Strategic Market-Based Interventions And Price Volatility in The Dairy Sector: A Case of Kiambu County, Kenya

Abstract

This study investigated the effects of strategic market based interventions such as forward contracting, price insurance mechanisms, market intelligence systems, cooperative bargaining capacity and product diversification strategies on price volatility of smallholder dairy farmers in Kiambu County, Kenya. The study was based on the theories of Price Theory, Risk Management Theory, Collective Action Theory and Market Information Theory. A cross-sectional survey design was used, and data was gathered using structured questionnaires. A total of 430 questionnaires were sent out, of which 172 were complete and usable, with a 40.0% response rate. Although this falls below the 70% threshold recommended by Mugenda and Mugenda (2003), the sample provided sufficient statistical power for analysis, and non-response bias is acknowledged as a limitation. Data were analyzed with SPSS version 26 with descriptive statistics, Pearson correlation and multiple regression analysis. The forward contracting, market intelligence systems and capacity for cooperative bargaining interventions all had the highest mean scores and a relatively low standard deviation, with a mean score of 4.5, SD = 0.82 for forward contracting, a mean score of 4.4, SD = 0.80 for market intelligence systems, and a mean of 4.2, SD = 0.88 for capacity for cooperative bargaining. Price insurance mechanisms and product diversification both had a mean of 4.3, but with higher standard deviations of 0.43 and 0.88 respectively. Overall, there was moderate to low price stability (mean = 3.5, SD = 1.05) and the price changes had significant effects on household budgets (mean = 4.1, SD = 0.98). There was no indication of multicollinearity in the data as all the VIF values were within the range of 1.466 – 1.672. The Pearson correlation analysis indicated that market intelligence systems was in a statistically significant negative relationship with price volatility (r = −.161, p = .036) and all other interventions had weak negative relationships that weren't statistically significant. Multiple regression analysis revealed that the overall model was statistically non-significant (F(5, 165) = 1.056, p = .387) and that the model explained only 3.1% of the variance in price volatility (R2 = .031, Adjusted R2 = .002), with none of the variables emerging as significant predictors. The results indicate that farm-level strategic interventions do not significantly explain the dairy price volatility in Kiambu County. Based on the study's findings, the study suggests that structural, seasonal and institutional factors are more likely to explain the volatility, and thus calls for systemic measures by policy and other stakeholders, including investments in market infrastructure, formal market price stabilization policies and participatory insurance arrangements.