Irrigation and Drainage最新文献

The aim of this study was to investigate the effects of improved subsurface drainage installation on nitrogen (N) loss in drain discharge (DD) and topsoil layer runoff (TLR). Data on DD and TLR, as well as on concentrations of total, nitrate and ammonium N in the runoff components, were collected from four sections of an experimental field in southern Finland (June 2007–December 2018). Supplementary drains were installed in one of the field sections in June 2014, and the data from that section were compared with those from three reference sections. Differences between the sections were statistically analysed based on annual and monthly values of runoff components and concentrations, as well as the loads of N fractions. The results revealed that improved drainage increased the N load in the DD, reducing the load in the TLR. Changes in N loads were more clearly driven by changes in the runoff volumes rather than by changes in the N concentrations in the runoff waters. Before the drainage improvement, most of the total N load was nitrate (53%), while the share of rest N (fraction of the total N after the mineral N fractions were subtracted) was 45%. After improved drainage, the percentages of nitrate and rest N were 73 and 26%, respectively. The results demonstrate the importance of agricultural water management as the key driver for controlling nutrient loads.

The ‘Kent’ mango is one of the main cultivars produced in the São Francisco valley. However, due to a lack of data, water management was carried out using coefficients from the Tommy Atkins cultivar. Thus, aiming to achieve greater water management efficiency, the aim of this study was to evaluate the growth, radiation and energy balance, evapotranspiration and coefficients of the ‘Kent’ mango in the lower-middle São Francisco valley in Brazil. The study was conducted in an orchard over two harvests between 2017 and 2018. The radiation and energy balance, evapotranspiration (ET_c) and crop coefficients (K_c) of the mango were estimated from micrometeorological data. The mean reference evapotranspiration (ET₀) and ET_c values were 5.47 and 4.40 mm day⁻¹ (vegetative growth, VG), 4.42 and 4.29 mm day⁻¹ (floral induction, FI), 4.08 and 3.48 mm day⁻¹ (floral induction + flowering, FI + FL), 4.51 and 3.63 mm day⁻¹ (fruit drop, FD) and 6.09 and 4.46 mm day⁻¹ (formation fruit + maturation fruit phase, FF + MF). Under the climate conditions of the São Francisco valley, K_c values of 0.80, 0.97, 0.85, 0.80 and 0.74 are recommended for the ‘Kent’ mango during the VG, FI, FI + FL, FD and FF + MF phases, respectively.

To verify the effectiveness of the intelligent decision method for fertigation, an automatic control system for fertigation in greenhouses was designed, and three intelligent decision methods based on evapotranspiration (T1), soil moisture (T2) and accumulated temperature (T3) were tested. Intelligent decisions included monitoring meteorological information, automatically monitoring soil moisture, utilizing fertigation application systems and using automated control modules. The system was stable and accurately controlled according to the decision scheme. The results showed that the average errors of the automated control system for decision-making and irrigation were 1.1 and 0.8%, respectively. The study findings serve as a reference for the integration of intelligent irrigation decision-making and control systems and for further improving the efficiency of water and fertilizer utilized. Compared with those of the control, the three intelligent decision-making methods increased the tomato yield by 8, 12 and 7%, respectively. In addition, the irrigation water and fertilizer levels decreased significantly compared with those in the control treatment. Although the accuracy of the soil water content (SWC) estimated based on ET and temperature in irrigation decision-making is low, the general trend is consistent with practice. In addition, the irrigation water use efficiency (IWUE) and partial factor productivity of fertilizer (PFP) were significantly improved. Similarly, the IWUE in T1 was the highest (60 kg m⁻³), and the PFP in T3 was the highest (669 kg kg⁻¹).

Drip irrigation is widely acknowledged for its water use efficiency, yet evaluating relative water uptake rates (RWURs, ratios between the water uptake rates and the irrigation rates) remains pivotal for effective system design and management. This article presents a novel method employing straightforward measurements of wetted soil surfaces around emitters or perpendicular to driplines, both with and without water uptake, emphasizing simplicity and practicality. The proposed method offers valuable insights into agronomic water use efficiency, facilitating the optimization of drip irrigation for both annual and perennial crops. While effective for intensively irrigated crops, the method does have limitations for smaller wetted areas and longer irrigation cycles, depending also upon a reasonable determination of the active root zone depth and the soil capillary length. Despite relying on a simplified water uptake model, the accessibility and cost-effectiveness of the method render it a valuable tool for assessing RWURs in diverse agricultural settings, contributing to the sustainable utilization of water resources in drip irrigation.

Technological interventions in tackling water scarcity in agriculture delve into the water productivity issue through available alternative water resources and on-farm techniques including both structural measures and agronomic practices accompanied by a sound water management approach. On-farm water management has been addressed under Congress Question 65 through deliberations on irrigation efficiency, soil moisture forecasts and hydrological predictions, efficient and timely distribution of water, controlling non-beneficial evaporation and reducing non-returnable losses of irrigation water at existing and state-of-the-art levels using technologies such as SCADA (Supervisory Control and Data Acquisition) and sensor technology. A wide range of land, water and crop-related important parameters such as soil degradation, soil moisture, zero tillage, conservation agriculture, water demand and supply, future irrigation demand, biologically degradable mulching materials, cost-effective design of drip irrigation, water quality and cultivation practices through simulation for evaluating environmental impact and adaptation capacity of farmers were addressed. The major outcomes inferred increasing water productivity through optimizing agricultural water use that led to higher crop yields. IoT (Internet of Things) technologies can provide accurate and dynamic information of water consumption and rainfall in real time and have the potential of adapting to climate change scenarios.

The Extended Hydrologic Prediction (EHP) project is a forecasting system development and deployment project for three major basins of India—Yamuna, Cauvery and Narmada. The forecasts will be produced at four key locations in each basin using a 4-week horizon. Forecasts are hydrologic naturalized (deregulated) flows. Regulated forecasts will require the addition of an operations model to account for management decisions and interventions. Forecasting streamflow over a multi-week horizon has a range of benefits to improve water resources planning for flood mitigation via reservoir pre-positioning, drought planning for shortage allocation, power maximization and many other benefits demonstrated globally. EHP is a challenging project from the research side, where there are limited deployed systems across the world at this horizon, which is a chance for EHP to make a significant contribution to the forecasting community, which will bring benefits to the stakeholders of these basins, and will be a model for future similar forecasting systems in India and around the world.

Poorly performing irrigation schemes in India require technical, managerial and institutional support on an urgent basis. In recent years, participatory irrigation management (PIM) has gained momentum as a strategy to improve the performance of irrigation systems, increase water use efficiency and promote equitable water distribution. This approach involves the active involvement of farmers and other stakeholders in the planning, implementation and management of irrigation systems. The state of Bihar has primarily an agrarian economy and irrigation is essential for agricultural production. However, Bihar's irrigation potential is being underutilized due to improper management of irrigation systems. In Bihar, the experiment of PIM in the Paliganj distributary of the Sone irrigation scheme was attempted as a local initiative by WALMI Patna, which was funded by USAID, and, thus, the need for an overall state policy and strategic plan was realized. Therefore, PIM has been implemented through the formation of water users' associations (WUAs) and farmers’ organizations (FOs) at different levels under the Bihar Irrigation Act of 1997, and Bihar Irrigation, Flood Management and Drainage Rules, 2003. The Bihar model of PIM is based on the macro-to-micro approach because of its geographical conditions and the size of the schemes in the state. This paper examines the implementation of PIM in Bihar and its impacts on farmers.

The Pleistocene aquifer serves as a vital water source for various activities in Ho Chi Minh City, Vietnam, including concentrated and individual exploitation. In the present study, the DRASTIC (D, depth of water; R, net recharge; A, aquifer media; S, soil media; T, topography; I, impact of vadose zone; C, hydraulic conductivity) model was used to evaluate the groundwater sensitivity of the study area. To analyse Ho Chi Minh City's Upper-Middle Pleistocene aquifer vulnerability, the analytic hierarchy process (AHP) method was used to optimize the DRASTIC score and include land use (LU) characteristics. Four distinct weights were used: DRASTIC, modified DRASTIC-LU, AHP-DRASTIC and modified AHP-DRASTIC-LU. This study identified low, moderate and high vulnerability for 12%, 55% and 33% of the DRASTIC-LU index values, respectively. The AHP-DRASTIC index classifies 61%, 26% and 13% of sites as low, moderate and highly vulnerable, respectively. The study reveals that 52%, 30% and 18% of the area are vulnerable to the modified AHP-DRASTIC-LU index classes. The most sensitive factors are shallow aquifer roofs, recharge and LU. The real-world accuracy of the DRASTIC models was tested using 106 groundwater nitrate concentrations. The modified AHP-DRASTIC-LU is the most accurate and appropriate model for the current research region.