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How should we turn data into decisions in AgriFood?
Liliya Serazetdinova1, James Garratt2, Alan Baylis3
1Knowledge Transfer Network, London, UK.
Journal of the Science of Food and Agriculture
|December 21, 2018
Summary
Harnessing AgriFood supply chain data can enhance food security and quality. Overcoming challenges like data silos and skills gaps is crucial for realizing the full potential of digitalization.
Area of Science:
- Agricultural Science
- Data Science
- Supply Chain Management
Background:
- The AgriFood supply chain faces pressures from food security needs, climate change, and consumer demands.
- Existing technologies generate vast data, offering opportunities to improve productivity, reduce waste, and enhance traceability.
Framework:
- Data utilization in livestock production for efficiency gains.
- Automation and robotics in crop production enabled by data insights.
- Enhancing food safety and provenance through data-driven approaches.
Implementation:
- Workshop discussions highlighted challenges: data interoperability, data silos, and a skills gap.
- Addressing challenges requires practical producer support, skills development, and industrial leadership.
Implications:
- Digitalization is revolutionizing the AgriFood supply chain.
- Cohesive leadership is needed to integrate disparate groups and maximize data benefits.
- Collaboration and data sharing are key to unlocking transformative improvements.
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