Abstract
On-farm experimentation (OFE) is a farmer-centric process that can enhance the adoption of digital agriculture technologies and improve farm profitability and sustainability. Farmers work with consultants or researchers to design and implement experiments using their own machinery to test management practices at the field or farm scale. Analysis of data from OFE is challenging because of the large spatial variation influenced by spatial autocorrelation that is not due to the treatment being tested and is often much larger than treatment effects. In addition, the relationship between treatment and yield response may also vary spatially. We investigate the use of geographically weighted regression (GWR) for analysis of data from large on-farm experiments. GWR estimates local regressions, where data are weighted by distance from the site using a distance-decay kernel. It is a simple approach that can be easily explained to farmers and their agronomic advisors. We use simulated data to test the ability of GWR to separate yield variation due to treatment from any underlying spatial variation in yield that is not due to treatment; show that GWR kernel bandwidth can be based on experimental design to accurately separate the underlying spatial variability from treatment effects; and demonstrate a step-wise model selection approach to determine when the response to treatment is global across the experiment or locally varying. We demonstrate our recommended approach on two large-scale experiments conducted on farms in Western Australia to investigate grain yield response to potassium fertiliser. We discuss the implications of our results for routine practical application to OFE and conclude that GWR has potential for wide application in a semi-automated manner to analyse OFE data, improve farm decision-making, and enhance the adoption of digital technologies.
Highlights
Precision agriculture (PA) uses data from multiple sources to map and analyse yield variability and manage crops [1]
A recent analysis of potassium cost to wheat grain prices [69] showed that the that the break-even ratio for the fertilisation of barley with MOP was 7.4; this analysis shows that MOP fertilisation is not profitable for this paddock
On-farm experimentation (OFE) is an inexpensive means of acquiring knowledge to support on-farm decision-making and improve the uptake of digital agriculture
Summary
Precision agriculture (PA) uses data from multiple sources to map and analyse yield variability and manage crops [1]. It has recently been defined as follows: precision agriculture is a management strategy that gathers, processes, and analyses temporal, spatial, and individual data and combines it with other information to support management decisions according to the estimated variability for improved resource use efficiency, productivity, quality, profitability, and sustainability of agricultural production [2]. While being largely driven by the desire to increase profitability, PA adoption is influenced by education, perceived ease of use, and access to expert consultants and contractors [11,12], which “initiates a learning process, enabling potential users to become more aware and confident about PA tools” [13]
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