Irrigation and Drainage最新文献

Hydroponic cultivation requires proper fertigation management to avoid over-fertigation and environmental damage. This 2-year study evaluated the effects of full irrigation, partial root-zone drying (PRD) and sustained deficit irrigation (SDI) on hydroponic strawberry cultivation to optimize water and fertilizer use. In the first year, various irrigation and fertilizer levels were tested, and on the basis of these results, the second year focused on full and half irrigation with different nutrient solution concentrations. This study evaluated plant morphology, physiochemical traits (e.g., chlorophyll, phenolics, and electrolyte leakage) and fruit quality (TSS/TA and L/D ratio) while calculating irrigation water productivity and biomass water use efficiency (WUE). Nutrient element contents were measured in the substrate and leaves. The second-year results indicated that irrigation treatments significantly influenced fruit quality and yield. The PRD, which was fertigated with a half-pot capacity (PRD2), was the best approach because it does not reduce nutrients, preserves substrate health and reduces fertigation by 50%. PRD2 is a drought resistance strategy that reduces vegetative growth and leaf surface area, leading to increased yield and improved berry quality. Additionally, these findings suggest that fertigation adjustments can improve plant stress resistance. These results demonstrate the potential for scalable and sustainable hydroponic strawberry production, offering practical applications for water-efficient agriculture in resource-limited environments.

Submersible drainage pumps are widely used in various applications, including irrigation and drainage, municipal wastewater projects, and sewage pumping. These pumps are suitable for handling mixtures consisting of solid–liquid components and have sophisticated two-phase flow characteristics and wear mechanisms. Therefore, the operational stability of submersible pumps is essential when transporting solid–liquid mixtures. This work aims to optimize existing pumps and enhance hydraulic performance via the computational fluid dynamics (CFD) software ANSYS-CFX. The test pump performance data were compared with the computational data to validate the pump model. The study revealed that altering the shape of the impeller hydraulic performance could improve by 4%, significantly reducing the erosion wear effects. Additionally, optimizing the casing shape could increase the efficiency by 4–5%, although practical implementation is challenging and improves the anti-wear erosion effects. The effects of the particle concentration and particle size were investigated and discussed under different operating conditions for various shape change models. The erosion rate increased with increasing particle concentration at both the leading and trailing edge of the impeller blades. Altering the pump's impeller can minimize the average erosion rate by at least 20%. The revised model significantly reduced erosion wear on the impeller blades.

Increasing water productivity and mitigating the impacts of climate shocks is central to achieving food and water security. This study employed a household survey and farmer field experiments in four districts of Odisha and Assam states in Eastern India to understand and build farmers agricultural resilience. Household survey of 1627 farmers showed that ‘access to irrigation’, ‘system rice intensification (SRI)’, and ‘better seeds and biofertilizers’ significantly reduce farmers' vulnerability to poverty. However, the adoption of efficient irrigation practices, critical for building agricultural resilience, remains low. To increase awareness and adoption through demonstration of benefits, farmer field experiments were conducted for efficient irrigation interventions, including soil moisture sensor-based (SMS) irrigation, drip, and sprinkler for non-paddy crops, and alternate wetting and drying (AWD) and SRI for paddy. The interventions led to 8–69% water savings, 2–220%, 15–600%, and 34–346% increases in crop yield, irrigation (IWP), and economic water productivity (EWP), respectively, with drip irrigation showing greatest increase, followed by sprinkler and SMS. The AWD and SRI interventions showed 26–59% water savings, 2–131%, 40–600%, and 39–550% increases in yield, IWP, and EWP, respectively. Results show that if implemented in existing irrigated vegetable areas, these interventions could reduce fallow lands by 4–60% in both states.

Wind drift and evaporation loss (WDEL) significantly affect the performance of low-pressure sprinkler irrigation systems. However, research on its characteristics and prediction under integrated water-fertilizer management remains limited. This study evaluated WDEL variability under diverse meteorological conditions in a low-pressure sprinkler fertigation system. The effects of operating pressure, fertilizer concentration and meteorological factors on fertigation uniformity and accuracy were investigated, with a focus on losses of fertilizer solution (WDEL_F), water (WDEL_W) and urea fertilizer (WDEL_U). Four machine learning models (multiple linear regression [MLR]; random forest [RF]; support vector regression [SVR]; artificial neural network [ANN]) were compared. The results revealed that WDEL reduced the uniformity coefficient (CU) of the sprinkler by up to 59% under an average wind speed (WS_avg) of 5.5 m s⁻¹ and a vapour pressure deficit (VPD) of 2.20 kPa. VPD impacted WDEL_F and WDEL_W (p < 0.01), whereas WS had the most substantial effect on WDEL_U (p < 0.01). SVR achieved the highest determination coefficient (R² = 0.792) for predicting WDEL_F, whereas ANN outperformed other methods for WDEL_W and WDEL_U, with R² values of 0.711 and 0.854, respectively. These results provide theoretical support for mitigating WDEL risks and improving water–fertilizer utilization efficiency under low-pressure sprinkler fertigation.

Evaluating soil matrix infiltration is essential for understanding water dynamics, runoff and groundwater recharge. Experiments using a double-ring infiltrometer with and without fine sand were conducted in forestland, tea gardens and paddy fields. A methylene blue tracer helped analyse soil profiles to detect macropores. Fine sand effectively blocked macropores in forestland and tea gardens but was absent in paddy fields, likely due to annual puddling, high moisture, clay content and bulk density. The basic infiltration rate (BI) remained consistent across land types, whereas accumulated infiltration depth (AI) was influenced by fine sand, except in paddy fields. Data collection was labour intensive, leading to regression models predicting infiltration based on soil properties. Three machine learning techniques (partial least squares regression [PLSR], the group method of data handling [GMDH] and random forest [RF]) were evaluated via the coefficient of determination (R²) and root mean square error (RMSE) metrics. Model 2 of PLSR performed best for forestland (R²: 0.933, RMSE: 0.908 cm) and tea gardens (R²: 0.838, RMSE: 1.156 cm), whereas Model 1 of PLSR was optimal for paddy fields (R²: 0.780, RMSE: 0.370 cm). GMDH yielded the best overall results, with RF ranking third but remaining useful.