Bayesian Spatial Models for Projecting Corn Yields
[1];
[4];
- Department of Statistics, Pennsylvania State University, University Park, PA 16802, USA
- Department of Statistics, George Mason University, Fairfax, VA 22030, USA
- Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA 16802, USA
- Thayer School of Engineering at Dartmouth College, Hanover, NH 03755, USA
Climate change is predicted to impact corn yields. Previous studies analyzing these impacts differ in data and modeling approaches and, consequently, corn yield projections. We analyze the impacts of climate change on corn yields using two statistical models with different approaches for dealing with county-level effects. The first model, which is novel to modeling corn yields, uses a computationally efficient spatial basis function approach. We use a Bayesian framework to incorporate both parametric and climate model structural uncertainty. We find that the statistical models have similar predictive abilities, but the spatial basis function model is faster and hence potentially a useful tool for crop yield projections. We also explore how different gridded temperature datasets affect the statistical model fit and performance. Compared to the dataset with only weather station data, we find that the dataset composed of satellite and weather station data results in a model with a magnified relationship between temperature and corn yields. For all statistical models, we observe a relationship between temperature and corn yields that is broadly similar to previous studies. We use downscaled and bias-corrected CMIP5 climate model projections to obtain detrended corn yield projections for 2020–2049 and 2069–2098. In both periods, we project a decrease in the mean corn yield production, reinforcing the findings of other studies. However, the magnitude of the decrease and the associated uncertainties we obtain differ from previous studies.
- Research Organization:
- Pennsylvania State Univ., University Park, PA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth And Environmental Systems Science (EESS); USDOE
- Grant/Contract Number:
- SC0016162; SC0022141
- OSTI ID:
- 2263286
- Alternate ID(s):
- OSTI ID: 2324982
- Journal Information:
- Remote Sensing, Vol. 16, Issue 1; ISSN 2072-4292
- Publisher:
- MDPICopyright Statement
- Country of Publication:
- United States
- Language:
- English
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