Irrigation and Drainage最新文献

This study focuses on the design and evaluation of permeable reactive barrier (PRB) filters for the purpose of mitigating nitrate levels in agricultural drainage water. SEEP/W and CTRAN/W software models were used to simulate the one-dimensional movement of dissolved nitrate pollutants within the porous medium of the drainage filter. The research comprehensively models and examines nitrate transport within the filter, considering the saturation of the soil column with a constant pollutant load from the initiation to the completion of the nitrate breakthrough curve, which is an absorption process. The results indicate that the software provides a robust approximation of the pollutant movement process within the drainage filter. However, the breakthrough curve derived from the software requires more time than laboratory experiments do to simulate pollutant transport in the filter environment. This study revealed nearly equal nitrate removal rates between the numerical and experimental models.

Existing policies promoting agricultural water conservation focus mainly on promoting water-saving irrigation through subsidy policies, but this approach leads to a rebound effect, making obtaining the desired water-saving result challenging. Moreover, many countries have begun to realize that the market-oriented allocation of water resources through water rights reform (WRR) may be a more effective way of managing water resources to achieve agricultural water conservation. On the basis of the WRR pilot policy in China, this paper uses an empirical difference-in-differences (DID) strategy to analyse whether the WRR can reduce agricultural water use. The empirical results show that the WRR can reduce water use by 7% on average, and this result still holds after contemporaneous policies and alternative outcome measures are considered. The possible mechanism is that the WRR has changed the planting structure for food crops and cash-crops and promoted water resource transfer from the agricultural sector to the non-agricultural sector. The heterogeneity analysis indicates that the water-saving effect of WRR is worse in areas with higher land potential or with better economic conditions. Therefore, the promotion of the WRR needs to consider agricultural production conditions and the economic situation.

Drip irrigation technology has obvious advantages in solving water shortages in agriculture. The sediment particles in the irrigation water not only hinder the proper functioning of the emitters but also sink into the soil with the water flow and change the soil gradation. Understanding the relationship between curved flow paths inside emitters and sediment particles is essential for sustainable agricultural practices and effective water management. Low-quality sandy water sources with different concentrations (1.8, 2.8 and 3.8 g/L) and particle sizes (0–0.054 mm, 0.054–0.075 mm and 0.075–0.1 mm) were configured and used for drip irrigation tests to compare the composition, characteristic parameters and parameters of sediment output from emitters with those of sandy water sources under irrigation conditions. The effects of different curved flow paths on sediment were systematically investigated to assess the effectiveness of the drip irrigation system. The results revealed that the specific surface areas of E1, E2 and E3 were 12.3–17.3 mg/mm², 2.3–9.7 mg/mm² and 4.5–8.1 mg/mm², respectively. D₅₀ and D₉₀ increased from 18.1–27.11 μm and 75.5–160.4 μm to 22.2–30.5 μm and 95.1–201.9 μm. Owing to the flocculation effect of sediment aggregation and the screening and blocking effects of the emitters, the proportion of clay and sand particles decreased, and the proportion of powder particles increased. The characteristic structural parameters of the emitter flow channel and the external characteristic parameters significantly affect some of the output sediment characteristic parameters. In addition, through the application of fractal geometry, we provide a new approach to assess the effects of water flow modelling and sediment particle variations within emitters on resistance to clogging, providing valuable insights for optimising irrigation strategies and promoting soil health.

Salinity and sodicity severely impact soil health and agricultural productivity by disrupting nutrient and water availability, ultimately threatening food security and ecosystem sustainability. This study examined the spatial and temporal distributions of soil salinity and sodicity under the Sille irrigation scheme in southern Ethiopia. Soil samples collected from two depths (0–30 cm and 30–60 cm) during both the wet and dry seasons revealed significant variations in salinity levels, ranging from nonsaline to very strongly saline. During the dry season, moderately saline areas constituted 43% and 44% of the 0–30 cm and 30–60 cm depths, respectively, with strongly saline areas at 0–30 cm varying (37%–38%) from the wet to dry season. Sodicity also varied significantly, with 39% of the area exhibiting strong sodicity at 30–60 cm depth during the dry season. A strong positive correlation was found between electrical conductivity (EC) and the exchangeable sodium percentage (ESP), as well as soluble sodium. Additionally, the soil water balance analysis indicated increased irrigation and groundwater recharge during dry periods. These findings highlight the need for integrated management strategies, such as selecting salt-tolerant crops and implementing effective salt-leaching practices, to increase productivity and ensure the sustainability of irrigated agriculture.

The lack of water in semi-arid areas presents challenges for agriculture, making it necessary to adopt strategies that optimise the use of this water resource. This study evaluated the effects of irrigation level on the growth, productivity, economic viability and water use indicators of different forage species in the semi-arid region of Brazil. The study was carried out in two production cycles, with a bifactorial arrangement for pasture grasses (Buffel, Aruana and BRS Kurumi) and for silage (Sorghum and BRS Capiaçu) and a unifactorial arrangement for the protein source species (Pornunça). Irrigation depths of 25%, 50%, 75% and 100% of the reference evapotranspiration (ET_o) were imposed. The growth rates showed linear and quadratic behaviour in the two cycles evaluated, with growth peaking at irrigation depths between 50% and 75% of the ET_o. Only BRS Kurumi was influenced by irrigation depth in the first cycle. For the silage grasses, only BRS Capiaçu was influenced in the first cycle, with 100% irrigation depth standing out. In the second cycle, the 100% irrigation depth favoured the dry matter yields of BRS Capiaçu and Sorghum. Irrigation should be adjusted according to the time of year for better water utilisation.

Climate-induced drying–wetting cycles have severe impacts on different soil infrastructures and agricultural productivity in semi-arid regions. Owing to their hydrophilic nature, superabsorbent hydrogels are gaining significant attention for reducing the negative influence of climate change on soil properties. Knowledge of the hysteretic behaviour of the water retention properties of hydrogel-amended soils is needed to understand their long-term hydraulic performance. This study examined the drying-wetting soil–water retention curve (SWRC) of a fly ash-based hydrogel (FAH) amended with three different textured soils for two consecutive cycles. The leachability of toxic heavy metals from the developed FAH was evaluated via the toxicity characteristics leaching procedure (TCLP). The efficacy of a predictive hysteresis model was investigated for estimating the wetting SWRC of FAH-amended soils from the measured drying SWRC. The inclusion of FAH particles significantly improved the SWRC of the soils used in the lower suction range (Ψ < 100 kPa). The hysteresis loop in the SWRC increased with increasing FAH content for both drying and wetting cycles. Moreover, width of the hysteresis loop decreases with the increasing number of cycles due to the degradation of the FAH polymer chain during the soil-FAH-pore water interaction. The predictive hysteresis model successfully captured this decrease in the hysteresis loop with increasing drying–wetting cycles for the FAH-amended soils.

Reference evapotranspiration (ETo) and crop coefficients (Kc) can be used for the estimation of crop water requirements. Estimating the ETo using the FAO-56 Penman–Monteith (FAO-56 PM) method requires various climatic variables that are not commonly available in developing countries. The emergence of long-term assimilated reanalysis products offers an alternative for consistent climate data. However, reanalysis data (RD) must be validated before being applied regionally. This document presents the evaluation of five reanalysis datasets (CFSv2, GLDAS, NLDAS, RTMA, and the NASA-POWER [NP]) to estimate ETo in an arid hydrological region of northern Mexico using daily information observed at 26 meteorological stations. Additionally, four different cases are examined, in which the FAO-56 PM and Hargreaves and Samani (HS) methods are used with reanalysis and measured data. According to the results, the RTMA data show the best performance for temperature, CFSv2 for wind speed, and the NP for solar radiation. Although NLDAS contains hourly information, it has low performance. The five datasets tend to overestimate ETo. The FAO-56 PM method combined with RD outperforms the HS method. In the study area, when observed data are not available, ETo can be estimated via the FAO-56 PM method and RD from different sets.

The growing demand for water in agriculture requires a thorough assessment of water quality to ensure sustainable irrigation practices. This study integrates a novel approach, the method of removal effects of criteria (MEREC), with principal component analysis (PCA), which delves into the intricate interplay of key water quality parameters within the Karun River Basin, a vital agricultural region in Iran. The key water quality parameters include total dissolved solids (TDS), electrical conductivity (EC), the sodium adsorption ratio (SAR), the permeability index (PI), total hardness (TH), the sodium percentage (SP), the magnesium adsorption ratio (MAR) and potential salinity (PS). The results revealed strong correlations between EC, TDS and SAR. PCA revealed that EC, SAR and PS were the primary contributors to the variations in water quality, explaining more than 89% of the total variance. The MEREC method revealed that PS was the most significant factor, followed by EC and SAR. The calculated water quality index (WQI) indicated that 74% of the water samples fell within the low-restriction category, whereas 26% exhibited moderate restrictions. These findings suggest that although the water of Karun River Basin is generally suitable for irrigation, the salinity and sodium content may pose challenges to agricultural productivity.

Agricultural productivity is crucial for population growth, but 83% of India's water usage is due to unplanned consumption. To address this, smart agriculture, which uses Internet of Things (IoT) technologies for efficient water management, is essential. Smart irrigation control is crucial for addressing water waste and promoting agricultural growth. To make agriculture smart, automation and IoT technologies emerged with the deep learning algorithms used in the research. Despite advancements, existing research models have drawbacks, including limited accuracy in predicting moisture content and inefficient resource utilization. To address the drawbacks of the existing techniques, a novel approach named distributed bidirectional long short-term memory optimized with the Jubatus classifier algorithm (JCA-DiBiLSTM) for automatic irrigation control is proposed. The proposed method integrates BiLSTM networks with a distributed computing architecture, enhancing predictive accuracy by capturing complex temporal dependencies in moisture content data. Additionally, the Jubatus classifier algorithm (JCA) optimizes the model parameters, improving the overall prediction performance. By leveraging JCA-DiBiLSTM, significant advantages are observed in predicting the moisture content of agricultural land. The model demonstrates enhanced accuracy, outperforming traditional methods and mitigating over- or under-irrigation risks. Furthermore, JCA-DiBiLSTM ensures an accuracy and specificity of 94% and minimizes the mean square error (MSE) to 5.5 while maximizing the sensitivity to 94%.