Wako University

Abstract Understanding how habitat structure and seasonal variation influence natural pest control is crucial for designing sustainable farming systems in tropical landscapes. This study experimentally quantified the contributions of birds and arthropods to pest suppression across tree-rich and tree-poor smallholder agroecosystems in the Kafa Biosphere Reserve, southwestern Ethiopia. Using 3,000 plasticine caterpillar models deployed over six weeks, it was assessed spatial and temporal variation in predation and evaluated how landscape complexity and prey traits shape predator activity. Overall, 33.9% of caterpillars exhibited predation marks, with arthropods responsible for 51.5% of attacks and birds for 46.9%. Total predation was higher in complex, tree-rich habitats (19.7%) than in simplified, tree-poor farms (14.2%). Birds exerted stronger predation pressure in complex landscapes, whereas arthropods dominated in simplified ones, demonstrating functional complementarity between predator guilds. Prey coloration significantly affected avian but not arthropod predation, indicating contrasting sensory foraging mechanisms. Predation activity peaked between the second and fourth weeks, coinciding with favorable microclimatic conditions. These findings reveal that habitat heterogeneity and seasonal dynamics jointly regulate biological control services in tropical smallholder systems. Promoting agroforestry, conserving native trees, and aligning farm management with periods of high predator activity can enhance the resilience of natural pest control. The study provides rare empirical evidence from African biosphere reserves, highlighting the ecological and practical value of maintaining multifunctional predator communities for biodiversity-based pest management.

This research has investigated the positive and negative impacts of forced migration on the hosting communities of Sherkole refugee camp area. The influx of huge number of refugees may put pressure on local people where both communities were competing with scarce resources. This study therefore aimed at investigating the socio-economic and the socio-cultural impacts of refugees on both communities of the study area. This study employed the qualitative research approach and used a purposive sampling technique for collecting data from participants. Hence, sources were gathered by interviews key informants and focus group discussions from the host communities were analyzed qualitatively. In addition to the primary sources, secondary sources were used to consolidate information that has been gathered through interviews, and focus group discussions. The findings indicated that the presence of refugees has both positive and negative impacts. The major positive findings were the socio-economic impacts like new job opportunities, small business activity, and availability of markets for local farmers around the refugee camp. Besides, along with refugees some professional persons arrived and provided social services for both receiving and refugee communities. On the other hand, as findings revealed that the socio-cultural impacts were regarded as negative impacts of refugees such as, conflict, insecurity, expansion of prostitution, rapping local girls, fighting each other to mention a few. Generally, as the results revealed both negative and positive impacts are witnessed. Finally, based on the findings, the researcher recommended: host community development, launching awareness creation project, positive intervention and improve social interaction.

Abstract Due to the rapid growth of the internet in recent years, social media has made it easier to create and share information via computer-mediated technologies. As a result of the social media revolution, people's communication and acquaintances have changed. The majority of people nowadays use social media to find and consume news rather than traditional news agencies. On the other hand, while online social media has grown in importance as a source of information and a method of connecting people together, detecting fake information remains a difficult problem to address. The majority of previous works presented appropriate machine learning models of fake news detections are concerned with specific group of language such as English. Despite the fact, the problem is not constricted to language. As a result, we implemented deep learning models on the Afaan Oromo language news dataset extracted from twitter and Facebook pages, to detect fake news on social media. In this study we annotated dataset of 1838 news text for train and testing purposes. Three popular deep learning methods used in the experiments: LSTM, GRU, and Bi-LSTM, to determine the best performing models, and each of the model performance is evaluated with various parameters. Then, when it came to recognizing fake news in news content written in the Afaan Oromo language, bidirectional-LSTM beat the other algorithms, namely unidirectional LSTM and GRU. The model is capable of making predictions on our dataset, with an f-1 score of 90%. Finally, python flask API server with spyder IDE was used for the web-based prototype development of the trained Bi-LSTM models for prediction of news.