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Projected climate impacts to S outh A frican maize and wheat production in 2055: a comparison of empirical and mechanistic modeling approaches
Author(s) -
Estes Lyndon D.,
Beukes Hein,
Bradley Bethany A.,
Debats Stephanie R.,
Oppenheimer Michael,
Ruane Alex C.,
Schulze Roland,
Tadross Mark
Publication year - 2013
Publication title -
global change biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.146
H-Index - 255
eISSN - 1365-2486
pISSN - 1354-1013
DOI - 10.1111/gcb.12325
Subject(s) - environmental science , crop , climate change , crop yield , productivity , crop simulation model , yield (engineering) , agriculture , simulation modeling , production (economics) , winter wheat , agricultural productivity , climate model , agronomy , agricultural engineering , atmospheric sciences , mathematics , ecology , biology , economics , physics , mathematical economics , macroeconomics , engineering , thermodynamics
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 ( MM s) and empirical models ( EM s) are rare despite both being used widely in this field. We combined MM s and EM s to project future (2055) changes in the potential distribution (suitability) and productivity of maize and spring wheat in S outh A frica under 18 downscaled climate scenarios (9 models run under 2 emissions scenarios). EM s projected larger yield losses or smaller gains than MM s. The EM s’ 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 MM s under elevated CO 2 accounted for much of the EM − MM difference, but EM s may have more accurately represented crop temperature sensitivities. Our results align with earlier studies showing that EM s may show larger climate change losses than MM s. Crop forecasting efforts should expand to include EM − MM comparisons to provide a fuller picture of crop–climate response uncertainties.