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Analyzing the ecological and behavioral effects of changes in irrigation practices in oases provides valuable insights for water resource management and the sustainable development of oasis agriculture in arid regions. Taking the Yanqi Basin as a case study, this research draws on long-term empirical data and remote sensing information to evaluate the ecological and irrigation behavior effects resulting from shifts in irrigation methods. And explores the deep societal causes behind these behavioral changes. The findings demonstrate: (1). Between 2000 and 2010, the rapid adoption of groundwater extraction and mulched drip irrigation (MDI) technology temporarily alleviated the water supply-demand contradiction. However, from 2010 to 2020, as the adoption of water-saving practices significantly expanded and agricultural irrigation areas grew substantially, the irrigation paradox emerged, where increased efficiency paradoxically led to greater water consumption. (2). From 2000 to 2020, the groundwater table depth in the irrigation district dropped by 8–16 m, total soluble salt content decreased by 2–5 g/L, and soil salinity decreased by 4–12 g/kg. The proportion of severely salinized and saline soil areas fell from 21.74% in 1999 to 9.75% in 2020. The longstanding salinization issues that had plagued the irrigation district were effectively mitigated with the widespread adoption of MDI. (3). The irrigation district’s vegetation ecological quality index ( VEQI ) showed a slow but steady upward trend in cultivated areas over the years. In contrast, natural vegetation areas such as forests and grasslands exhibited an initial increase followed by a decline. The trends in VEQI responded well to changes in irrigation practices. (4). The economic benefits driven by water-saving technologies and the expansion of cultivated land are deep societal factors behind the changes in irrigation behavior. These benefits also fostered improvements in users’ understanding and awareness of irrigation practices. The shift in irrigation methods in the Yanqi Basin has led to a decline in groundwater levels, an irrigation paradox, and moderate damage to natural vegetation. However, it has had a significant positive impact on improving regional groundwater quality and mitigating soil salinization. Furthermore, it facilitates the further exploration of regional water conservation potential, enhancing the research on the regional water and soil resource management system.

This study aimed to determine the farmers’ perceived impact of climate change on irrigation water and the adaptation measure adopted to mitigate its adverse effects. A binary logistic regression model was used to identified factors affecting the selection of adaptation measures. Partial Least Square-Structural Equation Modelling (PLS-SEM) was employed to compute the benefits of adaptation strategies. The study was conducted in two major cropping systems, i.e., the Cotton Wheat Cropping System (CWCS) and Rice Wheat Cropping System (RWCS) of Punjab, Pakistan, using primary data of 1080 farmers collected through a multistage sampling technique. Due to climate change there was deterioration in surface water and groundwater quality in CWCS than in RWCS. The farmer uses different adaptation strategies like water harvesting, crop diversification, increasing use of irrigation, laser land leveling to save water, making ridges, building a water harvesting scheme, changing irrigation time, high-efficiency irrigation system and water-saving technologies. Adaptation strategies used by farmer were affected by different socioeconomic, demographic and agronomic factors. Results of the binary logistic regression showed that age, farming experience, education, household size, farm size, tenancy status of owner, access to farm credit, information on weather forecasting, soil quality, tube well ownership, remittances, off-farm income, agricultural extension services provided for irrigation water, and information on climatic and natural hazards played a significant role in the selection of adaptation strategies for irrigation water. Results of PLS-SEM showed that adaptation strategies mitigate the adverse effects of climate change on irrigation water. Farmers’ awareness regarding the impact of climatic variability on irrigation water should be enhanced. Availability of credit to farmers should be improved on easy terms to facilitate the adoption of interventions for better irrigation water management. It is high time for policymakers to design effective, affordable, and workable policies to mitigate climate change vulnerabilities against irrigation water to improve the wellbeing of the farmers.

Continuous evaluation of groundwater quality is vital for ensuring its long-term sustainability. However, traditional assessment methods for various purposes face challenges due to cost and time constraints. In this study, machine learning (ML) models, including Gaussian Process Regression (GPR), Decision Tree (DT), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were employed to predict five irrigation water quality (IWQ) indices using only physical parameters (electrical conductivity (EC) and pH) and site conditions (Elevation, depth to water table, and distance to river). A dataset of 246 groundwater samples from the Eocene aquifer in Minia, Egypt, was collected and analyzed to measure groundwater quality parameters. Five combinations of the input parameters were utilized to calculate IWQ indices: sodium adsorption ratio (SAR), sodium percentage (Na %), total hardness (TH), permeability index (PI), and Kell’s ratio (KR). ML models were developed to estimate IWQ parameters based solely on physical measurements and site conditions. The results revealed that GPR, DT, SVR, and ANN strongly predicted all IWQ parameters during training. The results demonstrated that GPR accurately predicted groundwater quality, followed by DT, SVR, and ANN. The best performance of the GPR model was achieved during the fourth combination, which includes EC and distance to the river. The evaluation of GPR through the fourth combination revealed the highest accuracy with a correlation coefficient of 0.97, 0.82, 0.96, 0.87, and 0.81 in predicting SAR, %Na, TH, PI, and KR. The study emphasizes the capacity of machine learning models to efficiently employ readily available and quantifiable field data to predict IWQ characteristics. Moreover, the research findings, contributing to the second goal of the Sustainable Development Goals (SDGs), “No Hunger,” and the sixth goal, “Clean water and sanitation,” have the potential to enhance agricultural productivity and water conservation.

To mitigate the unfavorable effects of excessive water resources consumption, mainly induced by poor performance of irrigation practices, efficient water resource management strategies are required. In response to this need, we have, in an innovative way, enhanced the water resources management (WRM) strategies by both considering the regional conditions with the graph model for a conflict resolution (GMCR) decision support system, and linking the irrigation concept and water resources allocation theory to develop a coupled WRM simulation–optimization model. Typically, implementation of the modified WRM strategies may cause local conflicts because of losing the original water rights. To improve the current irrigation water allocation system with the minimum objections, the hypergame theory was utilized to enhance the capabilities of traditional GMCR models by including the parties’ misunderstandings in the negotiation process and assessing the partial perceptions rather than crisp options. Moreover, by dynamic monitoring of available water resources and water consumption patterns, a WRM simulation model was developed, which is applicable in real agricultural conditions of multi-agricultural zones with multi-crop and intercropping systems and variable water supply sources. The genetic algorithm was utilized to allocate the water resources and determine optimal WRM strategies with the lowest irrigation water shortage. The efficiency of the proposed framework was assessed in conventional agricultural zones in Oman. The recommended strategies not only address local conflicts during the implementation of optimal WRM strategies, but also demonstrate significant potential to reduce the water shortage as a serious environmental concern.

In this research, a full hydrodynamic model based on the numerical solution of Saint–Venant equations is described to simulate the advance phase of surface irrigation. The full hydrodynamic model is the complete form of Saint–Venant equations. This model is the most complex and accurate among all models and can be applied for analyzing the flow hydraulics and managing surface irrigation. The Preissmann finite difference scheme was used for implicit discretizing terms of the equations. The model presented herein is able to give cumulative infiltration and hydraulic properties including discharge, velocity and depth of flow for any time and distance which can be introduced as an upper boundary condition in water transport models in soil. The model was used to evaluate different situations and soil textures, and the results were compared with results of SIRMOD software, which indicated that relative error was less than 4%. The accuracy of the model was also evaluated in comparison with observed data, and the result showed that the model is able to estimate advance time with normalized root-mean-square error (NRMSE) of less than 8%. Conventional relationships of surface and subsurface shape factor overestimate them by as much as 4.7 and 17.2%, respectively, based on the inflow rate.

Due to global and regional climatic dynamics for a couple of decades, agricultural productivity, rural livelihood, and food security have been badly affected in Pakistan. This study was conducted in Punjab, Pakistan, to explore the farmers’ understanding of the impacts of climate change, adaptation strategies, determinants, and benefits on agriculture using data from 1080 respondents. Perceived risks by the farmers in the rice-wheat cropping system and the cotton-wheat cropping system were weed infestation, seed rate augmented, low-quality seeds, infestation of crop diseases and pests, change of cropping pattern, increase of input use, decrease of cropping intensity and productivity, decreasing soil fertility, increasing irrigation frequency, and increase of harvesting time. To alleviate the adverse influences of climate change, the adaptation strategies used by farmers were management of crop and variety, soil and irrigation water, diversification of agriculture production systems and livelihood sources, management of fertilizer and farm operations time, spatial adaptation, access to risk reduction measures and financial assets, adoption of new technologies, institutional support, and indigenous knowledge. Moreover, the results of Binary Logistic Regression indicate that adaptation strategies are affected by different factors like age, education, household family size, off-farm income, remittances, credit access, information on climatic and natural hazards, information on weather forecasting, land acreage, the experience of growing crops and rearing of livestock, tenancy status, tube well ownership, livestock inventory, access to market information, agricultural extension services, and distance from agricultural input/output market. There is a significant difference between adapters and nonadapters. The risk management system may be created to protect crops against failures caused by extreme weather events. There is a need to develop crop varieties that are both high yielding and resistant to climate change. Moreover, cropping patterns should be revised to combat the effects of climate change. To enhance farmers’ standard of living, it is necessary to provide adequate extension services and a more significant number of investment facilities. These measures will assist farmers in maintaining their standard of living and food security over the long term to adapt to the effects of climate change based on various cropping zones.

The application and promotion of water-saving irrigation technology are of great significance for maintaining food security and the sustainable development of agricultural water resources. Based on the characteristics of integrated agricultural technologies, a binary logistic model was used to analyze the impact of technology perception on farmers' adoption behavior regarding water-saving irrigation technologies using data from surveys of 775 wheat and maize farmers in the North China Plain. The results show the following: the perceived ease of use of technology significantly contributes to farmers' water-saving irrigation technology adoption behavior, but the effect of the perceived usefulness of technology is not significant. Government regulation plays a moderating role in the impact of the perceived ease of use of technology on the adoption of water-saving irrigation technologies by farmers. In addition to the perceived ease of use of technology and technology training, large-scale farmers are influenced by government advocacy and technology subsidies, while smallholders are mainly influenced by the perceived usefulness of technology. Therefore, the focus of future work should be on improving farmers' perceptions of the ease of use of water-saving irrigation technologies, expanding the scope of technical training and technical subsidies, and strengthening government advocacy and education.

In water-stressed agricultural regions, reuse of reclaimed water has emerged as a promising alternative that improves supply reliability, alleviates water scarcity and contributes to circular economy. The European Union has recently launched several initiatives to facilitate the adoption of water reuse for irrigation. However, its adoption is still far below its potential in most areas. This is the case of the Western La Mancha aquifer, in central Spain, where reclaimed water reuse is considered an alternative source to groundwater that may contribute to reduce overexploitation. A stakeholder-based fuzzy cognitive map (FCM) was developed to provide insights into the current situation of reclaimed water reuse in this area, as well as to explore the outcomes of different simulated scenarios (cost recovery, agricultural transformation, social awareness and political will increase). The FCM-based dynamic simulations showed that political will increase would generate the highest increase of reclaimed water reuse in agriculture in the study area, providing the highest increase of water reuse in agriculture. Agricultural extensification and increased social awareness delivered similar positive outcomes, however, only public awareness campaigns would increase water reuse, with agricultural extensification outcomes being more oriented towards the reduction of water abstractions and pollution. The cost recovery scenario was the only one that resulted in non-desired changes, mainly caused by reduced farmers' income due to higher costs of reclaimed water. Finally, the analysis also evidenced the key role that the newly enacted EU Water Reuse Regulation may play in promoting reclaimed water use, even reversing the negative outcomes of the cost recovery scenario.

Globally, agricultural water management faces significant challenges due to uneven water availability, crop diversity, and climate variability. Despite increasing access to smart irrigation technologies, adoption among vegetable farmers remains low. This study examines the willingness of Florida vegetable growers to adopt water-saving irrigation technologies, focusing on socio-economic factors, perceived barriers, and opportunities for enhanced outreach. A structured, pre-tested survey was conducted with commercial vegetable growers across Florida’s major vegetable-producing regions, collecting data on irrigation practices, familiarity with technology, satisfaction, and demographic characteristics. Results showed that satisfaction with current irrigation practices and willingness to adopt new technologies were significantly influenced by farm size, education, income, and crop diversity. Farmers managing multiple crops, small-scale growers expressed a strong interest in adoption, particularly when cost-share or technical assistance programs were available. Common barriers included high initial costs, lack of technical training, and skepticism about the reliability of water-saving technologies. This study highlights the need for targeted outreach strategies that consider demographic variability, farm size, and cropping systems. Based on the results, the policy measures that simplify access to incentive information, decision-support tools, and inclusive hands-on training programs can enhance technology adoption. While focused on Florida, the findings reflect broader patterns in adoption behavior across global small- to medium-scale farming systems. These insights are valuable for policymakers, extension agents, and researchers aiming to accelerate the adoption of precision irrigation for climate-resilient agriculture.

Recent improvements in initialization procedures and representation of large-scale hydrometeorological processes have contributed to advancing the accuracy of hydroclimatic forecasts, which are progressively more skillful over seasonal and longer timescales. These forecasts are potentially valuable for informing strategic multisector decisions, including irrigated agriculture, for which they can improve crop choices and irrigation scheduling. In this operational context, the accuracy associated with the forecast system setup does not necessarily yield proportional marginal benefit, as this is also affected by how forecasts are employed by end users. This paper aims at quantifying the value of hydroclimatic forecasts in terms of potential economic benefit to the end users, which allows for the inference of a relation between gains in forecast skill and gains in end user profit. We also explore the sensitivity of this benefit to both forecast system setup and end user behavioral factors. These analyses are supported by an evaluation framework demonstrated on the Lake Como system (Italy), a regulated lake operated for flood protection and irrigation supply. Our framework relies on an integrated modeling chain composed of three building blocks: bias-adjusted seasonal meteorological forecasts are used as input to the continentally calibrated E-HYPE hydrological model; predicted lake inflows are used for conditioning the daily lake operations; and the resulting lake releases feed an agricultural model to estimate the net profit of the farmers in a downstream irrigation district. Results suggest that despite the gain in average conditions being negligible, informing the operations of Lake Como based on seasonal hydrological forecasts during intense drought episodes allows about 15 % of the farmers' profit to be gained with respect to a baseline solution not informed by any forecast. Moreover, our analysis suggests that behavioral factors capturing different perceptions of risk and uncertainty significantly impact the quantification of the benefit to the end users, whereby the estimated forecast value is potentially undermined by different levels of end user risk aversion. Lastly, our results show an intricate skill-to-value relation modulated by the underlying hydrologic conditions, which is well aligned over an exponential function in dry years, while the gains in profit are almost insensitive to the improvements in forecast skill in wet years.