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Article

Assessment of the Use of Geographically Weighted Regression for Analysis of Large On-Farm Experiments and Implications for Practical Application

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Big Data in Agriculture, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
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Centre for Digital Agriculture, Curtin University, Kent Street, Bentley, WA 6102, Australia
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Food Agility CRC Ltd., 81 Broadway, Ultimo, NSW 2007, Australia
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Statistics for the Australian Grains Industry (SAGI-West), Curtin University, Kent Street, Bentley, WA 6102, Australia
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Department of Primary Industries and Regional Development, Western Australia, 75 York Road, Northam, WA 6151, Australia
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Author to whom correspondence should be addressed.
Agronomy 2020, 10(11), 1720; https://doi.org/10.3390/agronomy10111720
Received: 21 September 2020 / Revised: 4 November 2020 / Accepted: 4 November 2020 / Published: 5 November 2020
(This article belongs to the Section Precision and Digital Agriculture)
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. View Full-Text
Keywords: on-farm experimentation; precision agriculture; digital agriculture; spatial analysis; geographically weighted regression on-farm experimentation; precision agriculture; digital agriculture; spatial analysis; geographically weighted regression
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MDPI and ACS Style

Evans, F.H.; Recalde Salas, A.; Rakshit, S.; Scanlan, C.A.; Cook, S.E. Assessment of the Use of Geographically Weighted Regression for Analysis of Large On-Farm Experiments and Implications for Practical Application. Agronomy 2020, 10, 1720. https://doi.org/10.3390/agronomy10111720

AMA Style

Evans FH, Recalde Salas A, Rakshit S, Scanlan CA, Cook SE. Assessment of the Use of Geographically Weighted Regression for Analysis of Large On-Farm Experiments and Implications for Practical Application. Agronomy. 2020; 10(11):1720. https://doi.org/10.3390/agronomy10111720

Chicago/Turabian Style

Evans, Fiona H., Angela Recalde Salas, Suman Rakshit, Craig A. Scanlan, and Simon E. Cook 2020. "Assessment of the Use of Geographically Weighted Regression for Analysis of Large On-Farm Experiments and Implications for Practical Application" Agronomy 10, no. 11: 1720. https://doi.org/10.3390/agronomy10111720

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