Literature DB >> 23864352

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

Lyndon D Estes1, Hein Beukes, Bethany A Bradley, Stephanie R Debats, Michael Oppenheimer, Alex C Ruane, Roland Schulze, Mark Tadross.   

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.
© 2013 John Wiley & Sons Ltd.

Entities:  

Keywords:  DSSAT; South Africa; Triticum aestivum; Zea mays; climate change; crop model; downscaling; empirical; generalized additive model; mechanistic

Mesh:

Year:  2013        PMID: 23864352     DOI: 10.1111/gcb.12325

Source DB:  PubMed          Journal:  Glob Chang Biol        ISSN: 1354-1013            Impact factor:   10.863


  5 in total

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Authors:  Maisa Rojas; Fabrice Lambert; Julian Ramirez-Villegas; Andrew J Challinor
Journal:  Proc Natl Acad Sci U S A       Date:  2019-03-11       Impact factor: 11.205

2.  A doubling of atmospheric CO2 mitigates the effects of severe drought on maize through the preservation of soil water.

Authors:  B S Ripley; T M Bopape; S Vetter
Journal:  Ann Bot       Date:  2022-04-13       Impact factor: 4.357

3.  High Resolution, Annual Maps of Field Boundaries for Smallholder-Dominated Croplands at National Scales.

Authors:  Lyndon D Estes; Su Ye; Lei Song; Boka Luo; J Ronald Eastman; Zhenhua Meng; Qi Zhang; Dennis McRitchie; Stephanie R Debats; Justus Muhando; Angeline H Amukoa; Brian W Kaloo; Jackson Makuru; Ben K Mbatia; Isaac M Muasa; Julius Mucha; Adelide M Mugami; Judith M Mugami; Francis W Muinde; Fredrick M Mwawaza; Jeff Ochieng; Charles J Oduol; Purent Oduor; Thuo Wanjiku; Joseph G Wanyoike; Ryan B Avery; Kelly K Caylor
Journal:  Front Artif Intell       Date:  2022-02-25

4.  Global Synthesis of Drought Effects on Maize and Wheat Production.

Authors:  Stefani Daryanto; Lixin Wang; Pierre-André Jacinthe
Journal:  PLoS One       Date:  2016-05-25       Impact factor: 3.240

5.  Geographical patterns in climate and agricultural technology drive soybean productivity in Brazil.

Authors:  Jordana Moura Caetano; Geiziane Tessarolo; Guilherme de Oliveira; Kelly da Silva E Souza; José Alexandre Felizola Diniz-Filho; João Carlos Nabout
Journal:  PLoS One       Date:  2018-01-30       Impact factor: 3.240

  5 in total

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