Search Results (1,497)

Fig tree growth and development are highly susceptible to variations influenced by abiotic factors and management practices, including fertilization and training systems. This study aimed to evaluate the effect of organic and mineral fertilization on the photosynthetic, nutritional, and productive efficiency of fig trees subjected to different training systems in semi-arid regions. The experimental design was randomized blocks in a 5 × 4 factorial scheme, with three blocks and three plants per plot. The treatments consisted of five fertilizer sources (mineral fertilizer (NPK) applied at a dose of 126 g N, 90 g P, and 90 g K per plant (M); and four organic sources—cattle manure (CM), organic compost (OC), chicken litter (CL), and sheep manure (SM), all applied at a dose of 10 kg per plant); and four types of training systems (plants with two branches (2B), three branches (3B), four branches (4B), and espalier). Our results demonstrated that the mineral fertilizer (M) and chicken litter (CL) treatments yielded the highest results, particularly in photosynthetic performance. Fig trees fertilized with mineral fertilizer and subjected to the 3B system showed enhanced net photosynthesis (36.96 µmol m⁻² s⁻¹) and, consequently, higher productivity of 21.28 t ha⁻¹. Similarly, plants fertilized with chicken litter (CL) under the 4B system produced comparable results. These findings demonstrate that the use of mineral and organic fertilizers, combined with an appropriate training system, is a viable strategy for optimizing fig productivity and profitability in semi-arid conditions. Full article

Soil salinity and water scarcity are major challenges limiting agricultural productivity in arid and semi-arid regions. Quinoa (a climate-resilient crop) offers potential for sustainable food production under these harsh conditions; however, its growth and yield are often constrained by salt and water stress. This study evaluated the role of plant-growth-promoting rhizobacteria (PGPR) in enhancing Chenopodium quinoa Willd performance under deficit irrigation (DI) with saline water. A greenhouse pot experiment was conducted with four irrigation levels (40%, 60%, 80%, and 100% of the growth water requirement, GWR) and two water qualities (fresh water, EC = 0.8 dS m⁻¹; and saline water, EC = 6.0 dS m⁻¹), each tested with and without PGPR inoculation. The results showed that PGPR application significantly (p < 0.05) improved quinoa tolerance to salinity, leading to higher biomass, yield, and crop water productivity (CWP) under saline irrigation. Yield reductions were most severe at 40% GWR (53.9% and 82.6% under saline and fresh water, respectively), but PGPR inoculation mitigated yield losses, with increases of 83.3% and 130.8% under 40% and 100% GWR saline irrigation, respectively. Notably, PGPR did not show a clear effect with freshwater irrigation. In addition, inoculated plants exhibited improved nutrient uptake and reduced heavy metal accumulation. Overall, PGPR demonstrated strong potential to enhance salinity resilience and water-use efficiency in quinoa. Future studies should extend these findings under field conditions and investigate the long-term impacts of PGPR on sustainable crop production in saline- and water-limited environments. Full article

Large-scale vegetation loss induced by surface coal mining constitutes a critical driver of regional ecological degradation. However, the applicability of existing change detection methodologies based on remote sensing within complex mining areas under diverse climatic conditions remains systematically unverified. To address this gap and reveal nationwide disturbance patterns, this study systematically evaluates the performance of two algorithms—Continuous Change Detection and Classification (CCDC) and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr)—in identifying vegetation loss across three major climatic zones of China (the humid, semi-humid, and semi-arid zones). Based on the optimal algorithm, the vegetation loss year and loss magnitude across all of China’s surface coal mining areas from 1990 to 2020 were accurately identified, enabling the reconstruction of the comprehensive, nationwide spatio-temporal pattern of mining-induced vegetation loss over the past 30 years. The results show that: (1) CCDC demonstrated superior stability and significantly higher accuracy (OA = 0.82) than LandTrendr (OA = 0.31) in identifying loss years across all zones. (2) The cumulative vegetation loss area reached 1429.68 km², with semi-arid zones accounting for 86.76%. Temporal analysis revealed a continuous expansion of the loss area from 2003 to 2013, followed by a distinct inflection point and decline during 2014–2016 attributable to policy-driven regulations. (3) Further analysis revealed significant variations in the average magnitude of loss across different climatic zones, namely semi-arid (0.11), semi-humid (0.21), and humid (0.25). These findings underscore the imperative for region-specific restoration strategies to ensure effective conservation outcomes. This study provides a systematic quantification and analysis of long-term, nationwide evolution patterns and regional differentiation characteristics of vegetation loss induced by surface coal mining in China, offering critical support for sustainable development decision-making in balancing energy development and ecological conservation. Full article

Pharmaceuticals are emerging contaminants of global concern, but their occurrence and removal in semi-arid regions such as Algeria remain poorly documented. This study provides the first systematic evaluation of pharmaceutical and physicochemical parameters in two wastewater treatment plants (WWTPs) in Mascara: an activated sludge system (WWTP-1) and an aerated lagoon system (WWTP-2). Ten pharmaceuticals of different therapeutic classes were quantified using UPLC-HR-QTOF-MS in influent, effluent, and sludge samples, and removal efficiencies were compared using ANOVA and Principal Component Analysis (PCA). WWTP-1 showed higher efficiency, with >90% removal of COD, BOD₅, and ammonium, and near-complete elimination of sulfamethoxazole (99.9%) and atenolol (94%). In contrast, WWTP-2 achieved only moderate reductions (69% COD, 51% BOD₅) and low pharmaceutical removal, with negative efficiencies for persistent compounds such as carbamazepine, diclofenac, and ibuprofen. Weak correlations between macro- and micropollutants indicated that traditional indicators cannot predict pharmaceutical behavior. This work is the first to integrate physicochemical monitoring, pharmaceutical profiling, and multivariate analysis in Algerian WWTPs. The findings highlight the limitations of conventional treatment in semi-arid conditions and provide a critical baseline for adopting advanced technologies to mitigate pharmaceutical pollution in North Africa. Full article

In Africa, the folkloric practices involving plant-based remedies play a crucial role in livestock farming, often attributed to the limited access to modern veterinary services. The use of Acacia species (including those reclassified as Vachellia species) in ethnoveterinary medicine has garnered increasing interest due to their high protein content and medicinal (including anti-parasitic) properties, offering a sustainable source of fodder particularly in arid and semi-arid regions. However, scientific assessment of their efficacy and safety remains limited. This systematic review examines the ethnoveterinary uses, biological efficacy and safety of Acacia species across Africa. A literature search was conducted using PubMed, Google Scholar and Scopus, yielding 519 relevant studies published between 2001 and 2024. After applying the inclusion and exclusion criteria, 43 eligible studies were analyzed based on their relevance, geographical location and livestock disease applications. Plants of the World online database was used to validate the names of the species and authority. Ethiopia had the highest usage of Acacia species (25%), then Nigeria (20%) followed by both South Africa (15%) and Namibia (15%). Vachellia nilotica (Acacia nilotica) was the most frequently cited species (26.3%), followed by Vachellia karroo (Acacia karroo) (15.8%). Ethnobotanical records indicate that the different Acacia species have been traditionally used to treat conditions such as diarrhea, wound infections and complications such as retained placenta. Pharmacological studies corroborate the therapeutic benefits of Acacia species with evidence of their antimicrobial, anti-inflammatory, antioxidant and anthelmintic effects, though some toxicity concerns exist at high dosages. The systematic review revealed the efficacy and safety (to some extent) of Acacia species in livestock disease management, emphasizing their potential integration into veterinary medicine. However, the dearth of in vivo studies underscores the need for pre-clinical and clinical trials to establish safe and effective dosages for use in livestock. 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

Pearl millet is primarily grown under rainfed conditions in Sub-Saharan Africa. Early droughts are prevalent in the Sahel region, where pearl millet is widely cultivated, and they severely impact pearl millet growth and productivity by affecting plant stand and reducing plant density in the field. Consequently, genetic improvement for early drought tolerance is a promising strategy to enhance productivity in these regions. This study aims to identify pearl millet lines that are tolerant to water stress at the seedling stage by assessing various water-stress-tolerance traits. Two hundred pearl millet inbred lines were screened for drought tolerance by inducing water stress with polyethylene glycol 6000 (PEG 6000) in the laboratory. The experiment was repeated in the greenhouse using pot screening. The experimental design was an alpha lattice with 10 entries × 20 blocks in two replications. Four treatments (0 g/L, 115 g/L, 235 g/L, 289 g/L) were applied in the laboratory: one control and three concentrations of PEG 6000. Control and stress were applied in the greenhouse. Data were collected on germination rate and growth parameters, including root and seedling length, leaf length and width, and chlorophyll content. Results revealed significant differences among the pearl millet inbred lines under both drought and well-watered conditions. The inbred lines IP-16403 and IP-18062 were the most tolerant in both the greenhouse and laboratory. Water stress significantly reduced plant growth, although an increase in root length was observed in some lines. The number of days to 50% emergence was positively and strongly correlated with survival time (+0.45), while leaf width was negatively correlated with survival time (−0.29) and water stress tolerance (−0.37). The drought-tolerant and drought-susceptible pearl millet inbred lines identified in this study provide valuable genetic resources for enhancing pearl millet productivity in arid and semi-arid environments, especially in the face of unpredictable climate variability. Full article

Agricultural drought, increasingly intensified by climate change, poses a significant threat to food security and water resources in semi-arid regions, including Türkiye’s Konya Closed Basin. This study evaluates six satellite-derived indices—Vegetation Health Index (VHI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Precipitation Condition Index (PCI), Evapotranspiration Condition Index (ETCI), and Soil Moisture Condition Index (SMCI)—to monitor agricultural drought (2001–2024) and proposes a drought vulnerability map using a novel Drought Vulnerability Index (DVI). Integrating Moderate Resolution Imaging Spectroradiometer (MODIS), Climate Hazards Center InfraRed Precipitation with Station (CHIRPS), and Land Data Assimilation System (FLDAS) datasets, the DVI combines these indices with weighted contributions (VHI: 0.27, ETCI: 0.25, SMCI: 0.22, PCI: 0.26) to spatially classify vulnerability. The results highlight severe drought episodes in 2001, 2007, 2008, 2014, 2016, and 2020, with extreme vulnerability concentrated in the southern and central basin, driven by prolonged vegetation stress and soil moisture deficits. The DVI reveals that 38% of the agricultural area in the basin is classified as moderately vulnerable, while 29% is critically vulnerable—comprising 22% under high vulnerability and 7% under extreme vulnerability. The proposed drought vulnerability map offers an actionable framework to support targeted water management strategies and policy interventions in drought-prone agricultural systems. Full article

Climate change poses a significant threat to crop production, particularly in tropical and semi-arid regions. Sorghum (Sorghum bicolor (L.) Moench), a resilient C₄ cereal, has high photosynthetic efficiency and abiotic stress tolerance, making it a key crop for food, fodder, and feed security. This study evaluated agronomic and physiological traits influencing the yield performance of 20 sorghum varieties under field conditions in Kerala, India. The data were analyzed using a randomized block design (RBD) in GRAPES software, and a principal component analysis was performed in R. Variety CSV 17 exhibited the highest grain yield (GY) (3760 kg ha⁻¹) and harvest index (HI) (43), with early flowering, early maturity, a high chlorophyll content (CHL), and minimal nitrogen (N), phosphorus (P), and potassium uptake. Conversely, CSV 20 produced the highest stover yield (22.5 t ha⁻¹), associated with greater leaf thickness (LT), lower canopy temperature, taller plant height (PH), increased leaf number (LN), and extended maturity. Leaf temperature (T_leaf) was negatively correlated with the quantum yield of photosystem II (ΦPSII) and panicle length (PL), which were strong predictors of grain weight. The principal component analysis revealed that PC1 and PC2 explained 21% and 19% of the variation in the grain and stover yield, respectively. Hierarchical partitioning identified the potassium content (K%), CHL, T_leaf, leaf area index (LAI), ΦPSII, and LT as key contributors to the GY, while the SY was primarily influenced by the LN, nitrogen content (N%), maturity duration, PH, and ΦPSII. These findings highlight the potential of exploiting physiological traits for enhancing sorghum productivity under summer conditions in Kerala and similar environments. Full article

The quantitative assessment of acoustic rain enhancement technology is highly significant for improving the ecological environment. A scientific and accurate evaluation of its operational effects provides an important basis for continued government and public support and investment in artificial weather modification activities. To effectively analyze the effects of acoustic rain enhancement on the vegetation of grassland ecosystems in arid and semi-arid areas and to clarify its mechanism, this study constructed eight vegetation indices based on Sentinel-2 satellite data. A comprehensive assessment of the changes in vegetation within the grassland ecosystem of the experimental zone was conducted by analyzing spatiotemporal distribution patterns, double-ratio analysis, and difference value comparisons. The results showed that (1) following the acoustic rain enhancement experiment, vegetation growth improved significantly. The mean values of all eight vegetation indices increased more substantially than before the experiment, with kNDVI showing the most notable gain. The proportion of the zone with kNDVI values greater than 0.417 increased from 52.62% to 71.59%, representing a relative increase of 36.05%. (2) The rain enhancement experiment significantly raised the values of eight vegetation indices: kNDVI increased by 0.042 (18.68%), ARVI by 0.043 (18.67%), and the remaining indices also increased to varying degrees (9.51–12.34%). (3) Vegetation improvement was more pronounced in areas closer to the acoustic rain enhancement site. Under consistent climate conditions, vegetation growth in the experimental zone showed significant enhancement. This study demonstrates that acoustic rain enhancement technology can mitigate drought and low rainfall, improve grassland ecosystem services, and provide a valuable foundation for ecological restoration and aerial water resource utilization in arid and semi-arid regions. Full article

This study evaluated ten drought indices focusing on their ability to monitor drought events in Marathwada, a semi-arid region of India. High-resolution gridded monthly total precipitation data for 75 years (1950–2024) from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used to evaluate the drought indices. These indices were computed across six timescales: 1, 3, 4, 6, 9, and 12 months. A Generalized Autoregressive Conditional Heteroscedastic (GARCH) model was employed to detect temporal volatility in precipitation, followed by a second-order geospatial autocorrelation eigenfunction eigendecomposition using Global Moran’s Index statistics to geolocate both aggregated and non-aggregated precipitation locations. The performance of drought indices was assessed using non-parametric Spearman’s correlation to identify the strength, direction, and similarity of regional-specific drought events. The temporal lag interdependence between meteorological and agricultural droughts was assessed using a non-parametric Spearman’s cross correlation function (SCCF). The findings revealed that the GARCH model with a skewed Student’s t distribution effectively captured conditional temporal volatility and asymptotic behavior in the precipitation series. The model’s sensitivity enabled the incorporation of temporal fluctuations related to droughts and extreme meteorological events. The Bhalme and Mooley Drought Index (BMDI-6) and Z-Score Index (ZSI-6) were the most applicable indices for drought monitoring. Spearman’s cross-correlation analysis revealed that meteorological droughts influenced agricultural droughts with a time lag of up to 4 months. Full article

Environmental degradation and soil desertification are among the most severe environmental issues of recent decades worldwide. Over time, these processes have led to increasingly extreme and highly dynamic climatic conditions. In Brazil, the Northeast Region is characterized by semi-arid and arid areas that exhibit high climatic variability and are extremely vulnerable to environmental changes and pressures from human activities. The application of geotechnologies and geographic information system (GIS) modeling is essential to mitigate the impacts and pressures on the various ecosystems of Northeastern Brazil (NEB), where the Caatinga biome is predominant and critically threatened by these factors. In this context, the objective was to map and assess the spatiotemporal patterns of land use and land cover (LULC), detecting significant trends of loss and gain, based on surface reflectance data and precipitation data over two decades (2000–2019). Remote sensing datasets were utilized, including Landsat satellite data (LULC data), MODIS sensor data (surface reflectance product) and TRMM data (precipitation data). The Google Earth Engine (GEE) software was used to process orbital images and determine surface albedo and acquisition of the LULC dataset. Satellite data were subjected to multivariate analysis, descriptive statistics, dispersion and variability assessments. The results indicated a significant loss trend over the time series (2000–2019) for forest areas (Z_MK = −5.872; Tau = −0.958; p < 0.01) with an annual loss of −3705.853 km² and a total loss of −74,117.06 km². Conversely, farming areas (agriculture and pasture) exhibited a significant gain trend (Z_MK = 5.807; Tau = 0.947; p < 0.01), with an annual gain of +3978.898 km² and a total gain of +79,577.96 km², indicating a substantial expansion of these areas over time. However, it is important to emphasize that deforestation of the region’s native vegetation contributes to reduced water production and availability. The trend analysis identified an increase in environmental degradation due to the rapid expansion of land use. LULC and albedo data confirmed the intensification of deforestation in the Northern, Northwestern, Southern and Southeastern regions of NEB. The Northwestern region was the most directly impacted by this increase due to anthropogenic pressures. Over two decades (2000–2019), forested areas in the NEB lost approximately 80.000 km². Principal component analysis (PCA) identified a significant cumulative variance of 87.15%. It is concluded, then, that the spatiotemporal relationship between biophysical conditions and regional climate helps us to understand and evaluate the impacts and environmental dynamics, especially of the vegetation cover of the NEB. Full article

This paper presents a comprehensive methodology for assessing the resilience of landscapes to human impact in western Kazakhstan. The approach developed is based on integrating remote sensing data (MODIS, SMAP, NDVI and NDSI), the results of field surveys, and multi-criteria analysis methods in a GIS environment. The assessment covered over 50 landscape types and subtypes using ten key indicators reflecting climatic, geomorphological, soil, hydrological, and biotic characteristics. These indicators were normalised, aggregated and summarised to create an integral index of landscape resilience, which allowed four resilience classes to be identified, ranging from highly vulnerable to relatively resilient. The spatial analysis revealed that over 60% of the region’s territory is classified as high-vulnerability, predominantly within semi-desert and desert zones, which are vulnerable to climatic risks, degradation of vegetation cover and human activity. Verification of the results based on remote monitoring data for the period 2000–2024 and field observations confirmed the reliability of the developed methodology. The results obtained allow the identification of areas prioritised for environmental monitoring, restoration and sustainable land use in arid climate conditions. A plan of measures for regulation and restoration of ecosystems and spatial planning tools are proposed. The obtained data can be used for the development of regional environmental policy and sustainable land use strategies. Full article

Largescale vegetation reconstruction projects in the western and northern parts of China, along with climate change and increased humidity, have significantly boosted fractional vegetation cover (FVC) in the Mu Us Desert. However, this increase may impact the area’s vulnerability to drought stress. Here, we assessed the area’s susceptibility to hydrometeorological drought by analyzing the maximum correlation coefficients (MCC) derived from the spatiotemporal relationships between FVC and estimates of standardized precipitation evapotranspiration index (SPEI) for the area. The results of the study were as follows: (1) FVC exhibited an increasing trend throughout the growing seasons from 2003 to 2022. Although the region experienced an overall wetting trend, drought events still occurred in some years. MCC-values were predominantly positive across all timescales, suggesting that vegetation generally responded favorably to drought conditions. (2) The order of response of land covertype to drought, from greatest to lowest, was grassland, cultivated land, forestland, and sand land. Cultivated land and grassland exhibited heightened sensitivity to short-term drought; forestland and sand land showed greater sensitivity to long-term drought. (3) With a high FVC, the response of grassland and sand land to drought was significantly enhanced, whereas the response of cultivated land and forestland was less noticeable. (4) Low FVC grassland and sand land have not yet reached the VCCSW threshold and can support moderate vegetation restoration. In contrast, forestland and cultivated land exhibit drought sensitivity regardless of FVC levels, indicating that increasing vegetation should be approached with caution. This research offers a method to evaluate the impact of drought stress on ecosystem stability, with findings applicable to planning and managing vegetation cover in arid and semiarid regions globally. Full article

The Lower Submember of the Second Member of the Lianggaoshan Formation (LGS2-LS) in the Fuling area, southeastern Sichuan Basin, represents the deepest lacustrine depositional stage of the formation and constitutes an important target for shale oil and gas exploration. Based on core observations, thin-section petrography, X-ray diffraction, geochemical analyses, and sedimentary facies interpretation from representative wells, this study characterizes the lithofacies types, sedimentary environments, and depositional evolution of the LGS2-LS. Results show that the LGS2-LS is dominated by clay–quartz assemblages, with average clay mineral and quartz contents of 44.6% and 38.8%, respectively, and can be subdivided into shallow and semi-deep lacustrine subfacies comprising eight microfacies. Geochemical proxies indicate alternating warm-humid and hot-arid paleoclimatic phases, predominantly freshwater conditions, variable redox states, and fluctuations in paleoproductivity. Sedimentary evolution reveals multiple transgressive–regressive cycles, with Sub-layer 6 recording the maximum water depth and deposition of thick organic-rich shales under strongly reducing conditions. The proposed sedimentary model outlines a terrigenous clastic lacustrine system controlled by lake-level fluctuations, transitioning from littoral to shallow-lake to semi-deep-lake environments. The distribution of high-quality organic-rich shales interbedded with sandstones highlights the LGS2-LS as a favorable interval for shale oil and gas accumulation, providing a geological basis for further hydrocarbon exploration in the southeastern Sichuan Basin. Full article