Feature selection and explainable machine learning to identify climatic drivers of alfalfa yield in the Ogallala Region (USA)

IntroductionThe Ogallala aquifer region supports extensive alfalfa production but faces severe groundwater depletion and intensifying climatic volatility. This study aimed to develop a machine learning (ML) framework to predict regional alfalfa yield variability and identify the climatic factors ass...

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Hlavní autoři:	Sourajit Dey, Farshina Nazrul Shimim, Jiyung Kim, Prasad Deshpande, Xuan Xu, Bradley Whitaker, Mahendra Bhandari, Jamie L. Foster, Yuri Clements Daglia Calil, P. V. Vara Prasad, Jonathan Aguilar, Doohong Min, Gaurav Jha
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Frontiers Media S.A. 2026-06-01
Edice:	Frontiers in Agronomy
Témata:	alfalfa climatic variables feature selection machine learning Ogallala aquifer
On-line přístup:	https://www.frontiersin.org/articles/10.3389/fagro.2026.1789727/full
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