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CONFLICT PREVENTION THROUGH PEACEBUILDING AMONG AGRARIAN COMMUNITIES
(FACULTY OF SOCIAL SCIENCE ,FEDERAL UNIVERSITY OF OYE-EKITI,NIGERIA, 2026-04-29)
IRANOLA, O.M
This study highlights a concise steps to preventing conflict among agrarian community, using the principle of a peace building as a measure to forestall occurrences of conflict majorly among farmers. It aims to defining the term conflict, conflict prevention and atypical agrarian community; identify the importance and types of conflict in an agrarian community, explicit the processes of peace building in an agrarian community and give a classical strategy applicable to preventing conflicts in rural community through the process of peace building. The study identifies and explain types of conflict in agrarian community as pastoral-agricultural conflict / crop farmers-herdsmen conflict, town-country conflict, inter family and inter-clan-conflict, conflict of wages, and conflict of land and person-person conflict. It explains the concept of peace building and the process involved in building peace. The study concluded with a concise strategy for peace building and recommended the adoption of the method and process as explained taking into consideration the activities of spoilers and their proper handling.
IMPACT OF GOVERNMENT -PROVIDED PROCESSING EQUIPMENT ON BENEFICIARIES' LIVELIHOOD STATUS IN ONDO STATE NIGERIA , A MIXED -METHODS ASSESMENT
(FACULTY OF SOCIAL SCIENCE ,FEDERAL UNIVERSITY OF OYE-EKITI,NIGERIA, 2026-04-29)
IRANOLA, O.M
;
SHEHU, M
Ondo state benefited from the Federal Government recipient of credit, and through FADAMA provides support to beneficiaries with food processing and equipment. This study tend to assess the impact of the benefits of the processing equipment supplies to the beneficiaries on the poverty reduction level. The population of the study consist of 42 groups of farmers with a total of 450 beneficiaries .Multi stage sampling procedure was adopted. Based on the preponderance of processors, purposive sampling was used to sample 11 out of 18 Local government areas across the three senatorial district within the state. Random sampling technique was used to select 30.2% of the beneficiaries from each group of participant to give a sample size of 136, structured questionnaire was administered to the respondents by the trained enumerators. Data obtained were analyzed using descriptive (means, frequency and percentage) and inferential statistics t-test. Major findings revealed that majority of the respondents were male with average age 46.9 years, having secondary education, mean household of 5 person. It was also observed that majority of them had no source to credit, and primarily had food processing as their major occupation. Findings also revealed an Improve poverty reduction from the lives of the beneficiaries. The study concluded that the assistant received by the beneficiaries in form of processing equipment ,has significantly reduced the poverty level of the recipients ,and had positively impacted on the outputs and income and as well as substantial improvement on their lives. The study recommended among others that more awareness programs and enlightenment campaign should be mounted to sensitize the populace on the needs to be empowered economically and be self - employed especially in the present economic situation where there is scarcity of white collar jobs.
Hydraulic–oxygenation and nutrient–ionic axes structure fish assemblages in a tropical river: a pre-impoundment baseline from the Lower Gurara River, Nigeria
(Taylor & Francis, 2026-06-29)
Abubakar, Danasabe S
;
Meye, Jacob A.
;
Ayanwale, Adesola V.
;
Auta, Yohanna I.
;
Ukubuiwe, Azubuike C.
This study establishes a comprehensive pre-impoundment ecological baseline for the Lower Gurara River, quantifying how physicochemical gradients structure fish assemblages across spatial and seasonal scales in advance of hydropower regulation. The assemblage comprised 3,831 individuals representing 15 species and 11 families, reflecting a typical West African tropical river community. Multivariate analyses identified two dominant environmental axes: a “hydraulic–oxygenation axis” associated with coarse substrates, high turbulence, and elevated dissolved oxygen in upstream reaches, and a “nutrient–ionic axis” that intensified progressively downstream with increasing conductivity, sulphate, and phosphate. These gradients corresponded closely with observed community patterns. Rheophilic and benthic foraging taxa were most abundant at well-oxygenated upstream stations (S1–S2), whereas generalist and disturbance-tolerant species dominated depositional, nutrient-enriched downstream habitats (S3–S4). Species richness remained constant across stations, but evenness declined and dominance increased downstream, indicating an altered assemblage structure rather than compositional turnover. Seasonal patterns showed strong wetseason increases in abundance driven by flood-pulse connectivity, while α-diversity metrics remained comparatively stable, consistent with hydrology-mediated dynamics in tropical rivers. Interannual increases in total abundance from 2021 to 2022 reflected regional hydroclimatic variability, particularly sensitivity of low flows and flood extent. Collectively, these results demonstrate that linked hydraulic–oxygenation and nutrient– ionic gradients offer a robust, mechanistic framework for interpreting fish assemblage responses. The baseline also identifies key sentinel stations, S2 for aeration sensitivity and S4 for enrichment, and thermal change, and supports the development of threshold-based monitoring dashboards to guide adaptive management following impoundment. This dataset provides a defensible reference condition crucial for safeguarding ecological integrity during and after hydropower development on the Lower Gurara River.
Artificial Intelligence, Psychological Well-being and Workforce Productivity in Higher Education: An Educational Psychology Perspective on Sustainable Human Capital Development.
(4th International Research and Development Conference (IRDeC-2026), Kampala International University, Kampala, Uganda. https://irdec.kiu.ac.ug/2026/, 2026-10-12)
Baji, M. I.,
;
Musa, HUSSAINI
Artificial Intelligence (AI) is reshaping teaching, research and administration in universities, yet its psychological consequences for academic staff are under-examined. This paper proposes and specifies a mixed-methods study of how AI competence and perceived organizational support relate to work engagement, psychological well-being and workforce productivity among lecturers and academic support staff in selected African universities. Grounded in the Job Demands–Resources (JD-R) model and Self-Determination Theory (SDT), the study treats AI as both a job resource (automation, feedback, time savings) and a job demand (learning load, technostress, fear of displacement). An explanatory sequential design is proposed: a cross-sectional survey of about 400 staff using validated scales, analysed with descriptive statistics and structural equation modelling (SEM), followed by 20–24 semi-structured interviews analysed thematically. Because data collection is still to be completed, the Results section reports the pre-specified hypotheses, fit criteria and the pattern of findings anticipated from theory and prior evidence, not observed estimates. The expected pattern is that AI adoption raises productivity indirectly, through AI competence, organizational support and work engagement, and that its benefits weaken where technostress is high and psychological safety is low. The paper recommends integrating AI-competency training with well-being provision, organizational counselling services and evidence-based institutional policy. These actions support SDGs 3, 4, 8 and 9
Artificial intelligence and digital learning for workforce productivity and efficiency: Transforming continuing education and e-learning in African universities
(4th International Research and Development Conference (IRDeC-2026). Kampala International University, 2026-10-12)
Baji, M. I.,
;
Musa, H.
The Fourth Industrial Revolution has reshaped continuing education, compelling higher education institutions to adopt digital solutions that strengthen workforce productivity and efficiency, lifelong learning and institutional competitiveness. Artificial Intelligence (AI) has emerged as a transformative technology capable of personalising learning experiences, automating administrative processes, supporting data-driven decision-making and enhancing learner engagement. Despite this potential, many continuing education and e-learning centres in Africa continue to grapple with inadequate digital infrastructure, limited staff AI competencies, policy gaps and resistance to technological change. This paper examines how AI and digital learning technologies can improve workforce efficiency and productivity within continuing education programmes in African universities. Guided by the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), the study adopts a mixed-methods design involving administrators, lecturers, instructional designers and adult learners from selected universities. Data will be collected through structured questionnaires, interviews and institutional document analysis to assess AI readiness, digital competence, employee productivity, learner satisfaction and organisational performance. The study anticipates that effective AI integration will significantly enhance administrative efficiency, teaching quality, learner retention, staff productivity and institutional responsiveness to labour-market demands. It further argues that sustainable implementation depends on strategic leadership, investment in digital infrastructure, continuous staff capacity development, robust governance frameworks and clear ethical guidelines for AI deployment. The paper concludes that continuing education centres must reposition themselves as innovation hubs that leverage AI to advance flexible, inclusive and high-quality lifelong learning, offering practical recommendations for university administrators, policymakers and educational leaders seeking to strengthen workforce productivity and efficiency and accelerate digital transformation across African higher education. The study contributes directly to Sustainable Development Goals (SDGs) 4 (Quality Education), 8 (Decent Work and Economic Growth), 9 (Industry, Innovation and Infrastructure) and 17 (Partnerships for the Goals).