Thesis:

Monitoring and evaluation systems’ influence on Kenya climate smart agriculture project performance, a world bank-funded initiative in Uasin gishu county, Kenya.

In response to international donors' and other non-profit organizations' demands for transparency in project development, Kenya's agriculture sector has adopted an array of monitoring and evaluation systems that accurately monitor results. Nevertheless, some donor-funded agricultural initiatives stay stagnant or halt, underscoring the urgency to look into all possible variables impacting project outcomes. Recognizing M&E system contribution in agricultural initiatives funded by external donors is vital for maximizing the allocation of resources, developing project design, and positively impacting small-scale farmers' livelihoods. The study thus sought to understand how M&E systems influence agricultural productivity in KCSAP, a World Bank funded initiative in Uasin Gishu County Kenya. Specifically, the study determined how human capacity, work plan, routine program monitoring, and data use contributes to project performance, coordinating project goals, outcome optimization, decision-making, and resource allocation. The study was informed by three theories: the Theory of Change, Empowerment Evaluation Theory, and Utilization-Focused Evaluation Theory. The study process included data collection via structured questionnaires and interview guides to 250 respondents, consisting of monitoring and evaluation officers, farmers, and other project stakeholders. SPSS version 25 was used to process quantitative data, alongside regression analysis outputs such as Pearson correlation and ANOVA. The qualitative evidence acquired was examined thematically. The findings exhibited a 2.28 composite mean score, indicating a modest influence of M&E human capacity to Kenya- Climate Smart Agriculture Project. This was confirmed by a statistically significant positive correlation with a <0.001 P-value emphasizing the M&E human capacity contribution project succeeding, with a 43.5% performance variance. The composite 3.35 average score also established that the M&E work plan contributed relatively to Kenya- Climate-Smart Agriculture Project success. The <0.001 p-value confirmed its statistical significance in improving the project's outcome, corresponding to a variance of 39.9%. The key work plan attributes behind this positive outcome were explicit goals, regular revisions, and engagement with stakeholders. Routine program monitoring proved effective in enhancing farming practices, minimizing waste, and boosting yields, as a combined average score of 3.26. A p-value of 0.031 laid out the statistical importance of the variables' connection, pointing to its impact on Kenya- Climate-Smart Agriculture Project performance, making up a 39.9% variance. The analysis of data use and its role in Kenya- Climate Smart Agriculture Project yielded an encouraging outcome with a moderate 3.45 average rating, implying efficient exchange of knowledge, which facilitated access and decision-making. The findings confirmed that data use contributed to project performance. The above results show that M&E's human resources and work plan have the most significant effects on Kenya- Climate-Smart Agriculture Project performance. Data use, on the other hand, has a small effect, even though it is important. In this respect, the study recommends the use of advanced data management tools, personalized M&E training for stakeholders, and improvement of M&E frameworks tailored to agricultural projects. Further research could explore other factors influencing World Bank-funded project Performance, such as funding models, policy implementation, and the integration of technology.