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Projected climate impacts to South African maize and wheat production in 2055: a comparison of empirical and mechanistic modeling approaches
Authors:Lyndon D. Estes  Hein Beukes  Bethany A. Bradley  Stephanie R. Debats  Michael Oppenheimer  Alex C. Ruane  Roland Schulze  Mark Tadross
Affiliation:1. Program in Science, Technology, and Environmental Policy, Woodrow Wilson School, Princeton University, , Princeton, NJ, 08544 USA;2. Department of Civil and Environmental Engineering, Princeton University, , Princeton, NJ 08544 USA;3. Agricultural Research Council, Institute for Soil, Climate, and Water, , Stellenbosch, 2599 South Africa;4. Department of Environmental Conservation, University of Massachusetts, , Amherst, MA, 01003 USA;5. Department of Geosciences, Princeton University, , Princeton, NJ, 08544 USA;6. NASA GISS Climate Impacts Group/SSP, , New York, NY, 10025 USA;7. School of Bioresources Engineering and Environmental Hydrology, University of KwaZulu‐ Natal, , Pietermaritzburg, 3209 South Africa;8. Climate Systems Analysis Group, University of Cape Town, , Rondebosch, 7701 South Africa
Abstract: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.
Keywords:climate change  crop model  downscaling  DSSAT  empirical  generalized additive model  mechanistic  South Africa  Triticum aestivum  Zea mays
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