ISCF WAVE 1 AGRI TECH Agronomic Big Data Analytics for improved crop management

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Agricultural systems are complex, and must be managed if we are to achieve food security and maintain environmental quality. The management of complex systems in industry and commerce is being improved by the collection, processing and analysis of "big data" sets. For some years farmers have had the potential to collect big data sets on their crops and soils using GPS-driven monitors on the combine or tractor, data from satellite-borne sensors and the direct sampling and analysis of soils. This raises the question of whether agriculture can enter the big data era in order to solve management problems more quickly and robustly than through the conventional approach of field trials at a limited number of experimental sites. We contend that this is possible, but only by using methods to analyse the data that are biologically meaningful rather than by blindly mining data for correlations. This is a feasibility study to test two tailored big-data analytical methods on a large data set on arable fields from across the U.K. Two general approaches will be used, both of which have already been developed and published in the peer-reviewed literature, and used as research tools. The first is called boundary line analysis, a method to identify the maximum yield that a crop can achieve as a function of some soil or crop property that represents a factor (nutrient supply, canopy development) that may limit the potential yield. Boundary line analysis requires big data sets, but has the potential to give greater biological insight into the crop system, and to facilitate management decisions to remove limiting effects, than the relatively crude tools that are used in much data mining. The second approach is focussed on the analysis of yield maps produced by yield monitors on combine harvesters equipped with GPS. These maps show complex patterns of spatial variation, which are often hard to interpret usefully. When maps for two or more seasons are overlaid, the variability is even more complex. In past research we have shown that a pattern-recognition method called k-means clustering can be used to subdivide a field into regions within which the season-to-season fluctuations in yield are more or less uniform. One region may show consistent high yields, and another consistent low yields, while others fluctuate between seasons. Such regions are likely to represent parts of the field where the crop is subject to similar limitations. For example, where the soil available water content is relatively small yields may drop in drier years. A region with an emerging nutrient deficiency may show a steady decline in yield over a series of seasons. By relating the regionalization of the field, and each regions characteristic yield variations over time, to soil and other environmental information, we can hope to identify the key limiting factors at subfield scales, and by doing these analyses on big data sets, farm and regional scale patterns should also emerge. Within this project we shall show how a big agronomic data set can be most effectively analysed to allow the agronomy company which holds it best to advise their customers and obtain maximum value from the data that they collect. This will help to support improved management at farm scale, possibly including the use of precision agriculture methods to respond to within-field variation.