O. OMOREGIE

This study examined the adoption of Innovations by small-holders oil palm farmers in Ovia North East local government Area Edo state, Nigeria. This studies specifically examine the socioeconomic characteristics of smallholder oil palm farmers in Ovia North East Local Government Area, Edo State, Nigeria, the agricultural innovations smallholder oil palm farmers were aware of in the area, level of adoption of the innovations by oil palm farmers in the study area, the farmers information sources on oil-palm technologies, motivations for farmers’ adoption of oil palm technologies, constraints affecting the adoption of the innovations among smallholders oil palm farmers.A multistage sampling procedure was used to select 120 smallholders oil palm farmers from six purposively selected communities. Data were collected structural questionnaire, collected data were analysed using descriptive statistic (frequency count, percentage and mean). For the bjectives inferential statistics (multiple regression) was used to test the hypothesis. The results reveals that majority of the farmers in the area were predominantly male (63.33%), (67.5%) of the respondents were aged of 30- 49 years and married (50%). Also (70.83%) of the respondents cultivated a farm size of 1- 3 hectares, With annual income of ₦500,000-₦1,000,000 and had limited access to extension services, the had high awareness to traditional innovations such as pest and diseases managements, soil improvement technique. However, ICT- based Innovative tools, weather predictive information and Mechanized harvesting tools remained low .Adoption for fertilizer (mean = 3.33) and pest and disease practices (mean = 3.18) were relatively high but low for mobile application (mean = 1.45) and mechanized harvesting tools (mean = 1.35). The major motivation for farmers adopt of Innovations included: higher income (100%), fellow farmers/ peer influence (99.17%) and better crop yield (98.32%). The regression results reveals that, there is no significant relationship between most of the socioeconomic characteristics and adoption. However annual ix income (p= 0.0005), access to extension services (p= 0.000) and awareness of agricultural innovations (0.004) had significant influence in the adoption of Innovations. Key constraints were high cost of adoption (99.17%) limited access to credit (98.33%), weak extension services (95.00%), inadequate training opportunity (95.00%), and limited access to modern agricultural. Although response demonstrate readiness to adopt innovations however the limit outreach by extension agents and insufficient supports significantly impeded their adoption of oil palm innovations. This study recommended expanded extension outreach, targeted training programs, access to land, financial supports to increase the level adoption of oil palm innovations among smallholders' oil palm farmers and its productivity.

This study examined the socio-economic effects of artificial intelligence in Agricultural by farmers and extension agents in Edo state, Nigeria. A stratified sampling procedure was used to select 131 farmers and 82 extension agents that were used for this study. The specific objectives were analyzed using descriptive statistics ( frequency, percentage and mean rating ) why regression analysis was used to analyze the hypothesis. Findings revealed that the majority of farmers (62.4%) and extension agents (61.7%) were male, and most fell within the economically active age range of 31–50 years. Awareness level of AI technologies was high, 74.4% and 91.7% of farmers and extension agents respectively, satellite imagery (64.0% of farmers; 75.0% of extension agents), and climate precision models (60.0% of farmers; 81.7% of extension agents). However, advanced technologies such as remote sensing recorded low adoption (8.0% of farmers; 1.7% of extension agents). Adoption levels of Artificial intelligence(AI) varied and showed mixed sociocultural reactions towards AI technologies. The regression analysis shows that most socio-economic characteristics, sex (β = 0.162; p = 0.020), association membership (β = 0.258; p < 0.01), awareness of AI (β = 0.585; p < 0.01), and location (p = 0.058) of respondents have significant influence on awareness ( β = 0.585; p < 0.01 ) and adoption (Adjusted R² = 0.523 ) of AI technologies with some sociocul tural effects. The study concludes that while awareness and partial adoption of AI technologies are increasing, full integration into agricultural practice is hindered by sociocultural beliefs, limited infrastructure, and gaps in technical capacity