Random Forest Regression for Optimizing Variable Planting Rates for Corn and Soybean Using High-Resolution Topographical and Soil Data
M. R. Krause, S. Crossman, T. DuMond, R. Lott, J. Swede, S. Arliss, R. Robbins, D. Ochs, M. A. Gore
The paper develops a random forest regression model to optimize corn and soybean planting rates using topographical and soil data, finding that yield responses depend on complex site-specific interactions.
bioRxiv · Feb 17, 2020Plant Biology