Food Supply Chain: Pioneering a Digital Farm Wallet with a Consortium in New Zealand

Welcome everybody. Ora Kra As we say in New Zealand. My name is Claire Rich Luva and I was contracted to build an industry consortium and manage it. Today I will share my knowledge and expertise gained based on a digital farm wallet with you, which we have experienced in the agricultural sector in New Zealand. In our session we will cover the following content. I will take you through the journey of piring decentralized identifiers in the primary industry. We will discuss the challenges and how we overcame them and the things we have learned.

I will focus in particular in the challenges of a voluntarily core petition approach when there were no external regulatory requirements and forces driving the need for collaboration and cooperation and how we address these through an industry consortium approach. A little about Berg, who we are and what we do. We are providing guidance and support to the businesses seeking to enhance transparency in the food supply chain with digital proofs and establishing digital identities for real world applications.

We focus on really on practical deployment of digital identity from a platform neutral perspective and we support our clients in New Zealand and as well in Europe from initially consultancy and scoping project through technology selections and project management and implementation. But let's dive into the agricultural sector now. Is it going? Here we go, the agricultural sector. I would like to take you through the challenges and of course in the food supply chain we have issues with trust definitely as well here and increasing demand of verifiable proofs, digital or non-digital.

But we need more proofs. What's going on there? There are a lot of challenges but with data sharing, as you all know, especially in the sector as well, which I've outset here. The challenges which we are facing, were called the multiple problems. So we have multiple data sets, multiple stakeholders, multiple relying parties and receivers of data sets and multiple purposes why we have to share our data sets. I give you an example for bankers for example. One the same proof for sustainable evidence. What's going on on on the farm for sustainable finance or financial access.

The same information we need for exporters or importers for country compliance requirements and the buyers. So the brand owners wanted for consumers marketing purposes as well. The downtime sets needs to travel across the whole supply chain and through the whole ecosystem. So we have already an ecosystem where we have to share the data with.

However, a farm farm data is typically hold in silos and silo software systems which are not talking to each other. So we definitely have here a strong data interoperability problem as well. And we have an increasing number of stakeholders here and demands and only a few standards. And surprisingly in New Zealand we don't have a farm registry so we have to find ways how to do that.

And these, all these problems we haven't addressed really in a coherent industry approach. What we have done to address this, we have therefore our approach was to build an industry consortium and it's named the trust alliance, which is a non-for-profit organization and has the objective really to work on proof of concept, to explore decentralized technology, learn about the benefits and the limitations of the technology. We wanted really to demonstrate how we can enhance the trust across the whole value chain and deliver data sovereignty within the broader ecosystem.

A broad range of industry participants for part of this consortium. So we included our pharma association, we had our biggest retail chain participant participating. We had the food processors, the food producers, two big meat companies, a fertilizer company, a bank farm, software providers and other industry organizations who are helping us to come to this goal.

We explored a lot of use cases during the journey with our members and to simplify here that you see, we have to discover this use cases on the, here on the left side you see the data examples for data points which are being handled on farm and needs to be managed and shared. And on the right side you see the purposes why they have to be shared.

So you see for example the financial access the bank wants the greenhouse gas emissions verifiable credential ideally to to give access to a green loan Or we have to deal with data sets from animal we welfare or workers' health, whatever the dataset is. But you see already there are a lot of data sets on. The poor farmer has to manage all these different data sets but farmers wants to farm. They don't want to manage their data, they really want to farm and that is the thing where they're good at.

Here we have more examples of use cases 'cause we have discovered over 40 different use cases with this consortium where we potentially can deploy a verifiable credentials or decentralized identifiers with, so you can read through the list faster than I can speak it out, but it gives you an idea of what we have discovered. But for all of these many different use cases, data sharing was required and the same data sets in most of the use cases.

So we have identified that the poor pharma, especially in New Zealand, has to give seven times his farm boundary to different organizations, to the council, to the government, to the biosecurity authorization, to a a plant control auditor before he can start really farming. And that is of course as you can imagine, really frustrating And therefore that was one of the biggest problems which farmers were addressing to us and to our members. 'cause the farmers are the customers of our members as well. That problem needs to be solved.

So to prioritize the use cases here we were working on an outcome requirement to help them to prioritize and the audience which we have interviewed and worked with clearly said they want to capture data once and then being able to share it with permission protect and control it and don't overshare it because we explained to all of them what selective disclosure will mean and what opportunities are in there. So they were quite excited about that and ideally they requested as well as an outcome that the technology is enabling or at least supporting the trust and the trustworthy data sharing.

All these findings here were underpinned by workshops we held with them numerous of workshops and discovery discussions, a lot of talking and an analysis which we have done with them discovery processes. And we conducted really the on-farm analysis across the whole supply chain. And with that we are spec, we identified specific needs and prioritized our use cases. Oh sorry, I'm going quickly back 'cause the overall outcome requirements, I think I mixed up the flights but that's all right. We will go there till Friday afternoon. So why are we doing all of that?

The overall outcome requirements was really facilitating international market access and have a broader market opportunity here. So financial access I mentioned already and the operational efficiency when they can share it only once, that would be perfect. And reutilizing data sets, that would be awesome for them as well. So really turning the trans transforming the painful compliance work which the farmer has to do. And not only the farmer but the food processor as well.

Turning that really into an added value exercise by reutilizing compliance data sets and the overall goal coming from a brand promise to a brand proof, what does it really mean? It's really that they want to provide verifiable data sets that they are not greenwashing and really showing the good management practice on farm. So we went through this one so I can skip through this but that was really our mantra. Basically capture data once share in a permissioned way and trustworthy way and protect as much as you can and really being able to get data ownership back.

With all of these we have, we have identified the use of decentralized technology and the use of verifiable credentials and this was definitely for us a strong candidate to achieve these goals with this technology. But let's move to the working pilot building on the requirements of the members and we painted a picture and they said that's the future. And when we deployed our digital farm wallet pilot project in a productionized environment, the future is now. So we were able to realize parts of that to achieve this.

We are, yeah there the members were really asking that there can be really managing and sharing their data by themselves and not relying on software companies who are doing that on behalf of them. Let's dive into the pilot. Should we? So here we have a look at what we were really have done here out of our members organization. We had six organizations who actively were getting involved into this pilot.

So we had issuers, we had relying parties and we had of course holders in total These, we tried the wallet with over 250 farmers and you can imagine that the level of digitization is different or we had a broad range of farmers, some were digital, some not yet. So we did a lot of education and engagement there with them. This project was supported by the government, by our New Zealand government. So our ministry for primary industries who co-funded this project thanks to them. And I would outline a little bit what kind of credentials we put into the wallet.

We only started with four 'cause we said it's a pilot, we really should start simple. So we had our farm identifier in there, which we agreed on with our consortium members. Then we had a farm map. So the farm boundary map 'cause that was an element which needs to be shared quite often. Then everybody was around greenhouse gas, so we put a greenhouse gas value in there as well. And it arrived ESG credential which were, could be utilized then for the farmers for other purposes.

One really unique important thing I would like to outline here is we took a multi-vendor approach for the technology stack. And that was very unique because our core core petition model with the consortium. So we had meat producers competing against zaza to manage and we took the same approach for our vendors and I would like to thank you our final technology partners in DIO and Anon lab. Without them we couldn't have made it happen. It was really great work to work together with them to make it happen for the farmer.

So that was awesome and we were able to see how this core petition approach can work on the vendor side and as well on the consortium side. What are the benefits for the farmer? Let's explore. The farmer is really getting back ownership of his data. He receives efficiency and productivity because he can share his data in one set with his audience or the relying parties. He can do it once in a secure and trustworthy way and he can prove his good management practice.

And our pharma really in general, were highly supporters of this project because they understood even beyond the use cases which we have explored, they understood the big opportunity for them that they can really differentiate with this kind of approach of support of the technology and prove good management practice.

And of course they were super happy that they don't have to overshare their data and can only give the requested data sets and the verifiable credentials, which they really have to give because as the data privacy issue we have to deal with the fraud issue we have to deal with as well. So farmers are living on their farm. So the privacy issue is definitely higher than in other potentially businesses. We were proven to it. It really the pilot proof really the capability that we can really deliver real world benefits and most importantly it showed to the industry that decentralized technology.

And I want to make that, that really emphasizing on that point is really real and ready to being deployed. And that was one of our biggest outcomes here, what we were able to see and show to the industry. But it was not all easy. I can tell you coopetition models are not easy. And I think some of you have experienced that when you work in a consortium approach, a voluntarily approach, it's not easy. Let's tackle some of the different experiences which we had and the challenges which we had to overcome. So the level of readiness with our members were totally different.

One we're really ready, others not at all. So we had to deal with that. The technical competency were different and there was often a lack of skills and understanding digital identity. Why?

What, huh? I I'm not, we are not talking about individual identities, we are talking about digital identities for data set and certifications and that was pretty new to them. So we had to deal with that. And the motivational aspect were different as well because some were able to give some commitments, others not. And some were having as well fear of disruption of this new technology because it was a disruption of their current business model. So we had to face this as well.

And the, the finding the right balance between competitive pressure versus collaborative desire was really important. Well how we addressed that, we have grouped our members into four groups and I quickly go through them. So the active participants are the good ones who are really giving commitment and want to being part of that. The second one are the observers who are really there and observing and didn't really commit any resources but were on the 50 50 side then we had the free riders. They were only there because they had fomo.

They, they had to be managed in a different way. And the force group that was the biggest challenge was the derailer. They were in the tent to only observe and undermine if they could because they want to protect their own business model. And as the membership organization, that's quite tricky to manage these guys as well. But we have found out that it's better to have them in the camp. 'cause when you don't have them in the camp, then they actively can go against you. So that was quite tricky to manage.

And how we overcome that was really that we were trying really to manage these four groups in a different ways or in different ways and really address them depending on their requirements and their stages. And the observers could be turned into active participants. The free riders could be upgraded to observers and the derails hopefully to the free riders then. So we try to give a pipeline and to get them all into active managing active engagement members, which was talking, taking a lot of energy. So you can imagine it's, yeah political things are going on behind the scene but it was good.

So key things to overcome is really the pilot to really get broader involvement and let them feel and do things. We created this reference implementation because we talked a lot, we discussed a lot, we showed them and we demonstrated but they have to get their hands dirty when they really want to see the benefits and feel it they have to do it. Doing was for us the most important thing. And in parallel we established an evaluation process to discover the organizational readiness for our different members.

Yeah And we have to balance the right goals between, yeah, the goals between common goals of the industry consortium and the individual goals. That was a very, very important point here that we get the balance right because they all have their only own business cases and their own value propositions. So we have to work through that with them. And last but not least, the expectations. It was a bit of when you're managing and I would like to give you that yeah insight.

If you manage a consortium and you may even know it, it's between herding cats, twisting arms and inspiring them, encourage them and motivate them to go for the right things here. What are our takeaways? Ways? I would really say a competition model is a good way and a good approach for de for decentralized data sharing model. We have experienced some challenges so be prepared for them, plan and manage and organizational risk carefully as super vital and never underestimate the human factors you are working with humans.

Technology is not the key challenge, it is really the enforcement of collaboration, which is a little bit of a, yeah, tricky part here. And That's the first thing. The second thing is really what we learned is we have to communicate this technology is complimentary otherwise the derailer will never be upgrading to freeloaders or active participants. So it's really complimentary. It's not an either or one. And I really would like to encourage you take the multi-vendor approach because that was one requirement from our members to overcome scalability, flexibility, and interoperability.

And that was really, really to overcome this hurdle of deploying and productionizing to conclude it is doable even though it is a large amount of work. Our experience, you have to have solid experience in managing and collective addressing collectively. Addressing common problem statements is very important here.

And yeah, what's in what's in it for them is important and thank you very much. I together I think we can shape a future where trust is not just a commodity but a cornerstone of our digital existence. Thank you. And as we would say in New Zealand, Thank you so much. That was You. Here I am. Thank you for bringing us through that example, sharing that experience with everyone else who's also on the journey. We have a question from the audience. How long did it take to get this set up and rolling? That is a very good question.

So we started I think three and a half or four years ago to really start and we started to bring people together and define the coalition of willingness. They had all the same problems to bring them together. So the most time we really spent in education explaining the technology, how it will work and we, we always said, guys, you don't know how the internet works so we, we can explain you the benefits but when you're not from the technical side, I would say for deploying only the digital farm wallet, that was less done than less a few months.

So that was not the key part of the technology deployment. That was maybe three to four, five months. But the setting up the consortium and talking and helping them to identify all the use cases and the business benefits was taking the most of the time here. Thank you. And the political things I outlined the derailers. That's good. Yeah. Was there anything that was really surprising about the experience that you didn't expect to encounter? So I personally was really very overwhelmed with the positive feedback of the farmer.

'cause they are very sensitive about their data and they hate it when they have no control about their data. That was really good. And the opportunity when they understood the opportunity that they can have more international market access and that they get a bigger loan because they can provide a greenhouse gas emission of dataset to their banks. That was really good to, good to see. Absolutely. Thank you so much for your sharing. Thank you.