3° simpósio internacional de agricultura de precisão...3 6 9 12 15 18 21 24 27 30 33 3 to 6 6 to...

3° Simpósio Internacional de Agricultura de Precisão

16 a 18 de agosto de 2005 - Embrapa Milho e Sorgo - Sete Lagoas, MGwww.cnpms.embrapa.br/siap2005 / e-mail: [email protected]

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PRECISION FARMING FOR CEREAL CROPS – MANAGEMENT GUIDELINES

R J Godwin, G.A.Wood, J.C.Taylor

Cranfield University at Silsoe, Silsoe, Bedford MK45 4DT, UK

Abstract.

The results of a 6 year study to develop practical management strategies for spatiallydeveloping nitrogen are reported. The results show that there are significant advantages fromspatially varying nitrogen application rate in “real time” using the crop density of winterwheat in the spring period. Average economic benefits of £22/ha have been found togetherwith a one third reduction in the residual nitrogen. From these results a strategy has beendeveloped to aid farmers in varying the application rate of nitrogen given the crop status atselected growth stages. Yield maps where shown to be of little value for deciding upon thenext seasons nitrogen application; there are however, valuable indicators for thereplenishment of phosphorous and potassium. An economic analysis shows that benefitsfrom precision farming are attainable for relatively small areas of land but the critical factor isthe inherent field variability and the potential improvements to yield that are possible.

Keywords. Precision farming, nitrogen, fertilizer, cereals, NDVI, yield maps, managementguidelines.

1. Introduction

Precision Farming is the term given to a method of crop management by which areas of landor crop within a field are managed with different levels of input in that field. The potentialbenefits are the economic margin from crop production may be increased by improvements inyield or a reduction in inputs, and the risk of environmental pollution from agrochemicalsapplied at levels greater than optimal can be reduced. These benefits are excellent examples ofwhere both economic and environmental considerations are working together.

This paper provides an overview of a 6-year study, with the aim of developing practicalguidelines for the variable spatial application of nitrogen fertilizer. These have been fullyreported elsewhere Godwin et al (2002 a and b) and in the Special Edition of BiosystemsEngineering 84, (4), (2003) which contains a series of papers by the authors and theirassociates on a program of work funded by Home Grown Cereals Authority, AGCO andHydro Agri (now Yarra). To this, recent work on variable fungicide application has beenadded together with the results of active reflectance methods for assessing crop variability.

The duration of the study extended between 1995-2000 in the fields detailed in Table 1, whichwere selected to provide a range of soils typical of approximately 30% of the land used for



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arable production in England and Wales. These fields had predominantly been in cereals forseveral years prior to the experimental work

Table 1: Field details and location

Field name Location Soil Series* CroppingPattern

Far Sweetbrier Old Warden, Bedfordshire Hanslope Winter Wheat/Oilseed Rape

Onion Field Houghton Conquest,Bedfordshire

Denchworth/Oxpasture/Evesham

ContinuousWinter Wheat

Trent Field Goodworth Clatford,Hampshire

Andover / Panholes ContinuousWinter Barley

Twelve Acres Hatherop, Gloucestershire Sherborne / Moreton /Didmarton

ContinuousWinter Wheat

Far Highlands Old Warden, Bedfordshire Wickham / Evesham Winter Wheat

*after: Jarvis et al. (1984) and Hodge et al. (1984)

At the outset it was agreed that the reasons for any underlying field variation needed to beestablished prior to managing the crop in a spatially variable manner. Hence, uniform'blanket' treatments were applied in the 1995/6/7 seasons. Yield maps for these seasons,provided an indication of crop yield variation both in space and time. The effects of variableinputs were studied on all fields for the final three seasons.

A number of fields planted with uniform seed rate were subjected to variable inputs ofnitrogen. An additional two fields, Onion Field and Far Highlands, had variable nitrogeninputs applied across a range of seed rates that had been sown to create different crop canopystructures.

Over the past decade the technology has become commercially available to enable the farmerto both spatially record the yield from a field and vary both seed and fertiliser rates on a site-specific basis. Significant advances have also been made (Miller & Paice, 1998) to permit thespatial control of weeds on a site-specific basis by varying the dose rate of herbicidesdepending upon the weed density. However, the benefits of either an increase in yield and/ora reduction in fertilisers and agrochemicals have to be offset against the costs of investing inspecialist equipment to enable yield maps to be produced and variable applications to beimplemented.

A range of potential benefits has been reported, from various combinations of differentvariable application rate practices. Earl et al. (1996) postulated a potential benefit of



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£33.68 ha-1 could be possible combining variable nitrogen application and targetingsubsoiling to headlands for a crop of wheat in the UK, when wheat prices were £125 t-1.

Measured benefits in the range of - £11.14 to £74.09 ha-1 (-$6.37 to $42.38 ac-1) were reportedby Snyder et al. (1998) on irrigated maize in the USA. Schmerler & Basten (1999) measuredan average benefit of £38.60 ha-1 (60 DM ha-1) when growing wheat on a farm scale trialwhere both seed and agrochemical rates were varied.

Studies conducted by James et al. (2000) investigated the benefits of using historic yield dataas a guide to varying nitrogen application, for winter barley on a field with both clay loam andsandy loam soil types. Data from the 1997 harvest reported by Godwin et al. (1999) indicatedthat a modest benefit of could be possible.

Earl et al. (1996) estimated the costs of yield map production and the ability to apply fertiliseron a site-specific basis to be £10.46 ha-1 for an arable area of 250 ha, at 7% interest rateamortised over a 5-year period in the UK. Studies in the USA, by Snyder et al. (1998)estimated the cost of yield mapping and variable rate equipment, for nitrogen application, fortwo fields of 49 and 64 ha as £8.50 ha-1 ($11.88 ha-1). Schmerler and Basten (1999) reportedcosts of £15.46 ha-1 (49 DM ha-1) for a 7,100 ha German farm. The major reason for thehigher figures was the cost of the equipment to variably apply herbicides in addition to theseed rate and fertiliser.

This paper:

(i) reports on the results obtained from a series of agronomic studies.

(ii) examines the increase in revenues that have been achieved through the use ofprecision farming practices during a three-year study of 5 fields in cereal production inSouthern England (Godwin et al., 2002 a and b, 2003).

(iii) estimates of the costs of upgrading farm equipment, at the time of purchase, to a levelthat enables precision farming techniques to be practised.

(iv) compares the costs/benefits and analyses the potential returns from adopting precisionfarming technology for given farm sizes and levels of variability, and demonstrates thehow data has been made available to farmers to decide if adopting precision iseconomically viable for their farm.



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2. Determining the Inherent Variability

2.1 Crop yield

Typical variations in crop yield are presented in Figure 1, which shows that there is somesimilarity over the three-year period. The spatial trend map or average yield map (Blackmore,2000) for the period shows that, the yield range for this particular field is in excess of ±20%of the mean. The higher yielding zones occur to the west and the lower yielding zones to theeast of the 100% (or mean) contour. The variation in yield for the 4 main fields averagedbetween ±25 percent of the mean yield with a range of ±20% to ±33%.

80

90

100

110

120

Yield(% of grand mean)

1995

1997

1996

High Ave.Low

Fig. 1. Average yield map for yield at Trent Field, 1995 – 1997

2.2 Soil types

The fields were initially surveyed at a commercial detail level of approximately 1 augerhole/ha to provide an overview of soil textural and profile variation. These werecomplemented by "targeted" profile pit descriptions. The location of the profile pits wereselected to encompass

(i) the range of yields observed in the yield maps of 1994/95 and 1995/96,

(ii) the density of the crop from aerial digital photography captured in May 1996, and

(iii) soil maps based upon auger sampling at 100 m grid spacing.

Further studies with both soil coring apparatus (to a depth of 1 m) and electromagneticinduction (EMI) equipment increased the resolution to define soil textural boundaries (Jameset al, 2003). The latter technique is particularly useful for differentiating between soiltextures as shown in Figure 2, where the higher levels of conductivity indicate higher



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moisture content soils which if conducted at field capacity would indicate greater claycontent.

Objective techniques, using cluster analysis, have been developed which enable potentialmanagement zones to be determined using historic yield and EMI data (Taylor et al. 2003).Differences in soil nutrient levels have been identified between the management zones and,hence, form a basis for targeted sampling of soil nutrient status.

436850 436950 437050 437150 437250 437350 437450Easting (m)

140200

140250

140300

140350

140400

140450

140500

140550

140600

140650

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thin

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6

9

12

15

18

21

24

27

30

33

3 to 6 6 to 8 8 to 11 11 to 14 14 to 50

EMI ApparentSoil ConductivitymS/m

Fig. 2. EMI conductivity Trent Field 2nd February 1999

2.3 Soil fertility and crop nutrition

Detailed analyses of macro- and micro-nutrients in both soil water extract and plant tissuewere conducted at approximately 50 m grid spacing together with soil pH. These indicatedthat there was variation in nutrient levels in each of the fields (Taylor et al; 2003). However,with the exception of isolated areas with low pH, the levels were above the commonlyaccepted limits.

2.4 Crop canopy

Variations in crop canopy occur both in space and time in the same field. In order to obtainconsistent and reliable data for monitoring crop development for 'real time' management andto explain field differences, a light aircraft was equipped with two digital cameras fitted withred (R) and near infra red (NIR) filters (Wood et al, 2003). Field images obtained from aerialdigital photography (ADP) from a height of 1000 m give a pixel resolution of 0.5 m x 0.5 m.



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Normalised Difference Vegetation Index (NDVI) values were estimated from the followingequation:

RNIRRNIR

NDVI+−

=

The resulting images, such as Figure 3, show the effect of variations in crop developmentimmediately prior to the first application of nitrogen. These images are (i) immediatelyvaluable in discerning patterns of field variability, and (ii) provide detailed spatial data oncrop tillers/shoot density. These data, when calibrated against detailed agronomicmeasurements at targeted locations, were used in near "real time" to estimate crop conditionand potential nutritional requirements. The extension of this principle to farm scaleoperations, using 8 sampling points, provides an effective calibration between the cropindicators and NDVI. The cost of this technique, for 3 flights/year, has been estimated at£7/ha (Godwin et al., 2002b and 2003b).

Shoot Density (shoots m-2)

> 1000

800 - 1000

600 - 800

< 600

Fig. 3. Normalised Difference Vegetation Index (NDVI) image of Trent Field



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3. Variable Application of Nitrogen

3.1 Field layout

The main aim of this work was to develop an experimental methodology that could beemployed by farmers to determine an optimal application strategy for a given input in anyparticular field. To achieve this, it was important to use standard farm machinery for theexperiments.

The proposed design comprised a series of long strips, which ran through the main areas ofvariation within each field, an example of which is presented in Figure 4, where the treatmentstrip is interlaced with the field standard (Welsh et al., 2003a, b). The treatment strips werehalf the width of a tramline, nominally 12m. The fertilizer was applied using a pneumatic orliquid fertiliser applicator. These strip widths allowed the experiments to be harvested by thecombine harvester without harvesting the zones affected by the tramline wheel marks. Thecombine was equipped with a yield sensor, with a mean instantaneous grain flow error of 1%(Moore 1998).

3.2 Historic yield and shoot density studies

These treatment strips were established to test the following strategies:

(i) increasing the fertiliser application to the higher, or potentially higher, yielding partsof the field whilst reducing the application to the lower yielding parts.

(ii) reducing the fertiliser application to the higher, or potentially higher, yielding parts ofthe field whilst increasing the application to the lower yielding parts.

However, before these strategies could be implemented, the high, average and low yieldingzones had to be identified. Two methods were used: (i) historic yield data, as shown in Figure2.and (ii) shoot density data, estimated from NDVI data, as shown in Figure 3.

Using this approach, experimental strips (Figure 4) were established to give the followingtreatments:

Historic Yield 1 (HY1). High yield zone received 30% more nitrogen; average yield zonereceived the standard nitrogen rate; and the low yield zone received 30% less nitrogen.

Shoot Density 1 (SD1). High shoot density zone received 30% more nitrogen; average shootdensity zone received the standard nitrogen rate; and the low shoot density zone received 30%less nitrogen.

Historic Yield 2 (HY2). High yield zone received 30% less nitrogen; average yield zonereceived the standard nitrogen rate; and the low yield zone received 30% more nitrogen.

Shoot Density 2 (SD2). High shoot density zone received 30% less nitrogen; average shootdensity zone received the standard nitrogen rate; and the low shoot density zone received 30%more nitrogen.



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Standard N rate strips were located adjacent to each of the variable treatment strips to allowtreatment comparisons to be made, since classical experimental design and statistical analyseswith replicated plots was not possible.

Sta

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Y1)

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(S

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12 m

Var

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Low Yield

High Yield

UniformTreatments

VariableTreatments

Fig. 4. Plan of field experiments

3.3 Crop canopy management studies

The methodology for these studies was developed over three years in Onion Field, but wasextended to include Far Highlands in the final season. Seed rates of 150, 250, 350 or450 seeds/m2 were used to establish 24 m wide strips of wheat with a range of initial cropstructures. The strips were then subdivided into two 12 m wide sections, along which onereceived a standard field rate of nitrogen fertiliser (200 kg N/ha), and the other a variableamount dependant upon crop growth. Observations were made in near “real time” using theaerial digital photographic technique and crop canopy measurements described earlier.Appropriate flights were made prior to each of the three nitrogen application timings in theFebruary to May period, and crop growth (shoot populations at tillering and canopy greenGS30-31 and GS33) compared with benchmarks from the HGCA Wheat Growth Guide(1998).



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4. Results

4.1 Historic yield and shoot density studies

An example of the yield distribution along the variable treatment yield strips is presented inFigure 5 for the HY1 and HY2 strategies. The effect of increasing (160 kg N ha-1) anddecreasing (90 kg N ha-1) the nitrogen application rates to the high and low yielding zones incomparison with the field standards can be clearly seen. This shows that for Trent Field in1997/98 there were advantages of adding fertiliser to both the high and low yielding zonesand penalties for reducing the rate. The results in Table 2 indicate that there are no economicbenefits from HY1 and HY2 in Trent Field or Twelve Acres. The reason for this is due to thereduction in nitrogen application rate causing a significant yield loss in both the high and lowyielding zones, which are not compensated for by savings in nitrogen costs.

4

6

8

10

0 50 100 150 200 250 300

Distance along strip (m)

Yie

ld (

t ha

-1)

HY1StandardHY2

160

160

9090

125

125All 125

High yieldzone

Ave. yieldzone

Low yieldzone

Fig. 5. Combine yield of ‘Historic Yield’ treatments (HY1 & HY2) compared with astandard application along the treatment strips in Trent Field.

Shaded areas are transition zones and are deleted from the analysis (after Welsh, 2003a)



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Table 2: Economic consequences (£/ha) of 3 years of alternative nitrogen managementscenarios for all fields in comparison to a standard application rate

Strategy Trent Field Twelve Acres Far Sweetbrier Mean

HY1 -5.41 -21.23 -7.80 -11.48

HY2 -12.56 -21.88 5.85* -9.53*

SD1 4.98 -15.38 -13.00 -7.80

SD2 0.43 -15.17 33.58 6.28

*contains data from 1998/99 and 1999/00 only

Managing the crop using maps of the relative shoot density from NDVI data provided apositive benefit when more nitrogen was applied to the areas of low shoot density, and less tothe high density areas (SD2). The success of this, however, depended on the actual shootpopulations present, which differed between seasons. This occurred because there was littlevariation along the strip with a low shoot density, which from hindsight using the principle ofcanopy management would respond best to a uniform application of nitrogen.

Overall, the shoot density SD2 approach which uses a real-time assessment of the cropcanopy/ structure to control the nitrogen requirement appeared to offer the greatest potentialfor crop production. Nitrogen strategies based on historic yield maps (HY1 and HY2)showed no or very little benefit. Yield maps are, however, a valuable tool for:

(i) the replenishment of potassium and phosphorous removed by the previous crop, andidentifying the size of the zones needing particular attention from other field factors

(ii) identifying the size of the zones needing particular attention from factors such as theimpact of water-logging, pH and uneven fertilizer application.

4.2 Canopy management studies

The results presented in Table 3 are a comparison of both the yield and the economic (GrossMargin) performance of the variable and uniform nitrogen strips. Also shown are the mean ofthe variable nitrogen application rate and the uniform rate for both fields.

These show that regardless of seed rate in Onion Field both the yield and the gross marginsfor the variable nitrogen strategy exceeded those for the uniform practice. The similar datafrom Far Highlands show yield benefits at the lowest seed rate only. The other 3 seed ratesshow a small reduction in yield, which was economically compensated for by lower nitrogenapplication.



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Table 3 Nitrogen application rates (N) (kg N ha-1), Yield (Y) (t ha-1) and gross margin(GM) (£ ha-1) comparisons between variable and uniform nitrogen applicationstrategies (after Wood et al., 2000b)

Target Seed Rate (seeds m-2)

150 250 350 450

Plant population (plants m-2)

Onion field 100 143 177 200

N Y GM N Y GM N Y GM N Y GM

Variable N 243 6.31 366 227 7.24 432 188 7.23 434 192 7.47 441

Uniform N 200 5.92 349 200 6.63 394 200 6.87 403 200 6.69 381

Difference 43 0.37 17 27 0.53 38 -12 0.48 31 -8 0.75 60

Plant population (plants m-2)

FarHighlands

120 195 240 320

N Y GM N Y GM N Y GM N Y GM

Uniform N 197 8.24 437 189 7.77 397 135 7.79 406 144 7.77 391

Standard N 200 7.94 417 200 7.85 398 200 8.11 404 200 7.93 381

Difference -3 0.30 20 -11 0.08 -1 -65 0.32 2 -56 0.16 10

The financial benefits showed that in seven of the eight comparisons the variable Nmanagement out performed the uniform application. The maximum advantage to variable Nmanagement was £60/ha that was produced from a combination of higher yield (+11%) and aslightly lower total N input compared to the standard N approach.

Overall yield benefits were greatest where the mean application rate of the variable nitrogenstrips was approximately that of the field standard. On average, for the two fields, the overallbenefit of the variable nitrogen strategy was £22 ha-1. An analysis of the “responsive areas” tovariable nitrogen in both the shoot density and canopy management studies indicate thatbetween 12% and 52% of all fields responded positively and depending upon field and season

5. Environmental Implications

Whilst this work did not specifically address environmental implications of nitrogen usagepatterns was possible to draw some conclusions on the possible impact of precision farmingon the nitrogen balance. This was achieved by calculating the potential off-take of nitrogen inthe variable treatment compared to the standard from the strip mean grain yields, the average



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fertiliser N application rates, and the grain and straw nitrogen contents by assuming the strawyield was equal to 65% of grain yield.

The plant populations in Onion Field were generally low and in the lowest seed rate (whichproduced only 100 plants/m2) both the uniform and variable nitrogen programmes hadnitrogen off-takes which were significantly lower than the amount applied. This resulted in asurplus at the end of the season, see Figure 6.

Fig. 6: Surplus or deficit of applied nitrogen relative to off-take in grain and strawat Onion Field in 2000 (after Wood et al., 2002b)

However at the three higher plant populations the off-take from the variable N applicationswere higher than applied N resulting in a net reduction in N balances. Averaged over the fourseed rates, the N surplus for the variable treatments was 18.5kg/ha compared to 28kg/ha forthe uniform treatments. This represents a 34% reduction in the net amount added to the soilfrom the uniform application and this could have considerable environmental significance.

A similar analysis was conducted for Far Highlands by assuming the grain and straw nitrogencontents were similar to Onion Field, in this case the average saving from the variable Ntreatments compared to the uniform N treatments was 32.5kg/ha.

6. Potential benefits from adopting precision farming

The potential benefits considered in this paper arise from managing crop canopy in real timeby varying nitrogen in two fields, growing winter wheat (Wood et al., 2003b). Thedifference in the economic performance of variable and standard nitrogen application for arange of seed rates is summarised in Table 1.

Table 4 shows that the benefits of variably applying nitrogen in comparison with uniformapplication, based on the standard farm practice, range from -£1 ha-1 (Far Highlands @ 195plants m-2) up to £60 ha-1 (Onion Field @ 200 plants m-2). The mean benefit over all seed

4 6

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S e e d R a te [P la n t p o p u la tio n ]

(s e e d s m - 2 [p la n ts m - 2 ] )

N d

efic

it

N

su

rplu

s kg

ha-1 U ni fo rm N

V a r ia b le NB a y e s L s d @ 9 5 % c o n f id e n c e le v e l



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rates in Onion Field was £36.50 ha-1. The overall mean improvement in Onion Field and FarHighlands was £22 ha-1, marginally less than reported in Godwin el al. (1999).

Table 4 Economic comparisons of variable and uniform nitrogen application rates

Target Seed Rate (seeds m-2)

150 250 350 450

Establishment (plants m-2)

Onion Field 100 143 177 200

Gross margin £ ha-1

Variable N 366 432 434 441

Uniform N 349 394 403 381

Difference 17 38 31 60

Establishment (plants m-2)

Far Highlands 120 195 240 320

Gross margin £ ha-1

Variable N 437 397 406 391

Uniform N 417 398 404 381

Difference 20 -1 2 10

In addition to the benefits already mentioned, other factors listed in Table 5 need also to beconsidered.

Table 5 Other economic considerations

Factor Implication Penalty or Benefit

Water-logging Economic penalty Up to £195 ha-1

Fertiliser application errors Economic penalty Up to £65 ha-1

pH Economic advantage Up to £7 ha–1

Herbicide application* Economic advantage Up to £20 ha-1 *

Fungicide application** Economic advantage Up to £22 ha-1 **

* after Rew et al. (1997), ** after Secher (1997)



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During the course of this project three examples of the above additional benefits of precisionfarming were recorded. The cost of water logging, shown as up to £195 ha-1, was calculated

using a potential yield reduction of 3 t ha-1, experienced at one of the trial sites following awet period in the winter of 1998/99 (Wood et al., 2000). This could have been rectified for aone off cost of £50 ha-1 (Nix, 2000) for mole draining the site and clearing blocked drainoutlets that could have an economic life in excess of 5 years. Uneven distribution of fertiliserresulted in a yield penalty of up to 1 t ha-1. The cost of failing to rectify problems involvingpH levels was estimated to be up to £7 ha-1.

The collection of these data using yield-mapping techniques enables simple cost/benefitanalyses to be conducted to ascertain the scale and extent of the problem(s), from whichestimates of the cost of correction can be made to compare with the potential long-termbenefits.

Economic benefits resulting from the site-specific control of herbicide (Rew et al., 1997 andPerry et al., 2001) and fungicide (Secher, 1997) application are included in Table 5. Thereported cost savings for herbicides range between £0.50 and £20.70 ha-1, and were achievedby targeted application using patch-spraying techniques. A statistically significant yieldincrease of 0.3 t ha-1, equivalent to a revenue increase of £21.67 ha-1, has been achieved byvarying fungicide application rate according to crop canopy density. Albeit a more recentstudy by the authors in Bedfordshire, Hampshire and Lincolnshire showed that, whilst therewere regional differences in the desired dose rates, when using remote sensors to apply moreor less fungicide according to estimates of within field variability of canopy density, thisproved of little agronomic value and as a result no economic worth.

It can be seen from Table 5 that the potential economic penalties of normal field managementproblems can outweigh the highest increase in benefit achieved from spatially varyingnitrogen fertiliser. It is, therefore, imperative these problems are addressed prior toconsidering the use of spatially varying nitrogen.

7. Estimation of the costs of precision farming systems

7.1 Precision farming monitoring and control systems

A full precision farming (PF) system comprises hardware and software to enable variations incrop yield to be mapped and crop related treatments to be variably applied on a site-specificbasis. In reviewing the literature it is apparent the cost of practising precision farmingtechniques is dependent on:

(i) the level of technology purchased, i.e. a full or partial system,

(ii) depreciation and current interest rates, and

(iii) the area of crops managed.



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To determine the realistic cost for UK conditions an analysis was conducted, based on pricesquoted by main suppliers of precision farming equipment, in January 2001. It was apparentthat precision-farming systems could be categorized into four main classes.

Namely:

Class 1. Comprises a fully integrated system from an original equipment manufacturer.

Class 2. Comprises a full system from a specialist manufacturer.

Class 3. Comprises a full system, which is a combination of specialist and OEM.

Class 4. Comprises a basic system – from an OEM.

Most new combine harvesters can be fitted with yield mapping hardware, however, the degreeof integration between the yield mapping system and other components of the combineoperating and performance monitoring system varies between manufacturers. The systemsrange in functionality from fully integrated yield mapping and combine performancemonitoring systems, that can be removed from the combine and fitted to tractors or sprayersand include sub-metre DGPS (Class 1 at £11,363) through to low cost partial systems thatprovide full yield mapping functionality but reduced application rate control functions (Class4 at £4,500). The remaining two classes comprise, Class 2 (at £14,100) is a full precisionfarming system produced by specialist manufacturers, and Class 3 (at £16,150) is an additionof parts of Classes 1 and 2 which comprise an OEM integrated yield and combineperformance monitor with components from specialist manufacturers to be mounted in eithertractors or spray vehicles for variable application rate control. This has the added advantagethat the parallel systems enable both harvesting and application control to be undertaken at thesame time.

The basic system (Class 4 at £4,500) uses a non-differential GPS to provide positioninformation to +10 m. This provides the operator with the capability to produce yield maps ofa slightly lower resolution than those produced using full precision farming systems, butprobably sufficiently accurate for most management tasks. Variable application rates areachieved through changing the tractor forward speed whilst maintaining a constant materialflow from the applicator in use. The speed control is achieved by the operator manuallyattempting to match a target speed displayed on the on board vehicular computer screen. Thisprovides a limited range over which the application rate can be varied, dependant on thetractor transmission type, but does permit farmers to make initial ventures into precisionfarming management without a large capital outlay.

7.2 Assumptions used and the basis of the cost calculations

The costs are based on the following assumptions:

(i) one set of variable-application crop treatment equipment, i.e. PF-system, can ‘farm’ anidentical area to that harvested by the combine,



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(ii) operations involving variable application equipment are not conducted at the sametime as combine harvesting (with the exception of Class 3),

(iii) for multiple PF-systems the total area would be divided equally between units.

The basis of the additional costs associated with purchasing precision farming equipment aresummarised in Table 6 for all systems.

Table 6 Summary of the cost of precision farming equipment

PF equipment Cost Full system

Class 1 Class 2 Class 3

Basic system

Class 4

Initial capital cost £ 11 363 £14 100 £ 16 150 £ 4 500

Cost of capital 8.5 % 8.5 %

Depreciation all equipment 13% for 5 yr replacement 13% for 5 yr replacement

Maintenance

Combine 3.5% for 150 hrs use pa 3.5% for 150 hrs use pa

Tractor 8% for 1000 hrs use pa 0

Seed drill 7.5% for 150 hrs use pa 0

Fertiliser distributor 7.5% for 150 hrs use pa 0

Training £60 pa (£300 over 5 yr) £60 pa (£300 over 5 yr)

7.3 Annual cost per unit area

This has been calculated for a range of arable areas that could be managed using a single PF-system i.e. the vehicle mounted computer used to record yield when fitted to the combineharvester and control application rate when fitted to the tractor.

Fig 7 shows the total annual cost £ ha-1 for the range of systems. This shows that the cost ofthe basic system (Class 4) at £5 ha-1 for an area of 250 ha is significantly cheaper than the fullsystems. Values for the full systems range between £12 ha-1 and £18 ha-1. Doubling the areato 500 ha reduces the cost to £2.50 ha-1 and £6 - £9 ha-1 respectively. These figures showvery clearly the effect of the area per PF-system on the annual cost of the operation, with thecosts becoming asymptotic to the horizontal axis as the farmed area increases.



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0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800 900 1000

Area per PF-system (ha)

Ann

ual c

ost (

£ ha

-1)

Class 3Class 2Class 1Class 4

Fig 7 Total cost of four different Precision Farming systems

8. Other costs

8.1 Soil texture and chemical analysis

Soil texture can be determined by traditional manual surveying techniques from augersamples on an approximate 100 m grid basis or the more recently developed electromagneticinduction techniques (James et al. 2003). These costs are based upon a cost per hectare asgiven in Table 7 and should be viewed as a “one-off” investment.

Table 7 Typical fixed area costs

One-off cost £ ha-1 Annual Cost £ sample-1

Soil surveying (manual) 25 P, K, pH, Mg 9

Soil surveying (E.M.I.) 14-28 P, K, pH, Mg + Cu, B 20

Available N

a) upper, middle and lowersamples from 0.9 m deep core

100

b) 0.9 m core bulked together 40

Soil nutrient status is, however, determined upon a cost per sample; current sampling andanalysis costs for a range of nutrients are given in Table 7. This indicates that nitrogenanalysis is expensive if undertaken annually, with one sample per hectare, and explains whythere is great interest in targeting the samples needed for this and similar analyses based uponthe management zone concept reported in Taylor et al. (2003).



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8.2 Crop canopy status

The costs to assist in the management of the crop canopy in near real time using cropreflectance data relate to either aerial digital photography (ADP) or tractor-mountedradiometers (TMR). The hardware required for obtaining remotely sensed data comprises apair of digital cameras for use in ADP mounted in a light aircraft (Wood et al, 2003a) or atractor mounted radiometer (Boissard et al., 2001). The annual depreciation and maintenancecosts for these, summarized in Table 8, have been calculated using the same assumptions asused in the earlier sections. The TMR may also be hired.

For the farm scales in the UK it is most likely that a service provider, agronomy consultinggroup or a syndicate of farmers would make the substantial investment for the digital camerasystem for ADP. The cost would, therefore, be spread over a large area.

Table 8 Cost associated with acquiring crop reflectance data

TMR Purchased TMR Hired ADP Cameras

Hardware cost (£) 10,000 - 15,000

Annual costs (£)

Depreciation @ 13% 1,300 - 1,950

Maintenance @ 3.5% 350 - 525

Cost of capital @ 8.5% 850 - 1,275

Rental charges - 4,000 -

Total annual cost (£ pa) 2,500 4,000 3,750

Cost of ground calibration (£ ha-1) 4.85 4.85 4.85

In order to estimate the cost per hectare of acquiring the crop reflectance data using ADP ithas been assumed that:(i) each 3 hour flight could cover up to 3,650 ha and that each fieldwould need to be photographed prior to each application of nitrogen at 3 growth stages, and(ii) it is possible to make 2 flights per day.

The cost of data collection, is presented in Fig 8 this includes the cost of the plane, pilot,cameras and the technicians to perform the image calibration in the field. It can be seen thatthe cost is almost independent of the area flown above 1000 ha, and at 1500 ha (a typicalday’s work for collecting the ground calibration data) would cost £7 ha-1.

The cost of the tractor-mounted radiometer (TMR) is more likely to be borne by an individualfarmer or a small syndicate of farmers. The TMR could provide similar, but lower resolution,data to ADP for use in producing fertiliser application plans. The cost per ha has beencalculated, as a function of the area managed per radiometer, using the data in Table 8.



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The difference between owning (£10.75 ha-1) and renting (£13.75 ha-1) a TMR being £3 ha-1

at 500 ha, however, to be competitive with ADP it would be necessary to manage an area inexcess of 1500 ha.

0

10

20

30

40

50

0 500 1000 1500 2000 2500

Area per flight (ha)

Cos

t (£

ha-1

)

Fig 8 Cost of acquiring crop reflectance data using aerial digital photography

Currently the authors and their research students, Havrankova (2006), Morris (2006) andWilson (2006), are evaluating the advantage of a new radiometer (Crop Circle,http://www.hollandscientific.com/literature/CropCircle.pdf, Francis et al., (2005) and Sudduth etal., (2005)) which can be vehicle mounted and contains its own light source. The capital costof which is less than tractor mounted radiometer referred to above.

9. Breakeven analysis

The breakeven analysis has been based on a benefit of £15 ha-1. This has been calculated bysubtracting the £7 ha-1 cost of acquiring ADP data from the £22 ha-1 benefit achieved byvarying nitrogen application according to crop needs assessed using real time monitoring ofthe canopy in Onion Field and Far Highlands. In order to estimate the area per PF-systemrequired to break even the mean benefit of £15 ha-1 has been compared with the cost of thefour different classes of PF-system shown in Fig 7.

It can be seen from Fig 9 how the increase in system cost increases the area per PF-systemrequired for breakeven at an economic return of £15 ha-1. This shows that for a low cost basicsystem precision farming can be economically viable for areas in excess of 78 ha, rising to308 ha for the most expensive system.



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0 100 200 300 400 500 600 700 800 900 1000


Cos

t (£

ha-1

)

Class 3Class 2Class 1Class 4

£15 ha -1

Fig 9 Breakeven area per systems for a return of £15 ha-1.

10. Sensitivity analysis of field variability

The scale of any benefit obtained from adopting precision farming practices will ultimatelydepend on the magnitude of the response and the proportion of the field (%) that will respondpositively to variable management. The increase in yield required to break even for differentlevels of field variability has been calculated using the costs based on a Class 3 system andgrain at £65 t-1 is shown, as an example, in Fig 10. The proportion of the field (%) respondingpositively to variable nitrogen management is based upon data from the shoot density andcrop canopy studies.



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Fig 10 Field variability sensitivity analysis Class 3 (£16,000) system

It can be seen from Fig 10 that for an area of 250 ha, and response areas of 30% a minimumyield increase of c1.0 t ha-1 would provide a breakeven return. Figures similar to those abovehave been produced for all the Classes of precision farming system and incorporated intoTable 9 for practical on farm use. This requires farmers and/or agronomists to answer thefollowing questions and then refer to Table 9 to estimate the potential for their farm.

1. How large is the cereal area farmed by a precision farming system?

Choose the area up to 1000ha.

2. What is the proposed capital investment in the precision farming system?

Choose the system cost between £4500 and £16000.

3. What percentage of the total cereal area has the potential for improvement?

Estimate an area between 5% and 30% from agronomic assessment of field and yieldmap variability.

4. Am I likely to exceed the breakeven yield benefit shown in the table from applyingprecision farming techniques to those areas?

To illustrate this, it can be seen from Table 9 that cereal areas of:

1. 750ha with a system costing £4500, where 10% of the area would respond positively,is economic with a yield increase of 0.24t/ha on that area.

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

50 150 250 350 450 550 650 750 850 950


Yie

ld in

crea

se r

equi

red

in v

aria

ble

part

s of

the

. fie

ld to

ach

ieve

bre

akev

en @

£65

t-1 (

t ha-1

)

10%

20%

30%

50%

Percentage area of the field likely to produce a positive response

to variable inputs



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2. 500ha with a system costing £11000, where 20% of the area would respond positively,is economic with a yield increase of 0.48t/ha on that area.

Table 9 Practical Guideline to estimate the potential economic benefit of precisionfarming (HGCA, 2002)

Level of yield increase (t/ha) needed to justify investment*

System(investment)

% arearesponding

250 ha 500 ha 750 ha 1,000 ha

5% 1.44 0.72 0.48 0.3610% 0.72 0.36 0.24 0.1820% 0.36 0.18 0.12 0.09

Basic levelentry(£4,500)

30% 0.24 0.12 0.08 0.06

5% 3.81 0.91 1.27 0.9510% 1.91 0.95 0.64 0.4820% 0.95 0.48 0.32 0.24

Fullyintegratedsingle unit(£11,000) 30% 0.64 0.32 0.21 0.16

5% 5.68 2.84 1.89 1.4210% 2.84 1.42 0.95 0.7120% 1.42 0.71 0.47 0.35

Multipleunits forcombine andtractor(£16,000)

30% 0.95 0.47 0.32 0.24

*based on wheat at £65/t

11. Conclusions

1. Yield maps are indispensable for targeting areas for investigation and treatment byprecision farming practices and subsequent monitoring of results. They provide avaluable basis for estimating the replenishment levels of P and K fertilisers, however,they were not found to provide a useful basis for applying spatially variable nitrogen.

2. The spatial variation in canopy development within a field can be estimated using anADP technique for “real-time” agronomic management. This technique can beextended from field scale to farm scale for crops of similar varieties and plantingdates. The technique can be used as a basis for determining the most appropriateapplication rate for nitrogen, and as a guide for herbicide and plant growth regulatorapplication.

3. The application of nitrogen in a spatially variable manner can improve the efficiencyof cereal production through managing variations in the crop canopy. Between 12%and 52% of the area of the fields under investigation responded positively to thisapproach. In 2000 seven out of eight treatment zones gave positive economic returnsto spatially variable nitrogen with an average benefit of £22 ha-1.



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4. Simple nitrogen balance calculations have shown that the spatially variable applicationof nitrogen can have an overall effect on reducing the nitrogen surplus byapproximately one third.

5. Based upon nitrogen and cereal prices at £0.30 kg-1 and £65 t-1 respectively, and forequipment prices in the UK in January 2001 the benefits of the variable rateapplication of nitrogen provided an average improvement of £22 ha-1.

6. The annual costs per hectare of the systems vary between £4.67 ha-1 and £18.46 ha-1 forthe basic and most expensive system respectively for an area of 250 ha.

7. The benefits outweigh the additional costs of the investment for cereal farms greaterthan 80 ha for basic systems and 200 - 300 ha for the more sophisticated systems

8. The costs of detailed soil analysis prohibit collection from a dense grid of data pointsand targeted sampling is recommended.

9. Common field management problems can result in significant crop yield penalties andshould be corrected prior to spatially variable application of nitrogen.

10. The benefits obtained from precision farming practices depend upon the magnitude ofthe response to the treatments and the proportion of the field, which will respond.

As a result of these studies a flow chart has been produced to assist cereal farmers in thedecision making required for variable nitrogen application (HGCA, 2002) the outline ofwhich is given in Appendix1.

Acknowledgements

The authors would like to thank the sponsors of this work, Agco Ltd., BASF, Home-GrownCereals Authority and Hydro Agri and the contributions made by their collaborators, ArableResearch Centres and Shuttleworth Farms. We would also like to thank Jana Havrankova,Jim Wilson, David Morris and Robert Walker for their assistance. Thanks must also beextended to Messrs Dines, Hart, Welti, Wilson and Wisson who allowed us to use their fields.

References

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Francis, D.D., Schlemmer, M.R., Schepers, J.S., Shanahan, J.F., Luchiari, A. 2005.Performance of a Crop Canopy Reflectance Sensor to Assess Chlorophyll Content.http://hydrolab.arsusda.gov/RSinARS/cssa/francis.pdf.

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Appendix 1

Nitrogen management for Winter Wheat (HGCA, 2002)

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