Search Results (2,575)

This study assesses future agricultural drought risk in the Ganjiang River Basin under climate change and land use change. A coupled analysis framework was established using the SWAT hydrological model, the CMIP6 climate models (SSP1-2.6, SSP2-4.5, SSP5-8.5), and the PLUS land use simulation model. Key methods included the Standardized Soil Moisture Index (SSMI), travel time theory for drought event identification and duration analysis, Mann–Kendall trend test, and the Pettitt change-point test to examine soil moisture dynamics from 2027 to 2100. The results indicate that the CMIP6 ensemble performs excellently in temperature simulations, with a correlation coefficient of R² = 0.89 and a root mean square error of RMSE = 1.2 °C, compared to the observational data. The MMM-Best model also performs well in precipitation simulations, with R² = 0.82 and RMSE = 15.3 mm, compared to observational data. Land use changes between 2000 and 2020 showed a decrease in forestland (−3.2%), grassland (−2.8%), and construction land (−1.5%), with an increase in water (4.8%) and unused land (2.7%). Under all emission scenarios, the SSMI values fluctuate with standard deviations of 0.85 (SSP1-2.6), 1.12 (SSP2-4.5), and 1.34 (SSP5-8.5), with the strongest drought intensity observed under SSP5-8.5 (minimum SSMI = −2.8). Drought events exhibited spatial and temporal heterogeneity across scenarios, with drought-affected areas ranging from 25% (SSP1-2.6) to 45% (SSP5-8.5) of the basin. Notably, abrupt changes in soil moisture under SSP5-8.5 occurred earlier (2045–2050) due to intensified land use change, indicating strong human influence on hydrological cycles. This study integrated the CMIP6 climate projections with high-resolution human activity data to advance drought risk assessment methods. It established a framework for assessing agricultural drought risk at the regional scale that comprehensively considers climate and human influences, providing targeted guidance for the formulation of adaptive water resource and land management strategies. Full article

In China’s Loess Plateau, sustainable agricultural end products are affected by an insufficiency of water resources. Rising crop water use efficiency (WUE) through field management pattern improvement is a crucial plan of action to address this issue. However, there is no agreement among researchers on the most appropriate field management practices regarding WUE, which requires further integrated quantitative analysis. We conducted a meta-analysis by quantifying the effect of agricultural practices surrounding nitrogen (N) fertilizer management. The two experimental cultivars were Yunhan–20410 and Yunhan–618. The subplots included nitrogen 0 kg·ha⁻¹ (N0), 90 kg·ha⁻¹ (N90), 180 kg·ha⁻¹ (N180), 210 kg·ha⁻¹ (N210), and 240 kg·ha⁻¹ (N240). Our results show that higher N rates (up to N210) enhanced water consumption during the node-flowering and flowering-maturity time periods. YH–618 showed higher water use during the sowing–greening and node-flowering periods but decreased use during the greening-node and flowering-maturity periods compared to YH–20410. The N210 treatment under YH–618 maximized water use efficiency (WUE). Increased N rates (N180–N210) decreased covering temperatures (T_max, T_min, T_aver) during flowering, increasing the level of grain filling. Spike numbers rose with N application, with an off-peak at N210 for strong-gluten wheat. The 1000-grain weight was at first enhanced but decreased at the far end of N180–N210. YH–618 with N210 achieved a harvest index (HI) similar to that of YH–20410 with N180, while excessive N (N240) or water reduced the HI. Dry matter accumulation increased up to N210, resulting in earlier stabilization. Soil water consumption from wintering to jointing was strongly correlated with pre-flowering dry matter biological process and yield, while jointing–flowering water use was linked to post-flowering dry matter and spike numbers. Post-flowering dry matter accumulation was critical for yield, whereas spike numbers positively impacted yield but negatively affected 1000-grain weight. In conclusion, our results provide evidence for determining suitable integrated agricultural establishment strategies to ensure efficient water use and sustainable production in the Loess Plateau region. Full article

Drought and water scarcity are some of the most important challenges facing agricultural producers in dry environments. This study aimed to evaluate the effect of algae extract and zeolite in terms of their biostimulant action on water stress tolerance to obtain better growth and production of tomato Lycopersicum esculentum L. grown in an open field under suboptimum and deficient soil moisture content. Large plots had a suboptimum soil moisture content (SSMC) of 25% ± 2 [28% below field capacity (FC)] and deficient soil moisture content (DSMC) of 20% ± 2 [11% above permanent wilting point (PWP)]; both soil moisture ranges were based on field capacity FC (32%) and PWP (18%). Small plots had four treatments: algae extract (AE) 50 L ha⁻¹ and zeolite (Z) 20 t ha⁻¹, a combination of both products (AE + Z) 25 L ha⁻¹ and 10 t h⁻¹, and a control (without application of either product). By applying AE, Z, and AE + Z, plant height, plant vigor, and chlorophyll index were significantly higher compared to the control by 20.3%, 10.5%, and 22.3%, respectively. The effect on relative water content was moderate—only 2.6% higher than the control applying AE, while the best treatment for the photosynthesis variable was applying Z, with a value of 20.9 μmol CO₂ m⁻² s⁻¹, which was 18% higher than the control. Consequently, tomato yield was also higher compared to the control by 333% and 425% when applying AE and Z, respectively, with suboptimum soil moisture content. The application of the biostimulants did not show any mitigating effect on water stress under soil water deficit conditions close to permanent wilting. These findings are relevant to water-scarce agricultural areas, where more efficient irrigation water use is imperative. Plant biostimulation through organic and inorganic extracts plays an important role in mitigating environmental stresses such as those caused by water shortages, leading to improved production in vulnerable agricultural areas with extreme climates. Full article

As the world’s largest loess deposit region, the Loess Plateau’s vegetation dynamics are crucial for its regional water–heat balance and ecosystem functioning. Leaf Area Index (LAI) serves as a key indicator bridging canopy architecture and plant physiological activities. Existing studies have made significant advancements in simulating LAI, yet accurate LAI simulation remains challenging. To address this challenge and gain deeper insights into the environmental controls of LAI, this study aims to accurately simulate LAI in the Loess Plateau using deep learning models and to elucidate the spatiotemporal influence of soil moisture and temperature on LAI dynamics. For this purpose, we used three deep learning models, namely Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Interpretable Multivariable (IMV)-LSTM, to simulate LAI in the Loess Plateau, only using soil moisture and temperature as inputs. Results indicated that our approach outperformed traditional models and effectively captured LAI variations across different vegetation types. The attention analysis revealed that soil moisture mainly influenced LAI in the arid northwest and temperature was the predominant effect in the humid southeast. Seasonally, soil moisture was crucial in spring and summer, notably in grasslands and croplands, whereas temperature dominated in autumn and winter. Notably, forests had the longest temperature-sensitive periods. As LAI increased, soil moisture became more influential, and at peak LAI, both factors exerted varying controls on different vegetation types. These findings demonstrated the strength of deep learning for simulating vegetation–climate interactions and provided insights into hydrothermal regulation mechanisms in semiarid regions. Full article

Food security is crucial for social stability and economic development. Ensuring food security in the arid region of Northwest China presents unique challenges due to limited water and soil resources. This study addresses these challenges by integrating a comprehensive water and soil resource matching assessment with grain production forecasting. Based on data from 2000 to 2020, this research projects the food security status to 2035 using the GM(1,1) model, incorporating a comprehensive index of soil and water resource matching and regression analysis to inform production forecasts. Key assumptions include continued historical trends in population growth, urbanization, and dietary shifts towards an increased animal protein consumption. The findings revealed a consistent upward trend in grain production from 2000 to 2020, with an average annual growth rate of 3.5%. Corn and wheat emerged as the dominant grain crops. Certain provinces demonstrated comparative advantages for specific crops like rice and wheat. The most significant finding is that despite the projected growth in the total grain output by 2035 compared to 2020, the regional grain self-sufficiency rate is projected to range from 79.6% to 84.1%, falling below critical food security benchmarks set by the FAO and China. This projected shortfall carries significant implications, underscoring a serious challenge to regional food security and highlighting the region’s increasing vulnerability to external food supply fluctuations. The findings strongly signal that current trends are insufficient and necessitate urgent and proactive policy interventions. To address this, practical policy recommendations include promoting water-saving technologies, enhancing regional cooperation, and strategically utilizing the international grain trade to ensure regional food security. Full article

The objective of this study was to evaluate the physical-hydric properties of a Planosol under an Integrated Crop–Livestock–Forest (ICLF) system in the Agreste region of Paraíba, Brazil, after eight years of implementation, and to compare them with areas under a conventional cropping system and secondary native vegetation. The experiment was conducted at the experimental station located in Alagoinha, in the Agreste mesoregion of the State of Paraíba, Brazil. The experimental design adopted was a randomized block design (RBD) with five treatments and four replications (5 × 4 + 2). The treatments consisted of: (1) Gliricidia (Gliricidia sepium (Jacq.) Steud) + Signal grass (Urochloa decumbens) (GL+SG); (2) Sabiá (Mimosa caesalpiniaefolia Benth) + Signal grass (SB+SG); (3) Purple Ipê (Handroanthus avellanedae (Lorentz ex Griseb.) Mattos) + SG (I+SG); (4) annual crop + SG (C+SG); and (5) Signal grass (SG). Two additional treatments were included for statistical comparison: a conventional cropping system (CC) and a secondary native vegetation area (NV), both located near the experimental site. The CC treatment showed the lowest bulk density (1.23 g cm⁻³) and the lowest degree of compaction (66.3%) among the evaluated treatments, as well as a total porosity (TP) higher than 75% (0.75 m³ m⁻³). In the soil under the integration system, the lowest bulk density (1.38 g cm⁻³) and the highest total porosity (0.48 m³ m⁻³) were observed in the SG treatment at the 0.0–0.10 m depth. High S-index values (>0.035) and a low relative field capacity (RFc < 0.50) and K_θ indicate high structural quality and low soil water storage capacity. It was concluded that the SG, I+SG, SB+SG, and CC treatments presented the highest values of soil bulk and degree of compaction in the layers below 0.10 m. The I+SG and C+SG treatments showed the lowest hydraulic conductivities and macroaggregation. The SG and C+SG treatments had the lowest available water content and available water capacity across the three analyzed soil layers. Full article

Vegetation dynamics are complexly influenced by multiple factors such as climate, human activities, and topography. In recent years, the frequency, intensity, and diversity of human activities have increased, placing substantial pressure on the growth of vegetation. Arid and semi-arid regions are particularly sensitive to climate change, and climate change and large-scale ecological restoration have led to significant changes in the dynamic of dryland vegetation. However, few studies have explored the nonlinear relationships between these factors and vegetation dynamic. In this study, we integrated trend analysis (using the Mann–Kendall test and Theil–Sen estimation) and machine learning algorithms (XGBoost-SHAP model) based on long time-series remote sensing data from 2001 to 2020 to quantify the nonlinear response patterns and threshold effects of bioclimatic variables, topographic features, soil attributes, and anthropogenic factors on vegetation dynamic. The results revealed the following key findings: (1) The kNDVI in the study area showed an overall significant increasing trend (p < 0.01) during the observation period, of which 26.7% of the area showed a significant increase. (2) The water content index (Bio 23, 19.6%), the change in land use (15.2%), multi-year average precipitation (pre, 15.0%), population density (13.2%), and rainfall seasonality (Bio 15, 10.9%) were the key factors driving the dynamic change of vegetation, with the combined contribution of natural factors amounting to 64.3%. (3) Among the topographic factors, altitude had a more significant effect on vegetation dynamics, with higher altitude regions less likely to experience vegetation greening. Both natural and anthropogenic factors exhibited nonlinear responses and interactive effects, contributing to the observed dynamic trends. This study provides valuable insights into the driving mechanisms behind the condition of vegetation in arid and semi-arid regions of China and, by extension, in other arid regions globally. Full article

In the irrigation districts of Northern China, the flood resources utilization for deep storage irrigation, which is essentially characterized by active excessive irrigation, aims to have the potential to mitigate freshwater shortages, and long-term groundwater overexploitation. It is crucial to detect the effects of irrigation amounts on agricultural yield and the mechanisms under deep storage irrigation. A three-year field experiment (2020–2023) was conducted in the Guanzhong Plain, according to five soil wetting layer depths (RF: 0 cm; W1: control, 120 cm; W2: 140 cm; W3: 160 cm; W4: 180 cm) with soil saturation water content as the irrigation upper limit. Results exhibited that, compared to W1, the W2, W3, and W4 treatments led to the increased plant height, leaf area index, and dry matter accumulation. Meanwhile, the W2, W3, and W4 treatments improved kernel weight increment achieving maximum grain-filling rate (W_max), maximum grain-filling rate (G_max), and average grain-filling rate (G_ave), thereby enhancing the effective spikes (ES) and grain number per spike (GS), and thus increased wheat grain yield (GY). In relative to W1, the W2, W3, and W4 treatments increased the ES, GS, and GY by 11.89–19.81%, 8.61–14.36%, and 8.17–13.62% across the three years. Notably, no significant difference was observed in GS and GY between W3 and W4 treatments, but W4 treatment displayed significant decreases in ES by 3.04%, 3.06%, and 2.98% in the respective years. The application of a structural equation modeling (SEM) revealed that deep storage irrigation improved ES and GS by positively regulating W_max, G_max, and G_ave, thus significantly increasing GY. Overall, this study identified the optimal threshold (W3 treatment) to maximize wheat yields by optimizing both the vegetative growth and grain-filling dynamics. This study provides essential support for the feasibility assessment of deep storage irrigation before flood seasons, which is vital for the balance and coordination of food security and water security. Full article

In drought-prone regions without irrigation systems, effective agrotechnologies such as subsoiling are crucial for enhancing soil infiltration and water retention. However, the effects of subsoiling can vary depending on crop type and environmental conditions. Despite previous research, there is limited understanding of the contrasting responses of C3 (sunflower) and C4 (maize) crops to subsoiling under drought stress. This study addresses this knowledge gap by assessing the effectiveness of subsoiling as a drought mitigation practice on Haplic Chernozem in Northern Bulgaria, integrating ground-based and remote sensing data. Soil physical parameters, leaf area index (LAI), canopy temperature, crop water stress index (CWSI), soil moisture, and yield were evaluated under both conventional tillage and subsoiling for the two crops. A variety of optical and radar descriptive remote sensing products derived from Sentinel-1 and Sentinel-2 satellite data were calculated for different crop types. Consequently, the use of machine learning, utilizing all the processed remote sensing products, enabled the reasonable prediction of LAI, achieving a coefficient of determination (R²) after a cross-validation greater than 0.42 and demonstrating good agreement with in situ observations. Results revealed differing responses: subsoiling had a positive effect on sunflower, improving LAI, water status, and slightly increasing yield, while it had no positive effect on maize. These findings highlight the importance of crop-specific responses in evaluating subsoiling practices and demonstrate the added value of integrating unmanned aerial systems (UAS) and satellite-based remote sensing data into agricultural drought monitoring. Full article

Fly-ash-based bubble lightweight soil is widely used due to its environmental friendliness, load reduction, ease of construction, and low costs. In this study, 41 sets of 28 d compressive strength data on lightweight soils with different water–cement ratios, blowing agent dosages, and fly ash dosages were collected through a literature search and indoor tests. Using the compressive strength index and SEM tests, the correlation between the mix ratio design and the microscopic particle components was investigated. The findings were as follows: carbonation reactions occurred in lightweight soil during the maintenance process, and the particles were spherical; increasing the dosage of blowing agent increased the soil’s porosity and pore diameter, leading to the formation of through-holes and reducing the compressive strength and mobility; increasing the fly ash dosage and water–cement ratio increased the soil’s mobility but reduced its compressive strength; and the strength decreased significantly when the fly ash dosage was more than 16% (e.g., the strength at a 20% dosage was 17.8% lower than that at a 15% dosage). Feature importance analysis showed that the water–cement ratio (57.7%), fly ash dosage (30.9%), and blowing agent dosage (11.1%) had a significant effect on strength. ExtraTrees, LightGBM, and Bayesian-optimized Random Forest models were used for 28d strength prediction with coefficients of determination (R²) of 0.695, 0.731, and 0.794, respectively. The Bayesian-optimized Random Forest model performed optimally in terms of the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE), and the prediction performance was best. The accuracy of the model is expected to be further improved with expansions in the database. A 28 d compressive strength prediction platform for fly-ash-based bubble lightweight soil was ultimately developed, providing a convenient tool for researchers and engineers to predict material properties and mix ratios. 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

Desert plant communities play a vital role in sustaining the stability of arid ecosystems; however, they demonstrate limited resilience to environmental changes. A critical aspect of understanding community assembly mechanisms is determining whether soil texture heterogeneity affects vegetation diversity in arid deserts, especially under conditions of extreme water scarcity and restricted nutrient availability. This study systematically examined the relationships between plant diversity and soil physicochemical properties across four soil texture types—sand, sandy loam, loamy sand, and silty loam—by selecting four representative desert systems in the Hami region of Xinjiang, China. The objective was to elucidate the mechanisms through which soil texture may impact desert plant species diversity. The findings revealed that silty loam exhibited distinct characteristics in comparison to the other three sandy soil types. Despite its higher nutrient content, silty loam demonstrated the lowest vegetation diversity. The Shannon–Wiener index (H′), Simpson dominance index (C), Margalef richness index (D), and Pielou evenness index (Jsw) for silty loam were all lower compared to those for sand, sandy loam, and loamy sand. However, silty loam exhibited higher values in electrical conductivity (EC), urease activity (SUR), and nutrient content, including soil organic matter (SOM), ammonium nitrogen (NH⁴⁺-N), and available potassium (AK), than the other three soil textures. This study underscores the significant regulatory influence of soil texture on plant diversity in arid environments, offering new insights and practical foundations for the conservation and management of desert ecosystems. Full article

Semi-natural grasslands are some of the most species-rich habitats in Europe and provide important ecosystem services such as biodiversity conservation, carbon sequestration and soil fertility maintenance. This study investigates how different intensities of grassland management affect the composition of functional groups and soil chemical properties. Five grassland management systems were analyzed: Cut3—three cuts per year; LGI—low grazing intensity; CG—combined cutting and grazing; Cut4—four cuts per year; and HGI—high grazing intensity. The functional groups assessed were grasses, legumes and forbs, while soil samples from three depths (0–10, 10–20 and 20–30 cm) were analyzed for their chemical properties (soil organic carbon—SOC; soil total nitrogen—STN; inorganic soil carbon—SIC; soil organic matter—SOM; potassium oxide—K₂O; phosphorus pentoxide—P₂O₅; C/N ratio; and pH) and physical properties (volumetric soil water content—VWC; bulk density—BD; and porosity—POR). The results showed that less intensive systems had a higher proportion of legumes, while species diversity, as measured via the Shannon index, was the highest in the Cut4 system. The CG system tended to have the highest SOC and STN at a 0–10 cm depth, with a similar trend observed for SOC_stock at a 0–30 cm depth. The Cut4, HGI and CG systems also had an increased STN_stock. Both grazing systems had the highest P₂O₅ content. A tendency towards a higher BD was observed in the top 10 cm of soil in the more intensive systems. Choosing a management strategy that is tailored to local climate and site conditions is crucial for maintaining grassland stability, enhancing carbon sequestration and promoting long-term sustainability in the context of climate change. 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

The context of global climate change, water stress has a significant impact on the ecological function and landscape value of urban greening shrubs. In this study, three typical greening shrubs (Euonymus japonicus, Ligustrum × vicaryi, and Berberis thunbergii var. atropurpurea) in North China were subjected to a two-year field-controlled experiment (2022–2023) with four water treatments: full irrigation, deficit irrigation, natural rainfall, and extreme drought. The key findings are as follows: (1) Extreme drought reduced the color indices substantially—the G_CC of E. japonicus decreased by 40% (2023); the R_CC of B. thunbergii var. atropurpurea declined by 35% (2022); and the color indices of L. × vicaryi remained stable (variation < 15%). (2) Early-season soil water content (SWC) strongly correlated with the color index of E. japonicus (r² = 0.42, p < 0.05) but weakly with B. thunbergii (r² = 0.28), suggesting species-specific drought-tolerance mechanisms like reduced leaf area. (3) Deficit irrigation (SWC ≈ 40%) maintained color indices between fully irrigated and drought-stressed levels. Notably, B. thunbergii retained high redness (R_CC > 0.8) at an SWC ≈ 40%; E. japonicus required an SWC > 60% to preserve greenness (G_CC). The research results provide a scientific basis for urban greening plant screening and water-saving irrigation strategies, and expand the application scenarios of color coordinates in plant physiological and ecological research. Full article