Effectiveness of monitoring and evaluation strategies on milk production among county funded dairy programmes in Kakamega county, Kenya

Abstract

Monitoring and evaluation are vital components of project implementation, ensuring activities align with objectives. This research sought to investigate the effectiveness of M&E strategies on milk production among county-funded dairy programs in Kakamega County, Kenya. It was guided by specific objectives, including assessing team capacity, budget allocation, and stakeholder participation. The research employed the systems theory and stakeholder theory as guiding principles. It utilized descriptive research design. The study targeted 169 respondents, comprising farmers from the One Cow Initiative, county monitoring and evaluation department employees, and personnel from four smart farms within the county. Through interviews and questionnaires, data was collected from a census sample of all identified respondents. Analysis involved descriptive statistics using SPSS for quantitative data and inferential statistics to draw conclusions. The research found that milk production among county-funded dairy programs in Kakamega County presented varied perceptions among respondents. Farmers moderately agreed that milk sufficiency for personal use and sale was adequate, yet they faced significant challenges with milk storage and limited marketing access. Strong support for training programs was evident, with a composite mean score of 4.33, underscoring their importance in enhancing production. Concerns arose regarding insufficient budget allocation for monitoring and evaluation (M&E), although perceptions about training and logistics were more positive. Stakeholder participation was deemed crucial, with high mean scores reflecting community involvement in planning and implementation. The correlation analysis revealed positive relationships between milk production and key factors such as team capacity (r=0.624), budget allocation (r=0.541), and stakeholder participation (r=0.489), with team capacity having the strongest correlation. The regression analysis showed that the model explained 42.4% of the variance in milk production (R2=0.424) and after adjustments, 28.9% of the variation remained significant. The ANOVA results confirmed the model's significance with an F-statistic of 12.345 and a p-value below 0.001, indicating that team capacity, budget allocation, and stakeholder participation significantly influence milk production. In order to increase milk production, the study underlined the necessity of funding capacity-building, budget allocation, and community involvement. Recommendations included increased funding for M&E activities by the County Agricultural Department and ongoing training for farmers and staff. Future research should explore the long-term impacts of budget allocation and identify specific aspects of team capacity affecting production.