The Loess Plateau has a typical semi-arid climate, and the area suffers from very harsh ecological environment, severe soil erosion and water runoff, and uneven distributed precipitation. Due to the relatively low holding capacity, current rainwater-collecting and conservation facilities can only supplement a maximum of18 mm of water for crop production in each irrigation. In this study, mathematical models were constructed to identify the water requirement critical period of maize crop by evaluating response of each individual developmental stage to supplemental irrigation with harvested rainwater. In the transformed Jensen model, ETmin/Eta was used as the index of relative evapotranspiration. The use of relative yield and relative crop evapotranspiration was able to eliminate influences from unintended environmental factors. A BP neural network crop-water model for extreme water deficit condition was constructed using the index of relative evapotranspiration as the input and the index of relative yield as the output after iterative training and adjustment of weight values. Comparison of measured maize yields to those predicted by the two models confirmed that the BP neural network crop-water model is more accurate than the transformed Jensen model in predicting the sensitivity index to waterdeficit at various growth stages and maize yield when provided with supplemental irrigation with harvested rainwater.
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