This study aims to examine and challenge the impact of local government policy governance on the oil palm plantation sector in Riau Province, Indonesia. It was discovered that 1,628 million hectares of illegal oil palm plantations are located within forest areas. Plantation area and crop harvest areas are declining due to the increase in damaged old plants, low productivity of plantation crops, inadequate facilities and infrastructure conditions, low technology application, plantation business licensing, limited downstream plantation industry and marketing, assistance in changing the attitudes, behavior, and skills of farmers. The methodology used was exploratory qualitative to explore this topic, and the determination of research topics was conducted using Biblioshiny application analysis. Then, the data was analyzed using Nvivo 12 Plus software. The results of this study discovered that the policy governance of the oil palm plantation sector as a leading commodity in Riau Province, Indonesia, is influenced by three dimensions: firstly, the actor dimension; secondly, the structural dimension; and third, the empirical dimension of governance. This research contributes as a knowledge reference to oil palm plantations.

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.

In this study, the effect of roasting and boiling on the yield and oxidative stability of soya bean oil was investigated. The oil was soxhlet extracted and the oxidative stability was determined by the free fatty acid value, acid value and peroxide value. The results showed that the oil yield, free fatty acid value, acid value and peroxide value were significantly affected by roasting, boiling, and the thermal treatment time. The percentage oil yield in the control oil sample was 18.51%, which increased to 20.24% and 20.73% after boiling and roasting respectively, at 40mins. The corresponding free fatty acid and the peroxide value of the control oil sample were 0.14% and 2.04 meqO₂/kg, which increased to 0.82% and 6.60 meqO₂/kg by roasting, and 0.47% and 5.62 meqO₂/kg by boiling respectively. Thus the oil yield, free fatty acid value, peroxide value, and acid value increased with increasing roasting and boiling time.
The results indicate that roasting provides a higher oil yield than boiling, but boiled oil has higher oxidative stability than roasted oil.

This study aims to investigate what influences local workers over the age of 40 to work and stay employed in oil palm plantations. 414 individuals participated in a face-to-face interview that provided the study’s primary source of data. Exploratory Factor Analysis was used to analyse the given data. The study revealed that factors influencing local workers over the age of 40 years to leave or continue working in oil palm plantations can be classified as income factors, internal factors and external factors. The income factor was the most significant factor as the percentage variance explained by the factor was 26.792% and Cronbach Alpha was high at 0.870. Therefore, the study suggested that the oil palm plantation managements pay more attention to income elements such as basic salary, wage rate paid to the workers and allowance given to the workers since these elements contribute to the monthly total income received by the workers and in turn be able to attract more local workers to work and remain in the plantations.

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.

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.