement with the findings ofFigueiredo et al. (1997) and Mihm et al. (2006). Thegrowth rate of ovulatory follicle in two wave cycleswas 0.91+0.88 mm per day, was less than first wavefollicle (0.94+0.80 mm per day), which could be dueto lower plasma concentrations of progesterone and

RAMANA et al

58

presence of high FSH receptors (Knopf et al. 1989).Kulick et al. (2001) opined that the slower averagegrowth rate of the dominant follicle of the second intwo wave cycle was compensated by taking a longerperiod for reaching maximum diameter and ovulation.

The maximum diameter of dominant folliclesof first and second waves in two wave cycles reachedon 5.13 + 0.63 and 18.50 + 0.50 days and in threewave cycles, 6.45+ 0.88, 12.89+3.06 and 20.44+1.42days and regression started on 10.80+ 1.10 in firstwave in two wave cycle and 7.12+ 0.74 and 15.15+2.26 days in first and second waves of three wavecycle, which were in agreement with the Figueiredoet al. 1997) and Gaur et al. (2007) in Nellore cows.

Statistical analysis of the data of diameterof dominant follicle in two wave cycles revealed thatthe diameter of the first wave dominant follicle wassignificantly (P< 0.01) smaller than the second wavedominant follicle (ovulatory follicle). The growth rateof second wave dominant follicle was faster than itscounterpart in the first follicular wave.

The regression of dominant follicles of firstwave in two wave cycle and first and second wavesin three wave cycle occurs between 7-15 daysobserved in the present study were similar to thefindings of Figueiredo et al. (1997) and Gaur et al.(2007) in Nellore cows. The mean regression rate offirst wave in two wave cycle was 1.41+0.20 mm perday and in three wave cycle 1.20 + 0.33 and 1.48 +0.33 in first and second waves respectively.

The mean duration of phase of regressionof dominant follicles in first wave of two wave cycleswas recorded as 6.50+ 0.56 days and in three wavecycles 7.13 + 1.86 and 4.72 + 1.42 days respectivelyin first and second waves. The mean period of staticphase was 1.20±0.26 days in first wave of two wavecycle and 1.80±0.34 and 1.67±0.67 days for first andsecond waves of three wave cycles, respectively.The mean ovulation in natural estrus in postpartum

lactating Ongole cows was recorded as 20.90 ±0.46and 22.20±0.37 respectively in two wave and threewave estrous cycles. The mean duration of first andsecond wave in two wave estrous cycles inpostpartum lactating Ongole cows were 14.20±0.40and 9.10±0.45, respectively. Where as in three waveestrous cycles the duration of first, second and thirdwaves were 11.20±0.37, 10.60±0.40 and 6.0±0.31days, respectively. In the present study, in naturalcycle, the corpus luteum grew to a maximumdiameter of 15.93 + 0.37 and 17.8 + 0.37 mm in twoand three wave cycles on the day 13.1+1.50,14.60+0.56 and remained static up to 14.60+ 1.50and 15.90+0.45 days of the estrous cycles,respectively.

The above results were almost comparableto that observed by Figueiredo et al. (1997) in Nelorecows 15.56 + 0.44 and 17.69+0.80 mm in two andthree wave cycles, respectively. But Mayra et al.(1999) and Rhodes et al.(1997) reported higher sizein zebu cows.

The mean period of first exhibition ofpostpartum estrus was recorded as 109.50 + 4.56days. The incidence of estrus was recorded as 32.50% and 62.50 % during day and night time respectively.These findings were in corroboration with the reportof Venkat Naidu et al (2010)

It may be concluded that the knowledgeabout the follicular dynamics in postpartum lactatingOngole cows helps in planning and designing of thesynchronization protocols and reducing the serviceperiod, inter calving period etc, for improvingreproductive performance.

ACKNOWLEDGEMENT

The authors express their sincere gratitudeto the SVVU, Tirupati for providing facilities to carryout the research work at Cattle Project, Live StockResearch Station, Lam Farm, Guntur.

REFERENCES

Akter. Z, Talukder. A.K., Akter. T., Kabir. M.S.,Kamal. M.M., Bari. F.Y and Shamsuddin. M.2010. Ultrasonographic study of ovariancyclicity in Zebu cows of Bangladesh. 5 (2),65.

Alan Bennett Maia Alexandre Alves CiroTorresReinaldo Jose Mendes Streets, VicenteRibeiro Rocha Jr., Giovanni, Ribeiro e Carvalho,Jefferson Ferreira da Fonseca, Alberto NetoMarcatti Anderson George of Assisi-2003.

POSTPARTUM OVARIAN FOLLICULAR DYNAMICS AND ESTRUS ACTIVITY

59

Characteristics of follicular dynamics and lutealregression in cows of Gir and Nelore cows aftertreatment with cloprostenol. Journal of AnimalScience. 32(1) :1806-9290.

Borges, A.M., Torres, C. A.A, Rocha Junior, V.R.,Ruas, J.R.M., Gioso, M.M., Fonseca, J.E.,Carvalho, G.R., Maffili, V.V,–2004. Folliculardynamics and ovulation time of non-lactatingGir and Nelore cows during two seasons ofthe year. Arquivo Brasillerio de MedicinaVeterinaria e Zootecnia. 56:(3), 346-354.30 ref.

Borges, A.M., Torres, C. A.A, Ruas, J.R.M., RochaJunior, V.R., Carvalho, G.R –2001. Ovarianfollicular dynamics in crosbred Holstein-zebuheifers. Arquivo Brasillerio de MedicinaVeterinaria e Zootecnia. 53;5, 595-604. 18 ref.

Evandro S. Sartorelli, Luciano M. Carvalho, Bergfelt,D.R, Ginther, O.J., Ciro M. Barros. 2005.Morphological characterization of follicledeviation in Nelore (Bos indicus) heifers andcows. Theriogenology 63 2382-2394.

Figueiredo, R. A., Barros, C. M., Pinheiro, O. Land Soler J M P 1997 Ovarian folliculardynamics in Nellore Breed (Bos indicus) cattle.Theriogenology 47 : 489-1505.

Gaur, M and G. N. Purohit, 2007. Follicular dynamicsin Rathi (Bos indicus) cattle Veterinarskiarhi77 (2); 177-186.

Ginther, O.J., M.A.Beg., F.X. Donadeu and D.R.Bergfelt, 2003. Mechanism of follicle deviationin monovular farm species. Anim. Reprod.Sci., 78: 239 – 257.

Henao G Olivera – Angel M and Maldonado – EstradaJ G 2000 Follicular dynamics duringpostpartum anestrus and the first estrouscycle in suckled and non-suckled Brahman(Bos indicus) cows. Animal ReproductionScience 63 : 127 – 136

Knopf, L., J.P. Kastelic, E. Schallenberger and O.J.Ginther, 1989. Ovarian follicular dynamics inheifers: test of two-wave hypothesis byultrasonically monitoring individual follicles.Dom. Anim. Endocrin., 6: 111-119.

Krishna Mohan, U.K. Mishra, O.P Mishra, Singh, Cand Prakash B.S. 2010. Folliculardevelopment pattern in post partum anestroussahwal cows during Ovsynch protocol.IndianVet. Journal., 87: 448-450.

Kulick, L.J., D.R. Bergfelt, K. Kot, and O.J. Ginther,2001. Follicle selection in cattle: Follicledeviation and codominance within sequentialwaves. Biol. Reprod., 65: 839 –846.

Malhi, P.S., G.P. Adams and J. Singh, 2005. Bovinemodel for the study of reproductive aging inwomen: Follicular, luteal and endocrinecharacteristics. Biol. Reprod., 73: 45 – 53.

Mayra Elena Ortiz, D. avila Assumpcao, Ed HoffmannMadureira. Rubens Paes de arruda, EneivaCarla carvalho Celeghini, Pedro PauloGimenez Gumoes, Pedro Henrique Candini,Jose Antonio Visintin. Sao Paulo 1999. Braz.J. Vet. Res. Anim. Sci. 36 (6)

Mihm, M., P.J. Baker, J.L.H. Ireland, G.W. Smith,P.M. Coussens, A.C.O. Evans and J.J.Ireland, 2006. Molecular evidence that growthof dominant follicles involves a reduction infollicle stimulating hormone dependence andan increase in luteinizing hormone dependencein cattle. Biol. Reprod., 74: 1051 – 1059.

Peters A R and Lamming G E 1986 Regulationof ovarian function in the postpartum cow : anendocrine model. Veterinary Record 118 : 236-239.

Rajamahendran R and Taylor C 1990Characterisation of ovarian activity inpostpartum dairy cows using ultrasoundimaging and progesterone profiles. AnimalReproduction Science 22 : 171-180.

Rhodes, F.M., A.J. Peterson, P.D. Jolly, W.H.McMillan, M.Donnison, A. Ledgard, G. Partonand D.R. Hall, 1997. Bovine ovarian follicleand oocyte characteristics after emergence ofthe first follicular wave. Theriogenology, 47:149 (Abst.).

Rhodes, F.M., De’ath, G., Entwist le, K.W.1995Animal and temporal effects on ovarian

RAMANA et al

60

follicular dynamics in Brahman heifers. AnimalReproduction Science, 38(4). 265-277.

Roche, J. F., Crowe M. A and Bolland, M. P 1992Postpartum anestrus in dairy and beef cows.Animal Reproduction Science 28 : 371-378.

Satheshkumar, S., Palanisamy. A., Subramanian.A., Kathiresan. D., and Ramadass. P. 2008.Follicular Wave Synchronization using GnRHAgonist in Jersey Crossbred cows. Indian J.Anim. Reproduction, 29 (2) 154 – 158.

Venkata Naidu. G, Seshagiri Rao. A and Babu Rao.K, 2010. Progesterone profile in postpartumlactating Ongole (Zebu) cows. Indian Journalof Animal Reproduction 31 (1).

Viana, J.H.M.; Ferreira, A., de M.; camargo, L.S, deA. 2000. Follicular dynamics in zebu cattle.Pasquisa Agropecuaria Brasileira. 35 (12)2501-2509. 40 ref.

Vinoles, C., A. Meikle, M. Forsberg and E. Rubianes,1999. The effect of subluteal levels ofexogenous progesterone on folliculardynamics and endocrine patterns during earlyluteal phase of the ewe. Theriogenology, 51:1351-1361.

Wolfenson, D., G. Inbar, Z. Roth, M. Kaim, A. Blochand R.Braw-Tal, 2004. Follicular dynamics andconcentrations of steroids and gonadotropinsin lactating cows and nulliparous heifers.Theriogenology, 62: 1042-1055.

Zeitoun, M.M., H.F. Rodriguez and R.D. Randel, 1996.Effect of season on ovarian follicular dynamicsin Brahman cows. Theriogenology, 45: 1577-1581.

POSTPARTUM OVARIAN FOLLICULAR DYNAMICS AND ESTRUS ACTIVITY

61

Dietary fiber components have uniquechemical structures and characteristic physicalproperties (e.g., bulk/volume, viscosity, water-holdingcapacity, adsorption/binding or fermentability) thatdetermine their subsequent physiologic behavior(Schneeman and Tietyen, 1994). Individuals with highintakes of dietary fiber appear to be at significantlylower risk for developing coronary heart disease,stroke, hypertension, diabetes, obesity, and certaingastrointestinal diseases (James et al., 2009).

Khakra is a ready to eat, light crispy, crunchyflat bread snack of Gujarat in western India. It is avery versatile snack and can be eaten as fat freechips.Though khakra is gaining importance, thetraditional khakra is made up of oil and also it is madeup of refined flour which has very low fibre in it whichin turn increases the calorie load. Low fibre and highfat content in the diets are making people landing into various chronic life style disorders l ike

DEVELOPMENT AND EVALUATION OF FIBER ENRICHED KHAKRAM. KIRTHY REDDY, P. UMADEVI, P.S.S.SAILAJA and APARNA KUNA

Post Graduate Research Center, College of Home Science,Acharya N.G Ranga Agricultural University, Rajendra Nagar, Hyderabad-500030



diabetes,cancer etc. Hence, the present research wastaken up to prepare the khakras with fiber richingredients and without addition of any visible fatthereby making the product therapeutic.

The benefits of consuming foods rich in fiberare numerous, ranging from improved large bowelfunction to slowed digestion and absorption ofcarbohydrate and fat and reduced risk for certaindiseases (Ali et al. 1982).

Moreover, isoflavones contained in soybeansare effective cancer-preventive agents for loweringrisks of various cancers (El Gharras, 2009). Evidencealso points to the beneficial effects of soy isoflavonesin the prevention of cardiovascular disease (ElGharras, 2009). Their potential health benefits of soy-isoflavones include prevention of osteoporosis viaphytoestrogen effects of isoflavones, and preventionof neovascularization in ocular conditions (Zhu et al.,2005).

Ingredients Control (T1) (gms) T2 (Experimental with methi leaf powder)

(gms)

T3 (Experimental without methi leaf

powder(gms) Wheat flour 84 - -

Whole wheat flour - 46 48

Defatted soy flour - 15 15

Oats flakes flour - 20 20

Methi leaf powder - 2 -

Green chillies 10 10 10

Ajwain seeds 3 3 3

salt 4 4 4

Table 1. Khakras of different formulations

Research NotesJ.Res. ANGRAU 41(1) 56-60, 2013

62

There is therefore the need to develop a differentapproach to offer the weary consumers theopportunity to feed on improved formulations withsubstantive health benefits from wheat-soycombinations (Gomez et al., 2003).

Hence, an attempt was made to increasefibre content of khakra with no additional visiblefat.

Product development

Three products T1 ,T2, T3 were developed invarious combinations of whole wheat flour/oats flakepowder/defatted soy flour/methi leaf powder indifferent proportions which were given.

Procedure

Three products were developed using variouscombinations of whole wheat flour/ oats/ defatted soyflour/ methi leaf powder in different proportions. Roastand grind whole wheat, defatted soy bean and oatflakes in to fine powders. Methi leaves are blanchedat 650c for 2-3 minutes, dried and powered which was

only added in T2 sample. Mix all the above ingredientsincluding green chillies, salt and ajwain seeds withluke warm water. T1 sample is made up of only wholewheat flour, green chillies, salt and ajwani seeds. Allthese are kneaded in to individual doughs containingvarious ingredients. Keep it for 15 minutes a side.Make the dough into small balls of weight 7gms eachand hot press them for 45-60 secs.

Sensory evaluation Sensory evaluation was doneto select the most acceptable recipes with 5 pointHedonic rating scale.

Hedonic scale ratings Like extremely- 5, Likemoderately – 4, Neither like nor dislike – 3, Dislikemoderately – 2, Dislike extremely – 1.

The nutrient compositions of the recipes werecalculated for protein, fat, fiber, and energy by usingthe Nutritive value of Indian foods. (Gopalan et al,2004). The completed score cards after theevaluations were subjected to statistical analysis.(Snedecor,G.W and Cochran, W.G, 1983)

S.No. Colour Taste Flavour Texture Over all acceptability

Control (T1) 3.55 3.91 3.73 3.45 3.36

T2 4.18 3.82 4.45 4.55 4.36

T3 3.91 4.64 3.63 4.09 3.73

Se D 0.261 0.343 0.213 0.384 0.210

CD NS 0.705* 0.436* 0.789* 0.431*

CV 15.804 20.442 12.953 22.876 12.895

Table 2 Sensory evaluation of khakras

* significant NS- Non significant

Khakra of three formulations were subjected to thesensory evaluation and the scores are recorded forthe following attributes colour, taste, flavour, textureand overall acceptability were presented in Table 2.

Colour

No significant changes were observedamong the scores for colour in which T2 (4.18%)scored highest followed by T3 (3.91%) and lowestscore was recorded in T1 (3.55%). As per theobservations made by Trongpanich et al. 2001, itwas found that there was no significant difference in

the preferential scores in colour, odor and tastebetween the snack samples that contained 5 – 15 %DFC and the control sample at p < 0.05. However,adding DFC (Dietary Fiber Concentrate) in the snacksmade up of corn grits could improve the snack’stexture as the texture preferential scores of all thesnack samples which contained 10 %DFC werehigher than of the control one (Trongpanich et al.,2001). The darker colour of the crumbs of wholewheat bread and fortified breads and biscuits havebeen reported by several authors (Singh et al., 2000;

DEVELOPMENT AND EVALUATION OF FIBER ENRICHED KHAKRA

63

Akhtar et al., 2008; Serrem et al., 2011). The brownishbread appearance could be directly related to theincrease in fiber content (Hu et al., 2007)T2 = modifiedrecipe with 15gms defatted soy flour, 20gms oat

flakes and 2gms methi leaf powder, T3 = modifiedrecipe with 15gms defatted soy flour, 20gms oatflakes. T1 = control recipe made up of whole wheatflour.

Control (T1) T2 T3

Taste

Scores for taste of khakras recordedsignificant changes (p<0.05) among all the treatmentsT3 recorded highest score for the taste and lowestscore was given for T2 which was reported to have aslight bitter after taste even after blanching at 65°Cfor 2-3 minutes. This could be attributed to thepresence of methi leaf powder which was incorporatedto enhance the flavour, but it showed slight bitterafter taste.

Flavour

Significant differences (p<0.05) wereobserved among the scores which were given forflavour. Flavour of T2 (4.45%) scored highest whichthen followed by T1 (3.73%) and lowest was recordedin T3 (3.63%). High score was given for T2 may bedue to the incorporation of methi powder. Accordingto Awadesh et.al (2009) hardness, cohesiveness,springiness/elasticity, gumminess and chewinessvalues were increased with the increase in thedefatted soy flour levels in gulabjammuns.Appearance, colour, texture, flavour and overallacceptability of the gulabjamuns had improved withthe addition of 3.33% soy flour and decreasedthereafter.

Texture

Sensory score for the texture showedsignificant changes (p<0.05). Among all thetreatments T2 (4.55%) recorded highest score,

followed by T2 (4.09%) and lowest score was observedin T1 (3.45%). The highest score was observed inthe products made of whole wheat flour whichcontains more fibre and lowest score was given tothe product which was made with only wheat flour.The reason for low score of texture in product madeonly with wheat could be because of sogginess dueto lack of fibre in it.

Overall acceptability

Overall acceptability scores showedsignificant changes (p<0.05). Highest score wasgiven for T2 (4.36%), followed by T2 (3.73%) and T1

(3.36%) scored lowest. Therefore T2 was the mostacceptable product among three. Sensory analysisstudy conducted by Joel et al. 2011 showed that therewas no significant difference observed between thewhole wheat bread and the soy bread samples in thesensory attributes of crust colour and crumbappearance, While significant difference (p<0.05) wasobserved in texture, flavour and overall preferencerespectively. Fibers effect on palatability, as well asother sensory qualities of the diet, may affect energyintake (Drewnowski, 1998). Bulky, low energy-densefoods/diets are, for the most part, less appealing thanmore energy-dense foods or diets because energydensity and palatability have been shown to becorrelated. Drewnowski.,(1998). Therefore, theenergy-diluting effects of dietary fiber may reduceenergy intake by lowering the overall palatability ofthe diet.

KIRTHY et al

64

S.No. Protein (gms) Fiber (gms) Fat (gms) Energy (Kcal)

Control (T1) 8.3 6.2 0.8 286

T2 14.7 13.56 1.43 316

T3 15 13.78 1.47 322

SeD 0.490 2.588 3.657 1.393

CD 1.222 6.456 NS 3.474

CV 4.737 32.859 184.240 0.849

Table 3. Nutritional evaluation of Khakra

Nutritional quality of khakras are tabulatedin Table 3 and are presented in Fig 3. Significantdifferences (p<0.05) were observed in all thenutritional parameters that were measured except forfat. Protein was more in T3 (15g) which was more orless equal to protein content of T2 (14.7g). T1 (8.3g)recorded low protein content. Fibre content was alsomore in T3 (13.78g) and T2 (13.56g) and lowest fibrecontent was observed in T1 (6.2g). Compared to T2

(316kcal) and T3 (322kcal), T1 (286kcal) has lessenergy content. This was due to the addition ofdefatted soy flour and oat flakes powder increasedprotein content, fibre content and energy in T2 and T3

products.

No significant changes were observed in fatcontent among the treatments but there was a slightincrease in fat content of T3 (1.47g) and T2 (1.43g)

when compared to T1 (0.8g). The reason for not toomuch raise in fat content was soya flour which wasadded is defattted.

T2 was found to be the best and mostacceptable product among all the treatments. Protein,fibre, fat and energy content of T2 was high whencompared to other products. T2 scored high forsensory evaluation attributes like colour, flavour,texture and overall acceptability but taste wise T3

scored highest. Most of the respondents expresseda slight bitter after taste in T2 This could be attributedto the presence of methi leaf powder which wasincorporated to enhance the flavour, but a slight bitterafter taste was seen. So, further studies can becarried out using debitterised methi leaf powder toreduce the after taste.

REFERENCES

Akhtar, S., Anjum, F., Rehman S, Sheikh, M andFarzena, K. 2008. Effect of fortification on thephysico-chemical and microbiological stabilityof whole wheat flour. Food Chemistry. 112:156-163.

Ali, R., Staub, J., Leveille, G.A and Boylep, C., 1982.Dietary fiber and obesity. Dietary fiber in healthand disease. vahouny g. v. kritchevsky d. eds.plenum press new york, NY.

Awadhesh kumar singh, Dattatreya mahadev kadam,Mili saxena and R.P.Singh.2009. Efficacy ofdefatted soy flour supplement in gulabjamu.

African Journal of Biochemistry Research.3(4): 130-135.

Dewettinck, K., Van Bockstaele, F., Kuhne, B., Vande Walle, Courtens T and Gellynck X. 2008.Nutrit ional value of bread: Influence ofprocessing, food interaction and consumerperception. Review Journal Cereal Science.48: 243-257.

Drewnowski A.1998. Energy density, palatability, andsatiety: implications for weight control.Nutrition Reviews. 56:347-353.

DEVELOPMENT AND EVALUATION OF FIBER ENRICHED KHAKRA

65

El Gharras H. 2009. Polyphenols: food sources,properties and applications – a review.Internat ional Journal Food ScienceTechnology. 44: 2512-2518.

Gomez, M., Ronda, F., Blanco, Caballero, P andApesteguía A. 2003. Effect of dietary fibre ondough rheology and bread quality. EuropeanFood Research Technology. 216: 51-56.

Hu, GH., Yang, F., Ma, Z and Zhou, Q. 2007.Development of Research and application ofrice bran dietary fibre. China Food Addit., 84(5):80-85.

James., W, Anderson, Pat baird, Richard H Davis Jr, Stefanie ferreri, Mary knudtson, Ashrafkoravm, Valerie waters and Christine l william.2009. Health benefits of dietary fiber. NutritionReviews. 67(4):188–205.

Joel Ndife, L. O. Abdulraheem and U. M. Zakari. 2011.Evaluation of the nutrtional and sensory qualityof functional breads produced from wholewheat and soya bean flour blends. AfricanJournal of Food Science Vol. 5(8)466 – 472.

Schneeman B.O and Tietyen. 1994. Dietary fiber.modern nutrition in health and disease. shillsm. e. olson j. a. shike m. lea and fibiger

philadelphia, pa. 8:89-100.Serrem C, Kock H,Taylor J. 2011. Nutritional quality, sensoryquality and consumer acceptability of sorghumand bread wheat biscuits fortified with defattedsoy flour. Int. J. Food Science Technology.46: 74-83.

Singh R, Singh G and Chauhan GS (2000). Nutritionalevaluation of soy fortified biscuits. J. FoodScience Technology. 37: 162-164.

Snedecor, G.W and Cochran, W.G.1983.StatisticalMethods Oxford and IBH publishingcompany New Delhi.

Trongpanich K, Pracha Boonyasirikool, Sumalaisrikumlaitong, Chowladda taengpook andUdom kanjanapakornchai. 2001. Feasibilitystudy on snack production by using dietaryfiber concentrate from soymilk residue. NaturalScience. 35: 188 – 194.

Zhu D, Hettiarachchy N, Horax R, Chen P (2005).Isoflavone contents in germinated soybeanseeds. Plant Foods Human Nutrition. 60: 147-151.

KIRTHY et al

66

The soil N dynamics and path way of nitrogenlosses in dry sown rice system are different fromlowlands and result in different fertilizer nitrogenrecoveries. The alternate moist and dry soil conditionsmay stimulate nitrification-denitrification processesin dry sown rice, leading to loss of nitrogen throughN2 and N2O (Prasad, 2011). Hence, traditional lowlandrice fertilizer doses may not be optimum for aerobicrice. Further, aerobic soil conditions and dry tillagepractices, besides alternate wetting and drying areconducive for germination and growth of highlycompetitive weeds, which cause grain yield lossesranging from 50-91%, compared to conventionalproduction systems (Singh et al., 2006), in whichweeds are suppressed by standing water andtransplanted rice seedlings have a “head start” overgerminating weed seedlings. As the concept ofaerobic rice in Andhra Pradesh is new, relatively fewinsights into weed management and nitrogenfertilization exist. Hence, the present investigationwas planned out to find out the optimum dose ofnitrogen and effective weed management practice inaerobic rice.

Field experiment was conducted duringkharif, 2011 at Regional Agricultural Research Station,Warangal, Andhra Pradesh. The soil of theexperimental site was sandy loam in texture, mediumin available nitrogen (288 kg ha-1), low in availablephosphorus (7.6 kg ha-1) and medium in availablepotassium (73 kg ha-1) with a pH of 8.1. Theexperiment was laid out in randomized block design(factorial concept) with three nitrogen doses viz., 120,180 and 240 kg ha-1 and four weed managementtreatments, viz., pre-emergence application ofpendimethalin @ 1.2 kg a.i. ha-1 + post-emergenceapplication of pyrazosulfuron ethyl @ 30 g a.i. ha-1 at25 DAS, mechanical weeding at 20 and 45 DAS,

INFLUENCE OF NITROGEN AND WEED MANAGEMENT ON GROWTH AND YIELDOF AEROBIC RICE (Oryza sativa L.)

K. SANDYARANI, M. MALLA REDDY, R. UMA REDDY and P.V. RAODepartment of Agronomy, Agricultural College,

Acharya N.G. Ranga Agricultural University, Aswaraopet - 507 301


weed free check and weedy check replicated thrice.Rice variety ‘WGL-32100’ was sown by dibbling at30 cm row spacing with solid rows with a seed rateof 40 kg ha-1. Phosphorus and potash @ 60 and 50kg ha-1 were applied uniformly as basal in the form ofsingle super phosphate and muriate of potash,respectively. Nitrogen was applied in the form of ureaas per the treatments in three equal splits, each atbasal, active tillering and panicle initiation stage. Arange of mean minimum temperature of 19.7 to 26.10C and mean maximum temperature of 27.1 to 33.00C was recorded during the crop growth period. Atotal rainfall of 349.4 mm was received during thecrop season in 26 rainy days. Supplemental irrigationwas given as and when required to maintain the soilin moist condition. The weed density and dry weightwere recorded in each plot using a quadrant of 1 m ×1 m size. The data on weed density and dry weightwere subjected to square root transformation beforestatistical analysis.

Weed spectrum of the experimental fieldconsisted of three groups of weeds like grasses,sedges and broad leaved weeds. The observedsedges were Cyperus rotundus, Fimbristylisargentea; grasses were Cynodon dactylon,Echinochloa colona, Dinebra retroflexa, Panicumjavanicum and broad leaved weeds were Corchorusolitorius, Eclipta alba, Digera arvensis, Cyanotisaxillaris, Psoralea corylifolia, Ammannia baccifera,Euphorbea geniculata, Phyllanthus niruri, Portulacaoleracea, Abutilon indicum, Celosia argentia,Commelina benghalensis, Merremia emarginata,Gynandropsis pentaphylla and Partheniumhysterophorus. Among these, broad leaved weedswere dominant followed by grasses and sedges inaerobic rice.



67

Weed parameters like weed density, dryweight, weed control efficiency and weed index werenot significantly influenced by the application ofdifferent doses of nitrogen except weed density at60 DAS and weed dry weight at 15 DAS and harvest(Table 1 ). At 60 DAS, the weed density recordedwith 240 kg N ha-1 was significantly higher than 120kg N ha-1 but was at par with 180 kg N ha-1. Pre-emergence application of pendimethalin @ 1.2 kga.i. ha-1 followed by pyrazosulfuron ethyl @ 30 g a.i.ha-1 at 25 DAS registered significantly lower weeddensity at all the stages of observation compared toweedy check and mechanical weeding except at 60DAS where they were at par with each other (Table1). Similar trend was observed with respect to thedry weight of weeds at 60 DAS. Higher weed controlefficiency was recorded with herbicides thanmechanical weeding at all the stages which led tolower weed index in the former treatment. Theinteraction between the nitrogen levels and weedmanagement treatments was significant with respectto the dry weight of weeds at 15 DAS and harvestonly. The weed dry weight was significantly higherwith mechanical weeding compared to herbicidesapplication at all the doses of nitrogen both at 15DAS and harvest. Similarly, the dry weight of weedssignificantly increased at 240 kg N ha-1 compared to120 kg N ha-1 at 15 DAS and 180 kg N ha-1 as well atharvest except in the weed free treatment. This couldbe attributed to vigorous growth and development ofweeds owing to higher uptake of nutrients at higherrate of nitrogen application. Similar results werereported by Sharma et al. (2007).

Nitrogen removal by the weeds at harvestwas significantly higher at 240 kg N ha-1 over 120 kgN ha-1 but at par with 180 kg N ha-1 (Table 2). It wasalso significantly more with mechanical weedingcompared to herbicidal application which was at parwith weedy check due to higher density and dry weightof weeds in the latter treatment. Singh and Tripathi(2007) also reported similar results.

Application of 240 kg N ha-1 recordedsignificantly higher yield attributes and grain yield over120 kg N ha-1 but at par with 180 kg N ha-1 except the

test weight (Table 2). The straw yield was notsignificantly different among the different nitrogendoses. Increased yield under higher nitrogen levelsmight be due to adequate nutrient supply which wouldhave resulted in increased growth and yieldcomponents. Similar findings were reported byShekara et al. (2010). Among the weed managementpractices, pre-emergence application of pendimethalin@ 1.2 kg a.i. ha-1 followed by post-emergenceapplication of pyrazosulfuron ethyl @ 30 g a.i. ha-1 at25 DAS recorded significantly higher yield attributingparameters, grain yield and straw yield overmechanical weeding and it was comparable with weedfree treatment. The increased grain yield was mainlydue to effective control of weeds in herbicide appliedplots (Jayadeva et al., 2011). The significantly lowestyield attributing parameters and yield among thetreatments were observed with unweeded checkowing to severe crop-weed competition throughoutcrop growth period.

Nitrogen uptake by grain and straw of riceincreased significantly with increasing doses ofnitrogen upto 240 kg N ha-1 (Table 2). Among all theweed management practices, significantly highernitrogen uptake by grain as well as straw wasobserved with herbicides compared to weedy checkbut at par with mechanical weeding (Table 2).

The highest net returns and returns per rupeeinvested were obtained with the application of 240kg N ha-1 over other two doses. Among the weedmanagement practices, weed free check recordedhighest values followed by pre-emergence applicationof pendimethalin @ 1.2 kg a.i. ha-1 followed by post-emergence application of pyrazosulfuron ethyl @ 30g a.i. ha-1 at 25 DAS. These results corroborate thefindings of Jayadeva et al. (2011).

It can be stated that application of 180 kg Nha-1 was found to be optimum for aerobic rice in sandyloam soils of Telangana region and pre-emergenceapplication of pendimethalin @ 1.2 kg a.i. ha-1 followedby post-emergence application of pyrazosulfuronethyl @ 30 g a.i. ha-1 at 25 DAS was found to beeffective and economical weed management practicein aerobic rice during kharif season.

SANDYARANI et al

68

Tabl

e 1.

Influ

ence

of n

itrog

en d

oses

and

wee

d m

anag

emen

t on

wee

d de

nsity

, wee

d dr

y w

eigh

t, w

eed

cont

rol e

ffici

ency

and

wee

d in

dex

in a

erob

ic

rice

INFLUENCE OF NITROGEN AND WEED MANAGEMENT ON GROWTH

Dat

a su

bjec

ted

to s

quar

e-ro

ot (“

x+1)

tran

sfor

mat

ion;

Fig

ures

in p

aren

thes

es a

re o

rigin

al v

alue

s. P

endi

.-pe

ndim

etha

lin; p

y.et

hyl-p

yraz

osul

furo

n et

hyl;

M.W

-mec

hani

cal w

eedi

ng; D

AS

-day

s af

ter s

owin

g

69

Tabl

e 2.

Influ

ence

of n

itrog

en d

oses

and

wee

d m

anag

emen

t on

yiel

d at

trib

utes

, yie

ld, n

itrog

en u

ptak

e by

cro

p an

d w

eeds

and

eco

nom

ics

inae

robi

c ri

ce

SANDYARANI et al

Dat

a su

bjec

ted

to s

quar

e-ro

ot (“

x+1)

tran

sfor

mat

ion;

Fig

ures

in p

aren

thes

es a

re o

rigin

al v

alue

s.P

endi

.-pe

ndim

etha

lin; p

y.et

hyl-p

yraz

osul

furo

n et

hyl;

M.W

-mec

hani

cal w

eedi

ng; D

AS

-day

s af

ter s

owin

g

70

REFERENCES

Jayadeva, H.M., Bhairappanavar, S.T., Hugar, A.Y.,Rangaswamy, B.R., Mallikarjuna, G.B.,Malleeshappa and Naik, C.C.D. 2011.Integrated weed management in aerobic rice.Agricultural Science Digest. 31 (1): 58-61.

Prasad, R. 2011. Aerobic rice Systems. Advance inAgronomy. 111: 207-246.

Sharma, R.P., Pathak, S.K and Singh, R.C. 2007.Effect of nitrogen and weed management indirect-seeded rice under upland conditions.Indian Journal of Agronomy. 52 (2): 114- 119.

Shekara, B.G., Nagaraju and Shreedhara, D. 2010.Growth and yield of aerobic rice as influenced

by different levels of N, P and K in Kavericommand area. Journal of MaharashtraAgricultural Universities. 35 (2): 195-198.

Singh, B., Malik, R.K., Yadav, A and Nandal, D.P.2006. Weed management in direct seeded riceunder different crop establishment methods.Extended summaries in the NationalSymposium on Conservation Agriculture andEnvironment. Varanasi, Uttar Pradesh, 26-28October 2006. pp.332-333

Singh, K and Tripathi, H.P. 2007. Effect of nitrogenand weed control practices on performance ofirrigated direct seeded rice (Oryza sativa).Indian Journal of Agronomy. 52 (3): 231-234.

INFLUENCE OF NITROGEN AND WEED MANAGEMENT ON GROWTH

71

Maize is one of the most important cerealcrops in the world. It is widely grown twice a year incertain agro climatic zones of India and the world asa staple food. Only 20-25 percent quantity of the seedis invariably stored and used by farmers insubsequent seasons. The remaining 75-80 percentof the seed quantity is transported to market duringthe first season itself to meet the demand of thefarmers. The challenging task to overcome problemsin process of the seed storage is the seeddeterioration (viz., physiological or biochemicalchanges) which ultimately leads to loss of its vigourand viability. Priming is also thought to increaseenzyme activity and counteract the effects of lipidperoxidation (Saha et al., 1990). During priming denovo synthesis of á-amylase is also documented(Lee and Kim, 2000). Metabolic activities in seedsincrease with á-amylase activity, indicating higherseed vigour. Rapid and uniform field emergence, aretwo essential prerequisites to increase yield, qualityand ultimately profits in crop production. Uniformityand percentage of seedling emergence of direct-seeded crops have a major impact on final yield andquality.

The medium vigour (Six month old) seed lotsof five maize hybrids viz., AH 122, SMH-9, DHM111, DHM 115 and DHM 117 having germination upto minimum seed certification standard (MSCS) weredried to 9% moisture are used for the study during2010-11. The seeds were subjected to priming at 25oCwith distilled water (T1), PEG - 1.2 MPa (T2), KNO3

@ 0.5% (T3) along with untreated control (To). Theinvigoration treatments were given to the seedsby soaking in same quantity of water/aqueoussolution of chemical (1: 1 seed and water/solution)for 18 hours at ambient temperature followed by dryingback to original moisture content under shadedcondition. Immediately after drying the treated seeds

EFFECT OF SEED PRIMING ON BIOCHEMICAL CHANGES DURING SEEDSTORAGE OF MAIZE (ZEA MAYS L.) HYBRIDS

M. RAM KUMAR, P. S. RAO, V. PADMA and K. V. RADHA KRISHNADepartment of Crop Physiology, College of Agriculture

Acharya NG Ranga Agricultural University, Rajendranagar, Hyderabad-500 030


were stored in moisture impervious containers atambient condition. The seed samples (primed andunprimed) were drawn at 0, 2, 4 and 6 months aftertreatment for studying the enzymatic changes duringstorage. The fresh seed sample of 0.5 g washomogenized with 5ml of chilled citrate buffer (pH5.0) and centrifuged at 10,000 rpm for 15 minutes.The supernatant was used for assessing theactivity of á-amylase as per the procedure describedby Choi et al. (1996). Enzyme extract for peroxidasewas prepared by first freezing the weighed amountof seed sample (1g) in liquid nitrogen to preventproteolytic activity followed by grinding with 10 mlextraction buffer (0.1M phosphate buffer, pH7.5,containing 0.5 mM EDTA). Brie was passedthrough four layers of cheese cloth and filtrate wascentrifuged for 20 min. at 15000 G and the supernatantwas used as enzyme extract. The enzyme extractwas used for assesing the acivity of peroxidasedescribed by Castillo et al. (1984).

The α - amylase activity (mmol g-1 fr wt)was reported to be significantly different amongtreatments and hybrids. There was a significantinteraction between hybrids and treatments at allduration of time after storage (Table 1). Among thefive maize hybrids DHM 117 primed with water(Hydropriming) recorded maximum value (2.17 mmolg-1) of á- amylase activity while unprimed seeds ofAH 122 recorded least value (1.81 mmol g -1).Significant interaction between hybrids and treatmentsat all time intervals of germination at all the monthsof storage was noticed. The findings are in conformitywith the studies of Sung and Chang (1993); where inthe hydropriming was superior to PEG 6000 in sh-2corn hybrid and was more effective in improving theemergence which resulted due to higher á- amylaseand â-amylase activities. Similarly a laboratorystudy was conducted by Sathish and



72

Sundareshwaran (2010) who evaluated the influenceof seed priming on biochemical parameters of freshand aged seeds of maize hybrid (COH(M) 5) and itsparental lines UMI 285 (female) and UMI 61 (male).Seeds were soaked in water @ 1% KH2PO4, 3% KNO3

and 2 % CaCl2 solution for 6 hrs. The results revealedthat all the seed priming treatments affected thebiochemical activity of seeds. However, seed primingwith 1% KH2PO4 for 6 h significantly increased theprotein content, á-amylase and dehydrogenaseactivities in both fresh and aged seed lots of all the

three genotypes. The results are also in conformitywith Sathish and Sundareshwaran (2010) andWattanakulpakin (2012).

The peroxidase activity was observed atdifferent months of storage (Table 2). The hybrid DHM117 showed maximum peroxidase activity withhydropriming indicating the treatment is the mostreliable technique for increasing peroxidase activitythere by increasing the vigour during storage. Thesignificant interaction between hybrids and treatmentswas observed at all periods of storage. Priming

Hybrid ‘0’ Month 2 Months after storage S.No.

Treat ment Control HYDRO PE

G KNO3 Mean Control HYDRO PEG KNO3 Mean

1 AH 122 5.07 5.15 5.10 5.13 5.11 4.95 5.03 4.98 5.00 4.99

2 SMH 9 5.23 5.30 5.27 5.31 5.28 5.14 5.24 5.17 5.21 5.19

3 DHM 117 5.30 5.40 5.34 5.37 5.35 5.22 5.31 5.25 5.27 5.26

4 DHM 111 5.13 5.20 5.14 5.18 5.15 5.01 5.10 5.05 5.07 5.06

5 DHM 115 5.17 5.26 5.20 5.23 5.22 5.07 5.17 5.10 5.14 5.12

Mean 13.93 5.17 5.25 5.21 5.22 5.21 5.08 5.17 5.11 5.14

Hybrid (H) Treatment (T) HxT Hybrid

(H) Treatment (T) HxT

S.Em± 0.004 0.004 0.009 0.002 0.002 0.004

CD at 5% 0.013 0.012 0.027 0.006 0.005 0.012

Hybrid 4 Months after storage 6 Months after storage S.No

. Treat ment Control HYDRO PE

G KNO3 Mean Control HYDRO PEG KNO

3 Mean

1 AH 122 3.73 3.82 3.77 3.80 3.78 1.81 1.90 1.84 1.87 1.85

2 SMH 9 3.88 4.12 3.97 4.03 4.01 2.02 2.14 2.05 2.07 2.07

3 DHM 117 4.01 4.37 4.04 4.07 4.12 2.08 2.17 2.14 2.15 2.14

4 DHM 111 3.81 3.89 3.83 3.85 3.85 1.88 1.97 1.91 1.94 1.92

5 DHM 115 3.86 3.95 3.90 3.93 3.91 1.94 2.11 1.98 2.01 2.03

Mean 11.26 3.87 4.03 3.90 3.93 3.93 1.95 2.10 1.98 2.01

Hybrid (H)

Treatment (T) HxT Hybrid

H) Treatment (T) HxT

S.Em± 0.003 0.0032 0.007 0.006 0.005 0.012

CD at 5% 0.010 0.0091 0.020 0.018 0.016 0.036

Table 1. Effect of seed priming on á-amylase activity (mmol g-1 fr wt) of maize hybrids during storage

EFFECT OF SEED PRIMING ON BIOCHEMICAL CHANGES DURING STORAGE

73

Table 2. Effect of seed priming on peroxidase activity (EU/Litre) of maize hybrids during storage

Hy brid ‘0’ Month 2 Months after storage

S. No. Treatm

ent Control HYDRO PEG KNO3 Mean Con trol

HYDRO PEG KNO3 Mean

1 AH

122 86654.6 87252.0 86854.0 87054.0 86953.6 66346.0 66942.0 66545.0 66746.00 66644.75

2 SMH

9 88376.0 88971.0 88574.0 88771.0 88673.0 67671.0 68272.0 68203.3 68071.00 68054.33

3 DHM

117 88966.0 89566.0 89161.0 89369.0 89265.5 68232.0 68834.0 68435.0 68632.00 68533.25

4 DHM

111 87215.0 87816.0 87417.0 87616.0 87516.0 66545.0 67142.0 66745.3 66943.00 66843.83

5 DHM

115 87983.0 88380.0 87988.0 88183.0 88133.5 67107.0 67706.0 67305.0 67508.00 67406.50

Mean 13.93 87838.8 88397.0 87998.8 88198.6 88108.3 67180.2 67779.2 67446.73 67580.00

Hybrid(H) Treatment(T) HxT Hybrid(H) Treatment(T) HxT

S.Em± 0.77 0.69 1.54 0.52 0.46 1.04

CD (0.05) 2.21 1.98 4.43 1.50 1.34 3.01

Hybrid 4 Months after storage 6 Months after storage

S.No. Treat ment Control HYDRO PEG KNO3 Mean Con

trol HYDRO PEG KNO3 Mean

1 AH 122

43105 43702 43306 43508.0 43405.3 17003 17601.3 17202 17407 17303.3

2 SMH 9

44793 45394 44991 45191.7 45092.3 18694 19293 18894 19097 18994.5

3 DHM 117

45352 45958 45557 45756.0 45655.8 19256 19857 19456 19652 19555.3

4 DHM 111

43663 44266 43867 44065.3 43965.3 17564 18165 12433 17961 16530.8

5 DHM 115

44235 44830 44438 44632.0 44533.8 18128 18729 18328 18524 18427.3

Mean 44229 44830 44431.8 44630.4 4453

0.5 18129 18729

17262.

6 18528.2

Hybrid (H) Treatment (T) HxT Hybrid

(H) Treatment (T) HxT

S.Em± 0.85 0.76 1.71 0.65 0.58 1.30

CD at 5% 2.45 2.19 4.91 1.86 1.67 3.74

treatments showed superior values in comparisionto control. Syed et al., (2010) revealed that theactivity of catalase and peroxidase during acceleratedageing and repair priming treatment of maize (Zeamays L.) seeds. Seed priming with KNO3 wasperformed at different concentrations of (0.5, 1, 2.5and 4%) and seeds were soaked for 8, 12 and 24 h in

each individual concentration and the results showedthat there was significant difference for duration ofageing treatment on germination characteristics ofmaize seeds. Increasing ageing duration resultedhigher reduction in germination characteristics. KNO3

had positive effects on seed germination of agedseed. This was higher in application of 0.5% KNO3

KUMAR et al

74

for 8 h and 2.5% for 24 h. Antioxidant activity ofaged seeds increased after seed priming treatments.Seed priming with hormones was more effective thanseed priming with KNO3 in activation of antioxidant

REFERENCES

Castillo F I, Penel I and Greppin H, 1984. Peroxidaserelease induced by ozone in Sedum albumleaves, Plant Physiology, 74: 846-851.

Choi Y H, Kobayashi M and Sakurai A, 1996.Endogenous gibberellins A1 level and á-amylase activity in germinating rice seeds,Journal of Plant Growth Regulation, 15: 147-151.

Lee S S and Kim J H, 2000. Total sugars, á-amylaseactivity, and emergence after priming of normaland aged rice seeds, Korean Journal CropSciences, 45:108–111

Saha R, Mandal A K, and Basu R N, 1990. Physiologyof invigoration treatments in soybean (Glycinemax L.) Seed Science and Technology, 18:269–276.

Sathish S and Sundareswaran S, 2010. Biochemicalevaluation of seed priming in fresh and aged

enzymes. It was suggested that using seedenhancement treatments like seed priming couldimprove aged and non-aged seed performance.

seeds of maize hybrid (COH (M) 5) and itsparental lines, ISSN vol. 4: 2.

Sung F J M and Chang Y H, 1993. Biochemicalactivities associated with priming of sweet cornseeds to improve vigor, Seed Science andTechnology 21, 97–105.

Syed, Ata Siadat, Amir Moosavi and Mehran SharafiZadeh, 2012. Effects of seed priming onant ioxidant activity and germinat ioncharacteristics of maize seeds under differentageing treatment, Research Journal of SeedScience, 5: 51-62.

Wattanakulpakin, Songsin Photchanachai, KhanokRatanakhanokchai, Khin Lay Kyu, PanumartRitthichai and Shuichi Miyagawa, 2012.Hydropriming effects on carbohydratemetabolism, antioxidant enzyme activity andseed vigor of maize (Zea mays L.), AfricanJournal of Biotechnology, 11(15): 3537-3547.

EFFECT OF SEED PRIMING ON BIOCHEMICAL CHANGES DURING SEED STORAGE

75

The area of traditional cultivation of blackgram is confined to the South Asia and adjacentregions (India, Pakistan, Afghanistan, Bangladeshand Myanmar). About 70 per cent of world’s blackgram production comes from India and it is the largestproducer as well as consumer of black gram. Itproduces about 1.5 million tonnes of blackgramannually from about 2.5 million hectares of area withan average productivity of 400 kg per hectare. Blackgram output accounts for about 10 per cent of India’stotal pulse production. The major producing statesare Andhra Pradesh, Maharashtra, Orissa, MadhyaPradesh, Tamil Nadu and Uttar Pradesh. The studywas under taken in Karnataka with specific objectiveto estimate the costs and margins at different stagesin marketing channel of blackgram.

Combination of purposive and randomsampling techniques was used for selection of district,markets, market functionaries and farmers requiredfor the study. Two taluks in this district were selectedbased on the probability proportional to the area underthe study. Two villages from each taluk were selectedbased on area cultivated under pulses and obtainedyield. The required primary data were obtained from25 sample farmers from each village by interviewmethod making a sample of 100 from the district asa whole. Farmers are categorized into small and largebased on the land holdings.

It was also intended to study the marketfunctionaries involved at different stages of the valuechain of pulses, their marketing costs and margins.Commission agents (15), traders (15), processors(15), wholesaler (10) and retailers (10) were selectedat random. Secondary data pertaining to the agroeconomic features of the study area were collectedfrom tahaseldar office and Agriculture Department ofGulbarga district. Multiple linear regression model wasfitted to analyze the factors influencing the profitmargin of blackgram.

AN ECONOMIC ANALYSIS OF BLACKGRAM INGULBARGA DISTRICT OF KARNATAKA

DEEPAK HEGDE, D. V. SUBBA RAO, N. VASUDEV and K. SUPRIYACollege of Agriculture, ANGR Agricultural University, Rajendranagar, Hyderabad-500030


The cost of cultivation of blackgram wasestimated at Rs.27, 671. It increased with the sizeof holding from Rs. 27,044 for small farmers to Rs.28,307 for large farmers (Table.1). It was observedthat the operational costs accounted for a major sharein the total costs on all the categories of farms. Thetotal operational costs were Rs. 22,972, Rs. 24,057and Rs. 23,493 for small farmers, large farmers andthe sample as a whole respectively. Higheroperational cost of large farmers was due to hiringmore human labour and tractor services and incurringmore cost on manures and fertilizers and plantprotection chemicals.

The price spread was studied in twochannels. Commission agents were involved inchannel-I while traders were involved in channel-II.On an average, the producer incurred a cost of Rs.159 per quintal of blackgram towards soot, gunnybag, labour charges, transportation, weighing (Rs.)and miscellaneous in channel I. Commission agentincurred a cost of Rs. 90 in channel I. In channel II,blackgram producer incurred a marketing cost of Rs.199. Total marketing cost incurred by the trader wasRs. 135. Processor’s marketing cost and wholesalersmarketing costs were the same in both the channels.Total marketing cost incurred by retailer was Rs. 76.The results were in conformity with the findings ofBanerjee and Palke (2010).

Because of more marketing cost incurred byfarmers and more margins obtained by themiddlemen, producers share in consumer’s rupee wasless in channel II (73 per cent) than in channel-I (78per cent). Similar views were shared by Govind Pal(2002). Even though the producers share inconsumer’s rupee was less in channel-II whencompared to channel-I, most of the farmers in Jewargitaluk of Gulbarga district were selling their producein this channel.



76

The total costs (Rs. 798) and margins (Rs.633) were more in channel-II than channel-I, becauseof which the price spread was more and producersshare in consumer’s price was less in channel II.Price spread was Rs. 1093 and Rs. 881 in channel Iand II respectively.

Multiple regression model was employed tostudy determinants of profit margin per quintal ofpulses. Out of five variables included in the modelthree variables significantly explained the variationin profit margin per quintal of pulses (R2 = 0.92).Operating cost of production, marketing cost andgross price received by the producer were foundsignificantly influencing profit margin (Table 3).

The coefficient of determination (R2 = 0.92)

showed that about 92 percent of the variation in profit

was explained by the variables included in the model.

The result showed that, one rupee increase in

operating cost decreases the profit margin per quintal

by Rs.0.72. Reduction in the marketing cost increases

the profit margin per quintal of blackgram. If the

marketing cost is increased by one rupee, farmer

experiences less profit margin (Rs.5.66). If the market

price received by the producer is increased by one

rupee, the profit margin of blackgram will be

increased by Rs.0.74. Education level and fixed cost

of production were found to be non-significant.

Farmers Sl.No

Particulars

Small farmer Large farmer Pooled

1.Operational cost

a Human Labour 7995 8162 8104

i. Owned labours 5914 2471 5077

ii. Hired labours 2080 5692 3027

b. Bullock labour 5167 5016 5059

i. Owned labours 3468 3229 3304

ii. Hired labours 1699 1787 1754

c Tractor Power 608 1220 819

d. Seed 752 783 773

e. FYM 1750 1560 1643

f. Fertilizers 2998 3082 3052

g. Plant protection chemicals

2200 2660 2507

h. Interest on working capital

1503 1574 1537

Total operational cost

22972 24057 23493

a. Land Revenue 100 100 100

Table 1. Cost of cultivation of Blackgram (per hectare)

AN ECONOMIC ANALYSIS OF BLACKGRAM IN GULBARGA

(Rs.)

(Rs.)

77

Table 2 Price spread and marketing margin for blackgram (Rs/qtl)Blackgram

S.No Particulars Channel I (Rs/qtl) Channel II (Rs/qtl)

1 Producer

a) Gross price received 3371 3198

b) Marketing cost 159 199

Net price received 3211 2999

2 Commission Agent

a) Marketing cost 90 --

b) Margin 69

3 Trader

a) Purchase price 3198

b) Marketing cost -- 135

c) Selling price 3450

d) Margin 117

4 Processor

a) Purchase price 3529 3450


c) Processing cost 199 199

d) Selling price per quintal of Dall 5790 5790

e)

Selling price of per quintal of dal

multiplied with conversion factor

(CF)

3821 3821

f) Margin 166 245

5 Wholesaler



c) Selling price per quintal of dall 5950 5950

d) Selling price of per quintal of dal

multiplied with CF 3927 3927

e) Margin 106 106

6 Retailer




DEEPAK et al

dal

5 Wholesaler




d) Selling price of per quintal of dal

multiplied with CF 3927 3927

e) Margin 106 106

6 Retailer




78

Variables Coefficients Standard Error t Stat

Intercept 455.30* 176.86 2.57

Operating cost of production -0.72** 0.10 -6.85

Fixed cost of production 0.02 0.27 0.06

Marketing cost -5.66++ 2.62 -2.16

Market price of the produce 0.74** 0.09 8.01

Education level 455.30 176.86 2.57

F value 27.61

R2 0.92

Adj. R2 0.89

N 18

Table 3 Regression estimates of determinants of profit margin for blackgram Y= Profit margin per quintal of blackgram

++ Significant at 5 % level, * Significant at 5 % level, ** Significant at 1 % levelNote: Operating cost and fixed costs of production includes total of all components

REFERENCES

Banerjee, Gangadhar and Palke, L. M. 2010.Economics of Pulses Production andProcessing of India. National Bank forAgriculture and Rural Development.Occasional Paper - 51

Govind Pal. 2002. Marketing of gram in BlockShahabganj; district Chandauli, Uttar Pradesh(an economic analysis). Indian Journal ofAgricultural Economics. 57(3): 388

AN ECONOMIC ANALYSIS OF BLACKGRAM IN GULBARGA

79

Chickpea (Cicer arietinum L.) 2n=2x=16, isthe third most important food legume globally,occupying an area of 11.55 m ha with a production of10.46 m t (FAO STAT 2010). India is the largestproducer of chickpea in the world sharing about 72%of area and production globally and accounts for about30% and 38% of national pulse acreage andproduction respectively. Though India is the largestproducer of chickpea, the productivity is low (943kg/ha) compared to other chickpea producingcountries viz., Mexico (1809 kg/ha), Australia (1268kg/ha) and Ethiopia (1265 kg/ha) and the productionis not adequate to meet the domestic demand.Consequently India is importing chickpeas everyyear. Hence, there is every need to to improve theproductivity potential of chickpea through appropriatebreeding strategies. Choice of an appropriate breedingprocedure for improving a trait depends mainly onthe nature of gene action involved in the inheritanceof the character, thus, emphasizing the importanceof genetic analysis for yield and its components. Inthe present investigation, an attempt has been madeto assess the nature of gene effects for yield and itscomponent characters following the diallel analyses,so as to design breeding strategies for improvementof chickpea yield potential.

The experimental material consisted of sevenparents and also twenty-one F1s, derived from sevenparental genotypes viz., NBeG-3, JG-11, ICCV 05106,MNK-1, ICCV 95333, KAK-2 and Vihar, crossed indiallel fashion excluding reciprocals. Theexperimental material was sown in a RandomizedBlock Design with three replications during Rabi 2011-12. Data were recorded on five competitive randomlyselected plants per replication from each treatmentfor seven characters viz., days to 50 per centflowering, days to maturity, plant height, number ofbranches per plant, number of pods per plant, seed



GENE ACTION AND COMBINING ABILITY STUDIES IN CHICKPEA(Cicer arietinum L.)

B. REDDY YAMINI, V. JAYALAKSHMI, B. NARENDRA and P. UMAMAHESHWARIDepartment of Genetics and Plant Breeding, Agricultural College,Acharya N.G.Ranga Agricultural University, Mahanandi -518 503

yield per plant and 100-seed weight. Data is subjectedto combining ability analysis according to Model Iand Method II of Griffing (1956).

The analysis of variance revealed significantdifferences among the treatments for all the seventraits indicating considerable amount of variabilitythus justifying the use of material under study.Analysis of variance for combining ability (Table 1)revealed significant general combining ability (gca)and specific combining ability (sca) for all thecharacters studied, indicating the importance of bothadditive as well as non additive genetic componentsof variation in the expression of these attributes.Importance of both types of gene effects has beenobserved earlier in chickpea for seed yield and relatedattributes by Preethi Verma and Waldia (2010),Bharadwaj et al (2009) and Patil et al (2006).However, variance components indicated that themagnitude of the non additive (sca) variance wasconsiderably higher than additive (gca) variance forall the characters except plant height and 100 seedweight, indicating the preponderance of non additivegenetic effects (dominance and epistasis) incontrolling the expression of these characters. Thepredominance of non additive gene action wasreported by Sarode et al. (2001) for days to 50 percent flowering and days to maturity; Bhardwaj andSandhu (2009) for number of branches per plant,number of pods per plant and seed yield per plant.Gupta et al. (2007) reported the importance of additivegene action in inheritance of plant height and 100seed weight.

The estimates of gca effects (Table 2)indicated that parents NBeG-3, JG-11, ICCV 05106and Vihar were good general combiners for numberof pods per plant, where as NBeG-3 and JG-11showed significantly higher gca effects for numberof branches per plant. With regard to phenological


80

S. No. Character gca

(df=6) sca

(df=21) error

(df=54) ² gca ² sca ² gca

/ ² sca

h2

1 Days to 50% flowering 40.42** 12.15** 2.43 4.22 9.72 0.43 0.41

2 Days to maturity 9.49** 2.00** 0.62 0.99 1.38 0.71 0.50

3 Plant height (cm) 70.00** 6.30** 1.13 7.65 5.17 1.48 0.71

4 Number of branches

per plant 37.03** 18.75** 0.61 4.05 18.14 0.22 0.18

5 Number of pods per

plant 533.32** 360.87** 1.28 59.12 359.60 0.16 0.25

6 Seed yield per plant (g) 7.16** 30.70** 0.16 0.78 30.54 0.03 0.05

7 100 seed weight (g) 276.73** 20.19** 1.35 30.60 18.83 1.62 0.75

* Significant at Pd”0.05, ** Significant at P d”0.01, gca – general combining ability; sca – specific combiningability; s² gca – variance due to gca ; s² sca – variance due to sca. h2 - heritability in narrow sense

S. No.

Genotype

Character NBeG-3 JG-11 ICCV05106

M N K-1

ICCV95333 KAK-2 Vihar

S.Em of Gi

S.Em of Gi-Gj

1 Days to 50 percent Flowering -0.476 -3.03** -2.59** 1.97** 1.60** 0.49 2.04** 0.48 0.73

2 Days to maturity -1.00** -1.15** -0.30 0.85** 0.00 -0.185 1.78** 0.24 0.37

3 Plant height -2.02** -2.75** 0.53 4.92** 1.91** -2.63** 0.04 0.33 0.50

4 Number of branches per plant

2.22** 2.85** -0.01 -3.18** -1.08** -0.55* -0.25 0.24 0.37

5 Number of pods per plant 2.77** 12.19** 3.16** -12.95** -3.95** -2.22** 1.02** 0.35 0.53

6 Seed yield per plant 0.76** 0.46** -0.71** -1.41** -

0.56** 0.51** 0.96** 0.12 0.19

7 100 seed weight -3.71** -6.61** -3.34** 10.31** 3.00** -0.38 0.73* 0.36 0.55

Table 2. Estimates of general combining (gca) effects for seven yield attributes in Chickpea

*Significant at Pd” 0.05 , **Significant at Pd”0.01

traits, JG-11 and ICCV 05106 recorded significantdesirable negative gca effects for days to 50 per centflowering, while, NBeG-3 and JG-11 for days tomaturity, suggesting that these parents could be good

general combiners for breeding for earliness. The highgeneral combining ability for plant height and 100seed weight was recorded in MNK-1 and ICCV 95333.For seed yield per plant the genotypes viz., NBeG-3,

GENE ACTION AND COMBINING ABILITY STUDIES IN CHICKPEA

81

S. No Cross D 50 F DM PH NB/P NP/P SY/P

100 SW

1 NBeG -3 x JG-11 2.56 -1.77* 2.43* 1.81* -11.74** 3.42** 1.64

2 NBeG-3 x ICCV05106 2.44 0.71 -0.52 -4.76** 7.15**

-4.76**

-11.79**

3 NBeG-3 x MNK-1 -2.44 -1.77* -0.48 1.68* -4.44**

-3.44** -4.88**

4 NBeG-3 x ICCV 95333 1.26 0.75 -0.84 7.77** 31.99** 5.53** 0.6

5 NBeG-3 x KAK-2 1.37 0.27 -1.19 2.05** 4.32** 0.82* 5.52**

6 NBeG-3 x Vihar -3.85** -1.03 2.31* -0.43 3.64** 7.51** 1.27

7 JG-11 x ICCV 05106 2.67 -0.47 1.14 5.53** 23.63** 5.67** 1.35

8 JG-11 x MNK-1 6.11** 0.05 4.57** 1.04 -0.48 4.66** -2.15*

9 JG-11 x ICCV95333 -3.52* 0.23 -4.83** -2.23** -7.2** -4.38** -4.89**

10 JG-11 x KAK-2 -3.41* -1.25 1.55 2.02** 33.29** 6.1** -1.72

11 JG-11 x Vihar -5.3** -1.55* -0.21 -0.06 30.05** 0.96** -0.77

12 ICCV05106 x MNK-1 -2.33 1.86* 0.93 2.1** -0.36 0.66 1.33

13 ICCV05106 X ICCV 95333 -5.96** 0.05 2.87** 1.13 4.66** 0.06 5.85**

14 ICCV05106 x KAK-2 -2.85* 0.9 -1.57 -3.62** -15.4** -0.21 1.83

15 ICCV05106 x Vihar -2.41 -2.4** -0.3 7.63** 20.86** 8.78** -0.57

16 MNK-1 x ICCV 95333 -0.52 -0.44 0.86 -3.08** 0.66 2.33** 4.77**

17 MNK-1 x KAK-2 -3.41* -0.58 -4.61** 5.78** 11.51** 4.47** -6.82**

18 MNK-1 x Vihar 1.7 -0.55 0.79 -5.53** -8.45** -3.95** 2.37*

19 ICCV95333 x KAK-2 3.3* -1.07 1.6 3.69** 4.04** 4.61** 1.4

20 ICCV 95333 x Vihar 1.74 -0.36 -3.35** 0.59 0.69 -0.64 -2.26*

21 KAK-2 x Vihar 2.85* 0.16 2.92** -4.61** -6.99** 1.42 4.38** S.E of Sii-Sjj 1.64 0.83 1.12 0.82 1.19 0.42 1.23 S.E of Sij-Sik 2.08 1.05 1.42 1.04 1.51 0.53 1.55 S.E of Sij-Skl 1.94 0.98 1.33 0.98 1.41 0.49 1.45

Table 3. Estimates of specific combining (Sca) effects for seven yield attributes in Chickpea

* Significant at Pd” 0.05, ** Significant at Pd” 0.01 D 50 F= Days to 50% flowering, DM= Days to maturity, PH= Plant height, NB/P= Number of branches perplant, NP/P= Number of pods per plant, SY/P= Seed yield per plant, 100 SW=100 seed weight.

JG-11 and Vihar exhibits highly significant positivegca effects indicating that they were good generalcombiners for seed yield. So, these parents can beexploited for the development of improved lines ofchickpea, because high gca effects are mostly dueto additive gene effect or additive x additiveinteraction effects which are fixable (Griffing, 1956).

The sca effect is an important criterion forthe evaluation of hybrids. Among the various gene

interactions contributing towards sca, the additive xadditive type of gene interaction is fixable insegregating generations in self pollinated crops likechickpea. More over ultimate aim of a plant breederis to develop potential homozygous lines throughhybridization. The cross combinations with significantdesirable sca effects along with mean performanceand gca effects of the parents are listed in Table 3.JG-11 x Vihar had significant desirable sca effectsfor seed yield per plant and number of pods per plant

YAMINI et al

82

and also gave highly significant negative sca effectsfor days to 50 per cent flowering and days to maturity.A few crosses viz., JG-11 x ICCV 95333, JG-11 xKAK-2, ICCV 05106 x ICCV 95333 and ICCV 05106x KAK-2 also recorded highly significant sca effectsas well as low mean values for days to 50 per centflowering, indicating early maturity and these crossesinvolved only one good combiner suggesting additive

Character Crosses with high per se performance and significant sca effects

sca effects Per se performance

gca effects of parents

Days to 50 per cent flowering

JG-11 x ICCV 95333 JG-11 x KAK-2 JG-11 x Vihar ICCV 05106 x ICCV 95333 ICCV 05106 x KAK-2

-3.52* -3.41* -5.3**

-5.96** -2.85*

35.3 34.3 34.0 33.3 35.3

G x P G x P G x P G x P G x P

Days to maturity NBeG-3 x JG-11 -1.77* 88.3 G x G Plant height JG-11 x MNK-1

ICCV 05106 x ICCV 95333 4.57** 2.87**

47.7 46.3

P x G P x G

Number of branches per plant

NBeG-3 x JG-11 NBeG-3 x ICCV 95333 NBeG-3 x KAK-2 JG-11 x ICCV 05106 JG-11 x KAK-2 ICCV 05106 x Vihar

1.81* 7.77** 2.05** 5.53** 2.02** 7.63**

27.5 29.5 24.3 29.0 24.9 28.0

G x G G x P G x P G x P G x P P x P

Number of pods per plant

NBeG-3 x ICCV 05106 NBeG-3 x ICCV 95333 JG-11 x ICCV 05106 JG-11 x KAK-2 JG-11 x Vihar ICCV 05106 x Vihar

7.15** 31.99** 23.63** 33.29** 30.05** 20.86**

58.5 76.3 84.4 88.7 88.7 70.5

G x G G x P G x G G x P G x G G x G

Seed yield per plant

NBeG-3 x JG-11 NBeG-3 x ICCV 95333 NBeG-3 x Vihar JG-11 x ICCV 05106 JG-11 x MNK-1 JG-11 x KAK-2 ICCV 05106 x Vihar MNK-1 x KAK-2 ICCV 95333 x KAK-2

3.42** 5.53** 7.51** 5.67** 4.66** 6.1**

8.78** 4.47** 4.61**

20.7 21.8 25.3 21.5 19.8 23.1 25.1 19.6 20.6

G x G G x P G x G G x P G x P G x G P x G P x G P x G

100 seed weight ICCV 05106 x ICCV 95333 MNK-1 x ICCV 95333 MNK-1 x Vihar KAK-2 x Vihar

5.85** 4.77** 2.37*

4.38**

38.5 51.1 46.4 37.7

P x G G x G G x G P x G

Table 4. Specific combining ability effects, mean performance and general combining ability effectsof parents and promising crosses for yield and yield attributes

x dominance gene effects in these crosses. In thesecrosses, additive component present in goodcombiners and the complimentary epistatic effectsin F1 hybrid might work in the same direction tomaximize the desirable effects of this trait insegregants. Promising crosses viz., NBeG-3 x ICCV95333, JG-11 x ICCV 05106, JG-11 x KAK-2 andICCV 05106 x Vihar exhibited significant sca effects

GENE ACTION AND COMBINING ABILITY STUDIES IN CHICKPEA

83

coupled with high per se performance for seed yieldand yield attributes like number of branches andnumber of pods per plant. For plant height and 100seed weight ICCV 05106 x ICCV 95333 (poor x good)registered significant sca effect with high meanvalues. Hence this cross could be exploited foridentifying tall and bold seeded genotypes in thesegregating generations.

The results of the present investigationrevealed the preponderance of non additive geneaction for yield and yield components and thereforeheterosis breeding may be rewarding for improvingchickpea. But the practical production of hybrid gramis not biologically feasible due to small size andcleistogamous nature of the flowers and strong

hybridization barriers (Preethi Verma and Waldia,2010). In view of such problems, Jensen’s (1970)selective diallel mating system and its modifications(Frey, 1975) would be utilized for the creation andisolation of recombinants to breed superior chickpeavarieties. The crosses NBeG-3 x JG-11 for seed yieldand number of branches, NBeG-3 x ICCV 05106, JG-11 x ICCV 05106 and ICCV 05106 x Vihar for numberof pods per plant and NBeG-3 x Vihar and JG-11 xKAK-2 for seed yield per plant exhibited significantsca effects coupled with high per se performancewith good x good combiners. Due to additive x additiveeffects and their possibility of fixation, single plantselection could be practiced in segregatinggenerations to isolate purelines from these crosses.

REFERENCES

Bhardwaj, R and Sandhu, J. S. 2009. Componentsof variance analysis in chickpea. Jounal ofFood Legumes. 22(4): 254-255.

Bharadwaj, R., Sandhu, J. S., and Gupta, S. K. 2009.Gene action and combining ability estimatesfor yield and other quantitative traits inchickpea. Indian Journal of AgriculturalSciences. 79: 895-900.

Frey, K. J. 1975. Breeding concepts and techniquesfor self pollinated crops. Proceedings ofInternational workshop on grain legumes,ICRSAT, Patancheru, India. 257-278.

Griffing, B. 1956. A generalized treatment of the useof diallel cross in quantitative inheritance.Heredity. 10:31-34.

Gupta, S. K. Kaur Ajinder and Sandu, J. S. 2007.Combining ability in desi chickpea. IndianJournal of Pulses Research. 20(1):22-24.

Jensen, N. F. 1970. A diallel selective mating systemfor cereal breeding. Crop Science. 10:629-635.

Patil. J. V. Kulkarni, S. S and Gawande, V. L. 2006.Genetics of quantitative characters in chickpea(Cicer arietinum L.). New Botanist- InternationalJournal of Plant Science Research. 33:1-4.

Preethi Verma and Waldia, R. S. 2010. Diallelanalysis for nodulation and yield contributingtraits in chickpea. Journal of Food Legumes.23 (2): 117-120.

Sarode, N. D. Deshmukh, R. B. Kute, N. S.Kanawade, D. G and Dhonde, S. R. 2001.Genetic analysis in chickpea (Cicer arietinumL.). Legume Research. 24:3, 159-163.

YAMINI et al

84

The study was conducted at VegetableResearch Station, Agricultural Research Institute,Rajendranagar, Hyderabad,during rabi season of2008-09. Sixty genotypes of Brinjal collected fromvarious agro-climatic regions of India by N.B.P.G.RRegional station, Hyderabad were evaluated in aRandomized Block Design with two replications forfourteen quantitative characters. The mean data wereanalyzed following standard statistical techniqueswith the objective of studying the nature andmagnitude of genetic diversity available in thegermplasm. Genetic diversity is an important factorfor any heritable improvement. Knowledge of geneticdiversity is useful for selecting desirable genotypesfrom a germplasm for the successful breedingprogramme. The genetic divergence betweengenotypes was estimated using Mahalanobis D2

statistic (1936).

In the present investigation, The D2 valuewas used for the final grouping of the genotypes intoeight distinct clusters as presented in Table 1. ClusterV was the largest cluster consisting of 15 genotypeswhile cluster VIII consisted of single genotype i.e,MR/04-26. The mean inter and intra cluster D and D2

values (Table 2) suggest that the genotypes within acluster are less divergent than those of differentclusters. The intra cluster and inter cluster D2 valuesranged from 714.92 to 2452.22 and 2339.92 to35995.80 respectively. Lowest inter cluster D2 valuewas recorded between cluster I and II (2339.92)indicating close relationship and similarity for mostof the characters of the genotypes. Highest intercluster D2 value was recorded between clusters IIIand VIII (35995.80) indicating wider genetic diversityamong genotypes in these groups.

The percentage contribution of eachcharacter towards divergence in Brinjal is presentedin Table 3.Highest contribution towards divergence

GENETIC DIVERGENCE IN BRINJAL (Solanum melongena L.) BALAJI LOKESH, P.SURYANARAYANA REDDY, R.V.S.K.REDDY and N.SIVARAJ

Vegetable Research Station, A.R.I, Rajendranagar, Hyderabad-500030



was put forth by average fruit weight (44.12%)followed by plant spread(43.62%), average fruit length(4.52%), number of branches per plant (2.71%),number of flower clusters per plant (2.03%) andaverage fruit diameter (1.41%) respectively. Thissuggest that in order to select genotypes forhybridization, the material should be screened forimportant traits like average fruit weight, plantspread, average fruit length, number of branches perplant, number of flower clusters per plant and averagefruit diameter. Similar results were reported by SateshKumar et al. (2007).

The mean performance of genotypes ofclusters is presented in the Table 4. In calculation ofcluster means, the superiority of a particular genotypewith respect to a given character gets diluted by otherrelated genotypes that are grouped in the samecluster which are inferior or intermediary for thatcharacter in question. Hence, apart from selectinggenotypes from the clusters which have high inter-cluster distance for hybridization, it is also desirableto have selection of parents based on extent ofgenetic divergence with respect to a particularcharacter of interest. On the basis of the meanperformance of the characters in each cluster, thefollowing genotypes were identified as superior forfurther genetic studies. The genotypes IC-111431 andIC-203593 were regarded as superior for number offlower clusters per plant. MR/04-26 was superior fordays to 50% flowering, average fruit weight, shootand fruit borer incidence on fruit and fruit yield perplant.IC-111352 and IC-111428 were regarded assuperior for number of fruits per cluster.IC-13601 andPSR-11883 were regarded as superior for number offruits per plant. Similar genetic divergence studieson Brinjal in India have been carried out by manyresearchers (Bansal and Mehta 2007, Sherly andShanthi 2007, Nandan Mehta and Mayuri Sahu 2009).


85

Cluster No of genotypes

Genotypes

I 5 IC-089876-B,IC-111384,IC-111431,IC-111468,IC-203593 II 11 IC-104086,IC-345309,IC-89910,PSR-11836,IC-13637,

IC-111461-B,IC-111308,AR04-131,IC-111317, IC-345255,IC-135056

III 5 IC-245335,DBT/OR-37,MR/04-02,PSR 11773,MR/04-81 IV 11 IC-13601,IC-135920,IC-90930,IC-256208,IC-137751,

PSR-11883,IC-111086,IC-136280,IC-127024,AR/04-132,IC-111071

V 15 IC-111352,IC-111356,IC-383119,PSR-11891,IC-99649, IC-256150,IC-90767,IC-111444,IC-111074,IC-112851, IC-111428,IC-089905,IC-112755,MR/04-94,IC-136278

VI 8 IC-136006,IC-136245,IC-135934,IC-136088, IC-104086,AR/04-145,MR/04-88,IC-90087

VII 4 IC-135955,IC-74204,AR/04-477,IC-11404 VIII 1 MR/04-26

Table 1. Clustering pattern of 60 germplasm accessions of Brinjal on the basis of Mahalanobis D2

statistics

Table 2. Average intra and inter cluster D2 and D values for eight clusters in 60 germplasm accessionsof Brinjal

The figures in the parenthesis are D values

Cluster I II III IV V VI VII VIII

I 714.92

(26.73)

2339.92

(48.37)

6693.23

(81.81)

6998.93

(83.65)

3349.35

(57.87)

8799.52

(93.80)

4568.94

(67.59)

32737.66

(180.93)

II

1414.06

(37.60)

3047.26

(55.20)

5279.60

(72.66)

3970.72

(63.01)

10954.37

(104.66)

8873.22

(94.19)

31216.35

(176.68)

III

2452.22

(49.51)

7563.46

(86.96)

8260.76

(90.88)

17611.32

(132.70)

16941.25

(130.15)

35995.80

(189.72)

IV

1937.25

(44.01)

3064.44

(55.35)

5211.95

(72.19)

8650.18

(93.00)

14041.89

(118.49)

V

1701.21

(41.24)

3914.04

(62.56)

3986.40

(63.13)

18843.24

(137.27)

VI

1451.12

(38.09)

3899.03

(62.44)

10777.12

(103.81)

VII

1299.78

(36.05)

23498.27

(153.29)

VIII

0.00

(0.00)

LOKESH et al

86

Table 3. Percent contribution of characters towards diversity in Brinjal germplasm

S.No Character

Percent contribution

1 Plant height (cm) 0.23 2 Plant spread(cm2) 43.62 3 Number of branches per plant 2.71 4 Days to 50% flowering 0.00 5 Number of flower clusters per plant 2.03 6 Number of flowers per cluster 0.00 7 Number of fruits per cluster 0.00 8 Fruit length (cm) 4.52 9 Fruit diameter (cm) 1.41 10 Fruit weight (g) 44.12 11 Number of fruits per plant 0.23 12 Shoot and fruit borer incidence on shoot (%) 0.00 13 Shoot and fruit borer incidence on fruit (%) 0.28 14 Yield/ plant (kg) 0.85

REFERENCES

Bansal, S and Mehta, A. K. 2007. Genetic divergencein brinjal (Solanum melongena L.) HaryanaJournal of Horticultural Sciences 36(3/4): 319-320.

Mahalanobis, P. C. 1936. On the generalized distancein statistics. Proceedings of National Instituteof Sciences, India 12: 49- 55.

Nandan Mehta and Mayuri Sahu. 2009. Geneticdivergence in brinjal (Solanum melongena L.)International Journal of Plant Sciences,Muzaffarnagar 4(1): 123-124.

Satesh Kumar, Singh, A. K, Sharma, J. P and NeerjaSharma. 2007. Genotype clustering in brinjal(Solanum melongena L.) using D2 statistic.Haryana Journal of Horticultural Sciences36(1/2): 95-96.

Sherly, J and Shanthi, A. 2007. Diversity studies inbrinjal. Haryana Journal of HorticulturalSciences 36(1/2): 162-163.

GENETIC DIVERGENCE IN BRINJAL

87

Tabl

e 4.

Mea

n va

lues

of c

lust

ers

for f

ourt

een

char

acte

rs in

60

brin

jal g

erm

plas

m a

cces

sion

s

LOKESH et al

88

The project Irrigated AgricultureModernization and Water Bodies Restoration andManagement (IAMWARM) was introduced in 2007and funded by World Bank. Its objective was toimprove irrigation service delivery includingadaptation of modern water-saving irrigationtechnologies and ultimately to ensure food securityand improved farm incomes. Keeping this in view,present study was proposed to study the socio-economic impact of IAMWARM project on beneficiaryfarmers in Pudukkottai district of Tamil Nadu in theyear 2012. The implications of the study would beuseful to the project officials, implementingauthorit ies, funding agencies concerned, forextending project benefits to the farming community.

A study was undertaken purposively inPudukkottai district of Tamil Nadu as this project wasfirst implemented in Pudukkottai district under firstphase sub-basin of the project during the year 2007.Thus, it gave sufficient time interval to study theimpact. Four taluks were selected randomly and threevillages from each taluk were selected randomly. Tenbeneficiaries from each village were selected randomly



RELATIONSHIP BETWEEN PROFILE OF BENEFICIARY FARMERS AND THESOCIO-ECONOMIC IMPACT OF IRRIGATED AGRICULTURE MODERNIZATION

AND WATER BODIES RESTORATION AND MANAGEMENT (IAMWARM)PROJECT IN PUDUKKOTTAI DISTRICT

G. ABIRAMI, B.VIJAYABHINANDANA and T. GOPI KRISHNA Department of Agricultural Extension, Agricultural College,

Acharya N.G Ranga Agricultural University, Bapatla- 522 101

using simple random sampling procedure, thusmaking a total sample of 120 beneficiary farmers.Ex-post facto research design was followed. Datawas collected through interview schedule from thebeneficiary farmers of the project covering all aspectsof the socio-economic impact. To convert the datainto meaningful findings some statistical tools wereused. viz. a) Frequency and Percentage analysis b)Correlation analysis c) Multiple regression analysisand d) class interval.

a) Socio-economic impact of the project

Socio-economic impact of the project in SRItechnique was studied with eleven variables namely,knowledge, adoption, income, asset acquisition, yield,water use efficiency, participation in the projectactivities, labour use, social participation, cost ofcultivation, and empowerment. By adding andaveraging the scores of all the items, the individualscore for socio-economic impact was worked out. Itwas used to categorize the respondents into threegroups based on the class interval (exclusive) methodas low, medium and high level of socio-economicimpact.

Beneficiary Farmers

N=120

S.No Category

Frequency Percentage

1. Low (49-62) 30 25.00

2. Medium (63-76) 56 46.67

3. High (77-90) 34 28.33

Table 1. Distribution of respondents according to socio-economic impact

** - significant at 0.01 level of probability


89

Socio-economic Impact: A cursory look atthe Table 1 indicates that 46.67 per cent of beneficiaryfarmers had medium level of socio-economic impact,followed by high (28.33%) and low level of socio-economic impact (25.00%). The result was thecumulative effect of all the factors contributed to thesocio-economic impact. The reason for high level ofsocio-economic impact might be particularly due tomajority of the farmers obtained fine knowledge aboutSRI and adopted SRI technique fairly in their field.As a result they received more income and assetacquisition. Medium level of socio-economic impact

might be due to partial adoption of technology andinefficient management of labour. That results in morecost of cultivation. Low level of socio-economicimpact might be due to poor adoption & SRItechnology.

b) Relationship between profile of beneficiaryfarmers and the socio-economic impact

An attempt has been made to find out theassociation between independent variables anddependent variables through correlation coefficient(r) values. The results are presented in Table 2.

Table 2. Correlation coefficient between profile of beneficiary farmers and the socio-economic impact

N= 120

S. No Independent Variables ‘r’ values

1. Age -0.2119 NS 2. Education 0.4014** 3. Land Holding 0.3870** 4. Farming Experience 0.3537** 5. Information sources utilization 0.3793** 6. Training Received 0.4815** 7. Economic Motivation 0.5241** 8. Scientific Orientation 0.6113** 9. Innovativeness 0.5927** 10. Risk Orientation 0.6084**

The results presented in the Table 2 clearlyindicate that almost all computed ‘r’ values ofeducation, land holding, farming experience,information sources utilization, training received,economic mot ivation, scient if ic orientation,innovativeness and risk orientation with socio-economic impact were found positively significantrelationship at 0.01 level of probability. Whereas, agewith socio-economic impact had non-significant andnegative relationship.

** Significant at 0.01 level of probability NS = Non Significant

From this study it could be concluded thathigher the education, higher the land holding, higherthe experience in farming, higher the informationsources utilization, higher the training received,higher the economic motivation, higher the scientificorientation, higher the innovativeness and higher therisk orientation, the higher would be the socio-economic impact.

The probable reason for age, not influencingthe socio-economic impact can be explained that

ABIRAMI et al

90

adoption of innovation (in this context adoption ofSRI technique) in Indian agriculture does not differfrom non-adoption with respect to age, it depends onhow worthy the innovation is to a farmer for efficientcrop production. This trend was also noticed byMohammad et al (2009).

The reason for positive and significantcorrelation of education with socio-economic impactmight be due to the fact that, the education widenshorizons of the individual to get information fromvarious sources. This seems to be inter-related withfarmers to bring changes in their personal,psychological orientation, to adopt new ideas,practices and technologies and motivate the farmerstowards achieving high socio-economic impact. Thisresult was in agreement with the results of Manoj(2008) and Suresh and Rameshbabu (2008).

There was positive and significantrelationship between land holding and socio-economicimpact. This might be due to the fact that land holdingprovides the economic base for the farmers topractice new agricultural technologies. Land holdingalso provides regulated impetus to make optimumutilization of resources on farm for achievingmaximum profits. Similar results were reported byRameshbabu (2002) and Reddy et al. (2007).

It was found that farming experience hadpositive and significant relationship with socio-economic impact. This might be due to the fact thatexperience is the best teacher, farmer having moreexperience in farming, irrespective of age would knowthe difficulties and problems in farming better thanless experienced and who seek for new alternativefarm practices and adopt new production technologies.This result was in conformity with the results ofThyagarajan (2004) and Reddy et al. (2007).

Positive and significant relationship wasnoticed between information sources utilization andsocio-economic impact. This might be due to the factthat different information sources utilization updatedthe farmers with new production technologies andmotivate them to adopt it to improve their profits.This finding was in tune with the results of Reddy etal. (2007) and Mohammad et al. (2009).

From the Table 2. it was clear that there waspositive and significant relationship between trainingreceived and socio-economic impact. This might bedue to the fact that training is one of the means bywhich desired changes in knowledge and skills couldbe attained. An individual who receives trainingbecome more knowledgeable, skilful and developrationale and adopt improved farming practices ledto have more socio-economic impact. This might bethe reason for above result. The result was inagreement with the results of Basawarajaiah (2001).

Economic motivation was found to be positivelyand significantly associated with the socio-economicimpact. The reason could be that the farmers withmore economic motivation would be oriented towardsmore information sources utilization; risk bearing thatmight help them to adopt new production technologies.This finding was in line with the findings of Manoj(2008) and Suresh and Rameshbabu (2008).

The results furnished in the Table 2 indicatedthat there was positive and significant relationshipbetween scientific orientation and socio-economicimpact. It might be due to the reason that the farmershaving more scientific orientation would gather moreinformation from authentic sources like Krishi VigyanKandra, TNAU scientists, etc., and think rationallybefore applying into the field conditions, get the higherproduction and profits. Similar results were reportedby Manoj (2008) and Mohammad et al. (2009).

There was positive and significant relationshipbetween innovativeness and socio-economic impact(Table 2). This might be due to the fact that farmerswho are relatively earlier in adopting new agriculturalinnovations would orient towards more risk taking,more scientific orientation, maintain higher socialstatus. Their earliness to adopt innovations wouldhave resulted in higher socio-economic impact interms of increasing higher yields and income. Thisresult was in conformity with the results of Damodaran(2007) and Manoj (2008).

The correlation between risk orientation andsocio-economic impact was positive and significant(Table 2). It could be inferred from the finding thathigher the risk orientation, the higher would be the

RELATIONSHIP BETWEEN PROFILE OF BENEFICIARY FARMERS & IMPACT

91

socio-economic impact. This might be due to the fact

that higher risk oriented farmers adopt the innovations

and get more yield and higher income. Hence, such

type of relation existed in the study. This finding was

in line with the findings of Chandrasekhar et al. (2005)

and Manoj(2008).

c) Multiple Linear Regression of selectedindependent variables with socio-economicimpact.

An attempt was made to find out the amount ofcontribution made by the independent variables inexplaining the variation in the dependent variablethrough multiple linear regression. The results arepresented in Table 3.

Table 3. Multiple regression analysis of Profile of beneficiary farmers and the socio-economic impact

S. No Variables Regression coefficient (B) Standard error ‘t’ value

1. Age -6.3303 0.7039 -8.9927**

2. Education 0.7092 0.3255 2.1784NS

3. Land Holding 0.4241 0.4370 0.9704NS

4. Farming Experience 4.6575 0.6232 7.4733**

5. Information sources utilization -0.4021 0.0695 -5.7790** 6. Training Received

3.3325 0.5538 6.0164** 7. Economic Motivation

1.4766 0.2322 6.3584** 8. Scientific Orientation

0.3394 0.1763 1.9248NS 9. Innovativeness

0.7704 0.1235 6.2369** 10. Risk Orientation

1.7744 0.2070 8.5691**

The Ten independent variables with thesocio-economic impact of the project taken onMultiple Linear Regression Analysis gave the R2 (Co-efficient of multiple determination) value of 0.861. Itindicates that all the selected independent variablesput together contributed 86.10 per cent of the totalvariation in the socio-economic impact of the projectby the beneficiary farmers, leaving the rest to

R2 = 0.861; NS = Non Significant;** Significant at 0.01 level of probability

extraneous factors. The independent variables viz.,farming experience, training received, economicmotivation, innovativeness and risk orientationcontributed significantly to the socio-economic impactof the project. The variable scientific orientation wasnot having significant value, but the value is near tosignificant t –value (1.980272). So, it also could beconsidered as significant

REFERENCES

Baswarajaiah, V.2001. Impact of Edira WatershedDevelopment Programme on farm families inMahaboobnagar District of Andhra Pradesh.

M. Sc. (Ag.) Thesis submitted to Acharya NG Ranga Agricultural University, Hyderabad,India.

ABIRAMI et al

92

Chandrasekhar, V., Gangadharappa, N.R andSuresha, S.V. 2005. Knowledge level farmersabout selected technological interventions inTAR-IVLP. Mysore Journal of AgriculturalSciences. 39 (3): 410-414.

Damodaran, C. 2007. Irrigation management andsocio-economic changes among Cauvery olddelta farmers. M.Sc. (Ag.) Thesis submittedto Acharya N G Ranga Agricultural University,Hyderabad, India.

Manoj, A. 2008. Impact of Krishi Vigyan Kendra onfarmers Srikakulam district of AndhraPradesh. M. Sc. (Ag.) Thesis submitted toAcharya N G Ranga Agricultural University,Hyderabad, India.

Ca Mohammad Ajaz-ul-Islam, Masoodi, N.A.,Masoodi, T.H and Gangoo, S.A. 2009.Awareness and participation of beneficiariesin social forestry programme in Baramulladistrict of Kasmir valley. Indian Journal ofSocial Research. 50(4): 353-364.

Rameshbabu, C. 2002. Effectiveness of Indo-DutchOperational Research project on drainage andwater management for salinity control inPrakasam district of A.P. M. Sc. (Ag.) Thesissubmitted to Acharya N G Ranga AgriculturalUniversity, Hyderabad, India.

Reddy, P.T.S., Prabhakar, K and Gidda Reddy, P.2007. Analysis of influence of selectedindependent variables on knowledge of ricefarmers on Eco-friendly technologies. Journalof Research ANGRAU. 35 (2): 31-37.

Suresh, T.V and Ramesh Babu, C.H. 2008. Extentof part icipation of farmers in SujalaKalinganahalli Halla Watershed Project,Andhra Agricultural journal. 55(3): 405-407.

Thyagarajan, S. 2004. Rice production technology –adoption and constraints. Indian Journal ofExtension Education. 40 (3&4): 44-47.

RELATIONSHIP BETWEEN PROFILE OF BENEFICIARY FARMERS & IMPACT

93

The selection indices by discriminantfunction analysis were constructed based on the dataof a population of 800 F2 plants developed bycrossing three grain sorghum genotypes viz., 27 B,ICSB 38 and 296 B as a female parent and four sweetsorghum genotypes viz., SSV 84, SSV 74, URJAand NSSV 13 as a male parent. Majority of selectionindices were found to be more efficient than straightselection based on sugar yield alone. The selectionindex consisting of six character combination viz.,sugar yield, total biomass, fresh stalk yield, brix percent, juice yield and total soluble sugars was moreeffective with higher relative efficiency. While,selection based on five characters combinations viz.,sugar yield, total biomass, fresh stalk yield, brix percent and juice yield as well as four charactercombination viz., total biomass, fresh stalk yield, brixper cent and juice yield were also equally effectivein selection of plants for maximum sugar yield.However, selection index comprising six and fivecharacter combinations are of little importance inselection process as it includes derived parameterssuch as sugar yield and total soluble sugars. In thisregard it is suggested to go for four charactercombination which also manifested maximum relativeefficiency coupled with higher genetic advance.

The practical or economic value of a plant isaffected by several traits. Since, majority of theeconomic traits are polygenically inherited and theirexpression is subjected to varying degrees offluctuations due to environmental factors, eventuallydirect selection may not be useful for such characters.Efficiency of selection under such circumstances cansometimes be improved by taking into considerationsimultaneously the phenotypic values of a numberof plant attributes which are correlated with the

CONSTRUCTION OF SELECTION INDICES FOR F2 POPULATIONDERIVED FROM CROSSES BETWEEN

GRAIN SORGHUM × SWEET SORGHUM [Sorghum bicolor (L.) Moench]VEMANNA IRADDI, T. DAYAKAR REDDY, A. V. UMAKANTH, CH. RANI,

D. VISHNU VARDHAN REDDY and M. H. V. BHAVEDepartment of Genetics and Plant Breeding

Acharya N.G. Ranga Agricultural University, Hyderabad – 500 030



genotypic values (high heritability) of the charactersunder consideration.

The material for the present study comprisedof 800 F2 population of sweet sorghum crossesderived from parents having low and high sugarcontent developed at Directorate of SorghumResearch, Rajendranagar, Hyderabad. Thesepopulations were developed by crossing thecontrasting parents (27 B with SSV 84, ICSB 38 withSSV 74, 296 B with URJA and 27 B with NSSV 13)through hand emasculation and pollination duringkharif 2010 and the F1 plants of the two crosses weregrown during rabi 2010 - 11 and selfed to produce theF2 seeds, which were evaluated during summer 2012.

The technique of discriminant functiondeveloped by Fisher (1936) was adopted to know thetrue genotypic worth of yield and its components andto have computational formulae for construction ofselection indices which when applied to select plantscan bring about effective improvement in yieldcompared to straight selection for yield. Smith (1936)has illustrated the use of discriminant function in plantselection.

Formulation of selection indices throughdiscriminant function analysis Selection indiceswere formulated in F2 populations of sweet sorghumconsidering sugar yield and its five componentcharacters which had high correlation with sugar yield.Among six characters, sugar yield (X1) wasconsidered as dependent character, while othercharacters viz., total biomass (X2), fresh stalk yield(X3), brix per cent (X4), juice yield (X5) and total solublesugars (X6) were considered as independentvariables. In order to select plants with high sugaryield, discriminant functions were computed with


94

different sets of characters and the efficiency of eachindex was compared with direct selection for sugaryield and other combination of characters (Table 1).

A maximum of five component characterswhich exhibited high significant and positivecorrelation with sugar yield were used in F2 populationsfor construction of different selection indices. Theexpected genetic advance was computed for eachof the indices at five per cent selection intensity.The relative efficiency of all the indices was computedconsidering the relative efficiency of sugar yield as100 per cent. The estimated values of geneticadvance and relative efficiency for each combinationof character in F2 population have been tabulated inTable 1 and briefly discussed below.

Sixty three different selection indices wereformulated based on various combinations of sixcharacters considered for construction of selectionindices. Among these, higher relative efficiency of2756.36 coupled with high genetic advance (90.42)was exhibited by combination involving all the sixtraits (including sugar yield) of X1 + X2 + X3 + X4 + X5

+ X6, followed by the combination of five traits viz.,X1 + X2 + X3 + X4 + X5 which recorded relative efficiencyof 2734.65 with high genetic advance of 89.70compared to other combinations.

Among single character, X2 was highlyefficient with relative efficiency of 814.80 comparedto the direct selection based on sugar yield (X1) whoserelative efficiency was taken as 100. Whereas, amongtwo character combinations, maximum relativeefficiency of 1661.36 was observed for thecombination of X2 + X3 traits with high geneticadvance of 54.50, followed by X2 + X5 traits with arelative efficiency of 1287.74 and genetic advanceof 42.24. However, in case of three charactercombinations, the combination involving X2 + X3 + X5

exhibited higher relative efficiency of 2090.98 coupledwith high genetic advance of 68.59.

Among four character combinations, X1 + X2

+ X3 + X5 followed by X2 + X3 + X4 + X5 and X2 + X3 + X5

+ X6 exhibited high relative efficiencies of 2702.34,2455.56 and 2451.75 coupled with genetic advanceof 88.64, 80.55 and 80.42, respectively.

Since, sugar yield and total soluble sugarsare estimated or derived characters, more emphasis

during selection should be given on the directlymeasurable characters to get the accurate results.Hence, the combinations without sugar yield and totalsoluble sugars are considered for indirect selectionfor sugar yield. Among them, X2 + X3 + X4 + X5

combination exhibited maximum relative efficiencyof 2455.56 per cent with genetic advance of 80.55.

Path coefficient analysis revealed theintricacies of yield components while discriminantfunction is useful in knowing the extent ofimprovement that can be effected in yield by selectingplants based on different combination of componentcharacters. Smith (1936) opined that, selection indexis the basis in considering the correlated charactersfor higher efficiency in selection for yield. When yieldis associated with other characters, indirect selectionthrough such traits is sometimes likely to be betterthan straight selection for yield. But, when the numberof characters associated with yield is large, itbecomes difficult to select simultaneously for allthese characters. Under such circumstances,selection indices formulated by involving differentcombinations of characters with appropriateweightage to each character help in making theselection procedure easy.

In the present study, selection indexinvolving six character combination viz., sugar yield,total biomass, fresh stalk yield, brix per cent, juiceyield and total soluble sugars was more effective withhigher relative efficiency. While, selection based onfive characters combinations viz., sugar yield, totalbiomass, fresh stalk yield, brix per cent and juiceyield as well as four character combination viz., totalbiomass, fresh stalk yield, brix per cent and juiceyield were also equally effective in selection of plantsfor maximum sugar yield. Similar results of increasedefficiency by inclusion of yield as one of thecomponent in formulating selection indices werereported by Mahadevappa and Ponnaiya (1967) inpearlmillet, Paroda and Joshi (1970) in wheat, Agrawalet al. (1978) in dwarf rice, Rahangdale et al. (1987) inupland rice, Mannur et al. (1991) in soybean, Mathurand Gupta (1992) in niger, Singh and Khan (1998)and Nahar et al. (2002) in sugarcane.

When indirect selection scheme excludingsugar yield in formulating index is to be followed, theindex involving combination of four characters viz.,

CONSTRUCTION OF SELECTION INDICES FOR F2 POPULATION

95

Sl. No. Discriminant Function GA RE (%)

1 Y = 0.23X1 3.28 100.00

2 Y = 0.12X2 26.73 814.80

3 Y = 0.14X3 23.77 724.67

4 Y = 0.36X4 0.95 28.92

5 Y = 0.17X5 13.67 416.72

6 Y = 0.36X6 0.83 25.28

7 Y = 2.31X1 + 0.01X2 33.97 1035.68

8 Y = 1.42X1 + 0.05X3 28.04 854.85

9 Y = 0.06X1 + 1.62X4 4.92 150.08

10 Y = 6.44X1 – 0.94X5 22.82 695.72

11 Y = 0.06X1 + 1.80X6 4.80 146.39

12 Y = 0.65X3 – 0.26X2 54.50 1661.36

13 Y = 0.07X2 + 8.45X4 33.29 1015.02

14 Y = 0.57X5 – 0.02X2 42.24 1287.74

15 Y = 0.07X2 + 9.59X6 33.18 1011.37

16 Y = 0.10X3 + 5.75X4 27.49 838.08

17 Y = 0.05X3 + 0.36X5 36.67 1117.85

18 Y = 0.10X3 + 6.50X6 27.39 834.97

19 Y = 6.49X4 + 0.04X5 19.27 587.57

20 Y = 0.47X4 + 0.25X6 1.78 54.23

21 Y = 0.04X5 + 7.36X6 19.15 583.66

22 Y = 3.15X1 – 0.32X2 + 0.52X3 61.51 1875.03

23 Y = 0.79X1 + 0.04X2 + 8.07X4 37.17 1133.16

24 Y = 16.09X1 + 0.16X2 – 2.71X5 56.51 1722.60

25 Y = 0.79X1 + 0.04X2 + 9.15X6 37.05 1129.41

26 Y = 0.02X1 + 0.10X3 + 7.16X4 30.97 944.15

27 Y = 15.19X1 + 0.29X3 – 2.84X5 51.37 1566.14

28 Y = 0.02X1 + 0.10X3 + 8.12X6 30.86 940.82

29 Y = 4.41X1 + 3.81X4 – 0.65X5 24.30 740.65

30 Y = 0.06X1 + 1.36X4 + 0.65X6 5.75 175.32

31 Y = 4.40X1 – 0.65X5 + 4.29X6 24.16 736.68

Table 1. Discriminant functions, their genetic advance and relative efficiency over straight selectionfor sugar yield in F2 generation of the sweet sorghum crosses

VEMANNA et al

96

34 Y = 0.61X3 – 0.31X2 + 15.62X6 63.10 1923.78

35 Y = 0.05X2 + 14.19X4 + 0.10X5 50.80 1548.63

36 Y = 0.07X2 + 7.56X4 + 1.41X6 33.99 1036.31

37 Y = 0.05X2 + 0.11X5 + 16.13X6 50.66 1544.42

38 Y = 0.15X3 + 13.04X4 – 0.09X5 44.76 1364.64

39 Y = 0.10X3 + 5.14X4 + 1.09X6 28.13 857.65

40 Y = 0.15X3 – 0.09X5 + 14.83X6 44.64 1360.77

41 Y = 4.70X4 + 0.04X5 + 2.43X6 20.10 612.67

42 Y = 0.13X1 – 0.32X2 + 0.62X3 + 14.89X4 66.73 2034.16

43 Y = 25.21X1 – 0.22X2 + 0.81X3 – 4.64X5 88.64 2702.34

44 Y = 0.14X1 – 0.32X2 + 0.62X3 + 16.95X6 66.61 2030.74

45 Y = 13.16X1 + 0.15X2 + 4.80X4 – 2.24X5 57.71 1759.40

46 Y = 0.82X1 + 0.04X2 + 7.36X4 + 1.11X6 37.91 1155.67

47 Y = 13.19X1 + 0.15X2 – 2.25X5 + 5.35X6 57.59 1755.58

48 Y = 13.73X1 + 0.28X3 + 3.26X4 – 2.55X5 52.37 1596.68

49 Y = 0.06X1 + 0.10X3 + 6.05X4 + 1.57X6 31.65 964.76

50 Y = 13.76X1 + 0.28X3 – 2.55X5 + 3.61X6 52.26 1593.20

51 Y = 4.57X1 + 3.75X4 – 0.68X5 + 0.26X6 25.16 766.93

52 Y = 0.74X3 – 0.35X2 + 21.70X4 – 0.17X5 80.55 2455.56

53 Y = 0.61X3 – 0.31X2 + 10.07X4 + 4.58X6 63.86 1946.72

54 Y = 0.74X3 – 0.35X2 – 0.17X5 + 24.72X6 80.42 2451.75

55 Y = 0.05X2 + 11.34X4 + 0.11X5 + 3.61X6 51.56 1571.89

56 Y = 0.14X3 + 10.01X4 – 0.09X5 + 3.81X6 45.48 1386.50

57 Y = 21.88X1 – 0.23X2 + 0.81X3 + 5.40X4 –

4.12X5 89.70 2734.65

58 Y = 0.17X1 – 0.32X2 + 0.62X3 + 10.78X4 +

5.02X6 67.39 2054.36

59 Y = 21.93X1 – 0.23X2 + 0.81X3 – 4.12X5 +

6.02X6 89.59 2731.24

60 Y = 13.31X1 + 0.15X2 + 8.60X4 – 2.27X5 –

4.17X6 58.50 1783.51

61 Y = 13.89X1 + 0.28X3 + 6.57X4 – 2.57X5 –

3.61X6 53.12 1619.52

Sl. No. Discriminant Function GA RE (%)

GA = Genetic advance; RE = Relative efficiency

CONSTRUCTION OF SELECTION INDICES FOR F2 POPULATION

97

total biomass, fresh stalk yield, brix per cent andjuice yield which exhibited maximum relativeefficiency, same could be considered for selectionschemes. Bhat and Shariff (1994) in finger millet,Patil et al. (1997) in sunflower and Khulbe and Pant(1999) in mustard obtained similar results ofincreased efficiency through indirect selection using

REFERENCES

Agrawal, R. K., Lal, I. P and Richharia, A. K., 1978.Note on selection indices and path coefficientsin semi dwarf rice varieties. Indian Journal ofAgricultural Science. 48: 58-60.

Bhat, B. V and Shariff, R. A., 1994. Selection criteriain Finger millet (Eleusine coracana Gaertn.).Mysore Journal of Agricultural Sciences.28: 5-7.

Fisher, R. A. , 1936. The use of multiplemeasurements to taxonomic problems. Annalsof Eugenics. 7: 87-104.

Khulbe, R. K and Pant, D. P., 1999. Selection indicesin Indian mustard. [Brassica juncea (L.) Czern& Coss]. Crop Improvement. 26: 109-111.

Mahadevappa, M and Ponnaiya, B. W. X., 1967.Discriminant functions in the selection of pearlmillet (Pennisetum typhoids Stapf and Hubb.)population for grain yield. The MadrasAgricultural Journal. 54: 211-222.

Mannur, D. M., Salimath, P. M., Patil, S. S andParameshwarappa, R., 1991. Genetic studiesin interspecific crosses of soybean Glycinemax (L.) Merill × Glycine formosa. IndianJournal of Genetics and Plant Breeding. 51:471-475.

different combination of characters excluding yieldin selection index. Hence, it can be inferred that themaximum gain using indirect selection schemescould be achieved using highly correlated characterslike total biomass, fresh stalk yield, brix per cent andjuice yield, which is further confirmed by path co-efficient as well as discriminant function analysis.

Mathur, R. K and Gupta, S. C., 1992. Discriminantfunction analysis in Niger (Guizotia abyssinicaCass.). Crop Research. 5: 164-165.

Nahar, S. M. N., Khaleque, M. A and Miah, M. A.,2002. Correlation, path co-efficient andconstruction of selection index in sugarcane.Pakistan Sugar Journal. 17: 2-10.

Paroda, R. S and Joshi, A. B., 1970. Correlations,path co-efficients and the implication ofdiscriminate function for selection in wheat(Triticum aestivum). Heredity. 25: 383-392.

Patil, B. R., Rudraaradhya, M and Basappa, H., 1997.Construction of selection indices for varietalselection in sunflower (Helianthus annuus L.).Journal of Oilseed Research. 14: 172-174.

Rahangdale, S. L., Khoragade, P. W and Raut, S.K., 1987. Construction of selection indices forvarietal selection in upland Rice. Journal ofMaharashtra Agricultural Universities. 12: 223-224.

Singh, S. P and Khan, A. Q., 1998. Selection indicesfor cane yield in sugarcane. Indian Journal ofGenetics and Plant Breeding. 58: 353-357.

Smith, H. F., 1936. A discriminant function for plantselection. Annals of Eugenics. 7: 240-250.

VEMANNA et al

98

Dendrobium is one of the largest diversegenera of orchids, very popular among orchidsthroughout the world. In India, majority of commercialorchid farms are located in Tamil Nadu, Kerala,Karnataka and Agency areas of Andhra Pradesh.Dendrobium hybrids viz., Sonia-17, Emma White,New Wanee, Pampodour Blue Magic and Flame arebearing population in India. Increasing demand withinsufficient supply of orchid flowers in internationaltrade offer scope for making India a major exporter,having maximum genetic resources, varied climaticzones, availability of trained man power, lower costof production as compared to other orchid growingcountries. Very few studies are reported on thescreening of orchids. However, the presentinvestigation was carried out to evaluate Dendrobiumhybrids viz., Sonia-17, Emma White, New Wanee,Pampodour, Blue Magic and Flame for exportpurpose.

The study was conducted in the orchid farmNatural Synergies Limited, Nathanallur village,Kancheepuram district situated between 12052’ Nlatitude and 79051’ E longitude at an altitude of 102m MSL. Six hybrids of Sonia-17, Emma White, NewWanee, Pampodour, Blue Magic and Flame werescreened for their performance grown under 75 percent shade net. Growth parameters studied were plantheight, number of shoots per plant, number of leavesper plant, number of spikes per plant, length of spike,number of florets per spike, length of floret pedicel,and longevity of spikes on plant and in the vase.Observations on vegetative growth parameters wererecorded at 60, 120 and 180 days and floral charactersat 130 and 195 days after planting. The experimentwas laid out in Completely Randomized Block Design(CRD) with 4 replications and 6 treatments.

Among the hybrids studied ‘Blue Magic’produced maximum plant height (56.61 cm) andhybrid ‘Emma White’ produced minimum plant height

EVALUATION OF PERFORMANCE OF DENDROBIUM ORCHID HYBRIDSB. GOPALA RAO, P.T.SRINIVAS and M.H.NAIK

Department of Horticulture, Sri Venkateswara Agricultural College,Acharya N.G. Ranga Agricultural University , Tirupati- 517 502



(41.54 cm), while others showed intermediate heights.The hybrid ‘Flame’ had produced maximum numberof leaves per plant (12.87) and hybrid ‘New Wanee’recorded minimum number of leaves (6.44) per plant(Table 1). The hybrid ‘Flame’ showed distinguishabledifference in the spike number, length of spike, andnumber of florets per spike from others (Table 2).The number of spikes recorded was maximum in thehybrid ‘Flame’ (3.68). The results showed thatincreased number of spikes had positive andsignificant relation with the leaves and shoots. Thehybrid ‘Flame’ excelled other hybrids with a spikelength of 57.96 cm, while hybrid ‘Pompadour’ recordedspike length of 48.39 cm followed by ‘Blue Magic’(40.51 cm) and ‘New Wanee’ (37.86 cm), whereas‘Emma White’ and ‘Sonia 17’ recorded minimum spikelength of 32.49 and 33.16 cm (Fig. 1A). Maximumnumber of florets per spike (15.58) was recorded in‘Flame’, which might be due to production of longestspikes. Whereas minimum number of florets per spikewas recorded in ‘New Wanee’ (7.55) and ‘Sonia 17’(7.60), maximum floret pedicel length was recordedin ‘Emma White’ and ‘Blue Magic’. The vase life studyof different hybrids revealed that the cultivars ‘Sonia17’ (18.38 days) and ‘Flame’ (17.96 days) recordedlonger vase life, whereas shorter vase life wasobserved in ‘Pampodour’ (6.45 days) in tap water(Table 2)(Fig. 1B). The variation observed amongdifferent hybrids in respect of different aboveparameters may be attributed to genetic differencesin the hybrids studied. The above findings of thepresent investigation are in agreement with those ofBetonio (1966) and Chandrappa (2003).

Based on the above results, it appeared thatFlame’s performance is better than others, thoughits vase life is comparatively less but almost equalto that of Sonia 17. Hence it is recommended to grow‘Flame’ for commercial and export purposes.


99

Hybrid Plant height (cm) No. of leaves/plant No. of shoots/plant

Sonia 17 43.90 9.17 5.08

Emma White 41.54 8.87 6.68

New Wanee 43.52 6.44 7.55

Pampodour 49.15 10.62 6.91

Blue Magic 56.61 11.22 6.22

Flame 52.85 12.87 7.92

CD at 5% 2.01 0.23 0.35

Table 1. Plant height, number of leaves and shoots per plant of different Dendrobium hybrids (at 180days after planting) grown under shade net

Table 2. Spike yield, quality and vase life (at 195 days) of different Dendrobium hybrids grown undershade net.

Hybrid No. of spikes / plant

No. of florets / spike

Spike length (cm)

Floret pedicel

length (cm)

Vase life in tap water

(days)

Sonia 17 2.96 7.60 33.16 3.75 18.38

Emma White 2.46 11.76 32.49 4.07 13.84

New Wanee 2.02 7.55 37.86 3.29 9.24

Pampodour 2.09 13.45 48.39 3.68 6.45

Blue Magic 3.28 10.23 40.51 3.80 14.88

Flame 3.68 15.58 57.96 4.03 17.96

CD at 5% 0.36 1.16 2.59 0.20 0.88

RAO et al

100

Fig 1. Spike quality and vase life (in tap water) of different Dendrobium hybrids grown under shadenet.

REFERENCES

Betonio, G.I. 1966. Germplasm collection andevaluation of different Anthurium cultivars.Journal of crop science. 20: 12.

Chandrappa. 2003. Evaluation and effect of media,biofertilizer and growth regulators on growth

and flowering in Anthurium. Ph.D. Thesissubmitted to University of AgriculturalSciences, Bangalore.

EVALUATION OF PERFORMANCE OF DENDROBIUM ORCHID HYBRIDS

101

Sugarcane is the world’s largest crop and isgrown in over 110 countries. In 2009, an estimated1,683 million metric tons were produced worldwidewhich amounts to 22.4% of the total world agriculturalproduction by weight (FAO, 2009). India ranks secondin cane area and sugar production after Brazil. Thestates of Uttar Pradesh, Maharashtra, Karnataka,Tamil Naidu and Andhra Pradesh together producenearly 90 per cent of the cane and sugar in thecountry. Andhra Pradesh ranks fifth in sugar conearea of the country with a share of 4.83 per cent.The average production of Andhra Pradesh is about20.30 million tons contributing to 5.83 per cent ofthe total production of the country. In AndhraPradesh, the major sugarcane growing districts inTelangana, coastal Andhra and Rayalaseema regionsare Nizamabad, Visakhapatnam and Chittoor districtsrespectively.

The significant contribution of researchers,extension functionaries and farming community playspivotal role in achieving the above success. On oneside, the researchers developed sustainabletechnologies to meet the production requirements ofthe farmers followed by effective dissemination oftechnologies by the extension functionaries so as tobring the technologies to the farmers for adoption.On the other side, the farming community issuccessfully adopting those technologies so as toincrease the productivity levels of sugarcane. As thefarmers are the key contributors of production, thepresent study was taken up to study the profilecharacteristics of sugarcane farmers.

Ex-post-facto research design was adoptedfor the study. The investigation was carried out inChittoor district of Rayalaseema region. Fourmandals were selected in Chittoor district purposivelythat have highest area under sugarcane. From eachmandal 3 villages were selected purposively. From

PROFILE CHARACTERISTICS OF SUGARCANE FARMERS INCHITTOOR DISTRICT OF ANDHRA PRADESH

S. RAMALAKSHMI DEVI, P. V. SATYA GOPAL, V.SAILAJA and S.V. PRASADDepartment of Extension Education, S.V. Agricultural college, Tirupati-517502



each village 10 sugarcane farmers were selectedrandomly thus making a total of 120 respondents .The data were collected by personal interview methodthrough structured interview schedule.

The sugarcane farmers were distributed intodifferent categories based on their selected profilecharacteristics and were presented in the Table 1and interpreted through frequencies, percentages,mean and standard deviation.

Age

Majority (57.50%) of the sugarcane farmersbelonged to middle age category followed by young(31.66%) and old age (10.83%) categories. Theprobable reason for distribution might be that theagriculture in the present situation has been perceivedas a profitable enterprise in particular as sugarcaneis one of the remunerative crops for the farmers.Middle age and young age farmers were motivatedto cultivate sugarcane by adopting latest productiontechnologies and obtaining good returns.. Hence theabove trend was observed. It is in conformity withReddy (1997) and Gowda et al., (2011).

Education

Majority (90.00%) of the respondents wereeducated, 4.17 per cent were under can read andwrite only category and only 5.00 per cent wereilliterates. The probable reason for the abovedistribution might be that, as education was gainingimportance for the past three decades and broughtout awareness among the farming community aboutthe functional literacy. Majority of the sugarcanefarmers were under middle and young age lead tothe proper educational status among the farmingcommunity. It is in conformity with Latha (2002) andGowda (2011).


102

S.No AGE

Category Frequency Percentage MEAN S.D.

1. Young (<35 Years) 38 31.67 2. Middle(36-55 Years) 69 57.50 3. Old (>56 years) 13 10.83 Total 120 100.00

- -

EDUCATION 1 Illiterate 6 5.00 2. Can read and write only 5 4.17 3. Primary school 9 7.50 4. Middle school 26 21.67 5. High school 49 40.83 6. Intermediate 15 12.50 7. College level 10 8.33 Total 120 100.00

- -

FARM SIZE 1. Marginal farmer 4 3.33 2. Small farmer 65 54.17 3. Big farmer 51 42.50 Total 120 100.00

- -

FARMING EXPERIENCE

1. Low 19 15.83

2. Medium 83 69.17

3. High 18 15.00

25.35 11.12

Total 120 100.00 EXTENSION CONTACT

1. Low 17 14.17 2. Medium 76 63.33 3. High 27 22.50

9.99 2.20

Total 120 100.00 TRAININGS UNDERGONE

1 Low 37 30.83 2 Medium 60 50.00 3 High 23 19.17

1.825 1.0262

Total 120 100.00 SOCIAL PARTICIPATION

1 Low 14 11.67 2 Medium 81 67.50 3 High 25 20.83

10.75 3.3487

Total 120 100.00 MASS MEDIA EXPOSURE

1. Low 13 10.83 2. Medium 83 69.17 3. High 24 20.00

8.65 2.17

Total 120 100.00 ACHIEVEMENT MOTIVATION

Table 1. Profile characteristics of sugarcane farmers N=120

PROFILE CHARACTERISTICS OF SUGARCANE FARMERS

103

2. Medium 71 59.17 3. High 23 19.16

Total 120 100.00 SCIENTIFIC ORIENTATION

1. Low 22 18.33 2. Medium 74 61.67 3. High 24 20.00

11.88 2.73

Total 120 100.00 MANAGEMENT ORIENTATION

1. Low 15 12.50 35.72. 6.39 2. Medium 86 71.67 3. High 19 15.83 Total 120 100.00

INNOVATIVENESS 1. Low 20 16.67 17.64 4.52 2. Medium 79 65.83 3. High 21 17.50 Total 120 100.00

Farm size

It is evident from the Table 1 that 54.17 per cent ofthe sugarcane farmers were small followed by bigfarmers (42.50%) and marginal farmers (3.33%). Theprobable reason might be that, sugarcane as acommercial crop need to be grown in large farms soas to take up required farm management practicesand also to cope up with the post harvest managementsuch as transporting to the sugar factories or takingup jaggery preparation. It might be very difficult totake up all such activities under small holdingconditions with half to one acre of land which involvehigh investment leading to less profitability. It is inconformity with findings of Pandya (1995).

Farming experience

From Table 1 it is evident that 69.17 per centof the sugarcane farmers had medium farmingexperience followed by low (15.83%) and high farmingexperience (15.00%).The probable reason might bethat as majority of the farmers belong to middle agegroup and also there was awareness among thefarming community about the education which madethem to enter into farming after completing their

education. It is in conformity with Roy (2005) andReddy (1997).

Extension contact

From Table 1 it could be seen that 63.33 percent ofthe respondents were having medium extensioncontact followed by low (22.50%) and high (14.17%)extension contact. The probable reason f might bethat as the sugarcane crop is mainly grown underthe supervision of sugar factories and also the majorityof sugarcane farmers were educated, the farmersalways seek for timely extension support from factoryofficials, agricultural officers and the scientists fortheir day to day farm operations for betterproductivity. It is in conformity with Gattu (2001) andRoy (2005).

Trainings undergone

From Table 1 it could be seen that majorityof the respondents have undergone no. of mediumtrainings undergone (50.00%) followed by low(30.83%) and high (19.17%) number of trainings. Thismight be due to the fact that trainings are the toolsfor upgrading the knowledge and skills in a particulararea of operation. As sugarcane is the major

S.No

Category Frequency Percentage MEAN S.D.

DEVI et al

104

commercial crop, training on different technologieswill help the farmers in taking up the farm operationsin more viable and economical ways. On the otherside lack of awareness on the importance of trainingand also non availability of time to attend the trainingprogrammers might have contributed for the abovetrend. It is in conformity with Reddy (1997) and Roy(2005).

Social participation

From Table 4.7 and Fig 4.7 it could be seenthat majority of the respondents were having mediumsocial participation (67.50%) followed by high(20.83%) and low (11.67%) levels of socialparticipation. The probable reason for the above trendmight be that, being a member of society everybodyneeds to work together co operatively to achievehigher returns. As sugarcane is one of the majorcommercial crops involve year round investment rightfrom land preparation, selection of setts to the finaltransportation of sugarcane to the factories. The needof being a member or office bearer in such societieswhich directly involve in farming operations ofsugarcane is essential for taking up appropriate andtimely operations in farm production. It is inconformity with Reddy (1997).

Mass media exposure

Majority of the respondents were havingmedium mass media exposure (69.17%) followed byhigh (20.00%) and low(10.83%) levels of mass mediaexposure. The probable reason for this trend mightbe due to the fact that, as majority of sugarcanefarmers are young and middle aged and ninety fiveper cent of sugarcane farmers were educated hadinclination towards better utilization of different massmedia such as radio, T.V, news papers so as to takeup modern technologies in sugarcane production. Thefarmers with illiteracy and higher age might not beutilizing the mass media because of their personaland psychological limitations. It is in conformity withReddy (1997) and Sangeetha (2004).

Scientific Orientation

More than half (61.66%) of the respondentshad medium scientific orientation followed by high(20.00%) and low (18.33%) of scientific orientation.The probable reason might be that there were amplenumber of technologies developed by the scientists

and disseminated among the farming communityleading to successful adoption of those technologies.This might be because of higher scientific orientationamong the sugarcane farmers to adopt thosetechnologies as per the recommendations of thescientists for better returns. The education level,extension contact and mass media exposure directlycontributes for the scientific orientation among thesugarcane farmers. Less scientific orientation for fewfarmers might be due to complexity of thetechnologies and illiteracy of the farming community.It is in conformity with Reddy (1997).

Management Orientation

Perusal of the Table 1 reveals that majority(71.67%) of sugarcane farmers had mediumManagement Orientation followed by high (15.83%)and low (12.50%) management Orientation. Thismight be due to the fact that sugarcane crop requiresbetter management at each and every stage of itsoperations to get high net profit. Year round and timelydecisions are essential to cope up with theenvironmental and human resource management.Morever people might be adopting age old practices(traditional way) without proper resourcemanagement. It is in conformity with Reddy (1997).

Innovativeness

Findings from Table 1 show that majority(65.83%) of the respondents had mediuminnovativeness followed by high (17.50%) and low(16.67%) levels of innovativeness. The possiblereason might be that the farmers with highereducation, extension contact and mass mediaexposure were able to update their knowledge andskills time to time and ready to accept the newtechnologies in their farming. How ever illiterates andresource poor farmers might be lacking awareness,knowledge and risk taking ability to adopt suchtechnologies. It is in conformity with Hemanth (2002)and Gangadhar (2009) findings.

The results of present study indicated thatmajority of sugarcane farmers are middle and youngaged with required educational qualification and thereis every chance of motivating them towards adoptingsugarcane production technologies so as to enhancesugarcane productivity and also net income. As

PROFILE CHARACTERISTICS OF SUGARCANE FARMERS

105

extension contact and mass media exposure werethe major pillars for diffusing information to farmingcommunity there is every scope to improve thesetwo components so as to utilize the extensionpersonnel and mass media for strengthening theirknowledge and skills. Psychological variables ofsugarcane farmers i.e. Social participation, scientificorientation, management orientation and innovativeness

REFERENCES

Gangadhar, M.M. 2009. Communication factors andentrepreneurial behavior of cotton growers.Journal of Research, ANGRAU, Hyderabad.31(3):62-67.

Gattu, K.C. 2001. Production constraints of turmericcultivation in Karimnagar district of AndhraPradesh. M.Sc. Thesis submitted to AcharyaN G Ranga Agricultural University, Hyderabad.

Gowda, T.A., Babu, C.R., Naidu, G.R and Rao, V.S.2011. Profile characteristics of sugarcanegrowers in Mandya district of Karnataka. TheAndhra Agricultural Journal. 58(2):236-239.

Hemanth, K. B. 2002. A study on attitude, knowledgeand adoption of recommended practices byoriental tobacco farmers in Chittoor District ofAndhra Pradesh. M.Sc. (Ag.) Thesis, AcharyaN G Ranga Agricultural University, Hyderabad.

Latha, S.M. 2002. A study on knowledge andadoption of integrated pest managementpractices in cotton by farmers in Kurnooldistrict of Andhra Pradesh. M.Sc. (Ag.) Thesis

submitted to Acharya N G Ranga AgriculturalUniversity, Hyderabad.

Pandya R.D. 1995. Entrepreneurial behavior ofsugarcane farmers. Journal of ExtensionEducation. 6(4): 1299-1301.

Reddy, S. 1997. Information Management Behaviour(IMB)-An analysis of sugarcane research,extension and client system. Ph.D.. (Ag.)Thesis. Acharya N G Ranga AgriculturalUniversity, Hyderabad.

Roy, S. 2005. A study on the sustainability ofsugarcane cultivation in Vishakhapatnamdistrict of Andhra Pradesh. Ph.D.. (Ag.) Thesissubmitted to Acharya N G Ranga AgriculturalUniversity, Hyderabad

Sangeetha, V. 2004. Training needs of cotton growersof Madurai district of Tamilnadu. M.Sc. (Ag.)Thesis submitted to Acharya N G RangaAgricultural University, Hyderabad.

DEVI et al

were found to be medium and this indicates that these

variables are the inherent igniters for the human beings.

Hence, there is need to organize training programmes,

group discussions, brain storming sessions, exposure

visits etc. for sugarcane farmers and provide a platform

to utilize these variables effectively in sugarcane

cultivation.

106

Any efforts that successfully reduce thewater allocation for rice even by 20 to 30 per centwill help in averting both the food and water crisesas farmers can continue to grow more rice with lesswater.

Frequent drought over the past 10 years hasleft the rice farmers of Andhra Pradesh in doldrums.Andhra Pradesh experienced severe drought in 1999-2000, characterized by water shortages, fallinggroundwater levels and increased risk ofcontamination of surface water. Drought, followed bylow rainfall (534 mm annual rainfall) in the south-westand north-east monsoons during 1999 wasexacerbated by groundwater extraction. Agriculturalproduction was seriously reduced in kharif 1999.Thereafter, the thrust for conservative water-usagebecame the major concern for scientists and farmers.Depleted water resources, stagnated rice productivity,the growing importance of organic agriculture,increased production costs and the need for betterutilization of family labour among small and marginalfarmers, calls for a shift in cultivation practices. TheSystem of Rice Intensification (SRI) offers a way tonot only reduce the demand for water while growingirrigated rice, but also of simultaneously increasingrice production. SRI was introduced in AndhraPradesh in kharif 2003 in all 22 districts of the stateby Acharya N.G. Ranga Agricultural University(ANGRAU). Since 2003, ANGRAU and StateDepartment of Agriculture has taken several initiativesto promote SRI in Andhra Pradesh (www. sri-india.net,2009).

Today, India has one of the largest numbersof SRI farmers in the world. Official record indicatesthat SRI diffused first to Tamil Nadu State, followedby Andhra Pradesh (Prasad, 2006). Though AndhraPradesh was the first to start large scale promotionof SRI, but no substantial area could be covered

A STUDY ON DIFFUSION STATUS OF SYSTEM OF RICE INTENSIFICATION (SRI)IN ANDHRA PRADESH

K. NIRMALA and R. VASANTHA Department of Agricultural Extension

College of Agriculture, Rajendranagar, ANGRAU, Hyderabad – 500 030



during the last few years. Even after 9-10 years ofintroduction of SRI technology among farmers ofAndhra Pradesh, the pace of spread of technology isnot rapid.

Hence the present study was conceived toknow the status of SRI in terms of diffusion andadoption across the selected villages and mandalsof Mahaboobnagar district.

The present study was conducted inMahaboobnagar district as it has highest cultivatedarea under SRI during 2008-09. Ex-post facto researchdesign was followed. A sample of 120 SRI cultivatingfarmers from 12 villages of four mandals of thedistrict was selected randomly. Measurement ofdiffusion status was done under three dimensionsi.e, Diffusion Status, spread of SRI in selectedvillages (secondary data) and adopter categories.

Dif fusion status of System of RiceIntensification was operationalised as the extent ofspread of SRI technology among the farmers from2006-07 to 2010-11. Diffusion status was measuredwith the help of developed schedule comprising ofvarious items that are pretested. The score obtainedby a respondent on all items of diffusion status wereadded to get total score. Based on total scoresobtained, respondents were grouped into 3 categoriesof low, medium and high according to equal classinterval method.

The second dimension i.e. spread of SRI inselected villages was studied in terms of number offarmers adopting and number of acres. Year - wisedata was collected starting from 2006-07 to 2010-2011 (5 years) from secondary sources such asDepartment of Agriculture and NGOs.

The third dimension i.e. adopter categorieswas studied by categorizing adopters into fivecategories based on the criteria of innovativeness or


107

earliness in adoption i.e. the degree to which anindividual or others unit of adoption is relatively earlierin adopting new ideas than other members of socialsystem.

The year of adoption was taken as criteria todetermine the earliness of respondents in adoptionof SRI. Data on number of respondents adopting SRIfor the first time is collected year wise for five years,starting from 2006-07 to 2010-11 and accordingly therespondents were grouped into five adopter categoriesviz innovators, early adopters, early majority, latemajority and laggards. The data was tabulated anddepicted graphically.

Medium 52.50%

High 12.50%

Low 35.00%

Table 2. Comparision between total Acreage under Rice and SRI

Fig 1. Diffusion status of SRI technology

S.no Year Total Acreage

under Rice

Acreage under

SRI

% Total Number of Farmers

SRI

Farmers

%

1 2006-07 1964 6 0.30 982 15 1.52

2 2007-08 2244 63 2.80 748 101 13.50

3 2008-09 2777 245 8.82 925.67 238 25.71

4 2009-10 3438 166 4.82 1719 190 11.05

5 2010-11 3129 149 4.76 1564.5 172 10.99

Total 13552 629 4.64 5939.17 716 12.05

NIRMALA and VASANTHA

Distribution of respondents according to theirdiffusion status on SRI is depicted in Fig 1. Theprobable reason for medium to low diffusion statusof SRI among farming community may be becauseof inherent problems associated with SRI cultivationsuch as nonavailability of skilled labour, organicmanures, difficulties in land levelling and weedingcono weeder, gaps in research and extension, heavyrains in kharif etc.

If the above problems are overcome byresearch and extension agencies by taking necessarysteps, then there is a possibility for increase in areaunder SRI in the district.

108

The number of farmers cultivating SRI andacreage under SRI is compared with total number ofrice farmers and rice acreage in the selected villages.Percentages were calculated.

The percentage of SRI acreage over totalRice acreage is 0.30 per cent in 2006 -07, 2.80 percent in 2007-08, 8.82 per cent in 2008-09, 4.82 percent in 2009-10 and 4.76 per cent in 2010-11.Whereaspercentage of farmers adopting SRI over total Ricefarmers is 1.52 per cent in 2006 -07, 13.50 per centin 2007-08, 25.71 per cent in 2008-09 , 11.05 per

cent in 2009-10 and 10.99 per cent in 2010-11.

Results are depicted graphically in figure 1 and 2.

The secondary sources reported anincreasing trend both in area and number of farmersfrom the year 2006 (year of inception of SRI) to 2009,afterwards a gradual decline is clearly evident in theacreage and number of farmers adopting SRI inselected villages. The present study showed similartrend with respect to cumulative frequency reportedby Karthik and Manjunatha (2010).

Innovators

Early adopters

Early majorityLate majority

Laggards

0

5

10

15

20

25

30

35

40

45

50

2006-2007 2007-2008 2008-2009 2009-2010 2010-2011Years

Per

cent

age

adop

tion

of S

RI

Table 3. Distribution of the respondents based on their earliness in adoption (innovativeness) of SRItechnology

Fig.2. Categorisation of adopter on the basis of earliness in adoption (innovativeness)

S.No Year Adopter category N %

1 2006-2007 Innovators 2 1.66

2 2007-2008 Early adopters 11 9.17

3 2008-2009 Early majority 44 36.67

4 2009-2010 Late majority 40 33.33

5 2010-2011 Laggards 23 19.17

Total 120 100.00

Table 3 indicates that during 2006-07, (year

of SRI inception in the Mahaboobnagar district) SRIwas adopted by only a few members i.e only 1.66percent, who are termed as innovators. As SRI is a

new technology it was adopted only by a smallpercent of respondents. During the first year, therespondents (innovators) who are having highextension contacts and sources of information

A STUDY ON DIFFUSION STATUS OF SYSTEM OF RICE INTENSIFICATION (SRI)

109

adopted this technology. In 2007- 08 there was alittle increase in number of respondents adopting SRIi.e. from1.66 to 9.17 percent, the probable reasonscould be they might have got convinced by seeingSRI performance in innovators fields or there maybe increase in availability of implements ororganisation of good number of demonstrations andimproved extension contacts with concernedscientists etc. must have motivated the respondentsto adopt SRI. During 2008- 09, there was a rapidincrease in number of farmers adopting SRI i.e. from9.17 to 36.67 percent, the probable reason could bein order to prevent depletion of ground waterresources, which generally happens with conventionalrice cultivation, Government has announcedincentives to promote SRI in the form of providingmachinery and inputs at subsidised prices which hasshot up area under SRI. In 2009- 10, there was alittle decrease in adoption of SRI i.e from 36.67 to33.33 percent, the probable reason may be problemswith labour In 2010- 11 there was a drastic decreasein adoption of SRI due to intensified problems withlabour, non availability of inputs, difficulties in watermanagement, non availability of organic inputs, landlevelling, weeding operations which has reduced SRIcultivation. Study reported a large majority ofrespondents under early and late majority (70%).Similar findings were reported by Prasad (1997).

The curve (Fig.2 ) obtained on Adoptercategories is an incomplete bell shaped curve.

According to Rogers (2003) adoption of an innovationusually follows a normal bell shaped curve whenplotted over a time. Since the time tested period isshort in the present study, the curve could not be acomplete bell shaped curve. If the study was carriedout for longer period of time as done by Rogers thenthere is a possibility to obtain a complete bell shapedcurve for SRI cultivation also. Similar results werereported by Ryan and Gross (1943) in hybrid seedcorn in Iowa.

The status of diffusion of SRI is medium to lowin spite of multifarous efforts of government theaggregate area under SRI is not to the expectations.Though some farmers are able to continue this methodand reap benefits, some others have adopted SRIfor one season or two seasons and have discontinuedit, some others appreciated the method but did notadopt it. Lack of perception accuracy and operationaldifficulties might have discouraged farmers tocontinue SRI. Keeping in view of benefits of SRI,the government has to take measures to increaseits diffusion by popularising the benefits of SRIthrough interpersonal and mass communicationmedia, announcing incentives in the form of supplyof organic manures, subsidised markers andconoweeders. Funds should be earmarked toinnovative farmers and NGO’s who were interestedin developing modified implements, varieties ormethods in SRI that definitely helps in increasingarea under SRI.

REFERENCES

Karthik, K. B and Manjunatha, B. N. 2010. Adoptionof hybrid paddy seed production technologiesin Mandya District. Mysore Journal ofAgricultural Sciences, 44 (4): 863-865.

Prasad, S.C. 2006. System of Rice intensification inIndia: Innovation History and InstitutionalChallenges. WWF- ICRISAT Dialogue onWater, Food and Environment, Patancheru,Hyderabad. http:// wassan.org/Sri/documents/Shambu-SRI. pdf (21 July 2011)

Prasad, S.V. 1997 A critical analysis of diffusion andadoption of production recommendations of

rainfed castor in Nalgonda district of AndhraPradesh. Ph.D Thesis Acharya N G RangaAgricultural University, Hyderabad.

Rogers, E. M. 2003. Diffusion of Innovations. 5th ed.New York, London, Toronto, Sydney.Singapore: Free Press.

Ryan, Bryce and Neal C. Gross 1943. “The Diffusionof Hybrid Seed Corn in Two IowaCommunities”, Rural Sociology, 8:15-24.RS(E) Website: www. Sri-india.net, 2009.

NIRMALA and VASANTHA

110



Correlation studies and path coefficientanalysis were undertaken to know the interrelationship of yield components and physiologicalparameters related to salt tolerance and theirusefulness in selection programmes under salt stress.

In the present investigation 28 rice hybridsderived by crossing eight genotypes (RPBio-226,Swarna , CSR-27, CSR-30, CST-7-1, CSRC(S)7-1-4, SR26-B and CSRC(S)5-2-2-5 in half diallel mannerwere utilized based on their reaction to salinitytolerance and were evaluated during kharif, 2010under salt affected soils of Agricultural ResearchStation, Machilipatnam. Seedlings of 30 days old weretransplanted in the main field having electricalconductivity of 7.9 dS/m and pH of 7.7 followingrandomized block design with three replications. Therecommended agronomic, cultural and plantprotection measures were followed in conducting theexperiment. Genotypic and phenotypic correlationcoefficients were calculated among eight parentsusing the formulae suggested by Al-Jibouri et al.(1958) and their significance was tested by using the‘r’ table values (Fisher and Yates, 1963) at n-2degrees of freedom, where ‘n’ denotes the number oftreatments used in the calculation.

To estimate the direct and indirect effectsof the yield components on the yield, the statisticaltool employed was path coefficient analysis assuggested by Wright (1921) and illustrated by Deweyand Lu (1959). The path coefficients were obtainedby solving the ‘p’ normal equations following thematrix method given by Singh and Chowdhary (1985).

In the present investigation, the genotypic andphenotypic correlations amongst the traits followedalmost similar trend of association, the former beinga little higher in most of the cases, indicating the

CORRELATION AND PATH COEFFICIENT ANALYSIS FOR YIELD ANDPHYSIOLOGICAL ATTRIBUTES IN RICE (Oryza sativa L.) HYBRIDS UNDER

SALINE SOIL CONDITIONSM.SUDHARANI, P.RAGHAVA REDDY, G.HARIPRASAD REDDY and CH.SURENDRA RAJU

Seed Research and Technology Centre, Rajendranagar, Hyderabad-500030

existence of a strong inherent association betweenthe characters. Further, dissecting these associationsas direct and indirect effects through path analysisshowed direct contribution of each component traiton yield and indirect effect it has through associationon other component traits.

The yield component viz., plant height(0.5847), number of tillers plant-1 (0.7789), number ofproductive tillers plant-1 (0.5753), panicle length(0.8353), panicle weight (0.5500), number of filledgrains panicle-1 (0.7809), spikelet fertility per cent(0.7190), 1000-grain weight (0.5399), root/shoot ratio(0.4694) and harvest index (0.8128) were significantlyand positively correlated with grain yield (Table 1) inrice hybrids tested under saline conditions. On theother hand Na+/K+ ratio and SPAD chlorophyll meterreadings exhibited significant negative associationwith grain yield, while the effect of days to 50 percent flowering was non-significant. The findings ofearlier researchers viz., Bala (2001) for plant height;Zeng and Shannon (2000), Natarajan et al. (2005) fornumber of tillers plant-1; Ravindra Babu (1996),Natarajan et al. (2005) for number of productive tillersplant-1; Bala (2001) for panicle length; Buu and Tuan(1991), Ravindra Babu (1996), Natarajan et al. (2005)for number of filled grains panicle-1; Natarajan et al.(2005) for 1000-grain weight ; Sajjad (1990) and Balanet al. (1999) for harvest index were in line with thepresent readings. However, Asch et al. (2000)reported strong negative association of Na+/K+ ratiowith grain yield which is in agreement with the presentfindings. Under saline soil conditions plant height,number of tillers plant-1, productive tillers plant-1,panicle length, panicle weight, number of filled grainspanicle-1, spikelet fertility per cent, SPAD values andtest weight showed strong positive association withgrain yield plant-1 under stressed environment.


111

Tabl

e 1.

Gen

otyp

ic (r

g) a

nd p

heno

typi

c (r

p) c

orre

latio

n co

effic

ient

s am

ong

grai

n yi

eld,

its

com

pone

nts

and

phys

iolo

gica

l tra

its in

F1 h

ybri

ds o

f ric

eun

der s

alin

e so

ils

* S

igni

fican

t at

p=0.

05;

** S

igni

fican

t at

p=0.

01;

PH

(cm

): P

lant

hei

ght;

DF

F: D

ays

to 5

0% f

low

erin

g; T

T: N

umbe

r of

tille

rs p

lant

-1;

PT

: Num

ber

of p

rodu

ctiv

e til

lers

pla

nt-1;

PL

(cm

): P

anic

le le

ngth

; P

W(g

): P

anic

le

wei

ght

;

NF

GP

-1: N

umbe

r of

fille

d gr

ains

pan

icle

-1;

SF

(%

): S

pike

let f

ertil

ity p

er c

ent;

TW

(g)

: 10

00-g

rain

wei

ght;

GY

(g)

: G

rain

yie

ld (

g pl

ant-1

); S

ES

: SE

S f

or v

isua

l sal

t inj

ury;

RS

R: R

oot

/sho

ot r

atio

; H

I (%

): H

arve

st in

dex

per

cent

; Na+ /

K+

R: S

odiu

m P

otas

sium

rat

io; S

PA

D:

SP

AD

chl

orop

hyll

met

er r

eadi

ng.

SUDHARANI et al

112

Tabl

e 2.

Gen

otyp

ic (G

) and

phe

noty

pic(

P) d

irect

and

indi

rect

eff

ects

am

ong

grai

n yi

eld,

its

com

pone

nts

and

phys

iolo

gica

l tra

its in

F1 h

ybri

ds o

f ric

eun

der s

alin

e so

ils

Res

idua

l effe

ct =

0.08

005

PH

(cm

): P

lant

hei

ght;

DF

F: D

ays

to 5

0% fl

ower

ing;

TT

: Num

ber

of ti

llers

pla

nt-1; P

T: N

umbe

r of

pro

duct

ive

tille

rs p

lant

-1; P

L (c

m):

Pan

icle

leng

th; P

W(g

):P

anic

le w

eigh

t;

NF

GP

-1: N

umbe

r of f

illed

gra

ins

pani

cle-1

; SF

(%):

Spi

kele

t fer

tility

per

cen

t; T

W (g

): 1

000-

grai

n w

eigh

t; G

Y (g

): G

rain

yie

ld (g

pla

nt-1);

SE

S: S

ES

for v

isua

l sal

tin

jury

;

RS

R: R

oot /

shoo

t rat

io; H

I (%

): H

arve

st in

dex

per c

ent;

Na+ /

K+ R

: Sod

ium

Pot

assi

um ra

tio; S

PA

D: S

PA

D c

hlor

ophy

ll m

eter

read

ing.

CORRELATION AND PATH COEFFICIENT ANALYSIS FOR YIELD

113

At genotypic level, number of total tillers plant-1

(1.0876) exhibited highest positive effect on yield(Table 2), while substantial magnitude of positivedirect effect was also exerted by spikelet fertility(0.4417). On the other hand, moderate direct effectswere shown by SES for visual salt injury (0.2692)and root shoot ratio (0.2927). The direct effects ofproductive tillers plant-1 (-0.5877), panicle length(-0.3681) and panicle weight (-0.3495) were high, butnegative, while moderate influence in the samedirection was exhibited by SPAD chlorophyll meterreadings (-0.2290). The direct positive effects on yieldwere reported for number of grains panicle-1 (Sajjad,1990), harvest index (Tripathi et al., 2011) andproductive tillers plant-1 (Natarajan et al., 2005 andTripathi et al., 2011). Therefore, more emphasis may

be given to spikelet fertility per cent and number oftillers plant-1 while executing selections under salinesoil conditions.

The results of present investigation indicateselection under saline condition would be effectivefor number of total tillers per plant, spikelet fertilityper cent as they showed significant positiveassociation as well as direct effect on yield. Similarly,selecting the plants with low Na+/K+ ratio would helpfor yield improvement along with salt tolerance asthis trait showed significant negative association aswell as negative direct effect on grain yield understressed conditions Hence, these traits may beprioritized for developing ideotype(s) for salineenvironment.

REFERENCES

Al-Jibouri, H.A., Miller, P.A and Robinson, H.F. 1958.Genotypic and environmental variances andco-variances in an upland cotton cross ofinterspecific origin. Agronomy Journal. 50: 633-636.

Asch, F., Dingkunn, M., Dorffling, K and Miezank.2000. Leaf K/N ratio predicts salinity inducedyield loss in irrigated rice. Euphytica. 113: 109-118.

Balan, A., Muthiah, A.R and Boopathi, S.N.M.R.1999. Genetic variability, character associationand path coefficient analysis in rainfed rice,under alkaline condition. Madras AgriculturalJournal. 86 (1/3): 122-124.

Bala, A. 2001. Genetic variability, association ofcharacters and path coefficient analysis ofsaline and alkaline rice genotypes under rainfedcondition. Madras Agricultural Journal. 88(4-6): 356-357.

Buu, C.B and Tuan, T.M. 1991. Genetic study in theF2 crosses for high grain quality. InternationalRice Research Newletter. 16: 11.

Dewey, D.R and Lu, K.N. 1959. Correlation and pathcoefficient analysis of components of crestedwheat grass seed production. AgronomyJournal. 51: 515-518.

Fisher. R.A and Yates, F. 1963. Statistical Tablesfor Biological, Agricultural and MedicalResearch (6th Edition), Hafner PublishingCompany, New York,

Natarajan, S.K., Saravanan, S., Krishnakumar, S andDhanalakshmi, R. 2005b. Interpretations onassociation of certain quantitative traits onyield of rice (Oryza sativa L.) under salineenvironment. Research Journal of Agricultureand Biological Sciences. 1(1): 101-103.

Ravindra Babu, V. 1996. Study of genetic parameters,correlations and path coefficient analysis ofrice (Oryza sativa L.) under saline conditions.Annals of Agricultural Research. 17(4): 370-374.

Sajjad, M.S. 1990. Correlations and path coefficientanalysis of rice under controlled salineenvironment. Pakistan Journal of AgriculturalResearch. 11(3): 164-168.

Singh, P. K and Chaudhary, B. D.1985. BiometricalMethods in Quantitative Genetic Analysis (1st

Edition), Kalyani Publishers, New Delhi, India.

Tripathi, S., Verma, O.P., Dwived, D.K., YadavendraKumar., Singh, P.K and Verma, G.P. 2011.Association studies in Rice (Oryza Sativa L.)hybrids under saline alkaline environment.Environment and Ecology. 29(3) 1557-1560.

Wright, S. 1921. Correlation and causation. Journalof Agricultural Research. 20: 557-585

Zeng, L and Shannon, M.C. 2000. Salinity effects onseedling growth and yield components of rice.Crop Science. 40: 996-1003.

SUDHARANI et al

114

Selection of genotypes from the availablegenetic variation is crucial for any crop improvementprogramme. Estimating genetic diversity available inthe existing germplasm provides clue to the choiceof most desirable parents for use in hybridizationprogrammes. Selections based on physiological traitsthat confer water use efficiency have been suggestedfor improving drought tolerance in Groundnut. In thepresent investigation an attempt was made to identifymost diverse groundnut genotypes for practical plantbreeding programmes utilizing physiological traits likeSPAD Chlorophyll Metre Reading (SCMR), specificleaf area, crop growth rate (CGR), relative growth rate(RGR) etc.

Thirty genotypes of Groundnut wereevaluated during Kharif 2011 at Agricutural CollegeMahanandi, A.P. The experimental material wasprocured from the Groundnut Breeding Station,RARS, Tirupati comprising of diverse breedingmaterial generated in All India Coordinated GroundnutImprovement Programme. The experiment was laidout in a Randomized Block Design replicated thrice.Each genotype in a replication was grown in two rowsof 4.2 m length with a spacing of 30 cm between therows and 10 cm within a row. All the recommendedpackage of practices were followed to raise a goodcrop. Observations were recorded on five randomlychosen plants in each genotype in a replication for19 characters. The data collected was analyzed asper the standard procedures described byMahalanobis’s (1936) and Rao (1952).

Analysis of variance for both quantitative andphysiological traits in all the 30 genotypes under studyrevealed significant differences for all the charactersindicating the wealth of variability available in thegermplasm. Further the data was subjected to D2

analysis and the results were presented inTable 1 to 3.

GENETIC DIVERGENCE STUDIES FOR YIELD AND PHYSIOLOGICALATTRIBUTES IN GROUNDNUT (Arachis hypogaea L.)

D. NIRMALA, V. JAYALAKSHMI, B. NARENDRA and P. UMAMAHESHWARIDeptartment of Genetic & Plant Breeding, Agricultural College, ANGRAU, Mahanandi – 518 503



Based on D2 analysis all the 30 genotypeswere grouped into 14 clusters with a variable numberof entries in each cluster revealing the presence of aconsiderable amount of genetic diversity in thematerial (Table 2). Cluster I had maximum numberof 10 genotypes followed by cluster II with 6genotypes and cluster X with 3 genotypes. Remainingall other clusters possessed one genotype each.Cluster I alone had one-third of the total genotypesstudied indicating that the genotypes under study hadnarrow genetic diversity among them. Similarity inthe base population from which they have beenevolved might be the cause of genetic uniformity.However, the uni-directional selection potential forone particular character or a group of linked traits inseveral places may produce similar phenotypes whichcan be aggregated into one cluster irrespective ofgeographical diversity. Sudhir Kumar et al (2010),Awatade (2007) and Garajappa et al. (2005) reportedthat there is no correlation between genetic diversityand geographical diversity in the groundnut genotypesstudied by them.

Average inter cluster and intra cluster D2

values among the 30 genotypes were furnished inTable 2. The maximum intra cluster distance wasrecorded for cluster X (6.31) followed by cluster II(5.13) and cluster I (4.95) revealing substantialdiversity within the clusters. Maximum inter-clustervalues were observed between cluster III and clusterXII (12.35) followed by cluster V and cluster XIII(12.10) indicating maximum divergence between thegenotypes included in these clusters.

Cluster means for all the traits were given inTable 2. Cluster means for different charactersindicated that none of the clusters contained genotypewith all the desirable characters and so recombinantbreeding between genotypes of different clusters isneeded. Cluster XII showed higher cluster means for


115

weight of pods per plant (17.67), kernel weight perplant (12.93) and plant height (67.00) whereas, clusterXIV showed highest mean values for number ofmature pods per plant (19.20) and number of soundmature kernels per plant (31.6). Cluster X showedhigh mean values for harvest index (47.0), shellingout turn (82.18) and the genotypes of the clustersXIII possessed high mean value for 100 seed weight(59.33) and number of primary branches per plant(6.67).

Among various traits studied, the highestcontribution (Table 3) towards divergence was foundfor number of secondary branches per plant (29.89%)followed by CGR at 75 DAS to harvest (18.39%) CGRat 30-75 DAS (10.57%), 100 seed weight (8.51%),plant height (8.51%), SCMR (6.9%) and harvest index(5.75%). The manifestation of genetic diversity dueto number of secondary branches per plant wasreported by Muralidharan and Manivannan (2004),Garajappa et al. (2005), Dolma et al. (2010) and

Pavan kumar (2010) for harvest index, Sonone et al.(2011) for plant height and 100 seed weight. Theseresults corroborate with the findings of present study.

The data on inter cluster distances and perse performance of genotypes were used to selectgenetically diverse and agronomically superiorgenotypes. The genotypes exceptionally good for oneor more characters seemed to be more desirable.On this basis, CAUG-1, CSMG 2006-6, LGN 123, R-2001-2, and TCGS 150 were selected. Inter crossingof divergent groups would lead to greater opportunityfor crossing over and realizing hidden potentialvariability by disrupting the undesirable linkages. Theprogenies obtained from such diverse genotypesprovides a greater scope for isolating transgressivesegregants in advanced generations particularly insegmental allotetraploid like groundnut. Hence thesegenotypes could be utilized in a multiple crossingprogramme to recover desirable transgressivesegregants.

Cluster No. No. of genotypes

Genotype(s)

I 10 TCGS 876, ICGV 00351, Tirupati 4, TPT 25, TPT 1, ICGV 91114,

Narayani, DH 218, TCGS-913 ,TPT-2

II 6 TCGS 901A, UG 6, K 1392, TCGS 901 A, GPBD 4, PBS 30086

III 1 CSMG 2006-6

IV 1 TCGS-584

V 1 TCGS-150

VI 1 CTMG 7

VII 1 TG 68

VIII 1 CSMG 2006-6

IX 1 RTNG 2

X 3 K 1463, Greeshma, Bheema

XI 1 J 71

XII 1 CAUG 1

XIII 1 LGN 123

XIV 1 R-2001-2

Table 1. Distribution of 30 genotypes of groundnut in different clusters (Tocher’s method)

NIRMALA et al

116

Tabl

e 2

. A

vera

ge i

nter

clu

ster

dis

tanc

es fo

rmed

by

30 g

enot

ypes

of g

roun

dnut

GENETIC DIVERGENCE STUDIES FOR YIELD

117

Tabl

e 3.

Clu

ster

mea

ns fo

r 19

char

acte

rs in

30

geno

type

s of

gro

undn

ut (M

ahal

anob

is’s

D2 m

etho

d)

PH

= P

lant

hei

ght;

PB

= N

umbe

r of p

rimar

y br

anch

es p

er p

lant

; SB

= N

umbe

r of s

econ

dary

bra

nche

s pe

r pla

nt; M

P=

Num

ber o

f mat

ure

pods

per

pla

nt; I

MM

P=

Num

ber o

f im

mat

ure

pods

per

pla

nt; S

MK

= N

umbe

r of s

ound

mat

ure

kern

el; W

OP

= W

eigh

t of p

ods

per p

lant

; KW

= K

erne

l wei

ght p

er p

lant

; SO

= S

helli

ngou

t tur

n; 1

00S

W =

100-

seed

wei

ght;

HI =

Har

vest

inde

x; S

LA =

Spe

cific

Lea

f Are

a; S

CM

R =

SP

AD

chl

orop

hyll

met

er re

adin

g; O

.C =

Oil

cont

ent;

CG

R1

= C

rop

Gro

wth

Rat

e at

30

DA

S to

75

DA

S; C

GR

2 =

Cro

p G

row

th R

ate

at 7

5 D

AS

to h

arve

st; R

GR

1 =

Rel

ativ

e G

row

th R

ate

at 3

0 D

AS

to 7

5 D

AS

; RG

R 2

= R

elat

ive

Gro

wth

Rat

e at

75

DA

S to

har

vest

.

NIRMALA et al

Clu

ste

rN

o.

118

REFERENCES

Awatade, S.M. 2007. Genetic variability, charactersassociation, path analysis and geneticdiversity in groundnut (Arachis hypogaea L.)M.Sc. Thesis submitted to Dr. BSKKV, Dapoli.

Dolma, T., Sekhar, M.R and Reddy, K.R. 2010.Genetic divergence studies in Groundnut(Arachis hypogaea L.). Journal of OilseedsResearch 27:2, 158-160.

Garajappa, Dasaradha Rami Reddy, C., Naik, K.S.Sand Srinivasa Rao, V. 2005. Geneticdivergence in groundnut (Arachis hypogaea L.).The Andhra Agricultural Journal 52(3&4): 424-436.

Mahalanobis P. C 1936. On the generalized distancein statistics. Proceedings of National Academyof Sciences in India 2: 49-55.

Muralidharan, V and Manivannan, N. 2004. D2

analysis in groundnut. Legume Research27(4): 302-304.

Pavan Kumar, C 2010. Genetic divergence ingroundnut (Arachis hypogaea L.). M.Sc.Thesis submitted to Acharya N.G.RangaAgricultural University.

Rao, C.R.V 1952. Advanced statistical methods inbiometrical research. John Wiley and SonsInc., New York, pp.236-272.

Sonone, N.G., Thaware, B.L., Bhave, S.G., Jadhav,B.B., Joshi, G.D and Dhekale J. S. 2011.Multivariate studies in groundnut (Arachishypogaea L.). Journal of Oilseeds Research28 (1): 24-28.

Sudhir Kumar, I., Venkstaravana, P. and Marappa,N. 2010. Genetic divergence of new germplasm and advanced breeding lines ofGroundnut (Arachis hypogaea L.) studiedunder late Kharif situation. Legume Research33(2):124-127.

GENETIC DIVERGENCE STUDIES FOR YIELD

119

Dendrobium is the biggest genera in theOrchidaceae family and commands a major sharein foreign exchange. It is a water loving plant andneeds irrigation every day. Crops, in the absence ofdefinite recommendation are irrigated with differentmethods which have definite effects on the growth,development and performance. In such situationstandardization of method of irrigation is necessaryin realising the highest benefit of irrigation and saveboth cost and water. In view of this, the presentinvestigation was under taken to study the effect ofmethod of irrigation on vegetative and floralcharacteristics and longevity of Dendrobium hybridSonia-17.

The experiment was conducted at orchidfarm, Nathanallur village, Kancheepuram district,Tamil Nadu during the year 2006. There were fourtreatments replicated five times in a completelyrandomized block design. The Dendrobium hybridSonia-17 owing to its superiority for yield and qualitywas used for the study. The following are thetreatment details: T1: Mist irrigation, T2: Dripirrigation, T3: Hose irrigation, T4: Hand watering(control). Observations on various parameters likeplant height, number of leaves per plant, number ofshoots per plant, and number of spikes per plant,length of spike, number of florets per spike, floretpedicel length, longevity of spike on the plant and inthe tap water (vase life) were recorded at 180 and195 days after planting respectively.

Among the four methods of irrigationincluded, plants grown under mist recorded maximumheight (50.34 cm). Mist increases the humidity inthe green house, thus it retains mist in both containerand growing media which might enhanced the plantheight (Table 1). Misting increased plant growth and

INFLUENCE OF METHODS OF IRRIGATION ON PLANT GROWTH, YIELD,FLOWER QUALITY AND VASE LIFE IN DENDROBIUM ORCHID HYBRID

SONIA-17 UNDER SHADE NETB. GOPALA RAO, P.T. SRINIVAS and M.H.NAIK

Department of Horticulture, Sri Venkateswara Agricultural College,Acharya N.G. Ranga Agril. University, Tirupati-517502



yield by decreasing the canopy temperature andincreasing the relative humidity (Stalmakh et al.,1986),whereas lowest height of plants was recordedin hand watering (37.96 cm) which is on par with hoseirrigation (38.68 cm),other growth parameters likenumber of leaves per plant (11.09), number of shootsper plant (6.63) were maximum in mist irrigatedplants, whereas minimum in hand watering(8.84),(5.19). Gislerod and Nelson (1989) and Gislerodand Mortenson (1990) obtained similar results inChrysanthemum and Begonia.

Floral characters such as number of spikesper plant (2.91), length of spike (38.84 cm), numberof florets per spike (9.99) and floret pedicel length(4.31 cm) were maximum under mist (Table 1), whilehand watering recorded minimum number of spikesper plant (2.28),spike length (28.30 cm), number offlorets per spike (5.43) and length of floret pedicel(3.58 cm), this might be due to increase in productionsource (leaves) and nutrients which might have helpedin better synthesis of carbohydrates. Number offlowers and flower buds significantly increased withrelative humidity in Saintpaulia (Mortensen., 1986).

Longevity of flower spike on the plant wasthe highest under mist irrigation (94 days), whereasthe lowest under hand watering (76 days). Vase lifeof flower spike in tap water was maximum from mistirrigated plants (21 days) and hand watered plants(control) recorded minimum vase life (18 days)(Fig 1). Nair and Sujatha (2004) stated that orchidsrequire 60-80 per cent humidity to perform their bestbloom.

Hence, based on the above results, mistirrigation under shade net is ideal for successfulproduction of growth and blooms in the Orchid.


120

Table 1. Growth (at 180 DAP) and Flowering (at 195 DAP) characteristics of Dendrobium Orchid hybridSonia-17 grown under different methods of irrigation under shade net

Treatments Plant

height (cm)

Number of leaves

/plant

Number of

shoots /plant

Number of spikes

/plant

Number of florets

/Spike

Spike length (cm)

Floret pedicel

length (cm)

T1: Mist irrigation 50.3 11.0 6.6 2.9 9.9 38.8 4.3

T2: Drip irrigation 40.4 9.4 6.1 2.5 6.7 28.4 3.7

T3: Hose irrigation 38.6 9.0 5.6 2.5 5.9 29.6 3.8

T4: Hand watering (control)

37.9 8.8 5.1 2.2 5.4 28.3 3.5

CD at 5% 1.6 0.3 0.3 0.1 0.7 1.2 0.4

Fig 1. Longevity of spikes on the plant and in the vase at room temperature of Dendrobium Orchidhybrid Sonia-17 as influenced by method of irrigation

REFERENCES

Gislerod, H. R and Nelson, P. V. 1989. Theinteraction of relative air humidity and carbondioxide enrichment in the growth ofChrysanthemum x Morifolium Ramat. ScientiaHorticulturae 38: 305-313.

Gislerod, H. R and Mortenson, L. M. 1990. Effect ofrelative humidity and nutrient concentrationon nutrient uptake and growth by Begonia xHiemalis Fotsch. Schwabeland’. HorticulturalScience 25(5): 524-526.

Mortensen, L. M. 1986. Effect of relative humidityon growth and flowering of some green houseplants. Scientia Horticulture. 29(4): 301-307.

Nair, S. A and Sujatha, 2004. Growing orchids,prospects and advances, Floriculture Today9(7):27-28.

Stalmakh, E.A., N.D. Cherenkov and M.A. Kuzin,1986. Mist ir rigat ion is ef fective.Kartofeliovoshchi, 3: 17.

INFLUENCE OF METHODS OF IRRIGATION ON PLANT GROWTH

121

An attempt was made to estimate thepesticide residues in various vegetables (before andafter processing) grown in north coastal zone ofAndhra Pradesh. The fresh vegetable samples ofbrinjal, bitter gourd and tomato were collectedrandomly from three farmers in each of the threedistricts (Visakhapatnam, Vijayanagaram andSrikakulam). The pesticide residue content of theselected whole and processed vegetables (soakedin 3% salt water for 15 min) were analyzed for thepesticides namely chlorophyriphos, quinalphos,endosulfan, acephate, monocrotophos andcarbofuran.

Pesticide residue was determined by themethod of Sharma (2007). Non-ionic residues wereextracted with acetone / water, and the residues fromwere separated aqueous acetone to dichloromethane/ hexane phase. The traces of dichloromethane wereremoved and made up to the final extract withacetone/ hexane. Organophosphate residues weredetermined directly by gas chromatography withECD/NPD Detector.

The level of pesticide residue in all theselected whole and processed vegetables were belowdetectable level (BDL). The chromatogram of thesamples are given in the figures 1-6. Reddy et al.(1998) also found that the level of pesticide residuein vegetables sampled from Srikakulam was belowMaximum Residue Limits (MRL).

In field studies conducted in India, themaximum initial deposits of cypermethr in,fenvalerate and deltamethrin applied to cabbage at50, 50 and 12 g a.i./ha were 0.34, 0.96 and 0.25 mg/kg, respectively on heads, and 1.34, 0.08 and 0.30mg/ kg, respectively on leaves. These values werewithin the maximum residue limits of 2 mg/ kg forcypermethrin and fenvalerate on lettuce heads, and0.5 mg/ kg for deltamethrin on leafy vegatables. Most

STUDY ON PESTICIDE RESIDUES OF SELECTED VEGETABLES GROWN INNORTH COASTAL ZONE OF ANDHRA PRADESH

Y. PUNYAVATHI and V. VIJAYALAKSHMICollege of Home Science, Saifabad, Hyderabad - 500004



of the insecticide residues were found on the outerleaves and it was concluded that the residue levelsfound do not constitute any health hazard toconsumers (Singh et al. 1992). Chahal et al. (1992)investigated the persistence of residue of endosulfanand fenvelerate on okra and found that the residuesrequired a holding period of 1-3 days to become safefor consumption.

Persistence of fluvalinate and the safe intervalbetween the last application and the harvest of brinjal,okra, cauliflower and cabbage were determined attwo dose rates in field experiments in India. Residuesat 30 g a.i./ha persisted for 7, 10 and 15 days onbrinjal, okra, cabbage and cauliflower respectively.A post application holding period before harvest ofone day was suggested for these crops aftertreatment with fluvalinate (Agnihotri et al. 1992).Bordia and Gupta (1992) reported initial deposits of1.17, 12.80, 29.27 and 3.23 mg/kg when 0.05%monocrotophos, 0.1% carbaryl, 0.07% endosulfanand 0.05% fenitrothion, respectively, were applied infoliar application to cauliflowers in the field. Residuesof monocrotophos, endosulfan and fenitrothion fellto about 50% in 3 days and those of carbaryl in 7days. Residues of monocrotophos, carbaryl andfenitrothion were below the level of detection in 15days, and those of endosulfan in 21 days indicatingthat they have to be harvested only after 15 days /21 days respectively after application of pesticides.After 4 days tomatoes contained 1.02, 0.41 and 1.46ppm profenofos, pirimiphos-methyl andmethamidophos, respectively. Tomatoes wereconsidered to be safe for human consumption 1 dayafter treatment with pirimiphos-methyl and 8 daysafter treatment with profenofos or methamidophos( Abdalla et al. 1993).

The residues of most commonly usedpesticides (endosulfan, cypermethrin, dimethoate,


122

monocrotophos and mancozeb) on vegetables grownin India were observed by Dethe et al. (1995).Detectable levels of residues of endosulfan,cypermethrin, dimethoate, monocrotophos andmancozeb were observed in 33.3% samples oftomatoes (endosulfan, dimethoate andmonocrotophos), 73.3% samples of brinjal(endosulfan, cypermethrin , fenvalerate, quinalphos,dimethoate and monocrotophos), 14.3% samples ofokra (endosulfan), 88.9% samples of cabbage

4.5

5.0

5.5

6.0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

( endosulfan, fenvalerate and dimethoate) and 100%of cauliflower ( endosulfan, fenvalerate, dimethoate,cypermethrin, and monocrotophos). However, thelevels of pesticide residues were below the prescribedMRLs.

Moreover, pesticide residues in all the selectedvegetables were below detectable level (BDL) in bothwhole and processed vegetables which reflects thatthe pesticides used were highly volatile and requiredholding period was taken to harvest the product.

4.5

5.0

5.5

6.0

6.5

7.0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Fig 1. Chromatogram of multiple residues in whole Brinjal

Fig 2. Chromatogram of multiple residues in processed Brinjal

STUDY ON PESTICIDE RESIDUES OF SELECTED VEGETABLES

123

4.5

5.0

5.5

6.0

6.5

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Fig 3. Chromatogram of multiple residues in whole Bitter gourd

4.0

4.5

5.0

5.5

6.0

6.5

7.0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Fig 4. Chromatogram of multiple residues in processed Bitter gourd

PUNYAVATHI and VIJAYALAKSHMI

124

0

20

40

60

80

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Fig 5. Chromatogram of multiple residues in whole Tomato

0

200

400

600

800

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Fig 6. Chromatogram of multiple residues in processed Tomato

STUDY ON PESTICIDE RESIDUES OF SELECTED VEGETABLES

125

REFERENCES

Abdalla, E. F., Sammour, E. A., Abdallah, S. A andEi-Sayed, E. I. 1993. Persistence of someorganophosphate residues on tomato and beanplants. Bulletin of Faculty of AgriculturalUniversity Cario, Egypt. 44 (2): 465-476.

Agnihotri, N. P., Gajbhiye, V. T., Rai, S andSrivastava, K. P. 1992. Persistence and safewaiting period of f luvalinate on somevegetables. Indian Journal of Entomology. 54:299.

Bordia, J. S and Gupta, H. C. L. 1992. Residues ofmonocrotophos, carbaryl, endosulfan andfenitrothion in cauliflower. Indian Journal ofEntomology. 54 (2): 230-232.

Chahal, K. K., Singh, B and Singh, P. P. 1992.Persistence of endosulfan and fenvalerate onokra fruits. Indian Journal of Ecology. 19 (2):196-199.

Dethe, M. D., Kale, V. D and Rane, S. D. 1995.Pesticide residues in/on farm gate samplesof vegetables. Pest management inHorticultural Eco system. 1: 49-53.

Reddy, D. J., Rao, B.N., Sultan, M. A. and Reddy K.N. 1998. Pesticide residues in farm gatevegetables. Journal of Research ANGRAU. 26:6-10.

Sharma, K. K. 2007. Pesticide Residue AnalysisManual. ICAR, Directorate of information andpublications of Agriculture, New Delhi.

Singh, B., Singh, P. P., Bhattu, R.S and Kalra, R. I.1992. Residues of some synthetic Pyrethroidinsecticides on cabbage. Pesticide ResidueJournal. 4 (2): 134-141.

PUNYAVATHI and VIJAYALAKSHMI

126

Inspite of the suitable climate and otherconducive factors, the tea growers in Nepal are notable to fully reap the benefits of this highly exportoriented crop. This has resulted in a stagnation ofthe area of tea cultivation. The yields are particularlylower than the adjoining regions of India coupled withpoor quality of the produce. Against this background,a research was conducted to assess the level ofknowledge of the farmers on recommended teacultivation practices and provide insights on thereasons for the underperformance of the Nepal’s teaindustry.

An exploratory research design was adoptedfor the study. As tea cultivation is confined to Jhapaand Ilam, these two districts were selected for thestudy. Jhapa represents the Terai or plains while Ilamrepresents the hilly area. Three Village DevelopmentCommittees (VDCs) from each district were randomlypicked and 15 farmers from each VDC were selectedthus making a total of 90 respondents. A well preparedand pretested interview schedule consisting of all therecommended practices on tea cultivation wasprepared by consulting the experts in the field. Theknowledge schedule consisted of 32 items on teacultivation made in the form of multiple choices, fillin the blanks and true (or) false statements. Thecollected data were analyzed using equal classintervals, frequencies and percentages.

Majority of the respondents were of themiddle age group (50%) and educated up to schoollevel (81%). They had low experience in teacultivation (48%) and were semi-medium farmers(33%). Fifty per cent of the respondents had mediumsocio-economic status. Majority of them had lowextension contact (46%) with the extension agenciesand did not receive any training (39%). In case of

A STUDY ON THE KNOWLEDGE LEVEL OF FARMERS ON RECOMMENDED TEACULTIVATION PRACTICES IN NEPAL

KESHAV KATTEL, R. VASANTHA and M. JAGAN MOHAN REDDYDepartment of Agricultural Extension, College of Agriculture

Rajendranagar, ANGR Agricultural University, Hyderabad-500 030



market intelligence, majority of them had mediummarket intelligence (60%). A considerable percentageof respondents reported that labour availability wasdifficult for tea cultivation (57%) and they were nottimely available (69%). Majority of the respondentsexpressed that the inputs were readily available (41%)but not available timely (59%). Majority of therespondents (57%) had utilized loans from the lendingagencies. Majority of the respondents had mediumrisk orientation (56%), innovativeness (60%) andachievement motivation (59%).

Majority of the respondents (60%) hadmedium knowledge followed by low (18%) and high(16%) level of knowledge on recommended teacultivation practices. To get a better insight on thelevel of knowledge of respondents on various itemsof the tea cultivation practices an item analysis ontea cultivation practices was done as depicted inTable 1 below.

The item analysis revealed that cent per centof the respondents had knowledge on the items suchas ideal time for pruning and the recommended shadetrees for tea cultivation. A large majority of therespondents (>80%) had knowledge on ideal soil,best climate, recommended mulches for teacultivation, interval between two irrigations, optimumdistance between the plants, reasons for theoccurrence of collar canker disease, best time forplanting, plucking cycles for different seasons,recommended types of planting, use of urea in pitsat the time of planting and good winter and earlyspring rainfall improves yield.

Nearly, 50 to 80 percent of the respondentshad knowledge on optimum pH of soil for teacultivation, selection of planting material with at least


127

12 good mature buds, the optimum number of plantsper hectare, the height of the permanent frame,

various types of bio pesticides, the use of fertilizersin splits, depth of ground water table, doses of

S.No. Knowledge Items Correct

Response Incorrect Response

Percentage Percentage 1 Ideal soil for tea cultivation 88 12 2 The optimum soil pH for tea cultivation 60 40

3 The minimum number of clones that should be grown for tea cultivation in a given area 29 71

4 Recommended types of mulches for tea cultivation 91 9 5 The optimum number of plants per hectare of land 74 26

6 Symptoms of boron deficiency 12 88

7 What should be the height of Permanent frame? 76 24

8 Reasons for occurrence of Collar Canker disease 92 8

9 Optimum days between two consecutive irrigation 82 18

10 The ideal time for pruning 100 0

11 The depth of ground water table for tea cultivation 74 26

12 The percentage of total area that one single clone variety should occupy 0 100

13 The clone to seed ratio 19 81

14 The optimum distance for planting 83 17

15 The ideal depth of pits for planting 30 70

16 The symptoms of Potassium deficiency 21 79

17 Two types of planting generally recommended in tea cultivation 94 6

18 Chemical for the control of Black rot 19 81

19 The concentration for foliar application of Zinc Sulphate 33 67

20 The plucking cycle for flush season and for other seasons 90 10 21 The planting material should have at least 12 good mature

buds 76 24

22 Application of N and P fertilizers in 2 or more splits 77 23

23 Symptoms of blister blight on tea 59 41 24 Recommended shade trees for tea cultivation 100 0

25 The best time for planting of tea is April to June 91 9

26 Moderately hot and humid conditions are best for tea cultivation 90 10

27 Spraying of insecticides or fungicides indiscriminately increases the incidence of mites 56 44

28 Good rainfall during winter and early spring improves tea yield 93 7

29 Correct dose of application of Hexaconazole 78 22

30 Urea should not be applied in the pit at the time of plant ing 82 18

31 Various types of bio-fertilizers for tea cultivation 37 63

32 Various types of bio-pesticides for tea cultivation 56 44

Table 1. Item Analysis of respondents knowledge on recommended Tea cultivation practicesN=90

KATTEL et al

128

REFERENCES

Anonymous. 2010. Smarika: Tea and Coffee. NationalTea and Coffee Development Board. NewBaneshwor, Kathmandu.

Thapa, Ajit N. S. 2005. Concept paper on study ofNepalese tea industry- Vision 2020. Nepal TreeCrop Global Development Alliance (NTCGDA),Winrock International, Baneshwor,Kathmandu, Nepal.

hexaconazole, incidence of mites population due toindiscriminate sprayings and symptoms of blisterblight disease.

Less than 50 per cent of the respondentshad knowledge on minimum number of clones requiredin an area, the clone to seed ratio that should bemaintained, symptoms of boron and potassiumdeficiency in plants, control of diseases such as blackrot, recommended depth of pits for planting, varioustypes of bio fertilizers and Zinc sulphate concentrationfor foliar application. Surprisingly, none of therespondents had knowledge on the percentage of total

area that one single clone variety should occupy in agiven area of field.

The items which demand high technicalknowledge or advices from experts such asknowledge on nutrient deficiency symptoms, cloneto seed ratio, chemical for black rot control were notanswered by more than 80 per cent of the respondentsand 100 per cent of them had no knowledge on thepercentage of area that a single clone variety shouldoccupy. Unfortunately, these are the yielddetermining factors in tea cultivation and non adoptionof which leads to low yields.

Verma Pramod, Sonalika Gupta and D. K. Sharma.2010. Economic analysis of Himachal teaindustry- A study of co-operative factories andtea planters. Journal of Plantation Crops.38(3):194-200.

A STUDY ON THE KNOWLEDGE LEVEL OF FARMERS ON TEA CULTIVATION

129

The processing of cotton is a business whichis undertaken for the purpose of value addition to theproduce. The value addition to cotton takes place atthree main stages of processing viz., ginning,spinning and weaving.

The ginning of cotton is important for valueaddition to the produce, as the spinning mills acceptcotton only after ginning. Indian ginning industry isvery large in size and is spread over a large area incotton growing states. An attempt was made to studythe cost and returns, value addition in case of cottonginning and spinning industries and also the benefitcost analysis, which gives an intensified idea aboutthe profitability condition the unit.

The study was undertaken in Adilabad andGuntur districts of Andhra Pradesh. The study isbased on primary data (for the year 2010-2011)collected from ginning and spinning units. Amultistage random sampling technique was employedfor the selection of sample units. 20 ginning unitsand 5 spinning units were selected for the study. Thedata collected was subjected to analysis throughtabular analysis.

On an average, the total cost incurred in theprocessing of kapas to lint worked out to Rs. 4630.87per quintal of kapas. It is worth noting that the total

AN ECONOMIC ANALYSIS OF VALUE ADDITION TO COTTONE. RADHIKA, R. VIJAYA KUMARI and D.V. SUBBA RAO

Department of Agricultural Economics, College of Agriculture ANGR Agricultural University, Rajendranagar, Hyderabad – 500 030



variable cost (Rs. 4545.75 per quintal) formed asubstantial component (98.2%) of the total cost ofprocessing of kapas to lint. The total fixed cost beingRs. 85.12 per quintal, accounted for only 1.8 per centof the total cost of processing. In the total fixed cost(Rs. 85.12), salaries to permanent staff (Rs. 33.56)found to be the major component and accounted for0.72 per cent of total processing cost followed byinterest on fixed capital, insurance and depreciationon building and machinery, which accounted for 0.43per cent, 0.34 per cent and 0.2 per cent of the totalfixed cost respectively. Licence fee and taxestogether accounted for 0.12 per cent of the total costof processing of kapas to lint. Of the total variablecosts, the cost of raw material (Rs. 4238 / quintal)accounted for 91.51 per cent of the total processingcost, followed by interest on working capital (Rs.216.75 / quintal) accounting for 4.68 per cent,electricity charges (Rs. 49.97 / quintal) accountingfor 1.08 per cent and wages to casual labour (Rs.22.3 / quintal) for 0.48 per cent. The repair andmaintenance (Rs. 15.91 / quintal) and telephonecharges (Rs. 2.82 / quintal) altogether accounted for0.45 per cent of the total cost incurred in theprocessing of kapas. These results are in line withthe results of Mundinamani (2000) and Dodamani(2007).

S.No Particulars Amount (Rs)

1 Returns from main product(lint) 3978.81

2 Returns from by-product (seed ) 991.6

3 Gross returns 4970.41

4 Raw material cost (kapas) 4238

5 Value addition 732.41

6 Processing cost 392.87

7 Net value addition 339.54

8 Benefit-cost ratio 1.86

Table 1. Returns from processing of kapas to lint (for one quintal of kapas ginned)


130

The processing of one quintal of kapasresulted in 33 kg of lint, 65 kg of seed and 2 kg ofwaste. The gross returns obtained from ginning onequintal of kapas were Rs. 4970.41 of which the returnsfrom main product (lint) were Rs. 3978.81 and thatfrom byproduct (seed) were Rs. 991.6. Cost of rawmaterial (kapas) was Rs. 4238 with a value additionto the product in the process being Rs. 732.41 andcost of processing accounting Rs. 392.87. The netvalue added as a result of processing of kapas to lintwas Rs. 339.54 per quintal of kapas processed. Thebenefit cost ratio worked out to 0.86 in kapasprocessing. Similar results were observed withMundinamani (2000) and Dodamani (2007).

Among all the costs incurred in marketing ofginned cotton, the maximum cost was incurred onpacking material Rs.30.36 per quintal, accounting for37.47 per cent of the total cost of marketing. As thepacking material was very important in case of lintmarketing, it constituted the maximum percent. Thiswas followed by sales tax, miscellaneous costs andselling expenditure, which accounted for 25.02 percent, 20.33 per cent and 17.18 per cent of the totalcost of marketing of one quintal lint, respectively.These results are on par with the results ofShivakumar et al. (2001).

The average total cost incurred in theprocessing of lint to yarn was Rs.17201.64 per quintal,of which the total variable cost was Rs.15497.16

forming the major component (90.09%) of the totalcost of processing of lint. The total fixed cost beingRs.1704.48 per quintal accounted for 9.91 per centof the total cost of processing. Of the variable costthe cost of raw material (Rs.12000/quintal) accountedfor 69.76 per cent followed by electricity charges(9.82%) and interest rate on working capital (5.72%).The cost on wages to casual labour, repair andmaintenance, office maintenance, telephone chargestogether accounted for 4.76 per cent of the total costof processing.

In the total f ixed cost (Rs.1704.48),depreciation amounts Rs.1427.06 (8.27%), which wasfound to be major component. This was followed bysalary to permanent staff i.e., Rs.176.9 (1.02%) andinterest on fixed capital, insurance, taxes, licensefee, together accounting 0.58 per cent of the totalcost of lint to yarn. These results are in line withMundinamani (2000) and Shivakumar et.al (2001).

The processing of one quintal of lint on an averageyielded 73 kg of yarn and 27 kg of waste material.The gross returns obtained from processing (spinning)of one quintal of lint were Rs.20435, which comprisedof mainly returns from yarn (Rs.18250) and wastage(Rs.2185). The value addition in the process wasRs.8435. The net value added as a result ofprocessing of lint to yarn was Rs.5933.36 per quintalof lint processed. The resultant benefit-cost ratio was1.62.

S.No Particulars Amount (Rs) 1 Returns from main product(yarn) 18250 2 Returns from wastage 2185 3 Gross returns 20435 4 Raw material cost (kapas) 12000 5 Value addition 8435 6 Processing cost 5201.64 7 Net value addition 5933.36 8 Benefit-cost ratio 1.62

Table 2. Returns in processing of lint to yarn (for one quintal of lint spinned)

Among all the costs incurred in marketing ofspinned cotton, the maximum cost was incurred onpacking material, Rs.275.48 per quintal, accountingfor 47.9 per cent of the total cost of marketing. Thiswas followed by commission charges Rs.246.45

(42.85%) and export expenses, yarn sale expenses,yarn freight together accounting for 9.22 per cent ofthe total cost of marketing of one quintal yarn. Theseresults are in line with the results of Shivakumar etal. (2001).

AN ECONOMIC ANALYSIS OF VALUE ADDITION TO COTTON

131

Thus, the results of analysis indicated that anadditional value to the extent of Rs.2297.54 wascreated in the course of processing cotton kapas in

to yarn. The breakup of the same at different levelsof processing i.e., at ginning was Rs.339.54 (14.78%)and spinning Rs.1958 (85.22%).

S.No Stage of processing Net value addition (Rs) Per cent 1 Ginning 339.54 14.78

2 Spinning 1958 85.22

Total net value addition to cotton 2297.54 100

Table 3. Total net value addition to one quintal of cotton by processing

REFERENCES

Dodamani, M.T and Kunnal L.B. 2007. Value addition

to organically produced naturally- coloured

cotton under contract farming. Agricultural

Economics Research Review. 20:521-528.

Mundinamani, R.M. 2000. An economic analysis of

value addition to Cotton in Gadag district.

M.Sc. (Ag) Thesis submitted to University of

Agricultural Sciences, Dharwad.

Shivakumar, S., Sonnad, J.S and Basavaraj, H.

2001. Economics of cotton ginning and

pressing in Bellary District. Agricultural

marketing. 43(4): 9-12.

RADHIKA et al

132



Phytosterol potency in decreasing serum lowdensity lipoprotein (LDL) cholesterol levels and thusin protecting against cardiovascular diseases, hasled to the development of functional foods enrichedwith plant sterols. At present, several functional foodproduct types such as spreadable fats, yoghurts andmilk, with free phytosterols or phytosteryl fatty acidesters or phytostanyl fatty acid esters added at highlevels are available in the market especially in severalEuropean countries (Laakso, 2005).

When phytostanols and phytosterols areincluded in the diet in sufficient amounts, i.e. 2–3g/d, they efficiently decrease serum cholesterolconcentration by reducing the absorption ofcholesterol from the digestive tract. The averagereduction in total cholesterol is 10%, and 15% in LDLcholesterol. No changes occur in serum HDLcholesterol or triacylglycerol concentrations (Katanet al., 2003). In addition to the blood cholesterol-lowering effect, phytosterols have shown the followingactivities in animals:anti-cancer properties (with abeneficial effect upon the inhibition of colon cancerdevelopment) (Awad et al., 2003) andantiatherosclerotic, anti-inflammatory (Bouic, 2001)and anti-oxidative effects (van Rensburg et al., 2000).Sterols make up the largest proportion of theunsaponifiable fraction of lipids. Plant fats and oilscontain phytosterols as natural ly occurr ingconstituents. The most important natural sources ofplant sterols in human diets are oils and margarines,although they are also found in a range of seeds,legumes, vegetables and unrefined vegetable oils.Cereal products are a significant source of plantsterols, their contents, expressed on a fresh weightbasis, is higher than in vegetables (Phillips et al.,2005).

DEVELOPMENT OF PHYTOSTEROL ENRICHED FLAVOURED MILK M. PENCHALA RAJU , ANURAG CHATHURVEDI and APARNA KUNADepartment of Food Technology, Post Graduate and Research Centre,

Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad-500 030

The exact mechanism by which phytosterolsdecrease serum cholesterol levels is not completelyunderstood, but several theories have been proposed.One of them suggests that cholesterol in the intestine,already marginally soluble, is precipitated into anonabsorbable state in the presence of addedphytosterols and stanols (Jones and AbuMweis,2009). Another theory is based on the fact thatcholesterol must enter bile-salt and phospholipid-containing ‘mixed micelles’ in order to pass throughintestinal cells and to be absorbed into thebloodstream. Moreover, phytosterols may modulatethe action of key transporters involved in cholesterolabsorption. Cholesterol absorption is a very importantphysiological mechanism that regulates cholesterolmetabolism (Rozner and Garti, 2006). Phytosterolsmay reduce cholesterol absorption by competing withcholesterol for incorporation into the bile salts micellesor for uptaking of cholesterol by enterocytes throughNeiman Pick C1 Like1 (NPC1L1) transporter. Inaddition, phytosterols may enhance cholesterolexcretion back into the intestinal lumen through theadenosine triphosphate binding cassette G 5(ABCG5) and G 8 9ABCG8) transporters. Phytosterolcould also prevent esterification of the free cholesterolinto cholesterol esters and thus it’s assembling intothe chylomicrons. As a result of reducing cholesterolabsorption by phytosterols, the cholesterol synthesisrate increase, but the net effect is a reduction in LDL-cholesterol levels (Jones and AbuMweis, 2009).Phytosterol have been shown to inhibit the uptake ofboth dietary and endogenously produced (biliary)cholesterol from intestinal cells. Such inhibitionresults in a decrease in serum total and LDL-cholesterol levels. Levels of HDL cholesterol andtriglycerides do not appear to be affected by dietaryphytosterol consumption (AbuMweis et al., 2008). The


133

estimated daily dietary intakes of plant sterols amongdifferent populations range from 160 to 400 mg.(Berger et al., 2004). The average intakes ofphytosterols have been estimated between 140 and360 mg/day in Finland and 163 mg/day in the UnitedKingdom. (Piironen et al., 2004).

The amount of phytosterols we consumefrom the foods is not sufficient for attaining healthbenefits. Hence, incorporation of phytosterols in thefoods consumed can benefit in reducing the plasmacholesterol levels and reduce the risk of coronaryartery diseases. There is a lot of research evidenceshowing that maximum cholesterol lowering benefitis achieved with phytosterols at doses of 2-3 gm perday (Hallikainen et al., 2000, Jones et al, 2000, Makiet al., 2001). Phytosterols might also protect againstcertain types of cancers such as colon, breast &prostate. (Awad and Fink, 2000).

The enrichment of foods such as margarineswith phytosterols is one of the recent developmentsin functional foods to enhance the cholesterollowering ability of traditional food products(Anonymous, 2005). The phytosterol milk productsinhibit the uptake of cholesterol in intestinal Caco-2cells in vitro. Plant sterols in the milk matrix getstabilized using a proprietary crystal retardation andemulsification system (Poteau et al., 2003).

Flavoured milk is the milk in which littleflavour and colour has been added to make it morepalatable. The most common flavour for flavored milkis chocolate, with cocoa powder as ingredient. Other

common flavours for flavoured milk includestrawberry, banana and coffee. Less commonly useflavors that are available are lime, malt, mango,papaya, root beer, tropical fruits and vanilla. Withthe exception of chocolate milk, many of these flavorsare artificial. Flavoured milk should contain milk fatpercent equal to the minimum legal requirementprescribed for that milk. Nowadays, chocolateflavoured milk, fruit flavoured milk and many morevarieties are more popular in the market.

Objectives of the investigation

· To standardize and develop phytosterolincorporated flavoured milk.

· To study the acceptability of the developedproducts by sensory evaluation.

· To estimate the physico-chemical and nutritionalproperties of developed product.

The study was planned and conducted in thedepartment of Food Technology, Post Graduate andResearch Centre, Acharya N.G. Ranga AgriculturalUniversity, Rajendra Nagar, Hyderabad. For thepresent investigation, toned milk, cocoa powder,stabilizer (CMC-Carboxy Methyl Cellulose),Phytosterol powder was procured from ReducolTM

Original Powder ( Forbes medi – Tech lnc) USA basedcompany. Three products (flavoured milk) namely T1,T2, T3 and one control were prepared andstandardized. Phyotsterol powder was incorporatedin test samples at various levels as shown inTable 1.

Flavoured milk Ingredients used T1 T2 T3 Control

Toned milk 500ml 500ml 500ml 500ml Cocoa powder 4g 5g 6g 4g Sugar 35g 45g 55g 35g Stabilizer(CMC) 0.2g 0.3g 0.2g 0.2g Phytosterol powder 2g/100ml 2.5g/100ml 3g/100ml -

Table 1. Composition of different flavoured milk preparations

Flavoured milk was prepared by mixing all

ingredients which are mentioned in the Table 1.Themilk (toned milk) was preheated to 60°C/1 minute

and homogenized at 2500 psi at 55-60ºC/1 min and

then clarified. To the warm milk, the desired amount

of cocoa-mix, sugar and stabilizer were slowly added

and stirred so as to dissolve them properly. The cocoapowder was added in the form of syrup, and the

stabilizer in the form of solution. The mixture wasthen pasteurized at 71ºC/30 minutes, cooled rapidly

RAJU et al

134

to 5°C. The phytosterol enriched flavoured milk was

prepared by incorporating the phytosterols into the

flavoured milk. The phytosterols powder was

incorporated into the flavoured milk at various levels

as shown in Table 1. Phytosterol powder was added

(2g, 2.5 and 3g /100ml), homogenized at 2000 psi at

room temperature per 2 minutes, bottled by using

glass bottles and kept under refrigeration (5ºC) until

used. The received milk was homogenized to prevent

or delay the rising of cream. It could be homogenized

after addition of cocoa and sugar, but this increases

sedimentation. Stabilizer was added to delay or

prevent settling cocoa particles and also preventing

cream rising.

The developed products were subjected to

organoleptic evaluation for parameters like colour/

appearance, flavour, taste, texture/consistency and

overall acceptability by twelve (12) trained panel

members from Post Graduate and Research Centre,Acharya N.G. Ranga Agricultural University, Rajendra

Nagar, Hyderabad. A score card with five point

hedonic scale was used for the purpose. Organoleptic

scores for different parameters of sensory evaluationare presented in Table 2. As per the mean score

obtained the experimental product T1 was rated higherthan experimental products T2 and T3. The colour,taste, flavour and overall acceptability of theexperimental product T1 did not differ much from thecontrol. When compared to T1 (2g/100ml), phytosterolpowder per cent was high in experimental productsT2 (2.5g/100ml) and T3 (3g/100ml). As a result allthe variables scored lower than the control and T1.The overall acceptability scores was observed inwhich T1 (4.5) scored higher followed by control (4.4)and lowest score was observed for T2 and T3 (3.7%).After product development and acceptability studies,it was observed that the product T1 (combination oftoned milk, cocoa, sugar, stabilizer (500:4: 35:0.2)and phytosterol powder (2g/100ml)) was found to besuperior in all aspects. Hence T1 product was selectedfor further study.

Products S.No. Variables Control T1 T2 T3

1 Colour/appearance 4.3±0.8 4.4±0.8 3.6±0.6 3.6±0.6 2 Flavour 4.0±0.6 4.2±0.7 4.0±0.5 3.9±0.7 3 Taste 4.1±0.8 4.2±0.7 4.0±0.5 3.8±0.7 4 Texture/consistency 4.0±0.7 4.0±0.7 3.5±0.5 3.4±0.5 5 Overall acceptability 4.4±0.6 4.5±0.6 3.7±0.4 3.7±0.4

Table 2. Mean scores obtained for control and experimental products

Experimental product T1 and controlsamples were subjected to Physico- chemical andnutritional analysis. Physico- chemical and nutritional

Note: Values are expressed as mean ± SD.

Control: Flavoured milk with out phytosterols

T1: Phytosterols (2g) enriched flavoured milk,

T2: Phytosterols (2.5g) enriched flavoured milk,

T3: Phytosterols (3g) enriched flavoured milk.

composition of the phytosterol enriched flavoured milk(T1) and control samples are presented in Table 3.

DEVELOPMENT OF PHYTOSTEROL ENRICHED FLAVOURED MILK

135

Parameter Samples T1 Control

Fat (g) 3.0 3.1 SNF (g) 12.0 12.4 Specific gravity 1.043 1.044 Total solids (g) 14.52 15.50 Acidity (% of lactic acid) 0.18 0.18 pH 6.6 6.5 Protein (g) 2.98

2.96

Calcium (mg) 137.37

137.47

Phosphorus (mg) 123.54

123.34

Carbohydrates (g) 4.60

4.64

Sugars (g) 6.32

6.37

Table 3. Physico-chemical and nutritional parameters of experimental and control products (per 100gsample)

There was a slight variation between theexperimental and control values of Fat, SNF, Totalsolids, Specific gravity, Acidity and pH. Protein,calcium, Phosphorus, carbohydrate (CHO) and sugarscontent of control and experimental samples arealmost similar .Physico-chemical and nutritionalparameters of the experimental product did not differmuch from the control. The amounts of phytosterolwe consume from the foods are not sufficient forattaining health benefits. So incorporation ofphytosterols in the foods consumed can benefit in

reducing the plasma cholesterol levels and reducethe risk of coronary artery diseases. Plant sterolshave gained a prominent position in strategies to lowerCHD (Coronary Heart Diseases) risk because of theirserum LDL-Cholesterol lowering effects. So that thephytosterol enriched flavoured milk can be used asa good vehicle for reducing plasma cholesterol inhypercholesterolemic subjects. We can also use theincorporation of phytosterol enriched flavoured milkinto a balanced diet represents a practical dietarystrategy in the management of serum cholesterollevels.

REFERENCES

AbuMweis, S.S., Barake, R and Jones, P. 2008. Plantsterols/stanols as cholesterol lowering agents:a meta-analysis of randomized controlled trials.Food and Nutrition Research. 52-56.

Anonymous.2005. Phytosterol esters (plant sterol andstanol esters). Available: http://www.ifst.org/hottop29.htm.

Awad, A.B and Fink, C.S. 2000. Phytosterols asanticancer dietary components: evidence andmechanism of action. Journal of Nutrition. 130:2127-2130.

Awad, A.B., Roy, R and Fink, C.S. 2003. Beta-sitosterol, a plant sterol, induces apoptosisand activates key caspases in MDA-MB-231human breast cancer cells. Oncology reports.10: 497-500.

Berger, A., Jones, P.J.H and Abumweis, S.S. 2004.Plant sterols: factors affecting their efficacyand safety as functional food ingredients.Lipids in Health and Disease. 3:5.http://www.lipidworld.com/content/3/1/5.

RAJU et al

136

Bouic, P.J. 2001. The role of phytosterols andphytosterolins in immune modulation: a reviewof the past 10 years. Current opinion in clinicalnutrition and metabolic care. 4: 471–475.

Hallikainen M.A., Sarkkinen, E.S and Uusitupa, M.I.J.2000. Plant stanol eaters affectserumcholesterol concentrations ofhypercholesterolemic men and women in adose dependent manner.Journal of Nutrition.130(4): 767-776.

Jones, P.J.H and Abumweis, S.S. 2009. Phytosterolas functional food ingredients: linkages tocardiovascular disease and cancer. CurrentOpinion in Clinical Nutrition and Metabolic Care.12: 147-151.

Jones P.J., Raeini-Sarjaz, M and Ntanios, F.Y. 2000.Modulat ion of plasma lipid levels andcholesterol kinetics by phytosterol versusphytostanol esters. Journal of Lipid Research.41(5): 697-705.

Katan, M.B., Grundy, S.M., Jones, P., Law, M.,Miettinen, T and Paoletti, R. 2003. Efficacyand safety of plant stanols and sterols in themanagement of blood cholesterol levels. MayoClinic Proceedings. 78: 965-978.

Laakso, P. 2005. Analysis of sterols from variousfood matrices. European Journal of LipidScience and Technology. 107: 402-410.

Maki, K.C., Davidson, M.H and Umporowicz, D.M.2001. Lipid responses to plant-sterolenriched

reduced-fat spreads incorporated into aNational Cholesterol Education Program StepI diet. American Journal of Clinical Nutrition.74(1): 33-43.

Phillips, K.M., Ruggio, D.M and Ashraf-Khorassani,M. 2005. Phytosterol composition of nuts andseeds commonly consumed in the UnitedStates. Journal of Agricultural Food Chemistry.53: 9436-9445.

Piironen, V and Lampi, A.M. 2004. Occurrence andlevels of phytosterols in foods. In:Phytosterols as Functional Food Componentsand Nutraceuticals. Ed. P. C. Dutta, MarcelDekker, Inc., New York (USA). 1–32.

Poteau, E.B., Monnard, I.E., Piguet-Welsch, C.,Groux, M.J.A., Sagalowicz, L and Berger, A.(2003). Non-esterified plant sterols solubilizedin low-fat milks inhibit cholesterol absorption.European Journal of Nutrition. 42: 154–164.

Rozner, S and Garti, N. 2006. The activity andabsorption relationship of cholesterol andphytosterols. Colloids and surfaces. Aphysicochemical and engineering aspects.282-435.

Van Rensburg, S.J., Daniels, W.M., Van Zyl, J.M andTaljaard, J.J. 2000. A comparative study ofthe effects of cholesterol, beta-sitosterol, beta-sitosterol glucoside, dehydroe piandrosteronesulphate and melatonin on in vitro lipidperoxidation. Metabolic brain disease. 15(4):257-265.

DEVELOPMENT OF PHYTOSTEROL ENRICHED FLAVOURED MILK

137

Path analysis measures the direct andindirect effects of independent variables on dependentvariables. Knowledge of cause and effect relationshipmakes selection more effective. In the presentinvestigation an attempt was made to find out theeffects of component traits on grain yield of rice.

The experimental material comprising of 81genotypes (including 3 checks) obtained fromdifferent sources was cultivated during kharif, 2008at Rice section farm, Agriculture Research Institute,Rajendranagar, Hyderabad. All the genotypes weresown separately in the nursery on raised beds. Thirtydays old seedlings of each genotype weretransplanted in five rows of 6 m length by adopting aspacing of 15 cm between plants and 15cm betweenrows in a Randomized Block Design replicated twice.Recommended agronomic practices and plantprotection measures for raising a healthy crop weretaken up during experiment. Five plants of eachgenotype in each replication selected randomly fromcentral rows were used to record data. The meanvalues were considered for statistical analysis.

Genotypic correlations in general were highas compared to their phenotypic correlationsindicating strong inherent association between thecharacters which might be due to masking ormodifying effects of environment.

The correlation analysis indicated that grainyield was significantly associated with number ofproductive tillers per plant, plant height, number ofgrains per panicle and panicle length. Similar kind ofassociation was reported by Satish Chandra et al.(2009) for number of productive tillers per plant,number of grains per panicle and panicle length and

Madhavi Latha (2002) for plant height.

CORRELATION AND PATH ANALYSIS FOR YIELD AND ITSCOMPONENTS IN RICE (Oryza Sativa L.)

C.MANIKYA MINNIE, T.DAYAKAR REDDY and CH.SURENDER RAJUDepartment of Genetics and Plant Breeding, College of Agriculture,

Acharya N.G.Ranga Agricultural University, Rajendranagar, Hyderabad-500030

The data indicated that days to 50 percentflowering and 1000 gram weight had no association

with grain yield.

The characters that showed positive and

significant association with grain yield could beconsidered as criteria for selection for yield

improvement as these were mutually and directlyassociated with grain yield.

Days to 50 per cent flowering had significantpositive association with number of grains per

panicle. Similar result was reported by Madhavi Latha(2002).

Plant height registered positive and

significant association with number of productive

tillers per plant, panicle length, 1000 grain weight andnumber of grains per panicle, indicating that the

increase in panicle length and number of grains perpanicle and 1000 grain weight can be possible with

an increase in plant height. Similar results were also

obtained by Janardhan et al. (2001) for number ofproductive tillers per plant, panicle length and number

of grains per panicle and Yogameenakshi et al. (2004)for 1000 grain weight.

Number of productive tillers per plant was

positively and significantly correlated with number of

grains per panicle and panicle length. These resultsare in consonance with the earlier findings of

Janardhanam et al. (2001). It showed positivecorrelation with 1000 grain weight.

Panicle length had high significant positiveassociation with 1000 grain weight and number of

grains per panicle. Similar results were also obtainedby Yogameenakshi et al. (2004).




Path coefficient analysis was used tocompute direct and indirect effects of six characters

on grain yield. The characters number of productivetillers per plant, panicle length and number of grains

per panicle with positive direct effects on yield could

be considered as major yield contributing charactersin rice. These findings were in agreement with the

reports made by Karad and Pol (2008) for number ofproductive tillers per plant and panicle length and

Satish Chandra et al. (2009) for number of grains per

panicle.

On the contrary negative direct effects wereexisted by days to 50 per cent flowering, plant height

and 1000 grain weight. Gupta et al. (1998) alsorecorded similar kind of negative direct effect of these

traits on yield.

The characters, panicle length, number of

grains per panicle showed positive indirect effectthrough number of productive tillers per plant on grain

yield. Similar results were obtained by Gupta et al.

(1998). The panicle length exhibited positive indirect

effect through number of productive tillers per plant

and number of grains per panicle. Number ofproductive tillers per plant, 1000 grain weight and

panicle length showed positive indirect effect on grainyield through number of grains per panicle. These

results were in accordance with the results obtainedby Madhavilatha (2002).

The characters days to 50 per cent flowering,plant height and 1000 grain weight were not only

having negative direct effect on grain yield but alsohad negative association with the yield contributing

characters. This was in consonance with the results

of Gupta et al. (1998).

Path analysis revealed the importance of thecharacters, number of productive tillers per plant,

panicle length and number of grains per panicle ininfluencing the grain yield. Hence, selection should

be practiced for these characters in order to isolate

superior plant types for improvement of grain yield.

Character Days to 50 %

flowering

Plant Height (cm)

Number of productive tillers per

plant

Panicle Length (cm)

Number of grains per

panicle

1000 Grain Weight (g)

Grain Yield/

Plant (g)

P 1.0000 0.0622 0.0434 0.0992 0.2648** -0.2933** -0.0001 Days to 50% Flowering G 1.0000 0.0727 0.0109 0.0987 0.2590** -0.3024** -0.0181

P 1.0000 0.3113** 0.2865** 0.1525 0.1620* 0.3197** Plant Height (cm) G 1.0000 0.5046** 0.2958** 0.1606* 0.1845* 0.3964**

P 1.0000 0.1010 0.2048** -0.0175 0.8501** Number of productive tillers/plant

G 1.0000 0.1508 0.2512** 0.0233 1.0014**

P 1.0000 0.2383** 0.3725** 0.1047 Panicle Length (cm) G 1.0000 0.2546** 0.4021** 0.1599*

P 1.0000 -0.3270** 0.2483** Number of grains/panicle G 1.0000 -0.3425** 0.2711**

P 1.0000 -0.0329 1000 Grain Weight (g)plant

G 1.0000 -0.0157

Table 1.Estimation of correlation coefficients between yield and yield attributing characters

* Significant at 5% level ** Significant at 1% level

CORRELATION AND PATH ANALYSIS FOR YIELD AND ITS COMPONENTS

Table 2. Estimates of genotypic direct and indirect effects between yield and yield contributingcharacters

Character Days to 50 %

flowering

Plant Height (cm)

Number of productive ti llers per

plant

Panicle Length (cm)

Number of grains per

panicle

1000 Grain Weight

(g)

Grain Yield/

Plant (g)

Days to 50% Flowering -0.0418 -0.0109 0.0116 0.0067 0.0008 0.0156 -0.0181

Plant Height (cm) -0.0030 -0.1504 0.5389 0.0199 0.0005 -0.0095 0.3964**

Number of productive til lers/plant

-0.0005 -0.0759 1.0680 0.0102 0.0008 -0.0012 1.0014**

Panicle Length (cm) -0.0041 -0.0445 0.1611 0.0674 0.0008 -0.0207 0.1599*

Number of grains/panicle -0.0108 -0.0242 0.2682 0.0172 0.0031 0.0176 0.2711**

1000 Grain Weight (g) 0.0126 -0.0278 0.0249 0.0271 -0.0011 -0.0515 -0.0157

Bold = direct effects Residual effect = -0.1520

REFERENCES

Gupta, J. C., Kotoch, P. C., Kaushik, R. P andSharma, S. L. 1998. Cause and effectrelationship of yield and its components undercold stress condition in rice (Oryza sativa L.).Indian Journal of Agricultural Sciences. 68 (1): 13-15

Janardhanam, V., Nadarajan, N and Jebaraj, S. 2001.Correlation and path analysis in rice (Oryzasativa L.). Madras Agricultural Journal. 88 :719-720

Karad, S. R and Pol, K. M. 2008. Characterassociation, genetic variability and pathcoefficient analysis in rice (Oryza sativa L.).International Journal of Agricultural Sciences.4 (2) : 663-666

Madhavilatha, L. 2002. Studies on genetic divergenceand isozyme analusis on rice (Oryza sativaL.). M.Sc. (Ag). Thesis submitted to AcharyaN. G. Ranga Agricultural University,Hyderabad.

Satish Chandra, B., Dayakar Reddy, T., Ansari, N.A and Sudheer Kumar, S. 2009. Correlationand path analysis for yield and yieldcomponents in rice (Oryza sativa L.).Agricultural Science Digest. 29 (1) : 45-47

Yogameenakshi, P., Nadarajan, N andAnbumalarmathi, J. 2004. Correlation and pathanalysis on yield and drought tolerant attributesin rice (Oryza sativa L.) under drought stress.Oryza. 41 (3&4) : 68-70

MINNIE et al

140

THE JOURNAL OF RESEARCH ANGRAU

DECLARATION CERTIFICATE TO BE SUBMITTED BY THE AUTHOR(S)

Certified that the article entitled ___________________________________________________________

____________________________________________________________________________________

1. is based on my / our original research work / M.Sc / Ph.D thesis (strike off whichever is not applicable)

2. The article has been seen by all the authors and the order of authorship is agreed.

3. The results presented have not been published or submitted for publication else where in part or fullunder the same or other title

4. The names of the authors are those who made a notable contribution.

5. No authorship is given to anyone who did not make a notable contribution.

S.No. Name/s Present address Permanent address Signature

1.

2.

3.

CERTIFICATE BY THE COMPETENT AUTHORITY

( Professor & Head of the Department/ Principal Scientist of the station/ Associate Director of Research).

Certified that the article —————————————————————————————————

——————————————————————————————————————————————

authored by ——————————————————————————————————————————

——————————————————————— is fit for publication. It fulfills all the requirements for

publication in the Journal of Research ANGRAU.

Name :

Signature :

Office seal :

Note: In case it is not possible to obtain the signature of a particular author for reasons beyond his/herreach, the reasons thereof should be explained.

141

Statement about ownership and other particulars aboutTHE JOURNAL OF RESEARCH ANGRAU

Form IV (See Rule 8)

1. Place of Publication : The Acharya N.G. Ranga Agricultural University,Rajendranagar, Hyderabad - 500 030

2. Periodicity of Publication : Quarterly

3. Printer’s Name : Dr. P. Chandrasekhar Rao

Nationality : Indian

Address : Dean of Agriculture and Editor of Journal of Research,ANGRAU

Acharya N.G. Ranga Agricultural UniversityRajendranagar, Hyderabad.

4. Publisher’s Name : Dr. P. Chandrasekhar Rao


Address : Dean of Agriculture i/c and Editor of Journal of Research,ANGRAUAcharya N.G. Ranga Agricultural UniversityRajendranagar, Hyderabad.

5. Editor’s Name : Dr. P. Chandrasekhar Rao


Address : Dean of Agriculture and Editor of Journal of Research,ANGRAUAcharya N.G. Ranga Agricultural UniversityRajendranagar, Hyderabad.

6. Name and address of the : The Acharya N.G. Ranga Agricultural University,individuals who own the Rajendranagar, Hyderabad - 500 030 (A.P.)newspaper & partners orshare holders holding morethan one per cent of thetotal capital

I, Dr. P. Chandrasekhar Rao hereby declare that the particulars given above are true tothe best of my knowledge and belief.

Dated : Signature of Publisher

142

SUBSCRIPTION ENROLLING FORM

I/we, herewith enclose D.D. No...................................................................... .............

dated..................................for Rs. ............................... drawn in favour of Managing Editor, Journal ofResearch ANGRAU, Agricultural Information & Communication Centre, ARI Campus, Acharya N.G. Ranga

Agricultural University, Rajendranagar, Hyderabad - 500 030 as individual annual/individual life/Institutional

annual Membership for Journal of Research ANGRAU for the calendar year (January - December) ..................

S.No. Name of the Address for Name of the article Signatureauthors Correspondence contributed

1.

2.

3.

4.

Note : The receipt of payment will be sent only if a self addressed and stamped envelope is enclosedalong with your DD.

143

GUIDELINES FOR THE PREPARATION OF MANUSCRIPT

1. Title of the article should be short, specific, phrased to identify the content and indicate the nature ofstudy.

2. Names should be in capitals prefixed with initials and separated by commas. For more than two authorsthe names should be followed by ‘and’ in small letters before the end of last name. Full address of theplace of research in small letters should be typed below the names. Present address and E-mail ID ofthe author may be given as foot note.

3. The full length paper should have the titles ABSTRACT, MATERIALS AND METHODS, RESULTSAND DISCUSSION, REFERENCES-all typed in capitals and bold font - 12. The research note willhave only one title REFERENCES.

4. ABSTRACT: The content should include the year, purpose, methodology and salient findings of theexperiment in brief not exceeding 200 words. It should be so framed that the reader need not refer tothe article except for details.

5. INTRODUCTION : Should be without title and indicate the reasons which prompted the research,objectives and the likely implication. The review of recent literature should be pertinent to the problem.The content must be brief and precise.

6. MATERIALS AND METHODS : Should include very clearly the experimental techniques and thestatistical methods adopted. Citation of standard work is sufficient for the well known methods.

7. RESULTS AND DISCUSSION : Great care should be taken to highlight the important findings withsupport of the data well distinguished by statistical measures like CD, r, Z test etc. Too descriptiveexplanation for the whole data is not desirable. The treatments should be briefly expressed instead ofabbreviations like T1, T 2 etc. The discussion should be crisp and relate to the limitations or advantagesof the findings in comparison with the work of others.

8. REFERENCES : Literature cited should be latest. References dating back to more than 10 years arenot desirable. Names of authors, their spelling and year of publication should coincide both inthe text and references. The following examples should be followed while listing the references fromdifferent sources.

Journals and Bulletins

Abdul Salam, M and Mazrooe, S.A. 2007. Water requirement of maize (Zea mays L.) as influenced byplanting dates in Kuwait. Journal of Agrometeorology. 9 (1) : 34-41

Hu, J., Yue, B and Vick, B.A. 2007. Integration of trap makers onto a sunflower SSR marker linkage mapconstructed from 92 recombinant inbred lines. Helia. 30 (46) :25-36.

Books

AOAC. 1990. Official methods of analysis. Association of official analytical chemists. 15th Ed. WashingtonDC. USA. pp. 256

Federer, W.T. 1993. Statistical design and analysis for intercropping experiments. Volume I: two crops.Springer – Verlag, Cornell University, Ithaca, New York, USA. pp. 298-305

Thesis

Ibrahim, F. 2007. Genetic variability for resistance to sorghum aphid (Melanaphis sacchari, Zentner) insorghum. Ph.D. Thesis submitted to Acharya N.G. Ranga Agricultural University, Hyderabad.

144

Seminars / Symposia / Workshops

Naveen Kumar, P.G and Shaik Mohammad 2007. Farming Systems approach – A way towards organicfarming. Paper presented at the National symposium on integrated farming systems and its roletowards livelihood improvement. Jaipur, 26 – 28 October 2007. pp.43-46

Proceedings of Seminars / Symposia

Bind, M and Howden, M. 2004. Challenges and opportunities for cropping systems in a changing climate.Proceedings of International crop science congress. Brisbane –Australia. 26 September – 1 October2004. pp. 52-54

(www.cropscience 2004.com 03-11-2004)

Tables and Graphs : The data in tables should not be duplicated in graphs and vice versa. Mean data formain treatment effects should be presented with appropriate SE± and CD values wherever necessary.The 2 or 3 way tables should be furnished only if the results are consistent over years and aredistinguished to have consideration of significant practical value. SE± and CD values however,should be furnished in the tables for all interactions and should be explained in the results anddiscussion. The treatments should be mentioned atleast in short forms if they are lengthy, but notabbreviated as T1, T2 and T3 etc. The weights and measures should be given in the metric systemfollowing the latest units eg. kg ha-1, kg ha–1 cm, mg g-1, ds m-1, g m-3, C mol kg-1 etc.

Typing : The article should be typed in 12 pt font on A4 size paper leaving a margin of 5 cm on all sides.There should be a single line space between the rows in abstract and double line in rest. Verifythe manuscript thoroughly for errors before submitting it for publication.

Note : Latest issue of the Journal may be consulted. Further details can be obtained from the book“Editors style Manual, edn 4. American Institute of Biological Sciences, Washington DC”.

URL : http://www.angrau.ac.in/Publications.aspx

ESSENTIAL REQUIREMENTS FOR CONSIDERATION OF PUBLICATION OF ARTICLES

1. Research of not less than 2 years and of high standard will be considered as full length paper.If necessary, it will be considered for short communication.

2. Research of one year should be submitted in the style and format of short communication.3. The total number of pages should not exceed 10 for full paper and 5 pages for short communication

including tables and figures. The figures should be legible.4. Old research which terminated 5 years before the date of submission will not be considered.5. All the authors should subscribe for the Journal6. The manuscript should be submitted in triplicate as per the guidelines of the Journal to The Dean of

Post Graduate Studies and Editor, The Journal of Research ANGRAU, Administrative Office,Rajendranagar, Hyderabad – 500 030.

7. The manuscript should accompany the declaration certificate and subscription enrolment form.8. The authors should accept the editorial / referees comments until the quality of the paper is

improved.9. The revised manuscript should be submitted in duplicate along with a compact disk.

REVIEW PROCESS

The articles will be initially screened by the editors. It will be sent to an expert for peer reviewonly if it contains adequate original information and is prepared as per the guidelines. The author,then, may also be asked to revise it if the expert desires. After getting the article suitably revised andedited, it will be placed before the editor for a final decision. The accepted article will be finallychecked for language and grammar by the English editor before being sent to the press. The decisionhowever to publish the paper lies with the editor. Any article which is not able to meet the expectedstandard or is not prepared in conformity with guidelines will be rejected without assigning anyreason.

145

ESSENTIAL REQUIREMENTSFOR CONSIDERATION OF PUBLICATION OF ARTICLES

1. Research of not less than 2 years and of high standard will be considered as full length paper. Ifnecessary, it will be considered for short communication.

2. Research of one year should be submitted in the style and format of short communication.

3. The total number of pages should not exceed 10 for full paper and 5 pages for short communicationincluding tables and figures. The figures should be legible.

4. Previous research which terminated 5 years before the date of submission will not be considered.

5. All the authors should subscribe for the Journal

6. The manuscript should be submitted in duplicate as per the guidelines of the Journal to The Editor, theJournal of Research of Research ANGRAU, O/o Principal Agricultural Information Officer, AI&CC andANGRAU Press, ARI Campus, Rajendranagar, Hyderabad.

7. The manuscript should accompany the declaration certificate and subscription enrolment form.

8. The authors should accept the editorial / references comments until the quality of the article is im-proved.

9. The revised manuscript should be submitted in duplicate along with a compact disk.

10. DD may be drawn in favour of “Managing Editor, Journal of Research, ANGRAU” Payable at Hyderabad.

SUBSCRIPTION TARIFF

ANNUAL

Individual : Rs. 300/- author

Institution : Rs. 1200/-

LIFE

Individual (till retirement) : Rs. 1200/-

Reprints Charges : Rs. 100/- per page

1. Publications : The Editor - Journal of Research ANGRAU O/o Principal Agricultural InformationOfficer, AI&CC and ANGRAU Press, ARI Campus, Rajendranagar, Hyderabad.

2. Publications : The DD should be mailed to the Managing Editor - Journal of Research, ANGRAU- Press Agricultural Research Institute, Rajendranagar, Hyderabad - 500 030, A.P.

least in short forms if they are lengthy, but not abbreviated as T1, T2 and T3 etc. The weights andmeasures should be given in the metric system following the latest units eg. kg ha-1, kg ha-1 cm, mgg-1, ds m-1, g m-3, C mol kg-1 etc.

Typing : The article should be typed in 12pt font on A4 size paper leaving a margin of 10 cm on allsides. There should be a single line space between the rows in abstract and double line in rest.

Note : Latest issue of the Journal may be consulted. Further details can be obtained from the book“Editors style Manual, edn 4. American Institute of Biological Sciences, Washington DC”.

URL : http://www.angrau.ac.in/Publications.aspx

angrau.ac.in1)2013finalfor...2 the journal of research angrau (published quarterly in march, june,...

Documents