Search Results (135)

Net solar radiation is an essential parameter that characterizes surface energy exchange and plays a critical role in climate change, solar power generation, and agricultural irrigation. Although the global GLASS surface all-wave daily net radiation (NR) product exhibits high overall accuracy, a comprehensive quality assessment for continental China remains lacking, resulting in unclear regional applicability. Therefore, this study focuses on mainland China. Based on solar net radiation observations from 50 meteorological stations (2000–2016) and 37 ecological stations (2000–2020), four evaluation metrics were used: the correlation coefficient (R), mean bias error (MBE), root mean square error (RMSE), and coefficient of determination (R²). The results indicate that, during the study period, GLASS NR showed relatively small deviations from the observed values across most regions of China, with significant discrepancies observed only in southern Yunnan, Guangdong, Guangxi, and Hainan. Seasonally, GLASS NR performed better in autumn and winter than in spring and summer. Interannually, there was only a slight decline in data quality for a few individual years; however, overall, an upward trend was observed. Regarding land cover types, GLASS NR accuracy was lower for shrublands, forests, and grasslands, whereas it performed better for other land cover types. Overall, the GLASS NR product demonstrates high accuracy and good temporal continuity across mainland China. However, significant regional variations exist, and localized applications require optimization and refinement. This study provides valuable insights for improving net radiation products across multiple spatiotemporal scales. Full article

Maintaining agricultural viability and managing water resources under rising global temperatures requires understanding the complex relationship between climate variability and vegetation dynamics. This study investigated the effects of hydroclimatic variability and long-term trends on vegetation response in the Meriç-Ergene Basin, one of Türkiye’s most agriculturally productive and climate-sensitive regions. The monthly precipitation (pr), average temperature (Tave), reference evapotranspiration (ET0), and soil moisture (SM) were analyzed for 1975–2024 while the land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) were assessed between 2001 and 2024. Seasonal anomaly analysis revealed negative SM anomalies and frequent positive anomalies in the Tave, LST, and ET0, especially in spring and summer. The NDVI anomalies were more favorable in the spring and autumn but constrained in summer. Trend analyses (ITA/IPTA) showed increasing trends in the Tave, LST, and ET0, and declining trends in the SM. Correlation results indicated strong positive ET0–LST–Tave relationships (r > 0.90) and strong negative ET0–SM correlations (as low as −0.83). The NDVI showed moderate correlations with the LST but weak associations with the pr and SM, indicating a shift toward temperature-driven vegetation behavior. The findings demonstrate that vegetation dynamics, as represented by NDVI, are progressively affected by temperature anomalies. Warming trends specifically increase evapotranspiration demand and expedite phenological processes, resulting in stronger correlations between NDVI and both Tave and LST. This transition toward temperature sensitivity signifies that vegetation greenness in the study area is increasingly influenced by thermal factors rather than being solely limited by precipitation. These findings underscore the basin’s vulnerability to warming and drying, highlighting the need for climate-resilient agriculture, improved irrigation planning, and adaptive land use strategies. Full article

This study investigates how cover crop management and soil tillage influence the development and yield of cucumber and cabbage crops. Three cover crop treatments—blue lupin, black oats, and their mixture—were evaluated during the autumn/winter season, while Stylosanthes capitata (Fabaceae), pearl millet (Pennisetum glaucum, Poaceae), and their mixture were assessed during the spring/summer season, under both conventional tillage and no-till (direct seeding) systems. Cover crops were established in spring/summer (October–November) and, after their management, cucumber (Cucumis sativus L.) was cultivated from December to February. Subsequently, winter cover crops were grown from May to July, followed by cabbage (Brassica oleracea var. capitata) cultivation from July to September. Drip irrigation was used, and organic practices were employed for weed, pest, and disease management. Germination, seedling survival rate, and plant growth (height, number of leaves, foliage cover, and fruit or cabbage size) were evaluated. Finally, crop yield is considered by comparing harvest weight and quality to determine which combination of soil cover and planting method maximizes crop productivity and quality. Obviously, management differences that influence yield will be associated with soil properties. To better understand the causes of these yield differences, the influence of soil chemical properties was explored using multivariate analysis techniques (discriminant analysis) and neural networks. Multivariate techniques allow for the exploration of complex relationships among multiple variables simultaneously, facilitating the identification of key patterns or factors that influence crop yield. On the other hand, neural networks, using machine learning models, allow for the prediction of outcomes based on the soil’s physicochemical properties, as well as the identification of optimal combinations of factors that maximize crop yield. Discriminant analysis and neural networks showed that soil variables such as pH, organic matter (OM), cation exchange capacity (CEC), phosphorus (P), and potassium (K) were key determinants in differentiating the yield groups. Cabbage yield was most strongly associated with pH and OM, while cucumber yield responded more strongly to potassium and CEC. Full article

Climate change impacts on the Sado River (southwest Portugal) flow rates (FRs) were assessed for the first time under the 2041–2060 Shared Socioeconomic Pathways: 1–2.6 W/m² (SSP1-2.6), 3–7.0 W/m² (SSP3-7.0), and 5–8.5 W/m² (SSP5-8.5), using bias-adjusted and downscaled General Circulation Model (GCM) ensemble projections from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b-Sado). ISIMIP3b-Sado was used to estimate future precipitation and temperature changes, and as input for Hydrological Simulation Program—FORTRAN (HSPF) simulations. The HSPF projected decreases in the Sado FRs, mainly under SSP3-7.0 and SSP5-8.5, due to temperature increases and autumn/spring precipitation decreases. The FR decreases may lead to 29%/33% reductions in yearly accumulated riverine water volume under SSP3-7.0/SSP5-8.5 and a 31% summertime riverine water deficit increase under SSP3-7.0. Surface-water demand fulfilment in the Sado Basin could suffer a 22-day delay, and the wintertime precipitation range is projected to increase. Hence, in the near-future, summertime surface-water needs and reservoir recharge in the Sado Basin could become more dependent on wintertime precipitation. With Sado being an agricultural region, our results should prompt agriculture stakeholders and decision makers to improve wintertime surface water storage and management to sustain summertime crop irrigation needs. Full article

The imbalance between water supply and demand in the arid and semi-arid regions of northwest China has become increasingly severe, highlighting the urgent need to develop and utilize unconventional water resources. Return flow, originating from canal leakage and field drainage, is widely distributed in these regions. However, as it contains a certain amount of salts, long-term use of return flow can lead to soil salinization and degradation of soil structure. Therefore, the scientific utilization of return flow has become a key issue for achieving sustainable agricultural development and efficient water use in arid areas. This study was conducted in the Yinbei Irrigation District, Ningxia, northwest China. Water samples were collected from the main and branch drainage ditches and analyzed to evaluate the feasibility of using return flow irrigation in the area. In addition, based on two years of continuous field monitoring and HYDRUS model simulations, the long-term dynamics of soil salinity under moderate return flow irrigation over the next 20 years were predicted. The results show that the total salinity of the main return ditches consistently remained below the agricultural irrigation water quality standard of 2000 mg/L, with Na⁺ and SO₄²⁻ as the predominant ions. Seasonal variations in return flow salinity were notable, with higher levels observed in spring compared to summer. Simulation results based on field trial data indicated that soil salinity displayed regular seasonal fluctuations. During the rice-growing season, strong leaching kept the salinity in the plough layer (0–40 cm) low. However, after irrigation ceased, evaporation in autumn and winter led to an increase in surface soil salinity, creating annual peaks. Long-term simulations showed that soil salinity throughout the entire profile (0–100 cm) followed a pattern of “slight increase—gradual decrease—dynamic stability.” Specifically, winter salinity peaks slightly increased during the first two years but then gradually declined, stabilizing after approximately 15 years. This indicates that long-term return-flow irrigation does not result in the accumulation of soil salinity in the plough layer. Full article

The downstream irrigation district of the Yarkant River basin has experienced increasing soil salinization driven by shallow groundwater levels, constraining the sustainable development of regional agriculture. However, the dynamic relationship between soil salinity and groundwater depth in this region remains unclear, limiting the effectiveness of saline–alkali land remediation strategies based on groundwater level regulation. In this study, field data were collected in 2025 on total soil salinity, concentrations of eight major ions, groundwater depth, and groundwater salinity in the irrigation district. The spatiotemporal distribution patterns of soil salinity, groundwater depth, and groundwater salinity were analyzed, along with their interrelationships. The soils in the irrigation district are predominantly mildly to moderately saline. Overall, soil salinity exhibits clear seasonal patterns, characterized by accumulation due to evaporation in spring and autumn and dilution through irrigation in summer. The dominant anions in the soil were SO₄²⁻ and Cl⁻, while Ca²⁺ and Na⁺ were the dominant cations, indicating a chloride–sulfate salinity type. Soil salinity shows a significant positive correlation with groundwater mineralization. A clear Boltzmann function relationship was identified between soil salinity and groundwater depth, revealing a critical groundwater depth of 2.10–2.18 m for salt accumulation in the irrigation district. The critical groundwater depths corresponding to soil salinity and major salt ions, from lowest to highest, are Cl⁻ < Na⁺ < total salts < SO₄²⁻ < Ca²⁺. Random forest regression analysis identified the main factors influencing soil salinity and their relative importance, ranked from highest to lowest as follows: groundwater depth > Na⁺ > Cl⁻ > groundwater salinity > Ca²⁺ > SO₄²⁻ > Mg²⁺ > HCO₃⁻ > K⁺ > CO₃²⁻. Maintaining groundwater depth below the critical threshold and focusing on groundwater ions that strongly influence soil salinity can effectively alleviate soil salinization in the lower Yarkant River irrigation district caused by shallow, highly mineralized groundwater. Full article

In the context of global warming, agricultural drought risks are exacerbated by increasing atmospheric aridity. This study pioneers the application of the Bayesian Estimator of Abrupt Change, Seasonality, and Trend (BEAST) algorithm at a provincial scale to detect change points in vapor pressure deficit (VPD), leveraging high-density meteorological station data from Hunan Province to delineate the nuanced evolution of VPD and its implications for early drought warning. Key findings reveal the following: (1) The VPD in Hunan exhibits a spatial pattern of “higher in the south than north, higher in the east than west” and a seasonal variation of “summer > autumn > spring > winter”. (2) BEAST identified abrupt changes in VPD coinciding with critical phenological periods, such as the early rice transplanting period in early April, with spatial and temporal gradient differences (up to 25 days) that can guide irrigation resource scheduling; moreover, the months of change points have been consistently advancing during the study period. (3) The dominant factors of VPD exhibit regional and seasonal differentiation. Annually, the maximum temperature (contribution rate 57.1–60.6%) is the primary factor. (4) Extreme events with VPD > 1.5 kPa for three consecutive days covered 92 stations in 2022. Combining this with the critical growth periods of double-cropping rice, it is recommended to set VPD = 1 kPa as the drought early warning threshold for the northern and southern regions. This study provides a scientific basis for the prevention and control of agricultural drought by integrating climate diagnostics and crop physiological needs. Full article

Agricultural irrigation accounts for nearly 70% of global freshwater withdrawals, making sustainable water management crucial for food security and ecological stability—particularly in arid and semi-arid regions. However, dynamic water-saving thresholds at both inter-annual and intra-annual scales remain insufficiently quantified in current research. To address this gap, this study developed an integrated SWAT-MODFLOW model for the Hetao Irrigation District and quantified dynamic water-saving thresholds by simulating crop yield responses under a range of irrigation scenarios. The model was calibrated (2008–2014) and validated (2014–2016), demonstrating reliable performance (R² = 0.75, NSE = 0.74) in capturing local hydrological processes. Inter-annual scenarios assessed water-saving levels of 5%, 10%, 20%, and 30% under wet, normal, and dry years, while intra-annual scenarios adjusted seasonal irrigation volumes in spring, summer, and autumn with reduction gradients of 33%, 50%, and 100%. Results show that wet and normal years could achieve a water-saving threshold of up to 20%, whereas dry years were limited to 5%. Intra-annually, autumn irrigation offered the greatest saving potential (33–100%), followed by spring (33–50%). Spatially, crop responses varied substantially: the western part of the region proved particularly sensitive, with even the optimal district-wide strategy reducing local crop yields by 10–20%. This study quantifies dynamic water-saving thresholds and incorporates spatial heterogeneity into scenario assessment. The resulting framework is transferable and provides a basis for sustainable water management in water-limited agricultural regions. Full article

Dynamic canal-system scheduling faces the fundamental challenge of determining the optimal reduction in the current period’s water allocation to reserve sufficient water for remaining periods, thereby hedging against potentially greater future water shortages. Although forecast information has been widely incorporated to address this hedging problem, its effectiveness is heavily dependent on forecast accuracy. Integrating abundant historical canal scheduling data with forecast information provides a promising pathway to improve scheduling performance, yet relevant studies remain limited. This study introduces the concept of Target Residual Lump-Sum Water Quota (TRLSWQ) for each time interval and develops a novel “Bi-level, Two-stage” (BT) model for dynamically updated canal-system scheduling that jointly leverages TRLSWQ and forecast information. The model defines clear canal scheduling rules and effectively adapts to the hierarchical structure in canal system scheduling. The model is applied to the summer–autumn irrigation scheduling of the Yongji main canal and six associated sub-canals in the Hetao Irrigation Area, Inner Mongolia, China. The results indicate that compared with the conventional model, the BT model reduces the total water shortage index of sub-canals from 40.81 to 31.44 (a decrease of 22.9%) and increases the utilization rate of the water quota from 89.3% to 92.9% (an increase of 3.9%). Furthermore, this study clarifies the mechanism of canal scheduling deviations caused by forecast errors: early-stage rainfall under-forecasting induces excessive early-stage allocation, leaving no water for later periods, whereas early-stage over-forecasting leads to withheld early allocation and unused residual lump-sum quota in later stages. The BT model effectively balances shortage risks between current and future periods and offers a practical and robust strategy for improving dynamic canal scheduling in irrigation districts. Full article

Climate change represents a critical challenge for viticulture worldwide, primarily through increased heat stress, more frequent and severe drought periods, and unseasonal rainfall events. There is increasing evidence of its negative effects on both thermal regimes—potentially leading to accelerated phenology and unbalanced sugar-to-acid ratios—and hydric regimes—causing water stress that impacts berry development and final yield. The use of plastic covering in vineyards is a widespread technique, particularly in regions with high climatic variability such as the Mediterranean Basin (e.g., Southern Italy, Spain, Greece), aimed at protecting both vegetation and grapes from external factors such as hail, heavy rainfall, wind, and extreme solar radiation, which can cause physical damage, promote fungal diseases, and lead to berry sunburn. This study explores the impact of six distinct commercial plastic films, with varying optical properties, on the retrieval and accuracy of vegetation indices derived from Sentinel-2 imagery in a mid-season table grape vineyard (Autumn Crisp^®) in Southern Italy during the 2024 growing season. Laboratory spectroradiometric analyses were conducted to measure film-specific transmittance and reflectance factors from 200 to 1500 nm, enabling the development of a first-order linear spectral correction model applied to Sentinel-2 imagery. Vegetation indices (NDVI, CVI, GNDVI, LWCI) were corrected for plastic interference and analysed through univariate statistics and Principal Component Analysis. Results showed that after applying the spectral correction model, film T2 displayed the higher NDVI value (0.73). Films T3 and T4—characterised by high visible light transmittance (>39%) and low reflectance (<11% in the Red/NIR)—resulted in lower vine vigour and photosynthetic activity, with mean corrected NDVI values equal to 0.70, though still significantly higher than those of films T1 (0.65) and T5 (0.67). Films T6 and T1 were associated with greater water conservation, as indicated by the highest mean LWCI values (T6: 0.59; T1: 0.52), but lower chlorophyll-related signals, evidenced by the lowest mean CVI values (T6: 1.31; T1: 1.74) and GNDVI values (T6: 0.46; T1: 0.48). Among the corrected indices, NDVI demonstrated strong positive correlations with yield (r = 0.900) and total soluble solids per vine (TSS*vine, in kg), a key quality parameter representing the total sugar yield (r = 0.883), supporting its suitability as an index for vine productivity and fruit quality. The proposed correction method significantly improves the reliability of remote sensing in covered vineyards, as demonstrated by the strong correlations between corrected NDVI and yield (R² = 0.810) and sugar content (R² = 0.779), relationships that were not analysable with the uncorrected data; may guide film selection—opting for high-transmittance films (e.g., T2, T3) for yield or water-conserving films (e.g., T6) for stress mitigation—and irrigation strategies, such as using the corrected LWCI for precision scheduling. Future efforts should include angular effects and ground-truth validation to enhance correction accuracy and operational relevance. Full article

West Nile Virus (WNV) is an emerging arboviral threat in Europe, with rising incidence in Spain since 2004. In 2024, Spain experienced its largest outbreak, primarily in small urban areas of south-western regions. We report a subset of an emergency integrated vector management program, focusing on six municipalities accounting for one-third of all human WNV cases nationwide. Over four months, 725 potential larval sites were inspected during 4026 visits. Adult mosquitoes (n = 2553) were collected with suction traps, and immature stages (n = 4457) with dipper techniques, yielding 11 species. Culex pipiens s.l. was predominant, while Cx. perexiguus, though less abundant, was epidemiologically significant. Cytochrome Oxidase I (COI) gene phylogenetic analysis confirmed Cx. perexiguus, forming a distinct clade from Cx. univittatus. Immature mosquitoes were found in 18.6% of sites, especially irrigation canals, ditches, and backwaters near urban areas. Habitat differences in larval abundance were analyzed using generalized linear mixed models. Targeted larviciding with Bacillus thuringiensis var. israelensis (Bti) and focal adulticiding with cypermethrin totaled 259 interventions (70.4% larviciding, 29.6% adulticiding). A significant 63.9% reduction in larval abundance was observed after five consecutive Bti treatments, with some variation among treatment cycles (52.2–75.5%). Adult activity persisted into late autumn. This study provides the first comprehensive characterization of larval mosquitoes in Spain’s main WNV hotspot, highlighting the need for rapid, coordinated expert interventions and extended seasonal control to prevent future outbreaks. Full article

Ceratitis capitata (Wiedemann) or medfly is a polyphagous pest of fruit crops worldwide. The Asian-native larval parasitoid Diachasmimorpha longicaudata (Ashmead) is mass-reared at the San Juan Biofactory and is currently released for medfly control in Argentina. Information on parasitoid survival, reproduction, and population growth parameters is critical for optimizing the mass-rearing process and successfully achieving large-scale release. This study provides a first-time insight into the demography of two population lines of D. longicaudata: one mass-reared on medfly larvae of the Vienna-8 temperature-sensitive lethal genetic sexing strain and the other on larvae of the wild biparental medfly strain. The aim was to compare both parasitoid populations to improve mass-rearing quality and to assess performance on medfly in a semi-arid environment, typical of Argentina’s central-western fruit-growing region. Tests were performed under laboratory and non-controlled environmental conditions in semi-field cages during three seasons. Dl_(Cc-bip) females exhibited higher reproductive potential than did Dl_(Cc-tsl) females under lab conditions. However, both Dl_(Cc-bip) and Dl_(Cc-tsl) were found to be similar high-quality females with high population growth rates in warm–temperate seasons, i.e., late spring and summer. Dl_(Cc-bip) females were only able to sustain low reproductive rates in early autumn, a colder season. These results are useful for improving the parasitoid mass production at the San Juan Biofactory and redesigning parasitoid release schedules in Argentina’s irrigated, semi-arid, fruit-growing regions. Full article

Soil secondary salinization is a major limiting factor of sustainable agricultural production in arid and semi-arid irrigation zones, yet predictive tools for regional water–salt dynamics remain limited. The Yichang Irrigation District, located within the Hetao Irrigation Area, has experienced persistent salinity challenges due to shallow groundwater tables and intensive irrigation. In this study, we aimed to simulate long-term soil water–salt dynamics in the Yichang Irrigation District and evaluate the effectiveness of different engineering and management scenarios using the SaltMod model. Field monitoring of soil salinity and groundwater levels during summer and fall (2022–2024) was used to calibrate and validate SaltMod parameters, ensuring accurate reproduction of seasonal soil salinity fluctuations. Based on the calibrated model, ten-year scenario simulations were conducted to assess the effects of changes in soil texture, irrigation water quantity, water quality, rainfall, and groundwater table depth on root-zone salinity. Our results show that under baseline management, soil salinity is projected to decline by 5% over the next decade. Increasing fall autumn leaching irrigation further reduces salinity by 5–10% while conserving 50–300 m³·ha⁻¹ of water. Sensitivity analysis indicated groundwater depth and irrigation water salinity as key drivers. Among the engineering strategies, drainage system improvement and groundwater regulation achieved the highest salinity reduction (15–20%), while irrigation regime optimization provided moderate benefits (~10%). This study offers a quantitative basis for integrated water–salt management in the Hetao Irrigation District and similar regions. Full article

We develop a year-round, field-scale framework to retrieve soil moisture and map irrigation in an arid irrigation district where crop phenology and canopy dynamics undermine static, single-season approaches. However, the currently popular TVDI application is limited during non-growing seasons. To address this gap, we introduce a season-stratified TVDI scheme—based on the LST–EVI feature space with phenology-specific dry/wet edges—coupled with a non-growing-season inversion that fuses Sentinel-1 SAR and Landsat features and compares multiple regressors (PLSR, RF, XGBoost, and CNN). The study leverages 2023–2024 multi-sensor image time series for the Yichang sub-district of the Hetao Irrigation District (China), together with in situ topsoil moisture, meteorological records, a local cropping calendar, and district statistics for validation. Methodologically, EVI is preferred over NDVI to mitigate saturation under dense canopies; season-specific edge fitting stabilizes TVDI, while cross-validated regressors yield robust soil-moisture retrievals outside the growing period, with the CNN achieving the highest accuracy (test R² ≈ 0.56–0.61), outperforming PLSR/RF/XGBoost by approximately 12–38%. The integrated mapping reveals complementary seasonal irrigation patterns: spring irrigates about 40–45% of farmland (e.g., 43.39% on 20 May 2024), summer peaks around 70% (e.g., 71.42% on 16 August 2024), and autumn stabilizes near 20–25% (e.g., 24.55% on 23 November 2024), with marked spatial contrasts between intensively irrigated southwest blocks and drier northeastern zones. We conclude that season-stratified edges and multi-source inversions together enable reproducible, year-round irrigation detection at field scale. These results provide operational evidence to refine irrigation scheduling and water allocation, and support drought-risk management and precision water governance in arid irrigation districts. Full article

Evapotranspiration (ET) variation is typically influenced by climatic factors, which are considered the primary drivers of agricultural water requirements. Any changes in ET rates directly affect crop water demands. In this study, temporal trends and magnitudes of key climatic variables, and their impacts on reference evapotranspiration (ETo) during 1981–2020, were evaluated across 36 districts of Punjab, Pakistan. Positive serial correlations, ranging from 0.29 to 0.48, were identified and removed using the pre-whitening technique. Increasing trends in maximum temperature (Tmax) and wind speed (WS) across Punjab and its subregions were observed, while relative humidity (RH) exhibited both increasing and decreasing trends. No significant trends were detected for the minimum temperature (Tmin). On a monthly scale, in the Southern Punjab (SP) region, Sen’s slope estimated an increase in ETo, ranging from 0.239 mm/year in November to 0.636 mm/year in May, at a significance level of α = 0.05 (5%). At the provincial scale, significant upward trends in ETo were observed for the annual, Kharif, and autumn seasons, with Z-values of 2.04, 2.16, and 3.13, respectively, at α = 0.05 and 0.01. It was determined that, on an annual scale in Punjab, ETo sensitivity to climatic parameters followed the following order: Tmax > wind speed (WS) > Tmin > RH. The best-fitted models for Tmax, Tmin, WS, and RH were Gaussian, exponential, and spherical. ETo was found to increase spatially from North to South Punjab, with an approximate rise of 70–80 mm/decade. The results provide a scientific basis for understanding hydroclimatic drivers of ETo in semi-arid regions and contribute to improving climate impact assessments on agricultural water use. The observed ETo increases, particularly in South Punjab and lower Central Punjab, highlight the need for region-specific irrigation scheduling and water allocation. These findings can guide cropping calendars, improve irrigation efficiency, and increase canal water supplies to high-ETo areas, supporting adaptive strategies against climate variability in Punjab. Full article