One crucial metric for estimating a reservoirs and dam’s lifespan is sedimentation. It is dependent upon sediment output, which in turn is dependent upon soil erosion. The study area, the Aguat Wuha Dam, was located in Simada woreda, of northwestern parts of Ethiopia. And the study's goal was to use Arc GIS and RUSLE adjusted to Ethiopian conditions to assess potential soil erosion and sediment output from the watershed and identify hotspot locations for appropriate planning for erosion and sedimentation problem management techniques to make the outputs of the dam project more productive and effective for the proposed and suggested purpose of the dam. To predict the geographical patterns of soil erosion in the watershed, the Geographic Information System (GIS) was combined with the revised universal soil loss equation (RUSLE). A soil erosion map was produced using ArcGIS by utilizing all of the model's parameters, including Erosivity, erodibility, steepness, land use, land cover, and supportive practice factors. The watershed's yearly soil loss varies from 0 to 413.86 tons/ha. In order to determine the erosion hotspot area, the average annual soil loss value was discovered to be 9.24 tons/ha/year and was categorized into six erosion severity classes: low, moderate, high, very high, severe, and very severe. These findings indicated that 162.57 ha and 699.17 ha of the watershed were considered to be extremely and severely vulnerable to soil erosion, respectively. It was discovered that the anticipated sediment yield supplied to the outlet varied from 0 to 104.94 tons/ha/year. By standing from the implications of the assessments of the geological, geotechnical, topographical, and socioenvironmental considerations Watershed management is the most effective way to reduce the amount of sediment produced and the amount that enters the reservoir among the several reservoir sedimentation control options that are available.

The obtaining of new data on the transformation of parent materials into soil and on soil as a set of essential properties is provided on the basis of previously conducted fundamental studies of soils formed on loess-like loams in Belarus (15,000 numerical indicators). The study objects are autochthonous soils of uniform granulometric texture. The basic properties without which soils cannot exist are comprehensively considered. Interpolation of factual materials is given, highlighting the essential properties of soils. Soil formation is analyzed as a natural phenomenon depending on the life activity of biota and the water regime. Models for differentiation of the chemical profile and bioenergy potential of soils are presented. The results of the represented study interpret the available materials taking into account publications on the biology and water regime of soils over the past 50 years into three issues: the difference between soil and soil-like bodies; the soil formation as a natural phenomenon of the mobilization of soil biota from the energy of the sun, the atmosphere, and the destruction of minerals in the parent materials; and the essence of soil as a solid phase and as an ecosystem. The novelty of the article study is determined by the consideration of the priority of microorganisms and water regime in soil formation, chemical-analytical identification of types of water regime, and determination of the water regime as a marker of soil genesis.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.

Climate change is one of the most critical global challenges, driven primarily by the rapid increase in greenhouse gas concentrations. Carbon sequestration, the process by which ecosystems capture and store carbon, plays a key role in mitigating climate change. This study investigates the factors influencing carbon sequestration in subtropical planted forest ecosystems. Field data were collected from 100 randomly sampled plots of varying sizes (20 m² × 20 m² for trees, 5 m² × 5 m² for shrubs, and 1 m² × 1 m² for herbs) between February and April 2022. A total of 3,440 plants representing 36 species were recorded, with Prosopis juliflora and Prosopis cineraria as the dominant tree species and Desmostachya bipinnata as the dominant herb. Regression analysis, Pearson correlation, and structural equation modeling were performed using R software to explore relationships between carbon sequestration and various biotic and abiotic factors. Biotic factors such as diameter at breast height (DBH; R=0.94), tree height (R=0.83), and crown area (R=0.98) showed strong positive correlations with carbon sequestration. Abiotic factors like litter (R=0.37), humus depth (R=0.43), and electrical conductivity (E.C; R=0.11) also positively influenced carbon storage. Conversely, pH (R=-0.058), total dissolved solids (TDS; R=-0.067), organic matter (R=-0.1), and nitrogen (R=-0.096) negatively impacted carbon sequestration. The findings highlight that both biotic and abiotic factors significantly influence carbon sequestration in planted forests. To enhance carbon storage and mitigate climate change, efforts such as afforestation, reforestation, and conservation of subtropical forest ecosystems are essential.

In recent years, phytoremediation as a promising ecological restoration technique has emerged. Phytoremediation is a repair method that uses green plants to transfer, contain, or convert contaminants to the environment. Phytoremediation is a heavy metal, organic or radioactive element contaminated soil and water. The results show

that the use of plant absorption, volatilization, root filtration, degradation, stability and other effects, can purify soil

or water pollutants, to achieve the purpose of purifying the environment, so phytoremediation is a great potential, the development of the clean environment Pollution of green technology. The use of plants to repair contaminated soil is a cheap and durable bioremediation technique. The protection and management of Taihu Lake is an indispensable measure for the protection of Taihu Lake water, and the advantages of phytoremedry investment, low freight and

low leakage of pollutants show that its promotion has this unusual significance. This paper expounds the difference

of remediation soil between Taihu Lake Ecological Shelter Forest, and the comparison of the soil capacity of the

experimental tree species. Second, the correlation between the monitoring projects is discussed.

Himalayan ‘Ecotone’ temperate conifer forest is the cradle of life for human survival and wildlife existence. Human intervention and climate change are rapidly degrading and declining this transitional zone. This study aimed to quantify the floristic structure, important value index (IVI), topographic and edaphic variables between 2019 and 2020 utilizing circular quadrant method (10m × 10m). The upper-storey layer consisted of 17 tree species from 12 families and 9 orders. Middle-storey shrubs comprise 23 species representing 14 families and 12 orders. A total of 43 species of herbs, grasses, and ferns were identified from the ground-storey layer, representing 25 families and 21 orders. Upper-storey vegetation structure was dominated by Pinus roxburghii (22.45%), while middle-storey vegetation structure was dominated by Dodonaea viscosa (7.69%). However, the ground layer vegetation was diverse in species composition and distribution. By using Ward’s agglomerative clustering technique, the floral vegetation structure was divided into three floral communities. Ailanthus altissima, Pinus wallichiana, and P. roxburghii had the highest IVI values in Piro–Aial (Group 2), Piwa–Quin (Group 3) and Aial–Qugal (Group 2). The IVI values for Aesculus indica, Celtis australis, and Quercus incana in Aial-Qugal (Group 2) were not determined. Nevertheless, eleven of these species had 0 IVI values in Piro–Aial (Group 2) and Piwa–Quin (Group 3). Based on the CCA ordination biplot, significant differences were observed in floral characteristics and distribution depending on temperature, rainfall, soil pH, altitude, and topographic features. Based on Ward’s agglomerative clustering, it was found that Himalayan ‘Ecotone’ temperate conifer forests exhibit a rich and diverse floristic structure.