Global Forum on Food Security and Nutrition (FSN Forum)

Re: Contribute to shaping the design of the Agrifood System Technologies & Innovations Outlook (ATIO) Knowledge Base

Dear ATIO team

Please, find attached my submission on behalf of Bayer Crop Science for the FSN Consultation on the ATIO Knowledge Base. I appreciate the opportunity to contribute and look forward to any feedback or further discussions.

Best regards,
Stefano Marras
Director, Global Partnerships – UN Affairs
Bayer Crop Science

 

FSN Consultation

Contribute to shaping the design of the Agrifood System Technologies & Innovations Outlook (ATIO) Knowledge Base

Submission by Stefano Marras, Director, Global Partnerships – UN Affairs, Bayer Crop Science

  1. Given the description of the ATIO KB, how do you think it can help you and users like you? Describe one or more specific use cases that you wish the KB would address, like “I imagine I would be able to find innovative products that support farmers with access to credit and insurance specifically for one country, and I would be able to see information on their readiness and how they fare against adoptability criteria” or “I would like to use statistics to show a correlation between level of inclusivity / co- design of the solutions and their levels of adoption”.

Overall, for private companies in the agri-food sector, the ATIO KB can serve as a critical tool for decision-making, product development, market analysis, and investment prioritization, enabling to leverage science, technology, and innovation effectively.

  • Use Case 1: Identifying Innovative Products for Specific Regions

As a company involved in the agrifood system, the ATIO KB can be a useful resource for identifying innovative products tailored to specific regions. For instance, if we are looking to introduce a new product in a particular country, the ATIO KB can provide detailed information on existing similar products, their readiness levels, and how they fare against adoptability criteria such as profitability, accessibility, acceptability, and sustainability. This information can help companies make informed decisions about market entry and product development.

  • Use Case 2: Correlating Inclusivity and Adoption Rates

Another use case is utilizing the ATIO KB's statistical data to analyze the correlation between the level of inclusivity or co-design of solutions and their adoption rates. By accessing structured data on various innovations, we can identify patterns and insights that demonstrate the impact of inclusive and participatory approaches on the success of agricultural technologies. This can guide companies’ strategies for developing and promoting products that are more likely to be adopted by diverse user groups.

  • Use Case 3: Access to Grassroots Innovations

The ATIO KB's focus on grassroots innovations is particularly relevant for the training programs implemented by companies for their costumers (e.g. farmers, processors, retailers, consumers, etc). By accessing information on customer-led innovations and their contextual applications, companies can incorporate these grassroots solutions into their training modules, thus promoting locally relevant and sustainable practices. For example, companies might find innovative irrigation techniques developed by local farmers that can be scaled and adapted to other regions.

  • Use Case 4: Investment and Research Prioritization

The comprehensive data in the ATIO KB can help companies identify gaps and investment opportunities in the agrifood sector. By analyzing the lifecycle coverage of various technologies and innovations, we can prioritize research and development efforts in areas with high potential for impact and scalability. This strategic approach can enhance the companies’ product portfolio and contribute to the transformation of agrifood systems.

2. What do you make of concepts like policy innovation and social innovation? Can you think of examples? Is it useful for you to be able to find such content? In which form do you expect to find them? How would you use them?

Policy Innovation refers to the development and implementation of new and effective policies that address existing problems or improve upon current policies. In the context of agrifood systems, policy innovation could involve creating regulations and policies that support sustainable agricultural practices, promote the adoption of new technologies, or enhance food security. For example, a policy innovation might be the introduction of subsidies for farmers who adopt climate-resilient crops or the establishment of new frameworks for carbon farming initiatives.

Social Innovation involves the creation and implementation of new strategies, concepts, ideas, and organizations that meet social needs and foster social relationships and collaborations. In agrifood systems, social innovations could include community-supported agriculture (CSA), where consumers directly support local farmers, or the establishment of cooperatives that empower small-scale farmers and improve their market access. Another example could be the development of digital platforms that connect farmers with buyers, reducing intermediaries and ensuring fair prices for agricultural products.

Usefulness of Finding Such Content:

For a private company, being able to find content on policy and social innovations is highly useful. These innovations can provide insights into successful strategies and models that can be adapted or replicated in different contexts. Understanding policy innovations can help companies align their operations and solutions with local regulations and take advantage of government incentives. Social innovations, on the other hand, can guide companies in developing initiatives that create shared value for both the companies and the communities that they serve.

Expected Form of Content:

We would expect to find content on policy and social innovations in several forms:

  • Interactive Databases: Searchable databases that allow users to filter and browse through different types of policy and social innovations. These databases should include metadata such as the geographical location, target beneficiaries, and the specific issues addressed by the innovations.
  • Reports and Publications: Comprehensive reports that analyze the impact of various policy and social innovations on agrifood systems. These reports should provide data and evidence to support the effectiveness of these innovations.
  • Case Studies: Detailed descriptions of specific policy or social innovations, including their objectives, implementation processes, challenges faced, and outcomes. Case studies should highlight best practices and lessons learned.
  • Infographics and Visual Summaries: Visual representations of key information related to policy and social innovations. Infographics can make complex information more accessible and easier to understand at a glance.
  • Webinars: Interactive sessions where experts and practitioners share their experiences and insights on policy and social innovations. These sessions can facilitate knowledge exchange and networking among stakeholders.

Usage of Content:

We would use the content on policy and social innovations in several ways:

  • Strategic Planning: Integrate successful policy and social innovation models into the companies’ strategic plans to enhance their operations and community engagement efforts.
  • Product Development: Develop new products or services that align with innovative policies or address social needs identified through social innovations.
  • Advocacy and Partnerships: Advocate for supportive policies and collaborate with stakeholders to implement social innovations that benefit the agrifood sector.
  • Training and Capacity Building: Incorporate insights from policy and social innovations into the company’s training programs for farmers and other stakeholders to promote best practices and sustainable development.

 

3. How important is it to feature grassroots innovations? Looking at some records of grassroots innovations in the prototype, what would you like to see in the descriptions that you don’t see? Which dimension should we capture? What is most useful for grassroots use/application of innovations?

Grassroots innovations are crucial for several reasons. They often emerge from the practical experiences and needs of local communities, making them highly relevant and context-specific. These innovations can offer sustainable and affordable solutions that are tailored to the local environment and socio-economic conditions. By featuring grassroots innovations, the ATIO KB can ensure that the knowledge base is inclusive and representative of diverse innovation sources, thereby promoting a more equitable and holistic approach to agrifood system transformation.

Desired Descriptions for Grassroots Innovations:

When looking at records of grassroots innovations in the prototype, it is essential to include comprehensive descriptions that capture the following dimensions:

  • Context and Origin: Detailed background information on the local context where the innovation was developed, including geographical, cultural, and socio-economic factors. This helps in understanding the relevance and applicability of the innovation in similar contexts.
  • Problem Addressed: Clear explanation of the specific problem or challenge that the innovation aims to solve. This should include the impact of the problem on the community and how the innovation addresses it effectively.
  • Development Process: Information on how the innovation was developed, including the role of local knowledge, experimentation, and collaboration among community members. Highlighting the participatory nature of the development process can demonstrate the innovation's inclusivity and adaptability.
  • Implementation and Adoption: Details on how the innovation was implemented, including any pilot projects, trials, or scaling efforts. Information on adoption rates and feedback from users can provide insights into the innovation's effectiveness and potential for wider application.
  • Impact and Benefits: Quantitative and qualitative data on the benefits and impact of the innovation, such as improved yields, cost savings, environmental sustainability, and social benefits. This should include testimonials or case studies from users and community members.
  • Challenges and Lessons Learned: Discussion of any challenges faced during the development and implementation of the innovation, as well as lessons learned. This can provide valuable insights for others looking to replicate or adapt the innovation.
  • Scalability and Replicability: Assessment of the innovation's potential for scaling and replicability in other regions or contexts. This should include any necessary adaptations or considerations for different environments.
  • Sustainability: Information on the long-term sustainability of the innovation, including environmental, economic, and social dimensions. This can help in evaluating the innovation's viability and contribution to sustainable development goals.

Most Useful Dimensions for Grassroots Use/Application of Innovations:

For grassroots use and application of innovations, the most useful dimensions to capture are:

  • Accessibility: Information on how easily the innovation can be accessed and adopted by local communities, including cost, availability of materials, and required skills or knowledge.
  • Cultural Acceptability: Understanding of how well the innovation fits within the local cultural practices and values. Innovations that align with cultural norms are more likely to be accepted and adopted.
  • Scalability: Potential for the innovation to be scaled up or adapted to different contexts. This includes identifying any barriers to scaling and strategies to overcome them.
  • Environmental Impact: Assessment of the innovation's impact on the environment, including any benefits or potential negative effects. Sustainable innovations that protect or enhance the environment are highly valuable.
  • Economic Viability: Analysis of the economic benefits of the innovation, such as cost savings, increased income, or improved productivity. This helps in understanding the financial feasibility and attractiveness of the innovation.
  • Community Involvement: Degree of community involvement in the development and implementation of the innovation. Innovations that involve and empower local communities are likely to be more sustainable and impactful.

By capturing these dimensions, the ATIO KB can provide a comprehensive and valuable resource for grassroots innovators, policy makers, and other stakeholders, enabling them to identify, assess, and adopt effective and sustainable innovations.

4. How do you think branded commercial products should be featured on the ATIO KB? Data sources of technology-related information often feature individual models of technologies (for instance, different models of solar-powered irrigation pumps). Should the ATIO KB feature models? What is the “innovation” unit you expect to find?

The ATIO KB should encompass both broader categories of innovations and individual models of technologies (including branded commercial products). By featuring both individual models and broader categories of innovations with detailed, holistic information, the ATIO KB can provide a valuable resource for policy makers, investors, innovators, and potential adopters, helping them make informed decisions and promote the adoption of appropriate and sustainable technologies in the agrifood sector.

The knowledge base should include:

  • Categories of Innovations: Broader categories that group similar technologies or innovations together, providing an overview of the types of solutions available for a particular problem or application. This helps users understand the landscape of available innovations and identify trends and patterns.
  • Individual Models: Detailed profiles of specific models of technologies, including technical specifications, performance metrics, cost, and user reviews. This helps users compare and select the most appropriate model for their needs. Branded commercial products should be featured on the ATIO KB to provide a comprehensive view of available, real-world technologies and innovations in the agrifood sector. Including commercial products can help users compare different solutions, understand market trends, and make informed decisions based on a wide range of options. It also allows for the identification of gaps and opportunities for new product development.

The ATIO KB should provide holistic information on each innovation, including:

  • Technical Specifications: Detailed technical data such as capacity, efficiency, energy consumption, and other relevant metrics.
    • Use Cases: Examples of how the technology has been used in different contexts, including case studies and testimonials from users.
    • Adoptability Criteria: Information on the readiness, accessibility, acceptability, profitability, and sustainability of the technology. This includes any required complementary inputs and the socio-economic impact of adopting the technology.
    • Environmental and Social Impact: Assessment of the environmental footprint and social implications of the technology, including its impact on gender, minorities, and local communities.
    • Scalability and Replicability: Analysis of the potential for scaling the technology and replicating it in different regions or contexts.
    • Partnerships and Collaborations: Information on partnerships and collaborations that have supported the development and deployment of the technology, including funding sources and supporting organizations.

5. Here are two of the main taxonomies used in the prototype: types of innovations and use cases. Considering that there is no agreed standard for these categorizations, and that we are aligning them to those used in similar projects, are these “good enough” to start? Which major problems do you see? Please suggest changes or volunteer to help us improve them in the next months. Other taxonomies are here.

The taxonomies “types of innovations” and “use cases” are a good starting point. They provide a basic structure for organizing the information and make it easier for users to navigate the knowledge base. However, there are potential improvements and considerations to enhance their effectiveness:

Potential Limits and Suggestions for Improvement:

  • Granularity and Specificity: The current taxonomies might be too broad or too narrow in certain areas. It is essential to strike a balance between granularity and specificity to ensure that users can find relevant information without being overwhelmed by too many categories. We suggest conducting a thorough analysis of user needs and feedback to refine the categories and subcategories. This could involve creating more specific subcategories under broader categories to capture the diversity of innovations and use cases.
    • Dynamic and Evolving Nature: Innovations and their applications are continuously evolving. Static taxonomies may become outdated quickly. We suggest implementing a dynamic taxonomy system that can be regularly updated based on new insights, emerging trends, and user feedback. This could involve using AI-assisted routines to identify and incorporate new categories and subcategories as they emerge.
  • Interdisciplinary and Cross-cutting Innovations: Some innovations may span multiple categories or use cases, making it challenging to classify them under a single category. It is suggested to allow for multiple categorizations or tagging of innovations to capture their interdisciplinary and cross-cutting nature. This can help users find innovations that may not fit neatly into a single category but are relevant to multiple areas.
  • Alignment with Existing Standards: While there are no universally agreed standards, aligning with existing classifications used by similar projects can enhance interoperability and consistency. We suggest continuing aligning the taxonomies with those used in similar efforts by FAO and other international stakeholders. Engage in consultations with stakeholders and experts to ensure the taxonomies are relevant and useful.

Other Taxonomies:

In addition to types of innovations and use cases, other taxonomies that could be considered include:

  • Stage of Development and Readiness Level: Categorizing innovations based on their development stage, such as concept, prototype, pilot, or commercialized.
  • Accessibility Features: Categorizing innovations based on their accessibility to different user groups, including cost, availability, and ease of use.
  • Impact Dimensions: Classifying innovations based on their impact on various dimensions, such as environmental sustainability, social equity, economic viability, and gender inclusivity.

By incorporating these additional taxonomies, the ATIO KB can provide a more comprehensive and nuanced view of agrifood systems innovations, helping users make better-informed decisions and promoting the adoption of appropriate and sustainable solutions.

We are open to volunteering to help improve the taxonomies over the next months. This could involve participating in consultations, providing feedback based on the private sector’s expertise and user experiences, and collaborating with the ATIO KB team to refine the classification systems.

6. We are developing a chatbot-like search capability. Do you prefer the classic filter-based search or the chatbot search? Or the possibility of choosing either? Tell us how we can improve the search experience.

Classic Filter-Based Search:

The classic filter-based search allows users to narrow down their search results by applying various filters such as categories, tags, or specific criteria. This method is straightforward and efficient for users who know exactly what they are looking for and prefer a structured approach to finding information. It provides a clear and systematic way to explore the database, making it easy to compare different entries based on selected attributes. Advantages:

  • Precision: Users can precisely narrow down results using specific filters.
  • Structure: Provides a clear and organized way to browse through information.
  • User Control: Users have full control over the filtering criteria and can adjust them as needed.

Chatbot Search:

A chatbot-like search capability offers a more interactive and conversational approach to finding information. Users can ask questions in natural language, and the chatbot can provide relevant answers, suggest related topics, and guide users through the knowledge base. This method is particularly useful for users who are not sure how to formulate their search queries or prefer a more guided and dynamic search experience. Advantages:

  • User-Friendly: Offers a more intuitive and conversational way to search for information.
    • Guidance: Can help users refine their queries and suggest related information.
    • Accessibility: Makes it easier for users with varying levels of expertise to find relevant information.

Combination of Both:

Given the diverse needs of users, offering both classic filter-based search and chatbot search capabilities would be the ideal solution. This hybrid approach allows users to choose the method that best suits their preferences and specific needs at any given time.

  • Expert Users: May prefer the precision and control of the filter-based search.
  • Novice Users: May benefit from the guidance and ease of the chatbot search.

Suggestions:

  • Advanced Search Features: For the filter-based search, include advanced search features such as Boolean operators, proximity searches, and wildcard searches to enhance search capabilities.
  • Seamless Integration: Ensure that users can easily switch between the filter-based search and the chatbot search within the same interface. For example, the chatbot could suggest filters to apply based on the user's queries, and vice versa.
  • Contextual Assistance: Implement contextual assistance within the chatbot to help users understand how to use filters effectively. The chatbot could provide tips and examples of how to refine searches using filters.
  • Personalization: Allow users to save their search preferences and frequently used filters. The chatbot could learn from user interactions and offer personalized suggestions based on past queries and behaviors.
  • Feedback Mechanism: Include a feedback mechanism for users to rate the effectiveness of search results and provide suggestions for improvement. This feedback can be used to continuously refine both the filter-based and chatbot search functionalities.
  • Comprehensive Help Section: Provide a comprehensive help section that explains how to use both search methods, with tutorials, FAQs, and example queries. This can help users become more proficient in using the knowledge base.

By offering both classic filter-based search and chatbot search, and continuously improving these features based on user feedback and technological advancements, the ATIO KB can provide a versatile and user-friendly search experience that caters to a wide range of user needs and preferences.

7. We use Artificial Intelligence (AI) to enrich and automatically categorize the records: you will see an AI stamp at the end of descriptions that have been generated by AI: how good is the text generated? Is AI enriching the records in a meaningful way?

The AI-generated text in the ATIO KB has the potential to significantly enrich the records and provide meaningful insights to users. However, it is crucial to ensure that the AI-generated content is accurate, relevant, and of high quality. A robust human review process, continuous improvement of AI algorithms, and user feedback mechanisms are essential to achieving this goal. By leveraging AI effectively, the ATIO KB can become a comprehensive, dynamic, and user-friendly resource for agrifood systems innovations.

Benefits of using AI in ATIO KB:

  • Efficiency: AI can significantly enhance the efficiency of data processing and content generation. By automating the categorization and initial description of records, AI can handle large volumes of data quickly, ensuring that the ATIO KB remains up-to-date and comprehensive.
  • Comprehensiveness: AI can help identify patterns and connections within the data that might be overlooked by human curators. This can lead to more comprehensive and holistic records that capture the full spectrum of information relevant to each innovation.
  • Scalability: The use of AI allows the ATIO KB to scale its operations effectively. As more data sources are integrated and more innovations are documented, AI can manage the increased workload without compromising on quality.
  • Continuous Improvement: AI systems can learn and improve over time based on user interactions and feedback. This means that the quality of AI-generated content can continuously improve, becoming more accurate and insightful as the system evolves.
  • Contextual Information: AI can enrich records with contextual information that enhances their usefulness. This includes linking related innovations, providing background information on the socio-economic context, and highlighting key adoption criteria.
  • User-Centric Insights: AI can tailor the content to the needs of different user groups. For example, policy makers might need information on regulatory impacts, while farmers might be more interested in practical implementation tips. AI can generate content that addresses these diverse needs.
  • Interactive Features: AI can support interactive features such as chatbots and dynamic filtering, making it easier for users to find relevant information and insights. This enhances the overall user experience and makes the knowledge base more accessible.

The quality of AI-generated text should be assessed based on:

  • Accuracy and Relevance: The AI-generated text should be accurate, relevant, and context- specific. The information provided must align with the actual data and insights from the sources it draws upon. The AI should be capable of understanding the nuances of agrifood systems and the specific needs of the ATIO KB users.
  • Clarity and Readability: The text generated by AI should be clear, concise, and easily understandable. It should avoid technical jargon unless necessary and should be written in a way that is accessible to a broad audience, including policy makers, researchers, and farmers.
  • Depth and Insight: The AI-generated content should provide meaningful insights and not just surface-level information. It should capture the complexity and multifaceted nature of innovations, including technical specifications, use cases, and socio-economic impacts.
  • Consistency: The AI should generate text that is consistent in style and tone with the rest of the content in the ATIO KB. This ensures a cohesive user experience and maintains the credibility of the knowledge base.
  • Evaluation Process: To evaluate the quality of AI-generated text, it is essential to have a human review process in place. Experts in agrifood systems should review the AI-generated content to ensure its accuracy, relevance, and quality. Feedback from these reviews can be used to improve the AI algorithms.
See the attachments: