Abstract
Reliable yield estimation is crucial for food security and agricultural production especially in the intensively agricultural region. This study constructed a gridded yield estimation framework by driving machine learning models with remote sensing vegetation index and meteorological forcing. Among eight machine learning methods, support vector machine (SVM), k-nearest neighbor regression (KNN), and Gaussian process regression (GPR) models outperformed the others. Precipitation, temperature, and the fraction of photosynthetically active radiation are key factors for yield estimation. The yield estimation at county level and regional level were further conducted to explore the scale effect (estimation accuracy varies with spatial resolution). Different scales hold diverse spatial variability information. Finer scales that are more representative of spatial variability generally result in the better accuracy. This study demonstrates that a more accurate yield estimation can be achieved at a finer grid level, thus providing guidelines for agricultural planting structure.
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The data used in this research will be available (by the corresponding author), upon reasonable request.
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The study primarily used the following Python packages: sklearn and matplotlib. The code will be available upon reasonable request.
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Acknowledgements
The authors thank the Zhangye Statistical Bureau and Bayannur City Bureau of Agriculture and Animal Husbandry for providing the yield data. The authors also thank all the contributors for CMFD, MODIS and MIRCA datasets. The authors thank the reviewers for their constructive comments and useful suggestions on earlier versions of this manuscript.
Funding
This work was financially supported by the National Natural Science Foundation of China (51679233) and the Special Project on National Science and Technology Basic Resources Investigation of China (2021FY100703).
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Conceptualization, Jun Niu and Dehai Liao; Data curation, Dehai Liao and Na Lu; Formal analysis, Dehai Liao and Qianxi Shen; Funding acquisition, Jun Niu; Investigation, Qianxi Shen; Methodology, Jun Niu and Dehai Liao; Supervision, Jun Niu; Validation, Dehai Liao and Jun Niu; Visualization, Na Lu; Writing—original draft, Dehai Liao; Writing—review & editing, Jun Niu. All authors have read and approved the final manuscript.
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Liao, D., Niu, J., Lu, N. et al. Towards crop yield estimation at a finer spatial resolution using machine learning methods over agricultural regions. Theor Appl Climatol 146, 1387–1401 (2021). https://doi.org/10.1007/s00704-021-03799-3
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DOI: https://doi.org/10.1007/s00704-021-03799-3