 Okay, I think I'd be out of time. Should we get started? All right. Well, good morning, everyone. My name is Sumair Johal. I'm the Executive Director of Agstack, which is a project of the Linux Foundation that deals with digital infrastructure for food and ag. Today, we're actually unveiling a very exciting subproject called the Field Carbon Model subproject. So Agstack is an umbrella project within the Linux Foundation. We have several subprojects. And this is a very important first step in a longer journey, and we hope that you'd appreciate it and join this journey with us. As with everything open source, it's really about the evolution, and so with that, we'll jump right into it. All this work is available on GitHub, and that's the link. So let's first talk quickly about the motivation behind what we're doing. So first, from a broad perspective, agriculture can really provide a very powerful solution. It can be a net positive solution for climate change crisis and really through carbon sequestration at scale. The challenge with that is that we need, as a key enabler of that, a verifiable, trusted, and global, and really cheap field-specific carbon accounting system that accounts for what carbon is sequestered on a field in a way that people can find believable. And if that happens, that is a necessary, perhaps not sufficient, but a very necessary step in the enablement of agriculture to be essentially a solution for carbon-based climate change mitigation. So for that key challenge and key enabler, we are here to solve three key problems through what we like to call MRV or what the industry calls MRV, which is measurement, reporting, and verification. The first is to bring transparency to the actual methodology, which is a lot of times opaque. It's not really understood, and we want to change that completely. Number two is to enable scientific consensus. So there's lots of scientists doing lots of things along this area, but a lot of their work gets published in papers. That is not a digital-enabled solution that could be really worked like code, and so really create a way for the scientific consensus around code. And third is to reduce the cost through leveraging remote sensing and other digital methods of MRV so that you can actually do this at field scale globally. Those are the three big challenges around MRV today. So what we are doing is our intended design, again, this is the first step in a much longer journey, is to really create an open-source model repository with the first model in that repo, which is already there today, which we've created just recently. And this model is going to have three levels of models. So the levels of models are sort of important. The first one is the remote only. The second one is remote plus in situ metrology. And third one is remote in situ and activity on a field. So imagine an agriculture field anywhere in the world. Those would be the three levels of models. Each one will evolve as a separate code base, but all three are important at different levels of sort of granularity. We want to design this from a vendor-independent manner, so no one vendor should be favored in terms of enabling this. But it's also important that we engage private sectors and make it render-friendly to adopt and use while making sure it's independent and completely transparent. And today's sort of version 0.01 that you'll see today is actually designed from multiple data sources, from multiple agencies that are all planetary data sources. Central two is satellite data. Merit two comes from the NASA. SMAP and Merit two both comes from NASA. USDA Crop Data Layer provides a machine learning for crop classification. The soil grids presents the soil information from around the world. So very first order on-field, field carbon model. That's what we're trying to do. Let me introduce you to our panel. The first two people on our panel are going to present through a video. They couldn't be here today. Dr. Arthur Ensley is a research scientist at the numerical terodynamic simulation group, or NTSG at the University of Montana. He and John Kimball have been really the pillars of the NASA SMAP work that has been going on in carbon fluxes for the last decade. And John Kimball and Jerry Hatfield are co-chairs of our technical advisory committee. Dr. Jerry Hatfield has been at USDA as a remote science specialist and agroecologist for over 40 years, just recently retired, and has joined a company called AgroLogix, which actually I founded as a chief scientist. And we're really delighted to have Dr. Jerry Hatfield as well, weighing in on the technical advisory committee. Heather Atkinson is actually the technology professional for the last couple of decades in technology and a principal technical program manager at our peer sister project called OS Climate at the Linux Foundation. And so we are now doing this sort of cross-project work to go after common goals, which I'm delighted about. So welcome, Heather. And then Brian King is an agriculture data expert and head of digital and data innovation accelerator at the CGIR, which is a global research institution consortium of agriculture scientists. And so we have this great panel that will get into it. And with that, let me start with our first video and introduced by Dr. Arthur Ensley. Hi, my name is Arthur Ensley and I'm a research scientist at the University of Montana. Today, I'm excited to talk to you about the work that we're doing with CNER and AgStack on field scale carbon flex estimation. I want to acknowledge that Dr. John Kimball has also contributed to this work as a member of the AgStack Technical Advisory Committee. John and I both work at the University of Montana's Numerical Teradynamic Simulation Group, or NTSG, which was founded in 1987 by Nobel Laureate Dr. Steve Running. Steve and others at our lab developed foundational techniques in modeling leather and ecosystems. You might recognize the names of some of the models we've developed. We also generated the first global continuous and weekly estimates of ecosystem growth and net primary production, which you may know as the MODIS Mod 17 product. Well, recently, we are supporting NASA's Soil Moisture Active Passive, or SMAP mission through our ongoing development and maintenance of the SMAP on the core carbon product, which provides a global daily carbon budget. This carbon budget is made possible by upscaling observed CO2 fluxes from eddy covariance flux towers, such as the one you see on the left here. These towers and L4C both provide estimates of the net exchange of CO2 between ecosystems and the atmosphere. To do that, we quantify the difference between the CO2 released as part of heterotrophic restoration and the CO2 assimilated by plants as part of net primary production. This balance is related to the soil again at carbon state at a given time, which we're also able to estimate for surface soils. L4C has been an active development in annual recalibration since 2015. We've had several opportunities to demonstrate the value of this near real-time product. As one example, the recent Great Plains flash drought of 2017 showcased the potential for products like L4C to estimate the impacts of short-term climatic variation on carbon uptake and storage in terrestrial ecosystems. L4C predicted strong declines in GPP that were consistent with independent reports of low crop yields and poor-range lung conditions. Our new partnership with AgStack is focused on applying these types of models at field scale. We envision three levels of development and application. Level one is already available, free and open source on GitHub, as I'll demonstrate in a moment. We're using a model similar to L4C, along with downscale vegetation indices and globally available, created climate data sets to estimate field scale carbon fluxes. In level two, we plan to incorporate field-based microbiology and sensor data to improve the fidelity of these estimates for specific fields. Finally, at level three, we plan to provide support for integrating field management activities. With that said, I'd like to show you a brief demonstration of our current capabilities. This is a Jupyter Notebook that's available with the open source AgStack software on GitHub right now. I'm going to import some libraries for numerical data analysis and plotting. I want to show this map. I'm going to be showcasing one field just north of Des Moines, Iowa today. This is a satellite view of one of actually two fields in this area that we're working on. This code block right here shows pretty much all the code that's needed to get up and running with a simple estimate of the net carbon balance for this field. I'm going to walk through each one of these steps, but I just want to demonstrate how quick this can run for a single field. Straight to getting an estimation of the net carbon balance. What are the steps involved here? First, we need to read in some data. This may be something that you can get from something like Earth Engine. We're also developing tools to make it easier to access gridded climate data sets. Like the ones that are used to run the terrestrial carbon flux model in this demonstration. You can see this is just a CSV file for one field. For multiple fields or for say gridded data, we might have a three-dimensional data cube. We have multiple pixels or model resolution cells that we're estimating fluxes in. That's what our data look like. We have multiple fields here. I'm just going to stack those data sets together. The soil organic carbon state, I can get a good initial estimate of that from the soil grid 250 meter product. That's available globally at 250 meters. It's something that you could also use for a field that you're interested in or you might have field data. I do need to guess the SSE content as it's distributed through three different fields. I'll just start with this initial estimate which is pretty low for the surface soils for the top 5 centimeters. It's okay because we'll address that in a moment. The last thing I need to do is just read in some model parameters. These come from the SMAP level 4 carbon product. This parameter dictionary is freely available through the National Snow and Ice data center but it's also included in the repository. You can have a code associated with a certain land cover type, in this case serial crop names. Step 2, I just need to spin up the model. Just get this again at carbon state to a pseudo equilibrium. Then I'm already able to kind of plot some of the outputs from this model. I can see my two fields started out of balance in terms of the change in the annual NEE from year to year but they converge pretty quickly to a threshold that I can choose and change if I want to get a closer estimate. Now I have silicon at carbon pools that are much more realistic for surface soils in this particular region. I can just sum up the three pools if I want to get a total estimated silicon at carbon. The final state is simulations. This is the fun part. Again, we estimate the constituent carbon fluxes like GPP and heterotrophic respiration as well. I can estimate GPP. This is a completely vectorized calculation so I can get a time series of GPP pretty quickly. We can see that this time series dataset encompasses almost two entire growing seasons. If I want to get net ecosystem exchange, I do need to run the model forward because the model soil again at carbon state is dynamic and will change over time. This runs pretty quickly for just two fields and one interesting plot I could make of this particular example because both of these fields really share the same kind of surface meteorology is what is the difference in net ecosystem exchange between these two fields. We can see that it's fairly small but we can attribute this to the difference again at carbon content that's found in each field. We can see that a greater difference in spring for these two fields. In summer we see that the difference is here probably not limited by water availability so we just get a smooth they're changing the amount of carbon the relative difference in carbon just according to change in temperature in the available soil again at carbon as it builds up seasonally in the UK seasonally as well. So comparisons like this might be interesting to look at differences in different management histories or different management strategies for different fields and we're really just scratching the surface here I want to thank you for taking the time to attend this session today and I'm sorry I can't be there in person but I'm going to turn it over to Jerry Hatfield and Sumer Johal who will tell you more about this. So running quickly to the next one we have a presentation from Dr. Jerry Hatfield Jerry's career as a remote scientist-agricologist really ways brings the agriculture perspective into this so you'll see a slightly different variation of the same sort of idea presented through Jerry. It takes a few seconds to load so just bear with us. Good morning I'm Jerry Hatfield I'm one of the science team at AgriLogix and retired USDA ARS laboratory director and plant physiologist and work on the whole aspects of how do we understand the dynamics of carbon in agricultural systems that we can apply to looking at carbon sequestration or a slight delay as we go from one slide to another so please bear with us. You'd ask the question of why this interest of carbon in agriculture if we look at this from a standpoint that obviously carbon sequestration and carbon markets drive this process but you can really look at it from a different viewpoint and saying how much carbon is captured from the air and incorporated into the plant and soil and of that capture how much remains in that soil for sequestration and we know there's a lot of discussion about the permanence of carbon in agricultural systems and what that means if we look at this from a different perspective here's a corn can apologies for the choppiness perspective but if you think about this pathway of carbon into the soil there's a misconception that many people think that CO2 from the air just automatically gets into that soil volume out there but it's not a passive process as we see on that components of the right side is that in order to put carbon into the soil it requires a living plant to capture that carbon and we transfer that carbon as a simple sugar from the plant into the soil so when we look at this we think about quantifying carbon dynamics at different spatial scales on the left side there's just a variation of soil water holding capacity which is a direct function of how much organic matter is in the soil and then here's a picture of that soil profile and so we have to ask ourselves the question of how do we effectively sample the vertical and horizontal variation and just to put it in perspective if we take 12 one inch per acre that volume only represents 1.5 to the 10 to the minus 6 of the volume so if we look at where we're thinking about going is how do we build a field scale carbon model framework and if you just look at this we can begin to look at carbon sequestration as a function of how much below ground biomass is there and then what atomic practices affects that carbon loss we know that tillage puts a lot of carbon back in the atmosphere and that we can add all this and so utilizing these equations of gross primary productivity and then net primary productivity we can derive this with different remote sensing methods we can look at temperature and we've been looking at how do we quantify the vertical variation so over time agri-logics we built a system to begin to look at how do we effectively sample fields so if you want to sample a field and so ultimately what this requires is that we have to integrate multiple layers of data we can start with remote sensing then we can add field scale measurements we can add farmer measurements that then there's the yield map across fields we can look at what we have with soil survey results so there's a great deal of information that we can bring into sorry about the choppiness, sorry about that I think the main point was made but some of the audio was cut off so I can fill in the gaps later on for folks that want to know more so with that I'll hand it over to Heather to present the work that OS climate is doing in this area and we look forward to working together to move this project forward, Heather absolutely, thanks OS climate really is focused, I'm going to kind of take it up a little bit higher level than just the agriculture sector but OS climate is looking at what are the barriers that are preventing investments from flowing to climate aligned solutions and when we took a deep dive into that we realized that people weren't making those decisions because they didn't trust the data they didn't have transparency around that data and they didn't have the right analytical tools to make those decisions so OS climate was formed with three goals in mind one is to make sure we're creating an infrastructure what we're calling the data mesh that provides data that's trustworthy, that's transparent that data scientists can replicate results that one another have regarding their models and then also create multiple analytical tools that are needed by not only corporations but financial institutions by nonprofits by sovereigns themselves in terms of understanding what are the impacts of climate change not only from a physical risk perspective but how are they going to make that transition because it's going to be trillions of dollars of investment that has to be made and so having good tools that not only show transparent methodologies are kind of our key goals wanna go to the next? so right now we have a community of academic institutions that are providing the good science behind the models we also have financial institutions and banks that really need to rethink how they're investing that are part of our organization as well as a lot of IT and technology companies that are all coming together like Red Hat and Amazon are all partners and like I said we're pulling together a lot of the resources that people need to make these decisions but we're doing in a way where we're creating a data mesh which is a federated data platform where we're not copying let's say all the NASA data and all the NOAA data into one place but we're federating with those sources so people can get access to the data to the models and to the code that they need to be able to do the analysis on their own and make their own decisions and kind of have this ability to plug and play because we know no one model is the right model right it's combining all of those together looking at the trajectories of those models and understanding what the potential impact is gonna be so we have three main tools that are part of the OS climate platform one is on physical risk and resilience so it's taking a look at the hazards that are out there whether it's drought or flooding or whatever peril you can think of and looking at what are the vulnerability of a set of assets and those assets could be agriculture right that it could be farms and fields or they could be real estate and having the ability for people to bring in their assets, assess those hazards what's the vulnerability and then what's the probability of the impact once you know that you can start to take a look at transition analysis and how are you going to transition whether it's your corporation or your investment portfolio into climate aligned solutions and then finally sector alignment what that takes a look at and where we see good synergies with the carbon model is what are the emissions right a lot of corporations organizations have committed to net zero what are their trajectory in achieving that goal and where are they at and obviously agriculture can provide a way to sequester carbon and it's you know a carbon negative solution that we need go to the next slide please so just a quick this is just an example of the physical risk analysis tool you can see we have a UI that in this particular place is looking at real estate and flood inundation coastal inundation and what's the impact of that climate hazard of coastal inundation and pulling in vulnerability models and like I said we have this ability to do plug and play because we realize that no one model is the right model and it's the ability for people to do this evaluation over a series of models to understand what the potential impact and the probability of impact is go to the final slide and one thing that we're super excited to announce which again is related to the work that AgStack has going on is this week we announced the Sustainable Africa initiative so this work isn't just like I said for financial institutions and how they invest or corporations and how they transition but it's also talking about how do we create a public good so everything that we're building our tools our data our platform it's all open source and we've created the Sustainable Africa initiative where our goal is to provide the continent of Africa with this platform and the tools and the knowledge so that they can do this analysis themselves and we're starting in Nigeria with the agriculture sector most people may not know but 80% of Nigeria's revenue and income comes from oil and gas they're the 7th largest oil and gas producer in the world so when we think about the transition that has to take place in their country it's pretty significant because they use that money from oil and gas to feed their people to buy grain and things like that and obviously their particular area of the world is also subject to extensive chronic drought and flooding as well so working and giving them the tools so that they can come up with the adaptation programs for their agriculture sector we think is a vital public good that we will provide so if you're interested in learning more we'd like to volunteer we have the website up there thanks thank you very much Heather so I'm really excited to be working with our peer group within the Linux foundation on this vertical and OS climate provides a very broad horizontal view on climate risk and climate problems and we can sort of really focus on the agriculture piece so with that I'll transition to Brian King from the CGIR and Brian will introduce some of his slides thank you Sumer and hi everybody CGIR we can just go to the next slide sorry hi CGIR we've done so much influential science in the world that we sometimes think people should know who we are we're a consortium of 11 agricultural research institutes centered on really food security in developing economic contexts and we have geneticists and crop improvement experts economists sociologists, climatologists, hydrologists and really just about any domain that you can think of that intersects with global food security we have expertise in we're sometimes kind of a diffuse organization because each of these institutes is kind of a global institution in its own right and so over the last 10 years we've been finding ways to kind of harness our collective strength across those centers the fact that we preserve and regenerate some 700,000 food crops in gene banks around the world that we have research footprint or some level of research activity going on at any given time in over 100 countries and so I've been, it's been my pleasure and privilege to try to serve a digital innovation role cutting across those institutes those data stores and partnering with our researchers to try to achieve impact so I've already gone through this it's a really natural complement what I described that we should be working with the Linux Foundation via AgStack in that we default towards open on all of our products if there is some form of restriction there needs to be a very good reason in terms of the impact of that so for example we've partnered with global seed companies for example to accelerate development of drought tolerant varieties of crops particularly maize and the nature of that engagement was that we accommodated a level of restriction for a few years because the global seed companies and all of the smaller seed companies that they work with had the infrastructure to get more drought tolerant maize to more people more quickly but that's kind of the exception rather than the rule is international public goods and open by default and so having the strategic partnership with LF enables us to then look at the digital dimensions of that and what digital public goods for agriculture makes sense to co-develop and put into the world so this question about field scale measurement is a really hard problem several of the researchers from my organization starting 2011 I believe just kind of started reaching through our partner networks which are thousands of other organizations to start to level set a bit about what are the different measurement methodologies how crop specific are those what are the actual data acquisition questions and then how do we deal with the immense complexity around doing that down to small holder field scale so farms that are under a hectare is the kind of formal definition there can be farms that are smaller than that and so several of our researchers initiated a process over and it took a few years I can imagine to kind of start to level set on these things and start to build some kind of comparability across measurements to kind of introduce the rigor and harmonization that's needed at field scale. Let's go to the next slide so I was poking around for an infield small holder measurement picture I didn't easily find when they're out there I'm sure but this is actually from my host center in Colombia where you know it's a pretty appropriate tech low cost way to be measuring gases coming off of in this case a rice patty you know it looks like a big plastic jug and it's got a piece of pipe and then there's a valve on and you can basically at different points in the crop cycle you can pull out the gas and you can go and you can analyze those gas and so this is as field scale as it gets in terms of being able to generate a good quality measurement that doesn't cost you a lot of money to do I mean your biggest cost is probably the analysis of the gas themselves that once you send them off to somewhere but we have such a lab at my host center. The carbon flex towers that we saw you know I was looking into these recently I think the cheapest you could get at carbon flex towers about 50,000 US we have a couple at my institute there must be others around the organization again speaking to that defuseness and balkanization I mentioned but and those were in the neighborhood of 200,000 US and so you know this to me screams to you know for the need for open source IOT open source both on the hardware and on the software sides so that we can take that consensus around good quality measurement and we can turn it into actual devices and accelerated learning and accelerated technologies around that. Next slide. So I was really happy to see from OSC and learned by Heather their risk kind of specialized platform for looking at climate risk, climate shocks and those same researchers or several of those same researchers that I mentioned in that book chapter actually it's a whole book from 2016 that one was published took that work and then turned it into an analytic platform and so you know we too are looking at I mean obviously you can see geography and it's only been done for sub-Saharan Africa at this stage is my understanding so we look at climate hazards and so under the intergovernmental panel on climate change you know the definitions you know the different types of hazards can be kind of slow motion disasters a trend towards greater drought they can also be shocks they can be extreme weather events and so under climate hazards specialized analysis around the kinds of shocks you could be expecting and the kind of slow motion disasters that we should be prepared for as well using the scenarios from my PCC and then under exposure that's exposure to crops and to people and so you can you know poke around on the platform and you can go and you can look at cereal crops, legume crops livestock and I think a couple more I have to play around with it again but and then you can look at rural urban distribution as well and so being able to have at least a course scale understanding of okay well where are the most vulnerable folks smallholder you know farmers and what are the kinds of shocks that they should be getting ready for and then the last bit is draws on quite massive literature research in addition to many years of research across all of our institutes into adaptation options like what particular cropping practices would be relevant or what other options might there be for helping folks that are suddenly have to deal with one of these shocks last slide and so you know that data problem is still persists obviously I say GHC is no data for one particular recommended adaptation option you know so this needs to be you know if you go and you search adaptation solutions you can get by geography by crop and taking into account those hazards particular adaptation options particular practices that could be adopted I think this is a path that we're on of data and methods and models to read to capabilities and it's a path we must be on if we're going to even generate you know even course scale analysis like this this was used by the global commission on adaptation and it helped inform you know I think a few billion dollars worth of climate finance and development funding and so forth but it's still very core scale and we need some agility and adaptability in this space if we're going to be ready for the challenges in the coming years so thank you great thank you so much Ryan so let's have a couple of rounds of questions for the panel and then we'll open it up to questions from all of you and if there's nobody after us we're delighted to stay on and engage so first let me tell you why I'm here I'm a son of a farmer my dad, my grandparents were in farming and you know I went into tech and after about a dozen years decided that I wanted to do something more focused around transformational things that are global scale and farming and agriculture was a natural fit for me so let me turn it to first to you Brian you know this field based model you touched on it a little bit why are you here you know focus what about the field model work is important for CG and for you and where do you see this fit in I mean this the ability to take some of the global models and downscale them to the point where we can have a pretty good idea of challenges to be navigated at smallholder farming scale you know as I mentioned it's a really hard problem and it's the kind of problem that we can only solve through collaboration and open learning and open technologies and so you know being able to get the open science folks together with the open technology folks, open digital technology folks you know I think we can make some headway on this and accelerate our progress on this so that's why I'm here thank you same question for you definitely plus one what you said climate change isn't going to be solved by one org or one company it's going to take the entire global village to solve this and I think partnering with Eggstack especially on our sustainable Africa initiative and being able to get field level data is really really important and like I said we're not making as much progress to maintain that Paris Accord agreement of 1.5 degrees we're not on a trajectory to hit that and so being able to get this information and be able to look at other ways to mitigate some of the emissions that others are still pumping into the air I think is really important so thank you yeah so you know from that perspective one of the things that's really exciting possibility for agriculture is to create market incentives for farmers and producers to really change behaviors that give them through the incentive the means to essentially create more carbon sequestration so I'm envisioning carbon markets for agriculture as a new and additional revenue stream for producers imagine that that would change everything anybody who's been on a farm knows what margins and the lack thereof are all about I'm really excited about that there's so many hurdles let me point you know ask the question around challenges and hurdles so first with you Brian from your perch what do you see as the systemic challenges to this work as we progress particularly in light of our partnership we've been to an MOU with CGIR for being their digital partner on their journey they're the ag first we're sort of digital first and so together we can sort of go together and then we also have OS climate and others that have been sending us but back to you Brian what do you see as the challenges going forward I think there's there's the art of the generalizable is really important in this space having quicker insights that are you feel like or at least 60% right is needed sort of yesterday and so it's a really hard challenge to build a consensus it's a really hard challenge to kind of harmonize across methods and approaches I think in the climate space we're moving into an area where there is enough consensus and enough commonality that we can we can sort of you know compare or process approaches but we can also know what they're supposed to add up to so I think the biggest challenge now that that is at an inflection point at least in my perception is how do we as I mentioned turn that into capabilities how do we equip humanity or the sector with the agility to act on that intelligence and then learn from that and build new intelligence and have that kind of feedback between very localized things like data at a point where the data was captured with a plastic jug you know we know how that fits into the global system and what to do about it so it's the agility and the adaptability across scales I guess is the challenge now and I would add to it like the access I think is also really important the access to that information in a way that's meaningful one of the things that we're building is part of the data mesh is what we're calling the data exchange which allows people to be able to what we call the mere mortals take those important pieces whether it's that emission information or it's a model or it's a Jupiter notebook the technology to combine all that into actionable insights I think that's a big piece and then access and upskilling so one of the reasons why we're focused on Nigeria is we're going right now to nine Nigerian universities and giving them the tools and providing them that skill and that knowledge so that they can leverage that and I think that's the other piece is making it accessible to others great thank you so thank you to both of you for answering those questions I'd like to now open it up to the audience for any questions I think we're at time but I think there's nobody else behind us so we can is there anybody behind us there is okay so maybe we can ask maybe one question and then go outside for the rest go ahead please use the mic yeah we'll be outside for the rest so I live in Abbotsford just right up the road from Vancouver it's a it's a heavily agricultural town and I engage in a bit of climate activism work in that community but being a rural community you know a lot of the work that we do in climate is kind of victimized by the rural urban divide in that like people in urban centers care a lot about climate change and people in rural areas just culturally politically socially the climate movement isn't as strong in those areas but it's also where a lot of agricultural communities are and they're really the front lines of climate change so the data and the work you're doing here is really fantastic and what you mentioned about having carbon markets to be able to liberate these farms from agribusiness and to give them another revenue stream to be able to do good farming and to help the earth instead of having to maximize their yield constantly with the barrister margins like how is the data and systems that you're building going towards a carbon market like that and how realistic is it to expect that that might be a reality at some point one of you want to take a little bit of time to go outside just quickly there's a little longer question and we can get a bit longer answer outside but if you want to quickly address it before we end it I think there's with all the regulatory activities that are happening globally whether it's the SEC in the US or EU there is going to be a lot more pressure on emitters heavy emitters to find solutions and I think it makes a lot of sense in terms of them planting cover crops and other perennial crops so that you can get that sequestration and hopefully pay them to do those good work so I think the market trends and the pressures and the regulatory will all play a part in that in moving that forward great thank you so much so let's take the rest of the questions outside let's have a round of applause for our panel thank you and thank you for all of you for attending appreciate it