Climatic Change

, Volume 116, Issue 2, pp 177–189

Predicting climate change effects on agriculture from ecological niche modeling: who profits, who loses?

Article

DOI: 10.1007/s10584-012-0481-x

Cite this article as:
Beck, J. Climatic Change (2013) 116: 177. doi:10.1007/s10584-012-0481-x
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Abstract

The susceptibility of agriculture to changing environmental conditions is arguably the most dangerous short-term consequence of climate change, and predictions on the geography of changes will be useful for implementing mitigation strategies. Ecological niche modeling (ENM) is a technique used to relate presence records of species to environmental variables. By extrapolation, ENM maps the suitability of a landscape for the species in question. Recently, ENM was successfully applied to predict the geographic distribution of agriculture. Using climate and soil conditions as predictor variables, agricultural suitability was mapped across the Old World. Here, I present analogous ENM-based maps of the suitability for agriculture under climate change scenarios for the year 2050. Deviations of predicted scenarios from a current conditions model were analyzed by (1) comparing relative average change across regions, and (2) by relating country-wide changes to the data indicative of the wealth of nations. The findings indicate that different regions vary considerably in whether they win or lose in agricultural suitability, even if average change across the entire study region is small. A positive relationship between the wealth of nations and change in agriculture conditions was found, but variability around this trend was high. Parts of Africa, Europe and southern and eastern Asia were predicted to be particularly negatively affected, while north-eastern Europe, among other regions, can expect more favorable conditions for agriculture. The results are presented as an independent “second opinion” to previously published, more complex forecasts on agricultural productivity and food supply variability due to climatic change, which were based on fitting environmental variables to yield statistics.

Supplementary material

10584_2012_481_MOESM1_ESM.pdf (5.9 mb)
Esm 1Appendix 1: Model details and alternative GCMs. Appendix 2: Excel-Table with raw data and country-wide averages (PDF 5999 kb)
10584_2012_481_MOESM2_ESM.xls (9.7 mb)
SUPPLEMENT 2(XLS 9.65 MB)

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Department of Environmental Science, BiogeographyUniversity of BaselBaselSwitzerland