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Agronomy Journal Abstract - Biometry, Modeling & Statistics

Physical Modeling of U.S. Cotton Yields and Climate Stresses during 1979 to 2005

 

This article in AJ

  1. Vol. 104 No. 3, p. 675-683
    unlockOPEN ACCESS
     
    Received: Aug 10, 2011


    * Corresponding author(s): xliang@umd.edu
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doi:10.2134/agronj2011.0251
  1. Xin-Zhong Liang *ab,
  2. Min Xub,
  3. Wei Gaoc,
  4. K. Raja Reddyd,
  5. Kenneth Kunkele,
  6. Daniel L. Schmoldtf and
  7. Arthur N. Samelg
  1. a Dep. of Atmospheric and Oceanic Science, Univ. of Maryland, College Park, MD 20742, and Dep. of Atmospheric Sciences, Univ. of Illinois, Urbana, IL 61801
    b Earth System Science Interdisciplinary Center, Univ. of Maryland, College Park, MD 20740, and Division of Illinois State Water Survey, Institute of Natural Resource Sustainability, Univ. of Illinois, Champaign, IL 61820
    c USDA UV-B Monitoring and Research Program, Natural Resource Ecology Lab., and Dep. of Ecosystem Science and Sustainability, Colorado State Univ., Fort Collins, CO 80523
    d Dep. of Plant and Soil Sciences, Mississippi State Univ., Mississippi State, MS 39762
    e Cooperative Institute for Climate and Satellites, North Carolina State Univ., Asheville, NC 28801, and National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC 28801
    f USDA National Institute of Food and Agriculture, Washington, DC 20024
    g Dep. of Geography, Bowling Green State Univ., Bowling Green, OH 43403

Abstract

Climate variability and changes affect crop yields by causing climatic stresses during various stages of the plant life cycle. A crop growth model must be able to capture the observed relationships between crop yields and climate stresses before its credible use as a prediction tool. This study evaluated the ability of the geographically distributed cotton growth model redeveloped from GOSSYM in simulating U.S. cotton (Gossypium hirsutum L.) yields and their responses to climate stresses during 1979 to 2005. Driven by realistic climate conditions, the model reproduced long-term mean cotton yields within ±10% of observations at the 30-km model resolution across virtually the entire U.S. Cotton Belt and correctly captured the critical dependence of their geographic distributions on regional climate characteristics. Significant correlations between simulated and observed interannual variations were found across 87% of the total harvest grids. The model also faithfully represented the predictive role of July to August air temperature and August to September soil temperature anomalies on interannual cotton yield changes on unirrigated lands, with a similar but weaker predictive signal for irrigated lands as observed. The modeled cotton yields exhibited large, positive correlations with July to August leaf area index. These results indicate the model's ability to depict the regional impact of climate stresses on cotton yields and suggest the potential predictive value of satellite retrievals. They also provide a baseline reference for further model improvements and applications in the future study of climate–cotton interactions.

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