气候变化领域集成服务门户(CCPortal): Bayesian Spatial Models for Projecting Corn Yields

CCPortal

DOI	10.3390/rs16010069
	Bayesian Spatial Models for Projecting Corn Yields
	Roth, Samantha; Lee, Ben Seiyon; Nicholas, Robert E.; Keller, Klaus; Haran, Murali
发表日期	2024
EISSN	2072-4292
起始页码	16
结束页码	1
卷号	16 期号:1
英文摘要	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.
英文关键词	climate change; crop yields; Bayesian inference; Gaussian process; basis representation
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001140316000001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/309558
作者单位	Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; George Mason University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Dartmouth College
推荐引用方式 GB/T 7714	Roth, Samantha,Lee, Ben Seiyon,Nicholas, Robert E.,et al. Bayesian Spatial Models for Projecting Corn Yields[J],2024,16(1).
APA	Roth, Samantha,Lee, Ben Seiyon,Nicholas, Robert E.,Keller, Klaus,&Haran, Murali.(2024).Bayesian Spatial Models for Projecting Corn Yields.REMOTE SENSING,16(1).
MLA	Roth, Samantha,et al."Bayesian Spatial Models for Projecting Corn Yields".REMOTE SENSING 16.1(2024).