Projected climate impacts to South African maize and wheat production in 2055: A comparison of empirical and mechanistic modeling approaches

Lyndon D. Estes, Hein Beukes, Bethany A. Bradley, Stephanie R. Debats, Michael Oppenheimer, Alex C. Ruane, Roland Schulze, Mark Tadross

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61 Scopus citations


Crop model-specific biases are a key uncertainty affecting our understanding of climate change impacts to agriculture. There is increasing research focus on intermodel variation, but comparisons between mechanistic (MMs) and empirical models (EMs) are rare despite both being used widely in this field. We combined MMs and EMs to project future (2055) changes in the potential distribution (suitability) and productivity of maize and spring wheat in South Africa under 18 downscaled climate scenarios (9 models run under 2 emissions scenarios). EMs projected larger yield losses or smaller gains than MMs. The EMs' median-projected maize and wheat yield changes were -3.6% and 6.2%, respectively, compared to 6.5% and 15.2% for the MM. The EM projected a 10% reduction in the potential maize growing area, where the MM projected a 9% gain. Both models showed increases in the potential spring wheat production region (EM = 48%, MM = 20%), but these results were more equivocal because both models (particularly the EM) substantially overestimated the extent of current suitability. The substantial water-use efficiency gains simulated by the MMs under elevated CO2 accounted for much of the EM-MM difference, but EMs may have more accurately represented crop temperature sensitivities. Our results align with earlier studies showing that EMs may show larger climate change losses than MMs. Crop forecasting efforts should expand to include EM-MM comparisons to provide a fuller picture of crop-climate response uncertainties.

Original languageEnglish (US)
Pages (from-to)3762-3774
Number of pages13
JournalGlobal Change Biology
Issue number12
StatePublished - Dec 2013

All Science Journal Classification (ASJC) codes

  • General Environmental Science
  • Global and Planetary Change
  • Ecology
  • Environmental Chemistry


  • Climate change
  • Crop model
  • Downscaling
  • Empirical
  • Generalized additive model
  • Mechanistic
  • South Africa
  • Triticum aestivum
  • Zea mays


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