Search Results (1,417)

In this case study, we used the Delta Simulation Model II (DSM2) to study the salt balance at the land–water interface in the river delta of California’s San Francisco Estuary. Drainage, a source of water and salt for adjacent channels in the study area, is affected by channel salinity. The DSM2 approach has been adopted by several hydrodynamic models of the estuary to enforce water volume balance between diversions, evapotranspiration and drainage at the land–water interface, but does not explicitly enforce salt balance. We found deviations from salt balance to be quite large, albeit variable in magnitude due to the heterogeneity of hydrodynamic and salinity conditions across the study area. We implemented a procedure that approximately enforces salt balance through iterative updates of the baseline drain salinity boundary conditions (termed loose coupling). We found a reasonable comparison with field measurements of drainage salinity. In particular, the adjusted boundary conditions appear to capture the range of observed interannual variability better than the baseline periodic estimates. The effect of the iterative adjustment procedure on channel salinity showed substantial spatial variability: locations dominated by large flows were minimally impacted, and in lower flow channels, deviations between baseline and adjusted channel salinity series were notable, particularly during the irrigation season. This approach, which has the potential to enhance the simulation of extreme salinity intrusion events (when high channel salinity significantly impacts drainage salinity), is essential for robustly modeling hydrodynamic conditions that pre-date contemporary water management infrastructure. We discuss limitations associated with this approach and recommend that—for this case study—further improvements could best be accomplished through code modification rather than coupling of transport and island water balance models. Full article

Understanding the carbon emission efficiency (CEE) of maize production and its determinants is critical to supporting China’s dual-carbon goals and advancing sustainable agriculture. This study employs a super-efficiency slack-based measure model (SBM) to evaluate the CEE of five major maize-producing regions in China from 2001 to 2022. Kernel density estimation and the Dagum Gini coefficient are used to analyze spatiotemporal disparities, while a geographically and temporally weighted regression (GTWR) model explores the underlying drivers. Results indicate that the national average maize CEE was 0.86, exhibiting a “W-shaped” fluctuation with turning points in 2009 and 2016. From 2001 to 2015, the Southwestern Mountainous Region led with an average efficiency of 0.76. Post-2015, the Northern Spring Maize Region emerged as the most efficient area, reaching 0.90. Efficiency levels have generally become more concentrated across regions, though the Southern Hilly and Northwest Irrigated Regions showed higher volatility. Inter-regional differences were the primary source of overall CEE disparity, with an average annual contribution of 46.66%, largely driven by the efficiency gap between the Northwest Irrigated Region and other areas. Spatial heterogeneity was evident in the impact of key factors. Agricultural mechanization, cropping structure, and environmental regulation exhibited region-specific effects. Rural economic development and agricultural fiscal support were positively associated with CEE, while urbanization had a negative correlation. These findings provide a theoretical foundation and policy reference for region-specific emission reduction strategies and the green transition of maize production in China. Full article

Rizhao Reservoir, Shandong Province, China, as a key regional water supply hub, provides water for domestic, industrial, and agricultural uses in and around Rizhao City by intercepting runoff, which plays a central role in guaranteeing water supply security and supporting regional development. This study systematically collected 66 surface water samples to elucidate the hydrochemical characteristics within the reservoir area, identify the principal influencing factors, and clarify the sources of dissolved ions, aiming to enhance the understanding of the prevailing water quality conditions. A systematic analysis of hydrochemical facies, solute provenance, and governing processes in the study area’s surface water was conducted, employing an integrated mathematical and statistical approach, comprising Piper trilinear diagrams, correlation analysis, and ionic ratios. Meanwhile, the entropy weight-based water quality index (EWQI) and irrigation water quality evaluation methods were employed to assess the surface water quality in the study area quantitatively. Analytical results demonstrate that the surface water system within the study area is classified as freshwater with circumneutral to slightly alkaline properties, predominantly characterized by Ca-HCO₃ and Ca-Mg-SO₄-Cl hydrochemical facies. The evolution of solute composition is principally governed by rock–water interactions, whereas anthropogenic influences and cation exchange processes exert comparatively minor control. Dissolved ions mostly originate from silicate rock weathering, carbonate rock dissolution, and sulfate mineral dissolution processes. Potability assessment via the entropy-weighted water quality index (EWQI) classifies surface waters in the study area as Grade I (Excellent), indicating compliance with drinking water criteria under defined boundary conditions. Irrigation suitability analysis confirms minimal secondary soil salinization risk during controlled agricultural application, with all samples meeting standards for direct irrigation use. Full article

Micro- and Nanoplastic (MNP) pollution is an emerging challenge globally, posing a significant threat to both aquatic and terrestrial ecosystems worldwide. This review critically examines the sources, exposure routes, and impact of plastics, with particular focus on implications for the livestock sector. MNPs enter animals’ bodies primarily through ingestion of contaminated feed and water, inhalation, and dermal exposure, subsequently accumulating in various organs, disrupting physiological functions. Notably, MNPs facilitate the horizontal transfer of antimicrobial resistance genes (ARGs), exacerbating the global challenge of antimicrobial resistance (AMR). In agricultural environments, sources such as organic fertilizers, wastewater irrigation systems, surface runoff, and littering contribute to soil contamination, adversely affecting plant growth and soil health, which in turn compromises feed quality and ultimately animals’ productivity. This review synthesizes current evidence demonstrating how MNP exposure impairs animal production, reproduction, and survival, and highlights the interconnected risks to food safety and ecosystem health. The findings call for the urgent need for comprehensive research under controlled conditions to underscore the fine details regarding mechanisms of MNP toxicity and to inform effective mitigation strategies. Addressing MNP pollution is crucial for safeguarding animal health, ensuring sustainable livestock production, and promoting environmental sustainability and integrity. Full article

According to Victor Ernest Shelford’s ‘Law of Tolerance,’ organisms within ecosystems thrive optimally when environmental conditions are favorable. Applying this principle to ecosystems and agro-ecosystems facing water scarcity or environmental challenges can significantly enhance their productivity. In these ecosystems, phytocenosis adjusts its conditions by utilizing water with varying salinity levels. Moreover, establishing optimal drinking water conditions for human populations within an ecosystem can help mitigate future negative succession processes. The purpose of this study is to evaluate the quality of two distinct water sources in the Amudarya district of the Republic of Karakalpakstan, Uzbekistan: collector-drainage water and groundwater at depths of 10 to 25 m. This research is highly relevant in the context of climate change, as improper management of water salinity, particularly in collector-drainage water, may exacerbate soil salinization and degrade drinking water quality. The primary methodology of this study is as follows: The Food and Agriculture Organization of the United Nations (FAO) standard for collector-drainage water is applied, and the water quality index is assessed using the CCME WQI model. The Canadian Council of Ministers of the Environment (CCME) model is adapted to assess groundwater quality using Uzbekistan’s national drinking water quality standards. The results of two years of collected data, i.e., 2021 and 2023, show that the water quality index of collector-drainage water indicates that it has limited potential for use as secondary water for the irrigation of sensitive crops and has been classified as ‘Poor’. As a result, salinity increased by 8.33% by 2023. In contrast, groundwater quality was rated as ‘Fair’ in 2021, showing a slight deterioration by 2023. Moreover, a comparative analysis of CCME WQI values for collector-drainage and groundwater in the region, in conjunction with findings from Ethiopia, India, Iraq, and Turkey, indicates a consistent decline in water quality, primarily due to agriculture and various other anthropogenic pollution sources, underscoring the critical need for sustainable water resource management. This study highlights the need to use organic fertilizers in agriculture to protect drinking water quality, improve crop yields, and promote soil health, while reducing reliance on chemical inputs. Furthermore, adopting WQI models under changing climatic conditions can improve agricultural productivity, enhance groundwater quality, and provide better environmental monitoring systems. Full article

Sediment accumulation in irrigation channels poses a significant challenge to water resource management, impacting hydraulic efficiency and agricultural sustainability. This study introduces an innovative multidisciplinary framework that integrates advanced image analysis (FIJI/ImageJ 1.54p), statistical validation (RStudio), and vector field modeling with a novel Sinusoidal Morphodynamic Bedload Transport Equation (SMBTE) to predict sediment deposition patterns with high precision. Conducted along the Malacatos River in La Tebaida Linear Park, Loja, Ecuador, the research captured a natural sediment transport event under controlled flow conditions, transitioning from pressurized pipe flow to free-surface flow. Observed sediment deposition reduced the hydraulic cross-section by approximately 5 cm, notably altering flow dynamics and water distribution. The final SMBTE model (Model 8) demonstrated exceptional predictive accuracy, achieving RMSE: 0.0108, R²: 0.8689, NSE: 0.8689, MAE: 0.0093, and a correlation coefficient exceeding 0.93. Complementary analyses, including heatmaps, histograms, and vector fields, revealed spatial heterogeneity, local gradients, and oscillatory trends in sediment distribution. These tools identified high-concentration sediment zones and quantified variability, providing actionable insights for optimizing canal design, maintenance schedules, and sediment control strategies. By leveraging open-source software and real-world validation, this methodology offers a scalable, replicable framework applicable to diverse water conveyance systems. The study advances understanding of sediment dynamics under subcritical (Fr ≈ 0.07) and turbulent flow conditions (Re ≈ 41,000), contributing to improved irrigation efficiency, system resilience, and sustainable water management. This research establishes a robust foundation for future advancements in sediment transport modeling and hydrological engineering, addressing critical challenges in agricultural water systems. Full article

In water-scarce regions such as Jordan, accurate tracking of water flows is critical for informed water management. This study applied the Water Accounting Plus (WA+) framework using open-source remote sensing data from the FAO WaPOR portal to develop agricultural water accounting (AWA) for the Amman–Zarqa Basin (AZB) during 2014–2022. Inflows, outflows, and water consumption were quantified using WaPOR and other open datasets. The results showed a strong correlation between WaPOR precipitation (P) and rainfall station data, while comparisons with other remote sensing sources were weaker. WaPOR evapotranspiration (ET) values were generally lower than those from alternative datasets. To improve classification accuracy, a correction of the WaPOR-derived land cover map was performed. The revised map achieved a producer’s accuracy of 15.9% and a user’s accuracy of 86.6% for irrigated areas. Additionally, ET values over irrigated zones were adjusted, resulting in a fivefold improvement in estimates. These corrections significantly enhanced the reliability of key AWA indicators such as basin closure, ET fraction, and managed fraction. The findings demonstrate that the accuracy of P and ET data strongly affects AWA outputs, particularly the estimation of percolation and beneficial water use. Therefore, calibrating remote sensing data is essential to ensure reliable water accounting, especially in agricultural settings where data uncertainty can lead to misleading conclusions. This study recommends the use of open-source datasets such as WaPOR—combined with field validation and calibration—to improve agricultural water resource assessments and support decision making at basin and national levels. Full article

As potable water becomes limited, alternative water sources, such as reclaimed wastewater, for crop irrigation have gained attention. However, reclaimed wastewater for irrigation may expose edible crops to compounds of emerging concern (CECs), which may include pharmaceutics, hazardous waste, and volatile substances. Of these CECs, carbamazepine (CBZ) is of particular interest because only 7% of CBZ is filtered out during traditional wastewater treatment processing methods. Two trials were designed to evaluate the uptake and partitioning of CBZ in lettuce grown in a deep-water culture system (DWC) at low and high concentrations. The first trial (0 µg L⁻¹, 12.5 µg L⁻¹, 25 µg L⁻¹, and 50 µg L⁻¹) of CBZ had few effects on lettuce (Lactuca sativa var. capitata) growth, and low concentrations of accumulated CBZ were found in lettuce tissues. As a result, increased concentrations of CBZ were used in the second trial (0 mg L⁻¹, 21 mg L⁻¹, 41 mg L⁻¹, and 83 mg L⁻¹). Greater amounts of CBZ accumulated in plant tissues and the application of higher rates of CBZ negatively affected the growth and overall health of the lettuce. Further research is needed to determine the impacts of CECs on plant uptake and growth, as well as the environmental conditions. Full article

Rainwater harvesting (abbreviation: RWH) presents a valuable alternative water source for agriculture, particularly in regions facing water scarcity. However, contaminants leaching from roofing materials, such as asbestos cement (abbreviation: AC), may compromise water quality and affect plant physiological responses. This paper aimed to assess how simulated rainwater, reflecting the different levels of contamination (1, 2, 5, 10, and 20 mg/L), influences leaf temperature in tomato plants (Solanum lycopersicum), a known non-invasive indicator of plant water stress. The treatments were applied over a four-week period under controlled greenhouse conditions. Leaf temperature was monitored using infrared thermography. Results showed that higher treatment concentrations led to a significant increase in leaf temperature, indicating elevated water stress. These findings suggest that even low levels of contaminants originating from roofing materials can induce detectable physiological stress in plants. Monitoring leaf temperature offers a rapid and non-destructive method for assessing environmental water quality impacts on crops. The outcomes of this research have direct applicability in the safer design of RWH systems and in evaluating the suitability of collected rainwater for irrigation use. Full article

Environmental problems, along with the increasing energy demand and high electricity costs in the agricultural sector, justify the need to explore renewable energy sources in order to improve irrigation efficiency and sustainability. Therefore, the objective of this study is to analyse the feasibility of installing floating photovoltaic panels in the irrigation ponds of irrigation communities (ICs) in the province of Alicante. To this end, a practical case study based on the operating data of a photovoltaic installation on an irrigation pond, which shows 31% self-consumption and a 27% reduction in energy costs, is presented. Based on these results, this type of installation has been considered for the rest of the ponds in the province of Alicante, with an estimated total investment of EUR 130 million and annual savings of EUR 23 million in energy costs. Additionally, barriers such as the initial investment and the need for public financing for large-scale implementation are identified. Finally, it is concluded that the adoption of floating photovoltaic energy represents a key opportunity to reduce dependence on fossil fuels, mitigate environmental impact, and promote the circular economy in the agricultural sector. Full article

The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R² of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications. Full article

The yield of common bean (Phaseolus vulgaris L.) is limited by abiotic stresses such as drought and high temperatures, which frequently occur simultaneously under field conditions. This study examined 100 bean genotypes under three environmental conditions, namely, the rainy season (optimal conditions), full irrigation in the dry season (high-temperature stress), and terminal drought in the dry season (combined stress), via a 10 × 10 triple-lattice design. Agronomic parameters evaluated included days to flowering (DF), days to physiological maturity (DM), plant height (PH), aerial biomass (BIO), grain yield (YLD), and 100-seed weight (100SW). The natural temperature exceeded 35 °C during the reproductive stage of the dry season. Combined stress revealed differential adaptive mechanisms in the tested germplasms, indicating that the response to multiple stresses is more complex than the sum of individual stress responses. The average yield under optimal conditions was 1344 kg/ha, decreasing to 889 kg/ha (66.1%) under irrigation with high temperatures and to 317 kg/ha (23.6%) under terminal drought with high temperatures. Under terminal drought with high temperatures, the number of days to maturity decreased by 5%, and the seed weight decreased by 20%. The G69-33-PT and G-19158 genotypes presented high yields under high-temperature stress, with yields above 1800 kg/ha, suggesting specific physiological mechanisms for tolerance to elevated temperatures. Under combined stress, genotypes G69-Sel25, Pinto Mestizo, and Dalia presented yields above 680 kg/ha, indicating adaptations in terms of water use efficiency and tolerance to high temperature. The identification of genotypes with differential stress tolerance provides valuable genetic resources for breeding programs. The diverse origins of superior germplasms (bred lines, landraces, and commercial cultivars) highlight the importance of exploring various germplasms in the search for sources of abiotic stress tolerance for breeding projects aimed at developing cultivars adapted to climate change scenarios where drought and high temperatures occur simultaneously. Full article

Soil microplastic pollution poses a significant threat to the integrity of terrestrial ecosystems and agricultural sustainability. This review provides a comprehensive synthesis of recent progress on soil microplastic (MP) sources, ecological impacts, and separation technologies. Agricultural practices (e.g., residual plastic mulch and wastewater irrigation) and atmospheric deposition serve as primary drivers of contamination accumulation, with pronounced spatial heterogeneity observed across regions. Predominant MP types such as polyethylene, polystyrene, and polypropylene disrupt soil structure and biogeochemical processes through three core mechanisms: physical interference, chemical toxicity, and biological accumulation. These particles further form carrier–pollutant complexes, exacerbating ecotoxicological impacts across trophic levels. While emerging separation techniques like magnetic separation and solvent extraction demonstrate enhanced efficiency, their implementation faces challenges stemming from soil matrix complexity and high operational costs. This article underscores the need for global collaborative efforts to accelerate innovation in biodegradable polymers, offering practical pathways for sustainable soil management. Full article

The plateaus of north-central Mexico have an arid to semiarid climate and groundwater naturally contaminated with inorganic arsenic (iAs) and fluoride (F). Like other arid and semiarid areas, this region faces great challenges to maintain a safe supply of drinking and irrigation water. Studies conducted in the past few decades on various locations within this region have reported groundwater iAs, F, and nitrate-nitrogen (NO₃-N), and either their source, enrichment processes, health risks, and/or potential water treatments. The relevant findings are analyzed and condensed here to provide an overview of the groundwater situation of the region. Studies identify volcanic rocks (rhyolite) and their weathering products (clays) as the main sources of iAs and F and report that these solutes become enriched through evaporation and residence time. In contrast, NO₃-N is reported as anthropogenic, with the highest concentrations found in large urban centers and in agricultural and livestock farm areas. Health risks are high since the hot spots of contamination correspond to populated areas. Health problems associated with NO₃-N in drinking water may be underestimated. Removal technologies of the contaminants remain at the laboratory or pilot stage, except for the reverse osmosis filtration units fitted to selected wells within the state of Chihuahua. A recent approach to supplying drinking water free of iAs and F to two urban centers consisted of switching from groundwater to surface water. Incipient research currently focuses on the potential repercussions of irrigating crops with As-rich water. The groundwater predicaments concerning contamination, public health impact, and irrigation suitability depicted here can be applied to semiarid areas worldwide. Full article

The mean annual water balance is essential for evaluating water availability in a catchment and planning water resources. Climate change alters this balance by affecting precipitation, evapotranspiration, and overall water availability. This study analyses the impact of climate change on the mean annual water balance in the Catamayo catchment, a key water source for irrigation and hydropower in southern Ecuador and northern Peru. A Budyko-based approach was employed due to its conceptual simplicity and proven robustness for estimating long-term water balances under changing climatic conditions. Using outputs from 23 Global Circulation Models (GCMs) under CMIP6’s SSP2-4.5 and SSP8.5 scenarios, the results indicate increasing aridity, particularly in the lower and middle parts of the catchment, which correspond to arid and semi-arid zones. Water availability may decrease by 26.3 ± 12.3% to 33.3 ± 17% until 2080 due to negligible changes (statistically speaking) in average precipitation but rising evapotranspiration. However, historical precipitation analysis (1961–2020) reveals an increasing trend over this historical period which can be attributed to natural climatic variability associated to the El Nino-Southern Oscillation (ENSO), possibly enhanced by anthropogenic climate change. A novel hybrid method combining the statistics of historical precipitation deficits with GCM mean projections provides estimates of future precipitation deficits. These findings suggest potential reductions in crop yields and hydropower capacity, which (although not quantitatively assessed in this study) are inferred based on the projected decline in water availability. Such impacts could lead to higher energy costs, increased reliance on fossil fuels, and intensified competition for water. Mitigation measures, including water-saving strategies, energy diversification, and integrated water resource management, are recommended to address these challenges. Full article