26 June 2019

Efficient Assessment of Environmental Impacts on Crop Yields at the Regional Level

New toolset of crop-yield emulators enables computationally efficient assessment of environmental impacts on crop yields at national and regional scales, which can be used to advance integrated assessment research addressing land-use change.

Science

Environment-driven changes in crop yields may result in 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 crop models that project how yields of maize, wheat and other key crops will change over time. Combining the best features of the two most commonly used types of crop models, a new toolset of crop-yield emulators provide researchers with a simple, fast way to estimate crop yields of rain-fed maize, rice, soybean and wheat at the national or regional level. Such estimates can then be used as input for further modeling and analysis.   

Impact

Extending two earlier studies that confirmed the efficiency and effectiveness of theses crop-model emulators, this research provides a computationally efficient way to represent the national or regional impact of the environment on crop yields and land-use change within an integrated assessment model.

Summary

Globally gridded, 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. In a previous study, Elodie Blanc. a research scientist at the MIT Program for the Science and Policy of Global Change, developed crop-yield emulators that combine the best of both methods, “training” a statistical model to make reasonably accurate predictions based on the output of multiple process-based models. Now Blanc has extended the set of emulators to enable efficient assessment of rain-fed maize, rice, soybean and wheat yields at the national or regional level for a range of temperature and precipitation conditions.

Contact
Elodie Blanc
Massachusetts Institute of Technology
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