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Forecasting future crop suitability with microclimate data

Author

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  • Gardner, A.S.
  • Maclean, I.M.D.
  • Gaston, K.J.
  • Bütikofer, L.

Abstract

Against a background of unprecedented climate change, humanity faces the challenge of how to increase global food production without compromising the natural environment. Crop suitability models can indicate the best locations to grow different crops and, in doing so, support efficient use of land to leave space for, or share space with, nature. However, challenges in downscaling the climate data needed to drive these models to make predictions for the future has meant that they are often run using national or regional climate projections. At finer spatial scales, variation in climate conditions can have a substantial influence on yield and so the continued use of coarse resolution climate data risks maladaptive agricultural decisions. Opportunities to grow novel crops, for which knowledge of local variation in microclimate may be critical, may be missed.

Suggested Citation

  • Gardner, A.S. & Maclean, I.M.D. & Gaston, K.J. & Bütikofer, L., 2021. "Forecasting future crop suitability with microclimate data," Agricultural Systems, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:agisys:v:190:y:2021:i:c:s0308521x21000378
    DOI: 10.1016/j.agsy.2021.103084
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    Cited by:

    1. Kalpana Jain & Naveen Choudhary, 2022. "Comparative analysis of machine learning techniques for predicting production capability of crop yield," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 583-593, March.
    2. Talukdar, Swapan & Naikoo, Mohd Waseem & Mallick, Javed & Praveen, Bushra & Shahfahad, & Sharma, Pritee & Islam, Abu Reza Md. Towfiqul & Pal, Swades & Rahman, Atiqur, 2022. "Coupling geographic information system integrated fuzzy logic-analytical hierarchy process with global and machine learning based sensitivity analysis for agricultural suitability mapping," Agricultural Systems, Elsevier, vol. 196(C).

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