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

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Large data sets on crop yield and soil properties (as opposed to experimental data) rarely yield simple answers to conventional statistical analysis. It is unusual, for example, to find a strong relationship between crop yield and a soil property that can be expressed by a regression model . Similarly, the spatial variations of yield in a single field over successive seasons can be markedly different with small correlations between yield in any two seasons. This means that one cannot, in general, use yield maps to segment fields into zones with consistent yield performance. For this reason we propose that, to be agronomically informative, the analysis of big data sets requires hypothesis driven models. We will propose and test two such models, and then work with our commercial partner to integrate them into a data interpretation service.

First, we shall use boundary line analysis as a method to model limiting effects of environmental factors on crop yield. This was first proposed by Webb (1972), who suggested that the limiting response of a biological system, such as yield, (y) to an environmental variable, such as a nutrient concentration (x) is seen in the upper boundary of a scatter plot of y against x. A statistical formulation of this model, which can be fitted and tested by rigorous methods, has only recently become available through research by the PI and colleagues. It will be applied in the current project to examine potentially limiting effects on yield of a range of physical and chemical soil properties.

We shall address the problem of the complex spatio-temporal variation of crop yield using k-means cluster analysis of yield map sequences. The method identifies subregions of a field within which crop yields, over seasons, are more internally uniform than within the field/farm as a whole. We shall test the hypotheses that such subregions express underlying soil variation at farm scale in a way consistent with boundary line models.

This project will demonstrate the potential for focussed statistical analysis of Big Data sets from the agricultural sector using analytical models which embody clear agronomic concepts, and which will allow the identification of limiting factors on crop production at within-field to regional scales. This has the potential to improve the management of farm land, whether by identifying regional trends in limiting factors, reflecting climatic and geological constraints, or factors that apply to just part of a single field (e.g. where a potentially high-yielding subregion has developed a nutrient deficiency due to sustained high offtake-rates for nutrients in crop products and residues). As such, the methodology has the potential to support the implementation of regional strategies for advisors and agribusinesses at one spatial scale, and the use of precision farming technology for improved profitability and reduced environmental impact at another.

In order to have this impact the methodology must be applicable to a substantial volume of data representing a significant proportion of agricultural land, integrated with existing workflows for data collection and advice and support to growers. This must be achieved in a sustainable commercial framework. Because this is a catalyst project there is enormous potential to achieve this through the enhanced opportunity for the commercial partner AgSpace to offer big-data based services to its customers. There is an existing commercial relationship between AgSpace and large agri-business like BASF, Syngenta, Farmcare, IPF and Agrii. This means that the project will have an immediate reach, via these land managers, advisors and commercial organizations that directly manage or influence the management of a significant proportion of agricultural land in the UK and further afield.

These immediate impacts would be seen in land management. In addition, there is also considerable potential value of these big data approaches to agricultural research. The agricultural research market that this project wishes to access is very large in terms of financial value. There are 2 key sectors we wish to explore; firstly, the commercial sector is increasing R & D budgets annually to stay ahead of competition. For example, Syngenta invested over $1.4 billion in 2014, BASF crop production invest £215 million annually and Agrii invest over £1m annually into agricultural research and development (R&D), these are a few examples of existing AgSpace customers that offer a clear route to market. Second, there is considerable potential value of these methods to publically-funded agricultural research. One particular opportunity in the public sector is the imminent revision of Defra's RB209 recommendations on soil nutrient and pH management.