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

Climate variability and changes aff ect 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. Th is 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 geo- graphic distributions on regional climate characteristics. Signifi cant correlations between simulated and observed interannual variations were found across 87% of the total harvest grids. Th e 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. Th e modeled cotton yields exhibited large, positive correlations with July to August leaf area index. Th ese 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. Th ey also provide a baseline reference for further model improvements and applications in the future study of climate-cotton interactions.

[1]  J. Michaelsen.  Cross-Validation in Statistical Climate Forecast Models , 1987 .

[2]  B. Kimball.  Carbon Dioxide and Agricultural Yield: An Assemblage and Analysis of 430 Prior Observations1 , 1983 .

[3]  K. R. Reddy,et al.  Crop ecosystem responses to climatic change: soybean. , 2000 .

[4]  Huug van den Dool,et al.  An analysis of multimodel ensemble predictions for seasonal climate anomalies , 2002 .

[5]  John J. Read,et al.  Canopy reflectance in cotton for growth assessment and lint yield prediction , 2007 .

[6]  Pete Smith,et al.  Climate Change and the Global Harvest , 1998 .

[7]  J. Monteith.  Climatic variation and the growth of crops , 2007 .

[8]  K. R. Reddy,et al.  8 Crop Ecosystem Responses to Climatic Change: Cotton , 2000 .

[9]  K. Kunkel,et al.  Regional Climate Model Simulation of U.S. Precipitation during 1982–2002. Part I: Annual Cycle , 2004 .

[10]  S. B. Idso,et al.  Increasing atmospheric CO2: effects on crop yield, water use and climate , 1983 .

[11]  Vijaya Gopal Kakani,et al.  Cotton responses to ultraviolet-B radiation: experimentation and algorithm development , 2003 .

[12]  Daniel L. Schmoldt,et al.  A Distributed Cotton Growth Model Developed from GOSSYM and Its Parameter Determination , 2012 .

[13]  Jimy Dudhia,et al.  Regional Climate–Weather Research and Forecasting Model , 2012 .

[14]  Anthony C. Janetos,et al.  The Effects of Climate Change on Agriculture, Land Resources, Water Resources, and Biodiversity in the United States , 2008 .

[15]  A. J. Stern,et al.  Application of MODIS derived parameters for regional crop yield assessment , 2005 .

[16]  K. R. Reddy,et al.  Interactive effects of elevated CO2 and potassium deficiency on photosynthesis, growth, and biomass partitioning of cotton , 2005 .

[17]  David D. Parrish,et al.  NORTH AMERICAN REGIONAL REANALYSIS , 2006 .

[18]  Daniel Wallach,et al.  Parameter Estimation for Crop Models , 2001 .

[19]  Edward M. Barnes,et al.  Multispectral Reflectance of Cotton Related to Plant Growth, Soil Water and Texture, and Site Elevation , 2001 .

[20]  P. C. Doraiswamya,et al.  Crop condition and yield simulations using Landsat and MODIS , 2004 .

[21]  J. M. McKinion,et al.  Calibration of GOSSYM: Theory and practice☆ , 1993 .

[22]  B. Efron,et al.  A Leisurely Look at the Bootstrap, the Jackknife, and , 1983 .

[23]  F. Zwiers,et al.  Climate Predictions with Multimodel Ensembles , 2002 .

[24]  Vijaya Gopal Kakani,et al.  Simulating the impacts of climate change on cotton production in the Mississippi Delta , 2002 .

[25]  G. Grell,et al.  Regional Climate Model Simulation of U.S.–Mexico Summer Precipitation Using the Optimal Ensemble of Two Cumulus Parameterizations , 2007 .

[26]  M. Simpson.  Global Climate Change Impacts in the United States , 2011 .

[27]  B. Metz.  The Intergovernmental Panel on Climate Change , 2011 .

[28]  J. Morison,et al.  Intercellular CO_2 Concentration and Stomatal Response to CO_2 , 1987 .

[29]  J. Palutikof,et al.  Climate change 2007 : impacts, adaptation and vulnerability , 2001 .

[30]  Ayse Irmak,et al.  Evaluating methods for simulating soybean cultivar responses using cross validation. , 2000 .

[31]  J. Porter,et al.  The Physiology of Crop Yield , 2006 .

[32]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[33]  Thomas J. Jackson,et al.  Crop condition and yield simulations using Landsat and MODIS , 2004 .

[34]  D. Conway,et al.  A crop model cross calibration for use in regional climate impacts studies , 2008 .

[35]  William D. Batchelor,et al.  USING CROSS-VALIDATION TO EVALUATE CERES-MAIZE YIELD SIMULATIONS WITHIN A DECISION SUPPORT SYSTEM FOR PRECISION AGRICULTURE , 2007 .

[36]  O. Edenhofer,et al.  Mitigation from a cross-sectoral perspective , 2007 .

[37]  Wei Gao,et al.  Preliminary results of the coupled CWRF-GOSSYM system , 2005, SPIE Optics + Photonics.