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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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Abstract

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.

Subject Keywords

Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
Rainfed portion of U.S. Corn Belt
North Latitude
46.0000°
East Longitude
-83.0000°
South Latitude
37.0000°
West Longitude
-100.0000°

Temporal

Start Date:
End Date:

Content

How to Cite

Xu, T., K. Guan, B. Peng, S. Wei, L. Zhao (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, HydroShare, http://www.hydroshare.org/resource/3a80e06f59784a93a4e2acabf8a3ec93

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/
CC-BY

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