U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights


Quantifying the Indirect Impacts of Climate on Agriculture: An Inter-Method Comparison
Published: October 27, 2017
Posted: July 30, 2018

Capturing socio-economic feedbacks is critical to a comprehensive assessment of the impacts of climate change on agriculture.

The Science
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 models (IAMs).

The Impact
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.

Summary
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%.

Contacts
(BER PM)

Bob Vallario
Multisector Dynamics Research
Bob.Vallario@science.doe.gov

(PI Contact)

John Weyant
Stanford University
weyant@stanford.edu

Funding

This work was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, Multisector Dynamics Research activity, Grant no. DE-SC0005171.

Publications

Calvin, K. and K. Fisher-Vanden. “Quantifying the indirect impacts of climate on agriculture: an inter-method comparison.” Environmental Research Letters 12(11), 115004 (2017). [DOI: 10.1088/1748-9326/aa843c]

Related Links
Paper
Supplementary material

Topic Areas:

  • Research Area: Earth and Environmental Systems Modeling
  • Research Area: Multisector Dynamics (formerly Integrated Assessment)

Division: SC-23.1 Climate and Environmental Sciences Division, BER

 

BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER

Recent Highlights

May 10, 2019
Quantifying Decision Uncertainty in Water Management via a Coupled Agent-Based Model
Considering risk perception can improve the representation of human decision-making processes in age [more...]

May 09, 2019
Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Scenarios
Study provides country-specific urban area growth models and the first dataset on country-level urba [more...]

May 05, 2019
Calibrating Building Energy Demand Models to Refine Long-Term Energy Planning
A new, flexible calibration approach improved model accuracy in capturing year-to-year changes in bu [more...]

May 03, 2019
Calibration and Uncertainty Analysis of Demeter for Better Downscaling of Global Land Use and Land Cover Projections
Researchers improved the Demeter model’s performance by calibrating key parameters and establi [more...]

Apr 22, 2019
Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles
Representation of warm temperature events varies considerably among global climate models, which has [more...]

List all highlights (possible long download time)