Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/59509
Title: Analysis and forecasting of Australian rice yield using phenology-based aggregation of satellite and weather data
Contributor(s): Brinkhoff, James  (author)orcid ; Clarke, Allister (author); Dunn, Brian W (author); Groat, Mark (author)
Corporate Author: AgriFutures Australia
Publication Date: 2024-06-15
Early Online Version: 2024-05-18
Open Access: Yes
DOI: 10.1016/j.agrformet.2024.110055
Handle Link: https://hdl.handle.net/1959.11/59509
Abstract: 

Rice yield depends on factors including variety, weather, field management, nutrient and water availability. We analyzed important drivers of yield variability at the field scale, and developed yield forecast models for crops in the temperate irrigated rice growing region of Australia. We fused a time-series of Sentinel1 and Sentinel-2 satellite remote sensing imagery, spatial weather data and field management information. Rice phenology was predicted using previously reported models. Higher yields were associated with early flowering, higher chlorophyll indices and higher temperatures around flowering. Successive rice cropping in the same field was associated with lower yield (p<0.001). After running a series of leave-one-year-out cross validation experiments, final models were trained using 2018–2022 data, and were applied to predicting the yield of 1580 fields (43,700 hectares) from an independent season with challenging conditions (2023). Models which aggregated remote sensing and weather time-series data to phenological periods provided more accurate predictions than models that aggregated these predictors to calendar periods. The accuracy of forecast models improved as the growing season progressed, reaching RMSE=1.6 t/ha and Lin’s concordance correlation coefficient (LCCC) of 0.67 30 days after flowering at the field level. Explainability was provided using the SHAP method, revealing the likely drivers of yield variability overall, and of individual fields.

Publication Type: Journal Article
Source of Publication: Agricultural and Forest Meteorology, v.353, p. 1-19
Publisher: Elsevier BV
Place of Publication: The Netherlands
ISSN: 1873-2240
0168-1923
Fields of Research (FoR) 2020: 300403 Agronomy
401304 Photogrammetry and remote sensing
300206 Agricultural spatial analysis and modelling
Socio-Economic Objective (SEO) 2020: 260308 Rice
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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