Random Forests for Global and Regional Crop Yield Predictions-Reference-Cited by-同舟云学术

Random Forests for Global and Regional Crop Yield Predictions

Published:2016-06-03 Issue:6 Volume:11 Page:e0156571
ISSN:1932-6203
Container-title:PLOS ONE
language:en
Short-container-title:PLoS ONE

Author:

Jeong Jig Han,Resop Jonathan P.,Mueller Nathaniel D.,Fleisher David H.,Yun Kyungdahm,Butler Ethan E.,Timlin Dennis J.,Shim Kyo-Moon,Gerber James S.,Reddy Vangimalla R.,Kim Soo-Hyung^ORCID

Funder

Rural Development Administration

U.S. Department of Agriculture

National Institute of Food and Agriculture

USDA-ARS Headquarters Postdoctoral Research Associate Program

Directorate for Geosciences

David and Lucile Packard Foundation

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference50 articles.

1. Why are agricultural impacts of climate change so uncertain? The importance of temperature relative to precipitation;DB Lobell;Environ Res Lett,2008

2. Global food demand and the sustainable intensification of agriculture;D Tilman;Proceedings of the National Academy of Sciences,2011

3. On the use of statistical models to predict crop yield responses to climate change;DB Lobell;Agricultural and Forest Meteorology,2010

4. Decline in rice grain yields with temperature: Models and correlations can give different estimates;JE Sheehy;Field Crop Res,2006

5. A parsimonious, multiple-regression model of wheat yield response to environment.;S Landau;Agricultural and Forest Meteorology,2000

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