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Climate change impacts on global agricultural land availability

by Xiao Zhang, Ximing Cai
Environmental Research Letters ()
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Abstract

Climate change can affect both crop yield and the land area suitable for agriculture. This study provides a spatially explicit estimate of the impact of climate change on worldwide agricultural land availability, considering uncertainty in climate change projections and ambiguity with regard to land classification. Uncertainty in general circulation model (GCM) projections is addressed using data assembled from thirteen GCMs and two representative emission scenarios (A1B andB1employCO2-equivalent greenhouse gas concentrations of 850 and 600 ppmv, respectively; B1 represents a greener economy). Erroneous data and the uncertain nature of land classifications based on multiple indices (i.e. soil properties, land slope, temperature, and humidity) are handled with fuzzy logic modeling. It is found that the total global arable land area is likely to decrease by 0.81.7% under scenario A1B and increase by 2.04.4% under scenario B1. Regions characterized by relatively high latitudes such as Russia, China and the US may expect an increase of total arable land by 3767%, 2236% and 417%, respectively, while tropical and sub-tropical regions may suffer different levels of lost arable land. For example, South America may lose 121% of its arable land area, Africa 118%, Europe 1117%, and India 24%. When considering, in addition, land used for human settlements and natural conservation, the net potential arable land may decrease even further worldwide by the end of the 21st century under both scenarios due to population growth. Regionally, it is likely that both climate change and population growth will cause reductions in arable land in Africa, South America, India and Europe. However, in Russia, China and the US, significant arable land increases may still be possible. Although the magnitudes of the projected changes vary by scenario, the increasing or decreasing trends in arable land area are regionally consistent.

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