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.

本资源为下述参考文献中所使用的数据与代码存档：
徐涛、关凯、彭博、魏帅、赵亮（2021）：美国玉米带雨养玉米产量对气候、土壤与管理措施的时空响应机器学习建模，《前沿人工智能》（Frontiers in Artificial Intelligence），第4卷，文章编号647999，DOI: 10.3389/frai.2021.64799。
本研究通过整合气候、土壤属性以及播种日期等多类预测因子，采用随机森林（random forest）算法实现美国玉米带各县域雨养玉米产量的季内预测。