| dc.description.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. | en_US |
