This study examined the determinants of adoption of improved soybean seeds among farmers in southern Borno State, Nigeria. Data for the study were obtained from 360 respondents selected through multi-stage sampling procedure. Inferential statistical techniques namely the Logit model and the Tobit model were used to estimate the likelihood of technology adoption among respondents and the extent of adoption of improved soybean seeds by the respondents, respectively. Yield of soybean and distance to source of improved seeds were statistically significant factors (ρ ≤ 0.01) that influenced the likelihood of adoption of improved soybean seeds among the respondents. Farm size and distance of respondents to source of improved soybean seeds were statistically significant factors (ρ ≤ 0.01) that influenced the extent of adoption of improved soybean seeds among the respondents. Based on the findings of this study, it is recommended that improved technologies in the form of high yielding seeds varieties should be made available to farmers. Farm service centers should be established within reasonable distance from farming communities. This is the bring technologies closer to farmers, thereby reducing the risks that farmers have to encounter to get farm inputs.

This work was carried out as a collaborative effort among all the authors. YLI conceptualized the idea of the work, constructed the data collection instrument, analyzed the data and did the first reporting. AAI collected the data. DBB handled the data entry and assisted in the review. BOO provided the technical know-how, vetted the article and certified it for publication. All authors read and approved the final manuscript.

Soybean is one of the most important sources of protein known to man. It is the cheapest source of protein in terms of accessibility, especially in the developing countries. The importance of the crop informed its introduction to Borno State in 2004. this study examined the determinants of likelihood of improved soybean seeds adoption among farmers in southern Borno State. Data for the study were obtained from 360 respondents selected through multi-stage sampling procedure. Purposive and random sampling techniques were employed at the various stages of selection. Inferential statistical technique-the Logit model-was used to estimate the likelihood of technology adoption among the respondents. The results of the study revealed that farm size and expenditure on hired labor were the most important socioeconomic factors that significantly (p≤ 0.05) influenced the likelihood of adoption of improced soybean seeds among the respondents while yield of soybean (p≤ 0.01), household utilization of soybean (p≤ 0.05) and maturity period of soybean (p≤ 0.05) were the significant technology characteristics that influenced the likelihood of adoption of improved soybean seeds by farmers in the area. Based on the findings of this study, it was recommended that: labor-saving technologies be made available to farmers in order to cushion the effect of their expenditure on hired labor, farmers should also be linked to sources of financial support so as to enable them afford hired labor,

This paper reviews the capacity (number, quality and motivation)

Basically, climate change refers to any change in climate overtime, generally caused by natural variability and/or human activities. It has great devastating impact, particularly on agriculture and by extrapolation on farmers and the national economy. The frontline agricultural extension workers are expected to be among the principal stakeholders to teach farmers how to cope with climate change. Consequently, there is a need to develop appropriate teaching package for the training of the frontline agricultural extension workers, based on the myriad of adaptation strategies and practices available in the literature. This paper synthesizes the rationale for capacity building in climate change and the adaptation or coping strategies. The modules (train-the-trainer) for teaching agricultural extension workers and farmers are documented in the paper.