Greetings to everyone. Welcome to this exciting event. Food is more than just what we eat. The ways in which we produce, process, and consume food touches every aspect of life on this planet. It is a foundation of our cultures, of our economies, and our relationship with the natural world. And has the power to bring us together as families, communities, and nations. These are the introductory words to the objective set for the UN Food Systems Summit. In recent years, and during the preparation of the UN Food Systems Summit, agroecology has emerged as one of the most promising transformational solution to the problems of our food system. And now a broad coalition of action is forming and gaining momentum. Agroecology is a movement, a set of practices, but also a scientific discipline. In all these aspects, agroecology is a dynamic, inclusive, transformative power to change food systems. In 2019, the consortium of International Agricultural Research Centers, the CGIR, and French Research Institution conceived a joint initiative to address critical knowledge gaps about agroecological transitions to provide evidence to underpin advocacy and inform policymakers and donors about the potential of agroecological approaches. They produced a call for action, a call for agricultural research organization and researchers to play a leadership role in the global movement to bring about sustainability, health, equity, and food security transition in the agri-food system. This initiative quickly evolved into the creation of the transformative partnership platform TPP, which was officially launched in a side event of the Committee on World Food Security last June in Rome. The TPP is open to all individuals and organizations truly sharing the same vision. In this original call and in the TPP, we recognize that there will be changes in our scientists typically work. They have to be more inclusive. They should build wider and more balanced partnerships with diverse stakeholders and better engagement in co-creation of knowledge and innovation with practitioners and stakeholders, as well as better inclusion of traditional knowledge. Researchers and research organizations need to change mindsets, culture, and methodologies. A powerful way to involve the public is to engage everyone in the citizen science. This means that anyone and everyone can collaborate in research to increase global scientific knowledge. In our case, this approach is highly relevant because agri-ecology is based on inclusiveness and empowerment. The TPP, supported by the Swiss Agency for Development and Cooperation, wishes to launch this bold citizen science campaign to involve smaller farmers, farm workers, and food consumers in generating knowledge to accelerate and document agroecological transitions. This citizen science initiative is intended to provide evidence to support advocacy and inform policymakers and donors, but not only about the potential of agroecological approaches, but also about the quantity, the diversity, and the wealth of farmers innovation. This is particularly important for the definition of the respective country transformation pathways and strategies as foreseen in the next steps of the Food Systems Summit portfolio of action. This initiative is not a project of the coalition, but aims at complementing the work of different initiatives and all the very important research on agroecology. In the news released two days ago, the United Nations special rapporteurs on the rights to food, on human rights and the environment, and on extreme poverty, said, agroecology is one of the best ways to ensure that food systems fulfill human rights and respect the planet's environment. We think that agroecology should be primarily focused because it starts with the question of power dynamics. This initiative also aims to build on this premise by democratizing involvement in science. The first stakeholder should democratically decide what's the focus of the citizen science initiative should be and then engage in generating knowledge in support of agroecological transition. So during several meetings of the CGIR system council, we were told that gene banks are the crown jewels of the CGIR system. Scientists have collected almost one million crops varieties and are conserving them. But before that, millions of farmers worldwide have selected and bred these varieties. Small of the farmers are the designer of those crown jewels. Small of the farmers are feeding 70% of the world population and together with consumer they know very well. Well, the cultural and economic value of food and its relationship with the natural world, as it is mentioned in the objective of the summit. So let us work all together democratically creating a citizen science movement for agroecology. We will co-create co-design today and throughout the process, the floor is used, size this opportunity, share your inputs, ideas, suggestions and concern. Thank you. Thank you very much, Michelle. I'm Fergus Sinclair, I'm the Chief Scientist at Sinclair aircraft and co-convenor of the TPP that Michelle has just referred to. Can I have the first slide? I'm just going to introduce the first session, we have three parts to this, maybe you can put it into display mode, fantastic. So when we're talking about citizen science, there are sort of two aspects to it that are important, the extent to which science is responsive to the concerns and needs of citizens, and then the extent to which citizens are involved in actually providing information that generates scientific knowledge involved in the co-creation of that knowledge. And that original formation of the concept of citizen science from Alan Irwin last century is bang up to date with the way in which the European Commission is conceiving it now. Now some citizen science initiatives concentrate on one or other of those two, with I guess a little bit more emphasis quite often on the crowdsourcing of data, the collection of data. Next slide. So we're already in this initiative that's only been alive for a matter of days, collecting lots of examples of the way that citizen science is being applied in agroecology and related fields from collecting and monitoring biodiversity through sharing films about agroecological practices that farmers make on their own mobiles through connecting buyers and sellers of products and connecting information with those transactions. Next slide please. The whole concept of pharma networks. Next slide and pharma field schools that have been pushed by FAO where people are taking part in understanding and advancing principles. And then we've got quite a bit around rights and citizen science and meeting SDG targets, including around soil health. So there are lots of examples and these are just just a few that I'm showing here and we will be collating these to make them easy for you to access examples. Next slide. So today, we're going to kick off by looking at a series of examples that cover different types of approaches. And then we're going to have a critique of big data, and then try to sort of distill what the key issues are and then have some audience interaction with all of you to try to see whether we can start moving towards defining where this initiative should go. So with that, let me go to the first example that's being highlighted and each one of these is just five minutes. So tour, could you tell us about re greening Africa in in five minutes. Thanks. Thanks Fergus. So I'm going to walk you through a little bit of sort of the journey and some of the applications of the re greening Africa app and how we're using this in the context of what we call assisted crowdsourcing and the reason we call it assisted is that there's a lot of engagement that goes into this, this particular work and so it's basically working with with stakeholders across eight countries in East and West Africa. And the objective is, or the reason we developed the app was that we wanted to create a set of tools to allow stakeholders to basically, sorry, to basically track restoration at scale. So first of all, the app is available on the Google Play Store. So if you want to have a look at it, feel free just go to the Google Play Store on your Android. If you have an iPhone, I'm sorry. But just go on your Android and search for re greening Africa and it will come up and you can install from there. So this is available and you can use it for tracking your own interventions if you want it's open for anyone to use. So what we do in the app is we we collect information on people's restoration activities around tree planting around farmer managed natural regeneration which many of you know is quite common in West Africa and also in parts of East Africa and on nurseries. So looking at what what seedlings are available what species are available where are they available etc. And then there's the training or capacity development module as well. So I'm just going to take you through a few examples, just sort of a quick highlight. And this is just from Kenya. So in the app we obviously can we use it in the context of projects to look across different countries how many farmers are practicing for example tree planting and we have FNR or both as you see in that graph at the bottom. So in the, in the re greening Africa project which is where we developed the app initially but we've now sort of expanded it beyond that. We see that we have a lot of engagement in Rwanda, a lot of use Senegal Ghana Niger Kenya. At the in the top left here we have a map of Western Kenya, you can see the where it is in the relative to the rest of Kenya. And we see polygons on this map where users have used the app to walk their field boundaries. And that's a really important aspect of this is that we are actually tracking, not only what people are reporting to be doing but also where they are doing it so the kind of trees that are being planted or protected in the case of FNR, how they're doing it so what kinds of practices, where is this being done on farm and for what so it's, it's, it's really just a really rich data set that we're getting from this. If we zoom in just to look at an example, we see here, some, some of our stakeholders and partners on the ground measuring a tree. So essentially that trees geo referenced it sits within the field. You can see the field boundaries there on the map in those sort of greenish yellow. And so in other words, we can and we also capture the species and we do various measurements. So when people use the app they're submitting information that ranges in that we can use to look at biodiversity we can use it to look at the intensity of practices we can figure out where they're happening, and we can look at change over time. So, here's just an example from from Rwanda with some of the data as you can see some of the species that are being planted in the case of Rwanda it's mostly tree planting. And then the number of trees the survival rates which is of course a very important thing to keep track of, and where on farm people are planting their trees so it's it on the boundaries that in a wood lot is an improper and etc. If we go to Senegal, just jumping across the continent, we see that mostly it's FMNR so farmer managed natural regeneration. And these are the species on the left. And one of the things we see in these data sets is that in FMNR we have a lot more diversity in terms of the trees, then we see in tree planting. So that's a challenge that we're trying to address in a number of different projects around how do we bring in more diversity into the system so increasing biodiversity which of course is important for soil and land health. And it's also important in the context of agriculture. So you can see on the left again there a field boundary that one of the users has walked. So, the key thing here now, in the way that we usually we're using this information is that with those field boundaries, and with all that information that's being collected, we can connect the crowdsourcing to the science that we're doing. So in other words, we can take the field boundaries and combine that with assessments of land health, for example, soil carbon, which of course is a very important indicator of land health or soil health. And we can relate these things directly to for example climate neutrality goals, recreation targets, etc. And of course this is very important in this decade of land restoration. And we can then extract these land health indicators for the boundaries that people are walking, and then start looking at that various scales. And I'll just put a couple of graphics on the map here that show distributions of erosion on the top one and soil organic carbon in the left one by country within the project. So, we can then derive lots of insights into what is driving land degradation and we can, we can design more effective restoration methods to sort of directly target, target these challenges on the ground. So, a very important message. Yes. So a very important thing here is the connection and the crowdsourcing or the citizen science and the science. Thank you for this. Thank you to a wonderful. And just to remind you in the Q&A, you can vote for questions so that they go up to the top panelists will answer the questions as we're going on. But also, we will answer any that haven't been answered after the event and make it available to you. So let's go on to the Asian Farmers Association and Irish Bagela, who's going to tell us about the Forgotten Food Survey. Irish, you've got five minutes. Yes. Thank you for this. Good morning, good afternoon and good evening to all from the Philippines. It is such a delight to be part of this huge multi stakeholder and timely initiative so I am here representing the Asian Farmers Association for Sustainable Rural Development or AFA, which has 20 member organization in 16 countries with around 13 million small scale women and men producers engage in crops, livestock, fisheries, forestry, birding, pastoralism and further we collaborate with other CSOs and other farmers organization in the region, reaching around 30 million farmers and additional 12 countries. In the next few minutes, I will be sharing with you the process that we had undertaken for the farmer perception survey on traditional food crops, which is, we believe a good example of what farmers organization can do and how we can support these million voices initiative. So in the next three slides, you will see the partners that we work with. The perception survey is part of the collective action on Forgotten Food with Jifar Alliance, Biiversity, International and Siyat, Apaari, MS Swaminathan Research Foundation, Crops for the Future and Fire. So we have been given a small resource to be able to mobilize the national partners and subnational partners. So specifically for the survey it was undertaken with MS Swaminathan Research Foundation based in India, and with 25 national and subnational partners. So you will see in the slides the name of the National Farmers Organizations and CSOs that we worked with. So in the next slide, you can see that we have conducted these prior to original, the next slide please. Thank you. So we have conducted these survey prior to original webinar in June of this year. We use the survey result to develop a farmers declaration on Forgotten crops. We can share that with you in chat. Both the result and the farmers declaration were presented during the regional consultation. It was deliberated and was the basis to develop a regional manifesto both of the farmers organization and the research agencies, specifically Apaari and all its members. And in turn these regional, joint regional manifesto was then used to craft or develop a global manifesto on Forgotten food and it's being used to guide the partners and the development of a global work plan based on these manifesto and the farmers declaration is underway. So the survey was seen something that could facilitate the recognition of different stakeholders on the importance and value of indigenous species and knowledge of farmers and also seen as an effort to conserve these indigenous species and it's also used to highlight or to identify the needed support of the communities to continue to conserve these Forgotten crops. So we see this, we see this perception survey as a catalyst to have a discussion with various stakeholders. So as I have said it was done prior to June, so March to May, that was the duration and there were 18 questions identified under four categories. And I think one of the lessons we've learned during the survey is on the type of questions as we had a challenged processing open-ended question given the number of farmers that we were able to reach. In the next slide we'll show you the six step process that was involved in this initiative. So a joint designing of the questions, translating these questions into several languages of our national partners.  And then we did the actual survey. They did it through phone interview and some of them did an online survey. So the national farmers encoded the data or the answers in the Google form that we have created and then the data were consolidated by a Google sheet. And some of the data needed to be translated to English. Following that we had two consultations with our national partners, all that in a couple of months. So I already mentioned 19 countries were reached, majority of them are from India. Next slide please. And we were able to reach a total of 3,087 farmers in 19 countries. Yeah, sorry there's a delay in the slide. Next slide please. Okay, thank you. So that's the distribution I have mentioned that majority of the farmers who participated were from India. Next slide.  Yes, yes. I think that's my, this is my last slide for, yeah. So, as I mentioned, this is the farmers declaration. I will not go into detail but most of the survey result was reflected on this declaration. Next slide. We value this process. We see this process as a very important to our partners because it built our capacity at the regional and national level and that it was not just data gathering but it was also educational. And some of our partners are actually using it to engage their national government. So we see the same benefits and maybe more in this million voice initiative and we see this as an opportunity to amplify our voice and our contribution. Back to you for this. Thank you so much Irish a lot to get into a short time. So let's move to a somewhat more structured approach to interacting with farmers around particularly breeding objectives. David Van Etten from the Alliance of Biodiversity in Seattle is going to tell us about the exciting democratization of science in that area. Thank you, Fergus. Yes, thank you very much. So my name is Jacob Fett and I'll be talking about citizen science in agriculture experimentation so next slide please. So the work that we've done was mainly focused on the testing of varieties that includes land races, modern varieties, varieties created with parts, Patriot breeding for example as well. Many of these trials have common challenges so logistics, large plots and need large farms that's it's difficult to work with small holders, collective action bringing farms together to look at one plot. And then, very often these observations that farmers and do are kind of snapshots, because it's not their farm right they need to go to the neighbors farm to see it. Many of these trials are in a way kind of a replicate of the try to imitate on station trials and they want to be very precise. They're not always very representative of farmer conditions. Next slide please. So to address these challenges, we developed a citizen science approach to address a number of these so instead of kind of looking at farms and kind of paying for farms to be used for participate experiments. You're very inspired by how citizen science allows farmers to really participate as citizen scientists themselves right that's that's a principle in citizen science. Also that it requires less supervision training that digital support the things that happen so you've seen the presentation of tour or the things that happen in birds observation for example. But it also needs kind of a rethink of the statistics behind it so that that's one area of work that we've done, but it also allows us then to analyze environmental adaptation and it's very important for air ecology this kind of play specific solutions. Next please. So this is how the approach looks like it's a little bit cut, at least in my my screen, but it basically creates very small packages for each of the farmers and those are then traced digitally from start to finish so you you kind of design the experiment starting with a digital approach and all the data comes back to you from the farmers who do their observations to a single platform which then allows you to draw conclusions and also to communicate these results back to farmers in a very quick way so that they stay engaged. That is also a very important part of the approach. Next slide please. Yes, so kind of the ingredients of this. It's about simplifying the approach standardizing it and that allows you then to this to streamline it. And then you have all these kinds of benefits scaling this is what we call external validity so it's representative of farmers conditions themselves farmers grow really under how they would grow the crop. We can control the quality of the data. We know where the data comes from in terms of the GPS points and these kinds of things. A quick feedback that I already mentioned is really important, especially when you work with seeds, right you don't want to miss a whole cycle for example because you're just you're still analyzing the data. Next please. So this is how it looks like in practice a very small fields we've worked with this many crops and also with consumption testing. So what you see in the right down corner. The lower corner is sweet potato testing in Uganda, people testing different varieties but the actual products. Next please. This is a little example of what what you could do with this approach. This is from Nicaragua we work with more than 800. So 800 plots, about 300 farmers who generated data so we have GPS points on the left, we can then collect connected with climate data soil data etc and then on the right which you see is the kind of location Pacific variety recommendations that we then can generate from this also gives feedback to breeders who can take much better decisions about what farmers need etc. Next please. In conclusion, what does it do it streamlines on farm testing and trials with digital tools. We can involve farmers but without overloading them they do very small tasks each but it kind of adds up. So here external validity and more insight in in genotype environment interaction so the space as place specific recommendations. It's not very difficult to do. And it also gives us scope to involve different different organizations and doing it so different NGOs social enterprises, because it's easy and kind of scalable to do it. So we're also working on kind of new what we call business models. And so we configuring and scaling this also in a in a different way than it used to be done. Next slide please. Are you going to the end. Yes, this is the last slide just a overview of like the what we've done so far. So it's growing so we've created this software so people are now using it we're supporting people to to use it more and more. And I think that's the final slide, not mistaken. Thank you very much. Thank you, Jacob, really exciting and those numbers really beginning to add up now. So let us move to the last of these sort of highlight presentations and Christian and Palasana who's the country director for digital green in India is going to tell us a bit about what he's doing and answer some of the questions that are coming in the q&a live over the Christian you've got five minutes. Thank you so much. Digital green is our mission is to leverage the power of technology and the power of partnership to improve resilient sustainable productivity and income of small holder farmers. Next slide please. We are a not for profit located in our headquartered in San Francisco. We are working in East Africa and South Asia. We have reached out to close to 3 million farmers and it's going there by day. We have trained 41,000 frontline extension workers of the government and various studies have shown that our approach has primarily helped in adoption, increase the adoption rate by 43% improve the yield on an average by about 23% across various commodities. And most important, some of those extension work that we do has reduced the cost of extension by close to eight times. Next slide please. The three principles that we apply and it's very important number one is that we listen very close to the farmers. So whatever we do, though we do use technology, it is for the farmers and we typically co design it with farmers. The second is that we don't create parallel systems, we work with the governments on ground because they are the ones who are going to be there to provide support, and they are the one who is going to scale up initiatives. And third, we collect data, we have data of nearly 3 million farmers and we listen to that with data very carefully, and I will talk about that is a bit more later on. Next slide please. Yeah, there are three transformation initiatives and I will just focus on the first and third. The first one is the targeted digital advisories to inspire sustainable farm practice. The, you know, we know that the adoption of the ecology and sustainable farm practices needs a behavior change in motivation among farmers, and we have come up with this video based extension service that improves the efficiency and effectiveness of extension system. And this is typically by farmers with farmers and for farmers. That is what is this advisory services all about the second we have got a number of products and services that we offer to farmers. The third one, we are also revisioning data itself, how the data is being looked at at this point of time and can we actually transform the way data is being looked up through a farmer centric angle. Next slide please. The digital advisory service we typically produce we means that the community produce videos, they demonstrate those videos, it is the knowledge that we gain from the field itself, the community decides what videos, or what are those issues that they want to talk about through these videos, and instead of, you know, pamphlets leaflets and word of mouth these videos are shown in the community through people projectors, and this is, and this has helped significant scale from starting from about 1000 farmers about eight years ago, and we are reaching out to nearly 3 million farmers as of now. So close to 5000 videos are available in YouTube library and we have 67 million views as of now, as I speak to you right now, and we have also come out with the complete training package in virtual facility and more than 4000 frontline workers are as of now enrolled in those virtual facilities, and this is one part of it and this targeted advisories helps farmers to understand what needs to be done, how it needs to be done, and it's almost like you know carrying a demonstration farm to their homes through those videos. Next slide please. A number of products that I mentioned about this and Eric is under enterprise kd dashboard and marvy system. All these are direct to farmer oriented where farmers can use these for various applications. This is something that we are initiated right now it's called farmstack. Now, simply put we know that many institution many agencies collect data, everyone seems to be collecting data from farmers government onwards private institutions, but the farmers doesn't even know that the biggest asset that they own is their own data, right. And unfortunately, these data sets, which is owned by different organization that they're very particular about the ownership aspect, they don't speak to each other. So we were thinking about if these data sets can find a way to speak to each other, without losing the sovereign sovereignty or the control or the security aspects, we can actually provide value added service and support to the farmers farmers. So we had a use case couple of years ago we continued it for two or three other use cases, we were able to successfully integrate or bring together the weather data by a weather company, the soil data that is available with the farmer's profile that we were having to send out targeted advisories to farmers in case of potato farming in case of cashew nuts from et cetera, and it became a what we call as a blockbuster initiative in digital green. And then we further studied it and found that what we need is a protocol a strong protocol that will help these independent data sets to speak to each other. So farmstack is an open source community governed protocol, in which we have set that protocol and we are offering that peer to peer connectors at the organizational level, this farmstack helps them to create usage policies, the data owners can decide what to be shared with whom to be shared and how long it to be shared, they can come up with those usage policies, and then we help them to come up with this peer to peer data connectors. And we will not even see that data what is being exchanged, it's all between those data owners, and the most important part of it is related to farmers right now. How do we come out with a constant manager for farmers, and ensure that the farmers are not only owning that data that they're having they're controlling that data and they're giving consent to that data, and they're also able to discover what is that particular data being provided to them, and what is the value at that they will get because of this by sharing this data. So this is farmstack and we are we have a number of use cases till now. We are excited about its possibilities, and we are rolling out in a big way in the days to come. Next slide please. Are you closing up now. Right. So for example, this is one, you know, you know, we do quality certification through a tech service provider, the quality service certification is provided by the government and this data collection around quality helps us to send advisories customized advisories to farmers, and also it helps to into the market opportunities. But while all this were done, the farmers were being treated as simply mere recipients of the service. And once what funds that does right now is it builds a consent mechanism into that farmer is going to own that data into the data wallet, and they will decide who to share this data, when to share this data and for what purpose they want to share this data, that is farmstack. Next slide. Thank you so much. Okay, thank you Chris that's again a huge lot of different aspects that are really exciting ways of interacting. The video teed up for the next contribution, which is a critique of big data. Chris was already answering some of the issues that are going to come up. So I'm expecting the video to start at any moment. Yeah, thanks a lot for the invite and we apologize for not being able to participate in person as we need to be very brief I'll start right off with our presentation. In the first step I will attempt to assess the digitization of agriculture from rather critical stands and especially shed light on the role of what is called the platform economy. And I will then argue for why we need alternative that is open source technologies and commitment to data sovereignty in the agricultural sector. Yeah, first of all, that's well known many previously analog fields and farms are currently undergoing digital updates. The census robot stones big data analysis and artificial intelligence are being employed for the purpose of precision farming in order to increase the overall efficiency of agriculture and optimize the use of fertilizers and pesticides and the fields. And this is also possible by Google X. And whether many positive aspects to this development. And there's also another way of more problematic side to it, namely that the more network the farms and fields, the more dominant a production model common to us from our everyday life, the so called platform and bringing this into account, the greater the influence of big tech. As Nick Cernick has argued, the platform economy is the dominant economy of the digital present. It relies on a logic that has been developed and cultivated especially by the leading tech companies of our age Google Apple Facebook, and Amazon Microsoft As such, a digital platform can be about be defined as an intermediate infrastructure of exchange a kind of quasi market upon which products or service can be traded or conveyed in the same way as information and data. In this regard, many companies work in the field of precision agriculture have developed tools and apps that operate on platform economic principles. In other words, they can, they rely on data extraction data analysis and algorithm modeling, as well as platform economic structures and services established by big tech. As one example, the app farmwhip is designed as a knowledge platform using location weather data for field reports and AI for image recognition. It's aim is to automatically count the quantity of grown fruits for yield calculation, or to digitally identify plants that have been infected by pests and other diseases. Yet the app relies on a fairly powerful superior platform economic infrastructure, which enables the database to be set up and process in the first place Google Cloud, which alongside Amazon Web Services and Microsoft Azure is one of the largest clouds cloud platforms in this room. FarmWhip is thus a tool which is fundamentally based on the services of Google Cloud and other products of the company. And for example, from we've recently experimented with the variable Google Glass to leverage AI and vision compute capabilities to inspect animals in their environment. Another app that can hardly run without the essential infrastructure of Google is Cottonace by the Indian Badwani Institute for AI. And Google supports this app through a $2 million grant and its cloud services, which are used to help small farmers to identify the extent of pests or to predict disease trajectories. In addition, this app offers a network of experts that can provide immediate advice. Cottonace also serves as an information platform with the objective of reaching 2 million farmers by 2021. These examples illustrate a larger trend in smart farming, Google, Amazon and the like positioning themselves more and more as so called meter platforms providing essential infrastructure and expanding their influence in the agricultural sector as well. And so doing the companies integrate smaller platforms, rent out their services and structures and thus accumulate more and more data on farmer behaviors, production methods and knowledge. The benefit is not only a constant inflow of profitable data but also the emergence of a systemic dependency. As Christopher Meitz describes it and I quote, in order to actually receive the advantages and the very promised by precision equipment you means a farmer must simultaneously share exquisitely specific data about your farm operations data your labor generated for free. As a result, new services such as FarmWave or Cottonace as well as the farmers themselves can hardly use methods of precision agriculture without joining the ecosystem offered by meter platforms using their cloud services and even handing their personal data to them. On this ground, it is not difficult to predict a successful formation of monopoly power in the future, a formation in which a few corporations act less as producers than as hegemonic or as proprietary markets. As such, using hardware, software and server infrastructures, they could form an overarching ecosystem that operates as a wallet garden with several log in effects. This concentration of power is problematic, not least because the marketplace owned by a single company enables that company to set standards, define codes of conduct, control the barriers to market entry, and that is why it can more or less dictate who will be able to act. On that market when in under what conditions, and I will now expand on this aspect. Thank you very much. So in our view it is likely that the collection of data will lead to an even greater competitive advantage that will further strengthen monopoly power and what is also likely is that it will enable the creation of proprietary forms of knowledge which is an equally problematic point. Just to give you a brief example in this regard, based on the incoming data from its cloud service and AI image recognition services, Google will first know everything about the behavior of farmers and segment will also know which pesticide is needed to what extent and plus we'll be able to diagnose specific tests and diseases based on the extent of the data, and this much needed knowledge will be private and accessible only to those who are willing to pay for it. And in addition, this knowledge can then be used by big tech to develop their own services and products, or even be sold to agricultural insurance companies who might of course have an interest in assessing individual risk profiles. So the questions we should ask them are first whether such knowledge resulting from what Shoshana with terms data extractivism should lie in the hands of a few tech monopolies and second which forms of epistemic dependency might follow from this. And it is also vital to think about justice related issues of course and ask for instance, really benefit from the services in the end. There are issues that are at stake that can only briefly mentioned, including privacy issues, the ecological implications of expanded digitized infrastructures, problems relating to the automation of physical and mental labor algorithmic biases. And so on in recent years, it has become increasingly clear that as soon as Gaffa managed to implement their own data infrastructure on a global scale the hard it is to regulate them. And moreover the difficult it will be to develop alternative infrastructures that go beyond proprietary markets and the common framework of data extractivism so regulation of course is an equally important issue. It is no coincidence that Gaffa has established cooperative relationships with states and institutions alike and it has done so much to their own benefit. In the health and education sectors on the context of so-called smart cities, the public infrastructures often provided by tech monopolists, the data that is generated is usually privatized. So here more effective regulation, and a much more critical stance toward the tech sector and its narratives will be needed. And that also holds of course for the increasingly digital agricultural sector and agricultural policy. So to conclude then data and by that I specifically mean big data is not reducible to a neutral and a priori positive and natural process or development. Rather as Melvin Kranzberg pointed out decades ago technology is neither bad nor good nor is it neutral. And in this regard data and platforms are especially in the age of surveillance capitalism or platform capitalism, inextricably tied to commercial sectors, the dynamics and consequences of which need to be taken into account when discussing the expansion of the digital transformation into new realms that holds of course as well for agriculture and food production. So in our view it is probable that so-called big tech, specifically including leading tech monopolies such as Google, aka Alphabet, Felix pointed to it, will use opportunities such as smart farming to extend their own power and create related dependencies. And we've also observed how government have expanded what we call infrastructure power that is a form of power that circumvents political deliberation or regulation with the aim of establishing a new universal standard or a new norm. And Shoshana has referred to what she calls institutional facts as the outcome of new practices of data extractivism that are implemented when no effective instruments or regulation are available. As political scientist with Davies describes the logic of platformization in relation to government I quote, the ultimate objective of internet companies is to provide the infrastructure through which humans encounter the world. We want to be somewhere else, we click on Uber and when we simply want something Amazon will make it arrive, end of quote. One could follow this rather dystopian outlook and proclaim with regard to the digitization of agriculture. When a farmer wants to work effectively in the future he or she might need to work with Google Cloud services with Amazon Web Services and so on. So in short, he or she will depend on a monopolized tech infrastructure to be able to compete at all. With a bunch of new digital products in the realm of agriculture advertised trolling a narrative of empowerment and a language of sharing of commonality of transparency and democracy. Our focus should instead be on critically assessing the potential reproduction of inequalities hierarchies and forms of injustice in the sector. So last but not least and this is my last point in order to democratically benefit from the digitization of agriculture we need data alternatives. And by that I mean we need open source technologies and a fundamental commitment to collective data sovereignty, when only data but also forms of knowledge generated by data are accessible without any restriction. And on that note, I thank you very much and again sorry that we were unable to attend this meeting in person, but we are more than happy to respond to any questions or comments from your side. Thank you for your email addresses so be free to reach out. Thank you very much. So that that's a very challenging perspective for us and a number of things we need to keep in mind. I'd now like to ask you to Betty from IRD to try to sum up a little bit what the key issues other come from both the highlights and that critique. You have five minutes. Thank you very much for giving me this opportunity. So this last presentation actually rises the very issues that agricultural approaches for point criticized the concentration of power in the food sector, the control over knowledge generated by farmers. This has increased also the tendency of producers on on few technologies. But this data extraction approach which has been really advanced, been advancing in the recently due to more use of mobile phones and increase precision farming approach digitization is has been expanded and also risk to expand further in the future. But even in addition to this more to this data extraction through this more modern solution, even like traditional classical research approach has actually been in a bit data extraction approach involves considering bringing this knowledge back to the end users. And that is actually that earth of what agroecology, what to change. So to address this existing power dynamics, which has led farmers in increase dependency on your technologies, which has also reduced citizens and choices for their food and also reduce their inclusion in decision making and the food systems. This also has limited resources for researchers to work on alternative farming approaches to conventional farming. So citizen science can have can be actually used in a different way as we saw during these presentations. In some cases, it is just a crowdsourcing approach where citizens are just sensors and sending data and they volunteer in computing the data that they that they gather in their, in their local context. In some situation, they are already included in the basic interpretation of this data, and they're also are included in thinking on this on this data. There are then more inclusive inclusive approaches to citizens and science where when you have more parts battery science when the citizens or the farmers are participating in the problem definition and data collection. Or when we go more in the collaborative science and then farmers and citizens are really included in the problem definition in data collection and in the analysis. The technology actually promote those more inclusive forms of citizens and science so parts battery science and even more like collaborative science, where there is a mutual learning and and core sharing of knowledge, and where scientific knowledge and traditional and an intimate understanding of ecosystem are actually brought together to tackle the tangled issues related to agriculture to environment to nutrition and to climate. We know that agriculture is knowledge intensive and that's require specific data to to local context, and to be able to code design relevant and adaptive solution for those local context, and that kind of require a lot of resources so citizen science collaborative and inclusive science can be really a way to approach this knowledge intensive aspect of our ecology. And as we know also some issues like climate issues are cannot be resolved. We need a global perspective while actually depending on local data, and also many other natural and ecological processes they actually also require this approach, having the global perspective, but also relying on this local, local data. So that is how actually this citizen citizen science, which is not only being a data extraction approach but really being inclusive and collaborative can actually allow us to address the multiple issues that and tangled and unpredictable also issues which are related to agriculture to environment to nutrition and climate. So I just want to conclude that this citizen science can indeed be can support and can be necessary for agriculture, but only when more decentralized approach is taken. So the data which is collected is transformed and used as a public tool for knowledge sharing and knowledge generating. Fantastic you did that you've got a lot of key points in there and crystallize things really well. Now we're running 10 minutes late. I hope that a lot of you can stay on because it's now getting interactive. And we're doing a little bit of citizen science. And I'm handing over now to Mika born from shared, who is going to take us through a little bit of interactive participation. Thank you so much for this and as you've mentioned we've heard a rapid view through through different options and a critique so now we would really like to hear some feedback. Many of you are probably very familiar with Slido, but if you are not, please go to Slido.com. And then where it asked for the code at the top you put in million voices. The other alternative is to use the QR code that you can now see on the screen. So I'll just give you a moment to get to Slido.com. And then I'm going to launch the first poll. So coming back to what Fergus outlined at the beginning of the session. There's different elements of citizen science, but in your view, what is most important. Is doing the science and collecting the data, or all that citizens concerns and needs are being considered by the science. And I can see results coming in. So we have a rising number, feeling that citizens doing the science and collecting the data is critical. And now, almost being overtaken by citizens concerns and needs being considered by scientists. So very quiet, really quite even so we're going to give it a few moments to see which one will take over. It looks like citizens that concerns and needs being considered by the scientists is slightly in the lead. So now we're going to run to the next question. This is a word cloud. So just put in one or two question responses or words in response to the question. What aspects of agroecology, do you think citizen science can address. So just one word or two words, and it can be very broad so that the different elements of agroecology. So citizen science will be very helpful in addressing. And we can see quite a variety coming in around co creation power inequality co creation, obviously getting a number of different views, but a really wide number of topics with with this idea of co creation in the So we want to give people a few more moment soil and diversity, also very key topics for agroecology and the role of citizen science, just giving a few more moments for other ideas to come in. And it's wonderful to see co creation at the at the center, as well as having these other topics and elements around that. So soil complexity diversity and co creation. And I'm going to rush us through a little bit because we are running out of time and we don't want to take you past the hour too much. So just giving a few more moments for other additions. Agroforestry coming in now policy power inequality. Wonderful. I'm now launching the very last question and this is quite open ended so we can leave this one running as we pass back to Fergus to wrap up. And we've seen this also in the chat and the Q&A. But what are some of the challenges and opportunities you see with citizen science we've heard a critique of the big data. We've seen some different options. I've seen some comments in the chat around the accessibility, the data, the network access, and we can see ideas coming in here you can write a long sentence or a short one. Ideas around making it incentivize for people to share data accessibility is coming up both of access to input the data and also to see the results. Those that are non tech savvy, the inclusiveness and the ownership. And we can see a really useful amount of information coming in now. I would really like to use these comments these suggestions also what has come up in the Q&A to really inform the direction the priorities of this initiative as it moves forward the million voices initiative. And so I'm going to stop sharing my screen but please continue adding into this question as I pass back to Fergus to sum up and thank you all very much for your contributions. Thank you so much Mika you did that in a rapid time, so we're just actually on the hour at five. And what I want to stress at this point is that at the moment we've got an 18 month timeframe so this is just the beginning. But it's quite important that in a side event at UNFSS, we've actually started this engagement. So trying to get bottom up so that we have that pattern of the principle of working bottom up and top down at the same time to try to meet global objectives in locally relevant ways. So what we envisage happening after this is a series of more focused events at national and regional levels, where we'll explore in a little bit more detail what questions we can answer and address, not just at the farming aspects of agroecology but also at the consumption side and everything that makes up the food system. So I'd really like to thank everybody for taking part I think we had over 130 people online at the peak. Over 20 questions. We will make sure all the questions are answered one way or another. Please look out for further communication, continue to be involved, use the resources that will be available on the web take part in the discussions on the TPP website where we'll be able to interact with this initiative as it develops. And we can see that digital green are already reaching 3 million farmers. So it shouldn't be that difficult for us to be tackling citizen science issues with at least a million voices within the 18 month period that we set ourselves to get moving and to deliver some results from this initiative. So with that, I'd like to thank you all, and close this event. Thank you.


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OK, so hello everybody, my name is Steve Bramwell from University College in the centre of London in England. It's a pleasure to be here and thanks to the organisers for putting on this interesting conference. So I'm going to talk about Coulomb gases in magnetism, but the very specific aspect of Coulomb gases in spin models and how they can be realised in magnetic materials. And I'm going to be mixing theory and experiment. I'll keep it quite simple, just try and give some ideas that may be worth taking away. And I'll also give a bit of a personal take on the subject, although I obviously will review the subject to some extent as well. So spin model magnets are of course rather an old fashioned thing, they've been around for a long time. And as probably most people know it goes back to easing in 1925, the easing model of spins that just point up or down a two state spins with an exchange constant. And depending on the sign of the interaction, the ground state is either a ferromagnet or an anti ferromagnet. So the interactions are near neighbor and can either align or anti align the spins. Now, obviously, despite being a simple model, it's famous for the fact that in 1949 Onsaga provided an exact solution which is far from simple or trivial. Which is one of the sort of great moments of statistical mechanics in the 20th century. Much later on in the sort of 1970s, people started to do neutron scattering on real magnets. This is a measurement of Hirakawa and Aikida from 1971 on real material where they measure the order parameter. So that's actually this is an anti ferromagnet. So that's a staggered magnetization. They measure the order parameter of this material with neutron scattering. And the line there is Onsaga's exact solution. So the line goes through all the points. Now, I've always been rather delighted by this figure. You can justify the reason that this model applies to this magnet and I won't go into that in detail. It raises a few questions actually because this first of all, this is a three dimensional magnet, whereas this is a two dimensional model. But the main point I want to make is that in terms of model building, spin models offer tractable many body systems and experimental realizations thereof. And that's the short file recipe for discovery when you've got theory and experiment in such close harmony. So my particular personal interest in spin model magnets is I'm particularly like where you can get analogies between systems that are formally unrelated. And my own work over the years has been related to two major areas there. First of all, I've been interested in 2DXY magnets which map to the two dimensional Coulomb gas as has already been mentioned yesterday. We found all sorts of interesting connections like for example with order parameter statistics and statistics of turbulent flow. And this is added to a debate on long Gaussian statistics that goes as far as galaxy density distributions and that sort of thing. A bit closer to home as it were, this is a plot of a data collapse, experimental data collapse between a helium film and a magnetic film near the costless, thawless transition. So you get a lot of analogies in these systems between formally unrelated systems. My second major area that I've worked on is spin ice which is something I invented a long time ago in Mark Harris. And this maps to a three dimensional Coulomb gas. It's actually a magnetic analog of proton order in water ice. But it's a little bit more than that as well. There's been a variety of spin-offs, if you forgive the pun of spin ice. So first of all to so-called artificial spin ice which is micro magnetic arrays designed to mimic the spin model. And secondly to magnetic monopoles in spin ice that were introduced by Castle Overhead Talon by Rich Kinn. And this is the subject of that neutron scattering pattern that I'll show a few times in this talk. So I want to, that's just a little bit of background on my personal interests. So I'm going to talk about Coulomb gases and obviously I maybe don't have to say this but Coulomb gases are important. If you just look at this fish for example, it lives in an electrolyte, it lives in a Coulomb gas. But also the processes of life that go on in there are processes of electrochemistry largely. And they're the Coulomb interaction. And occasionally it's long range nature do play an important role. So there's a lot of Coulomb gas physics in that picture. So I'm going to stick to sort of pose some rather simple questions in this talk and indeed an next talk. I'll elaborate on them. So the first question is how can we create a Coulomb gas out of spins? We've already heard from yesterday that you can do that. But I wanted to try and sort of answer the question a bit more generally today. What kind of Coulomb gas do we get when we create a Coulomb gas out of spins? We'll see that there's a number of caveats that one has to think about but those are interesting in themselves. And finally the most interesting question perhaps is what kind of new insights do we get? So what's the point of all this? I mean is it worthwhile going to all this effort to make Coulomb gases out of spins? And I'll argue a little bit today but especially in the next talk that it is really worthwhile if you're interested in Coulomb gases to think about the spin realisations of Coulomb gases and the sort of things that give you different perspective on the problem which I think is interesting. So that's a sort of preamble and now my plan for today's lecture. So first of all I'm going to address that question how to create a Coulomb gas out of spins. I'm going to give just two examples. They don't necessarily exhaust the possibilities but the first is the well-known two-dimensional XY model that maps a two-dimensional Coulomb gas. And the second is spin ice that maps a three-dimensional Coulomb gas. And then I'm going to talk about magnetic monopoles in spin ice and finally draw some conclusions. So that's the plan. So the first question is how to create a Coulomb gas out of spins. Well let's start with a simple observation. Coulomb's law is long range interaction as we already heard. So it's algebraic one over r. Coulomb of course derive your text experimentally in 1785 just before the French Revolution. Good time to keep your head down and do science. But Maxwell's equations are local of course. So the energy for example is an integral over the electric field, the local electric field. So the question is can you simulate a Coulomb gas locally? Now a very elegant discussion and answer to this question has been given in the work of MAGS and co-workers. So particularly MAGS and Rosetto from 2002. And these workers were interested in simulating, in coming up with methods of simulation, practical methods of simulation of the Coulomb gas of electrolytes really. And as we heard yesterday, they start with the observation that a naive Monte Carlo approach would give a computer time that scaled as n squared, the number of particles in the system. So it's very inefficient. Avalt's summation does a bit better than that. But they were able to present an order in Monte Carlo algorithm. I'll just briefly say something about it without going into any particular detail. So they start with the observation that the energy goes as the square of the integral over the square of the field. But what they show is that you actually need to sample all solutions of Gauss's law. So this is Gauss's law, of course. And the general solution of Gauss's law is that the electric field is a gradient of a scalar potential plus the curl of effect potential. In terms of rather more simple terms, you can imagine the electric field as being Helmholtz decomposed into a longitudinal part with divergences which is the sort of physical part in electrostatics. And then a transverse part which is closed loops of field, and then a harmonic part which neither has divergence nor curl. So just in terms of pictures, that's a longitudinal part, field aligns beginning and ending on charges. What I'll call the transverse part is closed loops of flux. Then the harmonic part could be, for example, just a uniform field spreading to infinity. And what they show is that if you have these fields decoupled energetically and statistically, then you can use this as an algorithm to simulate the Coulomb gas. So they come up, for example, with, they sort of have a charge on a lattice site. They allow it to hop. It drags all of these fields with it. And then you have some moves that allow relaxation not only of the Poisson part of the field, the physical part, but also these other fields as well. So these are auxiliary fictitious fields in terms of the Coulomb gas problem, in terms of the electrical problem. But when you do that, you are able then to have an efficient order n Monte Carlo algorithm for simulating the Coulomb gas. So I won't go into that in any greater detail, but I can refer to all this interesting work on that subject there. So my interest in raising all this is what the relevance it has to spin systems. So it gives an idea of how to create Coulomb gas out of spins. So the spins essentially become the lattice electric fields in the problem. So we need to map spin fields, which are generally sort of discreet vector fields on a lattice in some way to lattice electric fields. And the components of the fields, the longitudinal and transverse components, need to be largely decoupled. And I'm going to show how this works in these two classes of spin model. First, the 2DXY model giving the 2D Coulomb gas with its logarithmic potential. And secondly, spin ice giving the 3D Coulomb gas with its familiar 1 over R potential. So just back to the plan then, that gives an idea on how to create a Coulomb gas out of spins. And I'm now going to talk about these models in detail. So first of all, the 2DXY model. Well, as most people probably know, the 2DXY model is just spins that rotate within a plane, also called the plane rotator model. And they interact via the cosine of their angular differences. So I could adopt product interaction between the vector spins. And for many purposes, you can do a sort of continuum approximation, where you introduce a spin angular difference field theta and take the gradient of that. There are two excitations in the problem, the spin waves, which are wave-like excitations, long, slow deviations of spins in which there isn't much difference between the angles of neighbouring spins. And then vortices, where you get these sort of rotational textures in the spin field. And of course, the main observation that came from Berezynski and Kostlitz and Thalys in the 1970s, I'll call them BKT, is that the spin waves and vortices are largely decoupled in this problem. So, for example, if you integrate this angular difference field round or the gradient of it around a plaquette, then you get plus or minus zero or two pi. And you can decompose this into two components, one for the spin waves and one for the vortices. And that starts off a sort of laser groundwork for an analytical approach to the model, which is extremely successful, mainly due to Kostlitz and Thalys. And what comes out of that is that the vortices form a symmetric two-dimensional Coulomb gas on a spin wave background. A symmetry is related essentially to the time reversal symmetry of magnetism. So, it has to be symmetric. And it's most famous for its pair and binding transitions. So, at low temperatures, the charges are bound into pairs. And at high temperatures, you can get free vortices or charges. And there's a famous BKT transition that separates these two phases. By the time BKT published, this was already established as a property of the Coulomb gas, at least to some extent, starting with the work of Salzberg and Prager back in 1963. So, this pairing transition, which is a property of a logarithmic potential. So, just in terms of the pictures of charges, you get pairs at low temperature, then a transition and then free charges as they unbind. And, of course, this, as has already been mentioned, gives a very good model of vortex interactions in two-dimensional superfluids, for example. And you get a really excellent agreement between theory and experiment on those systems, just picturing the vortices as a Coulomb gas. So, in terms of the electric field representation of what I was saying about earlier, so this is something I, me and my collaborators, particularly Peter Holesworth, have been interested in in the last few years, just because it draws together a lot of information about different models. And actually, we're very much inspired by Beautiful Paper by Valiton Beck from 1994. We don't really go much further than that, but we just made this connection to some of the things I was talking about earlier with the MAG's algorithm. This work was done by Michael Faulkner, a PhD student, who's now looking for postdocs, incidentally. So, you can already start to see the electric field description emerging just if you make the electric field a difference, angular difference field on the lattice. The spin difference field, the angular difference field, goes to the lattice-elect, maps to the lattice-electric field. And this is the actual mapping. It's very simple. And so, you can take any old spin configuration and create a lattice-electric field configuration that corresponds to it. And once again, this lattice-electric field has, is a general field. So, where you Helmholtz decompose it, it has all three components that I already mentioned. So, the longitudinal divergence-fool component, the transverse divergence-free component, and the harmonic component. And not surprisingly, it turns out that this is the vortex field. This is a field coming from the vortices. And this is a spinwave field. And I'll say more about the harmonic component in the next slide. And so, if you look at the actual spin models involved here, those of you who know the XY model will realise there's a kind of hierarchy of spin models. First of all, there's the full cosine XY model, which is the one that's sort of relevant to real magnets, as it were. And then you can make a harmonic approximation to that. And if you can actually, what we call the harmonic XY model, has a potential that sort of goes like that. It's a piecewise parabolic potential. And then there's the more famous villain model, which is basically a parabolic potential, plus vortices with any integer charge. It's not really a spin model, in the sense you can't simulate it with spins. It's a bit more subtle than that. And what we can show is that the villain model actually maps exactly to the Mags Rosetto two-dimensional Coulomb gas in the Gronchononic ensemble. This is an exact mapping. And so it answers the question immediately why this system with short-range interactions is able to model a Coulomb gas with long-range interactions. It's for the same reason that Mags and Rosetto's algorithm works in electrical systems. So these fields are perfectly decoupled, both energetically and statistically. As I mentioned, the villain model isn't really a spin model. The harmonic XY model is the nearest you can get to it while being a spin model. And this also maps exactly but with a temperature-dependent vacuum permittivity. And similarly, the XY model does something rather similar with some corrections. Okay, so one interesting thing that just came out of this. So this is really just another way of looking at an old and very familiar model. So maybe it's surprising something new would come out of it, but we did get something out of it which is what we call topological sector fluctuations and ergodicity breaking at the BKT transition. So the vortices are charged as a topological defect in the electric field, meaning that you can't remove them by stretching or bending the field lines. You have to sort of cut and paste if you want to remove the charges. Now the harmonic fields include a polarisation term and you can show that they include an integer value winding term. So this is done on a torus. So it's really done with periodic boundary conditions. We don't have the curvature of a torus, but we have the topology of a torus. And the harmonic fields, those without any curl or divergence, you can picture just wrapping around the torus like that. And they define to distinct topological sectors. They're excited with sort of integer values. And you can actually simulate this using the simulation technique already mentioned. And you can see the above, the costalous transition, you get these jumps where this field is excited. So this is when two vortices, for example, travel around the torus and join up again, leaving behind a field like that. So these are what we call topological sector fluctuations. Now with local dynamics, it turns out that the suppression of these below the costalous-sparaginski transition is very strict. They're fully suppressed. It's actually no gidicity breaking. Without going into too much detail, you can see it here, for example. So this is a sort of susceptibility for fluctuations of these fields. And it's a ratio between a susceptibility which allows global moves and one which has local moves only. And you can see it goes very much to zero below the transition, and it goes to unity above... Sorry, this has died. No, it hasn't. Above the transition, there's very big fluctuations in between. Now in the actual spin models, these fluctuations correspond to global spin twists. So the fields look like this. They're just uniform, the electric fields. And the spins look like that. So they kind of twist round as you go down. This is all quite new. I mention it because it's of general relevance to Coulomb gases. There appears to be some experimental evidence of this. I don't ask me to explain why, but this paper, if you're interested. And this would be in the phase field of the order parameter of the superconducting state. OK, so that was a quick look at the 2DXY model and just some sort of general lessons to learn from that. First of all, spin models provide lattice fields that map to the electric fields of a Coulomb gas. The Coulomb gas is symmetric and in the grand canonical ensemble. We can expect spin models rather generally to realise a general Coulomb gas in which you have these auxiliary field components, the transversal harmonic components. They exist and in any old spin model that they won't necessarily decouple perfectly. However, those auxiliary fields are interesting in their own right and lead to, for example, these topological sector fluctuations. And later on, I'll give other examples of what these auxiliary fields do. So just back to the plan. So that's the 2DXY model and now onto the second example, a spin ice, which is the three-dimensional Coulomb gas. And that's going to be the main part of my talk both today and tomorrow. So spin ice is a spin model realisation of a three-dimensional Coulomb gas. But how you actually get there is quite subtle. I want to give a brief history of spin ice so just to explain how we came up with this sort of rather strange concept. So if we go back to Nernst himself and the third law, I'll take that to mean just for simplicity, I'll take that to mean entropy zero at zero temperature. Of course, following Nernst's proposal of the third law, there was a lot of debate and a lot of experiments. Some of the most beautiful experiments came from William Joke who measured the residual entropy, the zero point entropy of real materials. And one particularly interesting one was water ice where he measured with very high precision and most extraordinary measurement as one of the most beautiful calorimetric measurements ever made, the entropy of water ice and discovered it was non-zero in the approach to zero temperature. And this was famously explained by Pauling in 1933 in terms of proton disorder. So if this is a fragment of the ice structure, you have oxygens which are the big ones and hydrogens which are the small ones. And so you have an ice rule which means you have two hydrogens close to each oxygen and two further away. And so this satisfies chemical bonding requirements. So two short covalent bonds and two longer so-called hydrogen bonds, these sort of electrostatic bonds. Now it turns out that when you go through the system applying the ice rule, you have a choice side by side but it's not a completely free choice. Nevertheless, you build up a number of equivalent states that grows exponentially with the size of the system. And when you take the logarithm of that, that gives you the entropy and Pauling calculated this on the basis of the ice rules and remarkably got a number, allog three halves per mole of water molecules that agreed with an experimental error with William Jokes experimental measurement. And hence it was largely accepted immediately. Now there's long been a sort of an interest in creating a magnetic analogue of proton disorder in ice. A significant effort was made by Anderson in 1956 who considered an antiferomagnet on this pyroclor lattice. This is a lattice of corner-linked tetrahedra in which spins can point up or down and it's frustrated because on one given tetrahedra the best you can do to satisfy antiferomagnetic interaction is two spins up and two spins down and you can see at least roughly speaking that this could map to too close, too far to an ice rule and indeed it does in a slightly complicated way actually but one has to just look closely at it to follow it. When I came into the field in around about the early 90s one thing that struck me was that people had been looking for this for years and never found it. There are no experimental examples of Anderson's model in the purest sense, at least not in the sense in an ideal sense at any rate. I think when you see something like that the most likely explanation is always that the model is lacking something that makes it maybe unphysical in some sense and far be it for me to say Anderson's model was unphysical but you'll see that in a sense it is. Now the problem that struck me and others and my colleagues was that the real issue here is that the local point symmetry on this lattice is not just up down like that, it's a trigonal point symmetry that goes along the long axes of the cubic structure and so the local point symmetry if you're going to get easing like spins in real material they're going to point in and out of this tetrahedron and you can't really do anything about that and instantly in real materials you always have some anisotropy that will reflect the point symmetry or nearly always you have to work very hard to avoid it. And so the idea that we had was that up down classical easing model was incompatible with the point symmetry so we studied models where the spins just pointed in or out and the first discovery was it kills the zero point entropy completely so when you have an anti-ferromagnetic model with in-out spins you get an ordered state, all spins in or all spins out alternating on different tetrahedra so Andersen's model falls to bits. However, not long after that we made a sort of what we thought was a very surprising discovery we were thinking about an allergist ferromagnet particularly in the context of these materials called homeium titanate and dysprosium titanate here they're nice sort of crystalline solids only the homeium and dysprosium is a magnetic and they're very big magnetic moments compound spins with huge magnetic moments and we sort of just wrote down our simple model for that and discovered to our enormous surprise that with ferromagnetic interactions the ground state of a tetrahedron is two spins in, two spins out and this maps this goes straight back to the ice model so rather remarkably by changing the symmetry of Andersen's model and changing the sign of the interaction you do generate an ice model and now the reason that in these materials that the spins point along these axes is a crystal field term that glits the free iron state such that the ground state is a doublet the maximum value of the spin it's a quasi-classical spin that really does point along those directions either in or out and it can flip between the two and so the water ice mapping is generated by ferromagnetic interactions plus the crystal field and so quite literally if you imagine putting a proton in every spin in a configuration of this ferromagnetic model then you get a configuration of protons in water ice there's a one-to-one mapping and you can carry over the statistical mechanics of water ice in its entirety almost at any rate to describe a spin ice and so this is why we called it spin ice for obvious reasons very direct mapping from spin model to the proton model and the ice rule there is therefore two spins pointing in, two spins pointing out of each tetrahedron okay so over the forthcoming years a lot of experimental verifications of all of this and one of the most beautiful was the Pauling entropy measured by Ramirez and colleagues and so they did a similar calorimetric experiment to the ones that had originally been done on ice and they measure the Pauling entropy in the ground state Over the years we and others have improved the neutron scattering treatment of these experimental systems such as this is about the best we've got so far this is a neutron scattering pattern straightforward neutron scattering no smoothing or adornment it's really a remarkable pattern those of you who know neutron scattering it measures spin correlation function in reciprocal space and you can by using polarised neutrons you can get all the components of this tensor now if you know X-ray or neutron scattering you'll know that if you have order periodic order then you get Bragg peaks we don't have any of those if you have say spin glass which has a kind of very subtle order but you just get a blob like a potato so we don't have that either what we have is something in between which has some considerable structure most notably including these narrow necks now we call these pinch points now come back to those in a moment now this is a simulation the one on the right that's experimental data that's a simulation just of the Pauling ice model and you can see it's pretty similar there's one or two discrepancies in fine detail that turn out to be interesting as I'll show later so just a word on these pinch points so these are rather fundamental to ice type systems so if we think about the ice rule two spins in, two spins out and if we grab these spins as link variables on a continuous flux that I'll call the magnetisation then the ground state because you can see this is a sort of conservation law as much flux flows into a tetrahedron as flows out and so the ground state then the ice rule state it consists of closed loops of flux the smallest of which is six membered rings larger loops even span the entire lattice and this particular correlation function or neutron scattering pattern reflect that very closely so you can show this in a very simple argument if you say the free energy is entirely entropic going as a square of the magnetisation an integral over that and then you simply impose the constraint that the magnetisation is divergence free then you can calculate the correlation function and you discover it gives these pinch points this wasn't originally discovered for spin ice but actually there are analogous arguments for ferroelectrics and for ice itself indeed and a lot of set of very nice papers by youngblood and axe in the 1970s discovered that but obviously to see it in a magnet is rather remarkable but back to the sort of electric field description so the electric field there for other obvious reasons the magnetisation of a spin field is the one that's going to map to the electric field directly again it has all its three components so in the ground state you only have the transverse component so the ice rule gives you that but the longitudinal component comes from defects in the ice rules H3O plus or OH minus defects so basically if you take a proton off it off it's water molecule and take it to another water molecule you get H3O plus an OH minus and you can separate these defects and in spin language this gives you three spins in and one out, three out and one in and these are divergences then in the effective electric field so that kind of charges in that sense and so again this transverse field is the ice rule configurations and the harmonic field again see next slide so just to complete the sort of overall picture of spin Coulon gases we'd expect topological sector fluctuations in spin ice as well I'll just briefly mention that this is mainly work of Ludovic Jobert and so system spanning loops transformed to harmonic fields so you have two types of loops in the ice rule states you have closed loops and system spanning loops rather remarkably you can show theoretically that there's an experimental signature of this there's a crossover in the magnetic susceptibility from the Curie law example C over T to the Curie law again but with the factor 2 you can actually measure this experimentally as well so this is the crossover of the Curie amplitude doesn't work perfectly we understand this but I won't go into that in fine detail and essentially entirely because of the long range interactions you have to use very perfectly spherical crystals in these experiments because of this very big de-magnetising fields ok so just to sort of complete that comparison with electrical Coulon gases and also the 2DXY model so comparison of with spin ice is in the XY model it's a spin difference field maps electric field here it's the spin field itself that maps electric field in spin ice and in both cases a Helmholtz decompose components nearly but not quite decoupled I'll come back to the not quite a bit later on because it's important in itself so back to the plan for today I think I'm more or less on time so I've gone through sorry that's not coming out very clearly ok so I'm on to magnetic models magnetic monopoles in spin ice now so magnetic monopoles in spin ice really come from two papers one by Rishkin in 2005 and the other by probably a slightly better known paper by Castle and Overmass and Sond in 2008 and the reason I wanted to explain sort of how these were discovered because it's a good example of why spin models are kind of useful so early on although we we sort of realised collectively in the field realised that we had a sort of Coulomb gas in spin ice we not ever thought about the effect of dipole-dipole interactions in the system because we thought that they would just make a mess of the whole thing because they're complicated and unisotropic and long range and we thought that simple picture that I've given would be totally ruined by adding dipole-dipole interactions into the mix but people who were interested in the material side of things rather than the model magnet side of things wanted a proper Hamiltonian for spin ice rather than the phenomenological spin ice Hamiltonian that Harrison I invented so this is the Hamiltonian they came up with and it's called the dipole-spin ice model coming from Shastri and Sidarton and Jingran colleagues and you can see it's a really ugly beast it's got a magnetic dipole-dipole interaction and then exchange interactions about third nearest neighbour very ugly thing and the question immediately rose was how could you square this Hamiltonian with the very simple observations that spin ice really worked for for the real materials and that was a big question in the field for quite a while and it was solved first by Jingran and then by Isaacow and colleagues and so it turns out that the dipole-dipole interaction has the property that it's self-screened in the ground state so you can map all of this to very good approximation onto an effective near-neighbour ferromagnetic exchange interaction hence that's why spin ice works at least in the ground state however the dipole-dipole interaction survives in excited states now then there were two lines of reasoning that led to the idea of magnetic dipoles in spin ice so one coming from the water ice community so Ryskin is a water ice physicist many years earlier in 1984 he'd written down a pseudo-spin Hamiltonian to describe electrostatic interactions in water ice itself and lo and behold it was the same Hamiltonian as this one with electric and magnetic interactions replacing each other now the other way of doing it was to go straight from this Hamiltonian and this is what Castelover, Mercer and Sondi did they found a way of retaining the long-range part of the dipole interaction whilst getting rid of one or two quadripole-connect corrections that occur at relatively short range and what they discovered and Ryskin discovered as well was that you actually have a Coulomb interaction between the H3O plus and OH minus type defects and that's what we call magnetic monopoles now the most important thing about magnetic monopoles is really shown in this picture here just to try and understand it so if you look at a tetrahedron where the ice rule is obeyed so there's as much flux going into it as there is coming out of it then in magnetostatic terms the divergence of M, the magnetisation is zero and hence the divergence of the H field of magnetism is also zero because there's no the divergence of B is zero of course because of Maxwell's law and so there's no charge density in the ground state whereas if you add some defects in so three spins in, one out three out, one in for example then we now have divergences in these fields and so you can immediately imagine that these are going to interact coulombically and indeed they do and you can show that as Ryskin did very simply actually by integrating a line of spins between the defects now these lines of spins that exist between the defects we sometimes call Dirac strings rather grandiose-ly because you know that a dipolar string monopoles at the end but the important thing is that the so there's for example a very short string going between two of these monopoles now but the important thing is these strings don't pair the monopoles in any way so you can draw strings in different ways the monopoles kind of exist on a string network but they're not paired and that's why you can call them monopoles because they're not paired yet they interact via the magnetic coulomb law with a well-defined charge and within a factor of two the charge is what you'd get if you took a magnetic dipole and naively stored it in half twice actually twice that value and that's all and this is mu naught the vacuum permeability so what these authors showed then was that real magnetic coulomb interactions exist on top of entropic ones real in the sense they come from the true dipole-dipole interaction in this Hamiltonian so in a sense we've combined an effective interaction with a real interaction fundamental one and the picture to have in mind is lots of little north and south poles interacting coulombically and moving around the system okay so I want to just mention the monopole conduction because this is an important puts an important caveat on the picture so this goes back to Rishkin's original treatment so in water ice if you wanted to conduct protons which it does it's a very good proton conductor you have to move the protons through the system so one way of doing that is to move them along the bonds oh I was about to say that so monopoles are dragged by real magnetic fields in the same way protons are dragged by real electric fields so if you want the system to conduct you have to push the protons along the bonds like this and that starts to separate charge so you've got positive charge down here and negative up there but you've also polarised the system now the similar thing in spin ice is shown here so you flip a spin you create two monopoles then you flip further spins restoring the ice rooms locally and you've left behind this string of a lined spin so again you've magnetised the system in this case you've polarised it or magnetised it so again that's another picture of the same thing so as monopoles move through spin ice or protons move through water ice they polarise the system so the monopole hopping drags the electric field as it were with it and polarises the spin system now in the context of water ice Jackard treated this theoretically by defining a configuration vector so this is just a vector that describes a spin order and a finite vector cos entropy now for spin ice Ryskin showed that the configuration vector is just the magnetisation divided by the charge and hence the entropy grows sorry the entropy is diminished by the square of the magnet the local magnetisation of the system so there's an entropic cost of monopole conduction and the thermodynamic equation of motion then is the rate of change of magnetisation with time is proportional to the field but then there's this reaction field that eventually brings the system to equilibrium so if I want to discuss this with respect to the magsresetto coulomb gas that I introduced earlier basically in that you hop a charge and then you relax the local fields but in water ice it turns out that the there's another kind of defect in the system so-called bonding defects where you have two protons on each bond or no protons these relax the polarisation and they allow a conducting steady state they allow you to pass protons from one side of the system to the other and it's actually quite a bit of magic that this happens it's really difficult to see intuitively but they do actually succeed in doing that but in classical spin ice we don't have that luxury and in spin ice all relaxation is mediated by monopole hopping the transverse relaxation is relaxed by monopoles only so all the fields in the system are relaxed by monopoles only and they're not entirely decoupled in this sense and that's really the origin of the of the jackard reaction field so I'll just say a quick word here about quantum spin ice I'm not going to do any justice to this it's a very topical field and a huge number of papers and very interesting ones it's less connected with experiment though than is classical spin ice I'm just going to mention one paper by Shannon and Benton and colleagues which is particularly nice one in my opinion so what you can do is these transverse fields so these closed loops of spins you can actually introduce some dynamics in those so you can introduce a tunneling sort of matrix element in those now in principle this could happen experimentally it doesn't in these particular materials that we study but in principle it could happen it could come from transverse terms in the spin operators but when you do that you can argue that the transverse M then transverse magnetisation then behaves like a lattice gauge field and along with the monopoles you then get a complete analogue of lattice, electromagnetism, quantum electromagnetism and you get photon excitations this is a dispersion and you can there's another picture of the dispersion of the photon excitations and how they relate to the field correlations in reciprocal space and in fact there's a sort of netexperimental sort of signature of this is when you have these processes going on it washes out the pinch points that you see in the classical pattern okay so I've more or less finished for today and I want to draw some conclusions so the three questions I posed at the start then were how can we create a coolant gas out of spins what kind of coolant gas do we get what kind of new insights do we get so in terms of the first question then the answer is really that you need to map the spin fields to electric fields and decouple the longitudinal and transverse electric fields and 2DXY model and spin ice although at first sight rather different actually very similar in that regard what kind of coolant gas do we get so we get a symmetric coolant gas it has to be symmetric because of time reversal symmetry of the spins and the kind of general coolant gas so it is a coolant gas but it includes all solutions to Gauss's law in general so there are longitudinal and transverse fields that will always be present and retain some coupling and in spin ice the monopole coolant gas is very much conditioned by the so-called Jackard field this reaction field coming from the polarization of the monopole vacuum as it were so finally done what kind of new insights do we get well I mean just by thinking in these ways you can see that you're always going to get topological sector fluctuations of the harmonic components magnetic monopoles in spin ice are in sense new insight by comparing water ice and spin ice we've already learned something about the for example the pseudo spin Hamiltonian of water ice so it was a worthwhile exercise already in that sense and emergent electromagnetism in quantum spin ice but most of the new insights I'm going to be talking about tomorrow so tomorrow having sort of set it up I'm going to focus on spin ice and show that it really is a model lattice coolant gas but I'm also going to show that it has emergent chemical kinetics of these monopoles you can write down chemical kinetic equations coupled equations and you can get non-equilibrium effects and all of that sort of thing and I'll argue that it's basically a model system for studying charge generation from a vacuum and I'll show both theoretical results and experimental results to try and convince you of that so that's my talk for today and thanks for your attention


The agent for the competition, C.S. Muggler. Thanks. Appreciate it. Hey guys, welcome. It's been a crazy couple of years, right? Anybody here compete last year or tried to compete last year? Good. I like repeat competitors. You just keep exposing yourself to this type of environment, right? This is competition. This is what it's all about. I don't understand that this is a competition. We're looking to keep going with this. This is an outstanding opportunity for soldiers, especially coming out of a pandemic, coming out of the types of situations that we've had to do over the last couple of years. This is what being a soldier is all about. Each one of your commands. What you're doing is, not only are you competing, but you're ambassadors for each one of your commands, each one of your units. In our hope, my hope as a senior non-commissioned officer, I'm sure every other senior non-commissioned officer in this room will agree, is that you are infectious. That you will spread this same type of desire and motivation amongst your formations, because that's what it's about, right?