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    Simulation and prediction of soybean growth and development under field conditions

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    S10ArtZhangSimulationNothomNodev.pdf (90.98Kb)
    Date
    2010
    Author
    Zhang, L.
    Zhang, J.
    Kyei-Boahen, S.
    Zhang, M.
    Type
    Journal Article
    Target Audience
    Scientists
    Metadata
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    Abstract/Description
    Thermal unit is often used as the main driving force in crop simulation models. However, simulation models built with this approach often do not lead to a satisfactory accuracy of prediction when it regards to soybean; mainly due to strong photoperiod influence on soybean and complicated interactions between photoperiod and temperature. This study tried to simulate and predict soybean phenological growth using calendar-day based approach. Field experiments were conducted at the Delta Research and Extension Center, Stoneville, Mississippi, USA. Five year (1998-2002) field data were used with 24 sowing dates from maturity groups (MG) III to MG VI soybean varieties. Three methods, artificial neural network (ANN), k- nearest neighbor (kNN) and regression were used to construct prediction models. Vegetative and reproductive growth stages were modeled separately. Results indicated that calendar-based prediction model in soybean growth calculation is a feasible approach. All three methods achieved the acceptable prediction accuracy. On average, prediction errors of ANN, kNN and Regression methods were 3.6, 2.8 and 3.6 days for vegetative stage and 4.4, 3.5 and 4.7 days for reproductive stages, respectively.
    Permanent link to this item
    https://hdl.handle.net/20.500.12478/2266
    IITA Subjects
    Grain Legumes; Plant Breeding; Plant Production; Soybean
    Agrovoc Terms
    Soybeans; Phenology; Prediction; Model; Regression; United States Of America; Artificial Neural Network; Simulation
    Regions
    Acp; North America
    Countries
    United States
    Journals
    American-Eurasian Journal of Agricultural and Environmental Sciences
    Collections
    • Journal and Journal Articles4835
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