Quantifying the Indirect Impacts of Climate on Agriculture: An Inter-Method Comparison
Much of the literature on the effect of climate on agriculture has focused on linking projections of changes in climate to process-based or statistical crop models. However, the changes in productivity have broader economic implications that cannot be quantified in crop models alone. How important are these socio-economic feedbacks to a comprehensive assessment of the impacts of climate change on agriculture? In this paper, researchers attempt to measure the importance of these interaction effects through an inter-method comparison between process models, statistical models, and integrated assessment model (IAMs).
The results demonstrate the important role of IAMs in climate change impact studies, and highlight the challenges that a modeler must face when attempting to couple yield impacts from crop or statistical models into an IAM. Issues related to differences in spatial, temporal, and sectoral resolution; and differences in base year data between crop and statistical models and IAMs must be addressed.
Researchers assessed the differences between process-based crop models, statistical crop models, and IAMs in their estimates of climate change impacts on agriculture. They find that IAMs show fewer negative effects than process-based and statistical crop models due to the inclusion of factors such as technological change, input substitution, and crop switching. They find the effect of these additional factors to be large, with the additional impact on yields ranging from 20%–40%. Some of these increases are due to the inclusion of technological change, a factor present in simulations both with and without climate change. Other factors (e.g. input substitution and crop switching) are induced by the inclusion of climate effects. The effect of these dynamics range from −12% to +15%.