{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T16:15:31Z","timestamp":1760890531453,"version":"build-2065373602"},"reference-count":80,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["KYCX23_1294","4210010673","SBK2020040616"],"award-info":[{"award-number":["KYCX23_1294","4210010673","SBK2020040616"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["KYCX23_1294","4210010673","SBK2020040616"],"award-info":[{"award-number":["KYCX23_1294","4210010673","SBK2020040616"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["KYCX23_1294","4210010673","SBK2020040616"],"award-info":[{"award-number":["KYCX23_1294","4210010673","SBK2020040616"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Improved agricultural production systems, together with increased grain yield, are essential to feed the growing global population in the 21st century. Global gridded crop models (GGCMs) have been extensively used to assess crop production and yield simulation on a large geographical scale. However, GGCMs are less effective when they are used on a finer scale, significantly limiting the precision in capturing the yearly maize yield. To address this issue, we propose a relatively more advanced approach that downsizes GGCMs by combining machine learning and crop modeling to enhance the accuracy of maize yield simulations on a regional scale. In this study, we combined the random forest algorithm with multiple data sources, trained the algorithm on low-resolution maize yield simulations from GGCMs, and applied it to a finer spatial resolution on a regional scale in China. We evaluated the performance of the eight GGCMs by utilizing a total of 1046 county-level maize yield data available over a 30-year period (1980\u20132010). Our findings reveal that the downscaled models created for maize yield simulations exhibited a remarkable level of accuracy (R2 \u2265 0.9, MAE &lt; 0.5 t\/ha, RMSE &lt; 0.75 t\/ha). The original GGCMs performed poorly in simulating county-level maize yields in China, and the improved GGCMs in our study captured an additional 17% variability in the county-level maize yields in China. Additionally, by optimizing nitrogen management strategies, we identified an average maize yield gap at the county level in China ranging from 0.47 to 1.82 t\/ha, with the south maize region exhibiting the highest yield gap. Our study demonstrates the high effectiveness of machine learning methods for the spatial downscaling of crop models, significantly improving GGCMs\u2019 performance in county-level maize yield simulations.<\/jats:p>","DOI":"10.3390\/rs16040701","type":"journal-article","created":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T06:00:25Z","timestamp":1708063225000},"page":"701","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources"],"prefix":"10.3390","volume":"16","author":[{"given":"Yangfeng","family":"Zou","sequence":"first","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8348-6477","authenticated-orcid":false,"given":"Giri Raj","family":"Kattel","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China"},{"name":"Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3052, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0332-8488","authenticated-orcid":false,"given":"Lijuan","family":"Miao","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1093\/biosci\/bix010","article-title":"Agriculture in 2050: Recalibrating Targets for Sustainable Intensification","volume":"67","author":"Hunter","year":"2017","journal-title":"BioScience"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.gfs.2014.08.004","article-title":"Food wedges: Framing the global food demand and supply challenge towards 2050","volume":"3","author":"Keating","year":"2014","journal-title":"Glob. 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