PRECISION FARMING APPROACHESTO SMALL-FARM AGRICULTRUE

Sakae ShibusawaFaculty of Agriculture,

Tokyo University of Agriculture and Technology3-5-8 Saiwai-Cho, Fuchu, Tokyo 183-8509, Japan

ABSTRACT

Precision farming implies a management strategy to increase productivity and economicreturns with a reduced impact on the environment. It is based on the application of informationtechnology to a description of variability in the field, variable-rate operations and the decision-making system. There are three technology levels and three strategies in the development ofprecision farming. Precision farming practices can be used on small farms as well as big ones,and they play a core role in rural development programs which are integrated with industry. Areal-time soil spectrophotometer was developed to describe soil variability in farmers' fields, to beused in precision farming.

Keywords: GPS, information level, precision farming, rural development, soil sensing

INTRODUCTION

Precison farming provides a new solutionusing a systems approach for today'sagricultural issues such as the need to balanceproductivity with environmental concerns. Itis based on advanced information technology.It includes describing and modeling variationin soils and plant species, and integratingagricultural practices to meet site-specificrequirements. It aims at increased economicreturns, as well as at reducing the energyinput and the environmental impact ofagriculture.

This Bulletin describes the concept ofprecision farming, and also its use in ruralareas, including those with small-scale farmswith diverse kinds of land use. A real-timesoil sensor developed in our laboratory is alsointroduced.

WHAT IS PRECISION FARMING?

The term "Precision Farming" or"Precision Agriculture" is capturing theimagination of many people concerned withthe production of food, feed, and fiber. Itoffers the promise of increasing productivity,

while decreasing production costs andminimizing the environmental impact offarming (NRC 1997, SKY-Farm 1999).

Elements of the technology

There are three fundamental elements inthis technology (Fig. 1) (Shibusawa 2000,2001).

Describing variability is the key concept.In particular, it is based on variation withineach field. Variability should be understoodin at least three aspects: spatial, temporal andpredictive.

Variable-rate technology (VRT) is usedto adjust the agricultural inputs according tothe site-specific requirements in each part ofthe field. If machines are used, this requiresvariable-rate machinery. On small farms,inputs can be applied manually. Variable-rateapplications need:

l Correct positioning in the field;l Correct information at the location; andl Timely operations at the site concerned.

Decision support systems offer a rangeof choices to farmers with respect to trade-offproblems where conflicting demands must betaken into account, such as productivity and

1

2

Fig. 1. How precision farming works

Needs ofmarkets andconsumers

Models for crop managementTraditional knowledge and wisdom

Refining of databaseManagementSoil info, crop info

EvaluationSafety+environment+profitability

Decision-makingVariable-rate technology

Decision support system

Mapping the

variability

Positioning + sensing

Rural distinction

protection of the environment. This approachhelps to optimize the whole farming system.

Information levels

There are four levels or stages in thequality of information. The lowest level isdata, followed by information, knowledge, andfinally wisdom (Fig. 2) (Shibusawa 1999,2000). The "data-stage" means a mass ofsignals and numerical values, which have nopractical value in themselves. The "infor-mation-stage" provides some meaning from a setof data, such as levels of excessive, appropriate ordeficient fertilizer use. The "knowledge-stage"implies that the information is individualized insome logical way which can enable someone tomake a decision, such as application guidelines.The "wisdom-stage" belongs to an individual whofinds an original solution, such as the approachof the skilled farmer.

Information technology tends to bepowerful in levels up to the knowledge-stage.The wisdom-stage requires the intellectual andcreative activities of farmers and researchers,if there is to be a break-through inaccumulated knowledge. Precision farmingneeds all stages of information in theagricultural production system, and also

requires good linkage between the stages. Inparticular, information technology should beclosely linked to farmers.

Scenarios

Developing system technology forprecision farming is shown in Fig. 3(Shibusawa 1999, 2000). First of all, weneed to describe and understand the variabilitywithin and between fields. Field sensors withGPS and monitors for machine applicationmake this easier. The next stage is todevelop machines which can be operated byremote control.

There are three steps in technologydevelopment, and three strategies for precisionfarming, as shown in Fig. 4 (Shibusawa 1999,2000). Step 1 is based on conventionalfarming technology, with intensivemechanization to reduce the labor input. Step2 involves the development of mappingtechniques, VRT machines, and introductoryDSS on the basis of information technology.Step 3 implies the maturity of wisdom-oriented technologies.

Scenario 1 is based on a "high-input andhigh-output" conventional strategy. Scenario 2has a strategy for "low-input but constant-

3

Fig. 2. Level of information

Informationtechnology

Artificialintelligence

Creativesolutions

Data

Wisdom

Knowledge

Information

↓

↓

↓

↓

!

!

Describe variability# Costs & yields# Soil & plants# Pests & blights# Environment# Management

Understand variability# Spatial# Temporal# Prediction

Fig. 3. Development of precision farming technologies

Mechanization (safety, environment)# Navigation & control# Variable rate operation# Management of IT networks

Optimization algorithms# Yield model# Crop management model# Environment impact model# Cost management model# Scheduling of farm work# Decision support system

GPS + sensorsMonitoring theapplication of inputs

output", and Scenario 3 aims at "optimizedinput-output" as the goal of precision farming.Advanced technology levels allow us tochoose freely between these three scenarios.Effective regulations will encourage progressin precision farming.

In Japan, technology at the moment is atthe level between Step 1 and 2, while thefarming strategy is changing from Scenario 1to 2. The shift from Step 2 to 3 involves adrastic change in the farming system. In themature stage of Step 2, each field hasinformation added which makes possible thebest management of that field. In Step 3, allfactors of the farming system are well-organized for precision farming. This allowsus to manage regional variability, as well asthe local variability within a single field.

What can precision farming achieve?

In general, a farming system involvesfive factors (Shibusawa 1999, 2000). Theseare: plant variety, field features, technology,regional infrastructure, and the motivation/intentions of the farmer. Better integration ofthese five factors can creat a competitivefarming system which suits local conditions.

Precision farming uses field maps,

variable-rate technologies and a decisionsupport system. As shown in Fig. 5,generating the field maps is in itself animportant source of information. Variable-ratetechnology not only increases productivity byre-organizing the three factors of technology,plants and fields, but also creates a betterlinkage with the regional infrastructure, e.g. byfollowing environmental regulations. Adecision support system provides the besttechnology, taking into account the aims andmotivation of farmers as well asenvironmental factors. In other words,precision farming brings about an innovationin the whole system of agriculture.

PRECISION FARMING FORSMALL-SCALE FARMS

Whether precision farming is feasible forsmall-scale farms is a leading issue foragricultural scientists and politicians in Japan.It should be noted that precision farming ischaracterized by variable management. A keypoint in precision farming is understandingvariability in the field.

There are at least two types ofvariability. One is within-field variability, theother is between-field or regional variability.

Fig. 4. Development of technology level and farming strategies

Step 1: MechanizationLabor saving/intensive

Step 3: WisdomFree scale PF

Step 2: IT-orientedMapping/VRT/DSS

Scenario 1: High-inputHigh-output,environmental concerns

Scenario 1: High-inputHigh-output/efficiencyEnvironmental concerns

Scenario 1: High-inputHigh-output & efficiencyEnvironmental concerns

Scenario 2: Low-inputOutput upkeep/efficiencyReduce environmentalimpact

Scenario 2: Low-inputAdaptive-outputLower impact

Sensing/monitoringInformation networksIntelligent machineryRegulations

Technology innovation

Scenario 3: Optimizedinputs & outputsEnvironment-symbiosis

VRT machineryOptimization algorithmsFarming innovations

Now

4

Within-field variability focuses on a singlefield, and the one plant variety beingcultivated. Between-field variability considerseach field as a unit on a map.

We need to consider what kind ofvariability is involved when we considerprecision farming for small farms. Whetherfarms are large or small, precision farmingshould mean improved farm management. Itshould give a higher economic return with areduced environmental impact.

On a single small farm, the farmer canunderstand fairly well what is going on ineach field. This makes possible variable-rateapplications to meet site-specific requirements,using the farmer's knowledge and skills.When it comes to an area of a few dozenhectares, containing many small fields,precision farming has to coordinate diversetypes of land use and many farmers withdifferent motivations, as shown in Fig. 6.

Regional precision farming must managea hierarchy of variability: within-field,between-field and between-farmers. High-techapproaches, such as a yield meter with GPS,are available for regional precision farmingcovering many small farms. Moreover,measures to conserve or improve theenvironment should be undertaken on asimilar scale.

From the point of view of developmentin a rural area which includes small farmsand local companies, precision farming offers

the possibility of developing a new kind ofindustry, by fusing agriculture to various kindsof industrial activity (Fig. 7).

If the multi-functions of agriculture arere-evaluated using information-added fields,value-added space of this kind can be seen asproviding new resources, such as newbiological materials, open-air classrooms andgreen tourism.

REAL-TIME SOIL SENSING

In Japan, paddy fields occupy half ofthe agricultural area, producing about tenmillion metric tons of grain every year.Paddy rice production is a kind of hydroponicsystem, with well-organized irrigation anddrainage facilities. In Japan, it is also highlymechanized.

Paddy rice production is very productive,but environmental concerns have becomenational issues in Japan. The problem is howto manage precisely the paddy productionsystem while considering the environmentalimpact. Solving this problem requires a goodunderstanding of what is happening in thepaddy field.

Shonk et al. (1991) developed a portablesoil organic matter sensor with photodiodesusing a single wavelength. It gave goodresults in predicting soil organic matter in therange 1.5 - 6%. Sudduth and Hummel (1991)investigated the feasibility of spectral

Fig. 5. Precision farming technologies make innovations in the agricultural system

Variable management

Decision support

Farmer's motivation& other factors

EnvironmentField map

Variable-rate technology

Regional systemRegulation, etc.

Plant + field

Technology+plant+field

IT-oriented

ProductivityOptimization

5

6

Fig. 6. Regional precision farming (PF) for small farms

Land use diversity

Wheat

Land use diversity

Uniform land use

One farmer

Between-field variability

Group of farmers

Hierarchical variability

Vegetables

Residential

Rice

Small-farm PFLarge-farm PF

Similartech.

Fig. 7. Role of precision farming in regional development

Value-added productsRegion networks

Multi-functions of agriculture

Re-evaluated region space

New industriesLocal brand namesBio-materialsEducationHealth & wealthAmenities, etc.

Environmental aspectsLandscape amenities

New farming system

Information-added products" Impact on markets Distribution &

IT industries

Precision farming

Information-orientedfields

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reflectance to sense soil organic matter. Aportable NIR spectrophotometer was designedto evaluate soil organic matter, CEC andmoisture content in a ploughed soil at a depthof 3.5 - 5 cm (Sudduth and Hummel 1993a,b).This approach can be useful to getinformation about the field surface, but westill need in situ soil sensing in the zone ofroot development for practical use in cropmanagement. Shibusawa et al. (2000) havedeveloped a real-time soil spectrophotometerwith an RTK-GPS to sense underground soilparameters at depths of 15 - 40 cm.

The objective of this work was to usethe soil spectrophotometer to generate detailedsoil maps of the paddy field, for theimplementation of precision rice farming.

Soil spectrophotometer

The soil spectrophotometer used in thisstudy is shown in Fig. 8. It was designed tocollect data on soil reflectance at depths of 15to 40 cm. The sensor system was composedof three main units: the external housing, thesoil penetrator and probes, and the externalsensing and monitoring devices.

The penetrator tip with its flat edge cutsthe soil in a uniform way. The plane edgebehind it smooths the soil to produce auniform surface. Inside the housing are sevenmicro optical devices. Two optical fiberprobes, using light energy at wave-lengths of400 - 2400 nm, are used for illumination,giving an illuminated area of about 50 mmdiameter on the soil surface. Two additionaloptical fiber probes are used to collect soilreflectance in the visible and NIR ranges.One fiber bundle passes reflected energy inthe 400 - 900 nm wavelength range, whilethe other optical probe carries reflected energyin the 900 - 2400 nm wavelength range. Amicro CCD camera is adjusted to monitor a75-mm focus point on the soil surface.

The sensor unit's housing includes thecore devices of the system, such as a 150 Whalogen lamp, a spectrophotometer (Carl ZeissLtd.), a FA computer (IBM, PC/AT, PentiumMMX), a RTK-GPS (Trimble MS740)receiver, etc. The spectrophotometer has a256-channel linear photodiode array toquantify the reflected energy in the 400 - 900nm wavelength range. A 128 channel linearphotodiode array is used to quantify the

reflected energy in the 900 - 1700 nmwavelength range. Data scanning time is justover four microseconds. Integration ofscanned data is carried out for each individualscan to get average values.

A video data recorder on the tractordisplays images of the soil surface during theexperiment. The displayed images are used tomonitor operations in case of emergency, suchas blockages or obstacles. The images alsoprovide information about which data shouldbe omitted from data analysis. The liquidcrystal monitor serves as a touch controlpanel, and a mouse and keyboard are alsoavailable for accessing the FA computer.

Field tests and results

The experiment was conducted in a 0.5ha paddy field on the Experimental Farm ofKyoto University, Japan in December 1999.The soil texture of the fields was 47% sand,30% silt and 23% clay. The working speedwas about one kilometer per hour. Scanswere at approximately one-meter intervals. Ittook approximately 20 microseconds to do thescan, and three seconds to record the data.The spectrum data was collected at intervalsof about 5 m, which gave more than 800locations for soil reflectance data. Theworking depth was 200 - 250 mm.

For calibration purposes, 25 soil sampleswere collected at the same location and depthas the scanning points and analyzed in thelaboratory for moisture, organic matter content,nitrate (NO

3-N), pH and EC. Fifteen samples

were used for calibration, and the remainingten samples were used for validation. Astandard moisture content was obtained, bykeeping the samples 24 hours in an oven at110oC. Soil organic matter content wasevaluated as the loss after four hours ofcombustion in an oven at 800oC. NO

3-N, pH

and EC were analyzed in the clear layer atthe top of muddy water, using portable ionmeters. The test muddy water was providedby diluting 5 g dried soil with 25 g distilledwater, stirring the mixture for 30 minutes, andthen leaving it to stand for 24 hours.

For the spectral reflectance, four stageswere followed (Marten and Naes 1987). Thefirst stage was linearization with a Kubelka-Munk transform, while the second stage waselimination of optical interference in the

8

spectral data with a multiplicative scattercorrection. The third stage was to reduce thenumber of wavelengths used for calibrationwith correlation analysis, including derivativeoperation. The final stage was the calibrationstage using the stepwise multiple linearregression analysis with S-Plus Data AnalysisSoftware. The calibration model wasquantified using the standard error forcalibration (SEC), standard error for prediction(SEP) and coefficient of determination (R2).

Semivariance analysis was performedusing the GS+ Geostatistics Software, and soilmaps were obtained by the block krigingmethod.

Results of calibration and validationanalysis (Table 1) produced higher scores ofR2 and fewer errors for the respective soilparameters. The second derivatives of lightabsorption tended to provide best-fit predictionmodels (I Made Anom et al. 2001).

With the prediction models, values forsoil parameters were evaluated at 860locations in the field. The means andstandard deviations were 48.4% and 6.5% for

moisture content, 9.51% and 1.06% fororganic matter (OM) content, 42.1 mg/100gand 11.0 mg/100g for NO

3-N content, 6.83

and 0.39 for pH, and 173.1 µS cm and 69.6µS/cm for EC. Based on these values,semivariance analysis (Table 2) wasperformed. Within the experimental field, thesoil OM content had the lowest spatialcorrelation (29.20 m), followed by the NO

3-N

content (34.50 m), moisture content (38.60 m),the pH (40.40 m), and the EC (46.60 m).

With the results of the semivarianceanalysis, the soil parameter maps were thendeveloped (Fig. 9). The maps wereinterpolated by block kriging with 10-neighborhood interpolation. Errors of krigedto observed values were estimated over 40grids, each 10 m square. Error means andstandard deviations were -0.35 and 2.25 formoisture content, -0/01 and 0.46 for SOMcontent, 0.08 and 2.72 for NO

3-N content, -

0.04 and 0.22 for pH, and 3.31 and 12.26 forEC (I Made Anom et al. 2001).

The distribution of variability in soilparameters shows some stripes running east-

Table 1. Results of calibration and validation for soil parameter prediction

Parameters Treatment Calibration (n=15) Validation (n=10) WavelengthR2

cal SEC R2val SEP (nm)

Moisture (% db) 2nd deriv. 0.908 1.893 0.655 3.111 606,1329,1499SOM (%) 2nd deriv. 0.948 0.259 0.647 0.559 606,1311,1238NO3-N(mg/100g) 2nd deriv. 0.803 3.699 0.539 4.741 589,1014pH 1st deriv. 0.714 0.101 0.541 0.127 957,1214EC(µS/cm) 2nd deriv. 0.735 23.975 0.650 41.718 456,984,1014

Table 2. Summary of semivariance analysis

Semivariance factorsNugget Sill Range Proportion R2 RSS

Parameters Model fit Co Co+C Ao or 3Ao C/(Co+C)

Moisture Spherical 16.20 47.64 38.60 0.66 0.95 34.74SOM Spherical 0.58 1.19 29.20 0.51 0.93 0.01NO3-N Spherical 47.50 136.20 34.50 0.65 0.94 302.40pH Spherical 0.06 0.174 40.40 0.67 0.99 1.29E-04EC Spherical 2474 5562 46.60 0.56 0.93 586760

9

Fig. 8. Revised soil spectrophotometer

Penetrator &probes

GPS antennaSensor units

Encoder

Potentio-meter

Optical fibers

VDRPhotometerVis & NIR

Touch panel FA computer

Halogen lamp

Fig. 9. Soil parameter maps of a 0.5ha paddy field using soil reflec-tance collected by the real-timesoil spectrophotometer. 860 datapoints at depths of 200 to 250mm depth.

(c) NO3-N

(a) Moisture

(d) pH

(e) EC

(b) SOM

50.0

40.0

30.020.0

10.00.0

0.0 20.0 40.0 60.0 80.0 100.0

Nor

th 68.065.062.049.046.043.040.037.0

East

0.0 20.0 40.0 60.0 80.0 100.0

East (m)

50.0

30.040.0

20.0

10.0

50.0

59.553.547.541.536.529.523.517.5

0.0

Nor

th

(m)

0.0 20.0 40.0 60.0 80.0 100.0

East (m)

20.0

30.040.0

10.0

0.0

Nor

th

(m)

11.010.510.09.59.08.58.07.5

0.0 20.0 40.0 60.0 80.0 100.0

0.0 20.0 40.0 60.0 80.0 100.0

East (m)

East (m)

Nor

thN

orth

40.0

20.0

10.0

30.0

50.0

0.0

7.67.47.27.06.86.66.46.2

50.0

30.020.0

40.0

10.0

0.0

260.0230.0200.0170.0140.0110.080.050.0

10

west. For example, there is a belt with ahigh moisture content and a high OM contentin the eastern part. Other belts have a highNO

3-N, a high EC or a low pH. The

irrigation inlet was located at the north-west,and the drainage gate at the south-east. Thismay have produced the striped effect, sincewater flowed from north to south.

CONCLUSION

Precision farming implies a managementstrategy to increase productivity and economicreturns with an reduced impact on theenvironment, by taking into account thevariability within and between fields.Variability description, variable-rate technologyand decision support systems are the keytechnologies for precision farming. Precisionfarming on a regional level is one way toapply this approach to small-farm agriculture.It may not only improve farm management,but may also promote the development ofrural areas.

A real-time soil spectrophotometer willbe commercially available in a few years.

REFERENCES

I Made Anom, S.W., S. Shibusawa, A. Sasao,S. Hirako. 2001. Parameters Maps inPaddy Field Using the Real-time SoilSpectrophotometer. Journal of theJapanese Society of AgriculturalMachinery, 63, 3: 51-58.

National Researach Council (NRC) (1997).Precision Agriculture in the 21st Century.(Committee on Assessing Crop Yield:Site-Specific Farming, InformationSystems, and Research Opportunities),National Academy Press, Washington DC.,USA, pp. 149.

Marten, H., T. Naes. 1987. Multivariatecalibration by data compression. Near-infrared Technology in the Agriculturaland Food Industries, P. Williams and K.Norris (Eds.). American Associatiion ofCereal Chemists, Inc. St. Paul, Minnesota,USA, pp. 57-87.

Shibusawa, S. 1999. Environmentally-friendly agriculture and mechanizationtrend in Japan: Prospects of precisionfarming in Japan. Proc. InternationalSymposium on Farm Mechanization for

Environmentally-Friendly Agriculture,Seoul, Korea. The Korean Society forAgricultural Machinery, pp. 53-80.

Shibusawa, S. 2000. Precision farmingApproaches for model and control.Proceedings of the XIV Memorial CIGRWorld Congress, Nov. 28-Dec. 1,Tsukuba, Japan, pp. 133-141.

Shibusawa, S. 2001. Precision farming:Approaches for small-scale farms. Proc.2nd IFAC-CIGR Workshop on IntelligentControl for Agricultural Applications, 22-24 August, 2001, Bali, Indonesia, pp. 22-27.

Shibusawa, S., A. Sasao, S. Sakai. 1999.Local variability of nitrate nitrogen in asmall field. Precision Agriculture '99,Proc. 2nd Euro. Conf. PrecisionAgriculture, J.V. Stafford (Ed.). UK, pp.377-386.

Shibusawa, S., H. Sato, S. Hirako, A. Otomo,A. Sasao. 2000. A revised soil spectro-photometer. Proc. 2nd IFAC/CIGRInternational Workshop on BIO-ROBOTICSII, Nov. 25-26, Osaka, Japan, pp. 225-230.

Shibusawa, S. et al. 2001. Soil mappingusing the real-time soil spectrophotometer.Proc. (on CD-ROM) 3rd EuropeanConference on Precision Agricultaure,Montpellier, France, June 18-20, 2001.

Shonk, J.L., L.D. Gaultney, D.G. Schulze,G.E. Van Scoyoc. 1991. Spectroscopicsensing of soil organic matter content.Transactions of the American Society ofAgricultural Engineers. 34, 5: 1978-1984.

SKY-farm. 1999. Opportunities for precisionframing in Europe: Updated report 1999.126 pp.

Sudduth, K.A., J.W. Hummel. 1991.Evaluations of reflectance methods for soilorganic matter sensing. Transactions ofthe American Society of AgriculturalEngineers. 34, 4: 1900-1909.

Sudduth, K.A., J.W. Hummel. 1993a.Portable, near-infrared spectrophotometerfor rapid soil analysis. Transactions ofthe American Society of AgriculturalEngineers. 36, 1: 185-193.

Sudduth, K.A., J.W. Hummel. 1993b. Soilorganic matter, CEC, and moisture sensingwith a portable NIR spectrophotometer.Transactions of the American Society ofAgricultural Engineers. 36, 6: 1571-1582.