交替沟灌玉米灌浆期茎流影响因子敏感性分析与模型适用性研究

doi:10.3864/j.issn.0578-1752.2018.02.004

Abstract

Abstract: 【Objective】The artificial neural network is used to predict the stem flow of maize with different treatments, which provides a new idea for estimating the water consumption of maize so as to make reasonable irrigation plan. 【Method】 The summer maize, variety of Xinong 985, was selected for study. The test set three treatments: alternative furrow irrigation high water treatment (alternate furrow irrigation, AFI1), alternate furrow irrigation low water treatment (alternate furrow irrigation, AFI2), and conventional furrow irrigation (conventional furrow irrigation, CFI). AFI1 and AFI2 each irrigation amount was 2/3 and 1/2 irrigation amount of CFI, respectively. In this paper, an artificial neural network is established to estimate the corn stem flow rate, and the model is compared with the principal component regression model, and the accuracy and stability of the two models are evaluated.【Result】(1) The response of different treatments to environmental factors was different. Meteorological conditions were the main factors affecting stem flow of the AFI1 treatment, and soil moisture was main factor affecting stem flow of the AFI2 treatment. (2) The influence of water content in different soil layers on stem flow was also different. It was found that the moisture contents of 10-20 cm and 20-30 cm layers were most correlated with maize stem flow. By using the uncertainty analysis method further analysis: For CFI and AFI1 treatments, 20-30 cm layer soil content was most closely related to sap flow changes, then 10-20 cm layer soil content. And for AFI2 treatment, 10-20 cm layer soil content was most closely related to sap flow changes, then 20-30 cm layer soil content; (3) After comparing the R² , RMSE and d-factor values of two models, the ANN model was more stable and accuracy, which was found to be the best model to predict the stem flow.【Conclusion】Compared with traditional methods, artificial neural network model can greatly enhance the prediction accuracy of stem flow, and it can provide some guidance for making reasonable irrigation plan of maize.

Key words: alternate furrow irrigation, maize stem flow, artificial neural network, layered soil moisture content

DU Bin, HU XiaoTao, WANG WenE, MA LiHua, ZHOU ShiWei. Stem Flow Influencing Factors Sensitivity Analysis and Stem Flow Model Applicability in Filling Stage of Alternate Furrow Irrigated Maize[J].Scientia Agricultura Sinica, 2018, 51(2): 233-245.

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Stem Flow Influencing Factors Sensitivity Analysis and Stem Flow Model Applicability in Filling Stage of Alternate Furrow Irrigated Maize

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