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


Type:
Article
Title:
Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy
Creator:
Silva, J.V.;
ORCID iD icon
Silva, J.V.
ORCID iD iconhttps://orcid.org/0000-0002-3019-5895
ScopusScopus ID
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Heerwaarden, J. van;
Heerwaarden, J. van
ORCID iD iconhttps://orcid.org/0000-0002-4959-3914
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Reidsma, P.;
Reidsma, P.
ORCID iD iconhttps://orcid.org/0000-0003-2294-809X
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Laborte, A.G.;
Laborte, A.G.
ORCID iD iconhttps://orcid.org/0000-0002-6689-8920
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Fantaye, K.T.;
ORCID iD icon
Fantaye, K.T.
ORCID iD iconhttps://orcid.org/0000-0002-7201-8053
ScopusScopus ID
mendeleyMendeley
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van Ittersum, M.K.
van Ittersum, M.K.
ORCID iD iconhttps://orcid.org/0000-0001-8611-6781
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Year:
2023
URI:
https://hdl.handle.net/10883/22678
Copyright:
CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
Journal:
Field Crops Research
Journal volume:
302
Article number:
109063
DOI:
10.1016/j.fcr.2023.109063
Place of Publication:
Amsterdam (Netherlands)
Publisher:
Elsevier B.V.
Citation:
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..

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CGIAR Initiatives

Initiative:
Excellence in Agronomy
Impact Area:
Nutrition, health & food security
Poverty reduction, livelihoods & jobs
Action Area:
Resilient Agrifood Systems
Donor or Funder:
Netherlands Science Foundation
Bill & Melinda Gates Foundation
CGSpace URL:
https://hdl.handle.net/10568/131409

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