Irrigation and Drainage最新文献

This study presents and assesses the novelty of a cutting-edge solar-powered automated irrigation system that incorporates a single-axis solar tracker. The research entails the meticulous development of a prototype, followed by comprehensive experimental scrutiny spanning 3 days, from 8:00 AM to 6:00 PM. In a unique approach, we benchmark the findings against previous research endeavours, highlighting the transformative potential of our innovative design.

Our innovative system harnesses a singular-axis solar tracking mechanism alongside moisture sensors and a water pump relay module, resulting in the creation of an autonomous irrigation system perpetually powered by solar energy. The results are noteworthy, showcasing the capability of a solar panel equipped with single-axis tracking to significantly boost photovoltaic output power. This configuration attains a remarkable 65% increase in total output power and a substantial improvement over the modest 52%–53% performance of fixed solar panels. This substantial divergence translates to a noteworthy 12%–13% difference in efficacy, underscoring the pioneering nature of our research.

The zenith of power output, ranging between 3.16 and 3.68 W, transpires from noon to 2:00 PM, further illustrating the system's viability. The integrated water pump exhibits commendable efficiency, attaining levels as high as 75%. This revelation underscores the transformative potential of automated irrigation systems endowed with single-axis solar tracking technology, auguring amplified system performance and heralding a new era of sustainable agricultural practices.

Soil pH is important for favourable plant growth. The water used for irrigation must have an optimal pH value. Such water can be prepared by passing it through a flow electric activator. An experimental setup with a flow electric activator was created to control the pH value of the activated water. A mathematical model was developed to establish the relationship between the activated water pH value and the power supply voltage and the performance of the anode and cathode chambers. The pH value of activated water varies in direct proportion to the power supply voltage and is inversely proportional to the performance of the set chambers. The pH value of activated water can be adjusted by two parameters: the voltage of the power source and the water supply.

Uneven sediment distribution affected by inlet velocity, particle size distribution and flow field characteristics leads to local clogging in a Y-type screen filter. This study revealed the detailed flow field characteristics and distribution of sediments on the screen via computational fluid dynamics combined with the discrete element method (CFD–DEM) simulations and experimental tests. The results showed that the distribution of the flow rate on the filter screen was extremely uneven, with the maximum flow rate being 11.72 times greater than the minimum rate. The flow rate was distributed symmetrically on the two sides along the outlet centreline, as shown by the unfolded drawing of the filter core. Numerical simulation and experimental tests using sandy water showed that the number and average particle diameter on the screen decreased, and the number and average particle diameter in the plug increased, with increasing inlet velocity. The sediments on the screen were distributed intensively, and the plugging extent coefficient and the anti-clogging performance improved. However, this process was more likely to cause local clogging and worsen the filtration performance. Therefore, low-speed filtering should be applied if the filtering effect is needed, and high-speed filtering should be applied if the filtering efficiency is favourable but the flushing frequency increases.

The agricultural sector faces a massive challenge in enhancing food production for the growing population with limited water resources. For effective and optimum utilization of fresh water, developing smart irrigation systems based on the internet of things (IoT) is essential for scheduling irrigation based on crop water requirements. In this study, an IoT-based irrigation system was developed and evaluated inside a greenhouse located in the experimental fields of Indian Council of Agricultural Research-Central Institute of Agricultural Engineering (ICAR-CIAE), Bhopal, India. Data on microenvironmental parameters such as temperature, relative humidity, light intensity, soil temperature and soil moisture were collected from the sensors developed inside the greenhouse. Soil moisture was predicted based on the field data collected via different machine learning techniques, such as the decision tree (DT), random forest (RF), multiple linear regression (MLR), extreme gradient boosting (XGB), K-nearest neighbour (KNN) and artificial neural network (ANN) methods, with three input combinations. The ANN (coefficient of determination [R²] = 0.942, 0.939) models performed well but were found to be less effective than the RF (R² = 0.991, 0.951) and XGB (R² = 0.997, 0.941) models in the training and testing phases, respectively. The RF and XGB models outperformed the other models, while the MLR (R² = 0.955, 0.875) technique underperformed. With respect to both the testing and training datasets, the models trained with all four inputs outperformed the models trained with two or three inputs.

Irrigation of rice using groundwater is considered one of the main contributors to north-west India's declining water level. The present study hypothesizes that zero-till direct seeding of rice (ZTDSR) with the optimum irrigation schedule may reduce irrigation compared to puddled transplanted rice (PTR). Crop growth stage-dependent predefined soil matric potential (SMP), that is, −15, −30 and −45 kPa based irrigation schedules either during the entire growing period or their combinations during the vegetative phase in ZTDSR, were compared with PTR for two consecutive seasons. The results showed that irrigation in ZTDSR at lower SMP at any growth stage caused adverse effects on yield. Irrigation at −15 kPa during the entire crop season with straw mulch was found to be the best schedule for ZTDSR. ZTDSR with −15 kPa irrigation, however, saved 36.2 cm of water and recorded higher water productivity but produced 20% less grain yield over the prevailing PTR. A higher groundwater system loss (GWSL) was found in the PTR (29.2 cm) than in the best ZTDSR (23.6 cm) schedule, which indicates better groundwater management in the ZTDSR than in the PTR. Hence, the ZTDSR has the potential to save irrigation, achieve higher water productivity and manage the depletion of groundwater resources in rice–wheat dominant north-west India.

Water resources planning and management are critical in intricate basins such as the Indus Basin, shared by India and Pakistan under the Indus Water Treaty (IWT) for food security, conserving the environment, sustainable economic development and supporting livelihoods. The present study assesses arable land loss within the Padshahi and Sindh Extension (SE) canal catchments over 54 years, utilizing high-resolution satellite imagery and Google Earth Engine's normalized difference vegetation index (NDVI) derivations for strategizing irrigation efficiency, minimizing water loss and ensuring sustainable utilization of limited water resources under the IWT. Results revealed that irrigated land has decreased from 5127 ha (1966) to 3501 ha (2020) in both canals. The Padshahi canal sees substantial loss (1278 ha), primarily due to the highest transitions from agricultural land/crop land (−69%) to built-up areas. The SE canal, experiencing shifts to horticulture and plantation, records relatively fewer changes in built-up areas (348 ha). The monthly variation in the NDVI clearly depicted the high demand for irrigation to cater to agricultural lands with the onset of the sowing season for paddy in the Padshahi (1900 ha) and SE (2600 ha) canals in May.

Despite being a significant sector in Ethiopia, agriculture is mainly run in rainfed system. However, it is imperative to look for irrigation systems and their suitability to the country's agriculture. The study's objectives were to (1) map areas appropriate for irrigable and rainfed agriculture and examine gaps with current active areas, (2) model possible development for irrigation and rainfed scenarios, and (3) offer evidence-based decision support for agricultural investment. Land features, agroecology, population density, market accessibility and length of growing seasons were considered as important indicators when determining land suitability for each scenario. Geographically weighted regression was used to model these indicators. The results show that approximately 359,360 (34%) and 13,802 km² (1.6%) are highly suitable areas for irrigation and rainfed agriculture, respectively. However, Ethiopia's production depends on areas moderately suitable for rainfed agriculture, but these areas are highly suitable for irrigation rather, indicating that it is unfortunate that the areas suitable for irrigation are used for rainfed agriculture so far. In terms of development potential, areas of approximately 71,317 (7%) and 347,435 km² (33%) had the highest and a high irrigation potential, respectively, while areas with rainfed agriculture had approximately 33,821 (3%) and 105,013 km² (10%) with the highest and a high development potential, respectively. These analyses suggest that the country has untapped potential for agricultural development in both scenarios, but this remains within the scope identified in this study.