Random Forests for Global and Regional Crop Yield Predictions

Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and r...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4892571/
https://www.ncbi.nlm.nih.gov/pubmed/27257967
http://dx.doi.org/10.1371/journal.pone.0156571

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4892571/
https://www.ncbi.nlm.nih.gov/pubmed/27257967
http://dx.doi.org/10.1371/journal.pone.0156571

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