Irrigation and Drainage最新文献

Drip irrigation systems are critical for sustainable agriculture, yet their design and management influence complex interactions among drip hydraulics, the transport of soil water (solutes), and plant growth. While these processes have been studied individually, integrated modelling remains underexplored in the current study. This study develops a coupled model combining a hydraulic analysis module, a soil water/solute simulation module, and a crop growth simulation module to simulate drip irrigation systems under various designs (network layouts, irrigation schedules, and soil conditions), which can inform designers and managers of precision drip irrigation systems. Field data from a cotton drip irrigation subunit in Xinjiang, China, were used to calibrate and validate the model, and the results demonstrated the strong accuracy of the model in predicting system performance (emitter pressure/discharge), soil moisture/salinity, and crop growth (LAI, biomass, yield) in the drip subunit (R² > 0.50, d > 0.82). Scenario analyses revealed that hydraulic nonuniformity (CU: 72%–94%) propagates to soil moisture (CU: 93%–99%) and yield (CU: 89%–99%), with increased irrigation depth mitigating heterogeneity and increasing yield. We emphasise the functionality and future applicability of our model in assisting drip network design and irrigation management strategies for drip irrigation systems.

This study investigated the influence of structural parameters on flow uniformity in micro-sprinkling hoses, a cost-effective irrigation tool that aids water conservation. The effectiveness of these hoses depends on the uniformity of the flow rates from their orifices. However, previous research has not adequately addressed how structural parameters affect flow uniformity in individual orifice groups. Five independent parameters were examined via an orthogonal experimental design: the folded diameter (D_N), orifice diameter (d), number of orifices (n), angle (α) and distance from the edge to the first orifice (s′). Flow uniformity was evaluated by the Christiansen coefficient. The results indicated that pressure has an insignificant effect on flow uniformity. Based on the range analysis and analysis of variance results, the order of significant factors affecting flow uniformity is D_N > s′ > d > α > n. The Christiansen coefficient initially increases with D_N and then decreases. The other four parameters exhibit a strong nonlinear relationship with the Christiansen coefficient. The optimal structural parameter combination for the best flow distribution is s′ = 2 mm, D_N = 50 mm, d = 1.5 mm, n = 11 and α = 60°. These findings offer valuable insights for improving micro-sprinkling hose design.

This study investigated the influence of structural parameters on flow uniformity in micro-sprinkling hoses, a cost-effective irrigation tool that aids water conservation. The effectiveness of these hoses depends on the uniformity of the flow rates from their orifices. However, previous research has not adequately addressed how structural parameters affect flow uniformity in individual orifice groups. Five independent parameters were examined via an orthogonal experimental design: the folded diameter (D_N), orifice diameter (d), number of orifices (n), angle (α) and distance from the edge to the first orifice (s′). Flow uniformity was evaluated by the Christiansen coefficient. The results indicated that pressure has an insignificant effect on flow uniformity. Based on the range analysis and analysis of variance results, the order of significant factors affecting flow uniformity is D_N > s′ > d > α > n. The Christiansen coefficient initially increases with D_N and then decreases. The other four parameters exhibit a strong nonlinear relationship with the Christiansen coefficient. The optimal structural parameter combination for the best flow distribution is s′ = 2 mm, D_N = 50 mm, d = 1.5 mm, n = 11 and α = 60°. These findings offer valuable insights for improving micro-sprinkling hose design.

This study presents the development of a cloud-based, automated variable-rate fertigation system by modifying an existing hose–reel irrigation machine (HIM). A 46-m-wide HIM spraying boom was segmented into four independently controlled sections, and a 300-L fertiliser tank was mounted on the spraying cart to deliver liquid fertiliser directly into each section line. A control system, programmed in C#, was designed to automate the application of irrigation water and fertiliser. Calibration through laboratory and field tests optimised the application accuracy. A cloud-based information and communication technology platform was developed. The soil moisture data from the LSE01 sensors were transmitted via LoRaWAN to field gateways and then to the Things Network (TTN) for authentication, routing and JSON conversion. The system achieved precise irrigation and fertilisation within 15–50 mm and 80–600 L/ha, respectively, with minor errors (3–5% for irrigation, 1–3% for fertilisation). The spatial resolution improved significantly, with the lateral resolution reduced from 46 m to 10.7 m and the longitudinal resolution increased from 0.17 to 1.5 m, depending on the desired irrigation water and liquid fertiliser rates. These results demonstrate that the developed hose–reel variable-rate fertigation machine offers accurate and efficient fertigation suitable for precision agriculture.

This study presents the development of a cloud-based, automated variable-rate fertigation system by modifying an existing hose–reel irrigation machine (HIM). A 46-m-wide HIM spraying boom was segmented into four independently controlled sections, and a 300-L fertiliser tank was mounted on the spraying cart to deliver liquid fertiliser directly into each section line. A control system, programmed in C#, was designed to automate the application of irrigation water and fertiliser. Calibration through laboratory and field tests optimised the application accuracy. A cloud-based information and communication technology platform was developed. The soil moisture data from the LSE01 sensors were transmitted via LoRaWAN to field gateways and then to the Things Network (TTN) for authentication, routing and JSON conversion. The system achieved precise irrigation and fertilisation within 15–50 mm and 80–600 L/ha, respectively, with minor errors (3–5% for irrigation, 1–3% for fertilisation). The spatial resolution improved significantly, with the lateral resolution reduced from 46 m to 10.7 m and the longitudinal resolution increased from 0.17 to 1.5 m, depending on the desired irrigation water and liquid fertiliser rates. These results demonstrate that the developed hose–reel variable-rate fertigation machine offers accurate and efficient fertigation suitable for precision agriculture.

Accurate prediction of farmland soil moisture is crucial for determining crop water requirements and establishing effective irrigation standards. However, the high costs and potential disruption of soil structure make direct measurement of soil moisture across various depths challenging. In this study, the long short-term memory (LSTM) and decision tree (DT) models were proposed to predict soil moisture at 3-, 5-, 10- and 20-cm soil depths based on soil temperature data, and the accuracies of the two models in predicting soil moisture at different depths were evaluated at half-hour and daily scales. The results revealed that the accuracies of the LSTM and DT models in predicting soil moisture at different depths at the half-hour scale were greater than those at the daily scale. The accuracy of the LSTM model was better than that of the DT model at different depths. Both models performed best at the 20-cm soil depth, followed by the 10-, 5- and 3-cm soil depths, with R² values ranging from 0.90–0.95, 0.81–0.95, 0.84–0.95 and 0.86–0.95, respectively. Therefore, the LSTM model is recommended for the prediction of soil moisture at different soil depths, providing valuable references for farmland management, irrigation decision-making and the formulation of drought prevention and mitigation measures.