The Thai Journal of Veterinary Medicine


This study aimed to establish a business model of reproductive biotechnology in small ruminant. Sets of questionnaires were administered to interview 115 farmers from different regions in Thailand. Data including age, educational background, farm size and type, source of replacement breeders, breeding technology, farm problems and attitude toward the technology were collected. Logistic regression analysis along with neutral network analysis was used to identify factors associated technology interest with P< 0.05. Results showed that among 115 participants, 72.2% were interested in reproductive biotechnology. In univariate logistic regression analysis, farmer’s age [OR=0.96, 95% CI (0.93,1.00)], educational background [OR=1.17, 95% CI (0.13,0.80)], production problems [OR=1.28, 95% CI (1.12,1.478)] and marketing problems [OR=1.40, 95% CI (1.18,1.67)] were independently associated with technology interest (P< 0.05). Similar to the neural network analysis, farmer’s age, overall farm problems, marketing problems, production problems and educational background were the primary factors influencing technology interest of farmers. Next, the data from 18 semi-structured interviews were interpreted to establish suggested business models of reproductive biotechnology package. This indicated that a single business model could not fit the expectation of all farmers. Thus, six business models were established and 2 models were initially implemented in 7 farms with a moderate successful rate. In conclusion, the implement ofreproductive biotechnology in small ruminant farm should be addressed to the young farmers with high education to improve the animal genetic value and sustain their livelihoods. However, the different farm managements play a key role in the success of these business models.



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