Abstract
FoodTech, intended as the use of disruptive digital technologies along the agri-food chain, features an outstanding potential to contribute to the SDGs, and in particular to help combat and eradicate hunger without a massive increase in food production. The chapter reviewed emerging applications of technologies like the Internet of Things, distributed ledger technologies and Artificial Intelligence at various phases of the agri-food chain, focusing in particular on smart and precision farming, value chain integrity, personalized nutrition and the reduction and prevention of food waste. The paper shows that it is important that the focus is not limited to one single technology, but to the whole “technology stack”, including sensing, big data analytics, 5G, blockchain and Artificial Intelligence. Moreover, weaker players such as small farmers and consumers are often unable to make the most of these technological developments, and this requires dedicated action in terms of training and education. Furthermore, blockchain and Artificial Intelligence can massively contribute to improving the agri-food chain: however, they feature important governance challenges, which can lead to undesirable re-intermediation effects (in the case of blockchain); and loss of user self-determination and agency, as well as privacy and integrity (in the case of Artificial Intelligence). Finally, any solution that relies on digital technologies will need to be inclusive, otherwise the risk will be to widen the digital divide: more generally, FoodTech needs to develop in way that is compatible with all SDGs, not only those related to the agri-food sector.
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Notes
- 1.
Distributed ledger technology (DLT) is a digital system for recording the transaction of assets in which the transactions and their details are recorded in multiple places at the same time. DLTs do not rely on centralized data storage or administration. Blockchain is a specific type of DLT in which a log of records is shared by means of blocks that form a chain. The blocks are closed by a type of cryptographic signature called a ‘hash’; the next block begins with that same ‘hash’.
- 2.
Data can be stored in various ways, including through remotely accessible, cloud-enabled solutions; through distributed databases; or through distributed ledger technologies such as blockchain. Some of these technologies are key enablers of value chain integrity, monitoring and trust, since they produce “audit trails” that enhance the verifiability of transactions and contractual performance across the value chain.
- 3.
For example, the CGIAR Platform for Big Data in Agriculture employs biologists, agronomists, nutritionists, and policy analysts to use Big Data tools to create AI systems that can predict the potential outcomes of future scenarios for farmers. The ultimate goal is to seamlessly integrate real-world data from farms around the world into algorithms that generate critical insights that can then be shared back with farmers. The CGIAR Platform is already showing results of potential benefits for smallholder farmers, such as for the Colombian Rice Farmers Federation. After multiple seasons of challenging rain patterns, rice farmers in Colombia were struggling to know when to plant their crop. Depending on whether there was going to be above average or below average rainfall, farmers would need to decide whether to plant earlier or later in the season. If there was going to be too much rain, they might decide not to plant at all that season.
- 4.
To calculate the crop-sowing period, historic climate data spanning over 30 years—from 1986 to 2015—for the Devanakonda area in Andhra Pradesh was analysed using AI. To determine the optimal sowing period, the Moisture Adequacy Index (MAI) was calculated. https://www.business-standard.com/article/companies/microsoft-ai-helping-indian-farmers-increase-crop-yields-117121700222_1.html
- 5.
Farm Data Accreditation Ltd, New Zealand Farm Data Code of Practice, ver 1.1, Cl 4. American Farm Bureau Federation, Privacy and Security Principles for Farm Data, https://www.fb.org/issues/technology/data-privacy/privacy-and-security-principles-for-farm-data/
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- 7.
https://newsroom.ibm.com/2018-08-09-Maersk-and-IBM-Introduce-TradeLens-Blockchain-Shipping-Solution. As many as 94 organizations are actively involved or have agreed to participate on the TradeLens platform built on open standards, including more than 20 port and terminal operators across the globe, global container carriers, customs authorities in five countries, custom brokers, cargo owners, freight forwarders, transportation and logistics companies.
- 8.
According to some commentators, the fact that Maersk owns a stake of the TradeLens and the intellectual property associated with the joint venture creates conflicting interests in the governance of the platform, in particular when it comes to attracting members that are also competing with platform owners. Commitment to profit-sharing and an open IP policy would probably remedy current problems. https://www.forbes.com/sites/andreatinianow/2018/10/30/how-maersks-bad-business-model-is-breaking-its-blockchain/#476280234f4d
- 9.
A good example of past attempts to increase verifiability through globally shared commitments to certify the origin and distribution of products was the Kimberley process, established in 2002 to break the link between diamonds and armed conflict. The scheme engaged participants from governments, civil society, and the private sector to eliminate the trade in “conflict diamonds,” or rough diamonds used by rebel groups to finance conflict with an aim toward overthrowing legitimate governments. Compliance was monitored with certificate data, statistics, and annual reports, among other types of information: but these monitoring efforts were largely unsuccessful: fraudulent certificates soon emerged in Angola, Congo, Ghana, and Malaysia. Could blockchain solve these problems? Only partly: for example, a startup called Everledger created a blockchain application that tracks assets over the course of their lifetimes, and claims to be able to drastically reduce the estimated USD45bn lost every year due to insurance fraud. In reality, blockchain and DLTs can help solve some of the associated problems (e.g. checking certificate numbers to avoid fraud by spotting duplicative certificates), but the problem of trust among the players in the supply chain shifts “upstream”, to the moment in which a given transaction is appended to the ledger.
- 10.
This follows an extended pilot in Vietnam, where more than 6000 companies are using it, including leading international food conglomerates like AEON, CP Group, Lotte Mart, Big C, Japfa, and CJ. https://www.foodingredientsfirst.com/news/globalized-blockchain-auchan-implements-food-traceability-technology-on-international-scale.html
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Id. With blockchain, vendors can remotely record a wide variety of predetermined measurements, including storage temperature, at each juncture in the supply chain. If temperature at point B varies dramatically from the temperature at point A and C, product managers can extrapolate this data to pinpoint problem areas and allocate resources accordingly.
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Renda, A. (2019). The Age of Foodtech: Optimizing the Agri-Food Chain with Digital Technologies. In: Valentini, R., Sievenpiper, J., Antonelli, M., Dembska, K. (eds) Achieving the Sustainable Development Goals Through Sustainable Food Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-23969-5_10
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