Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy
Tipo:
Título:
Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy
Creador/a:
Silva, J.V.;
Heerwaarden, J.;
Reidsma, P.;
Laborte, A.G.;
Fantaye, K.T.;
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van Ittersum, M.K.
Heerwaarden, J.;
Reidsma, P.;
Laborte, A.G.;
Fantaye, K.T.;
Fantaye, K.T.
https://orcid.org/0000-0002-7201-8053
Scopus ID
Mendeley
Items in this Repository
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van Ittersum, M.K.
Año:
2023
Copyright:
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Revista:
Field Crops Research
Volumen de la Revista:
302
Número de artículo:
109063
Lugar de publicación:
Amsterdam (Netherlands)
Editor:
Elsevier B.V.
Cita:
Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy. 2023. 302 DOI: 10.1016/j.fcr.2023.109063 Elsevier B.V..
Iniciativas del CGIAR
Iniciativa:
Excellence in Agronomy
Área de impacto:
Nutrition, health & food security
Poverty reduction, livelihoods & jobs
Poverty reduction, livelihoods & jobs
Área de acción:
Resilient Agrifood Systems
Donante o financiador:
Netherlands Science Foundation
Bill & Melinda Gates Foundation (BMGF)
Bill & Melinda Gates Foundation (BMGF)
URL en CGSpace: