SPATIAL TECHNOLOGY ADOPTION AMONGST AFRICAN DEVELOPMENT BANK COMMUNITY BASED AGRICULTURE AND RURAL DEVELOPMENT PROGRAMME BENEFICIARIES IN NIGERIA

Improved technology adoption for agricultural transformation and poverty reduction is critical in modern day agriculture. The study examined spatial distribution of technology adoption among African Development Bank-Community Based Agriculture and Rural Development Programme (AfDB-CBARDP) beneficiaries in Nigeria. Multi stage sampling procedure was employed where a total of 1020 farmers/Project beneficiaries across the 5 project implementing units (PIU) were selected. The data was estimated using descriptive statistics. The result obtained indicated that the mean value across the states for improved seed, post-harvest, dry season and fertilizer application technologies adoption stood at 83.7%, 81.9%, 75.8% and 51.8%. Similarly, the mean adoption rates for improved livestock, and fisheries technologies were reported to be 68.7%, and 50.6%, respectively. On a state-by-state basis, the weighted mean rates of adoption for all the agricultural technologies indicated that Adamawa, Bauchi, Gombe, Kaduna and Kwara have 58.0%, 70.2%, 66.9%, 67.7 and 50.0% respectively. The findings of the study revealed that the mean adoption rate among females Project beneficiaries for post-harvest technologies like multi-purpose threshers, rice dehaulers, rice milling, groundnut extractors, hammer milling machines and grinding machines were appreciably higher having percentage values of 66.3%, 56.2%, 70.1%, 60.1%, 59.1% and 54.9% respectively. In the same vein, distribution of technology adoption reveals that adoption of seed, fertilizer and post-harvest technologies ranked highest than fishery technologies across the Project implementing units. Technology adoption on gender basis further revealed that Groundnut, cowpea, and soybean technologies were highly adopted among participating male than female farmers. Concurrently, the study indicated an appreciable level of agricultural technology adoption across the Project implementing units. However, the mean adoption rate of Artificial Insemination technologies was low (25.5%) having fell short of the 40% before a farmer is considered to be an adopter.  The study recommended among others that where the overall adoption rate is lower, further research is required to identify the key constraints to adoption so that corrective measures can be found which can be used to improve adoption of future AfDB-CBARDP supported technologies.