Monitoring and Evaluation for Agriculture Training Course

Participants can map the system conditions that influence agricultural programme results.

• Farming systems analysis covering crops, livestock, labour and income sources
• Value-chain mapping from input suppliers through producers to end markets
• Constraints analysis for extension, finance, storage, transport and market access
• Food systems outcomes covering availability, access, utilisation and stability
• Production calendars aligned with cropping seasons, rainfall and market cycles
• Stakeholder mapping of ministries, cooperatives, traders, lenders and service providers

Participants can build a testable theory of change for an agriculture intervention.

• Theory of Change pathways linking services, behavioural change and outcomes
• Backward mapping from intended livelihood outcomes to required preconditions
• Causal assumptions documented for adoption, productivity, income and resilience
• External risks identified through political, climatic, market and conflict analysis
• Contribution claims distinguished from direct attribution claims in programme designs
• Outcome pathways differentiated for women, youth and land-poor households

Participants can convert a theory of change into a coherent logical framework.

• Logical Framework Approach linking objectives, indicators, sources and assumptions
• Goal and outcome statements written as observable changes over time
• Output indicators matched to accountable delivery teams and reporting cycles
• Means of verification assessed for cost, reliability and collection frequency
• Assumptions tested against programme influence and plausible external conditions
• Performance framework matrices connecting indicators, targets, responsibilities and data sources

Participants can specify relevant, measurable indicators for agriculture and rural development results.

• SMART indicator criteria applied to agriculture production and adoption measures
• Indicator classifications covering input, output, outcome, impact and context
• Crop yield indicators calculated using harvested area and production quantities
• Adoption measures defined by correct use, sustained use and disadoption
• Household income indicators separated from revenue, profit and consumption expenditure
• Dietary diversity measures selected for food security outcome monitoring

Participants can produce indicator reference sheets and defensible targets.

• Indicator reference sheets specifying definitions, numerators, denominators and disaggregation
• Unit-of-analysis choices among plots, farmers, households, enterprises and communities
• Disaggregation plans covering sex, age, disability, location and wealth status
• Target-setting methods using baselines, implementation capacity and historical performance
• Annual and seasonal target trajectories aligned with agriculture production cycles
• Proxy indicators assessed for validity when direct measurement is impractical

Participants can plan baseline and endline studies that support credible change measurement.

• Baseline timing established before service delivery and behaviour change occur
• Endline designs matched to intervention duration and expected outcome timing
• Comparison groups selected using eligibility criteria and exposure patterns
• Panel and repeated cross-sectional surveys compared for longitudinal measurement
• Recall periods chosen for harvests, sales, dietary intake and labour
• Baseline reports structured around indicators, context, gaps and recommendations

Participants can develop practical probability sampling plans for rural surveys.

• Sampling frames built from beneficiary registers, village lists and census data
• Simple random, systematic and stratified sampling methods compared
• Cluster sampling designed for dispersed settlements and fieldwork cost control
• Sample sizes estimated using precision, confidence and design effect assumptions
• Oversampling approaches planned for women farmers and minority groups
• Nonresponse protocols documented for absent households and inaccessible locations

Participants can create field-ready data collection instruments for agriculture monitoring.

• Questionnaire flow organised around respondent burden and logical skip patterns
• Closed and open questions selected for production, income and practice data
• Recall bias reduced through event calendars, units and bounded periods
• KoboToolbox forms configured with constraints, relevance logic and required fields
• Consent scripts address voluntary participation, confidentiality and data use
• Enumerator manuals specify probes, local units and observation protocols

Participants can select and apply qualitative methods to explain agricultural programme results.

• Key informant interviews planned with extension agents, traders and leaders
• Focus group discussions segmented by gender, age and livelihood group
• Seasonal calendars used to examine labour, food access and shocks
• Participatory rural appraisal tools applied to community priorities and change
• Outcome harvesting captures unexpected changes and contribution pathways
• Thematic coding frameworks connect respondent evidence to evaluation questions

Participants can design routine monitoring arrangements that produce timely management information.

• Monitoring plans schedule data collection, review meetings and reporting deadlines
• Farmer registration systems link households, services, plots and locations
• Extension visit records capture attendance, advisory content and follow-up
• Field verification protocols confirm assets, demonstrations and training records
• Supervisory dashboards track progress against targets, plans and seasonal milestones
• Exception reporting flags delayed activities, unusual values and coverage gaps

Participants can establish controls for accurate, complete, timely and secure M&E data.

• Routine Data Quality Assessment dimensions: validity, reliability, precision, integrity, timeliness
• Source document verification traces reported results to original field records
• Range checks and outlier detection identify implausible farm and household values
• Data cleaning logs document corrections, exclusions and unresolved anomalies
• Unique identifiers prevent duplicate beneficiary, household and plot records
• Data protection procedures define access, retention, anonymisation and secure storage

Participants can analyse routine and survey data for clear performance reporting.

• Descriptive statistics summarise counts, proportions, means, medians and distributions
• Cross-tabulations compare results by sex, location, wealth and programme exposure
• Baseline-to-endline changes interpreted using comparable definitions and samples
• Agriculture dashboards display seasonal trends, target progress and geographic coverage
• Charts selected to show distributions, comparisons, trends and contribution gaps
• Data narratives distinguish observed findings, interpretation, limitations and decisions

Participants can frame evaluation questions using appropriate criteria and evidence needs.

• OECD DAC criteria applied to relevance, coherence, effectiveness, efficiency, impact, sustainability
• Evaluation questions prioritised by decision value, feasibility and stakeholder interest
• Evaluation matrices connect questions, indicators, methods, sources and analysis
• Efficiency analysis examines costs, timeliness and resource use per result
• Sustainability questions assess local capacity, financing and institutional ownership
• Terms of reference specify purpose, scope, methods, governance and deliverables

Participants can select feasible designs for estimating intervention contribution and impact.

• Counterfactual logic distinguishes programme effects from external changes over time
• Randomised controlled trials assessed for ethics, feasibility and implementation readiness
• Difference-in-differences compares changes among participant and comparison groups
• Propensity score matching addresses observable differences between beneficiary groups
• Regression discontinuity uses eligibility thresholds where programme rules permit
• Contribution analysis tests causal claims through evidence and alternative explanations

Participants can integrate inclusion analysis into agriculture M&E design and reporting.

• Gender analysis examines access to land, inputs, finance and extension
• Women’s empowerment measures address decision-making, workload and control over income
• Youth indicators track employment quality, enterprise participation and asset access
• Disability inclusion questions identify barriers to participation and benefit
• Intra-household data collection avoids assuming one respondent represents all members
• Safeguarding referrals planned for disclosures arising during field data collection

Participants can measure environmental performance and resilience outcomes in agriculture programmes.

• Climate risk screening identifies rainfall, heat, flood and pest hazards
• Resilience indicators track absorptive, adaptive and transformative capacities
• Soil health measures include organic matter, erosion control and ground cover
• Water management indicators measure access, efficiency, functionality and maintenance
• Greenhouse gas considerations assessed for livestock, fertiliser and land-use activities
• Environmental safeguards monitored through mitigation actions, incidents and compliance records

Participants can convert M&E findings into accountable reporting and documented management action.

• After-action reviews identify evidence, decisions, owners and follow-up deadlines
• Pause-and-reflect sessions examine implementation assumptions and emerging field evidence
• Donor reports connect indicator results, explanations, risks and corrective actions
• Management response matrices assign actions to evaluation recommendations and owners
• Learning agendas prioritise uncertainties affecting programme strategy and resource allocation
• Feedback loops communicate findings to farmers, communities and local partners

Participants can resource and govern M&E functions within agriculture programme delivery.

• M&E budgets estimate staffing, travel, devices, surveys and data management
• Costed workplans align monitoring tasks with seasonal implementation milestones
• Roles and responsibilities assigned through RACI matrices and reporting lines
• Evaluation procurement criteria assess technical quality, independence and field capacity
• Data governance structures define ownership, approval authority and escalation routes
• Risk registers cover security, weather, access, fraud and political interference

Participants can build, review and present a complete M&E system for an agriculture programme.

• Theory of Change refined against intervention evidence, assumptions and stakeholder feedback
• Logical framework completed with indicators, targets, verification sources and risks
• Baseline, sampling and data collection plans integrated into implementation schedules
• Data quality controls, dashboards and reporting cycles specified for management use
• Evaluation questions and methods matched to priority decisions and resources
• Peer review tests coherence, feasibility, inclusion and seasonal measurement timing