159 Ichsani Wheeler and Tom Hengl – Open source, satellites and data to know what is happening on our planet

Ichsani Wheeler and Tom Hengl, two of the greatest scientists behind EnvirometriX and OpenGeoHub, discuss open data and open source solutions and how they will help the world come up with real solutions. They also tackle the crucial role of farmers and data analysis in our transition to a sustainable but profitable regenerative agriculture. 
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Terabytes of data are collected by satellites and remote sensing every day, but what is needed to turn those into decisions? How do we look back from 20 to 40 years ago, and how do we make predictions 20 to 40 years into the future? What do we need if we really want to revegetate the Earth with trees, plants, animals, and more life?

More about this episode on https://investinginregenerativeagriculture.com/ichsani-wheeler-tom-hengl.

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Transcript

SPEAKER_01: 0:00

Data, data, data. Terabytes of data are collected by satellites and remote sensing every day. But what is needed to turn that into decisions? How do we look back 20 and sometimes 40 years and how do we make models to predict the next 20 or 40 when we're planting trees, regenerating ecosystems, landscapes, etc. If we want to re-vegetate the earth with trees, plants, animals and more life, we need better data and better data tools. We only scratched the surface in this interview, but I really enjoy talking to you and asking my beginner questions to some of the smartest data scientists on this planet. Tempering climate change, bringing back rivers, preventing floods, and obviously lots and lots of nutrient-dense food. The promises of RegenEgg often sound straight out of a science fiction book. And for these promises to be met, we need to significantly scale regeneration to a landscape scale within this decade. Welcome to a new series where we look into the technologies needed to bring regeneration to a landscape scale. In this series, we'll look at already existing technologies, digital tech, ag tech, new financing, that can scale fast enough during the next 10 years. Technologies which put significantly more money into the pockets of farmers, landowners and land stewards who are regenerating their and our soils so they can go faster. And who ask the question, what is missing? What needs to be urgently developed over the next years? We're very happy with the support for this series by the Grantham Environmental Trust, which supports strategic communications and collaboration in solving the world's most pressing environmental problems. You can find out more at In March last year, we launched our membership community. Make it easy for fans to support our work. And so many of you have joined as a member. We've launched different types of benefits, exclusive content, Q&A webinars with former guests, ask me anything sessions, plus so much more to come in the future. For more information on the different tiers, benefits and how to become a member, check gumroad.com slash investing region egg or find the link below. Thank you. So welcome to another episode today with the co-founders of Envirometrics and OpenGeoHub. OpenGeoHub is an independent, non-for-profit research foundation promoting open source and open data solutions. They provide solutions and training in cutting-edge open geospatial and data services. And their expertise, and that's why they're here, lies in combining remote sensing with ground data for monitoring essential biodiversity variables at scale. And Envirometrics is a deep tech group helping businesses and organizations to find the right balance between satellites and samples. And also that's why they're here. They've used sensors, juggle big data and optimize modeling pathways and to solve the unsolvable, accurately measure and monitor soil nutrients through scale, depth and time. Welcome Ish and Tom. It's your intro. I took it from the website. Don't be, I mean, it's no pressure, no pressure, but I'm very excited to have you here. We talk about satellites a lot. We did a full series on how to bring regeneration to a landscape scale. And this is the final episode of that. And I'm very much looking forward to having this conversation, but let's start with a personal question for both of you. You can decide who goes first. Why and how did you end up focusing so much on soil?

UNKNOWN: 3:20

Yeah.

SPEAKER_02: 3:20

It's always soil. It was always soil from the beginning. I'm really happy everyone else is getting excited about soil now. No, but for me, I grew up in a permaculture farm back to the land. Permaculture was the first thing my parents taught me. So in there, it's got a fundamental ethic of earth care and caring for soil is part of that. Composting is a daily thing. So for me, that went through from childhood through to it professional training and I studied as a soil scientist. And I was always obsessed with organic matter, much dismay of my podology teachers. And then after that, did doctorate measuring and monitoring carbon on farms because I thought, okay, if we can get the farmers paid for it, they can transition their landscape usage. And that was back in 2013. And yeah, so for me, it's always been soil. And I'm really happy everyone's coming now.

SPEAKER_01: 4:20

And for you to

SPEAKER_00: 4:20

Well, let's say I'm not originally enthusiastic in soil when I look back when I was maybe a teenager or something. I'm an urban kid, so I got more inspired by wilderness. So I like wilderness and nature. And then that was one of the reasons. My parents, actually my mother, told me, why don't you go and study ecology? And then I went and studied forestry, forest engineering and forest ecology. And then there I I actually developed Infinity for science. Already when I was about 20, 21, I became a bit geeky, started fantasizing about different things, reading a bit, let's say, not a common literature. And then science-wise, I became interested in actually measurement and more like in the metric part. And then I got a position. They told me, well, this stuff you're interested in, we need your profile. I was then a best student or something. And then they gave me a scholarship. I says, we need you for soils and so I was kind of I finished with soils by demand actually but then later on I developed let's say passion for soil I especially like about soil that it's quite complex it's really challenging it's way more difficult to map soils and model soils than to model things above ground and also I met really some amazing researchers in soil science and so I came soil science came to my life a bit later but recently Recently now as an entrepreneur, we started looking at also the world and what's happening. And now I value soil a bit from a different perspective. I do see it as something actually fundamental for our survival also. And so now I'm looking more at the soil as intellectual, both as a scientist, as a human being. I'm thinking about what's important, what's happening, what's the process in soil, what happened last 50 years, last 100 years.

SPEAKER_01: 6:20

Very, very interesting. And so what gave birth to this open GeoHub? Let's start with the open side or the non-for-profit side because both are relatively open. And what was the need you saw there? What was the reason? What was the nudge to start that process? Which I will obviously link a lot of links below because it's a lot of visual things we're going to talk about in a visual way on audio where you have to see a lot of these things, but I will link them below. But what gave birth to open GeoHub? What was the reason to start that?

SPEAKER_00: 6:50

Well, I started flirting with the open source and the geeks in open source way back in 2002, 2003. And then in 2008, I think, 2007, we did some summer school because I started doing things and some people asked me, hey, can you run a course for PhD students? And then it was in Italy still. And then we went to south of Naples and we did a little summer school and it kind of felt good and I noticed in education that we are because the commercial companies they have much stronger marketing campaigns and so like you know if you buy a computer you will get Microsoft Windows on your machine and it's a bit tricky you know to get everyone to use Windows I think Bill Gates did very good there but then you start looking okay open source and then I fell in love a bit in it and then we started doing the summer school so the I think 12 years it was running and it became something very natural and a A lot of enthusiasm was around.

SPEAKER_01: 7:52

What were you teaching? What was the summer school about? What was happening at the summer school?

SPEAKER_00: 7:56

We wanted to promote the open source and sharing culture. Basically, it's about sharing culture, and it's about collaborating, collaboration, sharing culture. Open source is not about free software. I mean, everybody who goes for it for that reason is missing the point. The open source is about the sharing culture, and it's about collaborating in a larger group, so open development communities. And so that was really, for me, fantastic. And later on, I read books, you know, the people that started the Creative Commons and all this free culture movement. So I read and became kind of a bit my religion, you know. So when people, I met some, actually, once I met some people from Salt Lake City, and then they started saying, you know, we are a bit religious. But I said, I'm also very religious. Which religion is it? They say open source, open source. Anyway.

SPEAKER_01: 8:49

And in this case, what were you collaborating or what were you sharing? Because it wasn't software or it wasn't tools. It was something else.

SPEAKER_00: 8:56

Well, yeah, the software, I'm actually not a software engineer. So I met some people that made software and they made like, of course, the stars of our summer schools were people who make crucial software in open source, especially in open source spatial, spatial open source, geospatial.

SPEAKER_01: 9:11

And what is geospatial just as a complete newbie? What is geospatial? It sounds like satellite, but...

SPEAKER_00: 9:17

Well, yeah, yeah. So geospatial is like the complete newbie. is you know when you want to know where you are you open google maps you know that's a geospatial data and then if you look at google maps you can also see a satellite image that's another evolution of spatial data earth observation images then geospatial is also if you look at soil science you know you want to know how much soil carbon you have in soil so you need to go and sample and so where do you sample you need the geospatial sciences to help you with the sampling and then if from samples you want to estimate how much carbon you have again geospatial sciences so this is all just spatial

SPEAKER_01: 9:51

so where you are in which space like if you want to and we get to the soil carbon later but if you want to i'm looking out over a field and i want to measure the soil carbon you're not going to measure every centimeter obviously because way too expensive way too difficult way to time etc you need a certain number of spots and you're saying that a very good map a geospatial map that of course you took from above because otherwise it's not a map can tell you which spots you should actually sample and obviously i will tell you these are the minimum amount this is the best amount. This is the optimum, but make sure you sample here, here, here, and here. And then with some modeling software after we can have a pretty good estimate of the soil carbon that's on your field or other things that are in your field. Let's we start with soil carbon, but other things as well. And then we'll get to questions like, can you only do it with satellite without those sampling? Probably the answer is not, but actually you need, you need a good map to know where you are, where you need to drill, what you need to dig up, et cetera, et cetera.

SPEAKER_00: 10:45

Yeah. And that's the core of my expertise. It's called also predictive modeling. So using the methods from statistics and from data science and then using them to for real life problems or applied sciences so one of the question is like where do you sample or how many samples you need how do you estimate from samples quantities over the whole farm what is the uncertainty related with that what happens if you reduce number of samples what will happen with uncertainty what's the what there is for decision making based on the data that you collect so this is all predictive modeling

SPEAKER_01: 11:20

and this has to be open? Or what's the role of open sourcing?

SPEAKER_00: 11:24

With the OpenGeo Hub, we are very honest about it and it's a not-for-profit foundation and we promote open source, open data. And we do believe we wrote that pamphlet on Medium, we call it. Everybody has a right to know what's happening with the planet. I

SPEAKER_01: 11:38

will link it below.

SPEAKER_00: 11:40

So we wrote this pamphlet and we seriously believe that this open data should be under open data license. And it's also inspired by the decisions that in US Al Gore promoted back in 2008 to release all the USGS and NASA products open so especially like a Lancet and then later on European Union luckily they also took that model and so they released this data in open and we believe that it should become many of environmental data I think it should be open because the real problem of economy and future will be in environmental problems it shouldn't be about the data so we need to have the data open so everybody feels invited to do things and to explore and to find out what's happening

SPEAKER_02: 12:27

the value is not in the data it's in its use

SPEAKER_01: 12:31

yeah that was going to be my question like what if all this data is open we have like swimming pools full of data terabytes and terabytes and all these satellite images are there and even maybe i could find them what is holding me back or a farmer back to use them is it very difficult to use how does these maps look like do they need to be cleaned up probably all of the above but what's the how far is it from a practical thing for me to use like okay I have a satellite image how do I get that into a concrete answer to my question of where to sample like how big are those steps between the two

SPEAKER_02: 13:04

yeah so that's several entire disciplines of science to connect those things together and part of the core discipline of doing predictive mapping is taking points on the ground and relating those to a huge step of satellite images, the layers that give you the shape of the land surface, the slope, where the water is going to accumulate.

SPEAKER_01: 13:28

So it's not just a picture. No. Let's be clear. When you say a stack, it's all kinds of layers, which are like a different kind of picture of that piece of land.

SPEAKER_02: 13:37

Yeah, absolutely. You have the ones that track the land through time, like how much vegetation, how much light it's absorbing. And then you have ones that are like, give you whether you're in a valley depression or on the side of a And all these relationships can be stitched together with observations on the ground to create these predictive maps. And it's not just carbon. It's many things. We also use these techniques for water and for trees, like tree species prediction. So one of our PhDs is doing also land cover classification through time. Another one of the PhDs is doing. So it's actually quite a flexible framework. And it's really exciting because I think People are locking data. They're locking information. You have to come to them to get your answer. And we think...

SPEAKER_01: 14:27

Also in the satellite space, as all this data, as you mentioned, Tom, is open because most of the satellites are, let's say, run by government organizations that now, like the European Union and NASA, have released that. So everybody's building on top of that, but then they're still locking it. How does that work?

SPEAKER_00: 14:43

Well, so on one hand, you have this... So there's this knowledge and data gap problem. So on one hand... You have something like a NASA or European Space Agency, you have like multi-billion euro budgets, and they will make some amazing satellites that are really scientific. The people that design them are engineers, scientists, and they will then collect the data. And suddenly what happens with the Copernicus Sentinel, you will have like a terabyte of data flowing on a daily basis. And so the data is really exploding. And so there's this famous plot that shows the professional we have now going through the time that doesn't go exponentially. So we have this professional knowledge, which is kind of stagnating. And you have this exponential growth of data. And this is called this data gap. And so what happened is that we are in a danger of generating like super useful data that we are not able to extract information, right? Because the information has multiple levels. They always said that. So for example, there's the raw data. There's the raw data. That's the first thing that the satellite takes. the first image, then this image gets processed to remove artifacts and do corrections, and then you finish with something which is, let's say, still like a processed product, but it's not ready to do any decisions. Then you have to do analysis, ready data, then from analysis, ready data, you do analysis, then you can maybe make some decisions. And decision is really what people can work with, right? So when you open a Google Maps, if you say, well, I'm here, I need to get to Amsterdam or someplace, and you click a on a button and what Google provides you is a cloud service to calculate the optimal route given the traffic and then suggest to you this is the route. And that tells you now on this crossing you turn left, on the next crossing turn right. These are decisions, right? But behind the decision there's a lot of data. And so the problem now with satellite data for environmental sciences, for the land restoration projects and etc., the problem is that there's more and more data but we have less and less capacity to turn it into a decision. And then there's this famous...

SPEAKER_01: 16:51

So we need to build...

SPEAKER_00: 16:52

Yeah, there's a famous podcast also. One person said the problem of today's satellite images is it's not a commodity, you know. Many people cannot make use of it, you know, like out of 99% of data, very little use. And we are trying to fill in that gap. We try to make the data to convert it at least to like a decision-ready data or to decisions. Ideally, you would like to serve decisions, of course. That's the ideal.

SPEAKER_01: 17:16

Ultimately. And in these projects you've been working on, Anish, what's the... I wouldn't say the coolest because it's, but what's the most surprising or when somebody asks you, okay, tell me one project you've been working on both in the for-profit company, which we'll go into, but also with the PhDs, like what's the, let's say the people of the podcast here on investing in region ag and food should really know about what is possible now.

SPEAKER_02: 17:37

I would pick 20 years of soil carbon history, 2000 to 2019. We've predicted those in-house for Europe and for the USA. And they're both in pilot at the moment because we're looking to make updatable models so that a landholder can at least have an idea of what their carbon is and the uncertainty. And if they want to get a more certain answer, a more certain baseline for their farm, they're able to get a sampling design and then put the effort in to take some samples and get updated maps. So the calculation side of things is also automated the front end is still not open for people it's not ready yet but that's probably the most exciting

SPEAKER_01: 18:28

and it's based on satellite data obviously not on sampling but when i start sampling as a farmer i the machine gets better is that or gets better in predicting or it's also based on sampling

SPEAKER_02: 18:39

it's hundreds of thousands of public data points and i mean this is tom's core core discipline is taking these points and combining them

SPEAKER_01: 18:48

so and how close are we like how How far, how close are we? Like, is it an estimated guess? Or what would you say if somebody says, can you base decisions on this? Or is it a starting point for doing more research on my land if I would look at my land?

SPEAKER_02: 19:01

To be honest, what we're trying to enable is every farmer to do their own on-farm experimentation and to be able to have a system that gives them near real-time feedback, not specifically on their practices, but just from the point of view of the ecosystem. Because if you look at the world as an ecosystem system, which it is, you can monitor various vital signs, like a pulse, things like this. And this crosses over many different managements. So we really want farmers to be able to innovate themselves and track themselves because, I mean, I'm not driving the tractor. They are. So we're really trying to be conscious to enable and support rather than to dictate and try and capture all the value of carbon offsets, etc. from people. It doesn't...

SPEAKER_01: 19:51

Yeah, because we need to unpack that a bit later. But this would mean in Europe and the US, we could also track or look back in time of a few farms that have gone through transitions over the last 20 years. And that's probably where it gets interesting, where we know management has changed in 2008, or they started holistic grazing in X, or they did no two. And then compared with their neighbors, you get a very interesting picture, I'm assuming.

SPEAKER_00: 20:17

Yes. If I may add to the accuracy question so we are now in Europe and US I think we came to for the mineral agricultural source we came to about half percent accuracy the RMSE and but the more important is we took this path instead of like today I think many companies they just look okay what's the carbon you have now in the field and they consider it as a commercial service and for me that was really shallow I mean I felt both well also I felt like like, you know, well, you're missing the point here. I mean, come on, guys. So what we looked at, actually, we look at what they call soil forensics. So we try to get the best satellite data for last 20 up to 40 years. You have, you know, Lancet goes back 40 years, get the data, and then we do this so-called space-time machine learning. And then we can reconstruct what happened with the soil and on the large scales and using the biggest pool of data we could generate. I mean, we're ready to process millions of points. And then we discovered that there's an added value on putting all this data on a pile. So to go back to your question, what is the coolest thing about my research? When people ask me, I do have my top five picks, but let's start with the number one.

SPEAKER_01: 21:33

That's like the worst order, Tom. We cannot go to number one. If you're top five, we have to do at least three, two, one. Then people stay on.

SPEAKER_00: 21:41

But let me start something I'm really proud of. I did this. I helped make this solar agronomy data cube for Africa. It's all open data. It's something I I worked a long time, like 10 years. And in the end, we released the data openly at 30 meter resolution. It's like a four terabyte of data. But what we did, we took the global models and we took the data from US and Europe, which is paid by wealthy countries. And this data has been already paid. And I call it a gold mine for data science. So there are fantastic data, which is openly available. But nobody thought like you could use that to also help map the soils in Africa or that you could use Brazilian data to map soils in Africa, etc. And so we kind of transferred that knowledge from the wealthy countries to developing countries, let's say. And we did it without disruption. So we were like a Robin Hood, I tell my staff also. Like, I'm a Robin Hood of data science, but I'm not the Robin Hood that goes and steals from the rich to give to the poor. I go to the rich and I say, look, you have these extra things you're not using. Do you mind if we...

SPEAKER_01: 22:42

No, you go to the dustbin. You go to the dustbin behind the castle of the rich, and there you find it.

SPEAKER_00: 22:47

Exactly, exactly. I'm the Robin Hood that goes to the dustbin of the wealthy nations and then finds many interesting things that could be reused and I like that idea of this technology and knowledge transfer it really feels good when you do it properly

SPEAKER_01: 23:03

and then what can let's say African countries do with that like I'm in Ethiopia or I'm in Uganda and say okay wow I now have 30 meter data which sounds very cool but still it sounds very abstract what can I do with it how can I change my agriculture decisions or is that a few step away

SPEAKER_00: 23:20

yeah you wouldn't believe the africa is of course changing you have you have a less progressive more progressive countries but many countries in africa especially like western africa like if you go to cameron ganda what's happening you know google is bringing offices there there's uh incubators in ethiopia also very active lots of incubators lots of young companies startups uh and they need that they need that platforms you know they need something to develop on and when it comes to agronomy yeah we're very proud we made that uh agronomy, soil agronomy data cube. It's not perfect. I mean, it could be always better, but, you know, we are proud that we put it open and we just enable it for everyone to create a platform. They can, it's a field scale, you know, 30 meter resolution. You pick up even small scale farmers. They can see inside their farm where they have a nutrient problems, which are the nutrient problems, you know, anything about soils. They could estimate using some simple formulas. You can estimate the fertilizer requirement. They could see what are the limiting factors. What do they have to fertilize? focus on. Also for the regional planning offices, agriculture extension, they can all use that data. They don't have to wait until their national systems reduce or reproduce, something like that. We even made magic. We made maps of countries without having any points in these countries. People told us, how did you do that? Well, you know, we do global models. That's it. Globally, one gap.

SPEAKER_02: 24:42

Nature doesn't care what your country is called. You know, she's complex, but it's not infinite complexity. There is repetition. So those relationships, finding those environmental relationships in relation to the soil, that drives a lot of this transfer of knowledge. And that's also what we hope that if we can get it down to the farm level in like US and Europe and other countries where you can have farm level innovation, then the landholders themselves can start comparing amongst themselves and can start building their own on-farm science, essentially, because we don't have time for the agricultural scientists to do all the field trials to go, okay, you need to do A, not B. We have to sort this out real time. We have to make like, actually almost like a living experiment now. So that relies on the same concept that you can, a prairie over here is going in the same rainfall with the same soil type is going to be very actually similar to a prairie over there. with the same rainfall and the same soil type. Even though they may be on completely different continents, the species may be different. There are these driving factors in soil formation.

SPEAKER_01: 26:02

And do you see like this has to be like this layer of open source data cleaned up or to a certain extent processed so it can be used? Do you see the same as in a lot of open source software world where then there are companies built on top of that, like applications built on top that sort of plug in, obviously give back to it in a sense that keep the underlying data set growing and living and cleaned up, et cetera, not closing it? Or is there the same struggle with the closed systems that maybe want to use a bit of open data, but don't really want to contribute? Because we see an endless amount of satellite companies popping up or companies popping up saying they use satellite imagery to calculate solar carbon and sell it, for instance, et cetera. What do you see? Is it that easy? I And what do you see there in terms of, are we going through that same movement as the open source software movement there? We see some very successful companies on top, but the underlying layers are still relatively, let's say, open source.

SPEAKER_00: 27:02

Yeah, that's a question a business person will say, well, this open source can't make business. I mean, if it's all like open, you know, but this, of course, it's an excellent business model. And the typical example is like, for example, Android, you know, Android. If you look, Microsoft went and invested, I don't know, 4 billion or something into Nokia they said people like Microsoft Windows so they will love it on the mobile phones and we know that that was a flop and we know that Android actually ate for breakfast all the other operating system and that means it created a lot of revenue for anybody who chose Android 10 years ago right so that's an example of open source then you look at the servers world servers you know people don't know that but the world servers they run on Linux you know most of the backbone of world servers and all the companies they use actually Linux. So if we didn't have a Linux, we wouldn't have basically internet. And so what's the value of that, right? So there are many examples in business where the open source becomes a backbone. It becomes actually, it speeds up the business. As I said, with Android, it will speed up the business. I think it's just with the open source and open data. So it's not something that you have to choose business or no, it's more like about that you have to understand what is the priority. So when you look at the open data, so when you look at soil data, we We don't believe, we're not convinced that the profit should be made from the primary data. We think that the profit should be made from restoring land, from combating climate change, from saving people's lives. If you do a good service and if you do something good, you should be rewarded for that, but not with something like a primary thing.

SPEAKER_01: 28:40

Do you see that happening? Are you interacting with a lot of companies that are building on top of this layer?

SPEAKER_00: 28:47

Yeah, one of the big examples I gave examples for open source, and then the example for open data. Open data, if you look this earth observation industry, right? So somebody like Al Gore goes and says, hey, you know what? We're going to make CloudSat free. And in 2007, you still have to pay. And it was bureaucratic. So it's bureaucratic. It costs money. Even the money, it's, you know, the processing was done by government agencies. It was all clumsy. I mean, it only just slowed down things. So Al Gore goes like this. Look, Government pay for this data already. If we release it, the companies can grow on it. And as they grow, the economy grows, they pay more taxes and actually we will make profit. So they did a thorough analysis and they correctly concluded that if they release data openly, that they will make more profit. And so that's one of the nicest examples in environmental sciences is the NASA and European Space Agency releasing the data, just open license, download it, go nuts, make a copy, process it, build things, whatever. And it's actually to the benefit of business.

SPEAKER_02: 29:54

So we do actually get a lot of visitors from various startups and other like big organizations too. We've got probably more than 250,000. That was my last check. Downloads of open data layers, a lot of followings on the tutorials of how to do these more complex analyses, lots of following on the YouTube channel. There's like 870 lectures where you can follow along. I mean, they're not simple introductory. They're difficult. They're really hard, actually. So we seem to be fairly popular as Open Geo Hub with many startups and good because this has to be an enormous industry with many different businesses. It can't just, we won't succeed if it's just one business trying to eat everything and own all the things.

SPEAKER_01: 30:44

So we could say that Elgor had a bigger impact potentially on regeneration restoration and sustainability by opening up this data then his is inconvenient truth and generation management his big investment firm yeah that's interesting

SPEAKER_00: 30:58

it's possible probably we wrote in that pamphlet you know we exactly when we wrote like everybody has a right to know what's happening world it's really when you look before 2005 and now so look at the difference before 2005 you had it was exclusive business if you wanted to find out what's happening in Brazil let's say now Amazon, you know, you will have to trust scientists or you will have to like pay for some enormous amount to cover large areas. And so beyond 2005 and 2008 when they released it, so we are now in 2020. And now you can watch, observe at three meter whole world what's happening. Everybody can observe. And really it's a different arena now. So if you're a lousy farmer, if you're a lousy farmer in US, in Ghana, in Brazil, whatever. In

SPEAKER_02: 31:46

Australia,

SPEAKER_00: 31:47

in the US, in Europe. Everybody

SPEAKER_01: 31:50

can see.

SPEAKER_00: 31:52

Everybody can see it. Yeah, we think that's one of our basic business interests is to locate these good examples, locate these people and find them. Because these are patterns that are not, this is not something you see just on a satellite image. You know, you cannot say, oh, this is nice work. You know, you really have to use like a lot of machine learning and a lot of also ground data. And then you have to analyze 40 years because, you know, you cannot be a good farmer one year. You have to be a good farmer like 30 years.

SPEAKER_02: 32:45

And then you have to connect it to the farmer's knowledge. Yes. That's the piece, the critical part of this whole thing is that we simply, it's a false idea to think you could replace a landholder or someone who is stewarding a piece of land, especially the successful places, right? It's the farmer, what is the saying? The farmer's shadow is the best fertilizer. And it's true. So that's why we were just going, okay, can we actually find the places that are supposed to be, have gone through a regenerative cycle already? And can we show them these signals and these graphs and go can you explain what happened here because us looking at it we don't know the history of a site like we look across the continent and

SPEAKER_01: 33:30

yeah i remember you showing me an example in the netherlands of a farm that was basically i remember but please tell me and make it a visual story but i remember slowly a line going up which was i think around sun like the conversion of sun energy into basically into something useful and you could see the line going up and down very rapidly every year because they were on an annual cycle and then it changed so what happened there or what was paint a picture for us

SPEAKER_02: 33:58

so the advisor we took this signal to the advisor which is a signal it's like a two things it shows you the amount of light that's used throughout the seasons over 20 years

SPEAKER_01: 34:09

which is sort of the crucial bit like how much sunlight are you as a farmer converting into something how many solar panels do you have in this case leaves

SPEAKER_02: 34:16

absolutely I mean it's your big biggest lever in being able to optimize your farm.

SPEAKER_01: 34:22

And you can see that from above.

SPEAKER_02: 34:24

Yes,

SPEAKER_01: 34:25

of course. You can see that from above.

SPEAKER_02: 34:26

Yes. And so the issue we found was interpretation, right? Because we ourselves did not have the history, the site-specific knowledge of the place to go, why did this line shift up? And why did the fluctuation, which we could guess was cropping, why did that fluctuation change? And they transitioned from doing annual cropping to a much more diverse pasture system. So this one with many, many different species. And it showed up in the signal very distinctly, but we didn't know that until we took it to the advisor, hunted out who it was, took it to the advisor and went, can you explain it to us, please?

SPEAKER_01: 35:07

And what did he say or she?

SPEAKER_02: 35:09

Well, he said, well, in this year, it was like 2008 or nine, we so-and-so bought the farm and I advised to help transition the this farm was very run down, thin, sandy soils, and to transition to a regenerative dairying system. So, you know, you can argue whether the emissions from the cattle discount the sequestration in the soil, et cetera, et cetera. That's another argument. But the point is, is that you can transition a farm and we can see it from space. But if it's only us looking, we can't distinguish what is what, right? We can't distinguish what We can see something's changed, but we have no way to go, why did that change? And that's the critical part of on-farm experimentation that we'd really like to join with these signals. Because I don't think it's actually for us to interpret those signals. I think it's for the people on the landscape doing that job to interpret those signals and to see how to shift them. And does it respond? I mean, it's what I would like to do. Right. If I think if I was farming, that's what are the numbers I would like? What are the kind of signals I would like to see?

SPEAKER_01: 36:25

And then would it be possible to train the machine learning basically to find these spots around the world where maybe the biggest transition has taken place over the last 20 to 40 years? Or do you have to look specifically at, OK, we go to Gabe Brown's farm or we go to this indigenous tribe in this place? And because then we can I wouldn't say we can do a competition, but we can definitely see like Like, okay, where has it happened? And then ask the question, obviously, locally, why? And then repeat.

SPEAKER_00: 36:53

So with machine learning, I mean, it's heavily dependent on the training data, right? Machine learning, it's no magic stick. So the key is to have the highest quality training data that you can then correlate it with satellite images or changing climate or changing land use. And if you find that correlation, then it's a bingo. So it's really, at the moment, most of machine learning is basically, it's like a data microcosm. are looking for correlations, right? And if you find a correlation, it's a bingo. It means that you can apply that correlation on places where you don't have any samples. You can even extrapolate, et cetera. But there's another side which is focused not on machine learning, but on modeling processes. So understanding the processes and simulating processes. So that's like more like the people that are more background in physics and geophysics. And so what we are interested also as a company, it's a hybrid between the two so that we both become good in a modeling process The processes, by the way, you could do it also without any point data, training data.

SPEAKER_01: 37:53

So if you do this as a farmer, if you do this, this and this, we think that's highly likely this and this would happen.

SPEAKER_00: 37:59

Yeah, in very simple terms, like if your farmer says, look, I'm putting so much fertilizer, it's so much stones, da, da, da, and now calculate what will happen with that in five years. And you have formulas for, you know, so the formulas they calculate iteratively, step by step, what happens almost every day. And then you have all these processes the physical process, and you calculate, okay, this is in five years.

SPEAKER_02: 38:22

Yes, but we don't have time to do all of the scientific field studies on the new regenerative methods that are coming to light to get all those numbers, to train all those process models, to give us the response. So we're in this middle space where we kind of have some models that have been trained on long-term field trials. They do give us an idea, but then you also have landholders that have done things that science hasn't had decades to examine, right? So it's this really interesting tension. Do you just take everything back to how we think the environment works, or do we start actually just paying attention to where landholders have already regenerated to say, okay, well, you know what? We take that as the signal. So I don't want to say one is better than the other. They're different, but they can be combined.

SPEAKER_01: 39:17

One is definitely better. faster?

SPEAKER_02: 39:19

Well, one is definitely faster, but it, you know, I would love to look at Gabe Brown's wrench. I don't know where it is actually.

SPEAKER_01: 39:27

We can find it. Yeah. And there are some others. That's what I'm thinking, like, especially I'm thinking Ernest Goetsch, for instance, in tropical forestry and a few of these extreme examples that we've seen, at least we've seen from the imaging, like from, like from the drone imaging, et cetera, 30 years explosion, 20 years explosion of life, et cetera. And that would be very interesting to see, but it's same time i'm wondering taking one step back you mentioned our company tom and ish as well like what if you are working in this open data side of things and you're hoping others are building companies on top and are taking this data to make these decisions what made you decide to actually set up a for-profit company to be in that space as well

SPEAKER_00: 40:07

yeah so uh for me it's very natural for example to think like uh i mean i wish actually i wish that we just do not for profit i mean to be honest with you i wish Because...

SPEAKER_01: 40:18

There will be others doing,

SPEAKER_00: 40:20

yeah. Well, when we started the company, I had one consultant, financial consultant, and I come as a scientist, total geek, you know, I never thought about any business or anything. And total obsession with my field, you know, and a bit of also ego, trying to publish myself, whatever.

SPEAKER_01: 40:36

Which worked out, by the way. You're very published, let's say,

SPEAKER_00: 40:39

yeah. Yeah, but this advisor said, well, you know, what do you want to do? And then I'm like, wow, I actually, I don't know. I have to think. So I had some deep... reflection and then I wrote the list of things that I find important and I boiled it down to maybe like a list of 10 things and one of the things for example I would like to increase the education of people I noticed you know I have all this education I have like 20 years of top education and most of people don't get opportunities so I thought well I want to increase I want to help people get like not my level education but get in that direction then I also put like the climate change you know I'm really thinking about that now I saw that movie also, Don't Look Up. It's a really disturbing movie. And, you know, I realized actually this is no joke. I mean, you know, we have children and we have to take that seriously. It's really no joke. The climate change and it's going to be a horrible world, you know, if we don't do anything in 20 years. And I bet you that our children, they're going to look at us and say, what the hell were you doing? What did you do to our lives to, you know? And so this is the list of things I listed. I'm not going to go all the 10. So then I listed that. And then the guy says, well it looks like really ambitious you know so as you said you need to set up at least two companies so I do feel like sometimes a bit I understand Elon Musk that he has like 15 companies I fully understand it so and I said so let's set up if we want to do not-for-profit you know let's have a not-for-profit but if somebody comes and they're interested in business we're also interested in business but obviously it's a different stream you know

SPEAKER_02: 42:16

because capitalism is is a really strong engine and I feel like most I mean I come from this regenerative movement for my whole life long time I'm almost 40 and I feel like probably many of my compatriots will disown me for saying this but I think really only with the capitalistic engine are we going to have a chance of taking industrial society forward I think only with moving that much money and capital and effort and attention into ecosystem services and using that drive to money to transform the whole economic system, then I go, okay, I think we have a chance. Maybe. Just there. Because otherwise we all go back to

SPEAKER_00: 43:07

the hills. It's a bit more complicated, Shani, than that because, well, I saw this documentary and it said that capitalism is not interested in anything that can own. So if you look at the international seas, fish in the sea, air, you know, anything they cannot own, they're not interested. So I think it has to be like a triangle between business, politics, and science, I think. And it has to be, as to any solution, climate change, land restoration, only one corner is not going to do it. No,

SPEAKER_01: 43:41

no, of course,

SPEAKER_00: 43:41

of course. You need to get all three corners harmonized. And I notice also, you know, I'm not naive now, I look at the business. And I will tell you, my experience is that maybe 70% of business is politics. The business comes from politics. There's a political decision that makes a business. So I think the most important corner is actually politics. I think that's the, unfortunately.

SPEAKER_01: 44:04

But to change politics, we need to change business. I mean, it's a feedback loop that feeds itself. Why are we in bed with most input companies? It's because they have the politics on their side. Why is the gap in Europe not changing? I mean, there There's some very interesting lobby activities because the other lobby activities are a lot stronger. The same in the US with the farm bill, the same in Brazil to a certain extent, and the same in many other countries. But when you see examples, there's a few regions in India that stopped subsidizing fertilizer and put all the money into training of farmers and suddenly a whole region transformed. So there's, but we need the strong examples because I agree that one of the strongest organizational forms we have as a company, probably the other one is the social movement, but the company is something we have a lot of experience in building. building and mostly not necessarily for the good. But in this case, I think there's an enormous amount of work to be done. And the only way to get that amount of money is from the investment world. We've created so much money in the investment world that's looking for something.

SPEAKER_02: 45:02

Needs to go somewhere.

SPEAKER_01: 45:03

Yeah. Many of these things can really go faster with a bit of extra money and does extra people and does extra brains and does extra servers for Tom and does extra satellites and does et cetera, et cetera, et cetera. So you set up this company and what have you been mostly doing there that you couldn't do or cannot do in the OpenGeo Hub?

SPEAKER_02: 45:21

Yeah, so we've mostly been doing consultancies. So various groups that need pretty high level technical answers, like they need a methodology developed or they need like an actual workflow, an actual coded workflow. So not a report, like a functioning thing or a functioning data output. So mostly we've been doing that, but we actually get interest from individual landlords and small groups that want a lot. And it's been growing actually in the last couple of years. I'm surprised how much interest has grown so quickly. And we're not really set up to service a direct to customer or landholder, but we're trying to work out how to do that. because we think that it's not for us to tell a landholder what is going on on their land. I'd prefer they take all the tools and then they tell me what's going on or somebody else.

SPEAKER_01: 46:16

So you might need to bake the tools,

SPEAKER_02: 46:19

yeah. Yeah, well, we actually have them at the moment. We run them in-house, but it's a very different thing to run your own systems in-house that are automated versus, oh no, now I have a thousand landholders that I have to service in a couple of days. I

SPEAKER_01: 46:34

think Amazon AWS has some experience. in that like taking something that they're running in-house to actually to to sell to customers and it's a i mean you run a very different clean shop just as you're i don't know renting out a room on airbnb it's it's slightly different than if it's your own room it requires a different interface a different ux a different cleanness a different easiness frictionless yeah so you're in that process of how you're gonna interface with the world and not between brackets just i'm not saying just as in a bad way doing high level consultancy or consultancy with a few players this will be with a lot of them

SPEAKER_02: 47:10

yeah it's like trying to like i feel like how do i describe at the moment we're like an iceberg of information of data of all this really cool potential stuff and it has there's no bit above the water yet that you can build a door on and that that's the little piece we're concentrating on going how do we how do we do this and that's why all the pilots are so interesting because they bring out like issues simple issues that we didn't even think of like going to the field and sampling or for interpretation of maps, right? For which tree you want to plant where. Like we've got one grower who wants to replace ash trees where there's an ash die back on his farm. And so we're working out how to just combine these different predictions for different tree species that should grow well there. What do we do if the model says there are six different species on this one pixel that would do well? Do we just give him six species on a pixel? Like it seems like silly trivial stuff but it's really not

SPEAKER_01: 48:09

at all no no it's the underlying question of this whole series what to plant where or what to make walk where and why and you're saying that we're very close to offering that answer and then but is it also already possible let's say because we always we talked about the past a lot here to look into the future because obviously climate is changing fast and in many places very fast and will your predict of course it's always a prediction like say okay this six trees would probably do really well. But if we take this and this model into, or this and this climate model into consideration, we actually would say these three, because the other three are more at risk. Is that something we're getting to?

SPEAKER_02: 48:48

It's a frontier we're moving towards. Absolutely. I have to say that the, from, and I mean, it was a PhD's work. It's incredibly complicated to predict tree species, like way beyond what I expected.

SPEAKER_01: 49:05

Yeah, but the problem is if you, once you plant it, it's not that you can move them. Like it's a very, like you took a decision. Yes,

SPEAKER_02: 49:11

that has to be bought in and that's on the cards. The other thing to add to that is we also identified in that process of looking, scoping what bits of work next was that actually the rate at which the climate bands on the planet are moving is now going faster than the seed dispersal rate of the trees. So, you know, I used to think that I would just focus on agricultural land. That was all I, you know, I need to just focus on on fixing degradation there. And then over the last year, it's become apparent, I guess, to me, because I didn't actually know that much about trees, was that indeed, given the changes that are coming, we're going to have to basically pick everything up that we care about and move it ourselves.

SPEAKER_01: 49:54

Because the forest won't move,

SPEAKER_02: 49:56

yeah. It can't move fast enough. That's the thing. And that made me a bit depressed for at least a week of trying to process that.

SPEAKER_00: 50:05

Yeah, in general, modeling needs You know, when you look at, for example, like a planet, I mean, so you have these biomes and you have the vegetation, let's say, interacting with climate, also controlling climate, and this thing going in cycles, you know, through millions of years, I don't know. And when you, now we go as a human system, okay, now we're going to simulate it. And you realize actually it's very complex. It's like,

SPEAKER_01: 50:29

yeah, just the word you said there, like the vegetation controlling the climate already is probably, is, is such a, it's a hopeful message. I would say, but also a very complex one. How do you model that? Like we, we don't really know, and it might be a follow-up series we're going to do. It's a look at, okay, can we change the climate locally with vegetation?

SPEAKER_00: 50:48

Yeah, it's kind of like a dance. No, it's up to this climate. We have, you know, oxygen in the air and everything. I mean, it's like a control by life, but it kind of goes in like, it's like a dance between life and geochemical processes, you know, it dance up and down and then you have extinctions and things. Then you have the, also the ice ages, you know, they also disrupt everything and they move the species and the sea level goes up and down 200 meter I don't know plus minus so yeah it's a crazy actually system and now we go and we said we're going to forecast what's going to happen you know and it's like a 50 years in advance it's very difficult so but we realize what's important to make a system that is very let's say fast adjusting system or system that you like you feed with the new data every year and you really calibrate recalibrate and eventually it becomes you know like that's what they have in for example in meteorology they have a weather forecast at the beginning that you could predict maybe two three days you know what's weather going to be now they can go maybe two weeks you know and eventually they can also see there are some la nina il nino effects and then they predict next year you know this could happen it could be a dry year etc so we're slowly getting into predicting bit further and further

SPEAKER_02: 52:02

so changing the local climate in really all the local weather in in relation to the health of your soil, of the plant species you have there. I mean, for physical scientists, physical environmental scientists, it's a no-brainer. We kind of just go, well, of course. I mean, some of the early lessons I remember from my professors, they take you out in this big wheat field that's been wheat for 40 years. And right next to it is like a grazing pathway that's never been plowed, right? And they take a piece of soil from each one and they drop it in the water. And the soil under the wheat falls apart, it's muddy, and you pick up the soil that's been under the pasture, it's intact, right? So that structure is an expression of living systems passing organic carbon through the system. I mean, it's an expression of the biophysics of the system. So if you have a more structured soil that can hold itself together when it's wet, that means it actually can hold more water. That increase of water with your plant cover means you have more humidity in the area you get

SPEAKER_01: 53:10

which means it's cooler

SPEAKER_02: 53:11

yes it means it's cooler it means you take some of that heat and you transfer it into the evaporation of water and then because of the temperature differentials you get dew in an area if you have vegetation

SPEAKER_01: 53:23

so the underlying answer is always and we had it in i don't know if the podcast will be out yet when we publish this but is when in doubt plant more trees or when in doubt have more vegetation like vegetation life is by definition whatever climate model will look at better for us than no life.

SPEAKER_02: 53:40

Absolutely. I mean, the world is, I think, I found a paper on this topic about global fertility and nutrient cycling, right? It's a very interesting topic to me. It says the world is about 25% of the fertility in terms of nutrient cycling of what it was 100,000 years ago. That basically says the world is actually a very abundant place, much more abundant than it is now. and a huge capacity that could be filled up. So yes, more grass, more trees, more animals, more plants, more water, more soil carbon, all of it is good,

SPEAKER_01: 54:16

really. And so let's say I try to now, because we're coming out of this COVID period, let's say we're in a big theater, maybe in Wageningen University or in Amsterdam or somewhere or in London, there's a room full of investors and we're having this on stage. So we're having this conversation on stage and what would you tell them, not as advice, because obviously we don't give business investments advice but they walk out of that theater after the evening inspired and they're like okay this this data part is really gonna this is another magic wand but definitely something that can change what should they do where should they go look for where should they what would be the role for investors and business people in this space build all companies on top of it or do something else what do you see as the role for people that want that are not necessarily farmers not necessarily data scientists but that could build things in the space things as meaningful companies or invest in them?

SPEAKER_02: 55:08

Oof, that's a hard question. Build supportive things, build scaled things, scaled implementation, because trying something out on a pilot scale, like it teaches you the little things you never expected. But from day one, think about how do I make something that's available across continents, across the world, right? So scaled implementation, because you have to be able to cover enough landscape. That's the thing. If you're, you could have the best of If you can cover 10 farms, great. Give me 10 million farms. Give me 100 million farms. Like that's level of basic infrastructure, like roads, or just internet, like really that in developed worlds, you don't think of you turn the tap on, you get water. Something like that. Think of it at that level and at that scale. And then probably I would say look for domain expertise and experience. Like there's a lot of people that have been in this space for a long time that are now surprised actually with all the interest lately. But I think it's really important to be grounded in how much we do know already and how much can already be applied. And, you know, there's stuff I see or recently fertilizing with ground stone, right? Silicate weathering as a way to draw down carbon. And I mean, Bread from Stones was written in 1894, how to fertilize from pulverized rock dust, okay? And this is just in the Western science. It's not even touching on all of the indigenous knowledge. So I don't know, what would the advice be? The world is really different than what you see think it is the actual the living world maybe be humble

SPEAKER_01: 57:01

and if we switch the question what if you would be an investor so you're no longer the top data scientist in the world tom i'm sorry and you're no longer running this open geo hub and etc but you are in charge both of you actually and it is a tricky question because it's a lot of money let's say you're in charge of a billion dollars what would you invest in literally and this could be very long-term investments but it has to come back at some point and not necessarily through tax increase or to increase taxes. But what would it be if you had to put money to work, work? What would it be? Would it be a lot of pilot farms? Would it be launching your own satellite? Probably not. Would it be, what would you build? What would you invest in?

SPEAKER_00: 57:39

I think, you know, as a data scientist, of course, I'm biased. I see the data as like a golden resource, you know. And so there's a lot of data, you know. The problem is this data gap. So I look at, for example, DeepMind, a Google company, and they are amazing people there and they have computers you think about they have a budget i don't know 50 million a year i don't know the number i'm just guessing you know so they have all these resources to play games more or less right on the other hand you know we have no idea what's happening you know with the soils in tropics in the world we even have estimates of soil carbon the plus minus i don't know some crazy uncertainty you know we also like somebody asked you know what is this uh regenerative agriculture and land restoration you know and like people can only read papers and things you know it's not on the level that the high school kids, you know, know it. So my, as I told you, I noticed I like education, so I would absolutely invest into technology and knowledge transfer to make really, so only positive management, what Ishani said, you know, like increasing soil carbon, you know, making, boosting like ecosystem services of soil, of land. Any example in the world that people develop that works, and I'm talking, these are maybe like, I don't know, 1% of people. I want to scale it up to 98% you know that's what I will put my 1 billion probably so whoever helps that fastest upscale and you Ish

SPEAKER_02: 59:05

well I would say that this is a really difficult question I would say that to me, I mean, I would look at ecosystem services and monetization of those. So same area I'm looking at now, actually. But to me, money is a virtual agreement. We all agree it has a value, right? And ecosystem services are not virtual, right? It's the thing that allows us to eat, to breathe, to exist. So I would put probably most of it towards trying to make like a clearinghouse to facilitate transactions for ecosystem services. Like somewhere that you can actually scientifically rely, like a scientific, yeah, scientific stamp that says, no, this number is as best as we can make it within these error margins. Because, you know, nature doesn't lie, right? If a country doesn't report all of its methane emissions as we've seen in the news recently those emissions are still out there that extra warming is still there and just because we don't have it on our books yeah you know it still exists so i would i would go in that direction to go really we need just basic fundamental accounting like geo-accountancy globally so how you turn that into a business i don't know i'm expecting a stream of people to pitch to me how that is to be a business. That's someone else's problem.

SPEAKER_01: 1:00:35

That's someone else's problem. You're the investor side now. So that's someone else's problem.

SPEAKER_02: 1:00:39

Yeah, yeah, exactly. I don't have to worry about it. And then, yeah, probably actually really boring stuff like small scale hardware. I would go for like decentralized food processing, preservation equipment, handheld sensors, soil, water sensors, like to get it in the hands of people, not to make it an exclusive item. If this is going to work, it can't be exclusive. It has to be common and everyday so that's probably what I go for and I like the teaching side and the knowledge transfer but that was Tom's answer so

SPEAKER_01: 1:01:10

let's leave it to the other part of the family office yeah

SPEAKER_00: 1:01:12

we need to specialize also we need to specialize yeah yeah

SPEAKER_01: 1:01:15

and then last final question for both of you you can take it whoever wants to take it first if you had a magic wand so no longer an investor at the moment but if there's one thing and you only get one Tom not 10 or like what if you could change one thing anything in the sustainability or regeneration space, what would it be? Could be global consciousness, could be education, could be literally anything, but what would your magic wand do?

SPEAKER_00: 1:01:42

Yeah, economy, absolutely. This thing that when I read about this, that investors, like if they cannot own it, they don't value it. And this modern economy we have, I think it has to change. I will put it as a first priority because it's okay to, you know, to think in the, you know, dollars, euros, francs, whatever. It's okay. But it's not okay to not to calculate the cost of environmental damage it's not okay to monopolize knowledge and things it's not okay to like some fossil fuel industry made enormous profit you know for 40 years and then you estimate the one ton CO2 emission they estimate now I don't know social damage about$150 or something so they came up to the I don't know if you know but they estimated in nature paper that the total bills for the CO2 we made it so far it's about 15 trillion 15 trillion and so for me this economy that completely ignores it and the policies that don't care they ignore that that you know there's all this enormous cost and damages that nobody pays for that's absolute priority that has to change

SPEAKER_01: 1:02:50

and you

SPEAKER_00: 1:02:50

Ish

SPEAKER_02: 1:02:51

magic wand okay I would go after the subsidy system for production I would say that has to be completely re- It's a magic wand. I can do what I want. Okay. Subsidy system. No longer promoting damaging or health damaging systems for food production and various other activities to do with the environment. And to try to make like what we would call regenerative food the default. Because at the moment, the default most convenient is just causing more trouble. And the argument is used that it needs to be cheap for people to, you know, it has to be cheap. Otherwise people will go hungry. I just, we spend so little of our income on the West for our food. I don't buy that argument. So I would make, I'd go for the subsidies to change them. And then I would make regenerative food, the default option, right? That in the supermarket. So I don't have to think about it anymore.

SPEAKER_01: 1:03:53

I mean, the subsidies make it the cheapness and obviously the subsidy is come from the policy side of things, yeah. I know, that's why

SPEAKER_02: 1:03:59

I put them together and that's why it's my magic wand answer because I don't, yeah, it's such a big and powerful outcome of so many lobbies. I need some magic wand.

SPEAKER_01: 1:04:11

Absolutely.

SPEAKER_02: 1:04:12

And if I can

SPEAKER_00: 1:04:13

take the stick back just the last round. Oh!

SPEAKER_01: 1:04:16

There we go, yeah.

SPEAKER_00: 1:04:18

The greenwashing, greenwashing, it's horrible. It's, I don't know, we need more sanctions. It has to be more serious. It greenwashing, I saw it's happening. It will be more and more manipulation, fakes, you know, and that's also enormous damage because they create this illusion. Yeah, that's how you do it. You just, you know, you buy some carbon credits so you can continue doing what you want. You take some nature science, you say, oh, this guy says you plant one trillion trees so we can continue emitting, you know, it will solve the problem. So we need to stop the greenwashing and anybody manipulating it. It has to be legislation. It has to be more serious, you know, the same as like I don't know human rights or something I think this greenwashing should be at that level that it goes like that people can sue you know organizations and companies so anyway I give back the magic stick

SPEAKER_02: 1:05:09

we don't want to waste the window that we have you did say magic once

SPEAKER_01: 1:05:16

thank you both so much for sharing your time today and sharing your knowledge obviously and unpacking a few of the very almost magical things you guys have been doing and I really hope to be checking in soon again to see what you've been building what you've been uncovering and what you've been building on top of open data

SPEAKER_02: 1:05:35

thanks for having us good fun

SPEAKER_01: 1:05:37

thank you

SPEAKER_00: 1:05:38

for invitation yeah

SPEAKER_01: 1:05:40

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