A. Baseline study
The consulting firm will:
• Review the proposal, logframe and theory of change, including an assessment of the realism and testability of key assumptions underpinning the theory of change, M&E/MRV provisions, and related annexes. The approved project logframe and indicator framework, theory of change and M&E/MRV framework documents are attached to this TOR and the solicitation as Annexes III
• Review and provide technical recommendations on the existing baseline framework and indicator measurement approach.
• Confirm the indicators to be baselined at inception and define operational definitions, data sources, units of measure, disaggregation, and calculation methods.
• Generate baseline data on selected impact, outcome and output indicators from the logframe, including indicators requiring sex, age, disability and other disaggregation. The consulting firm shall propose a statistically defensible sampling strategy capable of generating representative baseline estimates for key farmer-level indicators. The methodology shall clearly explain:
◦ target population estimates;
◦ sampling frame construction;
◦ sample size calculations;
◦ confidence level assumptions;
◦ margin of error;
◦ treatment of clustering and stratification;
◦ non-response assumptions; and
◦ feasibility of county/value-chain and sex/age/disability disaggregation.
• Analyse the pre-project status of post-harvest losses; household income within agricultural contexts, including treatment of seasonality, recall periods, and multiple income sources; cold-chain access and use; investment conditions; policy and institutional readiness; awareness; skills; and jobs where relevant.
• Assess availability, quality, and limitations of secondary data and identify data gaps requiring primary data collection.
B. M&E/MRV Framework assessment
The consulting firm will:
• Assess the project’s M&E/MRV system, including the results framework, indicator definitions, targets, data sources, data collection responsibilities, reporting flows and quality assurance arrangements.
• Identify priority indicators and monitoring questions that should be tracked regularly during implementation, and recommend any refinements to indicators, targets, disaggregation or measurement approaches, with clear justification and cost/feasibility implications.
• Recommend a practical longitudinal or repeat-measurement approach capable of supporting comparison over time, including advice on panel tracking, cohort monitoring, sentinel sites, or repeated cross-sectional methodologies.
• Recommend immediate actions for project start-up relating to measurement, data management, adaptive management priorities and learning questions.
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