By N D COSSER and R EARL. School of Agriculture ... better targeting of inputs (Earl et al. 1997) .... Eds P C Roberts, R H Rust and W E Larson. ASA, CSSA ...
Aspects of Applied Biology 50, 1997 Optimising cereal inputs: Its scientific basis
Precision farming: Approaches to the management of field variation By N D COSSER and R EARL School of Agriculture, Food and Environment, Cranfield University, Silsoe, Bedfordshire MK45 4DT, UK Summary Precision farming is a term used to describe the optimal management of within field variability. Information to allow management of differing zones can be obtained from yield maps, field history, soil characterisation, plant tissue and soil samples. Knowledge is required to utilise this information if optimal treatment is requisite. Keywords: variability, precision farming, grain yield Introduction Precision farming is a term used to describe the management of variability within fields to optimise input usage. The potential benefit of such a system is that yield levels and associated inputs can be selected which will provide the most advantageous financial benefit. This method of increased profitability allows areas of the field with a low yield potential due to non manipulatable influences to receive lower input levels. This approach improves overall efficiency, therefore economic returns are increased and potential environmental damage is lessened. At the same time, inputs to areas of high yield potential, which are being restricted by manipulatable factors, can be increased. An understanding can also be obtained as to those areas of the field which suit conventional farming practice i.e. blanket application of inputs. Various approaches have been proposed over the last two decades for reducing the environmental impact of intensive agricultural systems. The philosophy behind these approaches, namely organic and low -input production, have been based on the assumption that environmental damage will be reduced if chemical inputs are reduced or eliminated. The uptake of organic crop production has remained unattractive to many farmers due to the radical change required in their farming systems. Other discouraging factors are the difficulties in obtaining a premium which is sufficient to subsidise the reduction in yield. Low -input farming systems are therefore more attractive to growers as agrochemicals can be applied, and the risk of crop failure is greatly reduced. Precision farming helps farmers to improve profitability through better targeting of inputs (Earl et al. 1997) The concepts of maximised return from areas within fields is attractive to farmers, however, a greater understanding of variability, and the interactions of factors contributing to variability is required to achieve this aim in practice. The development and application of technology to
compliment established agronomic principles provides a sound basis for approaching the development of practical guidelines for implementing the concept of precision farming.
Materials and Methods Optimising outputs from fields which exhibit within field variability requires some quantification of soil and crop physical and chemical parameters influencing grain yield and quality and therefore the profitability of growing the crop. Various data can be made available to assist in management decisions: Field history A knowledge of both long and short term field history can provide vital clues to yield variation. For example if a field was previously split and there had been a long term grass ley on one side, this could result in major nutrient implications for many years. Areas which were previously under different land usage e.g. site of an old building, land fill or old field entrance can be understood and management decisions taken accordingly. Yield maps Yield maps are themselves merely a historical record of crop performance, but they do provide useful information on yield variation and hence the first lead towards predicting potential variable yield in the following growing season. Trend maps, generated from yield maps from a number of seasons, can be used to determine yield stability in differing regions in the field or areas which suffer more in dry or wet seasons. These maps can be developed further to allow determination of differing gross margins over the field. Soil characterisation Profile pits provide a fundamental understanding of soil texture, depth, structure, rooting depth and drainage status (Millar 1997). Profile pits are expensive to excavate and so it is desirable to characterise a given yield using a minimum number of pits. The location of pits can be aided by historical yield maps, an auger soil survey and aerial digital photography. Crop, soil and water analysis Soil water availability, soil and tissue analyses can be carried out at appropriate times during the growing season to underpin agronomic decisions based on temporal and spatial variations. These data can be processed using mapping programmes to aid the targeting of remedial actions and applications of inputs. Aerial digital photography (ADP) ADP provides information on 'real time' spatial variability in crop development. Various digital filters can be applied to increase the range of parameters which can be assessed. ADP can provide information to aid the targeting of analysis on the ground, and at various growth stages (e.g. post emergence, post winter, tillering, anthesis, yield potential) to assist in the agronomic management of areas within fields.
Many companies have developed equipment which can variably apply their chosen input. However, despite the information provided by yield maps, it is unclear how to utilise the information available to variably treat fields to provide the optimum input levels. Experimentation is required to determine how all the available information and technology can be managed to optimise economic benefit. Investigations are currently being conducted, funded by Home Grown Cereals Authority, Massey Ferguson and Hydro Agri, at four locations representing 72% of soils in England's arable area. To demonstrate some of the concepts proposed, an early case study is reported from 12 Acres, Glebe Farm, Hatherop, Gloucestershire (NGR SP 1705 0630), an 8.75 ha field consisting predominately of Cotswold brash. An initial auger survey conducted by the Soil Survey and Land Resource Centre presented in Fig. 1., demonstrates the soil variation across the field. This information in conjunction with Aerial Digital Photography (Fig. 2) and historical yield maps (Fig. 3) were used to locate nine soil profile pits in characteristically differing areas of the field.
Fig. 1. Soil series map and locations of observations for 12 Acres, Hatherop, Gloucestershire
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Fig. 2. An aerial digital photograph of 12 Acres, Hatherop, Gloucestershire on 25 May 1997
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The protocol used to locate pits comprised siting the minimum number of pits possible consistent with characterising the three soil series identified from the soil survey, high and low areas of crop development from recent aerial digital photography and high and low yielding areas from the yield map. When the profiles were excavated, large differences in all horizons were observed over the field, Table 1 shows an example of four such pits.
Table 1. Soil profile descriptions from 12 Acres, Glebe Farm, Hatherop, Gloucestershire. Pit No.
Horiz on No.
Horizon depth (cm)
Horizon description
Texture (estimated) Cl San ay d%
Rooting depth
1
1 2 3
20 48 98
Stony clay topsoil Loose, blocky clay; small stones Large stones in firm sand infill
40 45 10
45 40 20
60
2
1 2 3 4
25 46 83 120
Dark clay; stony Dark, mottled clay; blocky; stony Vertical clay prisms; stoneless Olive clay; mottled, stony
45 40 55 35
35 30 35 35
80
3
1 2 3 4
25 46 79 110
40 50 5 5
40 35 15 15
85
4
1 2 3 4 5
24 46 88 128 138
Dark clay; stony Brown clay; large stones Sandy loam; large stones; calcareous Rubbly limestone Dark topsoil; small stones Brown clay; strong blocks; stony Strong brown clay; few small stones Brown clay; large stones Clay, mottled, medium stones
40 50 50 50 45
45 35 40 30 35
90
The profile descriptions, ADP images and yield maps provide a useful basis for identifying and targeting areas of the field for further investigation for the development of suitable guidelines for precision farming. Discussion Understanding variability The word 'variable' can be used to cover a wide range of situations e.g. yield differences leading to a 5 t ha' difference over a field, or differences between 970 ears 111-2 and 965 ears M-2, therefore, the differences between population means within a normal distribution and the levels of variation which is the result of a significantly different population.
For precision farming to be practically applicable, variation must be great enough to warrant differential treatment. The level of sampling must therefore be sufficient to detect feasible variation, but manageable to be of practical interest and remain within economic constraints. Agreeing with several other investigations (Jones et al. 1989; Cambardella et al. 1996; Khalcural et al. 1996), much of the variation observed within the four project sites for yield and ADP has been directly associated with changing soil types. Therefore, care needs to be taken that any decisions to spacially apply products are considered accordingly.
ADP has potential for showing real time variation within the crop canopy, but this project is also investigating methods of predicting yield maps (Taylor et al. 1997) so for example, optimal treatment of differing field zones can be applied to try and alleviate partially low yielding areas if possible. Good correlations have been formed between the Normalised Difference Vegetive Index (NDVI) [NDVI --(Infrared - Red)/(Infrared + red)] and various crop parametres at different stages of the crop development. Therefore, NDVI images can then be used to monitor spatial variation in crop development (e.g. plant density, fertile tillers etc.); the impact of spatial variation on crop performance (e.g. potential yield); and identification of soil and water management problems.
Variable applications require equipment which is capable of spacially variable treatments. Equipment is available in prototype form for spraying, drilling and cultivating. Spinning disc variable fertilizer applicators are now in commercial production. It is unlikely that farmers would vary the application of inputs to the level of application technology available. Therefore, a balance between what is practical and economically viable must be sort. Before variable applications of inputs can be applied it is necessary to: 1 Develop methodologies to identify and quantify variation. 2 Develop procedures for identifying the causes of variation. 3 Examine possible management actions to ameliorate problems / enhance production. 4 Analyse the economic implications of carrying out management actions. 5 Select and implement management actions from which economic benefit is likely to accrue. 6 Monitor actual profitability across the field at the end of the season.
Further requirements When variation, whether soil, plant tissue, images from ADP or yield maps, has been observed the
approach to manage this variability could be based on points 3 to 6 above. At present this knowledge is very limited, therefore, experimental work is needed to develop procedures and protocols for achieving these requirements. These experiments must be practical, repeatable and representative of a range of situations if progress in the management of within field variation is to be made.
Acknowledgements The authors are grateful to the Home -Grown Cereals Authority, Hydro Agri and Massey Ferguson for funding this work. Thanks are also expressed to the host farmers and Arable Research Centres for their help and support.
References Cambardella C A, Colvin T S, Karlen D L, Logsdon S D, Berry E C, Radke J K, Kaspar T C, Parkin T B, Jaynes D B. 1996. Soil property contributions to yield variation pattern. In Proceedings on the third international conference on precision agriculture. Eds P C Roberts, R H Rust and WE Larson. ASA, CSSA, SSSA. Madison, WI, 417-424. Earl R, Wheeler P N, Blackmore B S, Godwin R J. 1997. Precision farming - the management of variability. Landwards, Winter 1996, 18-23. Jones A L, Mielke C A, Miller C A. 1989. Relationship of landscape position and properties to crop production. Journal of Soil Water Conservation, 44, 328-332. Khakural B R, Robert P C, Mulla D J. 1996. Relating corn/soybean yields to variability in soil and landscape characters. In Proceedings on the third international conference on precision agriculture. Eds P C Roberts, R H Rust and W E Larson. ASA, CSSA, SSSA. Madison, WI, 117-128. Millarz D. 1997. Put precision farming to work. Crops, I March 1997, 12-13. Taylor J C, Wood G A, Thomas G A. 1997. Mapping yield potential with remote sensing. In 1st European Conference in Precision Farming, Warwick (In Press).
