AN EMPIRICAL ANALYSIS OF THE KNOWLEDGE-ATTITUDE ADOPTION NEXUS OF ARTIFICIAL INSEMINATION AMONG DAIRY FARMERS OF THE UPPER GANGETIC PLAINS REGION, INDIA

Abstract

Artificial insemination (AI) is a cornerstone of genetic improvement in dairy cattle, yet its adoption in smallholder systems of Uttar Pradesh remains incomplete and understudied. This study assessed the knowledge, attitude, and adoption levels of AI technology among dairy farmers in Meerut and Hapur districts of western Uttar Pradesh, located in the northern region of India, and identified socio-economic and cognitive predictors of adoption. A cross-sectional survey was conducted with 220 purposively selected dairy farmers across 18 villages of Meerut and Hapur districts using a pre-tested structured interview schedule. Knowledge was assessed by 20 dichotomous items (KAP instrument) and attitude by 12 Likert-scale items. Adoption was classified into four categories, Non-Adopter, Discontinuous, Partial, and Full. Statistical analyses included Kruskal-Wallis tests, Mann-Whitney U post-hoc comparisons, chi-square tests, Spearman correlations, and binary logistic regression. Among respondents, 60.9% were AI adopters (full or partial) and 27.7% were non-adopters. The overall mean knowledge score was 13.00 ± 2.69 out of 20; 63.6% had medium knowledge, 19.5% high, and 16.8% low. Mean attitude score was 40.40 ± 3.32 out of 60. Knowledge differed significantly across adoption categories (Kruskal-Wallis H = 73.99, p < 0.001), with full adopters scoring highest (15.74 ± 1.45). Attitude scores did not differ significantly (p = 0.068). Logistic regression identified knowledge score (OR = 2.59, p < 0.001) and distance to AI centre (OR = 0.61, p < 0.05) as the primary determinants of adoption. Crossbred cows (45.3%) were the predominant species, and government AI technicians served 54.1% of users. Heat detection was the leading barrier (23.0%).