Journal article
Assessment of the use of geographically weighted regression for analysis of large on-farm experiments and implications for practical application
Agronomy, Vol.10(11), Article 1720
2020
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.
Details
- Title
- Assessment of the use of geographically weighted regression for analysis of large on-farm experiments and implications for practical application
- Authors/Creators
- F.H. Evans (Author/Creator)A. Recalde Salas (Author/Creator)S. Rakshit (Author/Creator)C.A. Scanlan (Author/Creator)S.E. Cook (Author/Creator)
- Publication Details
- Agronomy, Vol.10(11), Article 1720
- Publisher
- MDPI
- Identifiers
- 991005543552507891
- Copyright
- © 2020 by the authors
- Murdoch Affiliation
- Separation Science and Metabolomics Laboratory; Centre for Sustainable Farming Systems
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Metrics
118 File views/ downloads
154 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- Citation topics
- 3 Agriculture, Environment & Ecology
- 3.45 Soil Science
- 3.45.1109 Soil Mapping
- Web Of Science research areas
- Agronomy
- Plant Sciences
- ESI research areas
- Agricultural Sciences