AgData

'What’s behind the ag-data logo? An examination of voluntary agricultural data codes of practice' by Jay Sanderson, Leanne Wiseman and Sam Poncini in (2018) 1 International Journal of Rural Law and Policy comments

In this article, we analyse agricultural data (ag-data) codes of practice. After the introduction, we examine the emergence of ag-data codes of practice and provides two case studies: the American Farm Bureau’s Privacy and Security Principles for Farm Data and New Zealand’s Farm Data Code of Practice. The case studies illustrate that the aims of ag-data codes of practice are inextricably linked to consent, disclosure, transparency and, ultimately, the building of trust. We go on to highlight the commonalities and challenges of ag-data codes of practice. In terms of commonalities, we consider that they are self-regulatory and voluntary; are principle-based; have a communicative function; and have attitude and behaviour change as key objectives. In terms of the challenges of ag-data codes, we argue that the key challenges are the need for an appropriate and agile ag-data normative framework; implementation and evaluation of ag-data codes; issues around trade mark-based logos; and evaluation of ag-data codes of practice. We conclude that while ag-data codes of practice may help change practices and convert complex details about agdata contracts into something tangible, understandable and useable, it is important for agricultural industries to not hastily or uncritically accept or adopt ag-data codes of practice. There needs to be clear objectives and a clear direction in which stakeholders want to take ag-data practices. Ag-data codes of practice need credible administration, accreditation and monitoring. There also needs to be a way of reviewing and evaluating the codes in a more meaningful way than simple metrics such as the number of members.

 The authors argue

Voluntary agricultural data (‘ag-data’) codes of practice have emerged since 2014. In part, their emergence is because of the increasing realisation of the potential benefit and value of ag-data, with many decisions and processes along the whole agri-food supply chain – from paddock to plate – being data enabled and data driven. Ag-data is collected and used for many purposes, including improving productivity and profitability. There is a myriad of different data collected from farms: machinery data that improves safety and efficiency of farm machinery; personal data of purchasing and finance history; and agronomic and agricultural data. In this article, our focus is on agricultural data. 

Ag-data is collected by sensors on tractors and drones and used for many purposes, including providing multi-spectral imagery, and showing crop health and moisture content. Software can aggregate and deploy ag-data to increase yields, improve farm profitability and sustainability, and ensure regulatory compliance and consumer satisfaction. Further uses of ag-data are found in supply chain logistics and in the ability to better respond to and manage issues such as crop or animal stress. Ag-data can also be linked from farm and packaging to transport and sales; assisting with food safety, healthy and ethical choices and differentiating markets and allocating resources. 

While the potential benefit and value of ag-data is immense, a major hurdle to realising the benefits is the tension between those who provide the data (ie, farmers and producers) and those who collect the data (ie, agribusiness and third parties). This tension limits the potential benefits of ag-data because, in large part, it results in problems of access and use of ag-data; fundamentally, farmers and producers do not trust agribusinesses with their data. A study identified that this lack of trust in the way agribusinesses deals with ag-data was identified as a major concern of Australian producers, with 56 per cent of respondents having no or little trust in agribusiness maintaining the privacy of their data.  Further evidence of a lack of trust between producers and agribusiness was found by the American Farm Bureau Federation, who, in 2016, conducted a survey of over 400 farmers and found, for example, that 77 per cent of those polled were concerned about which entities can access their ag-data. 

If digital agriculture and data are to transform agri-food networks, then trust around ag-data access and use needs to be fostered.  To this end, a range of initiatives are currently being investigated and implemented, including education and awareness programs, data co-operatives and other collaborative models.  Most notably, since 2014, voluntary ag-data codes of practice have emerged to not only help develop ‘good’ agdata practices but also to build trust in the way ag-data is managed.  Broadly stated, ag-data codes of practice act beyond legal mandates (ie, government legislation) and attempt to both harness the benefits of ag-data and protect producers’ privacy and security. More specifically, data codes tend to focus on the   key areas that give rise to mistrust: consent, disclosure and transparency around ag-data practices. For example, under the New Zealand Farm Data Code of Practice (‘NZ Farm Data Code’), organisations agree to disclose their practices and policies around data rights, data processing and sharing, and data storage and security. In the US, the American Farm Bureau Federation’s Privacy and Security Principles for Farm Data (‘Principles for Farm Data’) sets out data principles for agricultural technology providers including that ‘access and use of farm data should be granted only with the affirmative and explicit consent of the farmer’.  And the EU Code on Agricultural Data Sharing by Contractual Agreement (‘EU Code’) attempts to define key concepts and sets out general principles for sharing agricultural data including that ‘[t]he collection, storage and usage of the collected agricultural data can only occur once the data originator has granted their explicit, express and informed permission via contractual arrangement’.  Other countries (eg, Australia) are also contemplating the introduction of an ag-data code of practice. 

But are ag-data codes of practice a good idea? 

The aim of this article is to analyse the effect and usefulness of ag-data codes of practice. The next section examines the emergence of ag-data codes of practice and then discusses two case studies: the American Farm Bureau’s Principles for Farm Data and New Zealand’s NZ Farm Data Code. The case studies illustrate that ag-data codes of practice are inextricably linked to consent, disclosure, transparency and, ultimately, the building of trust. The section that follows highlights the commonalities and challenges of ag-data codes of practice. The article concludes with several observations, most notably that while ag-data codes of practice may help change practices and convert complex details about ag-data contracts into something tangible, understandable and useable, it is important not to uncritically accept or hastily adopt ag-data codes of practice. There needs to be clear objectives and a clear direction in which stakeholders want to take ag-data practices. In other words, stakeholders need to be sure about what they are trying – and able – to achieve with their ag-data codes of practice. There also needs to be a way of reviewing and evaluating the codes in a more meaningful way than simple metrics such as the number of members: for example, it is necessary to know something about whether the codes raise awareness and education around data practices, and whether they have encouraged changes in attitudes and behaviour. Ag-data codes need credible administration, accreditation and monitoring. Only with such added safeguards, will ag-data codes of practice have a chance of success.