Njehia, Bernard K.Wanjala, Simon P. Omondi2015-01-062015-01-062014-04International Journal of Current Research Vol. 6, Issue, 04, pp.6380-6388, April, 20140975-833Xhttp://www.journalcra.comhttp://www.journalcra.com/sites/default/files/5195.pdfhttp://ir-library.ku.ac.ke/handle/123456789/11876Value chain components are known to be important factors that determine the extent of commercialization and productivity in the agricultural sector. This study sought to assess the level of commercialization and variables influencing milk production in Butula and Butere districts of Western Kenya. 400 smallholder dairy farms were surveyed using proportional stratified random sampling, while qualitative data was collected through six focus group discussions, five informal interviews with Ministry of livestock staff and Kenya dairy Board. Household commercialization index (HCI) was used to estimate the level of commercialization. To assess which predictors are important in milk production, a total of eleven variables - Fodder, dairy meal, research technologies, credit, group membership, artificial insemination, extension, returns, linkages with buyers, community attitude and policy were put into Pearson’s correlation with milk production. Seven variables had a positive and significant correlation (p<0.01). To evaluate their collective and individual effect, multiple regression analysis was carried out. Results of the HCI revealed that the input market participation index was 0.32, while the output HCI was 0.46. The overall HCI in the area was 0.39 meaning that dairy farms in the area had a moderate market orientation. Multiple regression model explained 63.9% of the variance in milk production while the collective effect of value chain variables was found to be significant (P<0.001). The most important predictors explaining the variations in milk production were Fodder, dairy meal, research technologies, credit, group membership, artificial insemination, returns, and policy. Fodder and dairy meal had stronger beta co efficients and together explained 51% of the variation in milk yield. The results obtained suggest that multiple regression analysis may provide a rigorous and quantitative tool in selecting important value chain variables ex ante in an upgrading strategy since it goes a step beyond current qualitative approachesenValue chain predictorsHousehold Commercialization IndexCorrelationMultiple regressionMilk productionSmallholder farmsArticle