Novel approach to the analysis of spatially-varying treatment effects in on-farm experiments
© 2020 With increasing interest in on-farm experiments, there is a pressing need to develop rigorous statistical methods for analysing these experiments. The adoption of advanced technologies such as yield monitors and variable-rate fertilizer applicators has enabled farmers and researchers to colle...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier
2020
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/81646 |
| _version_ | 1848764396554158080 |
|---|---|
| author | Rakshit, Suman Baddeley, Adrian Stefanova, Katia Reeves, Karyn Chen, Kefei Cao, Zhanglong Evans, Fiona Gibberd, Mark |
| author_facet | Rakshit, Suman Baddeley, Adrian Stefanova, Katia Reeves, Karyn Chen, Kefei Cao, Zhanglong Evans, Fiona Gibberd, Mark |
| author_sort | Rakshit, Suman |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2020 With increasing interest in on-farm experiments, there is a pressing need to develop rigorous statistical methods for analysing these experiments. The adoption of advanced technologies such as yield monitors and variable-rate fertilizer applicators has enabled farmers and researchers to collect biophysical data linked to spatial information at a scale which allows them to investigate the role of spatial variability in the development of optimum management practices. A relevant topic for investigation could be: “what are the optimum rates of nitrogen and how/why do these differ across the field”? Although it has been recently understood that traditional statistical methods that are appropriate for analysing small-plot experiments are inappropriate for answering these questions, a unifying approach to inference for on-farm experiments is still missing and this limits the adoption of the technique. In this paper we propose a unifying approach to the analysis of on-farm strip experiments adapting the core ideas of local likelihood or geographically weighted regression. We propose a statistical model that allows spatial nonstationarity in modelled relationships and estimates spatially-varying parameters governing these relationships. A crucial step is bandwidth selection in implementing these models, and we develop bandwidth selection methods for two important scenarios relevant to the modelling of yield monitor data in on-farm experiments. Local t-scores have been introduced for inferential purposes and the associated problem of multiple testing has been described in the context of analysing on-farm experiments. We demonstrate in this paper how local p-values can be adjusted to overcome this problem. To illustrate the applicability of our proposed method, we analysed two publicly available datasets. Graphical displays are created to guide practitioners to make informed decisions on optimal management practices. |
| first_indexed | 2025-11-14T11:18:42Z |
| format | Journal Article |
| id | curtin-20.500.11937-81646 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:18:42Z |
| publishDate | 2020 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-816462021-03-25T02:21:21Z Novel approach to the analysis of spatially-varying treatment effects in on-farm experiments Rakshit, Suman Baddeley, Adrian Stefanova, Katia Reeves, Karyn Chen, Kefei Cao, Zhanglong Evans, Fiona Gibberd, Mark Science & Technology Life Sciences & Biomedicine Agronomy Agriculture Local likelihood Precision agriculture Geographically weighted regression Spatial nonstationarity Bandwidth selection Contour maps GEOGRAPHICALLY WEIGHTED REGRESSION OUT CROSS-VALIDATION FALSE DISCOVERY RATE R PACKAGE STATISTICAL-ANALYSIS MODELS AUTOCORRELATION TRIALS HETEROGENEITY PRECISION © 2020 With increasing interest in on-farm experiments, there is a pressing need to develop rigorous statistical methods for analysing these experiments. The adoption of advanced technologies such as yield monitors and variable-rate fertilizer applicators has enabled farmers and researchers to collect biophysical data linked to spatial information at a scale which allows them to investigate the role of spatial variability in the development of optimum management practices. A relevant topic for investigation could be: “what are the optimum rates of nitrogen and how/why do these differ across the field”? Although it has been recently understood that traditional statistical methods that are appropriate for analysing small-plot experiments are inappropriate for answering these questions, a unifying approach to inference for on-farm experiments is still missing and this limits the adoption of the technique. In this paper we propose a unifying approach to the analysis of on-farm strip experiments adapting the core ideas of local likelihood or geographically weighted regression. We propose a statistical model that allows spatial nonstationarity in modelled relationships and estimates spatially-varying parameters governing these relationships. A crucial step is bandwidth selection in implementing these models, and we develop bandwidth selection methods for two important scenarios relevant to the modelling of yield monitor data in on-farm experiments. Local t-scores have been introduced for inferential purposes and the associated problem of multiple testing has been described in the context of analysing on-farm experiments. We demonstrate in this paper how local p-values can be adjusted to overcome this problem. To illustrate the applicability of our proposed method, we analysed two publicly available datasets. Graphical displays are created to guide practitioners to make informed decisions on optimal management practices. 2020 Journal Article http://hdl.handle.net/20.500.11937/81646 10.1016/j.fcr.2020.107783 English Elsevier restricted |
| spellingShingle | Science & Technology Life Sciences & Biomedicine Agronomy Agriculture Local likelihood Precision agriculture Geographically weighted regression Spatial nonstationarity Bandwidth selection Contour maps GEOGRAPHICALLY WEIGHTED REGRESSION OUT CROSS-VALIDATION FALSE DISCOVERY RATE R PACKAGE STATISTICAL-ANALYSIS MODELS AUTOCORRELATION TRIALS HETEROGENEITY PRECISION Rakshit, Suman Baddeley, Adrian Stefanova, Katia Reeves, Karyn Chen, Kefei Cao, Zhanglong Evans, Fiona Gibberd, Mark Novel approach to the analysis of spatially-varying treatment effects in on-farm experiments |
| title | Novel approach to the analysis of spatially-varying treatment effects in on-farm experiments |
| title_full | Novel approach to the analysis of spatially-varying treatment effects in on-farm experiments |
| title_fullStr | Novel approach to the analysis of spatially-varying treatment effects in on-farm experiments |
| title_full_unstemmed | Novel approach to the analysis of spatially-varying treatment effects in on-farm experiments |
| title_short | Novel approach to the analysis of spatially-varying treatment effects in on-farm experiments |
| title_sort | novel approach to the analysis of spatially-varying treatment effects in on-farm experiments |
| topic | Science & Technology Life Sciences & Biomedicine Agronomy Agriculture Local likelihood Precision agriculture Geographically weighted regression Spatial nonstationarity Bandwidth selection Contour maps GEOGRAPHICALLY WEIGHTED REGRESSION OUT CROSS-VALIDATION FALSE DISCOVERY RATE R PACKAGE STATISTICAL-ANALYSIS MODELS AUTOCORRELATION TRIALS HETEROGENEITY PRECISION |
| url | http://hdl.handle.net/20.500.11937/81646 |