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

    Source: Journal of Applied Meteorology and Climatology:;2009:;volume( 049 ):;issue: 004::page 592
    Author:
    Shin, D. W.
    ,
    Baigorria, G. A.
    ,
    Lim, Y-K.
    ,
    Cocke, S.
    ,
    LaRow, T. E.
    ,
    O’Brien, James J.
    ,
    Jones, James W.
    DOI: 10.1175/2009JAMC2293.1
    Publisher: American Meteorological Society
    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.
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      Assessing Maize and Peanut Yield Simulations with Various Seasonal Climate Data in the Southeastern United States

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4209923
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    • Journal of Applied Meteorology and Climatology

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    contributor authorShin, D. W.
    contributor authorBaigorria, G. A.
    contributor authorLim, Y-K.
    contributor authorCocke, S.
    contributor authorLaRow, T. E.
    contributor authorO’Brien, James J.
    contributor authorJones, James W.
    date accessioned2017-06-09T16:28:02Z
    date available2017-06-09T16:28:02Z
    date copyright2010/04/01
    date issued2009
    identifier issn1558-8424
    identifier otherams-68372.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4209923
    description abstractA 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.
    publisherAmerican Meteorological Society
    titleAssessing Maize and Peanut Yield Simulations with Various Seasonal Climate Data in the Southeastern United States
    typeJournal Paper
    journal volume49
    journal issue4
    journal titleJournal of Applied Meteorology and Climatology
    identifier doi10.1175/2009JAMC2293.1
    journal fristpage592
    journal lastpage603
    treeJournal of Applied Meteorology and Climatology:;2009:;volume( 049 ):;issue: 004
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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