Analysis of factors influencing the adoption of dairy technologies in Western Kenya.

The necessity to improve dairy production in the less developed countries (LDCs) exists, and the Government of Kenya has recognised this as evidenced by its efforts in restructuring the dairy sector. The sector contributes substantially to the country’s gross domestic product (GDP). Studies have shown that with the restructure, smallholder farmers stand to benefit from dairy more than from other farming enterprises. However spatial differences in the rates of adoption of dairy technologies, in the face of the available opportunities, in part reflect the existence of impediments to dairy development in some parts of the country. Western Kenya, one of the country’s poorest areas, has shown low milk production levels, yet it has a high potential for dairy, hence the need to analyse factors contributing to the low production levels in the area. The study area consisted of seven districts: Bungoma, Kakamega, Vihiga, Nandi, Kisii, Rachuonyo, and Nyamira. Descriptive statistics and discrete choice were the methods used for analysis. The latter involved the binary choice probit model and conjoint (CJ) analysis. Two cross-sectional data sets were used. The first set of 1575 households across all the seven districts, was used to describe the area, and analyse the cause-effect relationships in the adoption of dairy technologies. The second data set of 630 households from four of the seven districts was used for valuation of cow attributes during the CJ valuation method. The Consumer theory was used in the theoretical framework of the study. Results of the descriptive analysis showed spatial variations in the following variables; proximity to urban areas, ethnicity, resource endowments among the male and female-headed households, priorities of the household head, disease prevalence, and adoption rates of dairy technologies. Results from the cause-effect analysis in the adoption of dairy technologies show various factors that influence adoption of dairy technologies. Apart from the land economic potential which is a main determinant in adoption, other factors were availability of extension, availability of income, land size, ethnicity, population density, experience of the household head with dairy technologies, cultural factors and gender. Milk yield was the most important attribute, followed by feed requirement and disease resistance. That household characteristics condition valuation of cow attributes was quite evident. Education, extension, off-farm income, ethnic factor, and households that preferred the Zebu for cultural purposes were critical determinants during the valuation. This causes variations in adoption rates and inefficiencies in the use of local resources. Policy interventions should be based on the fact that local resources should be mobilised to exploit the opportunities available to develop the dairy sector. More information is needed to reverse people’s attitude towards dairy, and extension services should give more information on feed resources and address the cultural practices that inhibit adoption of improved dairy technologies. Women should be supported because they showed a high potential to develop dairy