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Accounting for Weather Variability in Farm Management Resource Allocation in Northern Ghana: An Integrated Modeling Approach

Author

Listed:
  • Opeyemi Obafemi Adelesi

    (Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, Germany)

  • Yean-Uk Kim

    (Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, Germany)

  • Heidi Webber

    (Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, Germany)

  • Peter Zander

    (Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, Germany)

  • Johannes Schuler

    (Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, Germany)

  • Seyed-Ali Hosseini-Yekani

    (Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, Germany)

  • Dilys Sefakor MacCarthy

    (Soil and Irrigation Research Centre, School of Agriculture, University of Ghana, Accra P.O. Box LG 68, Ghana)

  • Alhassan Lansah Abdulai

    (CSIR-Savanna Agricultural Research Institute, Tamale P.O. Box TL 52, Ghana)

  • Karin van der Wiel

    (Royal Netherlands Meteorological Institute, 3731 GA De Bilt, The Netherlands)

  • Pierre C. Sibiry Traore

    (ICRISAT-Senegal, Dakar P.O. Box 24365, Senegal
    Manobi Africa PLC, Dakar P.O. Box 25026, Senegal)

  • Samuel Godfried Kwasi Adiku

    (Department of Soil Science, University of Ghana, Accra P.O. Box LG 245, Ghana)

Abstract

Smallholder farmers in Northern Ghana face challenges due to weather variability and market volatility, hindering their ability to invest in sustainable intensification options. Modeling can help understand the relationships between productivity, environmental, and economical aspects, but few models have explored the effects of weather variability on crop management and resource allocation. This study introduces an integrated modeling approach to optimize resource allocation for smallholder mixed crop and livestock farming systems in Northern Ghana. The model combines a process-based crop model, farm simulation model, and annual optimization model. Crop model simulations are driven by a large ensemble of weather time series for two scenarios: good and bad weather. The model accounts for the effects of climate risks on farm management decisions, which can help in supporting investments in sustainable intensification practices, thereby bringing smallholder farmers out of poverty traps. The model was simulated for three different farm types represented in the region. The results suggest that farmers could increase their income by allocating more than 80% of their land to cash crops such as rice, groundnut, and soybeans. The optimized cropping patterns have an over 50% probability of increasing farm income, particularly under bad weather scenarios, compared with current cropping systems.

Suggested Citation

  • Opeyemi Obafemi Adelesi & Yean-Uk Kim & Heidi Webber & Peter Zander & Johannes Schuler & Seyed-Ali Hosseini-Yekani & Dilys Sefakor MacCarthy & Alhassan Lansah Abdulai & Karin van der Wiel & Pierre C. , 2023. "Accounting for Weather Variability in Farm Management Resource Allocation in Northern Ghana: An Integrated Modeling Approach," Sustainability, MDPI, vol. 15(9), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7386-:d:1136076
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