Course Outcomes
Expected learning outcomes
Course Outcomes
- Understand practical AI applications in agriculture, food security and rural development.
- Use AI-supported insights for crop trends, climate risks and food security monitoring.
- Improve agricultural project planning, reporting, extension communication and decision-making.
- Apply AI tools to support land use, resource planning and rural livelihood programmes.
- Promote responsible AI adoption that considers data quality, local context and inclusion.
Curriculum
Course modules and outline
Course Outline
Module 1: AI in agriculture and food security
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 2: Smart agriculture and digital advisory services
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 3: Crop monitoring and production forecasting concepts
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 4: Climate data and climate-smart agriculture
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 5: Food security indicators and early warning support
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 6: Rural development planning and livelihood programmes
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 7: AI for agricultural project monitoring and reporting
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 8: Extension communication and farmer advisory content
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 9: Data quality, ethics and local context
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Module 10: Developing AI agriculture programme templates
- Key concepts, practical examples and sector-based discussion.
- Workplace application activity, templates and implementation considerations.
Target Audience
Who should attend?
Target Audience
Agriculture officers, food security specialists, rural development teams, NGOs, planners, government officials, climate programme teams, development project staff, extension officers, land officers, donor funded project teams and agricultural policy professionals.
Why Attend
Key course benefits
Practical capacity building approach
Designed for government, NGOs and public institutions
Real-world African case studies
Certificate of completion
Applicable tools and templates
Interactive facilitation and exercises
Course Enquiry
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