under the multiple linear regression set of and you know that you know in multiple linear regression the number of regression variables is more than one and in most of the practical problems what happen is that you know the number of regressors is very large and having the large number of regressor variables we may wonder you know whether some of them can be some of them are irrelevant and can be removed from the regression equation well so the basic idea you know behind this finding the best regression model is that we need to find an appropriate subset of regressors that can explain the variability in response variable well and finding this subset of regressor variable this problem is called you know variable selection problem well let me explain the thing in in detail there are two there are several algorithms to to solve this problem and those algorithms can be you know divided into two I mean that can be classified into two classes basically one approach is called all possible regression approach and the other one is called sequential selection well so first I will be talking about all possible regression all possible regression say here you know we need to consider all regression equations involving say zero regressors well so if if there are k minus one is the total number of regressors in the multiple linear regression model then you know the number of model having zero regressors is k minus one c zero and the model is basically y equal to beta naught plus epsilon okay so we we will also consider I mean of course the regression equations or models involving one regressor and the number of models number of such models is k minus one c one true two regressors k minus one c two regression models are there involving two regressor variables well similarly we go up to k minus one regressors so number of models involving k minus one regressors is one so total we have two to the power of k minus one regression models and you know these models these equations are evaluated according to some suitable criteria first one is called r square this is the coefficient of multiple determination or coefficient of determination and then we will be talking about the criteria adjusted r square and then ms residual and the finally I mean we will evaluate the equations based on the criteria mannows statistic and this one is denoted by cp okay and this is you know one approach that is you know all possible regression and the other approach is called sequential selection so I will be talking about this sequential selection later on and there are three algorithms of this type those are called forward selection backward elimination and the stepwise selection okay so today we will be talking about you know this all possible regression and how to evaluate so many I mean two to the power of k minus one regression equation based on these criterias well now if the number of regressors is four so usually we denote the number of regressors by k minus one so if k minus one is equal to four then k basically you know k denotes the number of k denotes the number of unknown parameters in the model well so if if there are k minus one regressors that means there will be k minus one regression coefficients and there is another unknown parameter which is the intercept so total you will have k unknown parameters well so if there are four regressors in the problem then there are two to the power of four sorry two to the power of four which is equal to sixteen possible regression equations and let me just I have those 16s you know regression equation so here I am considering the problem with with four regressor variable so this is the model which without any I mean with no regressor variable so number of such model is four c zero which is equal to one now these are the models involving one regressor variable so this one is involving x one the second equation is involving x two x three and x four so these are the four regression models involving one regressor variable and then we have you know six regression model involving two regressor two regressor variables so this one is involving x one x two x one x three x one x four x two x three x two x four x three x four and then next we have regression model involving three regressor variables so there are four c three that is equal to four such models regression model so this one is involving x one x two x three like that and this is basically the full model this involves all the four regressor variables so there are four c four that means one such model okay so when the when the number of regressors variable is four we have you know sixteen possible regressor regression models and we need to evaluate them with respect to some criteria and see the complexity of this this approach you know if you have a problem with say k minus one equal to ten that means the number of regressors is equal to ten then then there are you know two to the power of ten which is equal to one zero two four possible regression equations so clearly you know the number of equations or the number of regression models that need to be fitted you know that increases rapidly with with the number of regressor variables well so I mean but but still you know since in I mean most of the practical problems the number of regressor variable could be like twenty to thirty so but of course you can you can use computer to to fit all possible to do the power of twenty models also there is no problem well so next I will be talking about the criteria the first criteria I mentioned that criteria for evaluating subset regression model well so we need to evaluate those those subset models and the first criteria to evaluate them is I mean one criteria is coefficient of multiple determination and we denote this one by r square so before also I told I mentioned about this r square and we used to call it like coefficient of determination and hence since we are talking about you know multiple linear regression model here we call it multiple coefficient of multiple determination so we denote this by r p square well so this let r p square denote the coefficient of multiple determination for a subset regression model with p minus one regressors and intercept beta naught well so by r p square you know this p basically stands for the number of unknown parameters in the model so since there are p minus one regressors there will be p minus one coefficient and the intercept beta naught total number of unknown parameters is equal to p and and we denote the corresponding coefficient of multiple determination by r p square so this r p square is equal to s s regression p by s s t which can be written as one minus s s residual p by s s t right so what is this s s regression p and s s residual p they denote regression s s and residual s s for subset model with p minus one regressors okay and so basically the r p square is associated with the model when there are p minus one regressors in the model and r p square is a parameter which measures the proportion of variability in the response variable which is explained by the regression model involving p minus one regressors well so it is not I mean like you know r r square we know that r square increases as yeah when one observation you can make you know that that r this r p square this r p square it increases as p increases because you look at the definition of r p square r p square is equal to 1 minus s residual p by s s t and we know that s s residual this decreases this decreases as p increases so from here you know you can you can easily observe the observe that that r p square increases as as p increases and and this is maximum when p equal to k because you know p equal to k means p minus one is equal to k minus one that means we are talking about the full model and since we can have you know the problem we have maximum k minus one regressor variable and the s s residual it decreases as as the number of regressor variables increases so maximum number of regressor variable possible is k minus one so when this p is equal to k s s residual has the minimum value and hence the hence r p square has you know will have the maximum value okay so what we do here is that we we compute this the value of r p square so basically first we compute r one square this r one square is the case when the number of regressors is equal to zero so r one square means this will have so p equal to one that means p minus one equal to zero the number of regressors in the model is equal to zero that is the model if you consider the model y equal to beta naught plus epsilon so this is the model you know involving no regressor variable and it is not difficult to observe you know prove that when you have this model with no regressor variable then the coefficient of multiple determination is going to be equal to zero okay next we will be computing r two square given a set of data okay so r two square you know basically here p minus one so this is p so p minus one is equal to one so this one is r two square is associated with the model y equal to beta naught plus beta one x one plus epsilon so this is r two square is for the model with one regressor okay to illustrate all these things first I will consider one example well so this is quite famous data this is called the Hald cement data here we have one regress one response variable y and we have four regressor variable x one x two x three and x four and we have thirteen observations corresponds to the response variable and the regressor variable well now you know here we have four regressor variables and you may think that all the four regressor variables are not significant to explain the variability in y some of them might be you know irrelevant and whether that the whether some variables can be removed from the model without affecting the model predictive power well so for that you know we need to we need to select the regressor variables which regressor variables are best to explain the variability in the response variable y so that is the whole purpose of this lecture let me you know let me explain the all possible regression situation here using this example so there are four there are four regressor variables so these are the possible models these are the possible models with one regressor these are the possible models with two regressors and these are the possible models with three regressor variables and this is the model with four regressor variables now what we need to do is that we need to fit each of them and once you have the fitted equation or fitted model for for this type of you know for involving x1 you can compute the SS residual SS total and from there you can compute the coefficient of multiple determination let me you know fit at least one at least one equation for example you know I will I will fit this equation so I have this data I will try to fit a model between model of the form y equal to beta naught plus beta 1 x1 plus epsilon right so I will try to fit a model of the form y equal to beta naught plus beta 1 x1 plus epsilon for that you know that whole cement data I am not going into the detail of so this looks like a simple linear regression model so you know how to find beta naught hat so you consider only the data corresponds to the response variable and the data corresponds to the first regressor variable x1 and you know how to how to fit this model fitting this model means you know you have to find the least square estimate of beta naught and beta one hat so the fitted equation you can check that fitted equation is y hat equal to 81.5 plus 1.87 x1 so this is the fitted equation so once you have the fitted equation you can compute the residuals E i and once you have no E 1 E 2 up to E 13 you can compute the SS residual so SS residual is going to be E i square from 1 to 13 you you just check you know this is equal to 1 to 65 well so you have the fitted value E i and you have the original observation so from there you can get E which is equal to y minus y hat so we know all these things and the SS total is equal to for this data it is 2715.8 and hence the SS regression is equal to 1450 so I am just trying to give you some idea you know given a problem with 4 regressors or 5 regressors how to apply this all possible regression approach now we can have the ANOVA table for this ANOVA table so the ANOVA table for this problem I mean for this model is you know you write the source of variation degree of freedom SS MS and the F statistic variation due to the regression model so total variation the part remained unexplained residual the total degree total degree of freedom here is equal to 12 because there are 13 observations now the SS residual you know here you have two unknown parameters so basically you will be getting two normal equations and that means there are there are two constraints there are two constraints on the on the residuals so the residual degree of freedom is equal to 11 sorry is equal to 13 minus 2 because of the two unknown parameters in the model so the SS residual has degree of freedom 11 and the regression degree of freedom is equal to 1 and we have the SS regression value is 1450.1 residual is 1265 and the total is 2715 right and the MS value is 1450.1 and the MS value here is you know this is 115.1 and the F value is equal to 12.6 and the total is 12.6 well so what you want to say here is that once you so this unavoidable is associated with the model y equal to beta naught plus beta 1 x 1 plus epsilon similarly you have to fit the other four models involving one regressor variable that means y equal to beta naught plus beta 2 x 2 plus epsilon so for that model you will get another unavoidable similarly you fit y equal to beta naught plus beta 3 x 3 plus epsilon you will get another unavoidable y equal to beta naught plus beta 4 x 4 plus epsilon you will have the unavoidable associated with that model so basically you know there are there will be 16 possible regression models and for each of them you will have you have to fit the model you have to find out the associated unavoidable for your convenience of course you can use you know computer or some software package like SAS or S plus to do this job and then once you have you know all this unavoidables or the SS residual value SST value for every model you can you can compute the coefficient of multiple determinations so here the coefficient of multiple determination r square and this is 2 here p is equal to 2 because there are 2 unknown parameters and here r square is equal to r 2 square is equal to SS regression which is equal to 1 4 5 0 by 1 2 6 5 sorry by SST which is equal to 2 7 1 5 this is equal to 53.4 percent so here you know this this model is not that good because because it explain the model involving the regressor variable x 1 only this explains only 53 percent of the total variability in the response variable well so what I want to say now that look at this table here now we have computed the coefficient of multiple determination for this model that is 53.4 similarly you fit this model this model is also involving 1 regressor variable and that is x 2 you find out the corresponding unavoidable and then then you compute r square value okay so this is the r square coefficient of determination associated with this model and similarly you do for all the models here also you do for all the models here you can see that you know this model particularly it is a good one this one is involving x 1 and x 2 and the coefficient of multiple determination here is 97.9 percent that means which is maximum in this class so among the among the 2 variable among the regression equations involving 2 variables this one is best this is that means y equal to beta naught plus beta 1 x 1 plus beta 2 x 2 plus epsilon because you know almost 98 percent of the total variability in the response variable has been explained by this model well so similarly you have to you know this is really hectic job you know here you have to estimate all the models involving 3 regressors and you compute the r square value for all the models and this is the full model which involves all the 4 regressors and the coefficient of determination is 98.2 well now what we want to do is that we want to draw a graph the number of regressor variable p or basically p is the number of unknown parameters in the model along the x axis and and maximum r p square along the y axis. I hope you know you have observed that higher the value of r p square higher the value of r p square better the model is or the higher value of r p square indicates better fit. So, what I want to mean is that out of all this 6 model which involve 2 regressor variable this one is the best out of all this 4 regressor 4 models involving 1 regressor variable this one is the best because this has a maximum this has the max this model has the maximum coefficient of determination well. So, what we do in this graph is that here all possible models with p minus 1 regressors this is the maximum coefficient are evaluated using the criteria you know coefficient of multiple determination and the one giving the greatest r p square is tabulated. So, let me take this is my p equal to 1 p equal to 2 p 3 p 4 p 5. Now, when p equal to 1 that means there is only one unknown in the model that means p equal to 1 means p minus 1 equal to 0 that means there is no regressor in the model. So, this is the model and the r p square value is equal to 0. So, here the r p square maximum r p square is equal to 0. Now, for p equal to 2 p equal to 2 means the number of regressors in the model is equal to 1. So, out of this 4 models the maximum r maximum is 67.5. So, we will we will tabulate this one 67.5. Suppose, you know this is 20 30 40 50 60 may be 20 and then 40 60 80 100. So, 67.5 you can keep it here p equal to 3 p equal to 3 means the number of regressors in the model is 2 and the maximum 1 is 97.9. So, we will plot this one 97.9. So, for 3 it is almost here. Now, for p equal to 4 that means the number of regressors in the model is equal to 3 and the maximum is 98.2. So, for 4 it is 98.2 and for p equal to 4 it is equal to 5 means there are 4 regressors in the model and the coefficient of determination value is 98.2 again. So, we will plot 98.2 here well. So, what it suggests is that the algorithm is like that you know you start with with 1 regressor and add regressors to the model up to the point where an additional variable provides only a small increase in. So, based on this stopping criteria you can this small increase means there is no specific value of this small what you what you mean by small increase. So, either you know this model with 2 variable it has coefficient of determination value 97.9 which is of close to 98 percent of the variability is explained by this 2 regressor variable. Now, if you go for the 3 variable model then this one is the best or also this one is also the having the same multiple linear regression model. So, clearly you know you do not need to go for the you do not need to go for the 4 variable model either you choose the 3 variable model which is you know beta 1 y equal to beta naught plus beta 1 x 1 plus beta 2 x 2 plus beta 3 x 3 either you go for this model or you go for this model and according to the coefficient of multiple determination criteria this one is also not bad you know this is a model with with 2 regressors well. So, this this is how we we evaluate the different possible basically all possible models using some criteria. So, we talked about one criteria that is the coefficient of multiple determination and next we will move for the MS residual criteria residual mean square well. So, what we know is that SS residual P by P I mean you know this is the SS residual for the model which for the model with P minus 1 regressors when K minus 1 is the total number of regressor variables. We know that this one decreases as the number of regressor variable increases and here we are talking about MS residual which is equal to SS residual P let me denote it by P also MS residual P by N minus P N minus P is the degree of freedom for the associated model error degree of or residual degree of freedom for the associated model and here one thing you know I want to mention that you know for SS residual decreases as P increases, but this is not true for MS residual I mean MS residual may increase with P. So, reason behind this one is that what I want to say here let me let me write MS residual P which is equal to SS residual P by N minus P and also let me write MS residual P plus 1 which is equal to SS residual P plus 1 which is equal to SS residual P plus 1 by N minus P. We know that SS residual P is greater than or equal to SS residual P plus 1 because SS residual decreases as P increases, but the same thing is not true for MS residual here this could be this could be larger than the MS residual P. The reason is this you know the increase in MS residual P by N minus P occurs I mean this may be I mean larger this occurs when the reduction in SS residual P for adding a regressor to the model is not sufficient to compensate for the reduction the loss of one degree of freedom in denominator. Of course, I mean what I want to say here is that you know this one is of course smaller than this one, but if you add an irrelevant regressor in the model this will decrease, but the reduction here for adding one more regressor in the model the reduction in SS residual is if it is not sufficient to compensate you know one degree of freedom loss here then only it increases. So, if you if the newly added regressor variable is not relevant for the response variable or not relevant for the model then only you know the reduction in SS residual for adding this irrelevant irrelevant regressor to the model is not sufficient to compensate the one degree of freedom loss in the model then only MS residual increases. Well, so we learn how to how to evaluate you know all possible models using the MS residual criteria in the next class well. So, we will continue this this criteria MS residual in the next class. Thank you for your attention.


Well, very, very good morning, everyone. Thank you very much for joining today. This is part of the sixth series of the webinars that we're running as part of the Constructing a Digital Environment program. Details of the earlier webinars we've run are all on our website and on the YouTube channel, which we have, links to both of those will be popped onto the chat in just a moment. There's a Q&A section in the footer of the page and if you have questions for the colleagues today, please pop them in and we'll do our best to bring those in. So I'm Steve Halles, I'm one of the Constructing a Digital Environment, Digital Environment champions of the program. And the focus of this program really looks at supporting the development and the application of digital approaches in environmental science. And in this series, we're focusing on the demonstrator projects that we are supporting through the program. So there's a number of the demonstrator projects and each of the webinar looks at each one in turn. And this area that we're covering is a very important one because we live in a complex world and the human and natural worlds intersect, of course, even in ways that are unimaginably complex. And so in effect, we need improved and better tools to enable us to tease apart and understand the processes that are going on to understand the implications to our society and also both at a local city, regional and indeed national and global scale. And these are all very much key issues that were discussed even in COP 27 recently, of course. And so one of the things to notice the focus in these webinars is very much on the concept of the digital environment. And this is an approach whereby we are trying to represent natural processes and the world around us in digital forms and both methodologically and of course philosophically developing a digitally enabled environment for the benefit of all scientists, policy makers, businesses, communities and the public. Trying to understand these complexities that we have and we touch on an arc of technologies from capturing data with sensors through to the handling and transport of that data, the storage of that data, the analyses, the analytics that go without the processing and the visualization and decision support activities. And many of our demonstrators, as today will hear, pick off across that arc. And very much the other focus of these talks is the interdisciplinary nature of the projects that we are supporting. And that's one of the reasons why we're delighted to have the whole team from the Retina project with us today to give that full spectrum of insight into what it is to take part and to put together a project like Retina. And I'm very pleased to welcome all of you and Jagadish as the lead for this project. Jagadish, I wonder if I could ask you in a moment to introduce all of the colleagues on the team. And then I believe you're going to give us a bit of an overview with PowerPoint and then we'll get on with the discussion. So if that's okay, Jagadish, over to you. Thank you very much, Tiffan. Thanks for inviting us. And I'm very pleased to talk about Retina project and introduce our team this one. And before I ask the people to introduce themselves to our team, I would like to say that this project was led by Jane Sutton Institute with collaboration with Aberdeen Institute and CH. We have a huge number of people working in this project who are participating in this webinar as a basically subset of them. And also we have a really good mix of experts here including both from File Science, Computing Science, Biogy Chemistry, Modeling, Social Science and even the app development. It's a real diverse group. We are excited to show all these different perspectives how they come together to produce something innovative in this project. So I would like to introduce Pete Smith here. Hello, I'm Pete Smith from the University of Aberdeen. Keith Macalarch. Hi, I'm Keith Macalarch based at James Sutton Institute outside Aberdeen. Elizabeth Cowdery. Hi, I'm Elizabeth Cowdery. I'm a postdoctoral researcher at the James Hutton Institute. Thanks, Maxi. Damian. Hi, I'm Damian Bienkowski. I'm also a researcher at the James Hutton Institute based just outside Dundee. Thanks, Damian. David. Hi, I'm David Donnelly. I'm another Hutton scientist also based up in Aberdeen. And finally, Becky. Hi, I'm Becky Smith. I'm a research software engineer for the James Hutton Institute, also based near Dundee. Thanks, Ethan. I would like to share my presentation just to give you an overview of the project then you can have a discussion. Please, the floor is yours then. Let's give us a short overview. Hope everybody is able to see the flight. Yeah. And first of all, thanks for inviting us again. And I'm very pleased to talk about the digital project and which focus on developing a digital, monitoring the replacement reporting system. Just to give you a background, this is the UK greenhouse gas emissions by sector. As you can see from 1990 to 2020, there are substantial reductions were achieved in some of the sectors, especially energy sector, focusing on renewables. But I would like to highlight here the agriculture sector, which is almost state flat, more like. And that means there is a huge possibility that just the greenhouse gas emissions, but there is little progress was made in agriculture sector. So it is the same thing in Scotland. Scotland agriculture stands at the third position in terms of the source. If you see the change in the net emissions from last several decades, there is substantial reductions were achieved in energy sector and land use sector, especially increasing the forest areas. But when we come to the agriculture sector, there is a very little progress was made even though there is a huge potential in this sector. So if we want to achieve net zero by 2050, we need to make substantial reductions in every sector. As we can see in this graph, 1990 to 2018, there is reduction for achieve, but in order to hit 2050 net zero, the streak of the curve indicates that we need to make substantial reductions, including the agriculture sector. But to just go into the background of this one, agriculture sector Q is a huge potential in terms of harvesting the carbon into the soil. And there's a wider consensus on the scientific community that there is about this potential and the crop line worldwide could speak best between 0.9 to 1.85 petagram carbon per year. But soil organic carbon, but the complication here is that soil organic carbon content and the soils and greenhouse gas emissions cannot be easily measured, which is one of the key barriers for implementing programs to achieve net zero. So there is a need for credible, reliable monitoring verification reporting platform to reduce uncertainties in national reporting and also emission trading, especially operationalize the carbon market in agriculture sector, and also track toward net zero, because we need to inform the farmer where they are in terms of their journey towards net zero and make them make a conscious decision something. So the overall objective of the retina is to create an iterative near real-time digital monitoring and reporting system that forecast crop yield, greenhouse gas emissions and carbon sequestration at the farm level. So this will also create an end-to-end data pipeline from collection to the processing and understanding the data and to improve the way we visualize the data advancing modeling and nation making. So this will give a conceptual diagram how the entire retina works. We, in this project, we first look at the farm level, we look at the baseline, what is actually the baseline, what the farmers are doing. In order to do that, we need to have an, we doubt an automatic land parcel detection in the app. That means once you choose a land parcel, we integrated all the information and also see that a land parcel in the background. That means the farmers will be able to see what is with the existing databases, what is the soil carbon stock, what is the soil carbon levels, what is the nitrogen and all the set of information. And also the climate data with the existing databases.  That means we guide the farmers to take the soil samples based on the local information, which is available, which is pulled all the information from the existing databases. So this is more tailored to the local condition. That's what we call it as a smart sampling. So then we deployed the sensors. We are capturing the information at three different scales. One is at the plot scale, where we are deploying the soil temperature, moisture and other sensors into the fields. And also at the landscape level, we are running the drone to look at the crop growth and biomass estimation so that we can estimate how much is the soil carbon input into the soil. So again, we are using a remote sampling to track the fallow period and also plowing even and also the planting and harvesting. We are trying to track before and validate what is really going on in the field. So we are integrating all these information and also we are in order to, we are feeding into the model basically. So produce the output and pass on to the end user. So in order to run the model, there are three basic information we need. One is biophysical information, soil climate condition and the climate and the management information. So by biophysical information, we are collecting through the real-time monitoring with sensors, whereas the management information is still missing. That is the reason we have developed an app, which is called Retina app, which is used to collect the information and also provide the output to the farmers. So the app will, the all the information that is going to the app that will be utilized by the farmers for relation making on the field. So the app will, the farmers will enter the information, the management information to the app that will feedback into the model, into the system. So the model will run even for the future conditions and then predict what is the best management practice for the particular location and advise the farmers once they adapt that one, again, the feedback will continue. So this will be useful for two stakeholders, there are major stakeholders, one is the farmers and agri-food businesses. The farmers can utilize for the carbon farming and also better management, they journey towards the next zero. And in future, they can use it for carbon trades because it's a more digital monitoring and verification system where we can provide a digital reference to every activity so that it will provide more credibility to the system. And also it will substantially reduce the cost of monitoring and verification. So because we are using the digital system where it's currently in the market, they need to go physically and monitor and verify which is very expensive. So, and also agri-businesses if they want to go towards the next zero, like Netflix and many other companies which are rely on the supply chain, where the supply chain emissions are very high if they want to reduce the product emissions, they need to focus on supply chain. So they can use the system to monitor their emissions and track their emissions through the system because it produces a real-time update to that. So what are the benefits of regional technology? Yeah. So it's an iterative real-time model prediction that gives and also a complete data traceability. This is very important because it will provide a lot of credibility to the carbon market if you want to go back. And also data collection directly from the field without any input from the farmers. There are many tools existing that currently in the market which are very cumbersome, needs a lot of input from the farmers and the way you process and input to the farm information actually that will change the outcome. So we are bypassing that actually so that the farmers need not input much information here. And real-time data driven science bring more credibility and transparency and real-time data visualization for information at the farm level. For example, if the farmers have made any activity in the field. For example, if today the farmers love the land, the system immediately show what is the impact in terms of greenhouse gas emission and how the carbon is going to change in the field. So they can make an information. And we are using tier three models instead of tier two factors. That's also enhancement in terms of net quality and high quality soil and climate data such as an integration. And also this will pay a way for the digital space as we go along. So one of the three requisites for the disciplines is to real-time system, real-time system. So this is what actually we are trying to do. So in terms of pretty outreach, what we have done so far, we have focused on four areas. One is that we have an engagement with the industry in the scientific community and policy and public. We have presented several outcomes in the Royal Highland Show and Aroble Scotland where we have interacted with several farmers there. So that's one engagement we have and several are coming up this year too. The project runs up to this year all get sold. We have several events are planned. And also we have an engagement with the industry. We have several discussions with Singenta, Nestle, Yoval Yogurt, Agni Carbon. And also in fact, we put a bit to the depth of farming features in order to scale this technology. It's in the stage two, which we have, it went up to the stage two. So they have a lot of interest from the industry on this project. And also scientific community, we have presented some of the outcomes of this project in various international forums, soil against matter dynamics in CO and other conferences. And we have two scientific papers published and several are in the pipeline. And actually the technology we have developed in this retina led to several projects for our follow-on projects. Some of them were EU SENS project, 1.4 million and SOAR project, NECSOAR project, another 1 million and RESIS transition projects, which is 1.5 million, which are all based more or less on the things which we have done in retina. And also we have a lot of policy engagement during this last three years. And we are interacting with the government and also DEFRA have given several presentations in the DEFRA. And also we are working very closely with the Scottish government. And this is one of the picture we can show that this is the, I'm explaining to the, about the project to Mary Gusion, who's the Cabinet Secretary of the Scottish government. Thank you. So finally, the one thing I would like to say is that retina spin out. And this is what we have developed in the retina. We want to scale it up through the spin out that's called the carbon experts, which is in the pipeline. Thank you very much. I will stop here. Jack, thank you very much for that overview, really very comprehensive overview of the project. And it's good to get that sort of feeling for some of the impact that you're clearly having with the Scottish government as well. I mean, you've mentioned a number of challenges really. And I was thinking in terms of maybe Pete, I could sort of turn to you here. Some of the negative things, the emissions from this sector are growing and there needs to be ways to address that. And on the other hand, side of the coin, some of the positives in the sense of the opportunities for sequestration and the mention of things like credits. I'm really interested in that. What's your take, Pete, on the sort of size of the challenge and what that challenge is? Yeah, so the challenge is to get into net zero. And we know that there are going to be some emissions that are very, very difficult to abate, and we must offset those with sinks. So we already know that. And soil carbon sequestration offers the potential for a really big sink, about five gigatons of CO2 per year globally, which is about 10% of our current global emissions. Now that's a technical potential, not an economic potential, but it shows the size of the potential mitigation benefits that we can get from carbon sequestration. The biggest barrier, why it's not happening, is it's difficult to monitor, report and verify those changes. When you plant a forest, you can go and put your tape measure around trees and you can demonstrate that it's growing every year. And you've got a good estimate then of the increase in biomass. You can't do that with soils. You can't see the organic matter. So MRV is the biggest challenge. So what's stopping it being implemented more widely and stopping the farmers from getting the carbon credits or the benefits from it is the lack of MRV. So that's what the RETINA project aims to address. And if we can crack that nut, it could unlock the potential that soils have to form that real, real big sink that we know they have potential to do. So you see a way of actually carbon credits extending to the soil in this way through monitoring and that's really interesting. Yeah, okay. I mean, I sense then that one needs digital approaches to monitor the soil conditions. You've talked about sensors going in the ground. And of course, one of the thrust of the program that's constructing a digital environment is this notion of digital environment. I'm just wondering what the context is for your sort of interpretation team as in terms of the digital environment. Maybe, Kate, you have a view on the, how do you deal with digital environment in your work and how have you addressed this? Yeah, no, thanks, Steve. And I see it as a sort of a gradient like many others. We've been carrying out environmental modeling for 40, 50 plus years. And this was highlighted in the sort of the report by John Sidborn and others about the information management framework for environmental digital twins. This is a gradation from environmental models to maybe real-time digital twins. So picking up from where Pete left off to have these MRV systems, then it just needs to be digital to bring together the information and knowledge to provide those robust and transparent ways to enable that all parties have got confidence in the system. And Jagadish might want to say a bit more because his expertise in carbon MRVs. Yeah, what do you think, Jack? Yeah, I would certainly add to this. What actually we have been in the modeling for the last 20 years, at least myself. One of the limitations we see is that lack of data that is coming from the field, the models can learn about the system only if there is a data flow from the system. A continuous data flow really helps the model to actually learn about the system better and really mimic better. So this advent of digital technology, especially the sense of technology really help us because that's what actually data we will streamline the data from the field to the model. And then that will make it really powerful because the model field is capable of learning about the system and get better and better over a period of time. So that gives the, you know, are there particular areas where you could apply these approaches in other contexts or other areas that you think perhaps wouldn't be appropriate for this sort of approach and what are those and why? No, actually I see much more at the system level even though we are focusing here on the carbon and greenhouse gas emissions, but these models can be used for nitrogen optimization in the field and many other things related to the agriculture management actually, this can be extended and many models can be bring in into the system and into the framework actually, even the biodiversity elements of it, which can be included in the lack of part of it so that we can comprehensively look it into the environment and record, monitor and verify. This is very three elements which are lacking even in the government policies, many policies now targeting towards the results oriented approach. So when we want to look at the results, we need to know, we need to monitor and verify those results. So in terms of those objectives, those sort of monitoring and so on, we clearly the role of digital is key here. Let's turn our focus a little bit to the technology that you're using. I mean, it'd be interesting to know from you what sorts of technology are you using? You've mentioned sensors in the ground. You showed the photograph of a rather interesting looking sensor with the minister. Maybe some of the, who are the sensor people here? I'll jump in. It's Damia and myself kind of do a lot of the things with the sensors. So I have come on board to manage the, or to help manage the Laura-WAN enabled sensors. So we've got two sites. One is arable and one is upland pasture and they both have the same sensors across them. So each site has a weather station and a CO2 sensor. And then both sites have a differing number of soil, moisture and temperature profiles, they're called, which stick into the ground and take readings. One of the things that we've sort of had to manage is being able to lower works on line of site technology, which sounds great because it can go up to 10 miles and things unless there's a hill or some trees or a building. And so it can be a bit more complicated and working out what sensors can actually be seen by each gateway, which is the receiver for the sensors. We've also had issues in terms of the some of in the upland pasture, the ground gets more solid higher or closer to the surface. So we've had to use shorter probes, which means that they don't have as many measurements. Those ones were also attacked by some sheep. Sheep found the sensors very interesting. We've now built them little protective cages, which you can see, but to begin with, they were very interested in our sensors and did want to eat them. On the arable farm, we've had a similar issue in the attractor versus the sensor is bad. The sensors don't like that very much. So our original placement decision didn't take into account where the tractors would have to drive. And that's I think that's been a key learning point because we don't want those sensors run over and something that we would definitely do differently. Next time is not put them in the tram lines and yeah. So those are the sort of the Laura sort of radio wave technology sensors, the sensors that we've got in place at the moment. And Damien does some cool things for drones and stuff. So I'll let him talk about that. Well, I like the idea of having less tasty sensors. I think that's a key knowledge that I don't think other teams have mentioned at this point. So it's a new one for us. Damien, yes, I mean, you're using aerial platforms as well to capture to get there. Because on the one hand, you have a specific sensor at a point. And I guess, Becky, the challenge would be, how do you know that that point is representative of the area that you're trying to capture? But then on the other hand, Damien, you're using drones and aerial platforms to look at the whole sort of whole farm or whole enterprise. And how have you approached that? And what sort of challenges have you had with that? Yeah, so thanks very much, Stephen. Yeah, so we're writing that. I've been conducting aerial surveys of those two experimental sites that Becky was just mentioning. And the ambition was to produce maps of the distribution of above ground biomass. And to do this, I was using off the shelf UAV with multi-spectral sensors. And originally, so I find multiple surveys at each site over the growing seasons. And originally, because I'm quite new to the area of aerial imaging, I had sort of made an assumption that existing models of biomass that other people had produced and published might would be applicable. But we discovered quite quickly that the models that other people have produced based on their data, their sensors collected under different conditions, couldn't be applied. To the data that we were collecting. And so in tandem with the aerial surveys, we've also been collecting ground truth measurements of biomass. So, you know, cutting quadrats and drawing them at the arable site and using a GPS pressure plate meter at the upland pasture one. And so actually we are sort of still in the process of constructing our own biomass models to map the biomass over the fields to feed into those MRV. So that's kind of been one of the big sort of challenges. What was the reason why you couldn't use the models with the sensors you had? What did you find? They just gave bizarre results as to how much biomass. It's just that the situations that they had been trained on, those models weren't directly applicable to what we were doing, even though, you know, pasture grass and things like that. But it perhaps only been trained on a subset of situations of the heights of grass and things. And we were looking at a broader kind of range of data. And so areas where the grass is obviously very thin, it's still giving quite high values and things. So it just, it was clearly incorrect. And so we're having to develop our own ones. And those are sort of still under construction and to be fed into the MRV models going forward. Re-evaluate those models as well? Yeah, well, I mean, those models work for, you know, of course, we're using a slightly different, you know, set of sensors and, you know, different geographic situation. So, yeah. And we have other sort of challenges that I guess will come into play moving forward. My collection of data and the processing of it is very sort of manual, has a lot of manual steps. And I think for the future automation of that is probably going to be a bit of a challenge if you, if there was an expectation that a particular landowner would have sort of surveys done of their land, when to do it best time. I'm interested, and Jagdish mentioned that we're sort of paving the way I think was the phrase used for digital twins will be interesting to come back to that in a bit and see, you know, what would need to happen. You've mentioned automation being one thing. You know, so, you know, back in day when you're referring to sensors in the ground and obviously at aerial platforms, but of course there's other ways of capturing data as well. And, you know, there's a link that I suppose needs to be captured where people and technology interact as well, particularly if you're trying to build citizen science type approaches and, you know, I know, David, you've got your camera either. Yes, David, you've built a number of these sort of tools, and I think that's been factored into the project. And, you know, in our discussion before, I think you mentioned there was a short clip that you might you might even show some of the apps that you built, but what's your take on that sort of interface between people and technology in projects like this? I think the key thing is, and it's really no complicated, understanding that you're making the app for somebody else. You're not making the app for yourself. You're not making the app for research scientists. In this case, we're making the app for a farmer, so it's got to be suitable for a farmer. Even to the extent that, well, certainly this part of the world is, your archetypical farmer is a kind of son of the soil. So an app on a phone with tiny little buttons just isn't going to work. It has to be actually physically usable. So that's one aspect. The other aspect is, well, actually, what I should say, we did have plans for effective stakeholder engagement, but unfortunately COVID got in the way of those and they've been delayed. They had to be abandoned at last minute. So, but we have some more planned for the next few weeks and we'll try and get those, the outcome of those into the app. And we have a number of active farmers, actually the institutes, colleagues, either the spouses or farmers. Yes, mentioned the video. Do you want to run the video? And I can talk to the video, Jagadish. I think that would be good if you've got it though. We can, I mean, there was a very, while Jagadish is just doing that though, it was a very interesting talk. The last webinar all about the idea of co-creation of citizen science with apps. I mean, some of the concepts that the team were drawing could be of interest as you develop the thoughts in that area. Indeed. As I say, unfortunately, COVID really got in the way of the stage of the project and we were doing that. Yes, unfortunately. Jagadish, it matters not whether you run it or not, but if you wish to share the screen instead of going. Just talking to it. Yeah, so this is a whistle stop. So three minutes. So the app includes registration sections so you can request to be a user of the app. The information stored in a server straight through to site configuration. If you like to set up the digital farm, this is using an interactive map as you can see. Click on the GPS button to take it to your location. This is simulated and it's actually taken to the research farm in Glensaw. Change the background so we can see our fields. Click to pick your field. Now, that's communication and survey database. So grabs the field boundaries from your database in real time, records it remotely in our server and also on the device. Now, this is showing a map of soil carbon. Click anywhere and you can see the top soil carbon. And this is very rushed. I'm really sorry, but Jagadish mentioned sampling. So this is where we generate sample locations based on the environmental conditions at the site. The app also helps you to measure the samples by using the device sensor, so GPS, but I'll just skip on past that. And this section, we can look at a bit more detail. Jagadish mentioned extracting information from existing databases. So I can show an example of this here. Zoom into one of the fields we've selected. This is a part of our boundary research farm. So you can get this all stopped just by clicking on the polygon. Just in the computation. And there we have this total soil carbon for that field. Topsoil and subsoil. And so nitrogen. Calculated in real time from the not in solid database. We also record farm activity. So. I'm sure. So this is a historic timeline. Spring barley potatoes and winter wheat. We also have a Jagadish mentioned submit the farm activity to the database. So it gets included in the models. This is a little tool of God for recording the date and crop and activity associated with it. This all feeds into the database on the server, which is then used to improve the model and produce new outputs for the. The farm. I won't press send, but this would send the information to the database as well as storing information and database on the device. And then we'll go back to the time of sensor data that Becky's been talking about. It's not quite real time at the moment. I'm not sure whether the sensor is down, but that's the last recorded. Records for the. So you saw moisture. So temperature. On the site. Becky's looking confused. We'll come back to that. She began, isn't it? Probably. And finally, just to show some sample results. And. Carbon sequestration. Over a period of 50 years. And. This will quickly show to, yeah, too quick. And that's my three minute. Show of the app. We can, we can go back to it. But. One of the main challenges with this is using. Multiple technologies mean this is using. Ordinary survey data in real time. Grabbing is using mapping services from ourselves is using. Our data to compute special. And it's also using three different types of databases. Three or four different types of computer languages. Tying all these together is one of the big challenges because there are no, no standards. So you have to write the glue. It ties one thing to another thing to another thing. And make it all useful and meaningful. But one of the, one of the sort of observations here is that the, the beauty of systems like this is, is that the farmer should be familiar in a sense if they've been using Google maps and other other packages like that, they should take to what you've done very nicely. The idea of bringing up a map, changing the background, selecting things and so on. So it's, there's a familiarity. I think that's good. Another advantage presumably is that you don't need your stakeholders to have expensive software and other packages because they, all they have to have is the app and then, and then they're often away, I guess. Yeah. As long as we retain it as being free. Yeah. We can continue to use these services because all these services are predicated on not charging. Right. If you start to charge your product, then you have to enter into different agreement, see what the ordinance survey into a commercial agreement, which the ordinance survey would be happy to do. Yes. But then you have to start charging the farm and charge. I think as long as you can keep these things free upon of use, it does make things much more straightforward. You're putting a tremendous amount of power in people's hand and all that information that they can bring up for that, that location. Thanks very much, David. That's great. I mean, I'm just Elizabeth, if I, if I may turn to you, keep your videos going, David. Don't, don't, don't disappear. Elizabeth, you know, you've obviously been working on the, the technology of the rest of the project as well. I'm just wondering whether you have any thoughts about, you know, things that you've learned as the, as the project has progressed in your role and. Implementing the different technologies and the old, that's not it. Would you do things differently if you were starting again? Yes. Well, um, so, uh, I could talk about a couple of things actually. One thing I could talk about a bit is the automation side of things. Yeah. Which actually could, um, I think tie a little bit into, um, design as well. And actually, I don't know. Um, it might, since, since we sort of have been talking a little bit about automation, you did sort of ask questions about that. It might be useful to show that slide as well, because it does sort of tie in, uh, the things that David and, and David and Becky talked about as well. I think maybe, um, I don't know if Jagadish, if you want to, if you want to share it, I can control your screen. And flip through the things. This is never going to work. It's never going to work. Maybe I should, maybe I should share it then. Uh, shall I, shall I do that? Yeah, go for it. Okay. Um, one second. Just while Betsy's doing that, um, certainly from, from our perspective with the sensors and things, one of the things we have found is that one location of the sensors, which David highlighted with his video and had me very confused because I fixed that issue last night. Um, so. Um, the data is up and I checked the live screen and I can see the data is up and then I remembered it was a video you were using. You weren't demoing live. Um, is, yeah, I think having more time to play with the sensors and the technology before putting it into the project would have been nice. Um, and would definitely be something we would do. I would do if I was doing it again next time, because this is the first time I've worked with water sensors. Um, and we've custom built all of the sort of, um, the servers are custom configured all of the servers and things in the back end. So we control everything. Um, but it was a steep learning curve and that would have been good to come before the project. I think this, um, Becky and Elizabeth, you know, this is all about what are the learning points ready to share with colleagues and other folks who might be thinking of doing projects like this in the future. How are you doing with the video? So. Oh yeah. No, I actually, I suppose that's an interesting point is that, you know, when you are using open, open software, open data, open services like that, often you are your own maintainer, um, which has its benefits, um, because you have full control and open access, but often then you are also responsible for, uh, your own maintenance. Uh, and also, you know, uh, understanding the services yourself. And of course now so many things have such wonderful documentation that it is, is very easy. Well, I should say the learning curve is a lot less steep, but at the same time, um, for example, we did put a lot on Becky's plate, uh, asking to get up and running with the, the lower one, uh, servers, um, or, uh, uh, uh, I think sensors I should say, um, because that, that was, yeah, complete, completely new technology.  Um, but it is, it is a different, it is a different ballgame if you, if you take that, if you take that route. I'm just looking, um, um, thank you very much, um, Edward Darling, who popped a quest, uh, point into the, the chat. It looks like the red list revival, if I read that right, and their work with a life map, they're proposing having a discussion about potentially mutual interests, that sounds good. So something to follow up following this. And anyone else who'd like to pose a question to the team, please pop questions in. But if I may, perhaps, I think we've had a chance to discuss the technology, but one of the things that's interesting really is the policy implications of tools like this, and the way that technologies support the evolution and the prosecution of the policies, agricultural policies at the national level. Greg, if I may turn back to you, I'm just wondering what the scope of the policy engagement you've been able to have and demonstrate in the project is, and how that's gone? We have a couple of rounds of discussion with both the courts of government and DEFRA, and this one, and still we are in touch and plan several events in the next couple of months. The issue is that, as I mentioned before, even the DEFRA and also the courts of government, looking for the payments in future, they want to link for the results. But we need to provide the technology solutions. The reason why they can't do it at this point is, there's no credible way they can monitor and verify these things at the ground level. So this is one of the limitations. I think that's what I see the role of digital technologies can play a big role, especially projects like this, bringing different technologies together and make this work and monitor and verify, and that will allow the policy to roll on. One of the things we are looking potentially at this moment is courts of government is, courts of government already pledged 51 million for baseline soil monitoring. So the farmers can enroll, and that will be, they can do the soil testing freely that we paid with the government. And we want to utilize the data into this, where they can use the retina setup in their farm, so that they can be informed about the net zero, how they travel towards the net zero. And the government can use this one for monitor how the progress that is happening in terms of soil carbon stock across, once we scale it up. So there is a huge potential, but there are few limitations too. That's what we are discussing with the courts of government and also with the DEFRA. Thank you. David, you're showing the current view, I think that. Yeah, I was just to show the live stream from the... Oh, very good. Yes, yes, yes. One of the interesting things in these projects is the juxtaposition. And we've spoken, David, we spoke about the people and technology coming together. But, Pete, if I could turn to you as well, is the juxtaposition of the science and the policy objectives. And how have you sort of pitched that in this project? How has that gone? What are your sort of learnings from that that you could share? Well, I think the policy pull is there because as Jagadish has already mentioned, you know, the Scottish government and the UK, the Westminster government, is keen to pay for... You know, there's the Elm scheme in England is going to pay for public money for public goods. And there's an intention to pay for that on an outcome basis rather than an activity basis. So this issue of monitoring and verifying and reporting changes in soil carbon is important not only for carbon credits but for the private voluntary carbon markets, but also for the national inventories and for tracking our progress towards net zero in 2045 in Scotland and 2050 in the UK. So there's... You know, if we can get it right, governments I think will bite our hand off to use this system. So that's why it's so exciting to be involved in it because it's got a bunch of policy customers and government customers just waiting to use it if we can crack it and we're getting close to that. It could be a step change forward. Yeah, absolutely. Thanks, Pete. I mean, Kit, what are the challenges of actually doing that, of taking a project like you've got here and actually rolling it out in that sense? What are the sort of challenges that might exhibit in doing what Pete's just laid out? As I speak, highlighted that there is a demand and that's key because that's what's always highlighted, but as Mark Esno sort of often summarizes from the digital twin hub, which is the industry aspect of the digital twin activity, it's learning by doing, progress by sharing and it's actually getting into the details of specific projects like this one and finding what works but also trying to learn from others because sometimes others not only within research but also industry and the tech sector have solved a lot of these issues and I think one of the challenges, especially for researchers is being aware of, what are some of those options in terms of technologies and choosing what to use and what works where? Absolutely, yeah. I just want to add to what Kit mentioned here. One of the challenges we see is that we demonstrated this project in two parts at this point and there are so much diversity in the firm as we go along when we scale it up. We need to live up to that. We need to design the system where the system will cope with that diversity and also the scalability of it, the impact that we can see when we are able to scale it up. Can we scale it up to 100,000 hectares? Can we scale it up to 1 million hectares? So that is the next phase we want to go in whether what actually works we demonstrated here, we want to scale it up to really high land area so that we can see and produce a real-time system that will change the whole paradigm in this. And there are huge challenges in scaling up because for farm level operations, there are many things which we are using that works at the farm level. We don't need such an intense scaling software or even when we deliberately use the Docker system here so that we can multiply in future that one. So we need to see how this will operate when we scale it up to 100,000 hectares. So that's a challenge actually. Yeah, certainly is a challenge. Pete, as a distinguished scientist and the environmental scientist, how are these sort of digital approaches supporting the advancement of scientific understanding in projects like this? So what's the role of digital in helping further our understanding of environmental processes and so on? Well, as a modeler, I would say this, but we can use models to run different scenarios and to examine the science. Our best understanding is encapsulated in these models. It takes many years to set up and run a field experiment. I'm not saying we don't do those, we still have to do those, but they take five years and an awful amount of money to run. So we can use models, we can use the digital environment to explore the possibilities, to shortlist the things that we might do and we might want to look at in more detail and to base policy priorities on the outcomes of the models. But that has to be grounded in good data and we can then go and do longer-term experiments, also funded by UKRI through the standard grant mode to fill in our gaps and the information. But the digital environment allows us to explore much more possibilities quickly and cheaply. Interesting, yeah. And I mean, are there other... Elizabeth, if I could just come back to you. Are there areas where it doesn't work having digital environment approaches? So what's the learning for where it's appropriate and perhaps isn't appropriate? Well, I think it's interesting when I got this question, I wanted to turn it on its head and say, I think that I really benefited from going to the digital environment conference that happened a couple of months ago and seeing all the different versions of digital environment projects that are happening and seeing the interpretations of what that meant. And seeing that even though we can sometimes accomplish a lot, we're still not necessarily always very good at communicating it. And so I think that there's still a lot to be accomplished on the science communication side of these things, which is a goal point of this science project. And so although there is an automation side of this to accomplish the policy and management side of this and also to actually make the science worthwhile, the digitization won't actually, and also to be able to incorporate the new technology in from our peers. I saw lots of people trying to do the same things but in lots of different ways and we weren't necessarily communicating it very well with each other. And so I found that to be a major challenge that we all need to work on. And hopefully now that COVID is over, we can have more conferences, more communication amongst ourselves and we really need to hold ourselves to higher standards. I think communication is absolutely critical. And thanks for mentioning the conference. Yes, it was a fascinating time. We hope to run another conference this coming summer, perhaps in July, but so watch our website for that when we look for some more fascinating talks and that. We're beginning to run out of time but I have a sort of a last question. I'd just like to come back to perhaps Kirt and Damon for you. I mean, we've talked about this project like retina paving the way for digital twins. And I'd just like to get your take and others as well. What, where things stand with digital twins? What the opportunities are? What would need to perhaps develop in projects like yours to have a digital twin? What's the gap that we're trying to fill if we're paving the way towards a digital twin? And what's your perspective on digital twinning? So if I said, I mean, others have said like Mark, Esna, that federation is key and that's a social technical issue. It's about people, but also enabling it because especially one business isn't going to take it forwards. I think two other key aspects, one is privacy stroke security. You know, that's becoming more and more important where data isn't how it's looked after, but also how we do it sustainably. You know, there are different decisions we can make in terms of architecting sort of applications. And some of them have got lower carbon footprints than others. And what we do need to do is trying to do this work as sustainable as possible. Yeah, quite. Damian, what are your thoughts on digital twinning? And how projects like this can... Sorry, digital twinning isn't perhaps my background, but certainly one thing that springs to mind is, you know, what's the minimum sort of level of data you need to collect to, you know, accurately reproduce that?  So you need to work out, you know, what's the... When do you start to kind of saturate in terms of, for example, in the drone flying? How often does that need to be done? Can you get away with it during once in the season? Does it need to be three or four times to get accurate representation for your digital twin? I suppose that's a key challenge to make sure that you're collecting enough data to have a realistic representation. I'm not too much. But maybe, thank you very much, Damian. Maybe time for one last quick question before I turn to you, just for sort of your closing thoughts. But Becky, if I could, we've heard about the app that David's shown the app, but citizen science is something that we hear about a lot. And a lot of the projects, so the demonstrated projects we have are looking at citizen science. And I just wondering what, you know, what you're thinking is about the role that citizen science, for citizen science in gathering data and what might be some of the issues that you might anticipate in citizen science-type applications? I think in this area specifically, particularly when it comes to the sensors and things, we can't obviously expect everybody to be able to afford a drone, but even our sensors and the gateway and things, I think it's quite cost prohibitive for just anybody to be able to help us out. And so we need to find a way that the general public can assist, but without, you know, spending a lot of money. And I think that's sort of one of the issues in terms of what Damian was saying as well, in terms of, you know, about how much data to collect. So also how much of this is absolutely necessary if we can save them, you know, do they need four sensors per field or five or, you know, honing that to make it as accessible as possible to get as many people as possible involved in the collection of data? Yeah. I'll also add to that that one of the things that we're trying to show through the app, well, through the entire project is that it's possible to show things like simulations for one's farm, like Damian said, with essentially as little personal data as possible. So, for example, we have regional meteorological simulations that we have. We have regional soil maps from the James Hutton. And so we can start out with baseline data and do simulations without input from the user and then hopefully engage the user, show them where they can, you know, show them the potential that they can add to and then how the predictions of things like crop yield can improve with their input. And maybe just with personalized field management, you know, them telling us how they manage their crops, then maybe they're willing to invest in sensors for their soil or maybe they're willing to put up a tower to measure meteorological data, something like that. So it can be steps of investment and they can see how that improves their personal predictions. So it doesn't have to be one large investment and they can see that data and they can see how it's changing the model predictions. But they can also see something before they've actually submitted any data to begin with. So it's about enriching the data landscape. Exactly, enriching is a fantastic word. Yeah, yeah. I think that's a good observation. Thank you for that. So you've talked, Jagadish, about the history and you mentioned several decades of modeling leading up. And if we consider Restina as being on some kind of roadmap where that's behind you and it's led to what you're doing now, what's the sort of the roadmap going forward really just to finish off our discussion today? Where next with this? Yeah, we identified two streams of development in Restina. One is the technical development of Restina where we produced a functional prototype. The next step we want to do is the data really feed in, now the data is feeding into the model. Now what we want next step is to, the model should learn from the data and modify themselves to really mimic what actually predicts well in the field. That's the next step, which we haven't done in this project. That's going to the next technical development. And also the other aspect which we are looking at is the scalability aspect, which we are trying through the carbon extract, the spin out. Whatever we have developed, how we can scale it up to very large areas and produce it when in real time. So these are the two streams of work development which we are doing and several projects are already in the pipeline in that line. Thanks a lot, yeah. Well, look forward to hearing more in the coming months and years. Well, I think unfortunately that's all we have time for today. We've come to the end of our allotted period of time and I sincerely would like to thank all of the speakers today. Thank you so much for coming and joining us. I think to truly understand a complex project like Ratano, it's wonderful to have everyone here and it's clearly a project that draws on a number of disciplines and it's that juxtaposition of all of the different skills coming together, which makes it so fascinating. It's been a very useful discussion indeed about the role of digital environment in approaching these complex challenges and we've heard a lot about that. All the videos that we will take and including today will be popped onto our YouTube channel and Cameron has put the link to that, I think on the chat, thank you for that. So do come along and look at that and do subscribe to the channel if you wish as well. And what remains then is to thank you, the audience for listening today and to inform you that the next webinar in this series will be on Friday, the 17th of March, which is speaking about the project Sentinel and Dr. Paul Brown at Ferra and Team, the Sentinel of Treescapes for Plant Biosecurity and Risk. So I look forward to seeing you there and thank you very much for joining today. We'll call matters to a close now. Thank you very much. Thank you, Chief. Thank you for that opportunity. Thank you.


Microsoft's South Central US data center outage takes down a number of cloud services, but I actually like the headline from the register better Of course, they always have great headlines Thunderstruck as you're back in black out after voltage causes flick the switch So in short what happened here a lightning storm shook Texas facility all night long. I just I gotta admit I love that line, but Here we are almost 24 hours later. This is the refreshes who have got the latest page right here All right, this is the Azure status as of Wednesday, September 5th at 844 a.m. And we're still having warnings on here, which is better than an outage. They're still working on restoring services but Do we seen some failures in redundancy and I thought this was kind of interesting and I believe it could be related to this and This some of the application insight that's done What we seen was office 365 fail, which affected a lot of our clients people couldn't log in and It was kind of a mess So definitely crazy when this happens because there's not really when you're dependent office 365 and email and everything else There's not a plan B for that So it's feeling the interesting effects of the cloud and what I figured I do here was talk a little bit about the data center So 5150 Rogers Road in San Antonio, Texas is where this big giant data center resides I found the information on here. Apparently it's 477 the owls and square feet according to This article here I didn't measure it's definitely big. I kind of like how it's nondescript There's nothing more than an address in the building. We pulled back a little bit here to look at the data center itself It's big and I don't know why it says Chrysler Group on it that maybe is some management company that manages the property I don't know but this is 5150 Rogers and It's quite a big facility So it is definitely huge and apparently what happened was with any large data center the cooling towers Which they have some on this end and some on this end here I'm guessing and They're not always super open about what goes on inside these but this probably cools this side of the data center This cools that side, but the problem wasn't like a power outage The problem was related to the cooling system being taken offline and they didn't want the servers to melt Servers will melt and just destroy themselves if they don't have proper cooling And if you haven't ever been in a data center, the noise in them is a little bit loud And most of what you're hearing you hear all the fans and whatnot in the servers But you are really here in a whole lot of the cooling system going to try to remove all the heat as these systems generated It's always the trouble with big servers and data centers like this But it just had me thinking a lot about the lack of redundancy we have in the They use the phrase too big to fail It's it worrisome when they didn't have a smooth transition for the office 365 Now I get it if you're hosting your own app Inside of Azure and you want redundancy you host that app in another data center either Another company or another piece of Microsoft So you have geographical redundancy in case anything goes down That's part of the design what worries me is that Microsoft had so much Authentication being affected when one data center goes down and they have numerous ones when you look at the Azure status page You know, we've got East East US to Central North South Central, which is the Texas facility in question here West Central West US Canada East Canada Central Brazil South they have a lot of data centers here in the Americas And Then we have the Azure government ones Azure Pacific and then the Europe ones So there's a lot and I noticed that this application insights and I'm not an Azure expert So I'm not exactly sure what all application insights as I know. I mean, I read the blurb here It's actionable insights to application performance management But that appears to be affected throughout from Europe to there. So what I'm Surmising here, and I'm hoping there's a better debrief from Microsoft So I like when companies give us a this is what went wrong and the infamous several years ago Playbook failure at Azure, and I'm sorry at AWS when basically someone had Typed something and it was not filtered. So instead of say we're gonna take down 10 servers We added a zero and we took down a hundred and took down some of the systems over there so it's really interesting to Think about the scale and size when you talk about a 477,000 square foot data center And this is one and then our reliance on these things so many companies using it and relying on it and you are Disrupting of businesses you're disrupting the flow of business because we've become critically Attached to these systems. We don't always have a plan B to work without them So it's still interesting. It's still something to think about. It's you know a pain We mostly are you know, we're small companies So we're using a lot of self-hosted stuff, but we do use G Suite so GC were to go down We would certainly be very lost in terms of being able to send emails Which is a one of our primary communication systems and that's how a lot of our clients that are using office 365 where They were just stuck. They are like well, it's down and we're down with it Therefore insurance quotes can't go out processes can't be done billing can't be done There's just a plethora of things that go down So it's a lot to think about it's one of those You know, I'm hoping we get a really good debrief from them other than it exploded at a data center from a lightning strike But nonetheless, it's definitely something to think about when you're building your redundancy plans. How do you? Deal with it and it's probably good idea to at least have some process in place Obviously if you're dependent on an office 65 for email, you can't send emails What are you gonna do instead? What's your process for contacting your teams? What's your con process for what your staff is gonna do instead? So that's a little bit thinking and planning that people should have because there's this big assumption of this giant cloud server won't go down and it's redundant across multiple regions, but We still seem to have a single point of failure. This went down and took out things with it that people are very dependent on so Still interesting. It's still it's almost 24 hours later, and they're still just feeling the recovery So this is this happened and roughly I think it was on 10 a.m. Yesterday, and it's 850 a.m. Today And you know like I said things are getting better, but boy This is taking a long time to restore and making people hopefully wake up and think a little bit more about this and hopefully have a plan B Thanks Thanks for watching if you liked this video go ahead and click the thumbs up Leave us some feedback below to let us know any details what you like and didn't like as well because we love hearing Feedback or if you just want to say thanks leave a comment if you wanted to be notified of new videos as they come out Go ahead and subscribe and a bell icon that lets YouTube know that you're interested in notifications Hopefully they send them as we've learned with YouTube Anyways, if you want to contract us for consulting services You go ahead and hit launch systems calm and you can reach out to us for all the projects that we can do and help You we work with a lot of small businesses IT companies even some large companies and you can farm different Workout to us or just hire us as a consultant to help design your network Also, if you want to help the channel in other ways, we have a patreon. We have affiliate links You'll find them in the description You'll also find recommendations to other affiliate links and things you can sign up for on Lawrence systems calm once again Thanks for watching and I'll see you in the next video