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Library Resource
Publication évaluée par des pairs
France, Japon, Canada, Royaume-Uni, États-Unis d'Amérique, Allemagne, Australie, Nouvelle-Zélande
Globally, agricultural soils are being evaluated for their role in climate change regulation as a potential sink for atmospheric carbon dioxide (CO2) through sequestration of organic carbon as soil organic matter. Scientists and policy analysts increasingly seek to develop programs and policies which recognize the importance of mitigation of climate change and insurance of ecological sustainability when managing agricultural soils.
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Library Resource
Publication évaluée par des pairs
Canada, Chine, Fédération de Russie, Suède, États-Unis d'Amérique
Pan evapotranspiration (E) is an important physical parameter in agricultural water resources research. Many climatic factors affect E, and one of the essential challenges is to model or predict E utilizing limited climatic parameters. In this study, the performance of four different artificial neural network (ANN) algorithms i.e., multiple hidden layer back propagation (MBP), generalized regression neural network (GRNN), probabilistic neural networks (PNN), and wavelet neural network (WNN) and one empirical model namely Stephens–Stewart (SS) were employed to predict monthly E.
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