Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

CONTEXT: Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. OBJECTIVE: The aim of this study was to assess the performance of statistical and machine learning methods to ex...

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Detalles Bibliográficos
Autores principales: Silva, João Vasco, Heerwaarden, Joost van, Reidsma, Pytrik, Laborte, Alice G., Tesfaye, Kindie, Ittersum, Martin K. van
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Scientific Pub. Co 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10565834/
https://www.ncbi.nlm.nih.gov/pubmed/37840838
http://dx.doi.org/10.1016/j.fcr.2023.109063

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10565834/
https://www.ncbi.nlm.nih.gov/pubmed/37840838
http://dx.doi.org/10.1016/j.fcr.2023.109063

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