Global Forum on Food Security and Nutrition (FSN Forum)

My CONTRIBUTION TO SHAPING THE DESIGN OF THE AGRIFOOD SYSTEM TECHNOLOGIES & INNOVATIONS OUTLOOK (ATIO) KNOWLEDGE BASE (KB)

Dear Sir/ Madam

FSN Forum admin

Thanks for this appreciated initiative. As your synopsis on this crucial topic has indicated that "Agrifood system transformation to achieve the Sustainable Development Goals requires increased attention to developing, adapting and diffusing impactful science, technology and innovation (STI)". Your short summary also has stressed that "current levels and patterns of STI uptake are inadequate to facilitate needed agrifood system transformations, especially in today's low- and middle-income countries". I would like to comment as follows:

A useful tool for compiling data on agricultural technology and innovations may be the Agrifood Systems technology and Innovations Outlook (ATIO) Knowledge Base (KB). Data-driven insights on cutting-edge agrifood technology that improve flood resistance are one way it could be beneficial. In addition to country-specific readiness levels, adoption hurdles, and comparative effectiveness in minimizing flood risks in various agricultural contexts, it could contain information on flood-tolerant crop varieties, water-retaining soil amendments, or smart drainage systems.

Finding innovations that support biodiversity-friendly agriculture, such agroecological techniques, pollinator-friendly farming methods, or AI-powered biodiversity monitoring systems, is another way the KB could be helpful. Statistics demonstrating relationships between the adoption of innovations centred on biodiversity and long-term gains in ecosystem services or agricultural yields would be very helpful.

Furthermore, the KB might make it easier to compare agrifood innovations according to important standards like sustainability, scalability, and socioeconomic impact—especially when it comes to mitigating and adapting to climate change. Researchers' and politicians' decision-making would be aided by access to case studies, expert assessments, and actual adoption rates. The KB would be much more useful for people working at the nexus of agriculture, environmental sustainability, and technology innovation if it contained these kinds of insights.

In reference to: FAO. 2022. Introducing the Agrifood Systems Technologies and Innovations Outlook. Rome. I could add that: the ATIO needs to address more than simply farm-level output because post-farmgate activities account for more than 70% of the value addition represented in global consumer food expenditures (Yi et al., 2021). Besides, in the primary sectors of agriculture, fisheries, and forestry as well as in downstream processing, manufacturing, and distribution, the unwavering pursuit of ever-greater efficiency has had predictable, if unintended, consequences for working conditions, resilience to shocks, and human and environmental health as indicated by Herrero and co-workers  (2021).

Overall, the ATIO Knowledge Base holds the potential to bridge critical data gaps, enhance STI visibility, and foster a more inclusive, resilient, and sustainable agrifood system.
Wish you all the best in shaping the design of the agrifood system technologies and innovations.

Best regards,

Dr. Khaldoun Othman Al Sane

My conceptual vision is based on postulate that historically agrifood systems were based on two parallel approaches: a) market oriented production (in general understanding); b) internally consumption oriented activity.  The second one is more about the life style. And usually the size of land ownership dictates the prevalence/attractiveness of one approach or another.  

For countries with small arable landplots ownership it is very critical to support the SOCIAL innovations with aim to preserve/save farming spirit. Otherwise, we will continue to evidence the catastrophic migration processes.   

1. How can ATIO KB assist you and users like you?

The ATIO KB can be an opportunity to explore the significance of indigenous knowledge in food production and in climate change adaptation needed for building a resilient Agrifood system from grassroot level. Thus, it can be a contribution to big data generation and management and can be used as a reference base for ethical data sourcing. Furthermore, this is an opportunity to utilize these technologies towards expanding on local food markets to deliver more nutritious food. 

2. What's your take on concepts like policy innovation and social innovation?

The integration of these two concepts is vital since policy innovation tackles issues of bringing transformation within the technology aspect. Whilst social innovation deals with inclusivity of the human aspect more in relation to social dynamics.  The FAO Strategic Framework 2022-2031 underscores that both these concepts for innovation are important in transforming the agri-food systems for “better production, better nutrition, a better environment, and a better life, leaving no one behind”. Infusing both concepts is essential. 

The development of the Agrifood System Technologies and Innovation Outlook (ATIO) Knowledge Base marks a significant milestone in agrifood innovation and transformation. The FAO has the potential to provide a reliable and insightful platform for accurate data and innovations, benefiting researchers, businesses, institutions, and both the private and public sectors worldwide.

While the draft concept has been developed to a high standard with a strong knowledge base, I would like to offer a few suggestions at the country level to further enhance its effectiveness:

• Ensuring Data Accuracy: In developing countries, the reliability of primary data is critical. It should be collected, screened, and validated by a highly skilled and dedicated FAO team in each respective country. In many cases, data collection in these regions is based on assumptions rather than direct engagement with authentic sources and authorized individuals. Therefore, before analysis and interpretation, it is strongly recommended that data be gathered physically, thoroughly cleaned, and cross-verified with relevant sources.
• Incorporating All Relevant Data Sources: To ensure comprehensive and representative data collection, insights should be gathered from both the public and private sectors. This includes national statistics departments, ministries, universities, research institutes, regulatory authorities, companies, NGOs, industry associations, individual producers, farming communities, and international bodies. A multi-source approach will enhance the credibility and depth of the knowledge base.
• Publishing Quantitative Data on a Quarterly or Annual Basis: Providing country-specific data on a quarterly or annual basis would be highly beneficial for researchers and businesses that rely on regularly updated information for their analyses and decision-making.

These recommendations aim to strengthen the ATIO Knowledge Base’s accuracy, inclusivity, and practical relevance for global stakeholders. 

Sayed Samiullah Hakimi, Ph.D.

This proposal outlines prototype strategies for the Federal Agricultural Organization to drive a sustainable transformation in agrifood systems by prioritizing organic farming technologies. The objective is to enhance food security, environmental sustainability, and economic viability while gradually reducing dependence on non-organic agriculture.

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Shaping the agri-food system positively requires a shift toward sustainability, equity, and innovation. The following are some prototype ways to achieve this:

Regenerative Agriculture: Focus on farming techniques that restore and enhance soil health, promote biodiversity, and improve water management. This includes practices like crop rotation, agroforestry, and reduced pesticide use. These methods aim to replenish ecosystems rather than deplete them.

Circular Economy Models: Create closed-loop systems where waste from one process is used as input for another. For example, food waste can be turned into compost or bioenergy, while agricultural by-products can be utilized for other industrial purposes.

AgriTech Innovation: Invest in technology such as AI, drones, and IoT for precision farming. This allows farmers to optimize resource use (water, fertilizers) and improve yields while minimizing environmental impacts.

Local Food Systems: Strengthen local food systems by promoting urban farming, farmer’s markets, and short supply chains. This reduces food miles, supports local economies, and enhances food security.

Alternative Protein Sources: Encourage the development of plant-based and lab-grown proteins as more sustainable alternatives to conventional meat production. These innovations can reduce the environmental footprint of food production.

Sustainable Fisheries: Develop and support sustainable fishing practices, including aquaculture innovations, better management of marine ecosystems, and reducing bycatch.

Education and Consumer Awareness: Promote knowledge about sustainable food practices, nutrition, and the environmental impact of food choices. This can shift consumer demand towards more sustainable, ethical options.

Policy Support: Advocate for policy changes that incentivize sustainable farming practices, support local farmers, and address food insecurity. This includes subsidies for sustainable farming, fair trade, and responsible consumption.

Food Waste Reduction: Implement systems to reduce food loss at all stages, from production to consumption. This includes innovations in packaging, storage, and distribution systems that extend shelf life and make use of surplus food.

Inclusive and Fair Trade: Support initiatives that ensure fair wages for farmers, equitable access to land, and resources, as well as opportunities for marginalized groups in the agricultural sector.

These prototypes aim to create a more resilient, equitable, and sustainable agri-food system that balances productivity with environmental and social considerations.

Transforming the national agrifood systems, federal agricultural organizations must prioritize organic farming technologies, ensuring food security, sustainability, and economic viability. These prototype strategies integrate cutting-edge Agri-Tech solutions to scale up organic farming while gradually reducing dependence on conventional, chemical-based agriculture.

1. National Organic Agriculture Digital Platform

Prototype Idea: Organic Agri Hub

Objective: Establish a federal digital ecosystem to support organic farmers, monitor progress, and regulate organic farming practices.

   Features:

   AI-powered database of organic farming techniques and research

   Digital organic certification tracking and fraud prevention

   Interactive farmer advisory services using AI chatbots

   Open-access market insights for organic produce pricing

   Impact:

  Ensures transparency, knowledge-sharing, and easy access to organic farming best practices across the nation.

2. AI & IoT-Powered Precision Organic Farming

Prototype Idea: Smart Organic Farming Systems

Objective: Enhance productivity in organic farms using real-time data monitoring and AI-driven decision-making.

   Features:

   AI-based soil health analysis for organic fertilization

   IoT-enabled composting and natural irrigation control

   Drone technology for organic pest and weed management

   Impact:

  Reduces resource wastage, enhances yield efficiency, and eliminates chemical inputs gradually.

3. Blockchain-Based Organic Food Traceability & Security

   Prototype Idea: Organic Trace Blockchain

Objective: Establish a secure, tamper-proof organic food tracking system to ensure consumer trust and regulatory compliance.

   Features:

   End-to-end traceability of organic food from farm to consumer

   Smart contracts verifying certified organic produce

   AI-driven fraud detection for fake organic labeling

   Impact:

  Strengthens organic food credibility, increasing demand and supporting more farmers in transitioning to organic practices.

4. National Vertical & Urban Organic Farming Initiative

Prototype Idea: Organic City Farms

Objective: Integrate organic vertical farms and urban micro-farming into the federal food system.

   Features:

   Government-funded hydroponic and aeroponic organic farms

   AI-controlled nutrient distribution and organic composting

   Community-based organic farming initiatives in urban areas

   Impact:

Increases organic food production in cities, making organic food widely accessible and reducing reliance on chemical farming.

5. Climate-Smart & Regenerative Organic Agriculture

   Prototype Idea: National Green Farming Initiative

Objective: Regenerate soil health and biodiversity through advanced organic practices, minimizing the need for chemical-based farming.

   Features:

   AI-driven organic crop rotation and composting optimization

   Carbon credit systems rewarding organic farmers for sustainability

   AI-based monitoring of ecosystem health in organic farmlands

   Impact:

  Boosts long-term soil fertility, gradually phasing out synthetic fertilizers and harmful pesticides.

6. Agri-FinTech & Insurance for Organic Farmers

   Prototype Idea: Organic Agri Fund

Objective: Financially incentivize farmers to transition to organic farming through loans, insurance, and grants.

   Features:

   Government-backed subsidies for organic farmers

   AI-powered risk assessments for organic crop insurance

   Direct farm-to-market sales via digital e-commerce for organic produce

   Impact:

  Makes organic farming more financially viable, encouraging a shift away from conventional agriculture.

7. Bioengineered Crops & Sustainable Organic Inputs

   Prototype Idea: Eco Organic Bio Seeds

Objective: Strengthen organic crop resilience without genetic modification or chemical inputs.

   Features:

   Development of high-yield, naturally pest-resistant organic seeds

   AI-powered organic soil enhancement techniques

   Bio-based pest and disease control integrated with IoT smart monitoring

   Impact:

  Ensures organic farming remains productive and profitable, reducing dependence on chemical fertilizers and pesticides.

8. Federal Policy & Education for Organic Farming Expansion

   Prototype Idea: National Organic Transition Program

Objective: Establish a federal regulatory and educational framework for organic agriculture growth.

   Features:

   Government-backed training programs for organic farmers

   Public awareness campaigns promoting the benefits of organic food

   National targets for reducing chemical pesticide & fertilizer usage

   Impact:

  Ensures nationwide adoption of organic farming, gradually eliminating non-organic practices through education and policy incentives.

A Gradual Shift Towards a Fully Organic Future 🌿

Through federal leadership, strategic funding, and technological innovations, the agri-food system can transition towards organic farming, ensuring food security, environmental sustainability, and economic resilience.

•  Organic innovation is the future—let’s cultivate it together! ● 

1. How can ATIO KB assist you and users like you?

ATIO KB can assist in identifying opportunities and challenges in agricultural innovation, providing information on grassroots innovations and innovative solutions in agriculture. Can also utilize ATIO KB to research information on outstanding commercial products and companies offering innovative solutions in agriculture.

2. What's your take on concepts like policy innovation and social innovation?

The policy innovation and social innovation are crucial concepts in sustainable development. Policy innovation can facilitate the development of effective public policies, while social innovation can help create innovative solutions to social problems. Policy innovation can play a vital role in supporting agricultural innovation by providing financial and legislative support to companies offering innovative solutions in agriculture.

3. How should grassroots innovations be distinguished?

The grassroots innovations should be distinguished based on criteria such as social impact, effectiveness, and sustainability. Grassroots innovations should also be evaluated based on their alignment with local community needs. Systematic evaluation can play a crucial role in identifying effective grassroots innovations that can address social and economic challenges.

4. How should outstanding commercial products be presented on ATIO KB?

The outstanding commercial products should be presented on ATIO KB based on criteria such as quality, effectiveness, and sustainability. Information on each product, such as cost, benefits, and challenges, should also be provided. Presenting comprehensive information can play a vital role in assisting users in making informed decisions about outstanding commercial products.

5. Primary classifications used in ATIO KB

The current classifications ("types of innovations" and "use cases") are sufficient, but can be improved by adding additional classifications such as "sectors" and "geographic regions". Systematic classification can play a crucial role in assisting users in finding the information they need efficiently.

6. Search capability on ATIO KB

The classic filter-based search is optimal, but chat-based search can also be utilized to provide a more interactive search experience. Enhancing search capability can play a vital role in assisting users in finding the information they need efficiently.

7. Utilization of artificial intelligence in ATIO KB

The artificial intelligence can enhance ATIO KB by providing more accurate and rapid information. AI can be utilized to analyze data and provide recommendations on grassroots innovations and outstanding commercial products. Utilizing AI can play a vital role in assisting users in making informed decisions about grassroots innovations and outstanding commercial products.

This is a great initiative. In agriculture food system technologies, innovations, adoption and disseminating is a major issue particularly for small-scale farmers/producers particularly in Africa. In my observation scaling up and adoption of technologies and innovations from start-up entrepreneurs seem to be inefficient due to budget limitation. In addition, limted incentives iare alocate  for technological advancement and innovation in develping countries. indigenous knowledge should also be given much attention.

Nature-based solutions have been acknowledged as essential to transforming food systems. It is therefore important that agrifood-systems technologies and innovations be clustered on a biogeographical basis since environmental characteristics determine to a great extent locally-relevant solutions.

https://www.regionsunies-fogar.org/en/media-files/opinion-articles/867-moving-towards-territorial-food-systems-where-are-we-at

It is also important that they contribute to revitalize local economies for sustainable development and resilience https://www.uncdf.org/article/7177/territorial-food-systems-for-sustainable-development-issue-brief-for-un-food-systems-summit

Finally it would be important that the social and environmental impact of each technology/innovation be assessed with a view to ensure accountability.

Dear ATIO Team,

FSN Consultation 
Contribute to shaping the design of the Agrifood System Technologies & Innovations Outlook (ATIO) Knowledge Base 
Submission by:  Suleymen Abdureman Omer (MA Candidate in Public policy & Management) is BSc and MSc in Rural Development & Agricultural Extension (Rural Development stream) from Haramaya University, Ethiopia.

What is Crowdsourcing? 
To fulfil the demands of a growing population, agriculture must provide food and feed that is healthy, high-quality, and safe in an efficient, sustainable manner. Because of climate change and the necessity to preserve local agricultural production, food production will encounter challenges in the future. As a result, sustained effort is required to grow crops with higher and more reliable yields.
The current agricultural production system is based on low diversity and high chemical inputs, which undermines the long-term sustainability of food systems and the essential ecosystem services. The transition to diversified and sustainable production systems is essential and urgent, and depends on our ability to harness the transformative power of agricultural biodiversity. Such transitions, coupled with improved conservation practices, will reduce pressure on terrestrial and marine ecosystems by protecting productivity, restoring ecosystem services and improving ecological connectivity between protected areas. 
To improve crop productivity in regions where formal seed systems do not exist and a number of participatory techniques to variety development and transmission have been developed to target the diverse environments, crops, and varieties for Ethiopian farmers. Crowdsourcing, community seed banks, seed fairs, participatory variety selection and participatory plant breeding have all been utilized to promote innovation, access to seeds, and diversity in informal seed systems.  
Crowdsourcing is the process of enlisting a group of people to work toward a common objective or resolve a current issue, particularly to ensure the sustainable growth of agricultural operations. It has modern technology and social network. Social network is a source of information and concepts, which pushes contemporary businesses to utilize crowdsourcing and discover how to get the most out of it, including acquiring new knowledge. By using crowdsourcing, it is feasible to select the finest ideas submitted by a large number of responders as opposed to depending just on the credentials and experience of one individual. 
It is now simpler than ever for people to get together and offer ideas, time, experience, or money to a project or cause on many different levels and across many different industries. Crowdsourcing is the communal mobilization in action. It is a method of using people or groups of people, paid or unpaid, who are connected with one another through a shared interest to advance powerful increased results through their aggregated actions or activities. 
The crowdsourcing methodology is an alternative to conventional practices because it supports farmers’ on-farm selection of varieties based on seed performance in different agroecological and climatic zones of a given location. This way, the farmers got an opportunity to access different varieties of different crops. This has improved the informal seed system, through exchange and purchase of the varieties among farmers. 

Why Crowdsourcing? 
For many rural households around the world, access to new crop types, agricultural supplies, and expertise is essential. But discovering new technology frequently faces challenges: For instance, access may be hampered by the difficulty of reaching some isolated rural regions, the lack of established markets for seeds and crop inputs, or the lack of knowledge about new crop inputs or varieties. Due to insufficient inputs, the same cultivars are frequently cultivated year after year. 
Crowdsourcing is quickly becoming a popular innovation platform for organizations. The crowd appears to have a solution for every innovation issue; they can think up concepts for novel toys and provide answers to urgent scientific difficulties. In actuality, the crowd has great potential: a large, diverse group, consisting of experts and non-experts from over the globe, ought to have new viewpoints to provide ground-breaking ideas on a particular issue. Organizations may engage their participants and generate their interest in addition to accessing amazing ideas. 
By turning to an oversized cluster of individuals for ideas and solutions, crowdsourcing will generate plenty of advantages over internal thinking processes. 
Among the benefits of crowdsourcing is:  
➢  The ability to find unexpected solutions by involving a broader cluster of individuals in resolution a haul, an organization will gain access to hundreds or perhaps thousands of various approaches to come to  resolution. 
➢  Greater diversity of thinking  
➢  Reduced management burden 
➢  Faster problem solving 
➢  A rich source of data 
For agricultural sector, the crowdsourcing approach has been very effective  to disseminate seeds that match farmers needs in a very short period as after 2 years already several hundred farmers have the potential to use better adapted material, with a large snowball effect potential. With a significant potential for a snowball effect, the crowdsourcing technique has been very successful in rapidly disseminating seeds that are tailored to farmers' needs. After only two years, several hundred farmers are already able to use more suitable materials.
Crowdsourcing approach demonstrates how landraces can offer an immediate solution for reducing climate -related hazards and calls for greater use of the genetic resources preserved in gene banks; suggests that in order to sustainably manage these resources, local seed systems must be st rengthened;It demonstrates how farmers can offer extremely useful scientific data that can be applied to other fields of research as well as development outcomes. 
Crowdsourcing approach for variety selection using tricot improve seed systems through enriches variety recommendations, improves on-farm testing, engages and empowers farmers, contributes to a diversification of seed systems, supporting scaling of on-farm agricultural research, enables women to do their own variety selection, offers business opportunities for farmers, women and young people, gets researchers to learn farmers’ variety preferences.  
In this case, varieties are grown on  farmers' fields as  trial plot it has an additional advantage because it allows a larger number of farmers to participate. This helps to  collect extra data on performance at various altitudes or in various climatic conditions. 
They provide their input in an easy-to-read format, rating each package for several characteristics as "best, midrange, or worst". These farmer-generated data are then merged with socioeconomic and environmental variables and ranked using particular, cutting-edge statistical techniques. It has been feasible to show how varieties are differentially suited to various growth circumstances across broad areas using the tricot technique. Additionally, it enables a lot more farmers to take part in participatory trials, which directly impacts the spread of varieties. 
This strategy enables farmers serve as local scientists, testing, observing, contrasting, and trying novel agricultural methods and crop rotations to find what works best for them in terms of yield as well as resiliency, nutrition, taste, and resistance to pests and diseases. 
Generally, as part of their routine duties, development agents monitor the farmers throughout the process and offer advice. They can learn with farmers about how various kinds fare in various soils and micro-ecologies in this way. 
The crowdsourcing methodology supports the scalability of on-farm agricultural research, which enhances seed systems. Because seed distribution and evaluation are significantly simpler, information-based tasks can now be scaled to new levels that were previously impractical thanks to the tricot technique. Larger sets of types can be compared by providing farmers with alternative, partially overlapping combinations of technologies.
The method can engage a large number of farm households, which can help to get beyond the limited scalability and free-rider issue in existing participatory methodologies. As a result, it is possible to scale on-farm agricultural research using a crowdsourcing approach. 
Seed-based innovation must concentrate on the variety of crops that make up farmers' fields and diets if it is to provide food for the most vulnerable households. 
By adapting crop varieties to various production situations and diversifying their portfolio to reduce risks, a variety of crop species and varieties aid farmers in coping with environmental limitations. For instance, scientists found convincing evidence that increasing varietal diversity boosts agricultural output in the highlands of Ethiopia in a properly planned study.
When rainfall levels were low, the effect was considerably more pronounced. However, independent of production, crop and varietal diversity may also have immediate nutritional impacts. There is strong proof that dietary diversification and micronutrient intake are related. It is reasonable to anticipate that increasing crop diversification for farmers with limited resources will significantly improve the food security of their households. Therefore, the major strategy for improving nutrition for small-scale households is to enhance diversity through crowdsourcing approach. 
Validate varieties and enhance availability by distributing them through crowdsourcing approaches  
Main purpose/ core deliverables 
➢  The world is affected by climate change and consequently affecting agricultural productivity, that led farmers to be unsecured and vulnerable. The bad thing about climate change is its unpredictability. Therefore diversifying agriculture at species/variety level will secure farmers from the total loss of harvest.  
➢  Crowd sourcing approach uses multiple superior varieties (genotypes) that are tested by the community themselves after selection from diversity block as best in bad season and others best in good season. Then since the climate is unpredictable, when the season is bad the farmer will harvest  genotype that suits bad condition and if the season is good he/she will have a chance to get premium production from all genotypes. Therefore, diversifying varieties/genotypes is a means for food security at household level sustainably. These tool will enable to reach large farmers in few resources in short period of time. 
➢  Using diversified genotypes and/or species is also help farmers to be resilient from sporadic outbreak of diseases and pests. Since the genetic makeup of different genotypes and species is different, they will not be attacked by one time appearing disease or pest race all at the same time.

Suitability and adaptability of crowdsourcing to local knowledge         
The use of crowdsourcing approaches is strongly tied to traditional knowledge. The farmer preferred varieties identified in the previous step are distributed to farmers in small batches and tested under their own management practices and soil conditions. 
A big advantage of this approach is that growers can test varieties throughout the growing season and not just for one day. So they can really appreciate the seed's potential for uses that are important to them. , they can truly understand them.  
This approach makes it possible to reach out to a larger number of farmers and involve them fully in the assessment compared to traditional approaches. The costs to farmers are much lower because the extension or research system does not have to administer the trials themselves. 
Main land use and agro ecology for crowdsourcing approach 
There are specific crops and varieties that can be employed in each area and agro-ecology to increase production and resilience as well as offer new opportunities for a livelihood. 
Although using diverse crops, the crowdsourcing approach is appropriate for all agroecologies and soil types.
How to carry out crowdsourcing? 
Description of the technology 
The application of diversity agriculture in conjunction with other technologies is possible. A tricot plot design can be used as a manner of demonstrating and scaling out a crowdsourcing approach to rich an infinite number of farmers quickly. Diversity agriculture can be integrally used with other technologies. As a way of demonstration and scaling up a crowdsourcing approach can be used to rich uncountable number of farmers in short period. 
Innovation has produced a practical method for the field evaluation of varieties that might also be applied in the field of agricultural research.
Farmers participate in Tricot's crowdsourcing strategy as "citizen scientists," helping researchers with data generation and collection. The technique enables the split of large tasks into smaller, more manageable ones that may be distributed across a wide number of individuals through the use of an incomplete block design. The planting material is randomized into incomplete blocks of three varieties. Each farmer gets a package of seeds with three varieties that are coded to plant and score in their own field. 
The principles of tricot can be applied equally to other agricultural technologies, like fertilizers, compost, and other inputs, or post-harvest techniques.
Summary of  the Crowdsourcing 
Crowdsourcing is a method of enlisting a group of people to work toward a common objective or solve a pressing issue, notably to ensure the sustainable expansion of agricultural activities. By using crowdsourcing, it is feasible to select the finest suggestions made by a large number of responders as opposed to depending simply on the credentials and experience of one individual. By seeking ideas and solutions from a broad group of individuals, crowdsourcing is superior to internal thought processes in many ways.
This technique for variety selection improves seed systems by enhancing variety recommendations, enhancing on-farm testing, engaging and empowering farmers, aiding in the diversification of seed systems, supporting the scaling up of on-farm agricultural research, allowing women to make their own variety selections, supplying business opportunities for farmers, women, and young people, and helping researchers learn farmers' variety preferences. 
The primary techniques for putting crowdsourcing into practice include component identification, identifying farmers who are receiving various mixtures of varieties, seed preparation, site selection and planting, cultivation, measurements and observations, and proper harvesting.

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 / codesign 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 filterbased 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. 
 

Key Technologies and Innovations
1. Precision climate smart agriculture and Agricultural mechanization, urban agriculture
2. Genomics and Genetic Engineering: Improved breeding programs and disease resistance.
3. Artificial Intelligence (AI) and Machine Learning (ML): Predictive analytics, automated monitoring, and optimization.
4. Internet of Things (IoT): Real-time monitoring, automation, and data collection.
5. Blockchain: Transparent supply chain management and traceability.
6. Vertical Farming and Aquaculture: Sustainable, space-efficient production.
7. Synthetic Biology: Novel feed supplements and nutrient-enhanced products.
Grassroots Innovations
1. Community-based climate smart agriculture  programs: Localized, adaptive breeding strategies.
2. Indigenous knowledge sharing: Traditional practices integrated with modern technology.
3. Farmer-led research and development: Participatory approaches to innovation.
4. Mobile apps for farmers: Accessible, user-friendly tools for decision-making.
Global Initiatives and Collaborations
1. FAO's (Food and Agriculture Organization) Animal Production and Health Division: Global standards and guidelines.
2. World Organisation for Animal Health (OIE): International cooperation on animal health.
3. Global Alliance for Livestock Veterinary Medicines (GALVmed): Accessible veterinary medicines.
4. Haramaya University  and others: Research and development partnerships.
5. Agrifood innovation hubs: Regional centers facilitating collaboration and knowledge sharing.
Innovation Life Cycle
1. Research and Development: Basic and applied research.
2. Prototyping and Testing: Validation and refinement.
3. Pilot Scaling: Small-scale implementation.
4.Commercialization: Large-scale adoption.
5. Diffusion and Adoption: Global dissemination.
6. participatory varity selection and crowdsources 
Relevant Use Cases
1. Sustainable climate smart agriculture production: Environmental sustainability and climate resilience.
2. Improved animal welfare: Enhanced health and well-being.
3. Increased productivity: Efficient breeding, nutrition, and health management.
4. Enhanced food safety: Traceability and quality control.
5. Market access and trade: Global market integration.
6. Climate change mitigation: Reduced greenhouse gas emissions.
7. Nutrition and health: Biofortified products and nutrient-rich foods.
Future Directions
1. Integration of AI and biotechnology: Enhanced precision and efficiency.
2. Circular economy approaches: Waste reduction and valorization.
3. Inclusive and equitable innovation: Empowering small-scale farmers.
4. Global knowledge sharing: Collaborative research and development.
5. Policy and regulatory frameworks: Supporting innovation and sustainability.

Best Regrads,

Suleymen Abdureman Omer (MA Candidate in Public policy & Management) is BSc and MSc in Rural Development & Agricultural Extension (Rural Development stream) from Haramaya University, Ethiopia
Email: [email protected] ,  [email protected]
Mobile: +251920268645
https://www.linkedin.com/in/suleymen-abdureman-omer-63b091126
ORCID ID: https://orcid.org/0000-0001-7565-5851
https://www.researchgate.net/profile/Suleymen-Omer-4
https://scholar.google.com.au/citations?user=Jn3515AAAAAJ&hl=en