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    Machine Learning Crop Yield Models Based on Meteorological Features and Comparison with a Process-Based Model

    Source: Artificial Intelligence for the Earth Systems:;2022:;volume( 001 ):;issue: 004
    Author:
    Qinqing Liu
    ,
    Meijian Yang
    ,
    Koushan Mohammadi
    ,
    Dongjin Song
    ,
    Jinbo Bi
    ,
    Guiling Wang
    DOI: 10.1175/AIES-D-22-0002.1
    Publisher: American Meteorological Society
    Abstract: A major challenge for food security worldwide is the large interannual variability of crop yield, and climate change is expected to further exacerbate this volatility. Accurate prediction of the crop response to climate variability and change is critical for short-term management and long-term planning in multiple sectors. In this study, using maize in the U.S. Corn Belt as an example, we train and validate multiple machine learning (ML) models predicting crop yield based on meteorological variables and soil properties using the leaving-one-year-out approach, and compare their performance with that of a widely used process-based crop model (PBM). Our proposed long short-term memory model with attention (LSTM
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      Machine Learning Crop Yield Models Based on Meteorological Features and Comparison with a Process-Based Model

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4290390
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    contributor authorQinqing Liu
    contributor authorMeijian Yang
    contributor authorKoushan Mohammadi
    contributor authorDongjin Song
    contributor authorJinbo Bi
    contributor authorGuiling Wang
    date accessioned2023-04-12T18:52:19Z
    date available2023-04-12T18:52:19Z
    date copyright2022/09/30
    date issued2022
    identifier otherAIES-D-22-0002.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4290390
    description abstractA major challenge for food security worldwide is the large interannual variability of crop yield, and climate change is expected to further exacerbate this volatility. Accurate prediction of the crop response to climate variability and change is critical for short-term management and long-term planning in multiple sectors. In this study, using maize in the U.S. Corn Belt as an example, we train and validate multiple machine learning (ML) models predicting crop yield based on meteorological variables and soil properties using the leaving-one-year-out approach, and compare their performance with that of a widely used process-based crop model (PBM). Our proposed long short-term memory model with attention (LSTM
    publisherAmerican Meteorological Society
    titleMachine Learning Crop Yield Models Based on Meteorological Features and Comparison with a Process-Based Model
    typeJournal Paper
    journal volume1
    journal issue4
    journal titleArtificial Intelligence for the Earth Systems
    identifier doi10.1175/AIES-D-22-0002.1
    treeArtificial Intelligence for the Earth Systems:;2022:;volume( 001 ):;issue: 004
    contenttypeFulltext
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