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Analysis of field machinery performance based on daily soil workability status using discrete event simulation or on average workday probability

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  • de Toro, A.
  • Hansson, P. -A.

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  • 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.
    • Handle: RePEc:eee:agisys:v:79:y:2004:i:1:p:109-129
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    References listed on IDEAS

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    1. Papy, Francois & Attonaty, Jean-Marie & Laporte, Clotilde & Soler, Louis-Georges, 1988. "Work organization simulation as a basis for farm management advice (equipment and manpower, levels against climatic variability)," Agricultural Systems, Elsevier, vol. 27(4), pages 295-314.
    2. van Elderen, E., 1980. "Models and techniques for scheduling farm operations: A comparison," Agricultural Systems, Elsevier, vol. 5(1), pages 1-17.
    3. Van Elderen, E., 1980. "Scheduling farm operations," European Journal of Operational Research, Elsevier, vol. 4(1), pages 19-23, January.
    4. Edwards, William M. & Boehlje, Michael, 1980. "Machinery Selection Considering Timeliness Losses," Staff General Research Papers Archive 11065, Iowa State University, Department of Economics.
    5. Lal, H. & Jones, J. W. & Peart, R. M. & Shoup, W. D., 1992. "FARMSYS--A whole-farm machinery management decision support system," Agricultural Systems, Elsevier, vol. 38(3), pages 257-273.
    6. Etyang, Martin N. & Preckel, Paul V. & Binkley, James K. & Doster, D. Howard, 1998. "Field time constraints for farm planning models," Agricultural Systems, Elsevier, vol. 58(1), pages 25-37, September.
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    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.

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