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)”.
Youssef Travaly and Kevin Muvunyi at Brookings: “…AI in particular presents countless avenues for both the public and private sectors to optimize solutions to the most crucial problems facing the continent today, especially for struggling industries. For example, in health care, AI solutions can help scarce personnel and facilities do more with less by speeding initial processing, triage, diagnosis, and post-care follow up. Furthermore, AI-based pharmacogenomics applications, which focus on the likely response of an individual to therapeutic drugs based on certain genetic markers, can be used to tailor treatments. Considering the genetic diversity found on the African continent, it is highly likely that the application of these technologies in Africa will result in considerable advancement in medical treatment on a global level.
In agriculture, Abdoulaye Baniré Diallo, co-founder and chief scientific officer of the AI startup My Intelligent Machines, is working with advanced algorithms and machine learning methods to leverage genomic precision in livestock production models. With genomic precision, it is possible to build intelligent breeding programs that minimize the ecological footprint, address changing consumer demands, and contribute to the well-being of people and animals alike through the selection of good genetic characteristics at an early stage of the livestock production process. These are just a few examples that illustrate the transformative potential of AI technology in Africa.
However, a number of structural challenges undermine rapid adoption and implementation of AI on the continent. Inadequate basic and digital infrastructure seriously erodes efforts to activate AI-powered solutions as it reduces crucial connectivity. (For more on strategies to improve Africa’s digital infrastructure, see the viewpoint on page 67 of the full report). A lack of flexible and dynamic regulatory systems also frustrates the growth of a digital ecosystem that favors AI technology, especially as tech leaders want to scale across borders. Furthermore, lack of relevant technical skills, particularly for young people, is a growing threat. This skills gap means that those who would have otherwise been at the forefront of building AI are left out, preventing the continent from harnessing the full potential of transformative technologies and industries.
Similarly, the lack of adequate investments in research and development is an important obstacle. Africa must develop innovative financial instruments and public-private partnerships to fund human capital development, including a focus on industrial research and innovation hubs that bridge the gap between higher education institutions and the private sector to ensure the transition of AI products from lab to market….(More)”.
Book by James E. Addicott: “This book examines the precision farming revolution in Somerset, England. It reveals the reasons why local farmers invested in autonomous systems and traces the outcomes of adoption. It describes the local and global drivers of the fourth industrial revolution, from world population growth, climatic and ecological crises, profit driven farming and government agri-tech grants, to the Space Race era. A new cultural method of intelligence, ideas and thinking, new organisational and control powers, was precisely what precision farming offered farmers and off-farm firms, who were able to remotely monitor and control natural environments and aspects of on-farm activities. As a result of local farmers opting into precision farming systems the power dynamics of industrial agriculture were reorganised and this book will offer readers an understanding of how and why….(More)”.
Paper by M. Woods et al: “Citizens’ Observatories (COs) seek to extend conventional citizen science activities to scale up the potential of citizen sensing for environmental monitoring and creation of open datasets, knowledge and action around environmental issues, both local and global. The GROW CO has connected the planetary dimension of satellites with the hyperlocal context of farmers and their soil. GROW has faced three main interrelated challenges associated with each of the three core audiences of the observatory, namely citizens, scientists and policy makers: one is sustained citizen engagement, quality assurance of citizen-generated data and the challenge to move from data to action in practice and policy. We discuss how each of these challenges were overcome and gave way to the following related project outputs: 1) Contributing to satellite validation and enhancing the collective intelligence of GEOSS 2) Dynamic maps and visualisations for growers, scientists and policy makers 3) Social-technical innovations data art…(More)”.
Rishi Raithatha at GSMA: “In the new GSMA AgriTech report, Mobile Technology for Climate Resilience: The role of mobile operators in bridging the data gap, we explore how mobile network operators (MNOs) can play a bigger role in developing and delivering services to strengthen the climate resilience of smallholder farmers. By harnessing their own assets and data, MNOs can improve a broad suite of weather products that are especially relevant for farming communities. These include a variety of weather forecasts (daily, weekly, sub-seasonal and seasonal) and nowcasts, as real-time monitoring and one- to two-hour predictions are often used for Early Warning Systems (EWS) to prevent weather-related disasters. MNOs can also help strengthen the value proposition of other climate products, such as weather index insurance and decision agriculture.
Why do we need more weather data?
Agriculture is highly dependent on regional climates, especially in developing countries where farming is largely rain-fed. Smallholder farmers, who are responsible for the bulk of agricultural production in developing countries, are particularly vulnerable to changing weather patterns – especially given their reliance on natural resources and exclusion from social protection schemes. However, the use of climate adaptation approaches, such as localised weather forecasts and weather index insurance, can enhance smallholder farmers’ ability to withstand the risks posed by climate change and maintain agricultural productivity.
Ground-level measurements are an essential component of climate resilience products; the creation of weather forecasts and nowcasts starts with the analysis of ground, spatial and aerial observations. This involves the use of algorithms, weather models and current and historical observational weather data. Observational instruments, such as radar, weather stations and satellites, are necessary in measuring ground-level weather. However, National Hydrological and Meteorological Services (NHMSs) in developing countries often lack the capacity to generate accurate ground-level measurements beyond a few areas, resulting in gaps in local weather data.
While satellite offers better quality resolution than before, and is more affordable and available to NHMSs, there is a need to complement this data with ground-level measurements. This is especially true in tropical and sub-tropical regions where most smallholder farmers live, where variable local weather patterns can lead to skewed averages from satellite data….(More).”