Search Results (191)

As a vital component of the terrestrial ecosystem, grassland accounts for one-third of the global vegetation system. Grassland degradation has been exacerbated due to extreme overgrazing in China’s Inner Mongolia Autonomous Region (IMAR). While conservation was carried out via the Ecological Subsidy and Award Program (ESAP) to mitigate grassland degradation, little is known about its effectiveness in improving the biophysical conditions of grassland. This paper integrates the conceptual frameworks of total socio-environmental systems (TSESs) to assess how ecological systems respond to the ESAP, investigate the spatial heterogeneity of the ESAP, and explore the meddling effects of socio-environmental interactions on the ESAP. We integrated ecological, climate, and socioeconomic data and developed several hierarchical linear mixed models (HLMMs) to investigate how these factors interact with the ESAP in the IMAR. Our findings prove that the above-ground biomass between 2011 and 2015 responds significantly to variations in socioeconomic conditions and ecological communities. Available land resources, hospital and medical facilities, and net farmer and herdsman income are the most critical factors positively related to grassland productivity. Primary industries like mining, total consumer retail value, farming, forestry, animal husbandry, fishery productions, and GDP are the most damaging factors affecting biomass. Our study recommends a regionally or locally tailored ecological recovery policy, instead of a generalized one, in future efforts to conserve grassland. Full article

This study aims to predict the carbon sequestration capacity of Chinese grasslands to address climate change and achieve carbon neutrality goals. Grassland carbon sequestration is a crucial part of the global carbon cycle. However, its capacity is significantly impacted by climate change and human activities, making its dynamic changes complex and challenging to predict. This study adopts a fractional-order accumulation grey model, using 11 provinces in China as samples, to analyze and forecast grassland carbon sequestration. The study finds significant differences in grassland carbon sequestration trends across the sample regions. The carbon sequestration capacity of the grasslands in Xizang (Tibet) and Heilongjiang province is increasing, while it is decreasing in other provinces. The varying prediction results are influenced not only by regional climatic and natural conditions, but also by human interventions such as overgrazing, irrational reclamation, excessive mineral resource exploitation, and increased tourism development. Therefore, more region-specific grassland management and protection strategies should be formulated to enhance the carbon sequestration capacity of grasslands and promote the sustainable development of ecosystems. The significance of this study lies not only in providing scientific guidance for the protection and sustainable management of Chinese grasslands, but also in contributing theoretical and practical insights into global carbon sequestration strategies. Full article

Accurate livestock counts are essential for effective pastureland management. High spatial resolution remote sensing, coupled with deep learning, has shown promising results in livestock detection. However, challenges persist, particularly when the targets are small and in a heterogeneous environment, such as those in African rangelands. This study evaluated nine state-of-the-art object detection models, four variants each from YOLOv5 and YOLOv8, and Faster R-CNN, for detecting cattle in 10 cm resolution aerial RGB imagery in Kenya. The experiment involved 1039 images with 9641 labels for training from sites with varying land cover characteristics. The trained models were evaluated on 277 images and 2642 labels in the test dataset, and their performance was compared using Precision, Recall, and Average Precision (AP_0.5–0.95). The results indicated that reduced spatial resolution, dense shrub cover, and shadows diminish the model’s ability to distinguish cattle from the background. The YOLOv8m architecture achieved the best AP_0.5–0.95 accuracy of 39.6% with Precision and Recall of 91.0% and 83.4%, respectively. Despite its superior performance, YOLOv8m had the highest counting error of −8%. By contrast, YOLOv5m with AP_0.5–0.95 of 39.3% attained the most accurate cattle count with RMSE of 1.3 and R² of 0.98 for variable cattle herd densities. These results highlight that a model with high AP_0.5–0.95 detection accuracy may struggle with counting cattle accurately. Nevertheless, these findings suggest the potential to upscale aerial-imagery-trained object detection models to satellite imagery for conducting cattle censuses over large areas. In addition, accurate cattle counts will support sustainable pastureland management by ensuring stock numbers do not exceed the forage available for grazing, thereby mitigating overgrazing. Full article

Monitoring the status and dynamics of desertification is one of the most important parts of combating it. In this study, 30 m high-resolution information on land desertification and restoration in the Heihe River basin (HRB) was extracted from the land cover database. The results indicate that land desertification coexists with land restoration in the HRB. In different periods, the area of land restoration was much larger than the area of land desertification in the HRB, and the HRB has undergone land restoration. Upstream of the HRB, there is a continuing trend of increasing land desertification associated with overgrazing in a context where climate change favors desertification reversal. In the middle and lower reaches, although climate variability and human activities favor land desertification, land desertification is still being reversed, and land restoration dominates. Implementing the eco-environmental protection project and desertification control measures, especially the Ecological Water Distribution Project (EWDP), contributes to the reversal of desertification in the middle and lower reaches of the HRB. However, the EWDP has indirectly led to the lowering of the water table in the middle reaches, resulting in local vegetation degradation. Therefore, there is an urgent need to transform the economic structure of the middle reaches to cope with water scarcity and land desertification. Full article

The Arabian Peninsula, with its rugged mountains, wadis, alluvial plains, sand dune deserts, and diverse coastlines, spans over 3 million km². The Peninsula is situated at the crossroads of Africa and Asia and is a meeting point for diverse biogeographic realms, including the Palearctic, Afrotropical, and Indomalayan regions. This convergence of biogeographic zones has resulted in a remarkably diverse flora and fauna, which is adapted to the harsh and varied climates found throughout the Peninsula. Each of the countries of the Arabian Peninsula are biologically diverse and unique in their own right, but Yemen, Saudi Arabia, and Oman are the most diverse in terms of their landforms and biological diversity. The mountainous regions support a cooler and more moderate climate compared to the surrounding lowlands, thus forming unique ecosystems that function as refugia for plant and animal species, and have a high endemism of plant species. The desert ecosystems support a variety of lifeforms that are specially adapted to an extreme arid climate. Due to its long history of human habitation and subsistence agriculture, particularly in the mountainous areas, the Arabian Peninsula possesses unique crop varieties adapted to extreme arid climates, making them important genetic resources for the future in the face of climate change. The Arabian Peninsula, though rich and diverse in its biological diversity, has been greatly affected by human activities, especially in the last 50 years, including urbanization, habitat destruction, overgrazing, and climate change, which pose significant threats to the biodiversity of the region. This review presents the biogeography and background of conservation efforts made in the countries in the Arabian Peninsula and gives the progress made in botanical research and conservation practices throughout the Peninsula. Full article

Argania spinosa is among the most important species of the Moroccan forest in terms of ecological, environmental, and socio-economic aspects. However, it faces a delicate balance between regeneration and degradation in its natural habitat. Hence, the efforts to preserve and regenerate argan forests are crucial for biodiversity, soil quality, and local livelihoods, yet they face challenges like overgrazing and climate change. Sustainable management practices, including reforestation and community engagement, are vital for mitigating degradation. Similarly, exploiting the argan tree’s rhizosphere can enhance soil quality by leveraging its rich microbial diversity. This approach not only improves crop growth but also maintains ecosystem balance, ultimately benefiting both agriculture and the environment. This enrichment can be achieved by different factors: mycorrhizae, plant extracts, algae extracts, and plant growth-promoting rhizobacteria (PGPR). The benefits provided by PGPR may include increased nutrient availability, phytohormone production, shoot, root development, protection against several plant pathogens, and disease reduction. In this study, the effect of rhizobacteria isolated from the Agran rhizosphere was evaluated on germination percentage and radicle length for Argania spinosa in vitro tests, growth, collar diameter, and branching number under greenhouse conditions. One hundred and twenty (120) bacteria were isolated from the argan rhizosphere and evaluated for their capacity for phosphate solubilization and indole acetic acid production. The results showed that 52 isolates could solubilize phosphorus, with the diameters of the solubilization halos varying from 0.56 ± 0.14 to 2.9 ± 0.08 cm. Among 52 isolates, 25 were found to be positive for indole acetic acid production. These 25 isolates were first tested on maize growth to select the most performant ones. The results showed that 14 isolates from 25 tested stimulated maize growth significantly, and 3 of them by 28% (CN005, CN006, and CN009) compared to the control. Eight isolates (CN005, CN006, CN004, CN007, CN008, CN009, CN010, and CN011) that showed plant growth of more than 19% were selected to evaluate their effect on argan germination rate and radicle length and were subjected to DNA extraction and conventional Sanger sequencing. The 8 selected isolates were identified as: Brevundimonas naejangsanensis sp2, Alcaligenes faecalis, Brevundimonas naejangsanensis sp3, Brevundimonas naejangsanensis sp4, Leucobacter aridicollis sp1, Leucobacter aridicollis sp2, Brevundimonas naejangsanensis sp1, and Staphylococcus saprophyticus. The results showed that Leucobacter aridicollis sp2 significantly increased the germination rate by 95.83%, and the radicle length with a value of 2.71 cm compared to the control (1.60 cm), followed by Brevundimonas naejangsanensis sp3 and Leucobacter aridicollis sp1 (2.42 cm and 2.11 cm, respectively). Under greenhouse conditions, the results showed that the height growth increased significantly for Leucobacter aridicollis sp1 (42.07%) and Leucobacter aridicollis sp2 (39.99%). The isolates Brevundimonas naejangsanensis sp3 and Leucobacter aridicollis sp1 increased the gain of collar diameter by 41.56 and 41.21%, respectively, followed by Leucobacter aridicollis sp2 and Staphyloccocus saprophyticus (38.68 and 22.79%). Leucobacter aridicollis sp1 increased the ramification number per plant to 12 compared to the control, which had 6 ramifications per plant. The use of these isolates represents a viable alternative in sustainable agriculture by improving the germination rate and root development of the argan tree, as well as its development, while increasing the availability of nutrients in the soil and consequently improving fertilization. Full article

Accurate information about the amount of standing biomass is important in pasture management for monitoring forage growth patterns, minimizing the risk of overgrazing, and ensuring the necessary feed requirements of livestock. The morphological features of plants, like crop height and density, have been proven to be prominent predictors of crop yield. The objective of this study was to evaluate the effectiveness of stereovision-based crop height and vegetation coverage measurements in predicting the aboveground biomass yield of bermudagrass (Cynodon dactylon) in a pasture. Data were collected from 136 experimental plots within a 0.81 ha bermudagrass pasture using an RGB-depth camera mounted on a ground rover. The crop height was determined based on the disparity between images captured by two stereo cameras of the depth camera. The vegetation coverage was extracted from the RGB images using a machine learning algorithm by segmenting vegetative and non-vegetative pixels. After camera measurements, the plots were harvested and sub-sampled to measure the wet and dry biomass yields for each plot. The wet biomass yield prediction function based on crop height and vegetation coverage was generated using a linear regression analysis. The results indicated that the combination of crop height and vegetation coverage showed a promising correlation with aboveground wet biomass yield. However, the prediction function based only on the crop height showed less residuals at the extremes compared to the combined prediction function (crop height and vegetation coverage) and was thus declared the recommended approach (R² = 0.91; SeY= 1824 kg-wet/ha). The crop height-based prediction function was used to estimate the dry biomass yield using the mean dry matter fraction. Full article

As an important ecosystem, the wild fruit forest in the Tianshan Mountains is one of the origins of many fruit trees in the world. The wild fruit forest in Emin County, Xinjiang, China, was taken as the research area, the spatial and temporal distribution of the wild fruit forest was inverted using random forest and PLUS models, and the 2027 distribution pattern of the wild fruit forest was simulated and predicted. From 2007 to 2013, damage to the wild fruit forest from tourism and overgrazing was very serious, and the area occupied by the wild fruit forest decreased rapidly from 9.59 km² to 7.66 km². From 2013 to 2020, suitable temperatures and reasonable tourism management provided strong conditions for the rejuvenation of wild fruit forests. The distance of the center of gravity of the wild fruit forest increased, and the density of distribution of the wild fruit forest in the northwest direction of the study area also increased. It is predicted that the wild fruit forest in the study area will show a steady and slowly increasing trend in places far away from tourist areas and with more complex terrain. It is suggested that non-permanent fences be set up as buffer zones between wild fruit forests, ensuring basic maintenance of wild fruit forests, limiting human disturbance such as overgrazing, and reducing the risk of soil erosion. Full article

The remarkable adaptability and rapid proliferation of Prosopis juliflora have led to its invasive status in the rangelands of Kenya, detrimentally impacting native vegetation and biodiversity. Exacerbated by human activities such as overgrazing, deforestation, and land degradation, these conditions make the spread and management of this species a critical ecological concern. This study assesses the effectiveness of artificial intelligence (AI) and remote sensing in monitoring the invasion of Prosopis juliflora in Baringo County, Kenya. We investigated the environmental drivers, including weather conditions, land cover, and biophysical attributes, that influence its distinction from native vegetation. By analyzing data on the presence and absence of Prosopis juliflora, coupled with datasets on weather, land cover, and elevation, we identified key factors facilitating its detection. Our findings highlight the Decision Tree/Random Forest classifier as the most effective, achieving a 95% accuracy rate in instance classification. Key variables such as the Normalized Difference Vegetation Index (NDVI) for February, precipitation, land cover type, and elevation were significant in the accurate identification of Prosopis juliflora. Community insights reveal varied perspectives on the impact of Prosopis juliflora, with differing views based on professional experiences with the species. Integrating these technological advancements with local knowledge, this research contributes to developing sustainable management practices tailored to the unique ecological and social challenges posed by this invasive species. Our results highlight the contribution of advanced technologies for environmental management and conservation within rangeland ecosystems. Full article

Sub-Saharan African farmers have long been portrayed with very negative representations, at least since the beginning of coordinated European colonialism in the late 19th century. In the Sahel-Sudan area, agrosystems have been described as overgrazed, forests as endangered, and soils as overexploited, with local and traditional “archaic” practices. Against this background, the objective of this article is to focus on these agrosystems’ resilience, for which several criteria have been monitored. The approach used in this research was to synthesize observations from a large amount of material gathered over multiple years by the authors, drawing on our long-term commitment to, and inter-disciplinary study of, the evolution of surface hydrology, ecosystems, and agrosystems of West Africa. The positive trends in rainfall and streamflows, reinforced by farmer’s practices, confirm the overall regreening and reforestation of the Sahel-Sudan strip, especially in areas with high population densities, including the mangrove areas. The intensification of agricultural systems and the recovery of the water-holding capacity of soils and catchments explain the recorded general increase in terms of food self-sufficiency in the Sahel, as well as in crops yields and food production. Finally, we compare the neo-Malthusian discourse to the actual resilience of these agrosystems. The article concludes with a recommendation calling for the empowerment of smallholder farmers to take greater advantage of the current wet period. Overall, the speed of change in knowledge and know-how transfer and implementation, and the farmers’ ability to adapt to ecological and economic crises, must be highlighted. Far from being resistant to change, West African agriculturalists innovate, experiment, borrow, transform, and choose according to their situation, projects, and social issues. Full article

Maintaining a harmonious balance between grassland ecology and local economic development necessitates effective management of livestock resources. Traditional approaches have proven inefficient, highlighting an urgent need for intelligent solutions. Accurate identification of livestock targets is pivotal for precise livestock farming management. However, the You Only Look Once version 8 (YOLOv8) model exhibits limitations in accuracy when confronted with complex backgrounds and densely clustered targets. To address these challenges, this study proposes an optimized CCS-YOLOv8 (Comprehensive Contextual Sensing YOLOv8) model. First, we curated a comprehensive livestock detection dataset encompassing the Qinghai region. Second, the YOLOv8n model underwent three key enhancements: (1) incorporating a Convolutional Block Attention Module (CBAM) to accentuate salient image information, thereby boosting feature representational power; (2) integrating a Content-Aware ReAssembly of FEatures (CARAFE) operator to mitigate irrelevant interference, improving the integrity and accuracy of feature extraction; and (3) introducing a dedicated small object detection layer to capture finer livestock details, enhancing the recognition of smaller targets. Experimental results on our dataset demonstrate the CCS-YOLOv8 model’s superior performance, achieving 84.1% precision, 82.2% recall, 84.4% [email protected], 60.3% [email protected], 53.6% [email protected]:0.95, and 83.1% F1-score. These metrics reflect substantial improvements of 1.1%, 7.9%, 5.8%, 6.6%, 4.8%, and 4.7%, respectively, over the baseline model. Compared to mainstream object detection models, CCS-YOLOv8 strikes an optimal balance between accuracy and real-time processing capability. Its robustness is further validated on the VisDrone2019 dataset. The CCS-YOLOv8 model enables rapid and accurate identification of livestock age groups and species, effectively overcoming the challenges posed by complex grassland backgrounds and densely clustered targets. It offers a novel strategy for precise livestock population management and overgrazing prevention, aligning seamlessly with the demands of modern precision livestock farming. Moreover, it promotes local environmental conservation and fosters sustainable development within the livestock industry. Full article

Accurate estimation of livestock carrying capacity (LCC) and implementation of an appropriate actual stocking rate (ASR) are key to the sustainable management of grazing adapted alpine grassland ecosystems. The reliable determination of aboveground biomass is fundamental to these determinations. Peak aboveground biomass (AGB_P) captured from satellite data at the peak of the growing season (POS) is widely used as a proxy for annual aboveground biomass (AGB_A) to estimate LCC of grasslands. Here, we demonstrate the limitations of this approach and highlight the ability of POS in the estimation of ASR. We develop and trail new approaches that incorporate remote sensing phenology timings of grassland response to grazing activity, considering relations between biomass growth and consumption dynamics, in an effort to support more accurate and reliable estimation of LCC and ASR. The results show that based on averaged values from large-scale studies of alpine grassland on the Qinghai-Tibet Plateau (QTP), differences between AGB_P and AGB_A underestimate LCC by about 31%. The findings from a smaller-scale study that incorporate phenology timings into the estimation of annual aboveground biomass reveal that summer pastures in Haibei alpine meadows were overgrazed by 11.5% during the study period from 2000 to 2005. The methods proposed can be extended to map grassland grazing pressure by predicting the LCC and tracking the ASR, thereby improving sustainable resource use in alpine grasslands. Full article

Grassland plants that endure livestock grazing exhibit a dwarf phenotype, which can be transmitted to clonal offspring. Yet to date, it remains poorly understood whether such transgenerational dwarf effects alter the plants’ response to shade. Here, we conducted a common garden experiment under sunlight and shade conditions with clonal Leymus chinensis offspring, the parents of which had endured livestock overgrazing (OG) and non-grazing (NG) in the field, respectively. Plant morphological, physiological, and transcriptomic analyses were carried out. The results indicated that NG offspring showed greater shade avoidance than OG offspring. That is, NG offspring exhibited greater plasticity of vegetative height and leaf width, which may be contributed to their greater photosynthetic capacity and gibberellin (GA3) content compared with OG offspring when treated with shade. In addition, RNA-Seq profiling showed that differentially expressed genes in NG offspring were mainly enriched in RNA modification and metabolic processes, which facilitated rapid response to shade. Phytochrome interacting factors (PIFs) promoted downstream shade marker genes in NG offspring by significantly downregulating the expression of PHYC, SPY, and DELLA. Our findings suggest that light conditions should be taken into account to better understand transgenerational dwarf effects induced by livestock grazing on grassland ecosystems. These results provide new insights into the inducible factors of phenotypic variations in grassland plants that experience grazing. Full article

Aksu City, located in the southern region of Xinjiang, China, holds the position of being the fifth largest city in Xinjiang. It holds significant ecological importance as a vital functional region for the management of desertification in China. To safeguard the ecological security of Xinjiang and preserve the ecological stability of Aksu City, it is crucial to examine the relationship between ecological service value and ecological risk, as well as the geographical and temporal changes in land use characteristics in Aksu City. This study examines the evolutionary characteristics and spatial correlation between ecological service value and ecological risk in Aksu City, using Aksu City as a case study. The analysis is based on five periods of land use data from 2000, 2005, 2010, 2015, and 2020. The study revealed the spatial and temporal patterns of landscape ecological risk and ecosystem service value in Aksu City from 2000 to 2020 using the landscape pattern index, ecological service value estimation, and ecological risk index. In addition, the study explored the interrelationship between ecological service value and ecological risk. The findings indicated that: (1) Bare land constituted the predominant land use category in Aksu City, accounting for over 81% of the total land use transfer over a 20-year period, encompassing a total area of 459.83 km². (2) The total ecological service value (ESV) in the area experienced a decline of CNY 3.41 × 10⁸ within the study’s time frame, exhibiting a decrease rate of 6.73%. Notably, grass and shrubland emerged as the primary contributor to the ESV, accounting for 33.25% of the total. (3) The ecological risk index (ERI) in Aksu City, within the period of 2000–2020, showed an increase in the interval from 0.2686 to 0.2877. The results indicated a decline in the overall ecological condition. The ecological risk level in Aksu City from 2000 to 2020 was dominated by lower and medium ecological risks. (4) Moran’s I values in Aksu City between 2000 and 2020 ranged from 0.428 to 0.443, which suggested a positive spatial correlation between ESV and ERI in the study area. The primary factor contributing to the heightened ecological risk in the study region was predominantly attributed to human activities such as urban expansion, agricultural production, and overgrazing. Full article

Grasslands cover approximately 40% of the Earth’s surface. Thus, they play a pivotal role in supporting biodiversity, ecosystem services, and human livelihoods. These ecosystems provide crucial habitats for specialized plant and animal species, act as carbon sinks to mitigate climate change, and are vital for agriculture and pastoralism. However, grasslands face ongoing threats from certain factors, like land use changes, overgrazing, and climate change. Geospatial technologies have become indispensable to manage and protect these valuable ecosystems. This review focuses on the application of Google Earth Engine (GEE) in grasslands. The study presents a bibliometric analysis of research conducted between 2016–2023. Findings from the analysis reveal a significant growth in the use of GEE and different remote sensing products for grassland studies. Most authors reported grassland degradation in most countries. Additionally, China leads in research contributions, followed by the United States and Brazil. However, the analysis highlights the need for greater involvement from developing countries, particularly in Africa. Furthermore, it highlights the global distribution of research efforts, emphasizes the need for broader international participation. Full article