Search Results (125)

The planting area of the maize–soybean strip intercropping system has been increasing annually in the Hexi Corridor oasis irrigation area of China. However, long-term irrational water resource utilization and the excessive mono-application of fertilizers have led to significantly low water and nitrogen use efficiency in this cropping system. To explore the sustainable production model of high yield and high water–nitrogen productivity in maize–soybean strip intercropping, we established three irrigation levels (low: 60%, medium: 80%, and sufficient: 100% of reference crop evapotranspiration) and three nitrogen application levels (low: maize 230 kg ha⁻¹, soybean 29 kg ha⁻¹; medium: maize 340 kg ha⁻¹, soybean 57 kg ha⁻¹; and high: maize 450 kg ha⁻¹, soybean 85 kg ha⁻¹) for maize and soybean, respectively. Three irrigation levels without nitrogen application served as controls. The effects of different water–nitrogen combinations on multiple indicators of the maize–soybean strip intercropping system, including yield, water–nitrogen productivity, and quality, were analyzed. The results showed that the irrigation amount and nitrogen application rate significantly affected the kernel quality of maize. Specifically, the medium nitrogen and sufficient water (N2W3) combination achieved optimal performance in crude fat, starch, and bulk density. However, excessive irrigation and nitrogen application led to a reduction in the content of lysine and crude protein in maize, as well as crude fat and crude starch in soybean. Appropriate irrigation and nitrogen application significantly increased the yield in the maize–soybean strip intercropping system, in which the N2W3 treatment had the highest yield, with maize and soybean yields reaching 14007.02 and 2025.39 kg ha⁻¹, respectively, which increased by 2.52% to 138.85% and 5.37% to 191.44% compared with the other treatments. Taking into account the growing environment of the oasis agricultural area in the Hexi Corridor and the effects of different water and nitrogen supplies on the yield, water–nitrogen productivity, and kernel quality of maize and soybeans in the strip intercropping system, the highest target yield can be achieved when the irrigation quotas for maize and soybeans are set at 100% ET0 (reference crop evapotranspiration), with nitrogen application rates of 354.78~422.51 kg ha⁻¹ and 60.27~71.81 kg ha⁻¹, respectively. This provides guidance for enhancing yield and quality in maize–soybean strip intercropping in the oasis agricultural area of the Hexi Corridor, achieving the dual objectives of high yield and superior quality. Full article

Background/Objectives: Non-communicable diseases (NCDs) are strongly linked to beverage consumption. This study aimed to assess the average daily beverage intake of Saudi residents, energy intake from beverages, and the influence of socio-demographic factors, health behaviors, and health status on beverage intake. Methods: A nationally representative, cross-sectional study utilized stratified quota sampling to survey adults (≥18 years) across all 13 administrative regions of Saudi Arabia. Data were collected from April 2022 to December 2023 using the validated Arabic Beverage Frequency Questionnaire (ABFQ), assessing consumption patterns of 28 beverage types. Results: The study included 4385 participants (mean age: 36.1 ± 11.14 years, 65% male). Sweetened tea (28 mL/day), regular soft drinks (22.1 mL/day), and Saudi coffee (18 mL/day) were the most frequent beverages after water. Sweetened tea contributed to the highest average energy intake (33.2 ± 46.4 kcal/day). Consumption of sugar-sweetened beverages (SSBs) was higher among younger individuals (18–29 years: OR: 4.0, 95% CI [2.6–6.3]; 30–44 years: OR: 2.8, 95% CI [1.8–4.3]), males (OR:1.6, 95% CI [1.4–1.8]), and residents of specific regions [Al-Jawf (OR: 1.9, 95% CI [1.2–3.2]) and Jazan (OR: 3.2, 95% CI [2.2–4.7])]. Higher water intakes were associated with males (OR: 1.5, 95% CI [1.3–1.7]), higher education levels (OR: 1.4, 95% CI [1.2–1.8]), physically active (OR: 1.5, 95% CI [1.3–1.8]), and those overweight (OR: 1.6, 95% CI [1.2–2.3]) or obese (OR: 2, 95% CI [1.4–2.8]). Conclusions: This study provides a valuable insight into beverage consumption patterns among Saudi residents. The findings highlight the need for targeted public health interventions to promote healthier beverage choices, particularly among younger populations and those with lower socioeconomic status. Full article

Toxic and non-toxic strains of Raphidiopsis raciborskii coexist widely in natural water bodies, with the dominance of toxic strains directly influencing bloom toxicity. This study investigates how temperature affects the relative dominance of toxic R. raciborskii strains and the production of cylindrospermopsin (CYN). We conducted monoculture and co-culture experiments in nutrient-rich BG11 medium at three temperatures (16 °C, 24 °C, and 32 °C) using two pairs of strains (CS506/CS510 from Australia and QDH7/N8 from China). The results revealed that the Australian strains failed to grow at 16 °C, while the Chinese strains thrived. In a co-culture experiment, the Australian toxic strain CS506 exhibited the fastest growth at 24 °C, whereas the Chinese toxic strain QDH7 reached similar maximum cell densities across all temperatures but peaked more quickly at 24 °C and 32 °C compared to 16 °C. Regardless of temperature and strain pairs, toxic strains consistently maintained a higher relative abundance than their non-toxic counterparts. Analysis using the rate of competitive displacement (RCD) model indicated that higher temperatures accelerated the displacement of non-toxic strains by toxic ones. Total CYN concentrations in co-cultures increased with temperature, although the cell quota of CYN (Q_CYN) did not vary significantly across temperatures. In co-culture, the CYN production rate during the exponential phase was positively correlated with cell growth rate, but this correlation weakened or reversed in the stationary phase, likely due to changes in nutrient availability. These findings suggest that rising temperatures under eutrophic conditions may enhance the growth and competitive advantage of toxic R. raciborskii strains, thereby exacerbating bloom toxicity. Full article

Optimizing irrigation and nitrogen (N) management in saline soils is critical for sustainable cotton production in arid regions that have been subjected to climate change. In this study, a two-year factorial field experiment (3 salinity levels × 3 N rates × 3 irrigation quotas) is integrated with the RZWQM2 model to (1) identify water–N–salinity thresholds for cotton yield and (2) to project climate change impacts under SSP2.4-5 and SSP5.8-5 scenarios (2031–2090) in Xinjiang, China, a global cotton production hub. The results demonstrated that a moderate salinity (6 dS/m) combined with a reduced irrigation (3600 m³/hm²) and N input (210 kg/hm²) achieved a near-maximum yield (6918 kg/hm²), saving 20% more water and 33% more fertilizer compared to conventional practices. The model exhibited a robust performance (NRMSE: 5.94–12.88% for soil–crop variables) and revealed that warming shortened the cotton growing season by 1.2–9.5 days per decade. However, elevated CO₂ (832 ppm by 2090) levels under SSP5.8-5 increased yields by 22.6–42.1%, offsetting heat-induced declines through enhanced water use efficiency (WUE↑27.5%) and biomass accumulation. Critically, high-salinity soils (9 dS/m) required 25% additional irrigation (4500 m³/hm²) and a full N input (315 kg/hm²) to maintain yield stability. These findings provide actionable strategies for farmers to optimize irrigation schedules and nitrogen application, balancing water conservation with yield stability in saline-affected arid agroecosystems that have been subjected to climate change. Full article

Summer maize is an important grain crop in the North China Plain, but the problem of irrational application of water and fertilizer is becoming increasingly serious. Optimizing water and nitrogen management not only improves yield but also reduces water and fertilizer waste and environmental pollution. The Aquacrop model was calibrated and validated using a two-year summer maize field trial, and 16 different water and nitrogen scenarios were simulated and analyzed. In particular, the field trials were divided into 10 water–nitrogen treatments. The results showed that (1) the model has good applicability to the growth process of summer maize in the North China Plain. (2) Excessive water and nitrogen application would reduce yield by 5.6–33.7%, nitrogen bias productivity by 8.1–32.5%, and water use efficiency by 6.4–84.6%. (3) The optimal irrigation and nitrogen application program for furrow-irrigated summer maize is an irrigation quota of 360 mm in conjunction with nitrogen application of 240 kg/ha. This study provides a theoretical basis for a water-saving, fertilizer-saving, high-yield water and fertilizer management system for summer maize in the North China Plain. Full article

Quantitative assessment of the ability of the ecosystem service (ES) and its driving forces is of great significance for achieving regional SDGs. In view of the scarcity of existing research that evaluates the sustainability of multiple ES types over a long time series at the township scale in a typical Watertown Region, this study aims to address two key scientific questions: (1) what are the spatiotemporal changes in the ecosystem service supply–demand index (ESSDI) and ecosystem service sustainability index (ESSI) of a typical Watertown Region? and (2) what are the key factors driving the changes in ESSI? To answer the above two questions, this study takes the Yangtze River Delta Integrated Demonstration Zone (YRDIDZ) as the study area, utilizing multi-source remote sensing and other spatiotemporal geographical datasets to calculate the supply–demand levels and sustainable development ability of different ES in the YRDIDZ from 2000 to 2020. The main findings were as follows: (1) From 2000 to 2020, the mean ESSDI values for habitat quality, carbon storage, crop production, water yield, and soil retention all showed a declining trend. (2) During the same period, the mean ESSI exhibited a fluctuating downward trend, decreasing from 0.31 in 2000 to 0.17 in 2020, with low-value areas expanding as built-up areas grew, while high-value areas were mainly distributed around Dianshan Lake, Yuandang, and parts of ecological land. (3) The primary driving factors within the YRDIDZ were human activity factors, including POP and GDP, with their five-period average explanatory powers being 0.44 and 0.26, whereas the explanatory power of natural factors was lower. However, the interaction of POP and soil showed higher explanatory power. The results of this study could provide actionable ways for regional sustainable governance: (1) prioritizing wetland protection and soil retention in high-population-density areas based on targeted land use quotas; (2) integrating ESSI coldspots (built-up expansion zones) into ecological redline adjustments, maintaining high green infrastructure coverage in new urban areas; and (3) establishing a population–soil co-management framework in agricultural–urban transition zones. Full article

Currently, the world is facing a serious agricultural water crisis, which also affects grassland areas. Alfalfa, a key perennial forage legume, consumes about 10% of China’s pastoral irrigation water. Reducing irrigation generally results in a loss of hay yield, but the effects on alfalfa quality and its relationship to water use are less clear. In this study, we explore alfalfa quality under different irrigation deficits and its relationship to water use in the Hexi Corridor of China. Alfalfa water use, quality yield (relative feeding value yield (RFV_yield) and crude protein yield (CP_yield)), and quality water use efficiency (relative feeding value water use efficiency (WUE_RFV) and crude protein water use efficiency (WUE_CP)) were measured in a field experiment. Alfalfa quality showed a negative correlation with the irrigation quota (the determination coefficient for relative feeding value was 0.375 and for crude protein was 0.289). There was a positive correlation between quality yield and irrigation quota (the determination coefficient for RFV_yield was 0.570 and for CP_yield was 0.631). The higher irrigation quota increased quality yield, which compensated for its negative effects on alfalfa quality. The mild and moderate water deficit treatments showed lower WUE_RFV than both the severe and no water deficit treatments. Moderate or mild water deficit is recommended to be used for one-year-old alfalfa treatment. No water deficit is beneficial to improve the quality water use efficiency of two-year-old alfalfa. Full article

Seasonal freeze–thaw irrigation areas face challenges of soil salinization and water scarcity, requiring a deep understanding of soil freeze–thaw dynamics under the interaction between irrigation and groundwater. An in situ lysimeter experiment was conducted in the winters of 2020–2021 and 2023–2024 to investigate the effects of autumn irrigation (AI) timing (late AI conducted in late November and icing AI conducted in early December) and quota (0, 35, 135, 270 mm) on soil water, heat, and salt transport under varying groundwater levels in the Hetao Irrigation District, Northwest China. Results showed that AI had a strong short-term effect on the groundwater depth and there was a significant negative correlation between groundwater depth and air temperature on a monthly scale. The quota and air temperature during AI were the key factors in utilizing the “refrigerator effect”—where irrigation water pre-cooled by frozen layer accelerates soil freezing—to regulate soil water and salt transport under freeze–thaw cycles. The drastic reduction in AI water consumption lowered the groundwater level, highlighting air temperature as the dominant driver of soil dynamics. Thus, icing AI with low quota (35 mm) can optimize water use (water saving of 77% compared to the traditional quota of 150 mm) while maintaining soil moisture (an increase of 17.4% in water storage) and salinity control (a decrease of 41.6% in salt storage) in the root zone (0–40 cm) through the “refrigerator effect”, demonstrating its potential for sustainable irrigation in water-scarce cold regions. Full article

With the gradual cessation of budget quota standards and the emphasis on market-based pricing, accurately predicting project investments has become a critical issue in construction management. This study focuses on cost indicator prediction for irrigation and drainage projects to address the absence of cost standards for farmland water conservancy projects and achieve accurate and efficient investment prediction. Engineering characteristics affecting cost indicators were comprehensively analyzed, and principal component analysis (PCA) was employed to identify key influencing factors. A prediction model was proposed based on support vector regression (SVR) optimized using the dung beetle optimizer (DBO) algorithm. The DBO algorithm optimized SVR hyperparameters, resolving issues of poor generalization and long prediction times. Validation using 2024 farmland water conservancy project data from Liaoning Province showed that the PCA–DBO–SVR model achieved superior performance. For electromechanical well projects, the root mean square error (RMSE) was 1.116 million CNY, mean absolute error (MAE) was 0.910 million CNY, mean absolute percentage error (MAPE) was 3.261%, and R² reached 0.962. For drainage ditch projects, RMSE was 0.500 million CNY, MAE was 0.281 million CNY, MAPE was 3.732%, and R² reached 0.923. The PCA–DBO–SVR model outperformed BP, SVR, and PCA–SVR models in all evaluations, demonstrating higher prediction accuracy and better generalization capability. This study provides theoretical support for developing cost indicators for farmland water conservancy projects and offers valuable insights for dynamically adjusting national investment standards and improving construction fund management. Full article

This study addresses challenges such as insufficient irrigation water quotas, severe groundwater over-extraction, and conflicts around crop water usage within the mixed-cropping areas of the Inner Mongolia Yellow River Basin. Five evaluation factors—water resource utilization efficiency, irrigation rate, degree of development and utilization, supply modulus, and demand modulus—were selected for a gray relational analysis to assess the 2023 water resource carrying capacity. A crop structure optimization model was developed using machine learning, focusing on minimizing water use while maximizing economic benefits. The results indicate that groundwater resources are nearing critical levels, with many regions showing low carrying capacities and supply–demand conflicts. Key issues include unreasonable planting structures and excessive irrigation quotas, leading to significant water waste. To optimize resource utilization, it is recommended to reduce the food crop planting area by 0.0194 × 10⁴ hm² and increase economic and forage crops by 0.0106 × 10⁴ hm² and 0.0116 × 10⁴ hm², respectively. This adjustment would lead to a total water utilization reduction of 0.0289 × 10⁶ m³ per year, an increase in total yield of 4340.86 tons, and an increase in total economic benefit of CNY 6,559,200, thus leading the cropping structure towards greater rationality. The findings provide valuable insights for optimal water resource allocation in mixed-cropping irrigation areas. Full article

The aim of this study was to optimize water-saving and high-efficiency irrigation and nitrogen application scheduling for greenhouse tomato cultivation in North China. Using experimental data on water and nitrogen inputs, the DSSAT-GLUE parameter adjustment tool was employed to calibrate the genetic parameters of the DSSAT–CROPGRO–Tomato model. Simulations were conducted to assess greenhouse tomato growth, water use, and yield under varying water and nitrogen conditions. After calibration, the model showed average relative errors of 3.19% for the phenological stages, 3.33% for plant height, and 4.52% for yield dry weight, meeting accuracy standards. The results from the calibrated model indicated that increasing irrigation or nitrogen levels initially enhanced yield but led to diminishing returns beyond optimal ranges. The maximum tomato yield and water–nitrogen use efficiency were achieved with irrigation quotas between 320 and 340 mm and nitrogen applications between 360 and 400 kg·ha⁻¹. These findings provide a guideline for efficient water and nitrogen management for greenhouse tomatoes under drip irrigation conditions. Full article

This study investigates the impact of Aerated Subsurface Drip Irrigation (ASDI) on the growth and yield of mulched cotton, aiming to identify the optimal water-air combination pattern for ASDI in cotton cultivation. Conducted during 2021–2022, the experimental setup involved two aeration modes (aerated A₁ and unaerated A₀) and four irrigation quotas (W₁, W₂, W₃, and W₄), organized in a two-factor randomized block design resulting in eight distinct treatments. The findings revealed that ASDI significantly promoted soil moisture depletion from 0 to 40 cm during the cotton flowering and boll opening stages. Specifically, aerated A₁ reduced soil water content by 5.84% to 7.83% during the flowering stage and 7.45% to 13.39% during the boll opening stage compared to unaerated A₀. Additionally, both aerating and increasing irrigation quotas not only enhanced the cotton leaf area index (LAI) but also delayed leaf area decay, contributing to prolonged photosynthetic activity. Aerating also favorably influenced the distribution of above-ground biomass in cotton towards budding and boll stages, with the biomass share of buddings, flowers, and bolls averaging 62.98% under aerated conditions versus 62.27% under non-aerated conditions during the boll opening stage. Furthermore, aerating combined with increased irrigation quotas resulted in higher seed cotton yields, with aerated irrigation boosting yields by 1.79% in 2021 and 4.43% in 2022 compared to non-aerated irrigation. This approach also increased cotton’s water demand and average daily water consumption significantly (p < 0.01). Importantly, aerating improved IWUE, achieving 1.72 kg/m³ in 2021 and 1.62 kg/m³ in 2022 for ASDI, versus 1.69 kg/m³ and 1.57 kg/m³ for unaerated subsurface drip irrigation, respectively. In conclusion, from a water conservation and yield enhancement perspective, an irrigation quota of 337.4 mm during the reproductive stage under ASDI is recommended as an effective strategy for “one film three tubes and six rows” mulched cotton in Southern Xinjiang. Full article

To evaluate and compare the effectiveness of prediction models for Argentine squid Illex argentinus trawling grounds in the Southwest Atlantic high seas based on vessel position and fishing log data, this study used AIS datasets and fishing log datasets from fishing seasons spanning 2019–2024 (December to June each year). Using a spatial resolution of 0.1° × 0.1° and a monthly temporal resolution, we constructed two datasets—one based on vessel positions and the other on fishing logs. Fishing ground levels were defined according to the density of fishing locations, and combined with oceanographic data (sea surface temperature, 50 m water temperature, sea surface salinity, sea surface height, and mixed layer depth). A CNN-Attention deep learning model was applied to each dataset to develop Illex argentinus trawling ground prediction models. Model accuracy was then compared and potential causes for differences were analyzed. Results showed that the vessel position-based model had a higher accuracy (Accuracy = 0.813) and lower loss rate (Loss = 0.407) than the fishing log-based model (Accuracy = 0.727, Loss = 0.513). The vessel-based model achieved a prediction accuracy of 0.763 on the 2024 test set, while the fishing log-based model reached an accuracy of 0.712, slightly lower than the former, indicating the high accuracy and unique advantages of the vessel position-based model in predicting fishing grounds. Using CPUE from fishing logs as a reference, we found that the vessel position-based model performed well from January to April, whereas the CPUE-based model consistently maintained good accuracy across all months. The 2024 fishing season predictions indicated the formation of primary fishing grounds as early as January 2023, initially near the 46° S line of the Argentine Exclusive Economic Zone, with grounds shifting southeastward from March onward and reaching around 42° S by May and June. This study confirms the reliability of vessel position data in identifying fishing ground information and levels, with higher accuracy in some months compared to the fishing log-based model, thereby reducing the data lag associated with fishing logs, which are typically available a year later. Additionally, national-level fishing log data are often confidential, limiting the ability to fully consider fishing activities across the entire fishing ground region, a limitation effectively addressed by AIS vessel position data. While vessel data reflects daily catch volumes across vessels without distinguishing CPUE by species, log data provide a detailed daily CPUE breakdown by species (e.g., Illex argentinus). This distinction resulted in lower accuracy for vessel-based predictions in December 2023 and May–June 2024, suggesting the need to incorporate fishing log data for more precise assessments of fishing ground levels or resource abundance during those months. Given the near-real-time nature of vessel position data, fishing ground dynamics can be monitored in near real time. The successful development of vessel position-based prediction models aids enterprises in reducing fuel and time costs associated with indiscriminate squid searches, enhancing trawling efficiency. Additionally, such models support quota management in global fisheries by optimizing resource use, reducing fishing time, and consequently lowering carbon emissions and environmental impact, while promoting marine environmental protection in the Southwest Atlantic high seas. Full article

Water delivery management in China’s irrigation districts has traditionally prioritized the main canal system, often overlooking the water-saving potential of the final canals and field irrigation, which offer substantial opportunities to enhance water use efficiency and conserve agricultural water resources. This study summarizes and defines the integrated water management of final canals and field irrigation as terminal water management. An optimization method was developed to improve terminal water management, which includes optimizing irrigation quotas based on water balance and scheduling final canal water delivery to minimize seepage losses. A genetic algorithm was employed to solve the problem. The method was applied to the Hongjin irrigation district in Jiangsu Province, China. In 2020, paddy water management was observed, revealing that the irrigation amount for organic and traditional rice was 1113 mm and 956 mm, respectively. Conventional irrigation and water delivery practices have led to extensive drainage, significant rainwater wastage, and inefficient water use. The optimized irrigation quotas for organic and traditional rice resulted in water savings of 302.5 mm and 325.9 mm, respectively, compared to the 2020 monitored data. An irrigation event in early August during a 75% hydrological frequency year was selected as an example. With conventional scheduling, optimized final canal water delivery scheduling reduced the seepage losses from 6.3% to 4.6%, shortened the irrigation time from 17 h to 14 h, and stabilized canal flow rates. The proposed optimization method is a valuable tool for enhancing terminal water management and supporting better irrigation decisions in irrigation districts. Full article

Optimizing water and nitrogen management is an effective measure to reduce nitrogen fertilizer loss and environmental pollution risks. This study aims to quantify the impacts of different water and nitrogen management strategies on the soil microenvironment and yield of spring soybeans in southern Xinjiang. In this study, two irrigation quotas were established: W1—36 mm (low water) and W2—45 mm (high water). Three nitrogen application gradients were established: low nitrogen (150 kg·hm⁻², N1), medium nitrogen (225 kg·hm⁻², N2), and high nitrogen (300 k kg·hm⁻², N3). The analysis focused on soil physicochemical properties, enzyme activities, microbial community diversity, soybean yield, and soybean quality changes. The results indicate that the activities of nitrate reductase and urease, as well as total nitrogen content, increased with higher irrigation and nitrogen application rates. The W2N3 treatment significantly increased 0.15 to 4.39, 0.18 to 1.04, and 0.31 to 1.73 times. (p < 0.05). Alkaline protease and sucrase activities increased with higher irrigation amounts, while their response to nitrogen application exhibited an initial increase followed by a decrease. The W2N2 treatment significantly increased by 0.10 to 0.34 and 0.07 to 1.46 times (p < 0.05). Irrigation significantly affected the soil bacterial community structure, while the coupling effects of water and nitrogen notably influenced soil bacterial abundance (p < 0.05). Increases in irrigation and nitrogen application enhanced bacterial diversity and species abundance. Partial least squares path analysis indicated that water–nitrogen coupling directly influenced the soil microenvironment and indirectly produced positive effects on soybean yield and quality. An irrigation quota of 4500 m³ hm⁻² and a nitrogen application rate of 300 kg·hm⁻² can ensure soybean yield while enhancing soil microbial abundance. The findings provide insights into the response mechanisms of soil microbial communities in spring soybeans to water–nitrogen management, clarify the relationship between soil microenvironments and the yield and quality of spring soybeans, and identify optimal irrigation and fertilization strategies for high quality and yield. This research offers a theoretical basis and technical support for soybean cultivation in southern Xinjiang. Full article