The Future of Precision Farming
- The Development of a Precision Farming Information System and Economic Aspects -

Peter Wagner
Technische Universität München
Professur für Unternehemensforschung und Informationsmanagement
Alte Akademie 14
85350 Freising-Weihenstephan, Germany
phone +49-(0)8161-713406, fax +49-(0)8161-713408
e-mail: wagner@landw.uni-halle.de

ABSTRACT

Spatially-variable crop production, often known as precision farming, is being approached with a variety of methods which could be summarized as "mapping systems" and "sensor systems". It is likely that future, successful implementation of precision farming will rely on a combination of the mapping approach and the sensor approach. But, before this becomes to be true many problems have to be solved. One of the most challenging problem lies in combining the mapping and sensor approach to generate application maps. For this purpose an information model is sketched.

However, the acceptance of precision farming by farmers depends mainly on the fact whether it will be profitable or not. But the economic benefit of precision farming is still unknown. Positive effects may be caused by site-specific N management, adopted seed sowing rates and reduction of herbicide treatment. On the other hand, increasing yields up to three and more per cent are described. A break even analysis and some results of a literature analysis presented in the paper has more details on the economic aspects of spatially variable crop production. It has to be noted within this context, that the more heterogeneous a field appears, the more chances exist that precision farming will be profitable to the farmer.

Approaches and Information modells for precision farming

Basically two different approaches to precision farming are under discussion: The first is the mapping approach, the second is the real-time approach (sensor-systems). The design of information models depends an the chosen approach.

Mapping approach

The way in which mapping systems function is shown in figure 1. Past data is used in order to determine the necessary inputs for the current situation. Conclusions are drawn from the yields of former years and from the nutrients that are measured in the soil as to the amounts of fertilizers and seeds to be applied. In this way the application of herbicides can also be controlled. This approach is particularly well suited for low-yield areas, and thus for phosphate and potassium fertilizers, relatively constant weather conditions and exclusive cereal crop rotations. For high yield nitrogen fertilization the system reaches its limit, since nitrogen fertilization as a rule must be tuned to recent parameters rather than to past conditions.

Figure 1: Mapping-approach (AUERNHAMMER, 1998, modified)

Real-time approach

Real-time systems stand in contrast to the mapping systems. They apply inputs, especially nitrogen fertilizers, according to the needs of the plant population at the given moment. Sensors attached to the tractor give information on available soil moisture and the current plant N supply. The application of nitrogen fertilizer is based on such data. The principle of the approach is shown in figure 2. Real-time systems are suited for high yield areas with starkly varying weather conditions and are suitable for varied crop rotations, but basically exclusively for nitrogen fertilizing. Figure 3 shows how one such system looks like.

Figure 2: Real-time system (AUERNHAMMER, 1998, modified)

 

Figure 3: Real-time-fertilization

Combination of mapping and of real-time approach

Both mapping and the real-time systems per se have advantages and disadvantages. Real progress can be expected by linking both systems together as shown in figure 4. By means of yield maps and soil nutrient maps this approach attempts to explore the yield potentials meter by meter and apply nitrogen fertilizers according to the yield potential and the current conditions. By this approach the current situation of the plant population can be optimally dealt with and ecological and economic limitations taken into consideration at the same time.

Figure 4: Real-time approach with map-overlay (AUERNHAMMER, 1998, modified)

The approach is suited for high yield regions with starkly changing weather conditions and for fertilizing with nitrogen, phosphate, and potassium. Thus the approach is highly suitable for regions in Europe. A large part of the problems connected with it have not been solved yet. On September 1, 1998, a team at Weihenstephan started an interdisciplinary research project (http://ikb.weihenstephan.de/) with the aim of advancing this approach in the next 6 years. Particularly the development of sensors and models for the deduction of required amounts of fertilizer depending on the growth state of plants and the available soil moisture has just begun recently.

Information processing in precision farming

With the most used mapping systems available on the markets today the farmer alone decides how much input, e.g. nitrogen, will be applied to a site. This means that experience and intuition determine his decision. So far there are no reliably functioning models capable of computing the optimal amount of fertilizer with reference to the parameters of the soil and the plant population in small-sized grids. Such models are required, however, if we want to reduce the size of individually managed parts of fields and the more heterogeneous the known conditions of a location are. In some regions of Germany, for example, the quality of soil, the supply of nutrients and the availability of water vary greatly within a few meters. We can react to such variations only by managing the parts of fields on as small a scale as possible, for instance by keeping grids based on the working width of implements.

Figure 5: Information model based on the real-time approach with map overlay - components and data flow

Figure 6:: Data volume through precision farming and automated data logging (estimated)

The information models to be developed must be capable both of taking into account data from the past, e.g. in order to establish the yield potential for one part of a field, and of processing current conditions such as the sensor probed nitrogen supply of the plants or the sensor registered water availability of the grid in question. Setting up such models (outlined in figure 5) is a component not to be underestimated in the acceptance of precision farming by the farmers themselves. A first step is the standardization of the interfaces by a DIN-Norm (compare SPECKMANN, 1998, p.36). A working information model is one of the key issues of our common research project in Weihenstephan.

The probable volume of accumulated data in precision farming with automatic data collection per hectare, respectively a farm of 3oo hectares, is presented in figure 6. A certain working width and working speed were assumed for each of the following five activities: combine harvesting, tilling, sowing, spraying, and fertilizing. Working width and working speed of the activities define acreage performance. It is based on the assumption that measurements are taken each second. The number of data sets is computed by projection onto one hectare. The number of data attributes per set varies according to the measure taken. Each data set contains an identical header defining the type of data set, time, longitude, latitude and altitude and also allowing a statement on sensing quality. The number of further attributes depend on the performed task activity. For example, in the case of cereal harvesting such attributes could be the actual cutting width, the operational position of the cutting unit, the distance covered, and also yield related data such as amount of yield, grain losses or grain moisture. If the implements are provided with appropriate sensors further attributes can be considered. The number of data sets per year and hectare as well as the number of attributes per year and hectare can be computed from the number of tasks per year. The last column in the figure shows the number of bytes per year and hectare based on a data length of 6 bytes per attribute. In this case 1.3 megabytes would result. The annual volume of data for a 3oo-hectare farm would thus amount to 4o2 megabytes or almost half a gigabyte. This volume of data is, of course, impracticable. So suitable algorithms for the condensation and the computing of data must be found to reduce the data volume noticeably. However, it is still largely unknown which data are expendable.

Economics of precision farming

The profitability of precision farming is determined mainly by the following factors:

Necessary investments for precision farming

To run their farms on the principles of precision farming farmers must be willing to invest in technology and services. Basically there are three kinds of investment:

Some of the components are presently available, others will be available in the near future. As mentioned above it is the software based on the "application logic" of precision farming that is posing major problems.

R Kind of Investment
1 Equipping Tractor with LBS and DGPS 13000
2 Updating fertilizing technique 4000
3 Updating drilling technique 4000
4 Updating spraying technique 4000
5 Updating harvesting technique 10000
6 Office equipment (Software) 3000
7 Total 38000

Figure 7: Estimated investment needs for precision farming (mapping approach)

Figure 7 provides an overview of the investment costs for starting precision farming. The figures are mere approximations, which are based on the bids of different suppliers at the end of 1998. They are based on the assumption of the mapping approach.

The costs of investment is one thing. On the other hand there are the benefits of increasing yields and/or lowering the quantity of inputs. The reasons for the additional profit and the cost reduction lie in the different approaches of undifferentiated (traditional) crop management and of precision farming (compare WAGNER, 1999). The higher revenues and the potential cost reduction ought to cover the extra costs of investment. Basically we can go by the following: the more spatially heterogeneous a location, the greater the chances to reach the break even point between the necessary investment of precision farming and the lower costs of production and/or the increased yield.

Farm size and percentage of crops in the production program

With larger farms the yearly investment costs (annuity) can be spread over more hectares. This is especially important with investments that do not depreciate based upon utility performance, that is, performance-dependent depreciation, but with those which depreciate over time. For example, due to technical progress the use of a investment good no longer appears meaningful after a certain amount of time. Thus the investment good is outdated due to technological progress after a certain amount of time and should be replaced by a new object. These assumptions apply especially to investments in the area of precision farming. The following example will use a depreciation period of about five years. The calculations are based upon the German standard gross margins (performance class 3) for the crops wheat, corn (maize), rape, potatoes and sugar beets presented in Figure 8. The gross margins have been calculated without the compensation payments. The inclusion of the compensation payments would have no influence on the following presentation of the results.

R Ratio Unit Winter-
Wheat
Corn Winter-
Rape
Pot-
atoes
Sugar-
Beets
1 Performance  
2 Yield dt/ha 71,40 85,70 31,70 348,50 511,10
3 Price €/dt 13,29 12,24 22,69 8,91 5,65
4 Performance €/ha 949 1049 719 3105 2885
5 Variable Costs  
6 Seed €/ha 69 140 32 547 171
7 Fertilizer €/ha 114 121 115 144 170
8   thereof N €/ha 64 81 67 70 92
9 Plant Spray €/ha 121 89 121 170 231
10   thereof Herbizides €/ha 48 75 99 66 217
11 Machinery €/ha 215 263 187 358 370
12 Other €/ha 29 192 62 231 28
13 Total var. Costs €/ha 547 806 515 1450 970
14 Gross Margin1) €/ha 402 243 204 1655 1915
1) without compensation payments

Figure 8: Standard Gross Margins (1997/98) of selected Field Crops (KTBL, 1999, modified)

R Ratio Acreage (ha) 4)
Unit 100 400 800 1)
1 Investment Costs for PF 38000 38000 51000
2 Depreciation (5 Years) €/Year 7600 7600 10200
3 Interest (8%) €/Year 1917 1917 2573
4 Yearly Costs 2) €/Year 9517 9517 12773
5 Yearly Costs per Hectare €/ha 95,2 23,8 16
6 Necessary Yield Increase3) for Break-Even % 9 2,2 1,5
7 Necessary Cost Reduction for Break-Even
8 - Seed % 125,1 31,6 21
9 - N-Fertilizer % 139,5 34,9 23,4
10 - Herbicide % 134,2 33,6 22,5
11 - Total Seed, N-Fertilizer, Herbicide % 44,2 11,1 7,4
1) Higher Investment Costs due to equipping of a second tractor
2) without repairs, maintenance and labor costs
3) with the same price scale
4) crop percentage: Wheat 67,1%, Maize (Corn) 8,8%, Rape 16,7%, Potatoes 0%, Sugar Beet 7,3%

Figure 9: Break-Even-Analysis for an Cash Crop Farm (extensive crop rotation)

The results of a break-even analysis for the introduction of precision farming technology, depending on the size of the farm, are shown in Figure 9. An extensive cash crop farm is assumed, whose crop rotation consists to a large extent of wheat. In contrast, intensive crops like the sugar beet only take up 7.3% of the area. The results for the enterprise with 100, 400 and 800 hectares under cultivation show how high the changes of yield or variable costs would have to be under ceterus paribus conditions in order to cover the increased costs of introducing precision farming technology. The possible investment costs for precision farming technology are listed in row 1 (see Figure 7). For the farm size of 100 and 400 hectares they are 38 000 . Furnishing a second tractor with the necessary technology was calculated for the 800 hectare farm, therefore the investment costs here were 51 000 . The interest rate in row 3 was calculated at 8%. Average annual interest costs of 1917 per year arise with 7 600 depreciation. The yearly costs are measured per hectare in Line 5. It is shown that the costs drastically decrease with increasing enterprise size. Therefore, the necessary minimum increases in yield required (row 6) to cover these costs are lower, as the enterprise size increases. Thus, with the given organizational structure of 67% wheat, almost 9% maize, just under 17% rape, no potatoes and 7.3% sugar beets, for the 100 hectare farm to reach the break even point an increased yield of 9% is necessary. For the 800 hectare farm it is a mere 1.5%. Whereas the figures for the 400 and 800 hectare farm appear within the realm of the possibility, the necessary yield increases for the 100 hectare farm are impossible to achieve at the present level of knowledge. However, the additional investment costs do not have to covered by increased output alone. This can also occur by a possible reduction of the variable costs, especially seed, nitrogen fertilizer and herbicide are possibilities. The necessary percentual reduction to reach the break even point with these items is listed in rows 8,9, and 10. Here as well, the values for the 100 hectare farm appear unrealistic, even when one considers the total necessary savings in row 11. On the other hand, the herbicide savings by the use of precision farming technology can be up to 70%, as previous research has shown (see Figure 11). The break-even point for the 400 and 800 hectare farm appears easily attainable through herbicide savings alone. The total variable cost reduction of 11,1% and 7.4% for the 400 and 800 hectare farm respectively, appears within reach. If one considers the necessary reductions along with a possible output increase, then the use of the new technology appears to be meaningful in most cases with adequate farm size.

R Ratio Acreage (ha) 4)
Unit 100 400 800 1)
1 Investment Costs for PF 38000 38000 51000
2 Depreciation (5 Years) €/Year 7600 7600 10200
3 Interest (8%) €/Year 1917 1917 2573
4 Yearly Costs 2) €/Year 9517 9517 12773
5 Yearly Costs per Hectare €/ha 95,2 23,8 16
6 Necessary Yield Increase3) for Break-Even % 5,9 1,5 1
7 Necessary Cost Reduction for Break-Even
8 - Seed % 76,9 19,2 12,9
9 - N-Fertilizer % 129,5 32,4 21,7
10 - Herbicide % 94,2 23,5 15,8
11 - Total Seed, N-Fertilizer, Herbicide % 31,9 8 5,4
1) Higher Investment Costs due to equipping of a second tractor
2) without repairs, maintenance and labor costs
3) with the same price scale
4) crop percentage: Wheat 57,9%, Maize (Corn) 4,4%, Rape 3,7%, Potatoes 4,9%, Sugar Beet 29,2%

Figure 10: Break-Even-Analysis for an Cash Crop Farm (intensive crop rotation)

In Figure 10 the same presentation for a different organizational structure of a farm is shown; just under 58% wheat, maize 4.4%, rape 3.7%, potatoes 4.9% and sugar beets 29.2%. The necessary increases in output and/or reduction of variable costs to reach the break-even point decrease substantially with a higher share of intensive crops in the crop rotation. Thus only a one percent yield increase is necessary for the 800 hectare farm for instance, with the farm with the extensive crop rotation it was 1.5%. The same is true for the reduction of the individual variable cost positions. For example, for the 800 hectare farm, herbicide savings of only 15,8% are necessary, while for the same farm with extensive crop rotation it was 22,5%. Many farms will not reach such size. Nevertheless such farms do not have to do without the use of precision farming. Contractors, farming co-operatives or common area (land) pools are to be considered.

Another aspect is mentioned for the sake of completeness. Lower prices are threatening many products with the expected further liberalization of the agriculture market. As has been shown in the past, the input price will not go unaffected, a certain decrease is to be expected in the future as well. A reduction of the prices indicates the necessity of higher yield increases and/or even higher savings in inputs in order to reach the break-even point. In other words: a continuing liberalization of the agriculture market impedes the introduction of precision farming technology. However, with the European support- and subsidy-system it is certainly conceivable that if positive ecological effects can be proven through the use of precision farming, then investment bonuses and/or reduced interest rates for investments can be granted for these areas. This, on the other hand, would have a positive affect on the expansion of the new technology.

Figure 11 shows the results of a literature analysis dealing with the economic consequences of precision farming. Studies of herbicide cost reduction show that between 5o to 8o % of the costs for herbicides can be saved when treating only patches where weeds actually grow. The savings in terms of money depend greatly on the herbicide price, so that a generalization is not possible here. Several studies have dealt with yield benefits and saving potentials of inputs as shown in the center of the figure. SCHMERLER and JÜRSCHIK (1997 b, p.995), for example, show yield increase (wheat) of not quite 4 decitons per hectare and an average reduction of 25 kg nitrogen/ha on heterogeneous sites. All in all this amounts to about 5o in yield benefits and saving potentials per hectare. With sugar beets SWINTON/AHMAD (1996, p.1015) found yield and quality benefits as well as saving potentials as high as 16o/ha.

On the other hand there are the costs of precision farming. For investments in the (imperfect) technology HARRIS (1997, p. 953) amount to 3o to 35 per hectare for large farms (3oo ha). One can expect that these costs will decrease with the simultaneous improvement of the technology.

Author research object results
Green et al.
(USA, 1997)
spatially variable herbicide
treatment in peanuts
up to 70 % less herbicide use
Nordmeyer et al.
(Germany, 1997)
spatially variable herbicide
treatment in cereal grains
up to 80 % of the area need
not be treated
Gerhards
(Germany, 1997)
spatially variable herbicide
treatment in cereals
40-50 % less herbicide use
Scarlett et al.
(UK, 1997)
spatially variable seedbed treatment in winter-wheat up to 20% reduced seedrates
Snyder et al.
(USA, 1996)
spatially variable fertilization in corn 3-13% less N/ha
Ostergaard
(Denmark, 1997)
spatially variable N,P,K and
lime application in cereals
$ 40-50 economic advantage/ha1)
Schmerler/Jürschik
(Germany, 1997b)
spatially variable N-fertilization in cereals up to 3,9 dt/ha increased crop yield. On average 25 kg/ha less N
Swinton/Ahmad
(USA, 1996)
spatially variable N-fertilization in sugar beets 74 $/acre (~160 /ha) economic advantage1) and quality increases
Reetz/Fixen
(USA, 1995)
spatially variable N-fertilization
of all crops on a farm
43 $/ha economic advantage1)
Malzer et al.
(USA, 1996)
spatially variable N-fertilization
in corn
11-72 $/ha economic advantage1)
Schmerler/Jürschik
(Germany, 1997a)
calculated costs for GPS use (machi-nery and labor) for a 2000 ha farm increased costs of
35-40 /ha and year2)
Harris
(UK, 1997)
calculated costs for GPS use (machi-nery equipment) for a 320 ha farm increased costs of
30-35 /ha and year3)
1) regardless investment costs   2) incl. labor costs   3) without labor costs

Figure 11: Economics of precision farming (results of a literature analysis)

A final statement on the excellence of precision farming from the economic point of view cannot be made on the basis of the available studies. Generalizing, one can say, however that the higher the heterogeneity of the farmed location the more obvious the economic benefits of precision farming are. Intensive work is being done on remedying the imperfections of technology as well as on the decision criteria, for instance, for the rate of nitrogen fertilizing. It must be emphasized that the results of research are undoubtedly in favor of site-specific management. In the discussion on the applicability of the new technology one ought to take into account that not only questions of the economic viability for the individual farm should be considered but also that precision farming allows great progress in the reduction of the pressure that agriculture exerts on the environment. This applies particularly to the reduction of nitrogen leaching and to herbicide savings.

There are additional effects of precision farming as well. Through automated data acquisition the management of biological-technical systems will be as much improved as overall farm management. Significant progress in farm management can be expected. Simultaneously, so to say, as a spin-off, the farmer who has decided in favour of precision farming is provided with the documentation of his activities, which in Germany is required by fertilizing regulations and helps with applications for funds.

A further effect is that e.g. that soil compaction can be easily registered by sensors probing soil resistance in ploughing which allows immediate redress by on-the-spot reaction.

The technology of precision farming is also suited to make fleet management for optimization of co-operative machine use more efficient.

Precision farming is suitable not only for large farms but also for the management of minor fields in regions with small-scale agricultural structures. In this case the same crop can be grown beyond property borders on sites of various ownership, because the automated registration of data from input application and harvesting makes individual settlement of accounts for all owners possible. This, of course, needs an automated, very sophisticated site-specific cost accounting, which is under development in Weihenstephan right now.

Last but not the least we should also think of the changing ways of land use of tomorrow - with the technology of precision farming farm robots seem to become more real.

Precision farming is far more than site specific application of production factors, it is economically and ecologically optimized farming that allows better management.

REFERENCES:

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