Course Outline

Module 1: The Agricultural Innovation System & R&D Landscape

• The role of R&D in agricultural transformation

• Key actors and institutions in national and global innovation systems

• Current mega-trends: Climate change, digital agriculture, nutrition-sensitive research

• Case Study: Analysis of a successful national agricultural innovation system.

Module 2: Research Problem Identification & Prioritization

• Systems thinking and problem tree analysis

• Conducting situational analysis and literature reviews

• Stakeholder analysis and participatory priority setting with farmers (PVS, RRA)

• Formulating SMART research objectives and hypotheses.

• Practical Exercise: Defining a research problem for a given context using systems mapping.

Module 3: Research Design & Methodology Selection

• Overview of experimental designs (RCBD, split-plot, on-farm participatory trials)

• Quantitative, qualitative, and mixed-methods approaches

• Integrating gender and social inclusion in research design

• Sampling techniques and sample size determination.

• Workshop: Critiquing and improving a draft research design.

Module 4: Proposal Development & Grant Writing

• Anatomy of a winning proposal: narrative, logframe, budget, work plan

• Theory of Change and Impact Pathways

• Budget justification and financial management essentials

• Practical Exercise: Developing a logframe and draft budget for a mock Call for Proposals.

Module 5: R&D Project Management & Leadership

• Principles of project management for research: Agile vs. traditional

• Team management, leadership, and fostering collaboration

• Risk management and adaptive management in dynamic environments

• Simulation: Managing project timelines, resources, and team dynamics.

Module 6: Monitoring, Evaluation & Learning (MEL) for R&D

• Developing a MEL plan with process and outcome indicators

• Data collection tools and quality assurance

• Principles of impact evaluation (e.g., RCTs, quasi-experimental designs)

• Creating a culture of learning and adaptive management.

• Case Study: MEL in a complex, multi-partner research consortium.

Module 7: Data Management, Analysis & Interpretation

• Principles of good data management (ODK, KoboToolbox)

• Introduction to statistical software (hands-on with R or SPSS): descriptive stats, ANOVA, regression basics

• Interpreting results and dealing with non-significance

• Computer Lab Session: Hands-on data analysis of a provided dataset.

Module 8: Scientific Communication & Outreach

• Writing for scientific journals and technical reports

• Communicating to non-scientific audiences: policy briefs, extension bulletins, media

• Data visualization principles and tools

• Workshop: Drafting a policy brief from research findings.

Module 9: From Research to Impact: Scaling Innovations

• Understanding scaling pathways and models (NGO-led, private sector, policy)

• Business models for sustainable scaling

• Intellectual Property (IP) and technology licensing basics

• Engaging with incubators, accelerators, and investors.

• Case Study: Scaling a drought-tolerant crop variety in East Africa.

Module 10: Synthesis, Ethics & Capstone Project

• Ethics in agricultural research

• Capstone Project Presentation: Participants present a complete, condensed research proposal (incorporating all course modules) to a panel of "donors" (facilitators and peers) for feedback.

• Developing a personal/professional action plan.