Mapping 1-km soybean yield across China from 2001 to 2020 based on ensemble learning.

Zhang, M; Xu, X; Ou, J; Zhang, Z; Chen, F; Shi, L; Wang, B; Zhang, M; He, L; Zhang, X; Chen, Y; Hu, K; Feng, P

Mapping 1-km soybean yield across China from 2001 to 2020 based on ensemble learning.

Zhang, M Xu, X Ou, J Zhang, Z Chen, F Shi, L Wang, B Zhang, M He, L Zhang, X Chen, Y Hu, K Feng, P

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Publisher:: NATURE PORTFOLIO
Publication Type:: Journal Article
Citation:: Sci Data, 2025, 12, (1), pp. 408
Issue Date:: 2025-03-08

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Field	Value	Language
dc.contributor.author	Zhang, M
dc.contributor.author	Xu, X
dc.contributor.author	Ou, J
dc.contributor.author	Zhang, Z
dc.contributor.author	Chen, F
dc.contributor.author	Shi, L
dc.contributor.author	Wang, B
dc.contributor.author	Zhang, M
dc.contributor.author	He, L
dc.contributor.author	Zhang, X
dc.contributor.author	Chen, Y
dc.contributor.author	Hu, K
dc.contributor.author	Feng, P https://orcid.org/0000-0003-4845-9876
dc.date.accessioned	2025-07-15T05:56:09Z
dc.date.available	2025-02-28
dc.date.available	2025-07-15T05:56:09Z
dc.date.issued	2025-03-08
dc.identifier.citation	Sci Data, 2025, 12, (1), pp. 408
dc.identifier.issn	2052-4463
dc.identifier.issn	2052-4463
dc.identifier.uri	http://hdl.handle.net/10453/188305
dc.description.abstract	Soybean is a critical agricultural product in China, with domestic production unable to satisfy the substantial demand, leading to a huge reliance on imports. To support the scientific formulation of agricultural policies and the optimization of domestic planting structures, we developed a high-resolution annual soybean yield dataset for China (2001-2020), ChinaSoyYield1km. This dataset was generated by applying ensemble learning algorithms and spatial decomposition to a comprehensive set of multi-source data, including climate variables, remote sensing imagery, soil properties, agricultural management practices, and official yield records. The integration of these diverse datasets allows for a nuanced understanding of the factors influencing soybean yield at a 1-km resolution. The resulting dataset captures over 50% of the yield variability at the county scale, demonstrating superior accuracy compared to publicly available datasets with reductions in Root Mean Square Error (RMSE) ranging from 0.18 to 0.60 t/ha. It is anticipated that our dataset will enhance agricultural studies, planning, and policy-making related to soybean cultivation, providing a valuable resource for both the scientific community and government.
dc.format	Electronic
dc.language	eng
dc.publisher	NATURE PORTFOLIO
dc.relation.ispartof	Sci Data
dc.relation.isbasedon	10.1038/s41597-025-04738-x
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.mesh	Glycine max
dc.subject.mesh	China
dc.subject.mesh	Agriculture
dc.subject.mesh	Machine Learning
dc.subject.mesh	Remote Sensing Technology
dc.subject.mesh	Ensemble Learning
dc.subject.mesh	Agriculture
dc.subject.mesh	China
dc.subject.mesh	Remote Sensing Technology
dc.subject.mesh	Machine Learning
dc.subject.mesh	Glycine max
dc.subject.mesh	Ensemble Learning
dc.subject.mesh	Glycine max
dc.subject.mesh	China
dc.subject.mesh	Agriculture
dc.subject.mesh	Machine Learning
dc.subject.mesh	Remote Sensing Technology
dc.subject.mesh	Ensemble Learning
dc.title	Mapping 1-km soybean yield across China from 2001 to 2020 based on ensemble learning.
dc.type	Journal Article
utslib.citation.volume	12
utslib.location.activity	England
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2025-07-15T05:56:06Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	12
utslib.citation.issue	1

Abstract:

Soybean is a critical agricultural product in China, with domestic production unable to satisfy the substantial demand, leading to a huge reliance on imports. To support the scientific formulation of agricultural policies and the optimization of domestic planting structures, we developed a high-resolution annual soybean yield dataset for China (2001-2020), ChinaSoyYield1km. This dataset was generated by applying ensemble learning algorithms and spatial decomposition to a comprehensive set of multi-source data, including climate variables, remote sensing imagery, soil properties, agricultural management practices, and official yield records. The integration of these diverse datasets allows for a nuanced understanding of the factors influencing soybean yield at a 1-km resolution. The resulting dataset captures over 50% of the yield variability at the county scale, demonstrating superior accuracy compared to publicly available datasets with reductions in Root Mean Square Error (RMSE) ranging from 0.18 to 0.60 t/ha. It is anticipated that our dataset will enhance agricultural studies, planning, and policy-making related to soybean cultivation, providing a valuable resource for both the scientific community and government.

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http://hdl.handle.net/10453/188305