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Uncertainty is more than a number or colour: Involving experts in uncertainty assessments of yield gaps

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  • Schils, René L.M.
  • van Voorn, George A.K.
  • Grassini, Patricio
  • van Ittersum, Martin K.

Abstract

Yield gap analysis plays an important role in determining potential food availability. The Global Yield Gap Atlas maps yield gaps of crops from point to regional scale across the globe. The calculated yield gaps are based on comparisons between modelled potential yields with actual farmers' yields derived from statistical sources. The calculations are subject to uncertainty due to various sources, including measurement errors, modelling limitations, and scaling issues.

Suggested Citation

  • Schils, René L.M. & van Voorn, George A.K. & Grassini, Patricio & van Ittersum, Martin K., 2022. "Uncertainty is more than a number or colour: Involving experts in uncertainty assessments of yield gaps," Agricultural Systems, Elsevier, vol. 195(C).
  • Handle: RePEc:eee:agisys:v:195:y:2022:i:c:s0308521x2100264x
    DOI: 10.1016/j.agsy.2021.103311
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    References listed on IDEAS

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    1. Confalonieri, Roberto & Bregaglio, Simone & Acutis, Marco, 2016. "Quantifying uncertainty in crop model predictions due to the uncertainty in the observations used for calibration," Ecological Modelling, Elsevier, vol. 328(C), pages 72-77.
    2. van Dijk, Michiel & Morley, Tomas & van Loon, Marloes & Reidsma, Pytrik & Tesfaye, Kindie & van Ittersum, Martin K., 2020. "Reducing the maize yield gap in Ethiopia: Decomposition and policy simulation," Agricultural Systems, Elsevier, vol. 183(C).
    3. repec:wrk:wrkemf:22 is not listed on IDEAS
    4. S. Asseng & F. Ewert & C. Rosenzweig & J. W. Jones & J. L. Hatfield & A. C. Ruane & K. J. Boote & P. J. Thorburn & R. P. Rötter & D. Cammarano & N. Brisson & B. Basso & P. Martre & P. K. Aggarwal & C., 2013. "Uncertainty in simulating wheat yields under climate change," Nature Climate Change, Nature, vol. 3(9), pages 827-832, September.
    5. Ernesto Carrella, 2021. "No Free Lunch when Estimating Simulation Parameters," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 24(2), pages 1-7.
    6. Warren E. Walker & Vincent A. W. J. Marchau & Jan H. Kwakkel, 2013. "Uncertainty in the Framework of Policy Analysis," International Series in Operations Research & Management Science, in: Wil A. H. Thissen & Warren E. Walker (ed.), Public Policy Analysis, edition 127, chapter 0, pages 215-261, Springer.
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