The Top Five Emerging Technologies Expected to Impact Data Governance and Data Sovereignty

The prevalence of cloud services is one factor that has contributed to increasing concerns regarding data governance and data sovereignty for users and data providers. Both concepts are related to how digital data is controlled by the laws of a country, for example, when data brokers have to comply with the EU GDPR (General Data Protection Regulation). When associated with agri-food businesses, these concepts are even more pertinent, as they can help tip the scales in favor of smallholder farmers and producers.

We have identified five emerging technologies, adaptable to the agricultural sector, which we expect will be significant in reconfiguring data governance and data sovereignty to harness the value of digital data as a potential cash crop in agri-food systems. These emerging technologies are just the tip of the iceberg and together with policies, regulations, and public awareness, these technological tools could help bridge the gap between digital transformation and smallholder farmers.

Blockchain Digital Identification — Digital Identities Providing Transparency for Human Rights

What is it? Blockchain-based identity protocols that allow a platform to quickly and safely issue, exchange, and verify digital identities.

Why it matters: People’s participation in thriving societies and economies is dependent on identification. In some instances, nations cannot provide physical forms of identification (e.g., passports, IDs, birth certificates, visas, etc.) to many citizens, especially immigrants and refugees, thus creating hindrances to human rights and freedom. In other instances, corporations retain ownership over personal data with little scrutiny or recourse to security.

By cross-checking a person's multiple social media accounts, past documentation records linking to their social security number, passport, and other official papers, Blockchain Digital Identification creates a secure, unified, and interoperable way to verify identities. Additionally, this emerging technological solution also opens doors in terms of accessibility. Actors in the supply chain could open bank accounts, enter into secure, trustless contracts, and therefore capitalize on digital revenue opportunities previously unavailable to them within conventional supply chains or local banking infrastructure. Digital identities could thus be used for a variety of purposes, including participation in mobile crowdsensing platforms, and to help informal communities extend their access to financial assistance through blockchain-based chamas and national identifier systems.

One considerable constraint related to this emerging technology is that transactions occurring in the Blockchain consume a lot of energy. This is due to the employment of large banks of computers to authenticate these transactions, a process commonly known as mining. However, some solutions are emerging to tackle this issue, such as the method Proof-of-Stake Blockchain, an alternative to traditional consensus algorithms that relies on market incentives instead of computing power to verify transactions.

Ultimately, by joining the dots between these fragmented and often disparate systems, as well as connecting to the IoT within the supply chain, Blockchain Digital Identification allows individuals to be seen, and gives them the tools to participate in the supply chain and the wider economy, overcoming barriers and removing limits to their freedoms.

Integrated Operations Center — The Digital Layer Behind the Entire Supply Chain

What is it? An integrated control system that combines IoT communication devices, flow predictions, and automatic parameter adjustments to remotely control, monitor, and maintain large and complex facilities on a real-time basis.

Why it matters: The Covid-19 pandemic has highlighted the need and the demand for remote work. By introducing a high-level management system that can oversee operations, companies can have better control over their work environments, lower costs, reduce risks (such as minimizing human contact during contagious disease outbreaks), and optimize logistics and operations.

Having oversight over an entire supply chain, end-to-end, means organizations can be more efficient and resilient, with more reliable complex decision-making in real-time, particularly in relation to emergencies or unforeseen circumstances. With a flow of information coming from a myriad of many IoT units throughout the supply chain, quality control, transparency, and traceability can be delivered seamlessly.

Such a tool could be pivotal in providing insights into socio-economic, environmental, and ecological developments. For smallholder farmers, it could offer open platforms and protocols containing the distributed data collected by the Integrated Operations Center. It could assist in decision-making, help improve crop productivity and efficiency, and address the imbalances and inequities that affect more vulnerable regions and communities, particularly smallholder farmers.

Data Enclave — Privacy Protection in a Transparent System

What is it? A tool that automates data anonymization, sharing information derived from data rather than the actual data itself. It provides a confidential, protected environment in which authorized researchers can remotely access sensitive content.

Why it matters: This tool can help companies better manage their data and abide by privacy regulations by using subsets of data or extrapolations, rather than the sensitive or confidential information itself. This reduces the risk of data leaks, either accidentally or maliciously, and allows for secure access to data where needed, without it being downloaded or removed from the platform where it is securely stored.

As we consider opportunities in data providing a cash crop for farmers and producers, data enclaves could ensure data suppliers are compensated or reimbursed for the contribution of data, without their identities or their data being exposed to fraud or security risks. Such data can also feed into other platforms or models such as In Silico Farming, global land use optimization projects, or Mobile Crowdsensing Platforms, providing a wealth of information and insights for the benefit of all, without compromising identity or other sensitive information. This solution is beneficial when considering that the level of tech literacy in rural areas is considerably low, which frequently and systematically allows smallholder farmers to be more vulnerable to data abuse by big companies, who benefit from their data.

Machine Learning Weather Model — No Such Thing as Bad Weather

What is it? By combining computational fluid dynamics with machine learning and meteorological data sources, this system steadily improves the ability to forecast both weather and natural incidents with far-reaching results for individuals and businesses.

Why it matters: Developments in machine learning and access to more comprehensive meteorological data sources, including sensors and satellites, give a higher degree of accuracy to predicting complex weather systems. The model can be used for a range of purposes: to predict cyclones, atmospheric rivers, and forest fires, and also to monitor and assess changes in land use such as deforestation. By feeding into and drawing data from digital farming networks and precision agriculture platforms, such as AgriTech-as-a-Service or In Silico Farming models, data from weather models can support decision-making processes for farmers and producers in terms of planning and optimizing crop yields.

Weather models can also provide data for Integrated Operations Centers as a way for organizations to manage risk from natural disasters and increase resiliency, responsiveness, and sustainable land use management. Additionally, it can also become a crucial tool for crop insurance and credits, yet, to benefit not only banks and insurance companies but farmers too, laws and regulations will be needed. In the future, we anticipate farmers being remunerated for the data they provide to drive such models, which could, in turn, be favorable to facilitate the inclusion of small farmers in this digital ecosystem —an especially relevant feature in view of increasing climatic changes.

Computable General Equilibrium (CGE) — Sustainability Math: Where Data and Theory Meet

What is it? A computational model that combines existing and updated economic data with economic theory in order to estimate the possible impacts of policies, technologies, or other factors that can knock on the economy.

Why it matters: In the last two decades, CGE models have become a standard tool for economic simulations and analysis. Such models can flexibly capture direct and indirect inter-sectoral, inter-regional, and inter-temporal effects of policy, and therefore provide insights into policy reform and prioritization. This makes them particularly suited to measuring sustainable development, as was demonstrated by a study undertaken by the Centre for European Economic Research (ZEW). In this instance, a CGE model was used to assess the impacts of policies on a range of sustainable development indicators, in a single consistent framework.

Top-down and bottom-up CGE models have also been used to analyze water scarcity, land use and cropping patterns, and energy demands on a global scale. When using the data collected from small farms, CGE models could become a toolbox for policymakers and governments to visualize and contemplate the potential consequences of various long-term scenarios. The data could be oriented to forecast outcomes focusing on the wellbeing of smallholder farmers. Additionally, by using CGE models to simulate and assess policies, policy shocks, and changes in resource availability, they provide a significant opportunity to anticipate, mitigate and adapt to the consequences of the climate crisis and perhaps even develop an achievable, robust climate change mitigation policy.