A Simpler, Faster Way to Assess Environmental Impacts on Crop Yields
Regional and global changes in crop yields impact land-use change, with implications for carbon sources and sinks and the energy balance and hydrological feedbacks to the Earth system. To assess likely environmental impacts on crop yields, researchers typically run a combination of earth system and globally gridded crop models that project how yields of maize, wheat, and other key crops will change over time. The suite of models commonly used to simulate crop yields are computationally intensive and produce projections that vary significantly, indicating structural uncertainty. To generate projections that account for wide-ranging modeling uncertainty with far less computational resources, a new toolset of statistical emulators has been developed.
Extending an earlier study focused on maize yields, this research provides a computationally efficient way to represent the impact of the environment on crop yields and land-use change within an integrated assessment model.
Process-based crop models can simulate a wide range of weather and environmental conditions, but are computationally demanding. Statistical models, which are based on observed yield data, are much more efficient, but are hampered by incomplete data sets: crops are only grown under conditions where they do reasonably well most of the time, and hence these models are ill-equipped to estimate the impacts of scenarios well outside the bounds of observation. A third approach is to combine the best of both methods, “training” a statistical model to make reasonably accurate predictions based on the output of a process-based model, but predictions from more than one process-based model must be considered to account for uncertainty in the impact of the environment on crop yields. To that end, Elodie Blanc, a research scientist at the MIT Program for the Science and Policy of Global Change, has trained five simple statistical models to accurately replicate the outcomes of five process-based, globally gridded crop models under diverse environmental conditions. Using the statistical models to predict the responses of maize, rice, soybean, and wheat yields to variations in temperature and precipitation, Blanc found good agreement between predictions from the process and statistical models. The research, which appears in Agricultural and Forest Meteorology, draws upon a previous collaboration in 2015 with Benjamin Sultan of the University Pierre and Marie Curie in Paris.