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Water Demand Pattern and Irrigation Decision-Making Support Model for Drip-Irrigated Tomato Crop in a Solar Greenhouse

文献类型： 外文期刊

作者： An, Shunwei 1 ; Yang, Fuxin 1 ; Yang, Yingru 4 ; Huang, Yuan 4 ; Zhangzhong, Lili 1 ; Wei, Xiaoming 1 ; Yu, Jingxin 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

2.Beijing Agr Technol Extens Stn, Beijing 100029, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

4.Shijiazhuang Acad Agr & Forestry Sci, Intelligent Agr Res Ctr, Shijiazhuang 050041, Hebei, Peoples R China

5.Minist Agr & Rural Affairs, Key Lab Qual Testing Software & Hardware Prod Agr, Beijing 100097, Peoples R China

关键词： water demand pattern; fuzzy algorithm; solar greenhouse; tomato; decision making

期刊名称：AGRONOMY-BASEL （ 影响因子：3.949； 五年影响因子：4.117 ）

ISSN：

年卷期： 2022 年 12 卷 7 期

页码：

收录情况： SCI

摘要： The knowledge of crop water requirements is critical for agricultural water conservation, especially for accurate irrigation decision making in the greenhouse. Investigating the water demand pattern of the tomato in the solar greenhouse environment and constructing an appropriate irrigation decision-making model are urgently needed to improve irrigation water use efficiency. We designed four irrigation-level treatments: 100% ET0 (T1), 85% ET0 (T2), 70% ET0 (T3), and 55% ET0 (T4), and conducted a two-vegetation-season tomato planting trial under drip irrigation conditions in a solar greenhouse. The Pearson's correlation coefficient method analyzed the intrinsic linkage and influence between soil-crop-environment and tomatoes' water demand patterns. Indicators suitable for irrigation decision making in greenhouse tomatoes were selected, and regression functions were constructed for environmental and crop physiological parameters by combining path analysis and multiple regression methods. Finally, a fusion irrigation decision-making model was constructed by introducing a distance function in the Dempster-Shafer (D-S) theory primary probability assignment (BPA) synthesis algorithm and combining it with a triangular affiliation function. The results showed that: (1) the soil coefficient of variation was shallow > middle > deep, and tomatoes absorbed water mainly in the 0-60 cm soil layer; (2) the crop stem flow rate, net photosynthetic rate, and transpiration rate were positively correlated with irrigation water and had the highest correlation with net radiation, relative humidity, and relative humidity, with correlation coefficients of 0.9441, 0.9441, and 0.7679, respectively; (3) the constructed decision model had a significantly lower value of uncertainty than other methods, while the highest decision value could reach over 0.99, which achieved the best decision accuracy compared to other algorithms.

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