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


Authors:
Owners:
Resource type: Composite Resource
Storage: The size of this resource is 17.7 MB
Created: Apr 03, 2021 at 10:46 p.m.
Last updated: Apr 03, 2021 at 11:15 p.m.
Citation: See how to cite this resource
Sharing Status: Public
Views: 210
Downloads: 16
+1 Votes: Be the first one to 
 this.
Comments: No comments (yet)

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

Deleting all keywords will set the resource sharing status to private.

Resource Level 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

Comments

There are currently no comments

New Comment

required