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Machine Learning-Based Modeling of Spatio-Temporally Varying Responses of Rainfed Corn Yield to Climate, Soil, and Management in the U.S. Corn Belt
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|Created:||Apr 03, 2021 at 10:46 p.m.|
|Last updated:|| Apr 03, 2021 at 11:15 p.m.
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This resource is a deposit of the data and codes used in the reference below:
Xu, T., Guan, K,, Peng, B., Wei, S. and Zhao, L. (2021) Machine Learning-Based Modeling of Spatio-Temporally Varying Responses of Rainfed Corn Yield to Climate, Soil, and Management in the U.S. Corn Belt. Front. Artif. Intell. 4:647999. doi: 10.3389/frai.2021.64799
We used random forest to provide in-season prediction of county-wise rainfed corn yield in the U.S. Corn Belt by integrating various predictors including climate, soil properties, and management data such as planting date.
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