The study evaluated 33 accessions of groundnut in the field, consisting of 23 landraces from Nasarawa communities in Nigeria and 10 inbred lines. Assessment entailed the determination of plant survivorship, yield related parameters and pathological indices while genetic diversity study was undertaken using SSR and RAPD molecular markers. Data analysis was done on the Minitab 17.0 software. Significant variability was noted in all traits except in pod sizes, seed sizes and % infected seeds. About 33.3% of the accessions had a survival rate of ≥ 70.0% where 9/10 Inbred lines were found with overall yield (kg/ha) ranging from 4.0 ± 1.6 in Akwashiki-Doma to 516.8 ± 46.9 kg/ha in Samnut 24 × ICGV–91328. Five accessions (15.5%) had pathological indices of zero indicating no traces of any disease of any type and they included one landrace called Agric-Dazhogwa and four Inbred lines: Samnut 25 × ICGV–91317, Samnut 26 × ICGV–19324, Samnut 26 × ICGV–91328 and Samnut 26 × ICGV–91319. Coefficients of yield determination R2 by survivorship and pathological index were 50.6% and 15%, respectively. A fit model was established (Yield in kg/ha = –146 − 7.94 × Pi + 5.88 × S). Susceptibility to diseases depends on the type of variety (χ2(32) = 127.67, P = 0.00). Yield was significantly affected by BNR@30 (F = 5.47, P = 0.025, P < 0.05) and DSV@60*RUST@60 interaction effect (F = 4.39, P = 0.044, P < 0.05). The similarity coefficient ranged from 28.57 to 100 in plant morphology. Four varieties had no amplified bands with SSR primers whereas amplified bands were present only in four landraces accessions using the RAPD primer. The dendrogram generated by molecular data gave three groups where genetic similarity ranged from 41.4 to 100.0. The Inbred lines were noted for their high survivorship, good yield and disease resistance. Samnut 24 × ICGV–91328, an inbred line, had the highest yield but was susceptible to diseases. Among the landraces, Agric-Musha, Bomboyi-Dugu and Agric-Dazhogwa were selected for high survivorship and disease resistance. The selected inbred lines and landraces are valuable genetic resources that may harbour useful traits for breeding and they should be presented to the growers based on their unique agronomic values. The highest yielding inbred lines should be improved for resistance to late leaf spot diseases while the outstanding landraces should be improved for yield.

The multifaceted nature of the skills required by new-age professions, reflecting the dynamic evolution of the global workforce, is the focal point of this study. The objective was to synthesize the existing academic literature on these skills, employing a scientometric approach . This involved a comprehensive analysis of 367 articles from the merged Scopus and Web of Science databases. Science. We observed a significant increase in annual scientific output, with an increase of 87.01% over the last six years. The United States emerged as the most prolific contributor, responsible for 21.61% of total publications and receiving 34.31% of all citations. Using the Tree algorithm of Science (ToS), we identified fundamental contributions within this domain. The ToS outlined three main research streams: the convergence of gender, technology, and automation; defining elements of future work; and the dualistic impact of AI on work, seen as both a threat and an opportunity. Furthermore, our study explored the effects of automation on quality of life, the evolving meaning of work, and the emergence of new skills. A critical analysis was also conducted on how to balance technology with humanism, addressing challenges and strategies in workforce automation. This study offers a comprehensive scientometric view of new-age professions, highlighting the most important trends, challenges, and opportunities in this rapidly evolving field.

Farm households in developing countries are often involved in a variety of livelihood income-generating activities to achieve basic needs and enhance food security. However, little attention has been given to investigating the effect of livelihood diversification strategies on the adoption of agricultural land management practices. This study explored the nexus between livelihood diversification and Agricultural Land Management (ALM) practices in the Southern Ethiopian Highlands. Data for this study were gathered through a structured questionnaire, interviews, and focus group discussions. A total of 423 sample respondents were selected by using multistage random sampling techniques. The data were analyzed using the Inverse Herfindahl Hirschman Diversity Index (IHHDI), the multinomial logit model (MNL), and the probit regression model. The findings of the study revealed that on-farm income activities are the most dominant livelihood income strategies (69.1%), followed by non-farm (21%) and off-farm (9.64%). The multinomial logit model analysis demonstrated that variables such as sex, education, family size, distance to market, land size, extension contact, membership in cooperatives, and household income were the major drivers of farmers income diversification activities (p<0.05). The results of the probit analysis indicated that income from crop production, daily labor work, rents from farmland, and farm assets have a positive and significant effect on households' decisions to implement ALM practices. In contrast, incomes from remittance and migrant sources have a negative but statistically significant impact on the adoption of ALM measures. The farm household sources of income-generating strategies substantially affected the adoption intensity of ALM measures. Income generated from the on-farm sector alone cannot be considered a core income-generating activity for households or a means of achieving food security. Therefore, land management policies and program implementations should consider farmers’ livelihood diversification and income-generating strategies. In addition, such interventions need to promote sustainable farming practices, enhance innovation, and related measures for the adoption of ALM measures to ensure land sustainability.

The aim of this study was to assess the challenges of rural landholding rights of women in Boloso sore Woreda. The population that used as source of data were sample womenfrom four kebeles,Kebele land administration committee members,Woreda women,youth and children office head,Woreda women’s association president,Woreda agriculture office head and Woreda agriculture office rural land administration desk experts.four kebeles from 28 rural kebeles selected by using systematic random sampling. Data gathered using questionnaire were analyzed using SPSS where descriptive and inferential were used for the purpose. Secondary data were collected from different relevant literatures such as reports, research results documents and publications. As to the findings,women landholding trend in the study area was highly contrasts legally ensured equal holding and using rights of women with men.The community including women themselves perceive women independent landholding as taboo and prohibits it.Even if they hold by different means,the plot of land they got or held was small in size and not conducive for agriculture and house construction. The awareness of women on rural land registration and certification benefit was also poor. Thus,rural women should be initiated to organize and struggle for their equal landholding and administering rights.

The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI's predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.

In order to address severe siltation and enhance urban green spaces in Xianyang Lake, the research offers a sustainable solution by proposing an innovative integration of ecological dredging and landscape transformation. The key findings are as follows: Firstly, an ecological dredging mechanism was established by directly transporting sediment from Xianyang Lake to its central greenbelt, reducing dredging costs and environmental impact while creating a sustainable funding cycle through revenue from eco-tourism activities. Secondly, the landscape artistic conception of the central greenbelt was significantly improved by leveraging the natural distance between the lakeshore and the greenbelt, offering diverse viewing experiences and enhancing the cognitive abilities and urban life satisfaction of tourists. Thirdly, the project demonstrated substantial economic and social benefits, including revenue generation from paid activities like boat tours, increased public awareness of biodiversity through ecological education, and improved community well-being. The central greenbelt also enhanced the urban environment by improving air quality, mitigating the "heat island effect", and providing habitats for wildlife. This integrated approach serves as a model for sustainable urban development, offering valuable insights for cities facing similar ecological challenges. Future research should focus on long-term monitoring to further evaluate the ecological and socio-economic impacts of such projects.