Search Results (363)

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

In early May 2025, extreme wildfires swept across Manitoba, Canada, fueled by unseasonably warm temperatures, prolonged drought, and stressed vegetation. We explore how multi-source satellite indicators—such as anomalies in snow cover, precipitation, temperature, vegetation indices, and soil moisture in April–May—jointly signal landscape preconditioning for fire, highlighting the potential of these compound anomalies to inform fire risk awareness in boreal regions. Results indicate that rainfall deficits and diminished snowpack significantly reduced soil moisture, which subsequently decreased vegetative greenness and created a flammable environment prior to ignition. This concept captures how multiple moderate anomalies, when occurring simultaneously, can converge to create high-impact fire conditions that would not be flagged by individual thresholds alone. These findings underscore the importance of integrating climate and biosphere anomalies into wildfire risk monitoring to enhance preparedness in boreal regions under accelerating climate change. Full article

This study evaluates the agronomic potential of two types of vermicompost—one produced solely from wine industry residues (WIR) and one incorporating sewage sludge (WIR + SS)—under rainfed and deficit irrigation conditions in Mediterranean vineyards. The vermicompost was obtained through a two-phase process involving initial thermophilic pre-composting, followed by vermicomposting using Eisenia fetida for 90 days. The conditions were optimized to ensure aerobic decomposition and maintain proper moisture levels (70–85%) and temperature control. This resulted in end products that met the legal standards required for agricultural use. However, population dynamics revealed significantly higher worm reproduction and biomass in the WIR treatment, suggesting superior substrate quality. When applied to grapevines, WIR vermicompost increased soil organic matter, nitrogen availability, and overall fertility. Under rainfed conditions, it improved vegetative growth, yield, and must quality, with increases in yeast assimilable nitrogen (YAN), sugar content, and amino acid levels comparable to those achieved using chemical fertilizers, as opposed to the no-fertilizer trial. Foliar analyses at veraison revealed stronger nutrient uptake, particularly of nitrogen and potassium, which was correlated with improved oenological parameters compared to the no-fertilizer trial. In contrast, WIR + SS compost was less favorable due to lower worm activity and elevated trace elements, despite remaining within legal limits. These results support the use of vermicompost derived solely from wine residues as a sustainable alternative to chemical fertilizers, in line with the goals of the circular economy in viticulture. Full article

In arid agricultural production, exploring suitable water-saving irrigation strategies and analyzing their water-saving mechanisms are of great significance. Alternating partial root-zone drying irrigation (APRI), a water-saving strategy, enhances the water use efficiency (WUE) of alfalfa (Medicago sativa L.) This paper aims to clarify the physiological mechanisms by which the APRI method enhances the physiological WUE of alfalfa, as well as the differences between this water-saving irrigation strategy, conventional irrigation (CI), and their water deficit adjustments, in order to seek higher water use efficiency for alfalfa production in arid regions. In this experiment, alfalfa was used as the research subject, and three irrigation methods, CI, fixed partial root-zone drying (FPRI), and APRI, were set up, each paired with three decreasing moisture supply gradients of 90% water holding capacity (WHC) (W1), 70% WHC (W2), and 50% WHC (W3). Samples were taken and observed once after every three complete irrigation cycles. Through a comparative analysis of the growth status, leaf water status, antioxidant enzyme activity, and osmotic adjustment capabilities of alfalfa under different water supplies for the three irrigation strategies, the following conclusions were drawn: First, the APRI method, through artificially created periodic wet–dry cycles in the rhizosphere soil, provides pseudo-drought stress that enhances the osmotic adjustment capabilities and antioxidant enzyme activity of alfalfa leaves during the early to middle phases of irrigation treatment compared to CI and FPRI methods, resulting in healthier leaf water conditions. Secondly, the stronger drought tolerance and superior growth conditions of alfalfa under the APRI method due to reduced water availability are key factors in enhancing the water use efficiency of alfalfa under this strategy. Full article

Drought stress severely limits agricultural productivity, with atmospheric and soil water deficits often occurring simultaneously in field conditions. While plant responses to individual drought factors are well-documented, recovery mechanisms following combined atmospheric–soil drought remain poorly understood, hindering drought resistance strategies and irrigation optimization. We set up two VPD treatments (low and high vapor pressure deficit) and two soil moisture treatments (CK: control soil moisture with sufficient irrigation, 85–95% field capacity; drought: soil moisture with deficit irrigation, 50–60% field capacity) in the pot experiment. We investigated wheat’s hydraulic transport (leaf hydraulic conductance, K_leaf) and gas exchange (stomatal conductance, g_s; photosynthetic rate, A_n) responses to combined drought stress from atmospheric and soil conditions at the heading stage, as well as rewatering 55 days after treatment initiation. The results revealed that: (1) high VPD and soil drought significantly reduced leaf hydraulic conductance (K_leaf), with a high VPD decreasing K_leaf by 31.6% and soil drought reducing K_leaf by 33.2%; The high VPD decreased stomatal conductance (g_s) by 43.6% but the photosynthetic rate (A_n) by only 12.3%; (2) After rewatering, g_s and A_n of atmospheric and soil drought recovered relatively rapidly, while K_leaf did not; (3) Atmospheric and soil drought stress led to adaptive changes in wheat’s stomatal regulation strategies, with an increasing severity of drought stress characterized by a shift from non-conservative to conservative water regulation behavior. These findings elucidate wheat’s hydraulic–stomatal coordination mechanisms under drought stress and their differential recovery patterns, providing theoretical foundation for improved irrigation management practices. Full article

Soil moisture content (SMC) is a critical parameter for agricultural productivity, particularly in semi-arid regions, where irrigation practices are extensively used to offset water deficits and ensure decent yields. Yet, the socio-economic and remote context of these regions prevents sufficiently dense SMC monitoring in space and time to support farmers in their work to avoid unsustainable irrigation practices and preserve water resource availability. In this context, our study addresses the challenge of high spatial resolution (i.e., 20 m) SMC estimation by integrating remote sensing datasets in machine learning models. For this purpose, a dataset made of 166 soil samples’ SMC along with corresponding SMC, precipitation, and radar signal derived from Soil Moisture Active Passive (SMAP), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Sentinel-1 (S1), respectively, was used to assess four machine learning models’ (Decision Tree—DT, Random Forest—RF, Gradient Boosting—GB, Extreme Gradient Boosting—XGB) reliability for SMC mapping. First, each model was trained/validated using only the coarse spatial resolution (i.e., 10 km) SMAP SMC and IMERG precipitation estimates as independent features, and, second, S1 information (i.e., 20 m) derived from single scenes and/or composite images was added as independent features to highlight the benefit of information (i.e., S1 information) for SMC mapping at high spatial resolution (i.e., 20 m). Results show that integrating S1 information from both single scenes and composite images to SMAP SMC and IMERG precipitation data significantly improves model reliability, as R² increased by 12% to 16%, while RMSE decreased by 10% to 18%, depending on the considered model (i.e., RF, XGB, DT, GB). Overall, all models provided reliable SMC estimates at 20 m spatial resolution, with the GB model performing the best (R² = 0.86, RMSE = 2.55%). Full article

Differences in environmental conditions due to slope topography result in differences in evapotranspiration along slopes, but it is unclear how changes in environmental conditions affect the variations in evapotranspiration along slopes. Therefore, we monitored dry-day evapotranspiration (ET_d), solar radiation, vapor pressure deficit (VPD), and soil moisture downslope and upslope on a larch plantation hillslope from July to September 2023 to reveal the mechanisms driving ET_d variations. The results revealed that the difference in ET_d values between the downslope and upslope positions varied by month, with comparable ET_d values at both positions in July and higher ET_d values at the downslope position than at the upslope position in August and September. An ET_d model combining the effects of solar radiation, VPD, and soil water content was developed, which explained 68% of the variation in ET_d. The contributions of solar radiation, VPD, soil moisture, and their interactions to ET_d varied across slope positions, and ET_d was limited mainly by solar radiation downslope and by soil moisture upslope. Our study improves the understanding of the mechanisms governing the variations in evapotranspiration along slopes, and provides a new methodology for quantifying the effects of environmental differences between slope positions on evapotranspiration. Full article

Over the past four decades, Wadi Fatimah in western Saudi Arabia has undergone significant environmental changes that have contributed to desertification. High-resolution spatial and temporal analyses are essential for monitoring the extent of desertification and understanding its driving factors. This study aimed to assess the spatial distribution of desertification in Wadi Fatimah using satellite and climate data. Landsat imagery from 1984 to 2022 was employed to derive land surface temperature (LST) and assess vegetation trends using the Normalized Difference Vegetation Index (NDVI). Climate variables, including precipitation and evapotranspiration (ET), were sourced from the gridded TerraClimate dataset (1980–2022). LST estimates were validated using MOD11A2 products (2001–2022), while TerraClimate precipitation data were evaluated against observations from four local rain gauge stations: Wadi Muharam, Al-Seal Al-Kabeer, Makkah, and Baharah Al-Jadeedah. A Desertification Index (DI) was developed based on four variables: NDVI, LST, precipitation, and ET. Five regression models—ridge, lasso, elastic net, polynomial regression (degree 2), and random forest regression—were applied to evaluate the predictive capacity of these variables in explaining desertification dynamics. Among these, Random Forest and Polynomial Regression demonstrated superior predictive performance. The classification accuracy of the desertification map showed high overall accuracy and a strong Kappa coefficient. Results revealed extensive land degradation in the central and lower sub-basins of Wadi Fatimah, driven by both climatic stressors and anthropogenic pressures. LST exhibited a clear upward trend between 1984 and 2022, especially in the lower sub-basin. Precipitation and ET analysis confirmed the region’s arid climate, characterized by limited rainfall and high ET, which exacerbate vegetation stress and soil moisture deficits. Validation of LST with MOD11A2 data showed reasonable agreement, with RMSE values ranging from 2 °C to 6 °C and strong correlation coefficients across most years. Precipitation validation revealed low correlation at Al-Seal Al-Kabeer, moderate at Baharah Al-Jadeedah, and high correlations at Wadi Muharam and Makkah stations. These results highlight the importance of developing robust validation methods for gridded climate datasets, especially in data-sparse regions. Promoting sustainable land management and implementing targeted interventions are vital to mitigating desertification and preserving the environmental integrity of Wadi Fatimah. Full article

Clarifying spatiotemporal variations in transpiration and their influencing mechanisms is highly valuable for the accurate assessment of hillslope-scale transpiration and for the effective management of forest–water coordination. Here, the sap flow density, meteorological conditions, and soil moisture downslope and upslope of a Larix gmelinii var. principis-rupprechtii plantation hillslope were observed during the growing season (June to September) in 2023, China. The results revealed that transpiration per unit leaf area (T_L) was significantly lower at the upslope position than at the downslope position, with mean values of 0.21 and 0.31 mm·d⁻¹, respectively; these data were associated with the lower canopy conductance per unit leaf area induced by the higher vapor pressure deficit (VPD) and lower soil water content at the 40–60 cm soil depth at the upslope position. The temporal variations in the T_L were controlled by solar radiation, VPD, air temperature, and soil moisture at both slope positions, and the quantitative relationships established from these factors explained 89% of the variation in the T_L. The slope position did not affect the response functions between the T_L and the controlling factors but changed the contribution to the T_L. Compared with those at the downslope position, the contributions from solar radiation and VPD (air temperature) decreased (increased) at the upslope position, and the contribution of soil moisture was essentially similar at both slope positions. Transpiration mainly utilized water from the 20–60 cm soil depth; these results indicated that the soil water content at the 20–40 and 40–60 cm soil depths contributed more to the T_L than did that at the 0–20 cm soil depth. Based on our findings, changes in the environmental conditions caused by slope position have a critical impact on transpiration and can contribute to the development of hillslope-scale transpiration estimates and precise integrated forest and water management. Full article

The potato (Solanum tuberosum L.) is highly dependent on water availability, with physiological sensitivity varying throughout its phenological cycle. In the context of increasing water scarcity and greater climate variability, identifying critical periods where water stress negatively impacts productivity and tuber quality is essential. This study evaluated the physiological response of potatoes under different deficit irrigation strategies in field conditions, and aimed to determine the irrigation reduction thresholds that optimize water use efficiency without significantly compromising yield. Five irrigation regimes were applied: well-watered (T1; irrigation was applied when the volumetric soil moisture content was close to 35% of total water available), 130% of T1 (T2, 30% more than T1), 75% of T1 (T3), 50% of T1 (T4), and 30% of T1 (T5). Key physiological parameters were monitored, including gas exchange (net photosynthesis, stomatal conductance, and transpiration), chlorophyll fluorescence (Fv’/Fm’, ΦPSII, electron transport rate), and photosynthetic pigment content, at three critical phenological phases: tuberization, flowering, and fruit set. The results indicate that water stress during tuberization and flowering significantly reduced photosynthetic efficiency, with decreases in stomatal conductance (gs), effective quantum efficiency of PSII (ΦPSII), and electron transport rate (ETR). In contrast, moderate irrigation reduction (75%) lowered the seasonal application of water by ~25% (≈80 mm ha⁻¹) while maintaining commercial yield and tuber quality comparable to the fully irrigated control. Intrinsic water use efficiency increased by 18 ± 4% under this regime. These findings highlight the importance of irrigation management based on crop phenology, prioritizing water supply during the stages of higher physiological sensitivity and allowing irrigation reductions in less critical phases. In a scenario of increasing water limitations, this strategy enhances water use efficiency while ensuring the production of tubers with optimal commercial quality, promoting more sustainable agricultural management practices. Full article

The prediction of soil moisture conditions using multispectral data from unmanned aerial vehicles (UAVs) has advantages over ground measurements in terms of costs and monitoring range. However, the prediction accuracy for moisture conditions using spectral data alone is low. In this study, relationships between water deficits and phenotypic characteristics in oats were evaluated and used to develop a UAV multispectral-based water prediction model. The vegetation indices NDRE (Normalized Difference Red Edge), CIG (Chlorophyll Index), and MCARI (Modified Chlorophyll Absorption in Reflectance Index) were highly correlated with oat yield. Based on a multipath analysis in the structural equation modeling framework, irrigation (p < 0.01), leaf area index (LAI) (p < 0.001), and SPAD (p < 0.001) had direct positive effects on NDRE. Three distinct machine learning approaches—linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to establish predictive models between the Normalized Difference Red Edge Index (NDRE) and soil water content (SWC). The linear regression model showed moderate correlation (R² = 0.533). Machine learning approaches demonstrated markedly superior performance (RF: R² = 0.828; ANN: R² = 0.810). Nonlinear machine learning algorithms (RF and ANN) significantly outperform conventional linear regression in estimating SWC from spectral vegetation indices. Full article

This study investigated soil salinization processes in the Pavlodar region of Kazakhstan by comparing key soil parameters—namely, electrical conductivity (EC), pH, exchangeable sodium percentage (ESP), and sodium adsorption ratio (SAR) under irrigated and non-irrigated conditions across different agro-climatic zones and soil types (Haplic Chernozems, Haplic Kastanozems). The focus was on understanding the effects of irrigation and natural factors on soil salinization. Statistical analysis, including descriptive statistics and significance testing, was employed to evaluate differences between soil types, locations, and management practices. The research revealed secondary salinization (EC > 2 dS/m, ESP > 15%) in the topsoil of irrigated Haplic Kastanozems soils in the central Aksu district. This degradation was markedly higher than in non-irrigated plots or irrigated Haplic Chernozems in the northern Irtysh district, highlighting the high vulnerability of Haplic Kastanozems soils under current irrigation management given Aksu’s climatic conditions, which are characterized by high evaporative demand (driven by summer temperatures) and specific precipitation patterns that contribute to soil moisture deficits without irrigation. While ESP indicated sodicity, SAR values remained low. Natural factors, including potentially saline parent materials and likely shallow groundwater dynamics influenced by irrigation, appear to contribute to the observed patterns. The findings underscore the need for implementing optimized irrigation and drainage management, particularly in the Aksu district, potentially including water-saving techniques (e.g., drip irrigation) and selection of salt/sodicity-tolerant crops. A comprehensive approach integrating improved water management, agronomic practices, and potentially soil amendments is crucial for mitigating soil degradation and ensuring sustainable agriculture in the Pavlodar region. Further investigation including groundwater monitoring is recommended. Full article

Water use efficiency (WUE), as an important metric for ecosystem resilience, has been identified to play a significant role in the coupling of carbon and water cycles. The farming–pastoral ecotone of Northern China (FPENC), which is highly susceptible to drought due to water scarcity, has long been recognized as an ecologically fragile zone. The ecological restoration projects in China have mitigated land degradation and maintain the sustainability of dryland. However, the process of greening in drylands has the potential to impact water availability. A comprehensive analysis of the WUE in the FPENC can help to understand the carbon absorption and water consumption. Using gross primary production (GPP) and evapotranspiration (ET) data from a MODerate resolution Imaging Spectroradiometer (MODIS), alongside biophysical variables data and land cover information, the spatio-temporal variations in WUE from 2003 to 2022 were examined. Additionally, its driving force and the ecosystem resilience were also revealed. Results indicated that the annual mean of WUE fluctuated between 0.52 and 2.60 gC kgH₂O⁻¹, showing a non-significant decreasing trend across the FPENC. Notably, the annual averaged WUE underwent a significant decline before 2012 (p < 0.05), and then showed a slight increased trend (p = 0.14) during the year afterward (i.e., 2013–2022). In terms of climatic controls, temperature (Temp) and soil volumetric water content (VSWC) dominantly affected WUE from 2003 to 2012; VPD (vapor pressure deficit), VSWC, and Temp showed comprehensive controls from 2013 to 2022. The findings suggest that a wetter atmosphere and increased soil moisture contribute to the decline in WUE. In total, 59.2% of FPENC was shown to be non-resilient, as grassland occupy the majority of the area, located in Mu Us Sandy land and Horqin Sand Land. These results underscore the importance of climatic factors in the regulation WUE over FPENC and highlight the necessity for focused research on WUE responses to climate change, particularly extreme events like droughts, in the future. Full article

The effects of stomatal factors of plant leaves under partial root-zone drying (PRD) have been widely studied. However, the non-stomatal factors and the relationship between photosynthesis with soil moisture have not been explored. In this study, four treatments over-irrigation, full irrigation, moderate water deficit, and severe water deficit were investigated, aiming to evaluate the effects on the diurnal variation of alfalfa leaf photosynthesis under PRD and its relationship with stomatal and non-stomatal limitations, as well as soil moisture. The results showed that any levels of water deficit led to a decrease in the photosynthetic rate (P_n) of alfalfa leaves. Leaves under moderate and severe water deficit displayed a pronounced midday “photosynthetic lunch break,” while those under over- and full irrigation did not display this phenomenon. Before 11:30 a.m., the reduction in P_n was primarily due to stomatal limitations, as evidenced by reduced stomatal conductance (G_s) and decreased intercellular CO₂ concentration (C_i). After 11:30 a.m., non-stomatal limitations became the dominant factor, with both G_s and transpiration rate (T_r) continuing to decrease, while C_i increased, indicating a shift in the limiting factors. Under PRD with moderate water deficit, alfalfa experienced both stomatal and non-stomatal limitations within a single day, leading to a hay yield reduction of 18.6%. Additionally, over-irrigation helped to maintain higher P_n and T_r, increasing alfalfa yield and thus improving water productivity by 33.1%. The correlation coefficients between soil moisture content at 10 cm depths with alfalfa leaf P_n, T_r, and G_s on the photosynthetic measurement day were 0.9864, 0.8571, and 0.8462, respectively. At 20 cm, the correlation coefficients were 0.8820, 0.6943, and 0.6951, respectively. The study concluded that both stomatal and non-stomatal mechanisms contributed to reduced alfalfa P_n in water deficit of PRD. Furthermore, shallow soil moisture also played a crucial role in influencing photosynthetic performance. Full article

As a dominant ecological stress factor of climate change, soil drought has become a key challenge restricting food security. Based on soil moisture data, this paper uses the cumulative anomaly method, coefficient of variation, Sen + Mann–Kendall trend analysis, and center of gravity shift model to study the spatiotemporal changes in soil drought in Hebei Province from 2001 to 2020 and uses the optimal parameter geographic detector model to analyze the key factors affecting soil drought. The results show the following: (1) over the past 20 years, soil drought in Hebei Province has shown a trend of “first intensifying and then easing”, experiencing two turning points, and its spatial distribution showed significant agglomeration characteristics. (2) Soil moisture showed single-peak seasonal fluctuation, with severe drought from January to May, peak soil moisture from June to August, soil moisture balance from September to October, and soil moisture deficit intensified in winter. (3) Soil moisture stability showed spatial differentiation, being high in the northeast and low in the southwest. Soil drought in about 70% of the region has improved, and the center of gravity of drought-prone areas has moved to the southwest. (4) NDVI and altitude are the main drivers of soil drought spatial differentiation, and the multi-factor interaction shows a nonlinear enhancement effect. Among them, the parameter thresholds such as NDVI > 0.512 and altitude −32~16 m have a significant inhibitory effect on soil drought. This study can make a contribution to improving water resource management and increasing agricultural productivity in the region. Full article