This study analyzed the productivity and technical efficiency of cassava production in Ardo-Kola and Gassol Local Government Areas of Taraba State. Data were collected from 115 respondents using a structured questionnaire covering 2010/2011 farming season. Inferential statistics were employed in the analysis of data collected. Maximum likelihood estimate (MLE) for all parameters of the stochastic frontier production function and the inefficiency model for the cassava farmers in the study area was employed in the analysis. The study reveals that, farm size is the most important factor of production having an elasticity coefficient of 0.492 indicating that, output of cassava production is inelastic. Indicating that, an increase of 5% in farm size used in production of cassava, all things being equal; there would be a corresponding increase in the total output by 4.92%. Similarly, herbicide has an elasticity coefficient of 0.315 and was statistically significant at 5 %. This implies that, an increase in the quantity of herbicide would increase the output by 3.15 percent. Hired labour has an elasticity efficiency of 0.783 and is positively related to the total output of cassava. A 5 percent increase in hired labour will bring about an increase in the level of output. Despites challenges faced by cassava farmers in the study area, the study concludes that, farmers involved in cassava production have more than average technical efficiency and this means there is opportunity to increase cassava production in the study area. Similarly, the cassava production was profitable in the area. The research recommended public private partnership (PPP) to sensitize and educate farmers to enable them benefit from the new innovations and technology that abound in the agricultural sector.

The study examined the economics of small-scale maize production in Toto Local Government Area of Nasarawa State, Nigeria. A two-stage sampling technique was adopted for the data collection. Descriptive statistics, regression and gross margin analyses were used to analyse the data collected. The study revealed that majority of the respondents were within the active working age and most of them (83%) were male. The majority of the respondents were married and had a household size of five persons and farm size of 1-2ha. Results of the regression analysis revealed that the output of small-scale maize farmers was influenced by farm size, marital status and annual income at 1% and 5% respectively. A gross margin of N170,594.50 was earned from one hectare of maize farm with a return per naira invested of 2.40. The cost of labour constituted a greater proportion of the costs of production, accounting for about 58.38% and 39.52% of the total variable cost and the total cost respectively, the total cost. The problems militating against maize production in the study area were high cost of labour, pests and diseases, inadequate storage facilities, inadequate capital, marketing problems, transportation, poor access to credit facilities and high cost of inputs. The study, therefore, recommends that farmers should be properly educated by the extension agents on pest and disease control measures. Moreover, inputs should be made available to farmers at subsidized rates by relevant stakeholders.

The study assessed the determinants of adoption rate of rice production technologies introduced by Agricultural Research Outreach Centres in Nigeria. Data were collected using a multi-sampling technique. Data were analysed using simple descriptive statistics and multiple linear regression. Results revealed that respondents’ mean age was 50 years; level of formal education of farmers was low and farm size was 2.5ha on the average. Age, farming experience, years of schooling and number of extension visits were the socioeconomic determinants affecting rate of adoption. It was recommended that more villages should be selected with partnership between government and the private sector in order to cover more grounds and increase the rate of adoption of new technologies. Also, government and relevant stakeholders should prioritize establishment of the best extension teaching methods and systems as well as administration to help increase adoption rate of innovations and sustainability of the use of these technologies over time.

There has been contradiction among researchers regarding similarity or otherwise of technical efficiency estimate obtained from Cobb-Douglas and Translog frontier models. While some researchers believe that results obtained from the two functional forms, given the same data, were essentially similar, others disagreed. This study compared both functional forms to analyze technical inefficiency in dry-season tomato production in Jos-South Area of Plateau State. Data were collected from 60 dry-season tomato farmers sampled through three-staged random sampling technique. The analyses of the data were done using both Cobb-Douglas and translog frontier models. The results showed that the estimated elasticities, efficiency scores and inefficiency effects from Cobb-Douglas and Translog functional forms differ significantly. Therefore, the choice of functional form for efficiency analysis should be based on convenience, meeting selection criteria premise on the value of variance-parameters and objective of the study.

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