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Random Forest Regression for Optimizing Variable Planting Rates for Corn and Soybean Using High-Resolution Topographical and Soil Data
View ORCID ProfileMargaret R. Krause, Savanna Crossman, Todd DuMond, Rodman Lott, Jason Swede, Scott Arliss, Ron Robbins, Daniel Ochs, View ORCID ProfileMichael A. Gore
doi: https://doi.org/10.1101/2020.02.17.952556
Margaret R. Krause
*Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, 14853, USA
Savanna Crossman
†New York Corn and Soybean Growers Association, PO Box 133, Silver Springs, NY, 14550, USA
Todd DuMond
†New York Corn and Soybean Growers Association, PO Box 133, Silver Springs, NY, 14550, USA
Rodman Lott
†New York Corn and Soybean Growers Association, PO Box 133, Silver Springs, NY, 14550, USA
Jason Swede
†New York Corn and Soybean Growers Association, PO Box 133, Silver Springs, NY, 14550, USA
Scott Arliss
†New York Corn and Soybean Growers Association, PO Box 133, Silver Springs, NY, 14550, USA
Ron Robbins
†New York Corn and Soybean Growers Association, PO Box 133, Silver Springs, NY, 14550, USA
Daniel Ochs
†New York Corn and Soybean Growers Association, PO Box 133, Silver Springs, NY, 14550, USA
Michael A. Gore
*Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, 14853, USA
Article usage
Posted February 17, 2020.
Random Forest Regression for Optimizing Variable Planting Rates for Corn and Soybean Using High-Resolution Topographical and Soil Data
Margaret R. Krause, Savanna Crossman, Todd DuMond, Rodman Lott, Jason Swede, Scott Arliss, Ron Robbins, Daniel Ochs, Michael A. Gore
bioRxiv 2020.02.17.952556; doi: https://doi.org/10.1101/2020.02.17.952556
Random Forest Regression for Optimizing Variable Planting Rates for Corn and Soybean Using High-Resolution Topographical and Soil Data
Margaret R. Krause, Savanna Crossman, Todd DuMond, Rodman Lott, Jason Swede, Scott Arliss, Ron Robbins, Daniel Ochs, Michael A. Gore
bioRxiv 2020.02.17.952556; doi: https://doi.org/10.1101/2020.02.17.952556
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