IDEAS home Printed from https://ideas.repec.org/a/eee/agisys/v206y2023ics0308521x23000082.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0308521X23000082
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agsy.2023.103603?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thomas Vempiliyath & Maitri Thakur & Vincent Hargaden, 2021. "Development of a Hybrid Simulation Framework for the Production Planning Process in the Atlantic Salmon Supply Chain," Agriculture, MDPI, vol. 11(10), pages 1-17, September.
    2. Eriksson, Anders & Eliasson, Lars & Sikanen, Lauri & Hansson, Per-Anders & Jirjis, Raida, 2017. "Evaluation of delivery strategies for forest fuels applying a model for Weather-driven Analysis of Forest Fuel Systems (WAFFS)," Applied Energy, Elsevier, vol. 188(C), pages 420-430.
    3. Armaghan Kosari Moghaddam & Hassan Sadrnia & Hassan Aghel & Mohammad Bannayan, 2018. "Optimization of tillage and sowing operations using discrete event simulation," Research in Agricultural Engineering, Czech Academy of Agricultural Sciences, vol. 64(4), pages 187-194.
    4. Hironori Yagi & Tsuneo Hayashi, 2021. "Machinery utilization and management organization in Japanese rice farms: Comparison of single‐family, multifamily, and community farms," Agribusiness, John Wiley & Sons, Ltd., vol. 37(2), pages 393-408, April.
    5. Yagi, Hironori & Hayashi, Tsuneo, 2021. "Working conditions and labor flexibility in non-family farms: weather-based labor management by Japanese paddy rice corporations," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 24(2), February.
    6. Borodin, Valeria & Bourtembourg, Jean & Hnaien, Faicel & Labadie, Nacima, 2016. "Handling uncertainty in agricultural supply chain management: A state of the art," European Journal of Operational Research, Elsevier, vol. 254(2), pages 348-359.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agisys:v:206:y:2023:i:c:s0308521x23000082. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agsy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.