Adoption scores for buffalo-based technologies in the Philippines as influenced by socio-economic, technological, communication, and institutional factors

Eric Parala Palacpac. Department of Agriculture-Philippine Carabao Center; Erwin Manantan Valiente. Central Luzon State University; Rovelyn Tolosa Jacang. Department of Agriculture-Philippine Carabao Center; Ma. Teresa Malayao Manito. Department of Education Region VII

Adoption scores for buffalo-based technologies in the Philippines as influenced by socio-economic, technological, communication, and institutional factors

2022

Eric Parala Palacpac(Science City of Munoz, Nueva Ecija (Philippines). Department of Agriculture-Philippine Carabao Center) | Erwin Manantan Valiente(Science City of Munoz, Nueva Ecija (Philippines). Central Luzon State University) | Rovelyn Tolosa Jacang(Science City of Munoz, Nueva Ecija (Philippines). Department of Agriculture-Philippine Carabao Center) | Ma. Teresa Malayao Manito(Bohol Division, Bohol (Philippines). Department of Education Region VII)

The study aimed to analyze the adoption of 22 technologies on dairy buffalo production in selected sites in the Philippines. A total of 666 farmer-informants who were previously exposed to training and other extension support services on dairy buffalo production were interviewed using semi-structured questionnaire. Dichotomous (yes or no) frequency and percentage responses along five stages, i.e., 'awareness', 'interest', 'evaluation', 'trial', and 'adoption' were transformed to sigma (Z) scores for adoption. Frequency responses for 'number of years of adoption' were likewise transformed to sigma scores. The two sigma scores were added to get the total adoption scores for each technology. The total or combined adoption scores (dependent variable) for all technologies were then tested for linear correlation and multiple regression with selected socio-economic traits, farm characteristics, and other independent variables. Most of the farmer-informants had at least 75 percent adoption rate in animal health care, improved forage feeding, estrus detection, and feeding of calves with colostrum. Multiple regression analysis indicates that attribution scores, years of experience in dairying, technical assistance, animal inventory, distance of the farm from a buffalo R and D institution, access to information materials and income from dairying positively and significantly influenced adoption scores. To increase adoption, improving the attribution by farmers to technologies as regards their relative advantage, compatibility with existing farm operations, trialability, and simplicity should be given priority consideration in designing and implementing extension delivery systems since it is the most powerful predictor variable to adoption.

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