Emulating maize yields from global gridded crop models using statistical estimates

Abstract

The MIT Joint Program on the Science and Policy of Global Change combines cutting-edge scientific research with independent policy analysis to provide a solid foundation for the public and private decisions needed to mitigate and adapt to unavoidable global environmental changes. Being data-driven, the Program uses extensive Earth system and economic data and models to produce quantitative analysis and predictions of the risks of climate change and the challenges of limiting human influence on the environment-essential knowledge for the international dialogue toward a global response to climate change.To this end, the Program brings together an interdisciplinary group from two established MIT research centers: the Center for Global Change Science (CGCS) and the Center for Energy and Environmental Policy Research (CEEPR). These two centers-along with collaborators from the Marine Biology Laboratory (MBL) at Woods Hole and short-and longterm visitors-provide the united vision needed to solve global challenges.At the heart of much of the Program's work lies MIT's Integrated Global System Model. Through this integrated model, the Program seeks to: discover new interactions among natural and human climate system components; objectively assess uncertainty in economic and climate projections; critically and quantitatively analyze environmental management and policy proposals; understand complex connections among the many forces that will shape our future; and improve methods to model, monitor and verify greenhouse gas emissions and climatic impacts.This reprint is one of a series intended to communicate research results and improve public understanding of global environment and energy challenges, thereby contributing to informed debate about climate change and the economic and social implications of policy alternatives.

Aggregation of Gridded Emulated Rainfed Crop Yield Projections at the National or Regional Level

“…Blanc and Sultan (2015) and Blanc (2017) showed that the statistical emulators are overall able to replicate reasonably well the spatial patterns of yields crop projected by crop models in levels but also in term of changes overtime. However, as noted in Blanc (2017), "due to GGCM specificities, simulations are more suited to assess long-term trends in yields rather than interannual yield variability".…”

“…Additionally, as the weather effect on crops is expected to differ across soil types, the preferred estimation strategy estimates separate weather response functions for each soil order. Validation exercises by Blanc and Sultan (2015) and Blanc (2017) showed that, in general, the emulator reproduces relatively well the temporal and spatial patterns of climate change impacts on crop yields projected by GGCMs. Areas of disagreement regarding the sign of climate change impact on yields are limited and generally observed in areas where the projected yield impact is close to zero.…”

“…simplicity of statistical models, Blanc and Sultan (2015) and Blanc (2017) provide an ensemble of statistical tools emulating crops yields from global gridded crop models (GGCM) at the grid cell level (at a resolution of 0.5°x0.5° or roughly 50x50km) using a simple set of environmental variables.…”

“…To statistically estimate the determinants of crop yields, Blanc and Sultan (2015) and consider a parsimonious specification that only includes monthly precipitation, temperature and annual CO2 concentrations. Among various representations of environmental effects on crop growth, this set of variables was found to provide the best compromise in term of predictive ability and simplicity.…”

“…To benefit from the capabilities of processed-based models while preserving the application simplicity of statistical models, Blanc and Sultan (2015) and Blanc (2017) …”