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The phenology of soybean explicitly indicates environmental changes and strongly depends on temperature and day length. We adapted an artificial neural network model to predict the time to flowering in nine early maturing soybean accessions in the Northwest region of Russia. We added scaling constants for network inputs, optimized the high and low temperature thresholds and base day length, and implemented...
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