Salahou Mohamed Khaled, Chen Xiaoyuan, Zhang Yupeng, Lü Haishen, Jiao Xiyun
School of Biology and Agriculture, Shaoguan University, Shaoguan 512005, China.
Northern Guangdong Soil Environment Observation and Research Station, Shaoguan University, Shaoguan 512005, China.
Heliyon. 2024 Nov 5;10(22):e40116. doi: 10.1016/j.heliyon.2024.e40116. eCollection 2024 Nov 30.
The optimization of surface irrigation variables, i.e., the selection of the optimal combination of the inflow rate per unit width (q) and cutoff time (tco), is essential for obtaining high performance. The main objective of this study was to optimize irrigation variables by considering different irrigation requirements and the total loss model, which includes the irrigation water loss and crop yield loss. A correction factor for the irrigation quota (C) was introduced to achieve this objective. C considers different irrigation requirements, as it represents the ratio of the actual irrigation quota for certain irrigation to the designed irrigation quota (D). Uniform design theory was used to determine random combinations of q and C from a selected range. The q value ranged from 3 to 9 L m. s. Because the actual irrigation quota does not greatly deviate from the design irrigation quota, C should reach approximately 1.00. The selected C ranged from 0.80 to 1.38. To obtain higher crop yields, lower economic losses, and more reasonable design variables for surface irrigation, a total loss model for uneven border irrigation was established, which was defined as the objective function to optimize border irrigation design-the total loss model was combined with uniform design theory and the WinSRFR model to analyze different scenarios. The results showed that C has a clear meaning and exerts a favorable application effect on the irrigation performance evaluation indicators and can be used to design border irrigation systems. Based on the total loss model for uneven border irrigation, the optimal irrigation variables q and C for design irrigation quotas of 60, 80, and 100 mm are q = 6.22 L m. s and C = 1.17; q = 4.60 L m. s and C = 1.14; and q = 3.80 L m. s and C = 1.10, respectively. Compared with the conventional design results, q in the optimal design results based on the loss model decreased, C increased, and the total loss significantly decreased. Optimization of irrigation variables based on the border irrigation loss model could ensure favorable irrigation performance evaluation indicators, improve the water use efficiency, provide higher crop yields, and minimize the total economic losses caused by uneven irrigation.
优化地面灌溉变量，即选择单位宽度入流量（q）和停水时间（tco）的最佳组合，对于实现高性能至关重要。本研究的主要目标是通过考虑不同的灌溉需求和总损失模型来优化灌溉变量，该模型包括灌溉水损失和作物产量损失。为此引入了灌溉定额校正系数（C）。C考虑了不同的灌溉需求，因为它代表了特定灌溉的实际灌溉定额与设计灌溉定额（D）的比值。采用均匀设计理论从选定范围内确定q和C的随机组合。q值范围为3至9L·m·s。由于实际灌溉定额与设计灌溉定额偏差不大，C应接近1.00。选定的C范围为0.80至1.38。为了获得更高的作物产量、更低的经济损失以及更合理的地面灌溉设计变量，建立了畦灌不均匀总损失模型，将其定义为优化畦灌设计的目标函数——将总损失模型与均匀设计理论和WinSRFR模型相结合，分析不同方案。结果表明，C具有明确意义，对灌溉性能评价指标具有良好的应用效果，可用于设计畦灌系统。基于畦灌不均匀总损失模型，对于60、80和100mm的设计灌溉定额，最佳灌溉变量q和C分别为q = 6.22L·m·s和C = 1.17；q = 4.60L·m·s和C = 1.14；以及q = 3.80L·m·s和C = 1.10。与传统设计结果相比，基于损失模型的优化设计结果中q减小，C增大，总损失显著降低。基于畦灌损失模型优化灌溉变量可确保良好的灌溉性能评价指标，提高水分利用效率，实现更高的作物产量，并使不均匀灌溉造成的总经济损失最小化。
