Assessing Maize and Peanut Yield Simulations with Various Seasonal Climate Data in the Southeastern United States

D. W. Shin Center for Ocean–Atmospheric Prediction Studies, The Florida State University, Tallahassee, Florida

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G. A. Baigorria Agricultural and Biological Engineering Department, University of Florida, Gainesville, Florida

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Y-K. Lim Center for Ocean–Atmospheric Prediction Studies, The Florida State University, Tallahassee, Florida

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S. Cocke Center for Ocean–Atmospheric Prediction Studies, The Florida State University, Tallahassee, Florida

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T. E. LaRow Center for Ocean–Atmospheric Prediction Studies, The Florida State University, Tallahassee, Florida

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James J. O’Brien Center for Ocean–Atmospheric Prediction Studies, The Florida State University, Tallahassee, Florida

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James W. Jones Agricultural and Biological Engineering Department, University of Florida, Gainesville, Florida

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Abstract

A comprehensive evaluation of crop yield simulations with various seasonal climate data is performed to improve the current practice of crop yield projections. The El Niño–Southern Oscillation (ENSO)-based historical data are commonly used to predict the upcoming season crop yields over the southeastern United States. In this study, eight different seasonal climate datasets are generated using the combinations of two global models, a regional model, and a statistical downscaling technique. One of the global models and the regional model are run with two different convective schemes. These datasets are linked to maize and peanut dynamic models to assess their impacts on crop yield simulations and are then compared with the ENSO-based approach. Improvement of crop yield simulations with the climate model data is varying, depending on the model configuration and the crop type. Although using the global climate model data directly provides no improvement, the dynamically and statistically downscaled data show increased skill in the crop yield simulations. A statistically downscaled operational seasonal climate model forecast shows statistically significant (at the 5% level) interannual predictability in the peanut yield simulation. Since the yield amount simulated by the dynamical crop model is highly sensitive to wet/dry spell sequences (water stress) during the growing season, fidelity in simulating the precipitation variability is essential.

Corresponding author address: D. W. Shin, Center for Ocean–Atmospheric Prediction Studies, The Florida State University, Tallahassee, FL 32306-2840. Email: shin@coaps.fsu.edu

Abstract

A comprehensive evaluation of crop yield simulations with various seasonal climate data is performed to improve the current practice of crop yield projections. The El Niño–Southern Oscillation (ENSO)-based historical data are commonly used to predict the upcoming season crop yields over the southeastern United States. In this study, eight different seasonal climate datasets are generated using the combinations of two global models, a regional model, and a statistical downscaling technique. One of the global models and the regional model are run with two different convective schemes. These datasets are linked to maize and peanut dynamic models to assess their impacts on crop yield simulations and are then compared with the ENSO-based approach. Improvement of crop yield simulations with the climate model data is varying, depending on the model configuration and the crop type. Although using the global climate model data directly provides no improvement, the dynamically and statistically downscaled data show increased skill in the crop yield simulations. A statistically downscaled operational seasonal climate model forecast shows statistically significant (at the 5% level) interannual predictability in the peanut yield simulation. Since the yield amount simulated by the dynamical crop model is highly sensitive to wet/dry spell sequences (water stress) during the growing season, fidelity in simulating the precipitation variability is essential.

Corresponding author address: D. W. Shin, Center for Ocean–Atmospheric Prediction Studies, The Florida State University, Tallahassee, FL 32306-2840. Email: shin@coaps.fsu.edu

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