Search Results (65)

The Hetao irrigation area is one of the largest irrigation areas in the Yellow River Basin and a typical salinized agricultural area. Crop type shifts in this area can alter soil phosphorus (P) morphology and microbial functional diversity, thereby influencing soil P losses. However, few studies have elucidated the underlying mechanisms. In this study, soil samples were collected from four different crop planting areas: sunflower field (SF), corn field (CF), wheat land (WL), and vegetable and fruit land (VFL). Subsequently, the physicochemical properties, P fractions, and phosphate-solubilizing microorganisms (PSMs) were analyzed. The results indicated that when other lands shifted to SF, the soil pH increased significantly. Simultaneously, SOM, TN, and TP decreased significantly during the crop type conversion. Analysis of P fraction revealed that moderately active P, including NaOH-P_i, NaOH-P_o, and HCl-P_i, were the dominant fractions in the tested soils. Among them, HCl-P_i was the major component of moderately active P. The soil P leaching change point in the tested are was 6.25 mg Olsen-P kg⁻¹. The probabilities of P leaching in WL, VFL, CF, and SF were 91.7%, 83.8%, 83.8%, and 66.7%, respectively. Additionally, the sum of the relative abundances of the three PSMs in SF, VFL, WL, and CF were 8.81%, 11.88%, 8.03%, and 10.29%, respectively. The shift in crop type to SF exacerbated the soil degradation process. Both TP and residual P in the soil decreased. However, the NaHCO₃ slightly increased, which may have been due to the increased abundance of Thiobacillus and Escherichia. Full article

Juniperus turbinata is an endemic species of the Mediterranean region, recognized for its role in ecological balance. However, concerns have been raised regarding its state of conservation. The objective of this study is twofold: firstly, to assess the rate of degradation of this species in the eastern region of Morocco; and secondly, to monitor the rate of natural regeneration to determine whether this compensates for the rate of degradation. To this end, three populations of red juniper were studied: the littoral (Saidia), the semi-continental (Oued Elhimer) in Jerrada, and the continental (Ich and Abou Lkhel) in Figuig. The first study objective is based on the use of the Normalized Difference Vegetation Index (NDVI) based on satellite images from 1986, 2000, and 2022. The study’s second objective focuses on locating new regenerations of J. turbinata using the Global Positioning System (GPS). The results of this study are alarming, showing a significant loss in the area occupied by J. turbinata in the eastern region of three study sites between 1986 and 2022. The area of J. turbinata matorral decreased by 99.05%, 95.10%, and 89.32% in the coastal, semi-continental, and continental regions of Figuig province, respectively. Regarding the natural regeneration of J. turbinata plants, 121 regenerations were observed along the coast. In the semi-continental region, 27 regenerations were recorded. However, no regeneration was found at the Figuig site. The study reveals that, over the past four decades, the average annual decline J. turbinata in the three regions was 2.62%. Conversely, the average rate of regeneration was recorded as 0.03%. Projections indicate a precipitous decline in the distribution of J. turbinata, with probable extinction of the species in northeastern Morocco. Full article

Background/Objectives: Sarcopenia, characterized by the loss of muscle mass and strength, is prevalent in older persons and affects their quality of life. Nutritional intervention and physical activity play a key role in its prevention and treatment. This study aims to investigate the relationship between food consumption frequency as assessed through the Mini Nutritional Assessment (MNA) and probable sarcopenia, evaluated by grip strength, in Chilean older persons aged 65 or older. Methods: A correlational, cross-sectional study with a non-probabilistic sample of 155 older persons aged 65 or older was undertaken. Food consumption frequency was assessed using the MNA, and muscle strength was measured using a handgrip dynamometer. The authors analyzed the relationship between food consumption frequency, as assessed by the MNA, and the protein intake index with muscle strength. Results: Participants who consumed less than two servings of fruits and vegetables per day were 4.28 times more likely to have low muscle strength compared to those who consumed two or more servings per day (OR = 4.28; 95% CI: 1.59–11.45). No significant associations were found with the consumption of dairy products, legumes, meat, fish, poultry, or fluids. The protein intake index did not show a significant relationship with muscle strength. Conclusions: The results suggest that a diet rich in fruits and vegetables may have a protective effect on muscle strength in older persons. Promoting adequate intake of these foods could be critical in the prevention of sarcopenia in this population. Full article

Land use land cover (LULC) changes due to rapid urbanization pose critical challenges to sustainable development, particularly in arid and semi-arid regions like Saudi Arabia, where cities such as Abha are experiencing unprecedented expansion. Urban sprawl is accelerating environmental degradation, affecting key natural resources such as vegetation, water bodies, and barren land. This study introduces an advanced machine learning (ML) and deep learning (DL)-based framework for high-accuracy LULC classification, urban sprawl quantification, and ecosystem service assessment, providing a more precise and scalable approach compared to traditional remote sensing techniques. A hybrid methodology combining ML models—Random Forest, Artificial Neural Networks, Gradient Boosting Machine, and LightGBM—with a 1D Convolutional Neural Network (CNN) was fine-tuned using grid search optimization to enhance classification accuracy. The integration of deep learning improves feature extraction and classification consistency, achieving an AUC of 0.93 for Dense Vegetation and 0.82 for Cropland, outperforming conventional classification methods. The study also applies the Markov transition model to project land cover changes, offering a probabilistic understanding of urban expansion trends and ecosystem dynamics, providing a significant improvement over static LULC assessments by quantifying transition probabilities and predicting future land cover transformations. The results reveal that urban areas in Abha expanded by 120.74 km² between 2014 and 2023, with barren land decreasing by 557.09 km² and cropland increasing by 205.14 km². The peak ecosystem service value (ESV) loss was recorded at USD 125,662.7 between 2017 and 2020, but subsequent land management efforts improved ESV to USD 96,769.5 by 2023. The resilience and recovery of natural land cover types, particularly barren land (44,163 km² recovered by 2023), indicate the potential for targeted restoration strategies. This study advances urban sustainability research by integrating state-of-the-art deep learning models with Markov-based land change predictions, enhancing the accuracy and predictive capability of LULC assessments. The findings highlight the need for proactive land management policies to mitigate the adverse effects of urban sprawl and promote sustainable ecosystem service recovery. The methodological advancements presented in this study provide a scalable and adaptable framework for future urbanization impact assessments, particularly in rapidly developing regions. Full article

The Ili River Transboundary Basin is an important area within the Belt and Road Initiative, and its ecological security impacts China–Kazakhstan diplomatic relations and the building of the Belt and Road Initiative. Using the copula method, this study quantifies the vulnerability of vegetation to drought in the Ili River Transboundary Basin based on the Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The vulnerability of vegetation in the Ili River Transboundary Basin is highest in June, with the proportion of highly vulnerable areas reaching 63.29% under extreme drought conditions. As the drought severity increases, the probability of vegetation loss rises, with vegetation being affected the most in June. From May to June, drought-prone areas are mainly located in Almaty Oblast and East Kazakhstan. From July to September, drought-prone areas are mainly found in the Ili River Valley and southeastern Almaty Oblast. Rainfed croplands are most susceptible to drought, while, for irrigated croplands, higher drought severity enhances the mitigating effect of irrigation measures. Vegetation areas are most affected by drought in semi-arid regions, particularly in summer. These findings offer valuable scientific support for drought management and sustainable development in the region. Full article

The application of nitrification inhibitors (NIs) is an effective way to reduce soil nitrogen (N) losses and increase crop N uptake. Yet, the efficacy of NIs commonly varies with dosages, crop systems and soil environmental conditions. Hence, clarifying the suitable type and dosage of NIs is extremely important for structuring the best N management regime at a regional scale. Here, based on microcosm experiments, we evaluated the influence of three widely used NIs [Dicyandiamide, DCD; 3,4-Dimethylpyrazole phosphate, DMPP; 2-chloro-6-(trichloromethyl) pyridine, Nitrapyrin] on the nitrification activity of an intensively cultivated greenhouse soil. The results showed that both DCD and DMPP imposed a transient inhibition on nitrification (less than five days) regardless of the dosages applied, and, on the contrary, Nitrapyrin presented a persistent suppression, with a longer duration of the inhibition action by a higher dosage. Accordingly, the incorporation of Nitrapyrin at 2% of the applied N rate (w/w) is a recommendable dosage for local intensive greenhouse production. Further, we assessed the influence of various dosages of Nitrapyrin incorporation (0%, 0.25%, 0.5%, 2% and 5%) on the abundance and community of three groups of soil ammonia oxidizers [i.e., ammonia-oxidizing archaea (AOA), ammonia-oxidizing bacteria (AOB) and completely ammonia-oxidizing bacteria (Comammox Nitrospira)] by qPCR and high-throughput amplicon sequencing. Nitrapyrin incorporation strongly lowered both the AOB and Comammox Nitrospira abundances and their community richness even at the lowest dosage. Nitrapyrin incorporation also significantly altered the community structure of all of the tested ammonia oxidizers, and the average relative abundance of some major community members (i.e., the Nitrososphaerales Clade Nitrososphaera, Nitrososphaerales Clade A, Nitrosospira briensis Clade, Nitrosospira multiformis Clade, Comammox Nitrospira Clade A.2 and Comammox Nitrospira Clade A-associated) obviously responded to Nitrapyrin incorporation. Overall, our findings indicated that AOB and Comammox Nitrospira were more sensitive to Nitrapyrin incorporation as compared with AOA. The results obtained here highlight the importance of optimizing the type and dosage of NIs for N fertilization management in intensive greenhouse vegetable production. Nitrapyrin incorporation inhibits soil nitrification probably by suppressing the Nitrosospira multiformis Clade in the AOB community at the level tested herein. Full article

The rainfed cereal growing regions of Northern Kazakhstan experience significant yield fluctuations due to dependence on weather conditions. Early detection and monitoring of droughts is crucial for effective mitigation strategies in this region. This study emphasises the following objectives: (1) description of the current vegetation condition with a possible separation of short-term weather effects and (2) analysing trends of changes with their directionality and quantification. Terra MODIS satellite images from 2000 to 2023 are used. Differential indices—Normalised Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI)—are used to determine the characteristics of each current season. A key component is the comparison of the current NDVI values with historical maximum, minimum, and average values to identify early indicators of drought. NDVI deviations from multiyear norms and VCI values below 0.3 visually reflect changing vegetation conditions influenced by seasonal weather patterns. The results show that the algorithm effectively detects early signs of drought through observed deviations in NDVI values, showing a trend towards increasing drought frequency and intensity in Northern Kazakhstan. The algorithm was particularly effective in detecting severe drought seasons in advance, as was the case in June 2010 and May 2012, thus supporting early recognition of drought onset. The Integrated Vegetation Index (IVI) and Integrated Vegetation Condition Index (IVCI) time series are used for integrated multiyear assessments, in analysing temporal changes in vegetation cover, determining trends in these changes, and ranking the weather conditions of each growing season in the multiyear series. Areas with high probability of drought based on low IVCI values are mapped. The present study emphasises the value of remote sensing as a tool for drought monitoring, offering timely and spatially detailed information on vulnerable areas. This approach provides critical information for agricultural planning, environmental management and policy making, especially in arid and semi-arid regions. The study emphasises the importance of multiyear data series for accurate drought forecasting and suggests that this methodology can be adapted to other drought-sensitive regions. Emphasising the socio-economic benefits, this study suggests that the early detection of drought using satellite data can reduce material losses and facilitate targeted responses. Full article

The melon fruit fly, Bactrocera zonata (Coquillett) (Diptera: Tephritidae), is a notorious pest, posing a significant threat to a wide range of fruits and vegetables, leading to substantial agricultural losses worldwide. With growing concerns over chemical pesticide resistance and environmental safety, plant-based insecticides have emerged as eco-friendly and economically sustainable alternatives. In this context, the present study delves into the insecticidal potential of Ricinus communis extracts against B. zonata. The crude extract of R. communis was systematically fractionated using a series of organic solvents with increasing polarities. The fraction demonstrating the highest insecticidal activity was further purified for the isolation of bioactive compounds, employing advanced chromatographic techniques such as Column Chromatography, coupled with state-of-the-art analytical methods including Gas Chromatography-Mass Spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FTIR). Bioassays were conducted on B. zonata using the crude extract and its fractions in n-hexane, methanol, and ethyl acetate. Among the isolated compounds, 11,14,17-Eicosatrienoic acid was identified in both the methanol and ethyl acetate fractions. This compound exhibited remarkable insecticidal efficacy, with an LC₅₀ value of 1.36%, a linearity of R² = 0.64, and a statistically significant probability (p < 0.01). Particularly, 11,14,17-Eicosatrienoic acid emerged as the most potent bioactive agent against B. zonata highlighting its potential as a natural insecticide. These findings underscore the potential of R. communis as a valuable source of bioactive compounds for the sustainable management of B. cucurbitae. This study not only broadens the scope of plant-based pest control strategies but also opens avenues for further exploration of natural compounds in integrated pest management. Full article

With the intensification of global warming, droughts and heatwaves occur frequently and widely, which have a serious impact on the healthy growth of vegetation. The challenge is to accurately characterize vegetation vulnerability under compound heat and drought stress using correlation-based methods. This article uses the Haihe River Basin, an ecologically sensitive area known for experiencing droughts nine out of ten years, as an example. Firstly, using daily precipitation and maximum temperature data from 38 meteorological stations in the basin from 1965 to 2019, methods such as univariate linear regression and the Mann–Kendall mutation test were employed to identify the temporal variation patterns of meteorological elements in the basin. Secondly, the Pearson correlation coefficient and other methods were applied to determine the most likely months for compound dry and hot events, and the joint distribution pattern and recurrence period of concurrent high temperature and intense drought events were explored. Finally, a vegetation vulnerability assessment model based on Vine Copula in compound dry and hot climates was constructed to quantify the relationship of the response of watershed vegetation to different extreme events (high temperature, drought, and compound dry and hot climates). The results indicated that the basin’s precipitation keeps decreasing, evaporation rises, and the supply–demand conflict grows more severe. The correlation between the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) is strongest at the 3-month scale from June to August. Meanwhile, in most areas of the basin, the Standardized Normalized Difference Vegetation Index (sNDVI) is positively correlated with the SPI and negatively correlated with the STI. Compared to a single drought or high-temperature event, compound dry and hot climates further exacerbate the vegetation vulnerability of the Haihe River Basin. In compound dry and hot climates, the probability of vegetation loss in June, July, and August is as high as 0.45, 0.32, and 0.38, respectively. Moreover, vegetation vulnerability in the southern and northwestern mountainous areas of the basin is higher, and the ecological risk is severe. The research results contribute to an understanding of the vegetation’s response to extreme climate events, aiming to address terrestrial ecosystem risk management in response to climate change. Full article

Habitat loss due to wildfire is an increasing problem internationally for threatened animal species, particularly tree-dependent and arboreal animals. The koala (Phascolartos cinereus) is endangered in most of its range, and large areas of forest were burnt by widespread wildfires in Australia in 2019/2020, mostly areas dominated by eucalypts, which provide koala habitats. We studied the impact of fire and three subsequent years of recovery on a property in South-East Queensland, Australia. A classified Differenced Normalised Burn Ratio (dNBR) calculated from pre- and post-burn Sentinel-2 scenes encompassing the local study area was used to assess regional impact of fire on koala-habitat forest types. The geometrically structured composite burn index (GeoCBI), a field-based assessment, was used to classify fire severity impact. To detect lower levels of forest recovery, a manual classification of the multitemporal dNBR was used, enabling the direct comparison of images between recovery years. In our regional study area, the most suitable koala habitat occupied only about 2%, and about 10% of that was burnt by wildfire. From the five koala habitat forest types studied, one upland type was burnt more severely and extensively than the others but recovered vigorously after the first year, reaching the same extent of recovery as the other forest types. The two alluvial forest types showed a negligible fire impact, likely due to their sheltered locations. In the second year, all the impacted forest types studied showed further, almost equal, recovery. In the third year of recovery, there was almost no detectable change and therefore no more notable vegetative growth. Our field data revealed that the dNBR can probably only measure the general vegetation present and not tree recovery via epicormic shooting and coppicing. Eucalypt foliage growth is a critical resource for the koala, so field verification seems necessary unless more-accurate remote sensing methods such as hyperspectral imagery can be implemented. Full article

Droughts in the Weihe River Basin are occurring more frequently and are becoming more intense. These events negatively affect industrial production, economic development, and ecosystems. Studying how vegetation changes in response to them is of practical significance. We report temporal and spatial trends in vegetation cover, use a copula function to analyze relationships between drought and vegetation cover, and assess the probability of vegetation loss in different drought scenarios. A vegetation index trends upwards from north to south in this basin; from 2001 to 2017, vegetation cover also trends upward in most areas, although it decreases in areas with high vegetation cover. An escalated susceptibility to drought has been observed in the southern and eastern sectors, where proximity to the riverbank correlates with heightened drought sensitivity, particularly in zones of intensified vegetation density. The probability of vegetation loss at the same vegetation loss preset point gradually increases with increased drought severity. These results will facilitate the formulation of countermeasures to prevent and combat the effects of drought on vegetation and land management. Full article

Soil erosion risk assessment enables the identification of areas requiring priority treatment and avoids wasting human and material resources. The factor scoring method used in existing studies has high subjectivity, and the method of expressing erosion risk according to the soil erosion intensity ignores the random nature of the occurrence of erosion; therefore, neither method accurately reflects the risk of soil erosion. In order to address this issue, this study proposes a soil erosion risk assessment method that integrates the outcome and the probability of occurrence of soil erosion by means of a probabilistic statistical model. Subsequently, experimental research is conducted in the Dali River Basin. On the basis of long time-series data, using mathematical statistics as a tool and drawing on the empirical frequency formula, the probabilistic statistical risk assessment model is combined with the Modified Universal Soil Loss Equation (RUSLE) model to account for the probability of regional soil erosion at different intensity levels in the long time-series, which is combined with the intensity of erosion to carry out soil erosion risk assessment. The results of our study show the following: (1) The central and southwestern regions of the Dali River Basin (DRB) present medium and high levels of soil erosion risk, with the proportion of low-risk areas increasing annually, accounting for 78.97% of the DRB in 2020, while extremely high-risk areas account for only 0.40% of the DRB. (2) The major components impacting soil erosion risk in the DRB, as revealed by the geodetector, are the normalized difference vegetation index (NDVI) and slope, where the interaction between the two dominated the spatial variation in soil erosion risk. (3) Comparing the soil erosion risk and its status in the coming years, the proposed assessment method based on the occurrence probability can reveal the future soil erosion risk better than the traditional assessment method. Full article

During the large-scale vegetation restoration on the Loess Plateau, the introduction of exotic species with high water consumption, such as Robinia pseudoacacia L., led to widespread soil desiccation, and resulted in severe drought stress and increasing risk of forest degradation and mortality. Accurate assessment of drought-induced mortality risk in plantation forests is essential for evaluating and enhancing the sustainability of ecological restoration, yet quantitative research at the regional scale on the Loess Plateau is lacking. With a focus on Robinia pseudoacacia L. plantations, we utilized a coupled model of the Biome BioGeochemical Cycles model and plant supply–demand hydraulic model (BBGC-SPERRY model) to simulate the dynamics of the annual average percentage loss of whole-plant hydraulic conductance (APLK) at 124 meteorological stations over an extended period (1961–2020) to examine changes in plant hydraulic safety in Robinia pseudoacacia L. plantations. Based on the probability distribution of APLK at each site, the drought-induced mortality risk probability (DMRP) in Robinia pseudoacacia L. was determined. The results indicate the BBGC-SPERRY model could effectively simulate the spatiotemporal variations in transpiration and evapotranspiration in Robinia pseudoacacia L. stands on the Loess Plateau. The mean APLK and DMRP exhibited increasing trends from southeast to northwest along a precipitation gradient, with their spatial patterns on the Loess Plateau mainly driven by mean annual precipitation and also significantly influenced by other climatic and soil factors. The low-risk (DMRP < 2%), moderate-risk (2% ≤ DMRP ≤ 5%), and high-risk (DMRP > 5%) zones for drought-induced mortality in Robinia pseudoacacia L. accounted for 60.0%, 30.7%, and 9.3% of the study area, respectively. These quantitative findings can provide an important basis for rational forestation and sustainable vegetation management on the Loess Plateau. Full article

Droughts can affect forest ecosystems and lead to soil degradation, biodiversity loss, and desertification. Not all regions of Mexico are affected in the same way, as some areas are naturally more prone to drought due to their geographical location. Therefore, the objective of this work was to carry out a spatio-temporal analysis of the occurrence of droughts (severe and extreme) in Mexican forest systems, covering the period 2000–2021, and to study the area covered by these events in Mexican forest systems. This analysis was divided into three stages: the classification of land use and vegetation, spatial mapping and the classification of drought intensity, and an analysis of drought frequency and probability in forest systems. The results show that more than 46% of Mexico’s forest area experienced severe and extreme droughts during the 21-year period studied. Broadleaved forests were most affected by severe and extreme droughts, with a frequency of 6 years. The increasing frequency of droughts poses a major challenge to the resilience of forest ecosystems in Mexico, highlighting the need to implement climate change adaptation and forest management measures to protect the country’s biodiversity and natural resources. Full article

This study used descriptive statistical methods to investigate how the yield development of winter and spring barley was affected by annual weather variability within the vegetative, ear formation, anthesis, and grain-filling phases. Meteorological, phenological, and yield data from the agrometeorological field experiment in Berlin-Dahlem (Germany) between 2009 and 2022 were used. The results show that the lower yield variability in winter barley (cv = 18.7%) compared to spring barley (cv = 32.6%) is related to an earlier start and longer duration of relevant phenological phases, so yield formation is slower under generally cooler weather conditions. The significantly higher yield variability in spring barley was mainly the result of adverse weather conditions during ear formation and anthesis. In both phases, high temperatures led to significant yield losses, as has often been the case in recent years. In addition, a pronounced negative climatic water balance during anthesis was also a contributing factor. These meteorological parameters explained 82% of the yield variability in spring barley. New strategies for spring barley production are needed to avoid further yield losses in the future. Rising temperatures due to climate change could probably allow an earlier sowing date so that ear formation and anthesis take place in a generally cooler and wetter period, as shown for 2014. Full article