Modeling days suitable for fieldwork using machine learning, process-based, and rule-based models

Isaiah Huber, Lizhi Wang, Jerry L. Hatfield, H. Mark Hanna and Sotirios V. Archontoulis

Agricultural Systems, 2023, vol. 206, issue C

Abstract: Prediction of days suitable for fieldwork is important for understanding the potential effects of climate change and for selecting machinery systems to improve efficiency in field operations and avoid soil damage. Yet, we lack predictive models to inform decision-making at scale.

Keywords: APSIM; Optimization; Field workability; Data-driven models; Soil moisture (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:agisys:v:206:y:2023:i:c:s0308521x23000082

DOI: 10.1016/j.agsy.2023.103603

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