Adoption Drivers of Improved Open-Pollinated (OPVs) Maize Varieties by Smallholder Farmers in the Eastern Cape Province of South Africa

Abstract

This study assesses the determinants of adoption drivers of improved open-pollinated (OPVs) maize varieties by smallholder farmers in the Eastern Cape Province of South Africa. Cross-sectional data of multi-stage random sampling was used to randomly select 150 smallholder farmers in the province. Data collection was conducted through a well-structured questionnaire administered to 150 smallholder farmers. Descriptive statistics and a logistic regression model were used for the analyses. The inferential results reveal that the majority of farmers in the study area were females (70%) with an average age of 45 years and had six people in the households. Smallholder maize farmers spent an average of 10 years in school. The results show that smallholder maize farmers had a farm size of 3 ha and a household income of R 3565.00, which contributes highly to household and farm operations. Farmers had access to extension services and were members of farm organizations. The results show that hybrid is the most used maize variety rather than OPVs. The main reason, among other things, for the poor use and adoption of OPVs is the lack of knowledge about the seeds. The results drawn from the estimations show that socio-economic and institutional factors influenced the adoption of OPVs by farmers in the study area. Therefore, the study recommends strengthening the existing extension services to highlight the benefits of OPVs through training and farmers’ information days.

The effects of socio-economic factors on the food security status of rural households in the Eastern Cape Province: Evidence from farming households

“…The independent variables used in the analysis are shown in Table 2 above. Binary logistic regression is advantageous because it estimates the dichotomous outcome variables, which are more straightforward and flexible, to make the results more meaningful for interpretation (Sigigaba et al, 2021). This model was employed because it accommodates two categories in the dependent variable.…”