Uncertainties in estimating global potential yields and their impacts for long-term modeling

Mary Ollenburger, Page Kyle () and Xin Zhang
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Mary Ollenburger: University of Maryland Center for Environmental Science
Page Kyle: Joint Global Change Research Institute, Pacific Northwest National Laboratory, 5825 University Research Court
Xin Zhang: University of Maryland Center for Environmental Science

Food Security: The Science, Sociology and Economics of Food Production and Access to Food, 2022, vol. 14, issue 5, No 6, 1177-1190

Abstract: Abstract Estimating realistic potential yields by crop type and region is challenging; such yields depend on both biophysical characteristics (e.g., soil characteristics, climate, etc.), and the crop management practices available in any site or region (e.g., mechanization, irrigation, crop cultivars). A broad body of literature has assessed potential yields for selected crops and regions, using several strategies. In this study we first analyze future potential yields of major crop types globally by two different estimation methods, one of which is based on historical observed yields (“Empirical”), while the other is based on biophysical conditions (“Simulated”). Potential yields by major crop and region are quite different between the two methods; in particular, Simulated potential yields are typically 200% higher than Empirical potential yields in tropical regions for major crops. Applying both of these potential yields in yield gap closure scenarios in a global agro-economic model, GCAM, the two estimates of future potential yields lead to very different outcomes for the agricultural sector globally. In the Simulated potential yield closure scenario, Africa, Asia, and South America see comparatively favorable outcomes for agricultural sustainability over time: low land use change emissions, low crop prices, and high levels of self-sufficiency. In contrast, the Empirical potential yield scenario is characterized by a heavy reliance on production and exports in temperate regions that currently practice industrial agriculture. At the global level, this scenario has comparatively high crop commodity prices, and more land allocated to crop production (and associated land use change emissions) than either the baseline or Simulated potential yield scenarios. This study highlights the importance of the choice of methods of estimating potential yields for agro-economic modeling.

Keywords: Agricultural intensification; Yield gap; Global agricultural models; Scenario uncertainty (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s12571-021-01228-x

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