Rice productivity is low in Nigeria, and NERICA was introduced by West Africa Rice Development Agency (WARDA) to solve this problem. This study focused on NERICA productivity and efficiency in Kaduna State. A three-stage sampling technique was used with Kaduna State being purposively selected. Igabi and Soba Local Government Areas (LGAs) were randomly selected. Five villages were randomly selected from each LGA, with a total of 129 NERICA farmers selected and used for the analysis. There were technical and managerial inefficiencies among the farmers. NERICA production is characterized by increasing returns to scale with a value of 1.4954. The farmers were cost inefficient, hence there was room for improvement in NERICA production in the study area. The average productivity (AP) was 26.30. The average marginal productivity (MP) is 9.213. The average total factor productivity (TFP) is 12.87. Farmer's age and access to credit had negative influence on both AP and MP. Extension contact and adoption of NERICA technology positively influenced the AP and MP. Farmer's age had negative influence on total factor productivity. Formal education, farming experience, extension contact, farm commercialization and adoption of NERICA technology had positive influence on the total factor productivity of the NERICA farmers. Improvements in the supply of NERICA seed, extension services and level of farm commercialization are recommended.
In Nigeria, farming still depends largely on nature and therefore, vulnerable to climate variability. Climate variability affects production in a number of ways such as uncertainties with the onset of the farming season, flooding and drought in some areas .Farmers have responded by using adaptation strategies. The determinants of farmers' use of these strategies have not been adequately ascertained. In this study, farmers 'response to climate variability was examined. Primary and secondary data were used. A multi-stage sampling procedure was adopted in the collection of the primary data using structured questionnaires. Four vegetation zones out of seven where farming is mainly carried out were selected for the study. Using a list of farmers from the Agricultural Development Project, 470 farmers were randomly selected based on probability proportionate to the number of farmers in each vegetation zone. They were administered structured questionnaire. Data collected included socio-economic characteristics of the farmers, adaptation strategies they used and constraints associated with them. Data on climate were collected from Nigeria Meteorological Station, Oshodi. Data were analyzed using descriptive statistics, and multinomial logit models.
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