Comparing impacts of climate change and mitigation on global agriculture by 2050

Abstract

Systematic model inter-comparison helps to narrow discrepancies in the analysis of the future impact of climate change on agricultural production. This paper presents a set of alternative scenarios by five global climate and agro-economic models. Covering integrated assessment (IMAGE), partial equilibrium (CAPRI, GLOBIOM, MAgPIE) and computable general equilibrium (MAGNET) models ensures a good coverage of biophysical and economic agricultural features. These models are harmonized with respect to basic model drivers, to assess the range of potential impacts of climate change on the agricultural sector by 2050. Moreover, they quantify the economic consequences of stringent global emission mitigation efforts, such as non-CO 2 emission taxes and land-based mitigation options, to stabilize global warming at 2 • C by the end of the century under different Shared Socioeconomic Pathways. A key contribution of the paper is a vis-à-vis comparison of climate change impacts relative to the impact of mitigation measures. In addition, our scenario design allows assessing the impact of the residual climate change on the mitigation challenge. From a global perspective, the impact of climate change on agricultural production by mid-century is negative but small. A larger negative effect on agricultural production, most pronounced for ruminant meat production, is observed when emission mitigation measures compliant with a 2 • C target are put in place. Our results indicate that a mitigation strategy that embeds residual climate change effects (RCP2.6) has a negative impact on global agricultural production relative to a no-mitigation strategy with stronger climate impacts (RCP6.0). However, this is partially due to the limited impact of the climate change scenarios by 2050. The magnitude of price changes is different amongst models due to methodological differences. Further research to achieve a better harmonization is needed, especially regarding endogenous food and feed demand, including substitution across individual commodities, and endogenous technological change.

Regional climate projections for impact assessment studies in East Africa

“…In addition to the coarse resolution, the weak performance of the CanESM2 and GCMs in representing the current climate, compared to SDSM, could be due to the complex topography of the region and because GCMs provide an area average rather than point information. These models are already showing higher deviation, particularly high overestimation for T-max and T-min, and cannot be expected to be accurate in the future at a local scale as they are designed for largescale climate and impact assessment studies (Meijl et al 2018). The projected change in T-max and T-min is higher in the 2050s and 2080s, particularly under RCP8.5 and this is due to the expected change in emissions of greenhouse gases (IPCC 2013).…”

Integrated Solutions for the Water-Energy-Land Nexus: Are Global Models Rising to the Challenge?

“…Each RCP represents a specific projection of future global GHG emissions and climate forcing trajectory [31]. Although there is significant variability among GGCs, they generally predict that climate change will reduce crop yields in low-latitude regions and increase yields in high-latitude regions [32]. At the global level, significant uncertainty remains regarding the direction of change with impacts on future agricultural production in the range of ±10% by the end of the century [25].…”

Sensitivity of Agricultural Development to Water-Related Drivers: The Case of Andalusia (Spain)

“…Socio-economic drivers are in line with the Shared Socioeconomic Pathway (SSP) 2. Climate change impacts on crop productivity build on the Representative Concentration Pathway (RCP) 6.0., considering the median case crop yield scenario from the 15 possible Global Circulation Model (GCM) and Global Gridded Crop Model (GGCM) combinations, as in van Meijl et al [60]. The median scenario for RCP 6.0 draws on the combination of the GCM HadGEM2-ES [61] and the GGCM DSSAT [62].…”