18 pages, This study examines factors that appear to contribute to farmers’ adoption and discontinuation of poly house technology for off-season vegetable production. We collected cross-sectional survey data from a sample of 151 households in Kaski district, Nepal during October 2018. The data are analyzed using Heckman’s two stage sample selection model. The study reveals that the family members report being engaged in nonfarm sector that there is an increased probability of discontinuation of poly house technology. Farmers may be diverting their labor towards nonfarm activities that result in higher returns to labor and different risks. At the same time, the results indicate that farmers who did not receive training on vegetable production were more likely to discontinue poly house technology. It was also found that increasing farmers’ engagement with marketing activities increased the likelihood of farmers to continue poly house technology and increase household income. The provision of continued technical support (e.g., training), input supply (e.g., seeds, fertilizers) and market information are essential to sustain the adopted technologies. The study sheds light on the sustainability of technology adoption by underpinning the importance of extension services for longer-term adoption. We believe that the combined effect of various technologies would be associated with sustained adoption of the improved off-season technologies. This provides a new direction to operationalize farmer-oriented policies in agricultural extension and helps in devising programs for sustained adoption of technology.
16 pages, This study analysed the delivery of public agricultural extension services to the rural households of Idutywa, Eastern Cape. Primary data were collected from 75 participants. Descriptive and inferential statistics were used to analyse the data. Results revealed that there is generally a lack of access to extension services by households in the study area. Above all, the findings showed that access to agricultural extension services is influenced by limited movements, cellphone data, household size, and a limited number of farmers for training. Based on the control and treated variables, the Average Treatment Effect Treated from Kernel, Nearest Neighbours, and Radius matching methods were found to be negative which means that if farmers did not receive the program during the pandemic, the performance and yields were going to be very poor and low. The study recommends that extension officers should be empowered with modern tools to deliver need-based agricultural extension services in the future.