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

Author

Listed:
  • Huber, Isaiah
  • Wang, Lizhi
  • Hatfield, Jerry L.
  • Hanna, H. Mark
  • Archontoulis, Sotirios V.

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.

Suggested Citation

  • Huber, Isaiah & Wang, Lizhi & Hatfield, Jerry L. & Hanna, H. Mark & Archontoulis, Sotirios V., 2023. "Modeling days suitable for fieldwork using machine learning, process-based, and rule-based models," Agricultural Systems, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:agisys:v:206:y:2023:i:c:s0308521x23000082
    DOI: 10.1016/j.agsy.2023.103603
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    References listed on IDEAS

    as
    1. Wu, Lianhai & Wu, Lu & Bingham, Ian J. & Misselbrook, Thomas H., 2022. "Projected climate effects on soil workability and trafficability determine the feasibility of converting permanent grassland to arable land," Agricultural Systems, Elsevier, vol. 203(C).
    2. de Toro, A. & Hansson, P. -A., 2004. "Analysis of field machinery performance based on daily soil workability status using discrete event simulation or on average workday probability," Agricultural Systems, Elsevier, vol. 79(1), pages 109-129, January.
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