ADOPTION STATUS OF IMPROVED WHEAT VARIETIES IN KAILALI, NEPAL

Sudarshan Rokaya and Prem Pandey

DOI: 10.26480/bda.02.2025.117.122

ABSTRACT
Wheat is an important staple crop widely cultivated throughout Nepal, from terai to hill. However, the potential for greater production through high-yielding varieties is hindered by socioeconomic and institutional factors. This study aimed to identify the adoption status of improved wheat varieties as well as factors that influence adoption and challenges to adoption in the Kailali district. The study area was selected purposively which was Ghodaghodi Municipality (ward no. 11) and Kailari Rural Municipality (ward no. 8). The sample size was 93 farmers, selected using simple random sampling techniques, and they were interviewed using a semi-structured questionnaire. Data was analyzed using MS Excel and SPSS software. Descriptive statistics, index ranking and binary logistic regression model was used for data analysis. The study indicated that 64.5% were adopters and 35.5% were non-adopter of improved wheat varieties. Vijay (21.5%) had the highest area coverage followed by Gautam (13.9%), Banganga (10.7%) and Aditya (6.5%). The logistic regression model indicated that membership in organizations, access to credit, extension services, subsidies, education level, training, and landholding size all had a positive and statistically significant influence on the adoption of improved wheat varieties. The most critical problem in improved wheat adoption was the high cost of seeds with an index value of 0.76, followed by the unavailability of seeds on time (0.65), and poor quality of improved varieties (0.60). The survey results suggest to addressing the high cost and timely availability of quality seeds, enhancing farmer education, improving access to credit, providing subsidies, offering regular advisory and extension services, and implementing participatory training programs to increase the adoption of improved practices.

KEYWORDS
High-yielding varieties, Adopter, Non-adopters, Socioeconomics factors, Binary logistic model