Search Results (222)

The aim of this study was to assess the long-term adaptation and growth performance of 50 species introduced in 1991 on the island of Porto Santo, Madeira Archipelago, in order to guide afforestation and soil restoration under the island’s arid conditions, especially in biosphere reserves. The experiment was conducted in Alentejo, Pico Juliana and Matinho, three sites with different types of elevation, soil and exposure. A total of 502 experimental units (five plants each) were established with a completely randomized design in the three sites in 1991 to test the adaptation of 50 species from Mediterranean, African, Australian and American dry climates. Plants were grown in local nursery conditions and planted in rows with 1 × 4 m spacing. Soil properties were analyzed, and survival and growth (height and stem diameter) were monitored in 1991, 1992 and 2025. An analysis of variance was performed for the whole experiment, with the three sites showing significant differences in survival and height among species and sites thirty-four years after the planting. Some species showed high survival and growth, such as Pinus halepensis, Eucalyptus sideroxylon and Casuarina cunninghamiana. Others, like Schinus terebinthifolius and Thevetia neriifolia, showed good adaptation, and invasive behavior at the best sites, but their performance was strongly dependent on site conditions, with Alentejo being the most limiting site. This study demonstrates the long-term value of forest experiments and of long-term monitoring, providing rare data on species adaptation under semi-arid insular conditions. The findings support future afforestation strategies focusing on ecological suitability and invasiveness risk. Full article

Red-bed desertification represents a critical form of land degradation in subtropical regions, yet the coupled soil–vegetation processes remain poorly understood. This study integrates Sentinel-2 vegetation indices with soil fertility gradients to assess vegetation–soil interactions in the Nanxiong Basin of South China. By combining Normalized Difference Vegetation Index (NDVI)-based vegetation classification with comprehensive soil property analyses, we aim to uncover the spatial patterns and driving mechanisms of degradation. The results revealed a clear gradient from intact forests to exposed red-bed bare land (RBBL). NDVI classification achieved an overall accuracy of 77.8% (κ = 0.723), with mixed forests being identified most reliably (97.1%), while Red-Bed Bare Land (RBBL) exhibited the highest omission rate. Along this gradient, soil organic matter, available nitrogen, and phosphorus declined sharply, while pH shifted from near-neutral in forests to strongly acidic in bare lands. Principal component analysis (PCA) identified a dominant fertility axis (PC1, explaining 56.7% of the variance), which clustered forested sites in nutrient-rich zones and isolated RBBL as the most degraded state. The observed vegetation–soil pattern aligns with a “weathering–transport–exposure” sequence, whereby physical disintegration and selective erosion during monsoonal rainfall drive organic matter depletion, soil thinning, and acidification, with human disturbance further accelerating these processes. To our knowledge, this study is the first to directly couple PCA-derived soil fertility gradients with vegetation patterns in red-bed regions. By integrating vegetation indices with soil fertility gradients, this study establishes a process-based framework for interpreting red-bed desertification. These findings underscore the utility of remote sensing, especially NDVI classification, as a powerful tool for identifying degradation stages and linking vegetation patterns with soil processes, providing a scientific foundation for monitoring and managing land degradation in monsoonal and semi-arid regions. Full article

The present study aimed to determine the Genotype × Environment interaction (GEI), yield stability, and agronomic performance of 24 “Pinto” bean lines under semi-arid conditions in Central-West Mexico. All the lines possess a slow-darkening seed coat, a trait that prolongs visual quality and increases market value. The lines, which exhibit an indeterminate prostrate growth habit, were evaluated in three contrasting environments: irrigated, rainfed, and drought-stressed. A combined analysis of variance, Tukey’s test, and the additive main effects and multiplicative interaction (AMMI 2) model were applied to assess seed yield and agronomic traits. Average seed yield declined markedly across environments, from 2279 kg ha⁻¹ under irrigation to 593 kg ha⁻¹ under drought stress, with different lines performing best in each environment. AMMI 2 biplot analysis showed that the first two principal components explained 100% of GEI variability for seed yield, dry shoot biomass, total biomass, harvest index, pods per plant, and seeds per pod. Both genetic and environmental effects were significant, with notable GEI patterns. Despite pronounced environmental influence, several lines exhibited stable performance across environments. Line 11 consistently combined high yield and stability, positioning it as a strong candidate for cultivar registration and as a parent in breeding programs targeting semiarid regions. These results underscore the importance of multi-environment evaluation for identifying genotypes with broad or specific adaptation, contributing to genetic improvement and sustainable bean production under variable moisture regimes. Full article

Rangelands of arid and semi-arid regions are facing severe issues due to climate change, desertification, and overgrazing, which are subsequently leading towards the degradation of native shrubs. Several approaches have been practiced so far in the restoration of rangeland; however, optimization of irrigation practice is considered to be a promising strategy to restore native shrubs’ growth, by improving water distribution and plants’ physiological growth. In this study, a field experiment was conducted across three semi-arid sites, including Al-Tamiryyat (Al-Jouf), Al-Sahwa (Al-Madina), and Al-Fuhaihil (Thadiq), to assess the impact of drip and sprinkler irrigation on the growth dynamics of region-specific native shrubs. Plant growth parameters like plant height, stem diameter, and crown size ratio were measured monthly over 12 months (June 2024–June 2025). Analysis of Variance (ANOVA) revealed that drip irrigation significantly enhanced growth performance compared to sprinkler and control treatments across all sites. Plant height increased up to 142% in Haloxylon persicum, while the stem diameter of Vachellia gerrardii expanded by nearly 198% under drip irrigation. Crown size ratio exhibited site- and species-dependent temporal patterns, with drip irrigation consistently supporting greater canopy expansion. Meanwhile, the interactions between irrigation type, species, and time were significant at most sites, indicating temporal and contextual variability in growth dynamics. Overall, these findings demonstrate that drip irrigation has substantial potential for the rangeland restoration in water-limited environments. However, future studies should assess the long-term impact of irrigation practices for a better understanding of soil–plant–water interactions to ensure the restoration of rangelands in arid regions. Full article

The rapid expansion of photovoltaic installations in arid and semi-arid regions has altered regional water–heat regimes, triggering complex responses in vegetation recovery and soil processes. However, systematic assessments of ecological restoration under varying operational durations and microenvironmental interactions remain insufficient. Therefore, this study examines photovoltaic power stations operating for 1, 7, and 13 years within China’s temperate desert regions, alongside undeveloped control areas, to compare differences across four microenvironments: the front eave of photovoltaic panels (FP), underneath photovoltaic panels (UP), back eave of photovoltaic panels (BP), and interval between photovoltaic panels (IP). Combining analysis of variance, correlation analysis, variance partitioning analysis (VPA), and generalised additive models (GAMs), the study evaluates the coupling mechanisms between vegetation and soil. The results indicate that operational duration significantly enhances vegetation cover, biomass, and species diversity, with the 13 year operational zone demonstrating optimal restoration outcomes. Microenvironmental variations were pronounced, with vegetation and soil quality in the front eave zone surpassing other areas, while the inter-panel zone exhibited the weakest recovery. Key soil factors shifted with recovery stages: early-stage vegetation showed heightened sensitivity to soil water content (SWC), whereas later stages relied more heavily on soil organic matter (SOM) and nutrient supply. Variation Partial Analysis (VPA) revealed that soil factors in the 13 year operational zone accounted for 71.9% of the variation in vegetation cover. The operational lifespan of photovoltaic power stations, microenvironmental variations, and key soil factors collectively drive the restoration of thermophilic desert vegetation. This research reveals phased regulatory mechanisms during the restoration process, providing scientific grounds for optimising photovoltaic layouts and enhancing desert ecosystem stability. 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

Drought stress critically constrains agricultural productivity in arid and semi-arid regions, necessitating the development of drought-tolerant crop varieties for sustainable food security. This study evaluated drought tolerance in 222 foxtail millet (Setaria italica) germplasms from diverse Chinese agroecological zones from 2021–2023 at a specialized identification site in Xinjiang. Field experiments used a randomized complete block design comparing normal irrigation (3000 m³/ha) with drought stress (1800 m³/ha) across 12 morpho-agronomic traits including plant height, spike characteristics, biomass, and yield components. Drought stress significantly reduced all parameters, with yield exhibiting the highest sensitivity (drought tolerance coefficient = 0.58). Principal component analysis indicated that the first three components explained 82.70% of phenotypic variance, with yield-related parameters contributing the most to genotypic differentiation. Integrated evaluation using comprehensive drought tolerance coefficient (DTC), drought resistance index (DRI), and D-values classified germplasms into five categories: highly resistant (4.50%), resistant (11.71%), moderately resistant (57.21%), sensitive (16.22%), and highly sensitive (10.36%). Correlation and stepwise regression analyses identified five critical indicators: stem basal thickness, single plant biomass, spike weight, grain weight per spike, and yield. The predictive model demonstrated exceptional accuracy (R² = 0.9998), enabling efficient screening using the targeted traits. The elite germplasms T125 (92) and Baogu 23 (135) consistently ranked as the most drought-tolerant across all methods. These findings establish a robust methodological framework for evaluating drought tolerance in foxtail millet and provide practical selection criteria for developing climate-resilient cultivars. The identified germplasms and evaluation indices significantly contribute to agricultural sustainability in water-limited environments, supporting food security in regions that are increasingly affected by climate-induced drought stress. Full article

Performance analysis in elite football still faces significant challenges: traditional descriptive statistics often fail to capture tactical adaptability, and African teams remain underrepresented in the scientific literature despite achieving historic breakthroughs. The FIFA World Cup Qatar 2022 marked a turning point, with Morocco becoming the first African nation to reach the semi-finals. This study systematically analyzed the tactical, physical, and structural performance of the Moroccan national team across seven matches using official FIFA post-match reports. A three-level methodological framework was adopted: (i) descriptive analysis of key performance indicators (KPIs); (ii) visual profiling through radar charts, heatmaps, and passing networks; and (iii) exploratory modelling using principal component analysis (PCA) and clustering. Results revealed consistent defensive organization, low ball possession (<40% in five matches), and effective counter-attacking transitions, with pressing peaks against Spain (288 actions) and France (299 actions). PCA explained 76% of the variance, identifying two principal axes (physical intensity vs. technical mastery; verticality vs. build-up play) and clustering distinguished three match types: low-block defensive games, transition-oriented games, and open matches. These findings highlight Morocco’s tactical adaptability and sustained physical commitment. The study demonstrates how AI-enhanced analytics and multidimensional data visualization can uncover latent performance patterns and support evidence-based decision-making. Practical implications include actionable insights for performance analysts and coaching staff, particularly as Morocco prepares for the 2025 Africa Cup of Nations and the FIFA World Cups in 2026 and 2030. This integrative approach can serve as a model for federations seeking data-driven performance optimization in elite football. Full article

A pivotal food crop in arid and semi-arid zones, proso millet boasts remarkable economic value, making the breeding of stable high-yield varieties critical for industrial sustainability. This study employed a randomized complete block design to conduct a two-year multi-environment trial on nine new varieties across six representative spring-sown test regions in China. Analytical tools, including additive main effects and multiplicative interaction (AMMI) biplots, AMMI stability values (ASV), genotype and genotype × environment (GGE) models, and genotype by yield–trait (GYT) biplots were utilized to assess genotype–environment (G × E) interactions and screen superior genotypes. AMMI variance analysis showed extremely significant effects of genotype, environment, and G × E on yield traits (p < 0.01). G × E principal component analysis identified JS8, PS3, PS6, and PM4 as dominant genotypes. Based on ASV indices, varietal stability rankings were PS5 > YS13 > JS8 > PS3 > PS6 > PM4 > others. GGE analysis indicated PM4 had the broadest adaptability across tested environments, while JS15 exhibited specific adaptability in Datong. Huairen and Shuozhou were validated as ideal testing environments via an ideal environment plot. GYT biplots further confirmed that YS13, JS15, PS3, and PM4 excelled in comprehensive yield–trait combinations. These findings offer a scientific foundation for ecological adaptability evaluation, breeding material selection, and commercial variety promotion. Full article

This paper propose a practical field-based screening protocol for evaluating the risk of hamstring injury. This is done by discerning the most important factors that better explain the underlying structure among various measurements. Following a cross-sectional study design, ninety-nine professional and semi-professional football players were assessed at the team’s facilities during the preseason period. The collected data included aspects of demographic characteristics; previous injuries; athlete sense of burnout (Athlete Burnout Questionnaire (ABQ)); hamstring (HS) flexibility (passive single leg raise test); isometric hamstring strength (make and brake test); isometric quadriceps strength; single-leg triple hop for distance; endurance of the core muscles (prone bridge, side bridge and Biering–Sørensen tests); and hamstring strength endurance (single leg hamstring bridge test). Subsequently, Exploratory Factor Analysis was performed. Following a summarized dimension reduction process, the twenty-three assessment variables were grouped into a parsimonious model of six main risk factors. Specifically, the resulting model explains 55.7% of the total variance, comprising HS and core endurance (20.2% of the variance), HS strength (12.8%), previous injuries (8.9%), ABQ (5.8%), lower limb strength (4.1%), and strength limb symmetry (3.8%). The proposed model provides a practical protocol, facilitating sports scientists in evaluating the risk for HI in the highly complex reality of field-based situations. Full article

Background/Objectives: High-quality prognostic communication in pediatric oncology is essential to support informed decision making, foster trust, and honor goals of care. While families differ in their preferences for how prognosis is discussed, clinicians often rely on assumptions rather than directly eliciting these preferences, risking misalignment and distress. To address this gap, we aimed to characterize patient, parent, and oncologist perspectives on key variables, experiences, and circumstances that influence their preferences for prognostic communication. Methods: Semi-structured interviews were conducted with 85 participants (25 patients aged 12–25, 40 parents, and 20 oncologists) to elicit their preferences for prognostic communication and the reasons behind these preferences. Rapid analysis was conducted to generate themes and identify patterns and variances across participant cohorts. Results: Four distinct themes underpinning prognostic communication preferences were described by patients, parents, and oncologists: (1) personality, ideals, and values; (2) general life experiences; (3) prior interactions with the medical system; and (4) faith. Participants emphasized that personal identity and prior experiences significantly shaped how they wished to receive prognostic information. Clinicians and parents often linked preferences to core aspects of their professional or caregiver identities. Conclusions: Understanding the individualized factors shaping communication preferences can guide clinicians in tailoring prognostic discussions. Open-ended inquiry into identity, personal values, and past experiences can foster respectful, personalized communication in pediatric oncology. Further research is needed to determine best practices for prompting discussion about prognostic communication preferences that incorporates the reasons underpinning these preferences. Full article

The aim of this study was to examine teachers’ belief levels regarding education for sustainable development (ESD), to identify the factors behind these beliefs, and to reveal their suggestions for improving the quality of ESD. The study employed an explanatory sequential mixed-methods design. Data were collected from 409 teachers working at primary and secondary schools in Türkiye through the “Beliefs About Education for Sustainable Development Scale” and semi-structured interviews. The quantitative data were analyzed using an independent samples t-test, one-way Analysis of Variance (ANOVA), and Pearson product-moment correlation, and the qualitative data were analyzed through content analysis. The results indicated that the participants’ beliefs in ESD practices were high, but these beliefs were negatively affected by challenges due to SD goals, policymakers, students, and parents. In addition, the participants made recommendations for improving the quality of ESD to policymakers, the Turkish Council of Higher Education, the Ministry of National Education, and their colleagues. The findings of the study present significant implications for policymakers and educators for more effective implementation of ESD in the education system. Full article

Background: Exercise rehabilitation is a crucial component of stroke recovery, particularly for patients transitioning to home or community settings. However, there is currently a lack of self-reported scales designed to measure the level of engagement in exercise rehabilitation among patients with stroke. Objective: To develop and validate psychometric properties of the Engagement in Exercise Rehabilitation Scale for patients with stroke. Methods: The initial item pool was extracted from a literature review and a semi-structured interview with patients with stroke. The development and refinement of the items underwent expert consultation and cognitive interviews with patients with stroke. The items primarily covered patients’ perceptions, emotional attitudes, and specific engagement behaviors regarding exercise rehabilitation in home or community settings. A total of 260 patients with stroke were selected to test the reliability and validity. The psychometric proprieties test included construct validity, content validity, criterion-related validity, exploratory factor analysis, internal consistency reliability, test–retest reliability, and split-half reliability. Results: The final version of the Engagement in Exercise Rehabilitation Scale comprised 20 items. The scale’s content validity index was determined to be 0.976, while the item-content validity indices ranged from 0.833 to 1.000. Results from exploratory factor analysis indicated that this scale is unidimensional, with a cumulative variance contribution rate of 79.3%. The test–retest reliability of the scale was found to be 0.879, its split-half reliability was measured at 0.980, and its Cronbach’s α coefficient was calculated to be 0.986. Conclusion: The Engagement in Exercise Rehabilitation Scale for patients with stroke demonstrates accepted reliability and validity. The accuracy and generalizability of this scale necessitate further validation through additional large-sample studies involving diverse populations across multiple centers. Full article

The spatial distribution of soil heavy metals was influenced by both natural and anthropogenic factors, and the multi-scale characteristics of heavy metals played a key role in analyzing their influencing factors. Taking arsenic (As) of an oil refining site in Shandong as an example, the As was firstly decomposed into intrinsic mode functions (IMFs) at different scales and a residual using two-dimensional empirical mode decomposition (EMD). Secondly, the spatial variation scales of As, the IMFs, and the residual were quantified by their semi-variograms, respectively. Finally, local spatial correlation analysis and random forest model were employed to analyze the multi-scale features of As, the IMFs, the residual, and environmental variables. The results indicated that the As was decomposed into IMF1, IMF2, IMF3, and a residual using the two-dimensional EMD method, and the corresponding spatial ranges were 72.60 m, 159.30 m, 448.00 m, and 592.36 m, respectively. IMF3 had the highest percentage of variance with a value of 57.56%, indicating that the spatial variation of As was mainly concentrated on a large scale. There were correlations between As and aspect and land use type. However, after the scale decomposition of two-dimensional EMD, there were significant correlations between oil residue thickness and IMF1, land use type and IMF3, land use type, and aspect and residual, respectively. The IMFs and residual had a significant scale–location dependence on environment variables, and the impact of anthropogenic factors on As was mainly reflected at the small and medium scales, while the influence of natural factors was mainly reflected at the large scale. The developed method can provide a methodological framework for the spatial analysis and pollution control of soil heavy metals. Full article

Rangelands form the ecological and economic backbone of Namibia, yet the woody plant dynamics that sustain these landscapes remain sporadically quantified across the semi-arid interior. We investigated the characteristics (stand structure, regeneration, richness, diversity, composition, ecological importance, and indicator species) of woody communities along a pronounced south-to-north rainfall gradient (85–346 mm yr⁻¹) at five representative sites: Warmbad, Gibeon, Otjimbingwe, Ovitoto, and Sesfontein. Field sampling combined point-centered quarter surveys (10 points site⁻¹) and belt transects (15 plots site⁻¹). The basal area increased almost ten-fold along the gradient (0.4–3.4 m² ha⁻¹). Principal Coordinates Analysis (PCoA) arranged plots in near-perfect rainfall order, and Permutational Multivariate Analysis of Variance (PERMANOVA) confirmed significant site differences (F_3,56 = 9.1, p < 0.001). Nanophanerophytes dominated hyper-arid zones, while microphanerophytes appeared progressively with increasing rainfall. Mean annual precipitation explained 45% of the variance in mean height and 34% of Shannon diversity but only 5% of stem density. Indicator value analysis highlighted Montinia caryophyllacea for Warmbad (IndVal = 100), Rhigozum trichotomum (75.8) for Gibeon, Senegalia senegal (72.6) for Otjimbingwe, and Senegalia mellifera (97.3) for Ovitoto. Rainfall significantly influences woody structure and diversity; however, other factors also modulate density and regeneration dynamics. This quantitative baseline can serve as a practical toolkit for designing site-specific management strategies across Namibia’s aridity gradient. Full article