Training Course on Data Governance and Ethics in Digital Agriculture
Training Course on Data Governance and Ethics in Digital Agriculture equips participants with the knowledge and tools to address complex issues surrounding data privacy, ownership, interoperability, and trust in agri-data ecosystems.
Course Overview
Training Course on Data Governance and Ethics in Digital Agriculture
Introduction
As digital technologies revolutionize agriculture, the ethical governance and responsible handling of data have emerged as critical priorities. Training Course on Data Governance and Ethics in Digital Agriculture equips participants with the knowledge and tools to address complex issues surrounding data privacy, ownership, interoperability, and trust in agri-data ecosystems. By focusing on emerging trends like big data, AI, blockchain, and smart farming, this course empowers stakeholders to create sustainable and equitable digital agriculture systems.
Designed for agriculture professionals, policymakers, data scientists, and development practitioners, this intensive training bridges the gap between technology and ethics. It emphasizes the importance of fair data policies, regulatory compliance, stakeholder rights, and transparent data sharing in agriculture. The course promotes a human-centric approach to digital innovation, ensuring that technology serves both productivity and ethical stewardship in agricultural transformation.
Course Objectives
- Understand the fundamentals of data governance in digital agriculture.
- Explore ethical issues in the collection, sharing, and use of agricultural data.
- Evaluate global data protection laws (e.g., GDPR) and their relevance to agriculture.
- Design ethical and inclusive data sharing frameworks.
- Analyze the role of big data and AI in agricultural decision-making.
- Develop policy recommendations for ethical data governance.
- Address gender and inclusion in agri-data systems.
- Examine the implications of blockchain and digital IDs in agriculture.
- Learn how to implement data traceability and transparency systems.
- Integrate FAIR (Findable, Accessible, Interoperable, Reusable) data principles.
- Promote data sovereignty and local ownership of agricultural data.
- Identify risks and mitigation strategies in digital agricultural innovations.
- Foster cross-sectoral partnerships to support ethical data ecosystems.
Target Audiences
- Agriculture Extension Officers
- ICT/AgTech Professionals in Agriculture
- Policymakers and Regulatory Authorities
- Researchers and Academics in Agri-Tech
- Development Agencies and NGOs
- Digital Platform Developers and Engineers
- Data Scientists working in Agriculture
- Private Sector Agribusiness Professionals
Course Duration: 5 days
Course Modules
Module 1: Introduction to Data Governance in Agriculture
- Understanding data ecosystems in digital farming
- Core principles of agricultural data governance
- Key stakeholders and their roles
- Legal and institutional frameworks
- Risks and opportunities in digital transformation
- Case Study: India’s AgriStack Digital Ecosystem
Module 2: Data Ethics and Responsible Innovation
- Foundations of digital ethics
- Bias, discrimination, and exclusion in datasets
- Consent and informed participation
- Ethical AI applications in agriculture
- Building inclusive data cultures
- Case Study: Ethical Dilemmas in Kenyan Farm Data Collection
Module 3: Global Legal Frameworks and Policy Compliance
- Overview of data protection laws (e.g., GDPR, AUDA-NEPAD guidelines)
- National vs international data governance
- Policy harmonization for cross-border data flow
- Institutional responsibilities and accountability
- Legal case examples from Africa, Europe, and Asia
- Case Study: EU-GDPR Adoption in East African Agritech Projects
Module 4: Data Ownership, Sovereignty, and Farmers’ Rights
- Defining data ownership in agriculture
- Farmers’ digital rights and control
- Indigenous data governance models
- Addressing power imbalances
- Consent frameworks for smallholder data
- Case Study: Latin American Movement for Data Sovereignty
Module 5: Interoperability and FAIR Data Principles
- Challenges in data silos and fragmentation
- Benefits of standardized, shareable data
- Implementing FAIR (Findable, Accessible, Interoperable, Reusable)
- Role of open data in research and innovation
- Platforms enabling interoperability
- Case Study: The GODAN Initiative and FAIR Data Use
Module 6: Blockchain, Traceability, and Trust
- Blockchain fundamentals in agriculture
- Smart contracts and traceable value chains
- Enhancing transparency in agri-supply chains
- Decentralized identity and data wallets
- Blockchain ethics and environmental costs
- Case Study: IBM Food Trust Blockchain with Coffee Farmers
Module 7: Gender, Equity, and Inclusion in Agri-Data
- Gender disparities in access to digital tools
- Culturally responsive data practices
- Strategies to engage marginalized groups
- Gender-sensitive data policies
- Monitoring inclusion in digital rollouts
- Case Study: Gender-Aware Digital Mapping in Ghana
Module 8: Designing Ethical Data Governance Frameworks
- Principles of ethical data design
- Institutional capacity building
- Public-private partnerships and accountability
- Participatory policy development
- Developing codes of conduct and best practices
- Case Study: Rwanda’s National Ethical Data Policy for Agriculture
Training Methodology
- Expert-led presentations with interactive Q&A
- Real-world case study analysis and group discussions
- Hands-on sessions with digital agriculture tools
- Role-playing simulations and ethical decision-making games
- Collaborative policy framework development
- Knowledge check quizzes and action planning workshops
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.