Machine Learning-Based Modeling of Spatio-Temporally Varying Responses of Rainfed Corn Yield to Climate, Soil, and Management in the U.S. Corn Belt

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.本资源收录了以下参考文献中使用的数据集及代码： Xu, T., Guan, K., Peng, B., Wei, S. and Zhao, L. (2021) 基于机器学习的美国玉米带雨养玉米产量时空变化响应的建模。人工智能前沿 4:647999. doi: 10.3389/frai.2021.64799 本研究采用随机森林算法，结合气候、土壤属性及种植日期等管理数据等多种预测因子，对美国玉米带各县雨养玉米产量进行季节性预测。