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Mixed Methods Management for Agriculture Training Course

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Training Locations Kenya (Nairobi, Mombasa, Malindi, Kisumu, Nakuru, Nanyuki) · Tanzania (Dodoma, Zanzibar, Dar es Salaam) · Dubai UAE · South Africa (Pretoria, Cape Town) · Istanbul · Accra · Banjul more ▾
Groups & Payment Groups of 5+ receive one complimentary place — see group rates. Payment due at least 1 month before (Europe & Asia) or 2 weeks before (Africa programs).
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Classroom / In-Person
Same course & certificate — face-to-face
14 locations
Nairobi, Kenya Aug 24, 2026 (98)
Mombasa, Kenya Aug 24, 2026 (49)
Kigali, Rwanda Aug 24, 2026 (49)
Dar es Salaam, Tanzania Aug 24, 2026 (49)
Zanzibar, Tanzania Aug 24, 2026 (49)
Addis Ababa, Ethiopia Aug 24, 2026 (49)
Accra, Ghana Aug 24, 2026 (49)
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Format: Live instructor-led online training via Zoom / Microsoft Teams

Mixed Methods Management for Agriculture Training Course

Course Overview

Mixed Methods Management for Agriculture Training Course is designed to equip agricultural professionals, Monitoring, Evaluation, Accountability and Learning (MEAL) specialists, agricultural researchers, project managers, extension officers, policymakers, agribusiness managers, donor-funded project staff, development practitioners, and consultants with comprehensive knowledge and practical competencies in designing, managing, implementing, and interpreting mixed methods research and evaluation for agricultural programmes and projects. As ministries of agriculture, agricultural research institutions, universities, NGOs, international development organizations, agribusiness enterprises, and donor agencies increasingly adopt Results-Based Management (RBM), Monitoring, Evaluation, Accountability and Learning (MEAL), Sustainable Development Goals (SDGs), climate-smart agriculture, agricultural value chain development, food security, agricultural innovation systems, digital agriculture, impact evaluation, qualitative research, quantitative research, evidence-based policymaking, business intelligence, knowledge management, adaptive management, donor compliance, and institutional performance management, mixed methods research has become an internationally recognized approach for generating comprehensive evidence that combines statistical analysis with stakeholder perspectives. This course equips participants with practical skills to integrate qualitative and quantitative methods for effective agricultural programme planning, monitoring, evaluation, learning, and decision-making.

The course provides participants with practical experience in mixed methods research design, convergent design, explanatory sequential design, exploratory sequential design, embedded design, sampling strategies, survey design, questionnaire development, qualitative interviewing, focus group discussions, participatory rural appraisal, quantitative data analysis, qualitative coding, triangulation, integration techniques, agricultural indicator development, Theory of Change, Logical Framework Analysis, performance measurement, agricultural impact evaluation, dashboard reporting, donor reporting, policy analysis, and utilization-focused evaluation. Participants will gain hands-on experience integrating Microsoft Excel, Microsoft Power BI, SPSS, STATA, R, Python, NVivo, SurveyCTO, KoboToolbox, Open Data Kit (ODK), Geographic Information Systems (GIS), Agricultural Management Information Systems (AMIS), SQL databases, cloud collaboration platforms, artificial intelligence, machine learning, and business intelligence tools to evaluate crop production, livestock development, irrigation systems, climate resilience programmes, food security initiatives, agricultural extension services, agribusiness value chains, and rural livelihood projects.

Participants will further explore advanced concepts including experimental and quasi-experimental designs, participatory evaluation, developmental evaluation, outcome harvesting, predictive analytics, geospatial analysis, artificial intelligence-assisted research, machine learning applications, digital data collection, knowledge management, organizational learning, adaptive management, environmental assessment, gender-responsive research, sustainability evaluation, risk analysis, and continuous quality improvement. Practical exercises demonstrate how mixed methods strengthen agricultural monitoring and evaluation, improve donor compliance, enhance organizational accountability, support agricultural policy formulation, optimize programme implementation, and promote sustainable agricultural development through integrated evidence generation.

Through instructor-led workshops, practical research laboratories, quantitative and qualitative data analysis exercises, collaborative group projects, software demonstrations, GIS integration practical sessions, dashboard development workshops, peer learning activities, field-based simulations, and comprehensive agricultural case studies, participants will develop practical competencies in managing mixed methods research throughout the project cycle. Upon successful completion of the course, participants will possess the knowledge and skills required to design and manage high-quality mixed methods studies, strengthen Results-Based Management systems, improve agricultural programme performance, enhance institutional learning, and support evidence-based agricultural policies and investment decisions.

Course Objectives

  1. Understand the principles and applications of mixed methods research in agriculture.
  2. Design mixed methods research and evaluation frameworks for agricultural programmes.
  3. Integrate qualitative and quantitative research methodologies effectively.
  4. Develop appropriate sampling strategies and data collection instruments.
  5. Analyze quantitative and qualitative agricultural data using modern analytical tools.
  6. Apply triangulation techniques to strengthen evidence quality and decision-making.
  7. Integrate GIS, dashboards, and business intelligence into mixed methods research.
  8. Strengthen Monitoring, Evaluation, Accountability and Learning (MEAL) systems through mixed methods approaches.
  9. Improve donor compliance and evidence-based agricultural policymaking.
  10. Enhance institutional performance through comprehensive agricultural research and evaluation.

Organizational Benefits

  1. Strengthens institutional capacity in mixed methods research and evaluation.
  2. Improves evidence-based agricultural planning and policy development.
  3. Enhances Monitoring, Evaluation, Accountability and Learning (MEAL) systems.
  4. Supports donor compliance through comprehensive and credible evidence generation.
  5. Improves programme monitoring, evaluation, and impact assessment.
  6. Strengthens organizational learning and adaptive management.
  7. Enhances research quality and data reliability.
  8. Improves stakeholder engagement and participatory decision-making.
  9. Promotes innovation and continuous organizational improvement.
  10. Supports sustainable agricultural development, climate resilience, and food security.

Target Participants

  • Monitoring, Evaluation, Accountability and Learning (MEAL) Specialists
  • Agricultural Project Managers
  • Agricultural Researchers
  • Ministry of Agriculture Officials
  • Agricultural Extension Officers
  • Agribusiness Managers
  • Policy Analysts
  • Data Analysts
  • GIS Specialists
  • Programme Managers
  • Food Security Officers
  • Climate Change Specialists
  • Livestock Development Officers
  • University Lecturers
  • NGO and International Development Staff
  • Development Practitioners
  • Agricultural Consultants
  • Research Officers
  • Statistics Officers
  • Professionals involved in agricultural planning, monitoring and evaluation, research, project management, policy development, agribusiness, rural development, and institutional performance management.

Course Outline

Module 1: Foundations of Mixed Methods Research

  • Mixed methods principles
  • Results-Based Management (RBM)
  • Research paradigms
  • Theory of Change
  • Research ethics
  • Agricultural applications

Case Study: Designing a mixed methods framework for a national agricultural productivity programme.

Module 2: Mixed Methods Research Designs

  • Convergent design
  • Explanatory sequential design
  • Exploratory sequential design
  • Embedded design
  • Multiphase design
  • Design selection

Case Study: Selecting an appropriate mixed methods design for a climate-smart agriculture project.

Module 3: Quantitative Research Methods

  • Survey design
  • Questionnaire development
  • Sampling techniques
  • Agricultural indicators
  • Statistical analysis
  • Data quality assurance

Case Study: Conducting quantitative surveys to assess crop productivity improvements.

Module 4: Qualitative Research Methods

  • Key informant interviews
  • Focus group discussions
  • Participatory Rural Appraisal (PRA)
  • Observation techniques
  • Case studies
  • Ethical interviewing

Case Study: Exploring farmer perceptions of sustainable agricultural technologies through qualitative research.

Module 5: Data Collection Technologies

  • SurveyCTO
  • KoboToolbox
  • Open Data Kit (ODK)
  • Mobile data collection
  • Digital surveys
  • Cloud synchronization

Case Study: Collecting agricultural household data using digital mobile platforms.

Module 6: Data Analysis and Integration

  • Microsoft Excel
  • SPSS
  • STATA
  • R programming
  • Python analytics
  • NVivo qualitative analysis

Case Study: Integrating statistical findings with qualitative insights from agricultural extension programmes.

Module 7: Triangulation and Evidence Integration

  • Data triangulation
  • Methodological triangulation
  • Investigator triangulation
  • Theory triangulation
  • Integration techniques
  • Interpretation of findings

Case Study: Combining farmer surveys, interviews, and GIS data to evaluate irrigation programme performance.

Module 8: Monitoring, Evaluation, Accountability and Learning (MEAL)

  • Performance monitoring
  • Evaluation frameworks
  • Accountability systems
  • Learning agendas
  • Adaptive management
  • Results reporting

Case Study: Integrating mixed methods research into agricultural MEAL systems.

Module 9: GIS and Dashboard Reporting

  • Geographic Information Systems (GIS)
  • Microsoft Power BI
  • Dashboard development
  • Spatial analysis
  • Data visualization
  • Executive reporting

Case Study: Developing interactive dashboards and GIS maps for agricultural programme evaluation.

Module 10: Artificial Intelligence and Emerging Technologies

  • Artificial Intelligence
  • Machine learning
  • Predictive analytics
  • Automated coding
  • Text analytics
  • Smart decision support

Case Study: Using AI-assisted analytics to improve mixed methods evaluation of agricultural interventions.

Module 11: Research Reporting and Knowledge Utilization

  • Technical reports
  • Policy briefs
  • Scientific publications
  • Knowledge products
  • Stakeholder presentations
  • Evidence utilization

Case Study: Preparing a mixed methods evaluation report for a donor-funded agricultural development programme.

Module 12: Future Trends in Mixed Methods Management

  • Agricultural Management Information Systems (AMIS)
  • Business intelligence
  • Big data analytics
  • Cloud research platforms
  • Digital transformation
  • Future innovations

Case Study: Designing an integrated mixed methods management framework combining Microsoft Excel, Microsoft Power BI, SPSS, STATA, R, Python, NVivo, SurveyCTO, KoboToolbox, Open Data Kit (ODK), Geographic Information Systems (GIS), Agricultural Management Information Systems (AMIS), Artificial Intelligence, machine learning, Results-Based Management, Monitoring, Evaluation, Accountability and Learning (MEAL), business intelligence, predictive analytics, cloud collaboration, digital data collection, and knowledge management to strengthen agricultural policy implementation, climate resilience, food security, institutional accountability, donor compliance, and sustainable agricultural development.

General Information

  1. Customized Training: All our courses can be tailored to meet the specific needs of participants.
  2. Language Proficiency: Participants should have a good command of the English language.
  3. Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, mixed methods research workshops, quantitative and qualitative data analysis laboratories, statistical software practical sessions, NVivo coding exercises, GIS mapping demonstrations, Microsoft Power BI dashboard development, collaborative group work, peer learning activities, software demonstrations, field-based simulations, and comprehensive agricultural case studies. Our facilitators are seasoned experts with over a decade of experience in agricultural research, Results-Based Management, Monitoring, Evaluation, Accountability and Learning (MEAL), mixed methods research, business intelligence, GIS, digital agriculture, donor-funded programme management, and institutional performance improvement.
  4. Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).
  5. Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.
  6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.
  7. Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.
  8. Additional Services: Accommodation, pickup services, flight booking, and visa processing arrangements are available upon request at discounted rates.
  9. Equipment: Tablets and laptops can be provided to participants at an additional cost.
  10. Post-Training Support: We offer one year of free consultation and coaching after the course.
  11. Group Discounts: Register as a group of more than two participants and enjoy a discount ranging from 10% to 50%.
  12. Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.
  13. Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.
  14. Website: Visit our website at www.fdc-k.org for more information.

 

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