Training Course on Agricultural Project Monitoring and Evaluation for Impact

Training Course on Agricultural Project Monitoring and Evaluation (M&E) for Impact

Introduction

In today’s rapidly evolving agricultural landscape, the need for robust Monitoring and Evaluation (M&E) frameworks is more critical than ever. Agricultural projects face increasing pressure from donors, governments, and stakeholders to demonstrate measurable outcomes, cost-effectiveness, and sustainable impact. Training Course on Agricultural Project Monitoring and Evaluation (M&E) for Impact equips professionals with advanced M&E tools, data-driven techniques, and impact assessment methodologies tailored for agriculture. Participants will gain a deep understanding of how to track progress, measure success, and ensure accountability throughout the project lifecycle.

Designed for real-world application, this course integrates performance indicators, logical frameworks (LogFrames), participatory M&E, and results-based management into practical agricultural settings. Whether working on climate-resilient farming, food security, or value chain development, learners will master how to collect, analyze, and report data to optimize decision-making and maximize agricultural outcomes. By the end of the course, participants will be capable of designing and executing M&E systems that deliver meaningful insights and demonstrable change.

Course Objectives

1. Understand the fundamentals of agricultural M&E systems and frameworks.
2. Develop SMART indicators for agricultural impact assessment.
3. Design results-based M&E plans aligned with project goals.
4. Conduct baseline and endline surveys for performance benchmarking.
5. Apply Theory of Change and Logical Framework Approaches (LFA).
6. Use data visualization and dashboards to communicate results.
7. Implement gender-sensitive and inclusive M&E practices.
8. Conduct mid-term and final project evaluations.
9. Utilize GIS and remote sensing tools in agricultural M&E.
10. Strengthen stakeholder engagement through participatory M&E.
11. Analyze qualitative and quantitative agricultural data.
12. Integrate real-time monitoring technologies and mobile tools.
13. Develop actionable impact evaluation reports for decision-making.

Target Audience

1. Project Managers in Agricultural Development
2. Monitoring and Evaluation Specialists
3. Agronomists and Field Officers
4. Government Agriculture Officers
5. NGO and Donor Agency Staff
6. Research and Policy Analysts
7. Agricultural Consultants and Trainers
8. Academics and Graduate Students in Agriculture

Course Duration: 10 days

Course Modules

Module 1: Introduction to Agricultural M&E

• Purpose and scope of M&E in agriculture
• Key concepts: outputs, outcomes, impacts
• Differences between monitoring and evaluation
• Overview of agricultural project cycles
• Stakeholder mapping and roles in M&E
• Case Study: M&E design in a rural seed distribution program

Module 2: Designing M&E Systems

• Components of a strong M&E system
• Aligning M&E with project objectives
• LogFrame and Theory of Change development
• Establishing performance indicators
• M&E budget and staffing needs
• Case Study: Developing an M&E framework for a climate-smart agriculture project

Module 3: Results-Based Management (RBM)

• Principles of RBM
• Planning for results: inputs to impact
• Managing for outcomes
• Linking RBM with M&E systems
• Performance monitoring indicators
• Case Study: Implementing RBM in a livestock project in East Africa

Module 4: Data Collection Tools and Techniques

• Quantitative vs qualitative data
• Survey design and questionnaire development
• Focus group discussions and key informant interviews
• Mobile data collection tools
• Ethics in data collection
• Case Study: Using ODK for crop yield survey in Kenya

Module 5: Baseline, Midterm, and Endline Surveys

• Purpose and timing of surveys
• Sampling methods
• Data quality assurance
• Integrating findings into program planning
• Challenges in survey implementation
• Case Study: Conducting a baseline in a post-harvest loss reduction project

Module 6: Indicator Development and Management

• Criteria for good indicators (SMART)
• Selecting indicators for agricultural outcomes
• Custom vs standardized indicators
• Indicator tracking tools
• Disaggregation for gender, age, location
• Case Study: Indicator matrix for a food security project

Module 7: Participatory M&E (PM&E)

• Principles of PM&E
• Participatory tools (mapping, ranking, timelines)
• Involving farmers and communities
• Ownership and learning from data
• Feedback loops and adaptation
• Case Study: Community scorecard in a water harvesting project

Module 8: Gender and Social Inclusion in M&E

• Gender-sensitive indicators
• Ensuring equity in data collection
• Barriers to inclusion in evaluation
• Collecting sex-disaggregated data
• Tools for inclusive analysis
• Case Study: Gender impact study in women-led agribusinesses

Module 9: Data Analysis and Interpretation

• Cleaning and validating data
• Statistical tools and software
• Visualizing trends and outliers
• Triangulation of data sources
• Interpreting results for decision-making
• Case Study: Data dashboard for agro-input subsidy program

Module 10: Evaluation Techniques and Approaches

• Types of evaluations: formative, summative, impact
• Selecting appropriate evaluation design
• Mixed-methods evaluation
• Attribution and contribution analysis
• Reporting findings to stakeholders
• Case Study: External evaluation of a value chain development project

Module 11: Real-Time Monitoring and ICT Tools

• Remote sensing and satellite imagery
• Mobile-based reporting platforms
• IoT and precision agriculture tools
• Early warning systems
• Challenges and limitations of ICT in M&E
• Case Study: Mobile alerts for pest outbreak monitoring

Module 12: GIS and Mapping in M&E

• Basics of GIS in agricultural projects
• Mapping project beneficiaries and activities
• Spatial data analysis
• Integration with M&E data
• Visual storytelling with maps
• Case Study: GIS-based land use monitoring in agroforestry

Module 13: Communicating M&E Findings

• Writing effective M&E reports
• Data visualization and infographics
• Storytelling with evidence
• Audiences and channels for reporting
• Building a learning culture
• Case Study: Infographic reporting in a donor-funded irrigation project

Module 14: Risk Management and Adaptive Learning

• Identifying risks in M&E
• Risk mitigation strategies
• Incorporating lessons learned
• Continuous improvement in project planning
• Adaptive management principles
• Case Study: Adapting M&E plan during COVID-19 in a dairy program

Module 15: Designing Impact Evaluation Studies

• Difference between impact and outcome
• Experimental and quasi-experimental designs
• Contribution vs attribution
• Cost-effectiveness analysis
• Evaluating long-term sustainability
• Case Study: RCT-based impact evaluation of a fertilizer subsidy initiative

Training Methodology

• Interactive lectures and expert presentations
• Hands-on exercises using real project data
• Group work and collaborative problem-solving
• Field-based simulations and role plays
• Practical use of mobile and GIS tools
• Daily reflection and feedback sessions

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.