Report by Mercy Corps: “In our digital age, data is increasingly recognized as a powerful tool across all sectors, including agriculture. Historically, data on rural farmers was extremely limited and unreliable; the advent of new digital technologies has allowed more reliable data sources to emerge, from satellites to telecom to the Internet of Things. Private companies—including fintech and agricultural technology innovators—are increasingly utilizing these new data sources to learn more about farmers and to structure new services to meet their needs.
In order to make efficient use of this emerging data, many actors are exploring data-sharing partnerships that combine the power of multiple datasets to create greater impact for smallholder farmers. In a new Learning Brief looking at AgriFin engagements with 14 partners across four different countries, we found that 25% of engagements featured a strong data-sharing component. These engagements spanned various use cases, including credit scoring, targeted training, and open access to information.
Drawing on this broad experience, our research looks at what we’ve learned about data sharing to enhance service delivery for smallholder farmers. We have distilled these lessons into common barriers faced by data-sharing arrangements in order to provide practical guidance and tools for overcoming these barriers to the broader ecosystem of actors involved in optimizing data sharing for agriculture….(More) (Access the full length learning brief)”.
Tool developed by GODAN: “Codes of conduct, voluntary guidelines and sets of principles around how to transparently govern farm data are a recent thing. While laws and regulations that govern personal data are becoming more and more common, legislation still does not cover data flows in many industries where different actors in the value chain need to share data while protecting all involved from the risks of data sharing. Data in these value chains is currently governed through private data contracts and licensing agreements, which are normally very complex and over which data producers have very little negotiating power.
Codes of conduct have started to emerge to fill the legislative void, setting common standards for data sharing contracts. Codes provide principles that the signatories agree to apply in their contracts. Farm data is an example of such sensitive data flows. Farm data flows go from the farm through many other actors (extensionists/ advisory service providers/ ag tech companies, farmers’ associations, financial service providers, government…), before returning – aggregated and combined and in the form of services – back to the farm. Such flows potentially open up sensitive data that should only be shared with specific actors under specific conditions, or should be anonymised in order to avoid harming the farmer’s interests and privacy. This is especially true in the case of smallholder farmers, whose farm data often coincides with household data and personal data, and who are in the weakest position to negotiate their data rights.
This online tool, therefore, also has another important practical purpose: providing the conceptual basis for general, scalable guidelines for everyone dealing with the production, ownership, sharing and use of data in agriculture….(More)”.
EIT Food: “The Curating Citizen Engagement project will revolutionise our way of solving grand societal challenges by creating a platform for massive public involvement and knowledge generation, specifically targeting food-related issues. …Through a university course developed by partners representing different aspects of the food ecosystem (from sensory perception to nutrition to food policy), we will educate the next generation of students to be able to engage and involve the public in tackling food-related societal challenges. The students will learn iterative prototyping skills in order to create museum installations with built-in data collection points, that will engage the public and assist in shaping future food solutions. Thus, citizens are not only provided with knowledge on food related topics, but are empowered and encouraged to actively use it, leading to more trust in the food sector in general….(More)”.
Paper by Johanna Walker et al: “The smart city paradigm has underpinned a great deal of thevuse and production of open data for the benefit of policymakers and citizens. This paper posits that this further enhances the existing urban rural divide. It investigates the availability and use of rural open data along two parameters: pertaining to rural populations, and to key parts of the rural economy (agriculture, fisheries and forestry). It explores the relationship between key statistics of national / rural economies and rural open data; and the use and users of rural open data where it is available. It finds that although countries with more rural populations are not necessarily earlier in their Open Data Maturity journey, there is still a lack of institutionalisation of open data in rural areas; that there is an apparent gap between the importance of agriculture to a country’s GDP and the amount of agricultural data published openly; and lastly, that the smart
city paradigm cannot simply be transferred to the rural setting. It suggests instead the adoption of the emerging ‘smart region’ paradigm as that most likely to support the specific data needs of rural areas….(More)”.
Simon Roberts and Jason Bell at the Conversation: “The COVID-19 pandemic and consequent lockdown measures have had a huge negative impact on producers and consumers. Food production has been disrupted, and incomes have been lost. But a far more devastating welfare consequence of the pandemic could be reduced access to food.
A potential rise in food insecurity is a key policy point for many countries. The World Economic Forum has stated this pandemic is set to “radically exacerbate food insecurity in Africa”. This, and other supplier shocks, such as locust swarms in East Africa, have made many African economies more dependent on externally sourced food.
As the pandemic continues to spread, the continued functioning of regional and national food supply chains is vital to avoid a food security crisis in countries dependent on agriculture. This is true in terms of both nutrition and livelihoods. Many countries in Southern and East African economies are in this situation.
The integration of regional economies is one vehicle for alleviating pervasive food security issues. But regional integration can’t be achieved without the appropriate support for investment in production, infrastructure and capabilities.
And, crucially, there must be more accurate and timely information about food markets. Data on food prices are crucial for political and economic stability. Yet they are not easily accessible.
A study by the Centre for Competition, Regulation and Economic Development highlights how poor and inconsistent pricing data severely affects the quality of any assessment of agricultural markets in the Southern and East African region….(More)”
Report by Foteini Zampati et al: “Open Data offers a great potential for innovations from which the agricultural sector can benefit decisively due to a wide range of possibilities for further use. However, there are many inter-linked issues in the whole data value chain that affect the ability of farmers, especially the poorest and most vulnerable, to access, use and harness the benefits of data and data-driven technologies.
There are technical challenges and ethical and legal challenges as well. Of all these challenges, the ethical and legal aspects related to accessing and using data by the farmers and sharing farmers’ data have been less explored.
We aimed to identify gaps and highlight the often-complex legal issues related to open data in the areas of law (e.g. data ownership, data rights) policies, codes of conduct, data protection, intellectual property rights, licensing contracts and personal privacy.
This report is an output of the Kampala INSPIRE Hackathon 2020. The Hackathon addressed key topics identified by the IST-Africa 2020 conference, such as: Agriculture, environmental sustainability, collaborative open innovation, and ICT-enabled entrepreneurship.
The goal of the event was to continue to build on the efforts of the 2019 Nairobi INSPIRE Hackathon, further strengthening relationships between various EU projects and African communities. It was a successful event, with more than 200 participants representing 26 African countries. The INSPIRE Hackathons are not a competition, rather the main focus is building relationships, making rapid developments, and collecting ideas for future research and innovation….(More)”.
About: “The Food Systems Dashboard combines data from multiple sources to give users a complete view of food systems. Users can compare components of food systems across countries and regions. They can also identify and prioritize ways to sustainably improve diets and nutrition in their food systems.
Dashboards are useful tools that help users visualize and understand key information for complex systems. Users can track progress to see if policies or other interventions are working at a country or regional level
In recent years, the public health and nutrition communities have used dashboards to track the progress of health goals and interventions, including the Sustainable Development Goals. To our knowledge, this is the first dashboard that collects country-level data across all components of the food system.
The Dashboard contains over 150 indicators that measure components, drivers, and outcomes of food systems at the country level. As new indicators and data become available, the Dashboard will be updated. Most data used for the Dashboard is open source and available to download directly from the website. Data is pooled from FAO, Euromonitor International, World Bank, and other global and regional data sources….(More)”.
Paper by Bob Doherty et al: “In this article, we offer a contribution to the emerging debate on the role of citizen participation in food system policy making. A key driver is a recognition that solutions to complex challenges in the food system need the active participation of citizens to drive positive change. To achieve this, it is crucial to give citizens the agency in processes of designing policy interventions. This requires authentic and reflective engagement with citizens who are affected by collective decisions. One such participatory approach is citizen assemblies, which have been used to deliberate a number of key issues, including climate change by the UK Parliament’s House of Commons (House of Commons., 2019). Here, we have undertaken analysis of a citizen food assembly organized in the City of York (United Kingdom). This assembly was a way of hearing about a range of local food initiatives in Yorkshire, whose aim is to both relocalise food supply and production, and tackle food waste.
These innovative community-based business models, known as ‘food hubs’, are increasing the diversity of food supply, particularly in disadvantaged communities. Among other things, the assembly found that the process of design and sortation of the assembly is aided by the involvement of local stakeholders in the planning of the assembly. It also identified the potential for public procurement at the city level, to drive a more sustainable sourcing of food provision in the region. Furthermore, this citizen assembly has resulted in a galvanizing of individual agency with participants proactively seeking opportunities to create prosocial and environmental change in the food system….(More)”.
Paper by Payam Aminpour et al: “Sustainable management of natural resources requires adequate scientific knowledge about complex relationships between human and natural systems. Such understanding is difficult to achieve in many contexts due to data scarcity and knowledge limitations.
We explore the potential of harnessing the collective intelligence of resource stakeholders to overcome this challenge. Using a fisheries example, we show that by aggregating the system knowledge held by stakeholders through graphical mental models, a crowd of diverse resource users produces a system model of social–ecological relationships that is comparable to the best scientific understanding.
We show that the averaged model from a crowd of diverse resource users outperforms those of more homogeneous groups. Importantly, however, we find that the averaged model from a larger sample of individuals can perform worse than one constructed from a smaller sample. However, when averaging mental models within stakeholder-specific subgroups and subsequently aggregating across subgroup models, the effect is reversed. Our work identifies an inexpensive, yet robust way to develop scientific understanding of complex social–ecological systems by leveraging the collective wisdom of non-scientist stakeholders…(More)”.
Article by Griffin McCutcheon, John Malloy, Caitlyn Hall, and Nivedita Mahesh: “From the esoteric worlds of predictive health care and cybersecurity to Google’s e-mail completion and translation apps, the impacts of AI are increasingly being felt in our everyday lived experience. The way it has crepted into our lives in such diverse ways and its proficiency in low-level knowledge shows that AI is here to stay. But like any helpful new tool, there are notable flaws and consequences to blindly adapting it.
AI is a tool—not a cure-all to modern problems….
Connecterra is trying to use TensorFlow to address global hunger through AI-enabled efficient farming and sustainable food development. The company uses AI-equipped sensors to track cattle health, helping farmers look for signs of illness early on. But, this only benefits one type of farmer: those rearing cattle who are able to afford a device to outfit their entire herd. Applied this way, AI can only improve the productivity of specific resource-intensive dairy farms and is unlikely to meet Connecterra’s goal of ending world hunger.
This solution, and others like it, ignores the wider social context of AI’s application. The belief that AI is a cure-all tool that will magically deliver solutions if only you can collect enough data is misleading and ultimately dangerous as it prevents other effective solutions from being implemented earlier or even explored. Instead, we need to both build AI responsibly and understand where it can be reasonably applied.
Challenges with AI are exacerbated because these tools often come to the public as a “black boxes”—easy to use but entirely opaque in nature. This shields the user from understanding what biases and risks may be involved, and this lack of public understanding of AI tools and their limitations is a serious problem. We shouldn’t put our complete trust in programs whose workings their creators cannot interpret. These poorly understood conclusions from AI generate risk for individual users, companies or government projects where these tools are used.
With AI’s pervasiveness and the slow change of policy, where do we go from here? We need a more rigorous system in place to evaluate and manage risk for AI tools….(More)”.