Value chain predictors of milk production on smallholder dairy farms in Western Kenya: a multiple regression analysis

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Date
2014-04Author
Njehia, Bernard K.
Wanjala, Simon P. Omondi
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Show full item recordAbstract
Value 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 approaches
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http://ir-library.ku.ac.ke/handle/123456789/11876