Foro Global sobre Seguridad Alimentaria y Nutrición (Foro FSN)

Dear members of the FSN Forum community,

From the FAO Office of Innovation, we would like to sincerely thank you for your very elaborate and insightful contributions on the design of the ATIO Knowledge Base. We collected your feedback and are going to share a final report with all of you very soon.

All the recommendations and suggestions contained in your contributions will be considered in the further development of the ATIO KB, especially those that have been put forward by more than one participant - and there are many, which tells us that there are a common vision and common values that we all share.

In particular, there was almost unanimous insistence on the importance of context and local relevance (and some of you already illustrated your own real-world local scenarios), the crucial role of grassroots innovations, the consideration of indigenous / traditional knowledge, and the socio-economic and cultural dimensions of adoption. Also the comments we received on the search interface, the taxonomies and the use of AI are quite aligned (mainly elaborating on striking a balance between granularity and usability, and between the efficiency of AI and the accuracy of data, and the need to involve stakeholders in reviewing and updating taxonomies regularly).

Now, we have to refine the design and start addressing the practical aspects of the implementation.

The next step will be a more intense 4 half-day virtual expert consultation during which we will transform the recommendations received so far into an actionable plan and will design an initial form of governance for the ATIO KB. As mentioned in the introduction to this call on the FSN Forum, we would like to contact some of the participants in this discussion to continue the conversation and invite them to the expert consultation.

Looking forward to continued exchanges with you, and thanking you again,

Best regards,

the ATIO KB team

Fabrizio Bresciani, Delgermaa Chuluumbaatar, Valeria Pesce, Martina Miracapillo, Athira Aji

Office of Innovation (OIN)

Food and Agriculture Organization of the United Nations

Proposed Design for the Agrifood Systems Technologies and Innovations Outlook (ATIO) Knowledge Base (KB)

Nepal C Dey^1*, Md. Tofazzal Islam², Wais Kabir³ and Bidyuth K Mahalder⁴

^1*Chief Investigator, Resilient Agrifood System Research, Research and Entrepreneurship Development, Dhaka 1216 & *Fellow, AGU Fellow Mentoring Network, American Geophysical Union, Washington, DC 20009-1227

²Institute of Biotechnology and Genetics Engineering, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Salna, Gazipur -1706 

³Former Executive Chairman, Bangladesh Agricultural Research Council (BARC), &

Consultant, CIMMYT, Dhaka Bangladesh

⁴ Chief of Party, Bangladesh Climate Smart Project, ACDI/VOCA, Dhaka, Bangladesh

Introduction

The global food system faces growing challenges in meeting the demands of a rising population. The  Agrifood Systems Technologies and Innovations Outlook (ATIO) offers opportunities to enhance food production and trade, particularly for smallholder farmers, while also contributing to the achievement of the Sustainable Development Goals (SDGs).

The agricultural sector in developing countries, particularly in South Asia, including Bangladesh, is highly susceptible to natural calamities such as cyclones, floods, and droughts, which are exacerbated by climate change and fragile ecosystems. Coastal communities are increasingly affected by soil erosion and saline water intrusion due to rising sea levels, threatening their livelihoods. Low-lying areas, where two-thirds of the land remain submerged over six months, offer limited scope for agricultural production, often restricting farmers to a single crop cycle. This constraint exacerbates poverty in many South Asian countries, particularly in Bangladesh. Moreover, the overuse of chemical inputs like fertilizers and pesticides accelerates soil nutrient depletion and fertility loss, further reducing the availability of arable land.

Water management is critical, as agriculture consumes 80% of available water. Efficient agricultural water use, groundwater conservation, and improved surface irrigation practices are essential. Effective water governance and management should be prioritized. By doing so, the adverse impacts of climate change can be significantly mitigated. Shortening food value chains (FVCs) is crucial to lowering transaction costs, minimizing food loss, enhancing food safety, and environmental sustainability. Strengthening post-harvest infrastructure and processing facilities for perishable, nutrient-rich foods—such as fruits, vegetables, and non-cereal crops—will improve FVC efficiency and meet rising domestic demand.

In the aforementioned context within an era of rapid globalization and environmental uncertainty, the design of an effective Agrifood Systems Technologies and Innovations Outlook (ATIO) Knowledge Base (KB) is essential for promoting sustainable food systems. The ATIO Knowledge Base should adopt a user-centered design approach, ensuring it addresses the diverse needs of stakeholders involved in agrifood systems. This proposed design is prepared based on a comprehensive review of the literature using OpenAI (2024) ChatGPT driven search tools, the extensive experience of the authors, and insights from published research.

In recent years, digital technologies have emerged as powerful tools for enhancing agricultural extension services [Deichmann et al. 2016; Ortiz-Crespo et al. 2020; Mohammad and Dey, 2024). The rise of digitalization has enabled agri-tech entrepreneurs and startups to develop innovative business models tailored for smallholder farmers, reducing transaction and discovery costs (FAO, 2015). These emerging digital agriculture technologies have the potential to transform service delivery, improve decision-making, enhance value addition, and boost productivity, profitability, resilience, and sustainability across the food supply chain.

A recent study by Mohammad and Dey (2024) reviewed and proposed policies on improving agro-food system technologies, including digital applications in Bangladesh across various sectors. Crop advisory services, such as the Digital Crop Calendar and BRRI Rice Knowledge Bank, support farmers with essential information. Agronomic and agro-meteorological advisory platforms, including the Department of Agricultural Extension (DAE), Bangladesh Agricultural Research Council (BARC), and Krishi-call-centre-16123, provide real-time guidance (DAE, BARC). Agri-mechanization is facilitated by BRRI Machineries Bangladesh, while soil, seed, fertilizer, and irrigation advisory services are offered through SRDI and Mrittika apps (SRDI, BADC). Agribusiness and agri-marketing platforms like Sadai and Krishi Bazar enhance market access (DAM). Additionally, agri-finance is supported by the Farmer Loan app, and specialized applications cater to floriculture, fisheries, and animal science, including Motshyo Poramorsho and BLRI FeedMaster (BLRI, BdFISH).

Alam & Wagner (2013) examined a digital procurement system for Bangladeshi sugarcane producers, replacing paper-based orders with SMS-based transactions, improving efficiency and transparency. Chakraborty et al. (2022) developed e-Farmers’ Hut, an e-commerce platform enabling direct transactions between farmers and consumers, though challenges like internet accessibility persist. Ahmed (2022) highlighted iFarmer, an agri-tech platform providing financing, insurance, market access, and advisory services, while Tonmoy (2023) detailed its Sofol and Kri-Shop apps for financial requests and input delivery. New Age (2023) reported on iFarmer’s Agri Machinery Experience Centers, offering hands-on training and after-sales support.

The increasing adoption of digital solutions, such as mobile applications, remote sensing, and data analytics, offers real-time insights into weather patterns, pest outbreaks, and market prices. However, recent studies indicate significant duplication of digital agriculture services by public, private, and technology companies, highlighting the need for greater coordination and inclusive collaboration among supply-side actors. Moving forward, further research is essential to assess the impact of existing digital agriculture solutions and their effectiveness in supporting intended beneficiaries (Mohammad and Dey, 2024).

The ATIO is transforming agriculture, integrating smartphones, IoT, AI, blockchain, robotics, drones, and networked data systems. All these integrated innovations enhance productivity, streamline supply chains, reduce costs, and accelerate market access. The technological transformation empowers farmers with advanced tools for crop, fisheries, and livestock management, enabling data-driven decision-making and real-time troubleshooting.

Below is an outline of the proposed design, including use cases and user preferences, to guide its development.

1. Use Cases for ATIO KB

These use cases highlight how different users might interact with the ATIO KB:

A. Policymakers and Government Officials

• Access updated data-driven insights and evidence-based policy recommendations on agrifood system innovations.

• Summarized reports, policy briefs, interactive dashboards, and scenario analysis tools.

Preferred Features:

• Quick search and filtering options.

• Policy recommendations based on real-world case studies.

• Access to model simulations and forecasts.

B. Researchers and Academics

• Explore empirical studies, methodologies, global trends in agrifood technologies, and local adoption or practices.

• Peer-reviewed articles, data repositories, advanced analytical tools.

Preferred Features:

• Advanced search for research publications.

• Data visualization tools.

• Citation export functionalities.

C. Agribusiness and Private Sector

• Identify market trends, investment opportunities, producers’ challenges, and technological innovations.

• Business intelligence insights, technology adoption trends, funding opportunities.

Preferred Features:

• Market intelligence reports.

• Innovation tracking system.

• Networking opportunities with technology providers.

D. Farmers and Extension Workers

• Find practical, location-specific technologies and innovations to improve productivity, nutrient rich, and resilience.

• Simple, actionable guidelines, video tutorials, mobile-friendly access. 

• Technology like SMS, Automated Call and Interactive Voice Response can be used to give access to information, disseminate awareness and alert to technically challenged farmers who don't even have access to smart devices and the internet but have feature phones.

Preferred Features:

• Multilingual content with practical recommendations.

• Offline access for low-connectivity regions.

• Case studies of successful technology adoption.

E. Development Organizations and NGOs

• Design programs that support sustainable agrifood innovations in vulnerable communities.

• Impact assessments, identify best practices, project implementation guides, funding mechanisms.

Preferred Features:

• Open-access reports on successful interventions.

• Tools to measure the sustainability and scalability of innovations.

• Publication of innovation for global audience

• Community forum for knowledge exchange.

2. Users’ Preferences to Guide the ATIO KB Design

To ensure usability and relevance, ATIO KB should incorporate the following design principles:

• User-friendly Interface: Intuitive navigation, clean UI, and accessibility for all users, including those with limited digital literacy.

• Interactivity & Customization: Dashboards that allow users to personalize information based on interests and regions.

• Data-Driven Insights: Real-time analytics, trend visualization, and downloadable datasets.

• Integration with Existing Knowledge Systems: Links to FAO, CGIAR, AMISDP and other global databases.

• Multilingual and Multimedia Support: Content in multiple languages with infographics, videos, and podcasts.

• Mobile Optimization: Ensuring accessibility on low-bandwidth mobile networks for rural users.

• Collaboration and Networking Features: Forums, expert Q&A sections, and partnerships with universities and research centers.

By aligning the use cases with users’ preferences, the ATIO Knowledge Base can serve as a comprehensive, dynamic, and user-centered platform that accelerates the adoption of agrifood technologies and innovations globally.

References

Deichmann, U., Goyal, A., & Mishra, D. (2016). Will digital technologies transform agriculture in developing countries? Agricultural Economics, 47(S1), 21–33. doi:10.1111/agec.12300.

Ortiz-Crespo, B., Steinke, J., Quirós, C. F., van de Gevel, J., Daudi, H., Gaspar Mgimiloko, M., & van Etten, J. (2020). User-centred design of a digital advisory service: enhancing public agricultural extension for sustainable intensification in Tanzania. International Journal of Agricultural Sustainability, 1–17. doi:10.1080/14735903.2020.1720474

Mohammad, I. and Dey, N.C. (2024). Digital Agriculture Innovations in Bangladesh: A Situational Analysis and Pathways for Future Development. Thunderbird International Business Review, 2024; 0:1–25. https://doi.org/10.1002/tie.22421.

FAO (2015). FAOSTAT. Rome: Food and Agriculture Organization of the United Nations (FAO). Rome. Available at: http://faostat3.fao.org.

Alam, M. M., & Wagner, C. (2013). Assessing the impact of digital procurement via Mobile phone on the agribusiness of rural Bangladesh: a decision-analytic approach. Agribusiness and Information Management, 5(1), 31-41.

Chakraborty, S., Shamrat, F. J. M., Islam, M. S., Kabir, F., Khan, A. N., & Khater, A. (2022, April). Implementing E-Commerce Mobile and Web Application for Agricultural Products: e-Farmers' Hut. In 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 976-984). IEEE.

Ahmed, S. (2022). iFarmer: Reshaping farmers’ lives, becoming a one-step agro-solution. The Business Post. Available at https://businesspostbd.com/business-connect/ifarmer-reshaping-farmers-lives-becoming-a-one-step-agro-solution-2022-11-13

Tonmoy, S. R. (2023). iFarmer: The tech-enabled one-stop solution for smallholder farmers. The Daily Star. Available at https://www.thedailystar.net/supplements/accelerating-bangladesh/news/ifarmer-the-tech-enabled-one-stop-solution-smallholder-farmers-3263131

New Age. (2023, October 15). iFarmer launches Agri Machinery Experience centres in Bogura, Jaipurhat. Newagebd.Net. Retrieved March 20, 2024, from https://www.newagebd.net/article/215013/ifarmer-launches-agri-machinery…

OpenAI (2024) ChatGPT (Feb 11 version). Available at: https://openai.com (Accessed: 11 February 2025).

A general comment would be that as FAO, I would expect the organization to assume its normative role in a new discipline and ensure capacity development on the topic. The ATIO sounds quite ambitious in its scope, but one should also realise that it needs to take on a holistic approach to allow for different elements along the AFS to be linked. Therefore, the ATIO has to be comprehensive and may not necessarily achieve the same level of details in every field. However, where there is depth of understanding available (even using existing and established practices (which can still be applied in an innovative manner), then we should be able to demonstrate the power of a shared deep understanding of AFS processes.

As for the questions, I have attempted to answer then without repeating what others have already said so well before...

1. Given the description of the ATIO KB, how do you think it can help you and users like you? 

I would like to use the ATIO KB to provide me with an overview of the Technologies and Innovative approaches relevant to a problem area that I am facing – this problem may be procedural or technical, and the accuracy of the results would be based on the level of details I am able to provide in formulating the problem I am trying to solve (for myself or my stakeholders). I do not expect ATIO KB to give me the exact solution as an output but rather the aspects that I would need to consider as well as the potential solutions that may apply. So, I would be keen to see if the ATIO KB distinguishes between specific technologies and technology packages (the latter can also be innovations).

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?

Both a policy or social innovation would be addressing a problem area or a challenge, so I would more likely be looking for ATI that have been labelled as addressing similar challenges – which would mean that I am proposing the ‘Contextual challenge” or ‘Situational Problem area’ as one of the descriptors of ATI. I would be looking for shared experiences from the use cases to find ones that most closely resemble the challenge I am facing and try to find a common principle for achieving the solution. E.g. How do decide on the best model of water pumps suitable to tropical climate

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?

Based on the responses I have seen, there is a lot of demand for a range of fields and all along the AFS. However, I believe that the ATIO will become more responsive to needs of stakeholders if the grassroots innovations are included and discussions allowed or scheduled on these innovations as they emerge and mature. So, grassroots innovations as well as platform or communities of practice thereon to feed the taxonomy.

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?

I refer to my earlier question about differentiating between technologies and technology packages (where a technology package can include several technologies). Thus, irrigation pumps would be a technology, and solar powering would be another. In combination, they offer a technology package (which could also be seen by someone else as a technology). So, the innovation unit I would expect to find is the broad category but not the branded commercial name. For these, I would provide additional space to carry out such comparisons – or allow an external user or market place to enable such comparisons, but I would not expect ATIO to achieve this level of service.

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.

There are many taxonomies that have been mentioned already. Given FAO’s normative function, I would start with the two and allow different options to be tested by a community of practice on ATIO – something like a sandbox with the existing set of data and use cases. I would also suggest that there be a regular review of the taxonomy at specific periods. In the end ATIO could play the role of federating different authoritative sources to work towards harmonization of a standard.

In the same vein, I see that we have focused a lot on foresight in the classification but we should also be able to apply the same principles to hindsight. E.g. can we apply AGROVOC on existing databases of technologies an innovations that are already present on regional R&D organizations to demonstrate that an existing vocabulary/taxonomy fits well with existing ATI, and therefore improve our confidence that we can apply the same technique looking forward, with a proviso that it can be reviewed and improved on over time, just like AGROVOC?

If I would have wanted to add another taxonomy, I would also look at the Capacity Development of AIS approach for a classification of the ATI in the KB - five functional capacities and subsets within.

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.

The work of ATIO lends itself well to the use of AI, as chatbot today would be using this technology. I would want to have the possibility of both until I can see that the AI version is more intuitive.

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?

Yes, we should try to use AI and train AI to understand our logic in building categories. With the possibility of having a cheaper LLM installed locally and enriching it, this is something that a CoP on ATIO should experiment with.

 

Dear ATIO Team,

Thank you for providing the opportunity to comment on the ATIO KB. Below, please find our submission on behalf of GIZ’s Fund for the Promotion of Innovation in Agriculture (i4Ag). 

FSN Consultation

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

Submission by Fabiana Woywod and Till Rockenbauch, Fund for the Promotion of Agricultural Innovations (i4Ag), Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

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”.

As an implementing agency GIZ, has vast experience in setting up innovation partnerships for the promotion of agricultural innovations. Potential use cases include scouting (innovations and partners) and ideation (project design). A particular use case could be support to the development of scaling initiatives / strategies (e.g. design of context-specific innovation bundles). 

As a means to this end, we would ATIO expect to provide (additional) information on: 

• what innovations are applicable / promising in a specific context (e.g. agroecological setting, national / regional or institutional context)?
• how innovations could be bundled for synergies?
• relevant/common challenges/hurdles to consider/overcome (differentiated by intended scaling context)
• blueprints/roadmaps for scaling and dissemination

In addition, ATIO would be of interest as an outlet to share/disseminate proven GIZ innovations.

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?

Social/institutional innovations (e.g. farmer self-help groups, machinery rings, etc.) are drivers of social change and empowerment and, hence, should be equally represented at the platform (besides technological innovations). In addition, policy innovations (approaches to inclusive decision/policy making and conducive regulatory frameworks) are prerequisite for successful scaling of innovation (bundles). The platform could feature/suggest policy innovations as enablers/facilitators of particular agricultural innovations.

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?

ATIO’s emphasis on grassroots innovations is appreciated, as the discourse around (scaling of) agricultural innovations (in many cases) is dominated by linear / top-down approaches. For innovations to be inclusive, it is prerequisite to strengthen grassroots actors’ capacity to innovate and to ensure their representation in scaling processes. Rather than classifying an innovation as grassroot/non-grassroot innovation it would be helpful to provide information on the suitability of innovations to foster inclusive scaling and/or recommendations on how to ensure inclusive scaling (e.g. by bundling technical with social innovations). In addition, the use of the platform by grassroot organizations should be ensured/promoted (e.g. by paying attention to language and easy applicability).

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?

• Where possible, we suggest to avoid branding (in the presentation of the innovation). 
• Innovations with an open access licensing could be highlighted
• You might consider including an additional section/category “copyright”

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 “use case” taxonomy helps to compare Innovations when listed in the result gallery. Could be placed on top of thumbnail.
• Types and use cases are very detailed, which is good to match keyword search – however feels overwhelming and time-consuming to go through. Lower scaling readiness stages (1-3) most likely not relevant for non-scientific users.
• Use of LLM for search related suggestions could help. 
• Also, a digital dashboard to compare innovations could save time and increase user-friendliness, based on key metrics/scores (e.g. scaling potential, ease of use, cost-benefit ratio, etc. could further improve comparability 
• Tools for identifying synergies and compiling innovation bundles could help building context-specific solutions

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.

Chatbot search results often lack transparency. We would like to see an overview of the results to better control the search and tailor the assistance to the needs.

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?

• Description of innovations seems good but more conceptual clarity might be needed with regard to what actually constitutes particular innovations. Some “innovations” listed are resembling a range / bundle of (not necessarily well-defined) innovations. 
• Subtitles or graphics would help for skim reading. 
• AI-Reasoning reads consistent at a first glance; however, doubts remain due to vague/ambitious wording. Is AI checking the plausibility of the data base? How does it deal with vague/patchy data? For example, is AI able to distinguish between broad impacts claims (e.g. “the innovation improves income “) from reported impact (e.g. “the innovation has improved the income of x users by x percent”)? In addition, are there human plausibility checks (and if yes, how do humans deal with patchy data)?

We appreciate your efforts in drafting the ATIO KB, which we consider a promising tool with great potential for informed decision-making towards scaling the impact of agricultural innovations. We would be interested to stay updated on latest developments and would be glad (where possible and feasible) to accompany the conceptual refinement and implementation of the ATIO platform.

Aquatic Life Institute (ALI) appreciates the opportunity to provide input on the development of the Agrifood Systems Technologies & Innovations Outlook (ATIO) Knowledge Base (KB). As an organization dedicated to improving aquatic animal welfare and sustainability in food systems, we commend FAO’s Office of Innovation for advancing a participatory, open-access approach to cataloging agrifood technologies and innovations.

We believe the ATIO KB has significant potential to serve as a valuable resource for policymakers, researchers, agripreneurs, and stakeholders across the food system. However, to fully realize its impact, we encourage the following considerations to ensure that aquatic food systems and animal welfare are adequately represented within the Knowledge Base.

The Role of Aquatic Animal Welfare in Innovation and Policy Design
The transition toward sustainable agrifood systems must include meaningful consideration of aquatic animal welfare, particularly in fisheries and aquaculture. Given that over 2-3 trillion aquatic animals are caught in the wild or farmed annually, it is imperative that innovations in humane handling, species-specific welfare standards, and low-impact aquaculture systems are included in the ATIO KB.

We encourage the explicit inclusion of policy innovations that improve aquatic animal welfare, such as:

• Technology-driven welfare improvements, including species-specific stunning methods to reduce suffering at slaughter.
• Low-trophic aquaculture models that align with sustainability, biodiversity, and welfare considerations.
• Regulatory advancements in labeling, traceability, and transparency to improve consumer awareness of ethical production practices.

Grassroots and Low-Impact Innovations in Aquatic Food Systems
We strongly support the inclusion of grassroots innovations in the Knowledge Base, particularly those that advance sustainable, humane aquaculture and alternative aquatic proteins. Many small-scale, traditional, and Indigenous aquaculture practices demonstrate high-welfare, low-impact production methods that should be recognized alongside high-tech innovations.

To enhance the value of grassroots records, we recommend:

• Capturing welfare implications alongside environmental and economic dimensions.
• Highlighting low-cost, scalable, welfare-conscious solutions for small-scale producers.
• Ensuring co-design with fishers and aquaculture workers to reflect real-world applicability.

Traceability and Transparency in Aquatic Supply Chains
The ATIO KB can play a crucial role in advancing traceability and transparency in fisheries and aquaculture supply chains. Subsidies, policy incentives, and technological innovations should be evaluated not only based on productivity and efficiency but also on their ethical and sustainability implications.

We recommend that the Knowledge Base:

• Include welfare-centered transparency initiatives, such as welfare certification schemes and public reporting mechanisms.
• Track data on compliance with aquatic animal welfare standards at national and international levels.
• Integrate traceability technologies (e.g., blockchain for ethical seafood sourcing).
   

Ethical and Inclusive Technology Taxonomies
To ensure equitable and responsible categorization of agrifood innovations, we encourage the ATIO KB to:

• Use precise terminology when referring to aquatic animals (e.g., “fish populations” rather than “fish stocks” to avoid framing sentient beings as commodities).
• Align taxonomy categories with leading sustainability, biodiversity, and welfare frameworks.
• Capture the intersection of welfare, climate resilience, and public health when classifying innovations.
   

Search and AI-Generated Content
We appreciate FAO’s efforts to integrate Artificial Intelligence (AI) in enriching and categorizing Knowledge Base records. However, given the complexity of welfare-related innovations, we recommend ensuring that:

• AI-generated records are transparently flagged and verified by experts to prevent misinformation.
• Users can choose between chatbot-based and filter-based search to improve accessibility.
• Ethical concerns related to AI-generated decision-making in agrifood systems are considered.
   

The ATIO Knowledge Base represents an important step toward a more inclusive and well-informed agrifood innovation landscape. By integrating species-specific welfare considerations, grassroots solutions, and enhanced traceability mechanisms, FAO can ensure that this platform reflects a truly holistic and ethical vision for agrifood transformation.

We appreciate the opportunity to contribute to this consultation and look forward to continued engagement in shaping a more sustainable and humane future for aquatic food systems.

Sincerely,
Giulia Malerbi
Head of Global Policy
Aquatic Life Institute

In response to the provided questions:

1: I would like to see data on carbon sequestration rates into soil from different innovative farming practices, including those considered agroecology as well as external technological inputs. Also, data on how different farming practices and technological inputs influence polluting factors such as nutrient runoff and impact biodiversity. 

2: Policy innovation must be taken into a holistic context, over short-term and long-term horizons. In the short run, provisioning of training and support during adoption phases, diffusion parameters such as contact rate and adoption rates. Constraints to adoption due to cost and perceived risks of transitioning from existing technology and practice to technologies and practices. Also, the degree of exposure to uncertainties associated with external inputs. In the long run, damage to soil, water and the natural resources needed to support food production. These are just a few of the considerations that were built into a dynamic systems model of agroecology adoption in East Africa conducted by the Millennium institute and the BioVision Trust Africa and informed by stakeholders and experts in several East African countries.

3: Very important to include grassroots innovations, particularly in concert with more formalized research. Looking at the prototype I do not see agroecology (please excuse me if I am wrong about that!). I would like to see dimensions of grassroots/technical research cooperation bringing in indigenous and local knowledge, in application to  agroecology.

4: Will the ATIO KB be agnostic with respect to brands vis-à-vis quality assessments, national origin, etc.? I would expect innovation units to be of generic nature. 

5: yes, good enough for now. I would be pleased to volunteer to help improve.

6: Having both filter-based and chatbot would be greatly beneficial.

7: The AI generated text seems quite good. Noticed a few inconsequential typographical errors. I would need much more exposure to comment on the question of AI enriching the records meaningfully.  I am impressed with the scope of ATIO KB. Would be great to be able to query the system throughout its user components, for instance, under climate smart agriculture.

The challenges outlined in the "Forgotten Foods: A manifesto for the future of the food system?" article resonate powerfully with the consultation call for the design of the Agrifood System Technologies & Innovations Outlook (ATIO) Knowledge Base, especially when viewed through the lens of CropBASE—a global knowledge system dedicated to agricultural diversification. The manifesto argues that transitioning from a fossil-food system to one enriched with forgotten, underutilised crops can not only enhance nutritional security but also catalyse resilient, culturally sensitive innovations; CropBASE embodies this vision by integrating scattered qualitative and quantitative data on thousands of agrobiodiverse species into a single, accessible repository. In the context of the ATIO KB consultation, this integration suggests compelling use cases where users—ranging from policymakers to grassroots farmers—could, for example, access detailed information on innovative products that support credit and insurance mechanisms for farmers in specific countries, or employ statistical analyses to correlate inclusivity and co-design levels with successful adoption rates. Furthermore, the dialogue on policy innovation and social innovation, as raised by the consultation questions, finds a practical counterpart in CropBASE; the platform not only documents scientific and agronomic data but also captures cultural, traditional, and socio-economic dimensions that are essential for formulating policies which acknowledge both the technical and societal aspects of agrifood transformation. Equally, the call for featuring grassroots innovations in the ATIO KB becomes more tangible through CropBASE's examples, where localized, farmer-driven practices and innovations are documented with a rigor that invites further detail on aspects such as operational feasibility and community impact. Additionally, the discussions on how branded commercial products and distinct innovation units should be featured—ranging from individual models like solar-powered irrigation pumps to comprehensive agrifood systems—further highlight the need for a harmonized taxonomy; CropBASE’s capacity to categorize over 2,700 crops using diverse data points exemplifies how such taxonomies could be refined to address variations in quality, readiness, and adoptability. On the technological front, the envisaged chatbot-like search and AI-driven enrichment of records within the ATIO KB are perfectly aligned with the automated, intelligent categorization processes already in use in CropBASE, ensuring that users can both filter classic data and interact with dynamic AI interfaces to extract meaningful insights. In essence, linking CropBASE's global, AI-assisted repository of agrobiodiversity data with the ATIO KB’s participatory, multi-dimensional approach can generate innovative ideas that bolster agricultural development, foster policy and social innovations, and ultimately drive the transformation towards more sustainable and inclusive food systems.

Dear Co Facilitators, 

We addressed some of the points raised above recently in the climate technologies report which can be found here and would be happy to discuss further: https://doi.org/10.4060/cd2877en .

The report contains a multidimensional definition for climate technology which would be useful to discuss. The social and innovation elements are also important to ensure uptake of the technology. Further review and cases of this would be important to inform the discussion and bring in lessons learnt about which set ups can work.

Furthermore, it is not always easy to find consistent data on all technology options, so some discussion around data sources and platforms in a consolidated form and in a single access point would also be useful.

Happy to discuss further.

Best regards

Irini Maltsoglou (climate technology coordinator OCB, FAO)

As I have contributed to previous FAO discussions, please allow some comments on the Agrifood System Technologies & Innovations Outlook (ATIO) Knowledge Base (KB) that are often overlooked by our Agriculture Development efforts but failing to take into consideration could hinder the widespread acceptance of ATIO/KB innovations across smallholder communities.

1. First, don’t we have to consider ATIO/KB programs, not only for meeting the needs of smallholder farm families and local smallholder communities, but also the national needs?  With an ever-growing urban population, farming communities, even smallholder communities, must produce sufficient food surplus to meet the food security needs of urban areas.  If not, won’t governments have to spend limited foreign exchange resources on imported food to feed the urban population? This implies encouraging subsistence farmers to become more commercial. Doesn’t promoting subsistence farming result in poverty entrapment?
2. Second, do we need to consider the Dietary Energy Balance Deficit faced by smallholder farmers to assure we are not attempting to compel smallholders to exert more caloric energy than they have access to. There is a very high probability that most food systems innovations are compelling smallholders to exert more energy than they have access to as it requires some 4000 kcal/day (i.e. 1.2 kg of uncooked maize or milled rice) to undertake a full day of agronomic field work. However, too often smallholder farmers are lucky to have access to only 2500 kcal/day. Of this 2000 kcal/day are required for basic metabolism leaving only 500 kcal/day for physical exertion need for manual Agronomic field work. That is good for only a couple hours of diligent labor, perhaps paced over a couple more hours with less diligence and productivity. It should be noted that in Kenya the causal labor workday is only 5 hours. The result is extending the time required for basic agronomic tasks such as taking 8+ weeks for basic crop establishment. Will this delay render most of the ATIO/KB innovations null & void for most of smallholder communities cultivated lands. Something easily observed by looking at the crop land associated with any smallholder community and considering this a problem of hunger and exhaustion rather than education, motivation, or risk aversion. If limited available calories are hindering economic opportunity, how rational is it for smallholder farmers to emphasis high calorie crops over more healthy diverse foods that ATIO/KB innovations might advocate? How does the delayed crop establishment impact on potential yield, that normally declines with delayed crop establishment, and food security for families, communities and nations?  How much of the limited acceptance and scaling of innovations including ATIO/KB efforts will this account for? Who is looking at dietary needs in terms of optimizing economic opportunities to meet food security requirements? How often do we recognize smallholder farmers are hungry without factoring hunger as a major hindrance is scaling innovations? How often do ATIO/KB innovations require more labor than the indigenous Agricultural System they propose to replace?
3. How important is it to review the operational feasibility of ATIO/KB innovations to make certain smallholder farmers or other beneficiaries have access to the necessary labor or mechanization to implement innovations in a timely manner over all desired fields within a community that would allow them to fully benefit from the innovations? Who within the development effort is responsible for determining the labor requirements for timely implementation of ATIO/KB innovations? More important, how much labor is available across the farming community? What are the rational compromises farmers should make in adjusting the innovations to their limited operational capacity? Does this fall into an Administrative Void between the agronomists or other bio-scientists, who do an excellent job of determining what is physically possible and desirable, but saying nothing about what is required to extend small plot results across a smallholder farming community; and the economists or other social scientists that might determine the labor requirements for a cost/benefit analysis, but rarely address the available of labor across the community, let alone appropriate compromises if that labor is not available? Be careful in assessing available labor to make certain you are not “robbing Peter to pay Paul” as often available labor is other farmers opting for a day of casual labor at the expense of their farming operations, for a zero net benefit over the entire farming community. Shouldn’t the success of ATIO/KB innovations be based community wide acceptance and not just pilot farms demonstrate success? What would be the minimal percentage of a beneficiary communities cultivated land would you accept as utilizing ATIO/KB innovations to consider the innovation a success, below this percent would be considered a failure and the need to return to the proverbial drawing board for substantial modification to the innovations?
4. How critical is it in scaling ATIO/KB innovations to first facilitate access to contract mechanization? Won’t this enhance the area cultivated in the timely manner to take complete advantage of other crop husbandry activities, thus enhancing yield, family food security, and marketable surpluses to feed the urban populations? Will this then allow for wider acceptance of ATIO/KB innovations by the smallholder producers? What was the impact 30+ years ago of the shift from water buffalo to power tillers in paddy producing Asia? Did this halve the paddy establishment time, allow smallholder farmers to increase the land area they managed, double crop irrigated lands, when small combines became available produce 5 crops every 2 years, provide the labor needed for the “Tiger Economies” of southeast Asia, and allow these countries to become the major exporters to rice for the rest of the world? However, this substantial impact of mechanization was all farmers initiated and thus overlooked by the development effort. Also, look at Egypt where individually owner/operator contract tractor operators have done most of the initial land preparation for smallholder farmers throughout the Nile Valley and Delta for at least 40 years. Could you expect a similar response throughout the rest of Africa? Will enhancing access to mechanization promote wider acceptance of ATIO/KB innovations than continuing to badger farmers with extension/education programs they likely have a good basic understanding of but do not have the operational capacity to take advantage of over their entire holding.
5. In enhancing access to mechanization how critical is it to make certain this is by individual owner/operators and not any form of institutional or communal owner as such schemes have resulted in equipment being surveyed out of use with less than half the designed operating hours? Just visit any ADP in Nigeria and note the line-up of tractors surveyed out of service with only limited operational hours logged. Is the real challenge here to identify individuals within a smallholder community who can drift out of direct farming to become full-time mechanization service providers?  (Combining personal farming and tractor services would be a conflict of interest hindering both activities.) What kind of credit arrangements can be made to assist with the initial costs of purchasing the farm equipment as well as meeting some of the operating costs so their services can initially be offered on credit for payment after harvest?
6. Would addressing the dietary energy limits, and enhancing access to mechanization have more impact on scaling ATIO/KB innovations than extension/education programs? Are we content to “Count Coup”1 on the number of farmers trained through FFS or other extension activities, attributing limited acceptance to poor extension, or poor learning capacity of farmers with limited educational opportunity? Is this where we separate research/extension programs from development programs? Research/education programs managing small (6 x 10 m) plots in remote experiment stations with little restriction on operational resources vs smallholder farmers managing 1+ ha with limited operational capacity, while development projects being community based are in closer contact with the farmers? Thus, should their main concern be, while promoting the research/extension innovations, identifying and addressing what is hindering acceptance? If this was accepted as the primary objective of development projects, would we have identified and addressed the critical need to facilitate access to mechanization a couple decades ago? 
7. Is there a rational disconnect between research/extension personnel and smallholder farmers? Doesn’t small plot research/demonstrations emphasis maximum yield thus return to land. In contrast don’t smallholder farmers emphasis returns to labor resulting in making rational compromises in lowering managing one enterprise to enhance another so they can Maximize Total Returns to All Farm Enterprises!!? Does the concern to maximize returns to labor become more critical the more impoverished the family? 
8. If the Dietary Energy Balance Deficit, operational feasibility, and enhanced access to contract mechanization are not recognized, fully appreciated and addressed in ATIO/KB effort to assist smallholder communities will the scaling be limited to small pilot areas rather a major percent of the communities cultivate lands? If that is the case isn’t the effort little more than an expression of the donors’ good intentions without being a sincere commitment to achieving substantial change.  Is this acceptable?!! 

For an expansion of the ideas expressed above please review the referenced linked article reflecting on my 50+ years effort to assist smallholder communities. It more concerned with factual accuracy than political correctness, as only an emeritus professor can express. The link is: https://agsci.colostate.edu/smallholderagriculture/wp-content/uploads/sites/77/2023/03/Reflections.pdf

I hope this provides some food for thought and is helpful in your ATIO/KB efforts to assist smallholder farmers to accept and scale your innovations.

Thank you.

1 Counting Coup is an indigenous North American plains warrior tradition of winning prestige against an enemy without killing them. 

Dear Co Facilitators, 

The PSM thanks you for the opportunity to participate in the Consultations to "Contribute to shaping the design of the Agrifood System Technologies & Innovations Outlook (ATIO) Knowledge Base". In this regard, please see attached the contribution of the PSM.

Kind regards

Bibi Ally