Analysis of the drivers and intensity of adoption of climate-smart maize varieties among farmers in Kaduna State, Nigeria
Year: 2025
Type: article
Study design: Observational Study
Abstract: The study assessed drivers and intensity of the adoption of climate-smart maize varieties (CSMVs) among farmers in Kaduna State, Nigeria. A multi-stage sampling procedure procedure was employed to select 260 maize farmers across Samaru, Maigana, and Lere agricultural zones. Data were collected through structured questionnaires and analyzed using descriptive statistics, Probit and Tobit models. Results showed that 76.9% of the farmers adopted CSMVs, with SAMMAZ51 being the most preferred variety (49% of adopters) due to its high yield and stress resilience. Among adopters, 80.0% had formal education, while only 16.7% of non-adopters had formal education. The mean age of adopters was 33.2 years while nonadopters was 47.5 years. Invariably, younger farmers were more likely to adopt CSMVs compared to older farmers. The Probit regression analysis showed that education (p < 0.01), cooperative membership (p < 0.01), and access to credit (p < 0.05) influenced adoption decisions. Conversely, distance to seed sources (p < 0.01) and farming experience (p < 0.05) were negatively associated with adoption. The Tobit regression showed that education (p < 0.05), cooperative membership (p < 0.01), and extension contact (p < 0.05) positively influenced adoption intensity, while age (p < 0.01), household size (p < 0.01), and access to credit (p < 0.01) were significant barriers to adoption intensity. The study recommends improving access to credit, extension services, and training programs while strengthening cooperatives to address socioeconomic barriers. These measures will promote the inclusive adoption of CSMVs, enhancing agricultural productivity and resilience to climate change in the region.
Source: Agro-Science
DOI: 10.4314/as.v23i2.11
Language: English
Domain: Life Sciences
SDGs: Zero hunger, Climate action
Open Access status: diamond
OpenAlex ID: W4411610392