Agricultural Water Management最新文献

The temporal variability and spatial heterogeneity characteristics of the water-agriculture-ecology (WAE) nexus system have aggravated the difficulties in its synergetic management. Besides, in the inland river basin, the surface water and groundwater are tightly linked by the combination of canal and well irrigation. To address these issues, a spatiotemporal equilibrium-water transformation based water-agriculture-ecology nexus synergetic management (SEWT-WAE) model was proposed by incorporating a spatio-temporal robust optimization method and linear water transformation model. The SEWT-WAE model was then applied to the Aksu River Basin, an inland river basin of Xinjiang, China. The results indicated that the SEWT-WAE model was highly effective in achieving spatiotemporal equilibrium in groundwater balance and ecological water utilization, as well as in the integrated management of surface water and groundwater across upstream and downstream regions. The optimal synergetic management scheme was obtained based on the coordinated development degree. Compared to the current situation: (i) the irrigation amount provided by the surface water (groundwater) in the Tabei (Tanan) irrigation district was increased (decreased) by 21.4 % (70.2 %); (ii) the irrigated areas of grain crops and gardens were increased by 30.4 % and 20.1 %, respectively, while the irrigated area of cotton was decreased by 19.4 %; (iii) the ecological water utilization of the Populus euphratica forest was increased by 17.81 %. Overall, this study presents a new optimization model for achieving spatiotemporal equilibrium and conjunctive use of surface water and groundwater and provides decision support for WAE nexus synergetic management in the inland river basin.

Sustainable management of water, energy, and food (WEF) under climate change will be a significant challenge for arid agricultural systems. This study developed a fractional non-linear multi-objective programming (FNLMOP) model to optimize resource allocation and improve agricultural sustainability in these systems under climate change. The model was designed in the framework of the WEF nexus to simultaneously improved energy productivity (profit/energy), and water productivity (profit/water), while mitigating environmental damage (damage to groundwater resources/output) and ensuring food security in an arid watershed in Iran. The long Ashton research station weather generator (LARS-WG) and the coupled model intercomparison project 6 (CMIP6) were employed to project climate parameters for both future dry and wet conditions. The sustainability of the optimal solutions was then assessed using a hybrid criteria importance through intercriteria correlation (CRITIC)-VIKOR approach. The optimal solutions revealed a reduction in the land under cultivation and produced less water-intensive crops. The optimization model can ensure WEF security, enhancing agricultural system sustainability by optimizing crop cultivation patterns and resource allocation. Current crop choices were highly inefficient with the bigger changes being from the current crops to optimal crops. Climate change showed a substantial but lesser influence on optimal crop choice.

Fresh water shortage is a major problem for grain production in the low plain around Bohai Sea in the North China Plain. Relative abundance of shallow saline groundwater could serve as an alternative water resource for use during dry seasons. A continuous 8-year field study was conducted from 2015 to 2023 to assess the effects of salt content in irrigation water on soil salt accumulation and crop production. Fresh water (FW) with electrical conductivity (EC) at 1.6 dS/m and three levels of saline water (SW) with EC at 4.7 (SW1), 6.3 (SW2) and 7.8 dS/m (SW3) were used for irrigation. Results showed that a single irrigation event at the jointing stage of winter wheat increased grain production averagely by 18.6 %, 22.5 %, 12.9 % and 9.5 % compared with a rain-fed treatment (RF) under FW, SW1, SW2 and SW3, respectively. With an additional irrigation applied at flowering stage, both irrigations using FW increased the yield by 28.6 %, and both irrigations using SW2 increased the yield by 19.3 % compared with RF. Negative effects of salt on winter wheat overshadowed the positive effects of increased water supply under two irrigations both using SW. With an irrigation at maize sowing and the subsequent summer rainy season, the yield of maize following winter wheat was not affected by a one-time SW irrigation to the previous crop, but showed a 5.3 % yield reduction when two irrigations of SW were applied. There was no apparent salt accumulation in the top 1 m of the soil profile, but a slight increasing trend in the salt content in the 1–2 m layer of the soil profile under SW2 and SW3 irrigation. No apparent changes in soil physical properties were observed for continuous application of SW. It was suggested that SW with EC not exceeding 6.3 dS/m should be applied for a single irrigation during the winter wheat season. This practice could alleviate the fresh water shortage in this region and allow for the maintenance of a relatively stable yield of winter wheat and maize without the risk of salt accumulation in the soil.

Agricultural sustainability in many parts of the world is facing significant challenges due to water scarcity and the adverse effects of climate change. Agriculture uses 70 % of the total freshwater, and irrigation sustains 40 % of the global food supply. Various plant monitoring technologies and irrigation techniques were developed to improve the efficiency of agricultural water use. Although their benefits are widely acknowledged, conflicting findings and inconclusive results emerge when assessing their performances and effectiveness in monitoring plant water status. A systematic review using a rigorous protocol for research question formulation and eligibility criteria definition was conducted with the aim of assessing, from published literature, the performance of various plant (tree) based sensors for water stress monitoring. Initially, 496 articles were collected from four leading search engines for scientific peer-reviewed papers., the number of relevant manuscripts was narrowed to 124, using strict inclusion and exclusion criteria, from which meta-analysis was conducted and reported. Results showed that most studies were conducted in Spain and the USA, focusing on olive, peach, and almond cultivation. Crop Water Stress Index showed a better correlation with stomatal conductance (gs) (R² = 0.76) and with leaf water potential (ΨL) (R² = 0.75) compared to Xylem Water Potential (ΨS) (R² = 0.6). Maximum Daily Shrinkage (MDS) showed a coefficient of determination with ΨS equivalent to 0.68. On the remote sensing side, the most commonly used indices are the Normalized Difference Vegetation Index (NDVI) (n=22), Photochemical Reflectance Index (PRI) (n=11), and canopy temperature (n =94) with better correlation with ΨS for PRI and thermal. However, the finding is not conclusive due to the complexity of plant water relations and the influence of other factors such as environmental conditions, canopy structure, nutrient deficiencies, and plant diseases.

Shallow groundwater is a critical water resource for sustaining vegetation growth in arid and semi-arid environments and affects stand transpiration (T) dynamics. However, it is still difficult to quantify the impact of groundwater on T. Here, we introduced a novel groundwater response function in the Jarvis-type model (referred to as MJS_G) and tested its performance using Populus popularis sapflow data over two main growing seasons (2018–2019). The results showed that the performance of the MJS_G model depended on groundwater level. Specifically, when groundwater table depth was within 1.2–2.0 m, the precision of daily T simulation by the MJS_G model was higher than that by the MJS model without groundwater response function over two years, with an increase in Nash-Sutcliffe Efficiency (NSE) from 0.787 to 0.825. Furthermore, in contrast to the MJS model, the MJS_G model could better capture the diurnal course of T in 10:00–16:00, with a significant increase in NSE from 0.592 to 0.706. The improvement allows a more accurately estimate of tree water use under shallow groundwater fluctuations, which will help broaden the ecohydrological application of the Jarvis-type model to similar areas.

Water markets are increasingly advocated for combating agricultural water scarcity. However, in developing countries’ arid and semiarid regions, water market performance is greatly weakened by the insufficient demand-side and supply-side driving forces stemming from farmers’ limited awareness and capacity. Building an agent-based model and taking Hami City, Xinjiang in northwestern China as the case study area, this study explores the roles of technology-based and transaction-based subsidies in adjusting market driving forces and promoting the performance of agricultural temporary water markets. The results show that, without government support, water transactions are limited to only 8.054×10³ m³, which can hardly protect agricultural interests. However, with improved market driving forces, water transactions can be substantially increased—potentially exceeding 100×10³ m³—by technology-based and transaction-based subsidies This effect is particularly evident when the technology-based subsidy induces water shortages for recipients (e.g., retrieve 5820 m³/ha water entitlements) and its acceptance rate is below 50 %, and when the transaction-based subsidy approaches the average opportunity costs of selling water for traditional irrigation farmers (0.462 RMB/m³). Combinations of technology-based and transaction-based subsidies prove to be more cost-effective in improving water market performance and protecting agricultural benefits, owing to the enhanced market driving forces and the promoting effect of transaction-based subsidies on the implementation of technology-based subsidies. This promoting effect also helps to alleviate fiscal burdens. Furthermore, results indicate that irrigation technology promotion and water markets are complementary, and their dynamic interaction can facilitate the gradual transformation of irrigation practices in arid and semiarid regions of developing countries.

Agricultural water accounts for more than 70 % of the total global water usage, and the scarcity of global freshwater resources will largely limit global agricultural production. Precision irrigation is the key to improving water efficiency and achieving sustainable agriculture. Accurate and rapid access to crop water information is an essential prerequisite for precise irrigation decisions. Traditional moisture detection methods based on soil moisture and crop physiological parameters are faced with the problems of variable field conditions, low efficiency and lack of spatial information, which can be extremely limited in practical applications. By contrast, unmanned aerial vehicle (UAV) remote sensing has the advantages of low cost, small size, flexible data acquisition time, and easy acquisition of high-resolution image data. Therefore, UAV remote sensing has become an easy and efficient method for crop water information monitoring. This study systematically introduces the principles, methods and applications of crop water stress analysis using the UAV technology. First, the mechanism of crop water stress analysed by UAV is elaborated, focusing on the relationship between canopy temperature, evapotranspiration, sun-induced chlorophyll fluorescence (SIF) and crop water stress. Next, various UAV imaging technologies for crop water stress monitoring are presented, including optical sensing systems, red, green and blue (RGB) images, multi-spectral sensing systems, and hyper-spectral sensing systems. Subsequently, the application of machine learning algorithms in the field of UAV monitoring of crop water information is outlined, demonstrating their potential for data processing and analysis. Finally, new directions and challenges in UAV-based crop water information acquisition and processing are synthesised and discussed, with special emphasis on the prospects of data assimilation algorithms and non-stomatal restriction in monitoring crop water information in the future. This study provides a comprehensive comparison and assessment of the mechanisms, technologies and challenges of UAV-based crop water information monitoring, providing insights and references for researchers in related fields.

Irrigation patterns and K fertilization are the key measures used to improve the salt tolerance of crops in saline land. A pot experiment was carried out to assess the effects of partial root-zone irrigation and K fertilization on maize in saline land. For the study parameters, we used a split plot design with three irrigation treatments (conventional irrigation (CI), fixed root-zone irrigation (FRI) and alternate root-zone irrigation (ARI)) and two K application rates (0 (K0) and 120 (K1) kg K₂O ha⁻¹). Compared with the CI treatment, the FRI and ARI treatments reduced the transpiration rate (Tr) and MDA content but increased the root length density (RLD), root surface density (RSD), root volume density (RVD), root dry weight density (RDWD), proline content, plant K/Na ratio, photosynthetic rate (Pn), yield, water use efficiency (WUE) and K use efficiency (KUE). Compared with the FRI treatment, the ARI treatment increased RLD, RSD, RVD, RDWD, the Tr, the proline content, the plant K/Na ratio and the Pn but decreased the MDA content; these factors increased yield, WUE and KUE. K application decreased the MDA content; improved root growth; and increased the stomatal conductance, Tr, proline content, Pn, yield and WUE. Of all the treatments, the ARI-K1 treatment maximized RLD, RSD, RVD, RDWD, the Pn and yield while minimizing the MDA content. Therefore, ARI combined with K application can maximize resistance to salt stress and osmotic stress, delay senescence, and improve photosynthesis, yield and WUE.

The spatial gradients of protein and starch in wheat grains affected the grain milling characteristics and flour utilization. The increase in compound heat stress and drought stress (HDS) due to global climate change threatens wheat grain yield and quality parameters, but the impacts of extreme climate events on the gradients of protein and starch in wheat grains remain unclear. In this study, two-year, environment-controlled experiments with four heat stress levels (17/27, 21/31, 25/35, and 29/39°C) and three drought stress levels (30 %, 55 %, and 75 % field capacity) were conducted to investigate the effects of HDS on the gradients of protein and starch concentrations within the five grain layers. The results showed that HDS resulted in significantly greater protein concentrations, while resulting in lower starch concentrations in the wheat grain layers. Among the five layers, the endosperm layers exhibited the greatest increase in protein concentration under HDS, but the starch concentration under HDS decreased in the order of husk > aleurone > endosperm layer. HDS unevenly altered the protein and starch concentrations of the five grain layers. There was significant linear relationship between relative protein and starch concentration with accumulated heat degree days (AHDD). With a 1°C·d increase in AHDD, the protein concentration in the five grain layers increased by 1.17–2.19 %, while the starch concentration decreased by 0.62–0.90 %, depending on the drought stress levels. A significant linear relationship was also observed between the relative protein and starch concentrations and evapotranspiration (ET). A 1 mm increase in ET led to a protein concentration decrease of 0.58–0.76 % in P1-P5, with a corresponding starch concentration increase of 0.28–0.46 %, depending on cultivar and treatment stages. Our results indicate that HDS significantly impacts the grain quality parameters for the flour milling process and human diet and will provide important insights into adapting wheat quality to climate change.