Dataset Information

The Role of Climate Covariability on Crop Yields in the Conterminous United States.

ABSTRACT: The covariability of temperature (T), precipitation (P) and radiation (R) is an important aspect in understanding the climate influence on crop yields. Here, we analyze county-level corn and soybean yields and observed climate for the period 1983-2012 to understand how growing-season (June, July and August) mean T, P and R influence crop yields jointly and in isolation across the CONterminous United States (CONUS). Results show that nationally averaged corn and soybean yields exhibit large interannual variability of 21% and 22%, of which 35% and 32% can be significantly explained by T and P, respectively. By including R, an additional of 5% in variability can be explained for both crops. Using partial regression analyses, we find that studies that ignore the covariability among T, P, and R can substantially overestimate the sensitivity of crop yields to a single climate factor at the county scale. Further analyses indicate large spatial variation in the relative contributions of different climate variables to the variability of historical corn and soybean yields. The structure of the dominant climate factors did not change substantially over 1983-2012, confirming the robustness of the findings, which have important implications for crop yield prediction and crop model validations.

SUBMITTER: Leng G

PROVIDER: S-EPMC5018813 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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The Role of Climate Covariability on Crop Yields in the Conterminous United States.

Leng Guoyong G Zhang Xuesong X Huang Maoyi M Asrar Ghassem R GR Leung L Ruby LR

Scientific reports 20160912

The covariability of temperature (T), precipitation (P) and radiation (R) is an important aspect in understanding the climate influence on crop yields. Here, we analyze county-level corn and soybean yields and observed climate for the period 1983-2012 to understand how growing-season (June, July and August) mean T, P and R influence crop yields jointly and in isolation across the CONterminous United States (CONUS). Results show that nationally averaged corn and soybean yields exhibit large inter ...[more]

PMID: 27616326

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Project description:Watershed integrity is the capacity of a watershed to support and maintain the full range of ecological processes and functions essential to sustainability. Using information from EPA's StreamCat dataset, we calculated and mapped an Index of Watershed Integrity (IWI) for 2.6 million watersheds in the conterminous US with first-order approximations of relationships between stressors and six watershed functions: hydrologic regulation, regulation of water chemistry, sediment regulation, hydrologic connectivity, temperature regulation, and habitat provision. Results show high integrity in the western US, intermediate integrity in the southern and eastern US, and the lowest integrity in the temperate plains and lower Mississippi Valley. Correlation between the six functional components was high (r = 0.85-0.98). A related Index of Catchment Integrity (ICI) was developed using local drainages of individual stream segments (i.e., excluding upstream information). We evaluated the ability of the IWI and ICI to predict six continuous site-level indicators with regression analyses - three biological indicators and principal components derived from water quality, habitat, and combined water quality and habitat variables - using data from EPA's National Rivers and Streams Assessment. Relationships were highly significant, but the IWI only accounted for 1-12% of the variation in the four biological and habitat variables. The IWI accounted for over 25% of the variation in the water quality and combined principal components nationally, and 32-39% in the Northern and Southern Appalachians. We also used multinomial logistic regression to compare the IWI with the categorical forms of the three biological indicators. Results were consistent: we found positive associations but modest results. We compared how the IWI and ICI predicted the water quality PC relative to agricultural and urban land use. The IWI or ICI are the best predictors of the water quality PC for the CONUS and six of the nine ecoregions, but they only perform marginally better than agriculture in most instances. However, results suggest that agriculture would not be appropriate in all parts of the country, and the index is meant to be responsive to all stressors. The IWI in its present form (available through the StreamCat website; https://www.epa.gov/national-aquatic-resource-surveys/streamcat) could be useful for management efforts at multiple scales, especially when combined with information on site condition. The IWI could be improved by incorporating empirical or literature-derived relationships between functional components and stressors. However, limitations concerning the absence of data for certain stressors should be considered.

Dataset Information

The Role of Climate Covariability on Crop Yields in the Conterminous United States.

Publications

The Role of Climate Covariability on Crop Yields in the Conterminous United States.

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