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QGIS Essentials for Agriculture Training Course

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How to Register Click View Schedule for your preferred location, select your training dates, then register as an individual, group, or online participant. You will receive an invitation letter and invoice promptly after submission.
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).
Virtual / Online
Live, instructor-led — join from anywhere
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Aug 24, 2026 Sep 4, 2026 10 days Virtual Onsite
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Classroom / In-Person
Same course & certificate — face-to-face
14 locations
Nairobi, Kenya Aug 24, 2026 (98)
Dar es Salaam, Tanzania Aug 24, 2026 (49)
Addis Ababa, Ethiopia Aug 24, 2026 (49)
Accra, Ghana Aug 24, 2026 (49)
Pretoria, South Africa Aug 24, 2026 (49)
Dubai, UAE Aug 24, 2026 (69)
Istanbul, Turkey Aug 24, 2026 (49)
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Format: Live instructor-led online training via Zoom / Microsoft Teams

QGIS Essentials for Agriculture Training Course

Course Overview

QGIS Essentials for Agriculture Training Course is designed to equip agricultural professionals, GIS specialists, agricultural researchers, monitoring and evaluation specialists, extension officers, agribusiness managers, policymakers, environmental officers, donor-funded project staff, and development practitioners with comprehensive knowledge and practical skills in applying Quantum Geographic Information System (QGIS) for agricultural mapping, spatial analysis, precision agriculture, land resource management, climate-smart agriculture, and evidence-based decision-making. As ministries of agriculture, agricultural research institutions, universities, NGOs, international development organizations, donor agencies, and agribusiness enterprises increasingly adopt Results-Based Management (RBM), Monitoring, Evaluation, Accountability and Learning (MEAL), Sustainable Development Goals (SDGs), digital agriculture, climate-smart agriculture, precision agriculture, agricultural value chain development, food security, land information systems, environmental management, remote sensing, agricultural planning, spatial data infrastructure, agricultural monitoring, and evidence-based policymaking, QGIS has become one of the most widely used open-source Geographic Information Systems for agricultural spatial analysis and decision support. This course equips participants with practical competencies to collect, manage, analyze, visualize, and report agricultural spatial data for improved planning, monitoring, and sustainable agricultural development.

The course provides participants with practical experience in QGIS interface navigation, spatial data management, coordinate reference systems, GPS integration, vector and raster data processing, digitizing, georeferencing, spatial querying, land suitability analysis, agricultural zoning, watershed analysis, terrain analysis, remote sensing integration, spatial statistics, map production, agricultural performance monitoring, environmental assessment, dashboard development, and geospatial reporting. Participants will gain hands-on experience integrating QGIS, Microsoft Excel, Microsoft Power BI, SurveyCTO, KoboToolbox, Open Data Kit (ODK), GPS devices, SPSS, STATA, R, Python, PostgreSQL/PostGIS, Agricultural Management Information Systems (AMIS), drone imagery, satellite imagery, artificial intelligence, machine learning, cloud GIS platforms, and business intelligence tools to analyze crop production, livestock systems, irrigation schemes, soil fertility, climate resilience, food security, agribusiness operations, and agricultural development projects.

Participants will further explore advanced concepts including precision agriculture, geospatial data science, artificial intelligence-assisted mapping, machine learning for land classification, predictive spatial analytics, drone mapping, remote sensing, climate risk assessment, environmental monitoring, agricultural forecasting, digital governance, knowledge management, organizational learning, adaptive management, and continuous improvement. Practical exercises demonstrate how QGIS strengthens agricultural planning, improves monitoring and evaluation systems, enhances donor compliance, supports agricultural policy formulation, optimizes resource allocation, and promotes sustainable agricultural development through advanced geospatial analysis.

Through instructor-led workshops, practical GIS laboratories, spatial analysis exercises, GPS field demonstrations, agricultural mapping projects, collaborative group assignments, software demonstrations, remote sensing practical sessions, and comprehensive agricultural case studies, participants will gain practical competencies in applying QGIS to real-world agricultural datasets. Upon successful completion of the course, participants will possess the knowledge and skills required to design and manage agricultural GIS projects, strengthen Results-Based Management systems, improve agricultural monitoring, enhance institutional decision-making, and support sustainable agricultural development through geospatial technologies.

Course Objectives

  1. Understand the principles and applications of QGIS in agriculture.
  2. Develop practical skills in managing agricultural spatial data using QGIS.
  3. Create professional agricultural maps and geospatial visualizations.
  4. Perform spatial analysis for agricultural planning and resource management.
  5. Integrate GPS, remote sensing, and mobile data collection with QGIS.
  6. Conduct land suitability and environmental assessments.
  7. Develop GIS-based dashboards and agricultural reports.
  8. Strengthen Monitoring, Evaluation, Accountability and Learning (MEAL) systems using geospatial information.
  9. Improve donor reporting and evidence-based agricultural decision-making.
  10. Enhance institutional performance through GIS and spatial analytics.

Organizational Benefits

  1. Strengthens agricultural GIS and spatial analysis capacity.
  2. Improves evidence-based agricultural planning and land management.
  3. Enhances Monitoring, Evaluation, Accountability and Learning (MEAL) systems.
  4. Supports donor compliance through spatial reporting and mapping.
  5. Improves agricultural project monitoring and environmental assessment.
  6. Promotes precision agriculture and digital transformation.
  7. Strengthens institutional decision-making using geospatial intelligence.
  8. Enhances natural resource management and climate resilience.
  9. Improves agricultural productivity through spatial planning.
  10. Supports sustainable agricultural development, food security, and environmental conservation.

Target Participants

  • Agricultural Researchers
  • GIS Specialists
  • Agricultural Project Managers
  • Monitoring and Evaluation Officers
  • Ministry of Agriculture Officials
  • Agricultural Extension Officers
  • Agribusiness Managers
  • Environmental Officers
  • Land Management Officers
  • Food Security Officers
  • Climate Change Specialists
  • Irrigation Engineers
  • Surveyors
  • Data Analysts
  • ICT Officers
  • University Lecturers
  • Donor Project Coordinators
  • NGO and Development Project Staff
  • Agricultural Consultants
  • Professionals involved in agriculture, GIS, environmental management, rural development, monitoring and evaluation, research, digital agriculture, and institutional performance management.

Course Outline

Module 1: Introduction to QGIS for Agriculture

  • QGIS interface
  • GIS fundamentals
  • Results-Based Management
  • Agricultural GIS applications
  • Spatial data concepts
  • Project setup

Case Study: Establishing a GIS framework for a national agricultural monitoring program.

Module 2: Spatial Data Management

  • Vector data
  • Raster data
  • Coordinate reference systems
  • Data organization
  • Data quality
  • Metadata management

Case Study: Managing agricultural land parcel and crop production datasets.

Module 3: GPS and Mobile Data Integration

  • GPS data collection
  • SurveyCTO integration
  • KoboToolbox integration
  • Open Data Kit (ODK)
  • Data synchronization
  • Field verification

Case Study: Collecting GPS coordinates for agricultural field surveys.

Module 4: Map Creation and Visualization

  • Layer management
  • Map symbology
  • Labels
  • Layout design
  • Cartographic standards
  • Map publishing

Case Study: Producing agricultural land-use and crop distribution maps.

Module 5: Spatial Analysis Techniques

  • Buffer analysis
  • Overlay analysis
  • Spatial queries
  • Proximity analysis
  • Terrain analysis
  • Hydrological analysis

Case Study: Identifying suitable agricultural land for irrigation development.

Module 6: Remote Sensing and Imagery Analysis

  • Satellite imagery
  • Drone imagery
  • Image classification
  • Vegetation indices
  • Land cover analysis
  • Change detection

Case Study: Monitoring crop health using satellite imagery and QGIS.

Module 7: Agricultural Resource Mapping

  • Soil mapping
  • Water resource mapping
  • Crop suitability analysis
  • Livestock mapping
  • Agricultural zoning
  • Climate mapping

Case Study: Developing crop suitability maps for climate-smart agriculture.

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

  • Spatial monitoring
  • Indicator mapping
  • Performance assessment
  • Impact evaluation
  • Organizational learning
  • Adaptive management

Case Study: Integrating GIS into agricultural MEAL systems for project monitoring.

Module 9: GIS Reporting and Dashboards

  • Microsoft Power BI integration
  • Interactive dashboards
  • Spatial visualization
  • Reporting automation
  • Decision support
  • Executive reporting

Case Study: Developing GIS-enabled dashboards for agricultural performance reporting.

Module 10: Artificial Intelligence and Precision Agriculture

  • Artificial Intelligence
  • Machine learning
  • Precision agriculture
  • Predictive analytics
  • Smart farming
  • Automated mapping

Case Study: Using AI and GIS to predict crop productivity and optimize farm management.

Module 11: Environmental and Climate Risk Assessment

  • Climate vulnerability mapping
  • Environmental monitoring
  • Disaster risk mapping
  • Watershed management
  • Land degradation assessment
  • Sustainability analysis

Case Study: Mapping drought vulnerability and climate risks affecting agricultural production.

Module 12: Future Trends in Agricultural GIS

  • Cloud GIS
  • PostgreSQL/PostGIS
  • Big data analytics
  • Internet of Things (IoT)
  • Agricultural Management Information Systems (AMIS)
  • Future geospatial innovations

Case Study: Designing an integrated agricultural geospatial information system combining QGIS, Geographic Information Systems (GIS), GPS, Remote Sensing, SurveyCTO, KoboToolbox, Open Data Kit (ODK), Microsoft Excel, Microsoft Power BI, SPSS, STATA, R, Python, PostgreSQL/PostGIS, Agricultural Management Information Systems (AMIS), Artificial Intelligence, machine learning, Internet of Things (IoT), Results-Based Management, Monitoring, Evaluation, Accountability and Learning (MEAL), predictive analytics, precision agriculture, and digital reporting 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, QGIS computer laboratories, GPS field demonstrations, remote sensing practical sessions, agricultural mapping workshops, spatial analysis exercises, dashboard development practicals, software demonstrations, collaborative group work, agricultural GIS projects, and comprehensive agricultural case studies. Our facilitators are seasoned experts with over a decade of experience in Geographic Information Systems (GIS), agricultural monitoring and evaluation, Results-Based Management, digital agriculture, remote sensing, precision agriculture, business intelligence, donor-funded project 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, GPS receivers, drones (where applicable), 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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