DOE Highlight: Efficient assessment of environmental impacts on crop yields at the regional level

DOE Highlight: Efficient assessment of environmental impacts on crop yields at the regional level
Jan 01, 2018

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.


The 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.   


The 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.



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.


BER PM Contact

Bob Vallario


PI Contact

Elodie Blanc

MIT Joint Program on the Science and Policy of Global Change



The study was funded by the U.S. Department of Energy (DOE) Office of Science under the grant DE-FG02-94ER61937 and other government, industry and foundation sponsors of the MIT Joint Program.



E. Blanc, 2017: Aggregation of Gridded Emulated Rainfed Crop Yield Projections at the National or Regional Level, Journal of Global Economic Analysis, 2(2): 112-127, doi:10.21642/JGEA.020203AF.


Photo: Corn Field in Cedar Falls (Source: P. L. Tandon)


Presentation Upload: 

Associated Joint Program People: