A Crop Yield Change Emulator for Use in GCAM and Similar Models: Persephone v1.0

The Science

Future changes in Earth system state will impact agricultural yields and, through these changed yields, have profound impacts on the global economy. The results of past crop modeling efforts require too much computation to interact dynamically with the Global Change Assessment Model (GCAM). Previously, researchers have had to rely on public archives of prerun scenarios provided by crop modelers, making exploration of feedbacks among socioeconomics, Earth system changes, and crop yield changes impossible. Persephone v1.0 evaluates arbitrary Earth system changes and produces yield changes in response by emulating the Coordinated Climate-Crop Modeling Project (C3MP) sensitivity test dataset from the Agricultural Model Intercomparison and Improvement Project (AgMIP). The breadth of data included in the C3MP dataset also allows Persephone v1.0 to provide a measure of uncertainty associated with a yield change.

The Impact

With the Persephone yield responses, a new variety of agricultural impact experiments will be open to GCAM and other economic models. Because Persephone v1.0 doesn’t need precomputed Earth system inputs to produce yield changes, it can interact directly with GCAM to examine new, more complex questions. For example, Persephone can be used with GCAM to examine the economic impacts of a multiyear drought in a key agricultural region and how those economic changes can, in turn, impact the drought itself.

Summary

To examine the feedback loop among socioeconomics, Earth system changes, and crop yield changes, rapidly generated yield responses with some quantification of crop response uncertainty are desirable. The Persephone v1.0 response functions presented in this work are based on the AgMIP C3MP sensitivity test dataset and are focused on providing GCAM and similar models with a tractable number of rapid-to-evaluate dynamic yield response functions corresponding to a range of C3MP dataset yield response sensitivities. This is one solution to the challenge of linking detailed, process-based crop models to global, multiregion, multisector models such as GCAM in a computationally tractable way.

Using given training data, Persephone employs a Bayesian framework to estimate the distributions of agricultural response function parameters. Future versions of Persephone can use this Bayesian framework to explore different functional forms and different crop yield training datasets as more become available. This approach is relatively novel in the field of crop yield response functions and was noted as a strength by reviewers. Further, future projections of yield changes from Persephone were placed in the context of more detailed process-based crop modeling results. It was found that the Persephone results were largely consistent with past crop modeling efforts. This emulator is supported by an open-source R package (github).

Principal Investigator(s)

Mohamad Hejazi
Pacific Northwest National Laboratory
[email protected]

Funding

Abigail Snyder, Katherine Calvin, and Meridel Phillips were supported by the U.S. Department of Energy, Office of Science, as part of research in the Multisector Dynamics, Earth and Environmental System Modeling program. Alex Ruane’s work was supported by the National Aeronautics and Space Administration Climate Impacts Group under the Modeling, Analysis, and Prediction Program.

References

Snyder, A., K. V. Calvin, M. Phillips, and A. C. Ruane. “A crop yield change emulator for use in GCAM and similar models: Persephone v1.0.” Geoscientific Model Development 12, 1319–1350 (2019). DOI:10.5194/gmd-12-1319-2019