Hi, this is Arvind. I secured all derangue 106 in IFS 291. Sankar IS has been an important part of my preparation. I enrolled for GSPCM pre-storming and IFS Intrived Guidance Program. At this moment, I want to thank Karthikeyan sir for his expert guidance and other faculty members. All the best for the future aspirants. Work hard. Believe in yourself. You will reach your destination.


OK, so we're starting right on time, 11.20. I'm a German person, so I'd like to be really sharp. Thanks for coming to a talk on AIOps. I'm really glad that so many people made it here. And as I saw a talk today on MLOps, and as we use the term AI and ML interchangeably nowadays, so if you talk to a salesperson, you probably talk about AI if you want to get money. If you talk to a data scientist, you talk about machine learning. And if you talk to an engineer, you probably talk about an SQL query. So the term AIOps means, how can we use AI to help operations versus the term MLOps is rather used for how can we operate machine learning in an operational environment? So this talk is about anomaly detection with Prometheus. Just a quick question. How many of you folks in the room would consider yourself an operations guy? Maybe raise your hands. And how many would consider yourself as an engineer software developer? And how many AI data scientists? That's good. OK, great. So lesson learned should be, don't be afraid of AI and ML. We're trying to make things easy. Now the clicker doesn't work anymore. What happened? So this is how I look out on the internet. On GitHub, I'm a software engineer slash manager, hacker, zombie slayer. I'm from northern Germany, a city called Kiel, which is close to Hamburg and Denmark. We do a lot of shipping. Only people, not containers, so we're not cloud native yet. We're still mode one. I'm working for a stealth startup called Red Hat. This is our brand new logo. Check it out. It's on the internet. It's really new. You can get some swag down there at the booth. And I'm working in the office of the CTO, also known as Octo, which is like an octopus. We get to play with a lot of cool stuff, like making sure things don't explode or implode. But in the essence, we're trying to define the gravity of the next technology that Red Hat's looking into or trying to invest. So it looks dangerous, but it's actually a lot of fun. And just to give you a little bit of understanding what my team is working on is we're trying to understand how Red Hat sees AI. So first, we want to make sure that AI as a workload, a machine learning as a workload, runs very good on Red Hat products. So we want to make sure that if you're running your workload on OpenShift, say, then it should be a really good experience as you would do it on your local machine. And then we have a project called FOP, which is taken from the Expand series. That's the thing where all the evil scientists work. And those guys are looking into making AI stacks really good and give you recommendation on AI stacks and software stacks in general. And they're also recompiling TensorFlow specifically for your environment. And we're able, just by recompiling it, to squeeze like 10% out of the TensorFlow project. And as a side note, I love talks with a lot of pointers and links so that you can do something after the talk. So I spread all those small post-its on my slides. You can take pictures during the talk, or at the end, there will be a summary slide with all the post-its on it. Then we have the Open Data Hub, which is another great project, because data is the foundation. Data is the new oil, people say. Software has become so ubiquitous. So now people are looking how to use everything out of data. And we started a project called the Open Data Hub on Open Data Hub I.O. It's a one-click solution to create a data science platform in your Kubernetes cluster. So if you go to the operator hub and click on the button, you can install the Open Data Hub in your cluster, which will give you SAF for storing, which will give you a spark for analyzing stuff, which will give you a Jupyter Hub for your notebook requirements. It's really easy. And you could try these things out that I'm showing here in this talk on your local install or in your cluster at home. And then we concentrate on AI-powered products and services. So for example, we have a service called Red Hat Insights, which uses some machine learning technology to identify problems in your data center. Or we are trying to make OpenShift really stable by looking into the metrics that the clusters provide. And clusters provide metrics nowadays. We have Prometheus. So we're trying internally to help the OpenShift team make better decisions if their development, their cluster rollout is going well. So this is what this talk is about. How can we look into Prometheus metrics a little bit more intelligently? We'll look at Prometheus, what Prometheus is. How do we set up some long-term storage to analyze your data? Then we look at the definition of an anomaly. And then we look into how we could integrate it into your existing monitoring setup. So a quick word of notice to set expectation. This talk will not give you a shiny product and the holy grail of monitoring. It will also not give you a ready solution that you can just plug into your monitoring setup and turn it into this old school spider demon from Doom. You also don't get a success story how we created or transformed our messy setup into a really advanced AI monitoring solution. But I'll give you the tools and the scripts to get you started. We'll look into some questions and answers to problems that you might face in your journey. And the good thing, it's all open source. So you will find everything that we do on the internet, which nowadays is the equivalent to GitHub if we were talking code. So what's Prometheus? Who knows what Prometheus is in this crowd? Keep your hands open up. And who played with Prometheus? And quite a bunch. And who uses Prometheus in a production environment? Cool. So then to get everybody on the same page, here's a quick architecture diagram of how the Prometheus setup looks like. And everybody loves architecture slides right there. So easy to decipher. Let's start simple. So we have this Prometheus guy. And as we're in the Kubernetes world, we have to name it from Greek gods. So Prometheus is the guy that's brought fire back to the people, so hence this flame. And then we have targets. And these are the things that Prometheus monitors, or wants to monitor. And those targets expose metrics just via HTTP endpoints. Really simple. The metrics are the current state of the application or the target that you want to monitor. So that's really important to understand, because we can't go back in time and ask the application, how was your state five minutes ago? But it's Prometheus adding that timestamp to the monitoring data that you want to scrape. It gets tricky if you're running in a disconnected environment like in your cloud provider, which might give you the data back in a time slice interval. So you want to make sure that your data scientists understand that domain or that problem. And then Prometheus stores the metrics in its time series database, which is essentially really performant database for this kind of data. And you can query the TSTB with a powerful query language called PromQL, which is kind of weird to understand if you're coming from an SQL background. So you will be dealing with vectors and some multiplications. But there are tutorials all over the internet to get you started. And now metrics don't mean nothing if you don't get notified. So Prometheus can store rules, which will trigger targets. And then it'll push out alerts to a thing called alert manager, which is also part of the distribution. And this guy will take care that you're being notified. So in essence, Prometheus is made in its core for monitoring and alerting built around a very powerful and capable time series database. So what do we need for machine learning? Anybody? Data, exactly. So the giant hat wants data. Let's give him some data. Before we give him data, we obviously want to give him enough data, because giant hat wants really a lot of data. And Prometheus will only store it for like a day, two days, and then throw it away because it's made for monitoring. It's not made for storing it for long term. So we set out, so this project is like a year old. And I would show a bit of the progression here. We set out to look into a thing called Thanos, which I thought was also a Greek god. But my kids corrected me. He's from some Marvel movie. But he looks really powerful. So he can store the blobs in object storage. So he basically takes that TSTB and stores it in S3, SAF, or whatever. And then it has the Prometheus API on top of it. So in essence, it would give you unlimited retention until you're running out of storage. And you can query the historical data right via the PromQL and Prometheus API. So it's pretty transparent. And it also can down sample your data. So if you have it on a minute scale, but you only want to retain it on a five minute or a 10 minute scale, so it can also run these jobs in the background. But at that time, it didn't work out so well in our OpenShift cluster. Maybe it was OpenShift. Maybe it was Thanos, I don't know. So we looked into something else, like influx to be. It's also a great thing to store time series database, time series data. Because in essence, influx is also time series database. It has also nice integration with Prometheus because you can just configure a remote endpoint. And Prometheus will send every sample over to influx, store it there. But the unfortunate thing is it really eats RAM for breakfast. So it tries to keep everything in memory, to keep everything fast. And we ended up with like 6, 12 gigs of RAM after just a day. So it wasn't really suited for our use case. On the other hand, your data scientist will love it because you can connect your pandas data frame, which is a tooling that the data scientists work in, writes to influx. So they will have a really easy way to get into the influx database versus Prometheus they would need to learn how to query Prometheus. You could solve this problem if you would buy an influx cluster license and scale out. But unfortunately, they have the open core model. So they will only give you one node for free, which is OK. So if you want to use influx or you have influx already in your environment, you can easily use that. So we created a Prometheus scraper part that would just query the Prometheus API, store the JSON blobs in some SAP storage and some S3 storage, put it away, and store it there. Storage was cheap, and we just had it backed up there. We wrapped it into a container, configured it via an environment variable, and pointed to your Prometheus instance, job done. The good thing about it, you don't have to talk to your monitoring guys to configure their monitoring setup to move their data to somewhere else, which they might not want, but we just need access to their Prometheus store, and we just get the data out, which is pretty straightforward. And then you can use something like Apache Spark to query those JSON files. And as I mentioned, it's part of the open data app, so you would get that for free in your notebook. And this is a screenshot of such a notebook where your data scientist would work in. He just points it to the S3 endpoint, and then with SparkQL, it feels like a database. So you get your pandas data frame. You can query it with SparkQL, which really feels like SQL, over all those thousands and millions of JSON files, and they just feel like a database, which is great. Oh, and it also has some integrated, nice function like you can create the standard deviation of a large time series in a clustered environment, which would not be possible in a single node environment. So Apache Spark is really good for processing large data sets and doing ETL. Then we revisited TANOS a couple of weeks, months ago, and we now have it actually running in a production setup. So all the OpenShift4 clusters that you install will send back a subset of their telemetry Prometheus data to Red Hat. And we're capable of storing like 360,000 metrics per hour. And we store it off into a TANOS store for long term. So there's a blog post about it, how we set it up, and it's all open source. So TANOS works great now. And I think it's a de facto thing how to store Prometheus data for long term. So what do we really need for machine learning? Not just data, but we need consistent data. So we need to understand how the data looks like. There's a rough saying in the data science community that 80% of your time you will spend on the nature of your data, and just 20% on writing your neural networks and doing your actual AI stuff. So a lot of cleansing goes into this. So we needed to translate what the monitoring guys know about Prometheus metrics to our data scientists. So let's look at the Prometheus metrics, metric types. We have a gauge, which is essentially a time series. Then we have a counter, which is a monotonically increasing time series. We have a histogram, which is a cumulative histogram of values. And then we have a summary, which is slightly different. So a gauge goes up and down. A counter just goes up easy. Histogram, a little bit harder. So in a typical histogram, you would bucket your data into slices and count how many values fall into one bucket. So I have 100 in the zero to five bucket, et cetera. A summary is slightly different because it not only counts how many things fall into that bucket, but also give you a concrete value out of this bucket. So if you want to know, oh, my latency is actually 499 in that bucket. And in a histogram, you would only know that it's from 400 to 1,000. So you get an actual value out of it. A metric in essence, and I think I need to speed it up a bit. A metric is in essence a series of data points which consists of labels and the measurement, so value, and the timestamp. So we would have Huplit operations as the metric name. And then you give it some labels like hostname and operation type, and that is one time series in Prometheus. So you have to be very careful choosing your labels. So use something that is finite in your labels. If you put some infinite data in your labels, you might have a combinatorially explosion and you're just using them wrong because you're creating a lot of time series. And then you have like the values. So here's an example what you would store in these values. So monitoring is basically hard. Prometheus, for example, doesn't enforce a schema. So slash metrics, the endpoint can expose anything it wants. And as the slash metrics endpoints is provided by your application and your developer decides, oh, I'm gonna change that metric, then your monitoring guys don't get updated because there's no schema there. So their alerts don't trigger anymore. They think everything is good, but the developer just changed his metric name. So it's hard. And then we have a lot of metrics. So in a typical Kubernetes cluster, Kubernetes cluster with CNode exporter and some of the services being monitored here end up with 1000 metrics, which is a lot to understand. So the state of art in monitoring right now, I think it's fair to say it's dashboarding and alerting. And the dashboards are created by the subject matter experts which understand their stuff and they create dashboards and do some alerting. So there are no tools like to explore the metadata in metrics and that's what we tried to create and the tools where you start from are Jupyter notebooks. So here are some examples of these notebooks that live in GitHub that you can run, point it to your data, run the notebook again and you get the same results pretty straightforward. So looking at the metadata like the labels of your metrics would be the first step, like how many metadata is actually there? So here's a screenshot of something where we see, what is it, like the five month over five months, we see the unique labels plotted and you see that it started out with fewer labels and then continued to have a little bit more labels, et cetera. Here's another way to display the labels. So you see a clustering few of those. We used a technique called T-distributed stochastic neighborhood encoding which I can barely pronounce. I mean, I practice it a little bit but you don't necessarily need to understand it because there's a notebook. You can just run it and you get this nice plot and then you see some smaller classes and you can ask yourself as the monitoring guy, why are there some smaller classes of labels? What's wrong there? Looking at anomaly types, if we want to define an anomaly in a time series, we need to understand the components of a time series. So a time series might have a trend so it might go up or down. It might have some seasonality, like it goes in the morning, it fluctuates and in the evening, it goes down and it's the same every day and then you do some crazy shopping event and it goes really, really high up. So that's the seasonality that you could extract from those metrics. Or it might have an irregularity, so there's a sudden spike. And basically an anomaly is something where the time series doesn't look like we expected it to look like. So we might look at the seasonality, oh, it's going up and down but now it just goes down so that will be an anomaly or you could have a seasonal anomaly. We looked at the tool called Profit from Facebook. It's still maintained and it's a pretty nifty Python library. So point it to your time series. The black dots is the actual data and it will project or predict the data how it would look like in the future. So it gives you an upper value, it gives you a lower value, so that's a bounded window where your values should land in and it gives you the actual value where it thinks it should be. And it also predicts the trend in the upper picture. So in this case it goes up and in the lower picture you see the daily seasonality of your time series. Which is pretty straightforward and pretty easy to use. Then you could pretty straightforward say, oh, I'm looking at my value and compare it to the actual value and if it's not the same, then I create an alert. But I think you don't wanna do that because maybe it's just an outlier so we also looked into different ways how to actually determine that it's an anomaly. And the easiest thing would be an accumulator approach where you just count up a counter if you see such a deviation and you increase the counter and if you don't see it, you decrease the counter again but on a higher level, so you subtract two and if your counter goes above a certain threshold, you would call it an anomaly. And here's also some tweaking and playing in-game in place. All right, so let's look at our architecture setup so far. We have OpenShift or the Kubernetes cluster. We have the application. We have Prometheus monitoring application. Then we store the data in SAF or in TANOS. We have a Jupyter notebook for the data scientists to play with. We have Spark installed. And guess it looks a little bit too complicated to do it in half a day and we're in the Kubernetes world.  So we kind of expect that as well from these tools. So you want to play, right? So we wrapped it into a, obviously into a container. So the pink box is the thing that you can readily deploy. It would have the forecaster profit model there and some other models in there. It would store its forecast in a PBS. And then we export the predictions. We have the same way that we do our actual monitoring. So we just provide an Prometheus endpoint. We expose the slash metrics thing for our values. So you can hook it right up to your Prometheus instance or another Prometheus instance that scrapes this anomaly detector. It's all up on GitHub in our AICOE org. It has a Docker file so you can build your container yourself. It has a OpenShift build pipeline or it's on Quay IO so you don't need to even build it. You would configure it via a environment variable pointed to your metric name that you want to do the anomaly detection on and down there are the predictions. You can set up some alerting rules to be fired if there's an anomaly set to one and let your monitoring guys look into it. So demo time. Everybody loves demos. Let me see how I get out of SK. So I don't have it mirrored here so I have to. So here's my OpenShift cluster. We're installed Prometheus and the training pod which is doing the anomaly detection and the forecasting. Here's the slash metrics endpoint that is being exposed by this pod and you see here if we zoom in a little bit. Predicted node load one Fourier, Y hat upper, okay. So that seems to be our prediction. Can go to the Prometheus UI and it has this nice graph but that's just a bad UI for doing some initial explorations. So you wanna have a Grafana dashboard where you actually can visualize it. So also the Grafana dashboard is also something that comes with this deployment. So here on the upper graph we see the profit model, how it predicts the upper and the lower bound and the actual value of the load of one of those of the pods, no of the node in this cluster. You see it's going up and down pretty nicely and it also retrains the model. So these points here is when we retrain the model because it needs to look back. So you might have an online training model which adjusts like online all the time on incoming data. Profit doesn't work that way but it needs a look back and then does its computation, et cetera. And the red line is the actual load. And here we have a spike which seems to be weird. Let's see that's another prediction by Fourier analysis. Fourier analysis is much better at predicting actual time series. You can see it as the blue lines match up quite nicely with the actual value unless not here. So there seems to be something wrong. Unfortunately, profits didn't detect this. So the anomaly detector inside for profits didn't detect it as an anomaly and I would need to look closer. Maybe I need to adjust some thresholds there but Fourier detected it as an anomaly. Which is good. So this setup you would get just with a deployment YAML for your Kubernetes cluster and you can play with it. Maybe node load one is not the best thing to play with but you would work with some of your application developers or you look at projected disk usage or CPU usage, pick something that you're familiar with and start playing. I think that's the lesson learned and here just to show off the nice cool thing that's the OpenData app website. Let's go back to the presentation. Go into, can I go into full screen again? So we made Big Hat happy. He likes what we have and he can go away. So here's the picture with all the links to those notebooks, et cetera. And as you are all pointing the camera to me, may I also point the camera to you and make a nice selfie for the folks at home. Everybody cheer, you like the talk, yes. All right, that's it. Question time, let's back up slides. I have five minutes for questions, which is great. So are you still using your custom storage or is this now just calling the Thanos query layer to do all of its computation? We're now using, so we're applying that to an actual internal team, the TOF team, which deploys a graph database and they don't know how to operate it because there's no Kubernetes deployment there, so it exposes some metrics but they don't want to create alerts for it. And we store it in our Thanos backend because we have all our Prometheus instances set up with Thanos as a sidecar so it automatically stores it long-term. And we also have a Python library that can query a Thanos or Prometheus backend for the data scientists with some notebooks so that they can just create a pandas data frame out of the Prometheus or Thanos setup just by some two lines of Python. So that's our internal setup. It's pretty straightforward. Thanks, I'm sorry. Thanks a lot for sharing and I do have a quick question is that for monitoring system that sometimes when we have enamel, like we have like two hours very crazy and system goes very high, how do you just rule out these of normal? Because after these like two hours period of normal and this part of dirty data we call it will just affect the predictor of next data points. How do you just rule out in a real time system? Yeah, so if you have two hours that are where the system is crazy and you expect it to be crazy because you know that your users always check the website after they come from lunch, then the model would see it because you train it on a month worth of data or a year worth of data. The model would expect it as a normal behavior, so it wouldn't trigger an alert. If you're saying that it's not normal in these two hours, then I hope the model would fire an alert. And if the behavior of your folks change and they don't check the website after they are coming from lunch, then you need to either retrain the model which is done here on an hourly basis but you might want to configure it to a larger time frame, obviously. Yeah, your model adjusts. We haven't looked into online training. So that's something that we don't do yet in this setup. We have another prototype called the Flatliners also on that organization which uses ReactiveX in the Python framework which does some online computation but I wouldn't call it AI and machine learning, it's just some statistical analysis so far. But there's a setup to do streaming data analysis with the Prometheus data. There's the mic is coming. Hi, what issues, if any, did you have with Prometheus monitoring? What are the challenges? What were the challenges? So the first challenge was to get data out of Prometheus but I think that's solved now for our setup but you will face the same challenge in your setup because giving a sidecar to your Prometheus, to the monitoring guys is something that they would object to and you need to store your data somewhere. And then the other challenge is definitely understanding the problem domain, understanding the nature of Prometheus because it's another thing to learn for a data scientist. So that took a lot of time, understanding from QL and then also getting the buy-in from the subject meta experts because they think they have the monitoring under control and I think to a certain degree they have it under control but actually convincing them that what we do can provide some value to them is also not so straightforward. So we're still struggling internally finding stakeholders and finding users of this stuff that we're building. But then we're in the CTO office so that's what we're used to and we're always feeding edge and that's what we like. Thank you, just a quick follow-up. So the way you got data out of it is there's a Thanos provide syncs to, is that how it happens or is there a pull? Well, Thanos is just storing the data away. So it takes Prometheus blob, Time Series puts it into S3 or Ceph stores it there, done. And then you query Thanos and it's Prometheus over all those blobs in the S3 bucket. Okay. Okay, I'm timed out, sorry guys. If you have questions, come here or I'll probably be at the Red Hat booth.


Family Theatre presents Elizabeth Scott and Edmund O'Brien. Jew Edmund O'Brien, Ed Begley and Carlton Young in The Man Without a Country. To introduce the drama, your hostess, Elizabeth Scott. Thank you, Gene Baker. Family Theatre's only purpose is to bring to everyone's attention a practice that must become an important part of our lives if we're to win peace for ourselves, peace for our families and peace for the world. Family Theatre urges you to pray. Pray together as a family. Edward Hale was born of a prominent and distinguished Boston family. His father, owner and editor of the Boston Daily Advertiser, his uncle, Edward Everett, the author and statesman and his great uncle Nathan Hale, the martyr spy. His best known work was the story we present now and it's with real pleasure that we bring you Edmund O'Brien as Philip Nolan, Ed Begley as Ingham and Carlton Young as Danforth in Edward Hale's The Man Without a Country.  The same thing you are, Danforth, crossing Lake Superior. You mean you're not the captain of this magnificent steam ship? I'm afraid not, Danforth. I've been pretty much of a landlubber since the Navy retired me. How is everything on board the Levant? Couldn't be better. And how's old plane buttons? Haven't you heard? He died about six months ago aboard my ship. Maybe it's for the best. I have a clipping on it here somewhere. Probably since the New York Herald. Here it is. Nolan died on board U.S. Corvette Levant. Latitude 2 degrees, 11 minutes south. Longitude 131 degrees west. The 11th day of May, Philip Nolan. Not a very impressive obituary, is it? No one cares about the death of an obscure old man. This should have read, died May 11th, The Man Without a Country. It all began with the Aaron Burr affair. Philip Nolan was as fine a young officer as there was in the Legion of the West, as the Western Division of our army was then called. But, as luck would have it, the ambitious Burr, who during the war with Spain had visions of taking over Mexico and setting himself up as emperor, marked young Lieutenant Nolan and caused him to be invited to a reception in Burr's honor. He outdid himself to recruit Nolan for his man... I've been keeping an eye on you, Lieutenant Nolan. I think you're just the man I've been looking for. Thank you, sir. I hope I can be of service. You can be of great service. You were brought up along the Mexican border were you not, Lieutenant? Yes, sir. I spent all my childhood there. As you might know, I have some definite ideas about Mexico and the Texas Territory. I have heard about that, sir. How would you like to become part of my organization? In what capacity, sir? I'm going to need good men to lead my expedition. You know the country. How would you like to be your colonel in my army? A colonel, sir? I get over, Lieutenant. I have to return to Washington tomorrow, and when we return south again, we'll discuss this further. I assume that you're interested. Oh, yes, sir, I am. All you have to do, sir, is to tell me when to act. It won't be long, my boy. It won't be long. And when we act, I promise you great things. Great things, Lieutenant. The next year, Barrick's life was very tame for Nolan. He availed himself of Aaron Burr's permission to write him.  And one day, a fellow officer came to him. Hey, Nolan, have you heard? Your friend Aaron Burr has been arrested for treason. Treason? Yes. And I wouldn't be surprised if, well, if you weren't on the list, too. Yes, Nolan was on the list of those suspected of treason. And there was evidence enough against him. Everyone knew that he was sick of the service and would have obeyed any order to march anywhere with anyone who would follow him. As long as the order was signed by command of His Excellency Aaron Burr. So that when the acts fell, Lieutenant Nolan was one of the first to feel its sharp blade. Lieutenant Nolan, you have been found guilty of treason against the United States who uniform you wear and whose food you eat. Do you have anything to say to disprove treason against the United States of America? Damn the United States! What? Lieutenant Nolan, did I hear you correctly?  I wish I may never hear of the United States again! Prisoner, hear the sentence of the court. Your wish is granted. The court decides, subject to the approval of the president, that you never hear the name of the United States again. Ah! Marshal of the court, you will take the prisoner to Orleans in an armed boat and deliver him to the naval commander there. Yes, sir. And Mr. Marshal, see that no one mentions the name of the United States to the prisoner while he's on board ship. Provo Marshal delivered Nolan aboard the intrepid at Orleans. Captain Shaw received the orders in his cabin. What, this is ridiculous, Lieutenant Neal. Never to hear about nor see anything pertaining to the United States? How can we prevent it? Are we supposed to be mutes while he's aboard? I'm sure I don't know, sir. What do they think we talk about and read about while we're at sea? Our home and our families. Not anymore, sir. And reading matter. He certainly must read. What else is there for him to do? And what can he read without coming across some reference to the United States? Orders are to delete all reference to the United States in evidence. But he'll surely talk to the men. They might make a slip, and I can't discipline a man for mentioning the country he should be proud of. Well, sir, it would take some of the load off the men if he kind of switched around a bit. They could take turns having him to mess, and when he's around the men, you could see that he is always accompanied by an officer, sort of as a security measure. Oh, yes, yes, it could be done that way, but confounded man, why my ship? Why, in all the blooming Navy, do they have to prick on me? Those first few months on board the old intrepid posed a good many problems. As you can imagine, the conversation was more than stiff. The officers groping for harmless topics. One went on, and possibly to relieve the strained conversation, it became the custom for the officers when they were with Nolan to read aloud from cleared books. One afternoon, Nolan took his turn at reading the supposedly harmless work of Sir Walter Scott. Less liked he still, that scornful jeer mispries the land he loved so dear. High was the sound, as thus again the land resumed his minstrel strain. That ends the fifth canto. Would someone else care to read for a while? You're doing far better than I could, Mr. Nolan. You can take my turn, if you will. Canto sixth. Breathes there a man with soul so dead who never to himself hath said, this is my own, my native land, whose heart hath near within him burned as home his footsteps he hath turned from wandering on a foreign strand. If such there breathe, go, mark him well, for him no minstrel raptures swell. High though his titles proud his name, boundless his wealth as wish can claim, despite these titles, power and pelf, the wretch consented all in self. You'll excuse me, gentlemen. They didn't see Nolan for two months after that, and when they did, he was a changed man. He never entered in with a young man exactly as a companion again, and took on a shyness that was to last throughout his life. Soon after this, he was transferred to the warren, the first of some 20 such transfers, which brought him sooner or later into half of our best vessels. Transfers which kept him from the country he hoped he might never hear of again. But it was on that second cruise that he met Mrs. Graff. The warren had put in at the port of Naples, and a ball was arranged on board. It was agreed that Nolan could be invited, as long as one of the officers stayed with him. After a while, everyone got so friendly, they began to relax their guard a little. That's when he met Mrs. Graff. I beg your pardon, ma'am, but aren't you Miss Rutledge? I'm not Miss Rutledge any longer, Mr. Nolan. I am now Mrs. Graff. May I offer my congratulations, Mrs. Graff? What are you doing here in Naples? My husband had some business here. We're going to France in a few days. I've been doing a good deal of traveling myself at late. I believe we're to sail for Turkey next. Some day, perhaps, I'll go there, too. It's a wonderful country. Tell me, what do you hear from home, Mrs. Graff? Home, Mr. Nolan? I thought you were the man who never wanted to hear of home again. Excuse me, Mr. Nolan. And Nolan saw some action against the British. As a direct result of that engagement, Captain Shaw started the proceeding to obtain a pardon for him. You see, Nolan was back on the intrepid with Shaw again, when they were overhauled by a big English frigate. The American Navy got its baptism of fire that day. And Nolan was everywhere at once, looking after the wounded, doing what he could to help. It happened that a round shot from the enemy entered one of our gun ports. I'll show you how we used to use it in the artillery. Give me that rammer. And you, bring shot. Yes, sir. Don't carry shot that way, man.  Put your back into it. Put it in the artillery. I'm all for it. Give me that rammer. We'll give them another. But, Phillip, she's trapped under the mantle. You'll be burned to death.  You can't go up there. Nolan, you'll be exposed to enemy fire. I don't go, Phillips. We'll burn. Give me that knife. Yes, sir. Good. I'll be up in no time. I shall never forget this day, Mr. Nolan, and you never shall. I've mentioned you in my dispatches, and I've also written a letter to the Secretary of War, asking him to give you a pardon. A pardon, sir? It's no more than you deserve today. We're all very grateful, Mr. Nolan. And I would consider it an honor if you would accept this sword. But it's your sword. It was my sword. It's yours now, if you'll have it. Although nothing came of that letter requesting a pardon, Nolan never gave up hope. It was about that time that headquarters in Washington began to ignore the whole transaction, and Nolan's imprisonment went on because there was nobody to stop it. Six years after the war, I was appointed midshipman, and it was on my first voyage that he first came into my life. I had seen him on board, of course, with his plane button tunic. The plane, because the buttons with the United States insignia had been removed. I took him for a lay chaplain, knowing nothing of his strange story. We had him to mess once a week. While we were cautioned, never to mention home, I was too green to ask why.  I need some men to go with me, Lieutenant. Call out a boarding party. Aye, aye, sir. Bowson, turn out the starboard watch. Aye, sir. I go along, sir.  There wouldn't be much for you to do, and it might be dangerous. The danger doesn't worry me, sir, and it would be a change. Yes, I suppose it would. I'll show the natives that were on their side. Aye, aye, sir. These slavers certainly are a cut for a bunch. I'll be glad to get them in irons. The natives don't know what to make of it. They're almost as much afraid of us as they are of the pirates. That's bad. None of my men can speak this dialect. Perhaps I can help, sir. Can you speak this native jargon? No, sir, but I can speak a little Spanish, and cruel and native should understand Spanish. Well, it's worth a try anyway. Singham's coming out. Tell them they're free, and that these rascals will be hanged as soon as we can get rope enough. Have a go at it, Nolan. Yes, I'll do my best. I'm sorry, sir, but I couldn't make them understand a word. Mr. Nolan says he speaks Spanish. He's going to give it a try. What? Looks like he's found someone there now, sir. He seemed to understand him all right. Poor devils, I imagine it's been months since they've seen their homes and friends. And what of Mr. Nolan, sir? How long has it been since he's seen his home and his friends? Oh, Lord. Nolan's as white as a sheet. He must be going through the torches of the damned. Well, here he comes, sir. Fight it, sir. The headman says, not palmless, take us to our house.  He says he has a mother and father back there who will die if they don't see him. And another said he has not heard a word from home in six months. Tell him yes, yes, yes. Yes. Well, then we shall take them to the mountains of the moon if they wish. If I have to sail this schooner through the great white desert, they shall go home. It was after mess that night that Nolan called me over to a corner of the ward room. Youngster, come here. Yes, sir? Let what happened this afternoon show you what it is to be without a family, without a home, without a country. And if you are ever tempted to say a word or do a thing that you'll put a bar between you and your family, your home and your country, pray God in His mercy to take you that instant home to your own heaven. I shall never be tempted, sir. And as for your country, boy, and for that flag, never dream a dream but of serving her as she bids you, though the service carry you through a thousand hells. By all that's holy, sir. I thought of doing anything else. Oh, if someone had only said that to me when I was your age. A lot of water has passed under the bridge since then, Danforth. And I'll have to admit I didn't have the courage or the rank to do anything about a pardon for Nolan. And now, and now it's too late. Don't blame yourself, Captain Ingham. You weren't the only one lacking in courage. I didn't appreciate what a great man he really was until the night he died on board the Levant. It was late one afternoon that the doctor told me Nolan would like to see me. I'll never forget my amazement when I first entered that forbidden room. He had made a shrine of his bunk. Stars and stripes would try to step around a picture of Washington. And above the picture, he had painted a majestic eagle with lightning blazing from his beak and his foot just clasping the whole globe over shadowed. Here you see, Captain Danforth, I have a country. I see you have, sir. I know I'm dying, Danforth. I can never get home. Surely you can tell me something now. Wait, wait. Do not speak till I say what I am sure you know. That there is not on this ship that there is not in America, God bless her, a more loyal man than I. Oh, Danforth, how like a wretched night's dream. A boy's idea of personal fame or of separate sovereignty seems when one looks back on it after such a life as mine. But tell me, tell me something of home. Tell me anything, Danforth, before I die. Mr. Nolan, I'll tell you everything you were asked about. Only where shall I begin?  Look, here on this flag I've drawn. I know there are 34 states, but their names, tell me their names. The last I know is Ohio. My father lived in Kentucky, and I have guest Michigan, and Indiana, and Mississippi. They make 20, but where are your other 14? You have not cut up any of the old ones, I hope? No, sir. This one is Texas. Oh, I'm glad it became part of my country. My brother died there, you know. You see, I've marked a gold cross on my map. And this is California, and here's Oregon. Oh, I rather suspected Oregon, because when we put in there, I was never allowed to land. Tell me, tell me, Danforth, has anyone never tried to serve our country as did Aaron Burr? No, no, sir. Everyone likes it as it is.  There must be no more Burrs. Oh, God forgive him, for I'm sure I do. Like a monster that I had not told him everything before. Who were we to play the tyrant over that old man? I did my best to tell him all I could about his beloved country before he died. It was a hard thing to condense the history of half a century into that talk with a sick man. I told him everything I could think of that would show the grandeur of his country and its prosperity and how he did drink it in and enjoy it. He grew more and more silent, and yet I did not think him tired or faint. When I had finished, he asked me to bring him the Book of Public Prayer. Just hand it to me. It will open at the right place. Thank you. Would you please kneel, Danforth? Yes, sir. For ourselves and our country, O gracious God, we thank thee that notwithstanding our manifold transgressions of thy holy laws, thou hast continued to us thy marvelous kindness. Amen. Danforth, I have repeated those prayers night and morning for 55 years. And Danforth, look in my book. I thought he was tired and would sleep, but when the doctor and I went in an hour later, he had breathed his life away with a smile. I looked in his Bible and I found this mark. They desire a country, even a heavenly, before God is not ashamed to be called their God. For he hath prepared for them a city. And in the Bible was this piece of paper. Read it, Ingham. Burry me in the sea. It has been my home and I love it. But will not someone set up a stone for my memory at Fort Adams or at Orleans that my disgrace may not be more than I ought to bear? Say on it, in memory of Philip Nolan, lieutenant in the army of the United States, he loved his country as no other man has loved her. And no man deserved less at her hands. Scott again, I suppose everyone feels that his own problems are unique. We forget that others have borne even greater sorrows. We forget too that men and women in trouble have always found help when they turn to God in prayer. The families of yesteryear had troubles as great as ours. But they knew what to do about them. They prayed. As a family, they asked God's help every day in the year. Family prayer helped build the strong, God-fearing homes which made our nation great. Family prayer today in our homes can renew in our land the spirit and faith of those who've gone before us. And be assured, the family that prays together stays together.  Good Family Theatre has brought you Edmund O'Brien in The Man Without a Country with Elizabeth Scott as your hostess. Ed Begley was heard as Captain Ingham and Carlton Young as Captain Danforth. Others in our cast were Walter Burke, Ralph Moody, High Everback, Wally Mayer, Jack Carroll, and Virginia Euler. Edward Everett Hale's classic was adapted by Robert Hecker with music composed and conducted by Harry Zimmerman, was directed for Family Theatre by Jaime Del Valle. This series of Family Theatre broadcasts is made possible by the thousands of you who felt the need for this type of program, by the mutual network which has responded to the hundreds of stars of stage, screen and radio who have so unselfishly given of their time and talent to appear on our Family Theatre stage. To them and to you our humble thanks. This is Gene Baker expressing the wish of Family Theatre that the blessing of God may be upon you and your home and inviting you to join us next week at this time when Family Theatre will present Robert Young, Hans Connery and Mary Ship in Serenau de Bergerat. Join us, won't you? It originates in the Hollywood studios of the world's largest network. The Mutual Broadcasting System.


Everybody Matt here for latent sports cards ripping 2023 Panini legacy football hobby six box full case break. I'm sorry six box break Number seven pick your team. We have Adam K Jets Andrew H Lyons Andrew s chiefs Anthony D Giants Chris F Seahawks Chris B Panthers Texans Christopher s Saints Darrell R Rams David a bucks Edward P Broncos Greg L Steelers James D Ravens Joe P Packers John a Dolphins Corey s Commanders Kyle H Falcons Laurie R Eagles Matt T Patriots Matthew in Vikings Matthew M Jags Matthew F Raiders Matthew R bills Titans Nathan B Chargers Patrick S Browns Paul B Colts Robert R Bears Seth B Bengals Todd C Niners Trevor T Cowboys and whining H Cardinals all right, let's do it Yeah, we pulled an ultraviolet all-star Today about honey No So we have two legacy football breaks and then Close out with us. I like to draft picks full case There's Justin Herbert to 199 and de hop under the lights gold ice to 25 Mingo and Carter Kyler Orange Mani to 125 Jackson Smith and Jigba Seahawks rookie Grab up your spots for Leafs of herlite random hit style for us is going live over on the latin 2 channel live from the national Mike Parsons bronze mini 75 right Yeah, 75 Simpson and Marvin Mims Yeah, so yesterday we did four six boxers No dare-to-tears in the first three six boxers and then three dare-to-tears and the last one Patrick Mahomes under the lights Will let us rookie and Clayton tune Heinz war Orange to 199 Zay flowers and Dalton Kincaid One second if I tear them that I then we have to ship the cards inside to whichever team Get some so no, it was just hope we just ship them Brown's auto to 50 Aussie Newsome Paris Johnson Joey Porter Jr. and Josh Downs Miles Murphy and Bijan Robinson Peter Skaronski rookie red to 99 Zay flowers prize prospects tank bigsby and Cedric Tillman Joe burrow under the lights silver Jaron Hall and Tyree Wilson Trevor Lawrence to 100 screen or moon Or now Washington and CJ Stroud rookie Redemption lions right that is under the lights Ruby autographs Jameer Gibbs Lions right. Yeah, you go lions Yeah, I can post here in a second Jackson Smith and Jigba and Michael Meyer No, not yet Matt. This is box one 20 door set Tanner McKee and Brian Branch and Smith rushy rice Derek Henry to 100 Zach Evans and Christian Gonzalez All right, Timmy Kind of blocker many Anderson Nolan Smith rookie silver Jalen Hyatt and Devin. How do you pronounce that a shot? Shane Shane Zach Evans rookie red to 99. Yeah, WWE was With a spoon, yeah This is Jags to 249 rookie dual Jersey auto tank bigsby NFL had zero tanks And in the league and now they have to after one draft It's true Johnson and the same division You also have no Josh Allen's I don't think they ever had a Josh Allen in history and then back to back years Yeah, Josh Allen's to 10 that is Hinden Hooker rookie Prize prospects purple you go lions Andre Carter and Zach Charbonnet I think on this prize ones you can flip it around and it has a little bit of most Dak under the lights bronze or orange to 175 Let's go JJ and TJ what? We'll do it. I'll roll a single die here for the For the duals unless it's an auto relic or one of one top one two three bottom four five six That is one duals go top rice young tank Dell Brand James Jackson Smith and jigba So Thomas, how's it going? Jordan battle rookie to 199 Anthony Richardson prize prospects rookie Jalen Carter and Tanner McKee this is Jalen Hyatt rookie auto that is Giants Will lavish prize prospects Sean Tucker and Jonathan Mingo for the ages Alex Smith Harry Wilson and Parker Washington. It is Trenton Simpson rookie auto Ravens Darnell Washington Hinden Hooker rookie and Joachim Jarrett she writes and Paris Johnson Ray Lewis to 199 Jonathan Ogden Traction Zach Charbonnet Sam Smith brown ski And Anthony Richardson Ridley green to 100 Bob Weatherspoon Rookie to 149 Don't take cold pepper Gonzalez and tank Dell will let us under the lights mini well Anderson Will Anderson and Zach Evans doing good here Just about done for the day. I have one more of these and then a case of select draft picks jaren Hall rookie silver if you're Hutchinson Paul Kraus red to 299 Jarrett Weatherspoon and Hyatt redemption That is lions futures dual patch autograph silver Jameer Gibbs Another one for the lion Smith Ryan Tannehill orange mini 125 Bryce young rookie and Andre Carter No, I'm not super bad. No, I'll have to be out of the way. So Sam can get like start getting set up for tonight Brock Purdy under the lights Jackson Smith and Jigba Yeah, yeah, let's do it D hop to 299 red Hannah McKee and Brian branch Jake said for every 6,000 likes we get He will go one week Demanding everybody call him boltron Jake the snake if we get 5,000 likes, you'll record your own. I'm a snake 400 Panini points random to all at the end Zay flowers prize prospects April showers bring Zay flowers. I missed it. I'll have us rookie and Clayton tune Tank Dell red 299. I'm sure your delivery was much smoother though Tank Dell He said it so much now that he has it done Will Anderson who's this way will Anderson prize prospects? Will Anderson will Anderson Zach Evans That video of my saved on YouTube Yeah for that Brad right. Yeah, Joe Montana that kid of dominance blue to 50 Jared Witherspoon and Hyatt cardinals 35 under the lights auto blue Clayton tune very tidy auto Von witherspoon that dude definitely makes us read more I Wanterspoon to 100 100% Smith actually I do too, but that doesn't It'll be a lunch about me Anthony Richardson rookie Ray Lewis 150 Zay flowers legacy chrome rookie Zay flowers Don't concede Jalen Hyatt Many orange to 125 Race Jones drew as our raider Jameer Gibbs. Yeah, the Legends cards a lot of them have like players on teams that you wouldn't expect I'm not sure on him. I know like Kshon booty under lights over Bucks there's a booty there at Johnson Green to 100 Jordan Addison Quentin Johnston Hey, thanks for the follow Eva. 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Committed Ohio State Sean Watson 299 Chris Henry is really talented. You know kept his head straight CJ Stroud rookie Vikings 249 rookie Jersey Otto Jaren Hall Anthony Richardson prize prospects rookie Chris Carter Jody Porter jr. And Josh Downs will Leviss prize prospects rookie The Sean Robinson rookie and Miles Murphy the last box. Oh, how'd you do? One more six boxer of legacy will close it out with select draft picks wicks and Smith Jared Goff 299 Rashi Rice Ronsky and Anthony Richardson rookie Robinson jr. Red Mini that is to 50 Mander's color match Tankdale. Oh nice. 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All right, that's for 400 points May just wet it till the end All right, let's do this random top spot. I'll get 400 points. We'll randomize 10 times. Good luck one two three four five six seven eight nine and ten chiefs get the points Casey All right, we had Lions futures dual patch autographs silver Jamiro Gibbs Lions under the lights Ruby autographs Jamiro Gibbs Patriots Otto Ben coats That's a trend Trenton Simpson rookie Otto Ravens Jalen Hyatt rookie Otto Giants Clayton tune rookie Otto Cardinals Tank Bisbee rookie dual Jersey Otto to 249 Jags Jaren Hall rookie Jersey Otto to 249 Vikings This is Falcons rookie dual Jersey Otto the John Robinson to 100 Browns Otto to 50 Ozzie Newsom and Cardinals under the lights Otto blue Clayton tune that was to 35 That'll do it for the break. Thank you everybody number eight is up next


Welcome to Precision Agriculture in the Southeast. I'm here with Dr. John Fulton, our precision ag specialist for Auburn University and our Extension leader. And our topic today is yield monitoring, John. But before we get into that, we were talking before it started guidance system. Why this dome? You know, I got guidance system in my phone here, and it's a little bitty. Why the big, what's under the dome? Well, there's several components that's all integrated in here, number one, you can see deer's radio, they use the the transmit between the base station and the rover, which would be on the tractor, but internally here to the the donors, not only the GPS antenna, there's receiver and also built in some basically mechanical features to minimize any of the errors that are associated when we think about the transmission of electromagnetic waves or the bouncy of those waves off of metal. We want to try and do everything that we can. So it's direct signal, not a reflected signal. We want the direct signal being caught by that antenna and being used by the receiver to compute that and anything that can be done to to improve that positioning, as we talked about earlier, is going to keep us down to that centimeter level accuracy, especially when we get to at that. It's not something round under there. And there's like a metal plate right there. They probably have some type of metal plate or plane in there that helps eliminate some of those potential errors. Well, sorry for the distraction, John. Tell us about yield monitoring. Well, yield monitoring is our lesson here. And, you know, really, it's it's a key ingredient, key tool in any precision egg program at the farm. We've really seen the growth of yield monitoring in the last few years. And a lot of that stem from number one, folks really building their precision egg programs at their farm and the requirement to have yield monitor data in order to do things like how do I develop zones? How do I go out and apply inputs on a variable rate aspect? So yield monitoring really provides a key ingredient to help direct and develop those strategies. So we're going to kind of go through the parts of this from a yield monitoring, just what are the components? That's an important thing. And hopefully along the way, tell a little bit about some of the potential errors or what to look out for. Because at the end of the day, we want to have good quality data that can be used back in the program or within the business to make decisions with. So I think it's imperative right off the front that while everyone has this concept of what yield monitoring and we're going to talk about yield mapping at another module. But, you know, it's very important to understand what the basic components, what's measuring various parameters on the harvester to come up with this yield estimate, whether that's bushels per acre in grain or pounds per acre in cotton. There's particular sensors that are used or in most cases, multiple sensors used to derive that yield. But to understand those potential errors, because at the end of the day, if we're going to use this back in our program to make decisions or help us push forward in our precision ag program, you know, it's only as good as the data that's provided. So we want to make sure that in this lesson that we kind of hit those one and two, what those components are, what those potential errors are, just to be in recognition of that. So as I'm out harvesting, maybe I need to check something in the morning or every morning to ensure that I'm getting quality data. In the business today, you know, as we stand, the big push is quality data. And really coming at this because so many of these companies are providing services today and are really requiring farmers to provide quality yield data. So this, you know, right off the bat, we talk about what a yield map, it really can, you know, it's a multitude of things that we can start to drive. Essentially it's the ability to look at yield spatially on a field by field basis. But the interesting point that we got to kind of keep in mind, it does not point out what the yield limiting factor is. And so it only shows you what the response is. And so ground truth either going back out and looking at those areas, maybe low yielding or high yielding and kind of looking at those in a separate lens today versus just treating that field as a whole is important. But the yield map is not going to tell you what the due cause of that is. So yield monitoring, you know, I think it's just critical today as we see all these services that are rolled out by companies that they all require yield data, you know, that piece. It's very important. If you, you know, we try and use a lot of other data layers and absence of yield monitoring. But at the end of the day, the yield map shows the money. Where the money's coming from as we've been talking about market. If we can figure out how to either generate more money or not invest as much, you know, today's business operation at the farm is how do I best invest my money to get the maximum return? And that's going to be critical here. We basically collect in geo reference going back to the DGPS. We're using DGPS. We got a point. We're attaching some yield estimate to that point. And so all of a sudden now we can generate a map and do some spatial analysis and understanding the variability out in the field. We provide instantaneous yield and moisture measurements. In the case for grain, for cotton yield monitors, we're just getting yield. That pounds per second or pounds per acre. John compared to, you know, just the farmer driving a combine without a yield monitor. He knows when the combine, he can hear the grain going through and he knows when he's hit a sweet spot versus he's running a long time. But this really helps a farmer understand his farm. He knows where his sweet spots are and where his trouble spots are. And you take wet years and dry years and over time you learn how, what to expect, how to treat that land, what to do to improve it. So it's really not just an issue. It's not just data. Now, one of my favorite things is to look on Facebook and like in wheat harvest and somebody's taking a picture of their yield monitor and it's showing 120 bushels. That's like, ah, yeah. I bet that's not farm wide. That's a sweet spot. Absolutely. But it really helps people know what they're doing. Well, and I think it's an instant feedback. Absolutely. That instantaneous feedback in the cab. And, you know, probably the most common point the farmers have made is I never knew that much difference existed. I knew there was highs and lows, but, you know, there was 50 or 70 bushel from a high to low perspective out in that field. And so it really brings the quantification of that into sight. And wow, hey, you know, maybe I need to, can I capitalize knowing that and change some management strategies. You know, at the end of the day, we want that yield map. So that's kind of yield monitor. We're just trying to get spatial context on yield across the field, bringing that back and using that as a data layer today and decision making. Key uses, and I'm just going to throw out three. Really, it's, you know, you'll find out that once you got the yield monitor, there's a lot of uses for it, whether that's loading trucks out, knowing what the real moisture content of the grain is. But from a kind of a strategic standpoint, diagnosing crop production problems, you know, had a weed issue here. I got a fertility issue. I got a pH issue, but identifying those and correcting those and again managing to what the crop's telling us or what at the end of the year that we're seeing. What's that spatial yield potential? You know, ultimately, we talk about nutrient management. You know, can we take some of this data that we collect over time and really start to establish our nitrogen management? Maybe even potentially P&K, but more importantly, making sure our pH is maintaining within range across the whole field. The other thing is ultimately is establishing what your true yield potential is for the field. And just think about that. If I can start to drive that and have a lot better history and knowledge for that field, I should be able to manage it from a profitable standpoint much better. Then finally, what we always encourage people is, you know, I can start to do things on my farm because I got this report card now at the end of the year to say, you know, I was looking into this. I kind of did one field or did some strip trials like down at the bottom. And all of a sudden, you know, is that the best investment for me? Maybe I need to look at other practices. But there's a lot of potential, a lot of key uses that I can use to help me improve my operation. Talk quickly. We're not going to go in depth. Mainly today, Mark, we're going to spend time on grain yield monitors. They've been around since the early 90s. But today, there's basically two commercial available yield monitors out there. There's grain yield monitors. There's cotton yield monitoring technology. There's some other ones out there that are being developed. But I would not say they're commercially spread at this point. But cotton, we're really seeing an increase in cotton yield monitoring here in Alabama. There are essentially two ways or two companies. John Deere with their microwave technology. You can see their sensor on the bottom right. If you jump up on any of the new John Deere combines, you'll see that sensor right behind each one of the shoots. It's a plastic shoot and not a metal shoot today because of the microwave has to cannot go through the metal or it gets attenuated by the metal. The other style has been out for a while as the ag leaders and optical. Essentially, I have to cut two holes on either side of the shoot. I've got essentially a light beam, so a midder, and then I got a detector on the opposite side. And I can put those on all the shoots or a portion of the shoots. And again, all we're looking at is cotton flow through that shoot and we're estimating how much pounds per second. And we know our distance traveled. We know we're with a cut so we can calculate a pounds per acre, make an assumption on turnout and ultimately get the bales per acre in the data set. So that's cotton yield monitoring. We won't spend a lot of time. But like I mentioned, there's been a tremendous increase in cotton producers collecting yield data here in the state of Alabama in the last two years. Pretty standard today on all the John Deere combines coming out. Is it cotton pickers? Yeah, as accurate as a grain monitor would be. They do pretty good.  So calibration is required. And I would think that if you're doing a major change in terms of bowl size and density, definitely you need to recalibrate to make sure they're giving you the best results out. Not just once a season, but as you change varieties. And if you got the capacity to weigh several loads, you can essentially take a not only one basket but multiple baskets. And if you can get that weight, you can always come back and post calibrate these these cotton yield monitors, which is a nice aspect. You just got to have a way to weigh the amount that you harvest it. So we're going to jump into grain monitor grain yield monitors. Those are all the components. Again, we've got the DGPS, we've got some kind of display up in the cab. In this case, most all the grain yield monitors in the US use what we have as an impact plate at the top of the clean grain elevator. There's some other styles that use optical sensors. We'll talk a little bit about that. But then today we have all these measurements being made primarily we got grain flow, we got moisture, and we got GPS and we got ground speed or your that's those are our primary what's actually being measured. Everything else is assumed cutting with density of that grain. And we also need to know if the headers up or down that tells us if we're harvesting or not. That's just a way to start and stop data collection. So those are the components. We're going to jump down real quick mark and tell you how we calculate yield because this is an important aspect to understand. Again, thinking back what we're measuring, we're measuring mass flow, which is pounds per second. On most of these, we're measuring ground speed. So we know over a given time how far we've traveled. Okay. And so we got mass flow in the equation to calculate yield that we're measuring. We've got the test weight, which we assume and at the bottom, depending on what grain that you're doing corn, soybeans or wheat, you input that. And then we're also looking at how far we travel, you know, usually on a per second basis for over one second, if we know how far we traveled, that gives us the distance we know our swath width. And so we know the area covered so we can take that pounds per second up there on the top and calculate a bushels per acre measurement. It's simpler than it looks, john.  But what first sight that was intimidating. A little bit. But we're just making a couple measurements are cut with as an operator. You put that in I'm 25 feet on a platform I'm 20 feet, you know, or so many rows on 30 or 36 inch spacing. And so all we need is that distance traveled, we got our mass flow, and we convert that to a bushels per acre. Okay, the second thing to remember is we we have to sell this crop on grain. So we want to compute what the marketable yield. So most will have that was the wet yield. Okay, so that might be tell me what the yield is that let's say 20% moisture content. But we know when we get to the get to the market and sell, we're not going to sell it at 20%. We're going to get basically sell it for corn 15 and a half percent some cases 15%. But most of the time it's going to be 15 and a half. And so we have to we want to adjust that down to what we call the dry yield or marketable yield of most cases. And so we take that measured harvest moisture content. Okay. And you look at that equation. And basically, now I can adjust my yield bushels per acre to what I want to be able to sell. And most cases, all of our analysis are going to be done at that marketable yield or dry yields. And then we try and conform it all to 15 and a half for corn, or 13 for soybeans. So if I'm actually harvesting at 12, I'll adjust it up to 13. If I'm at 18, I just it down to 13 for soybeans. And so we're always at the same moisture content. So that's all we're doing on a on a combine, measuring a few parameters, moisture, mass flow, distance traveled, we're taking that into these equations and computing a blue bushel per acre measurement. And that's what that's what we all want to know. It's what we want to take to the coffee shop. Does the yield monitor test moisture? Or do you have I mean, we have the moisture testers that are in common use. So it's got a moisture sensor. I'll show you that here in a minute. But you got to calibrate it to typically with a high quality moisture sensor to make sure that it's given the proper moisture content to the yield calculation or the date data set. Here's what it looks like. We got a yield beta bit in most cases, all it is is point data mark. And in this case, we've chosen to kind of put a gradient green, high, red, low. But if you look up close down, all we're doing is collecting points. Typically, we're going to collect points every one second. But we can adjust that in most of these yield monitors because that's a lot of points for some of these fields. I mean, we're we're not we're not talking just 5000 we're typically you know, just a normal field 40 to 80,000 points. You think about storage and processing that. But most people keep it at one second. So every one second, I'll get data but I could adjust that to, you know, every two seconds. In some cases, there's other programs that are other displays that might give you the capacity to do it on distance.  So every five foot, 10 foot, whatever that distance you put in, but most time it's on second, we're collecting data on a per second basis. Here's the point representation. Just really looking down even closer. And all we're saying mark, if I know the distance travel, I know my cut with, I'm taking that point and I'm assigning that area, which is essentially a rectangle. In most cases, I'm taking that area. And I'm signing that area to that single point. So just to think about that in the calculation. This is what it looks like. This is just an example, data file, raw data file that we got out of. One of the yield monitors in this case, an ag leader, you can see over the last time I got my longitude latitude, I got a mass flow measurement and pounds per second, I'm collecting that every one second. So essentially, I've got a direct, you know, way to calculate this bushels per acre. But that's what the data looks like. And there'll be other attributes field name, elevation, maybe the serial number, there's a lot of other attributes. But then a day we got our longitude, latitude, mass flow, distance traveled, typically an inches. And then we have our moisture content. And we can calculate that yield using equation for every single point. All this is done internal to the yield monitor mark. But I can get this text data out. But that's what it looks like. I just got rows and rows and rows of data ultimately, that we can all sudden process and use. That's too much data. That's why we want to keep it simple. We take a picture of the yield monitor at 120 bush on week, right? Yeah, yeah, the picture is good enough for me. So yeah. And so going back, I'm just going to go through this real quick and we'll talk. I mentioned this earlier, the impact sensor is really the primary sensing mechanism that's used in the US. But we also have these volumetric flow sensing. So there's a light sensor or photoelectric sensor that's used. We're just going to talk about those two today because those are pretty much all that we'll see in the US. The impact sensor, you'll see here, this is an example from John Deere. Grain comes up the clean grain elevator gets accelerated across there's a plate with a sensor attached to it on the top that the deflection or the momentum of that grain deflects that plate. And I basically have a relationship there between how much it deflects two pounds per second. And that's all we're doing. And again, most all these you could climb up in the grain tank, go over the top of the clean grain elevator and easily see probably a mass flow sensor sticking out. And you can take that off and you can see that that mass impact plate as we call it. Here's some examples. You see ag leader on the bottom left. And that's the most prevalent some of the older John Deere's combines. You'll see an impact plate like there in the top that's kind of yellow. Most of them are have some kind of covering on it. But there's also in the bottom right, you'll see ag codes. Again, all those are are pretty much at the top of that clean grain elevator mark. Just to mention this, most all these are not linear response. And that's why calibration will talk about calibration. But just remember, just the it's not linear. So as I press on that sensor, it doesn't give me a linear output. And so I just can't use one data point and have a calibrate. I need multiple data points to make that response curve of that particular sensor is mounted in the combine. So that's just something a lot of people forget about is this non linearity of these sensors. But we try and treat them as linear in nature. So that generates potential errors, and we don't want that. The photoelectric, you'll see this on the caterpillar combines in particular, Raven has a system, I think tremble uses very similar. But basically, you got in a light emitter that kind of about halfway up or towards the bottom of the clean grain elevator on one side, you'll see a light source. And then on the other side, directly across from it, you got basically a detector. And all you're looking as the interruption or how much interruption of light you get is green. So as we fill that paddle up, you can have more light, you know, less light transmitted. If it's empty, you got should have full light intensity in there. All we're looking at is taking that and converting that to some kind of biometric, you know, cubic meters per second. And then we take that and calculate our yield. So this is popular amongst some of the kind of third parties providing yield monitors out there. Here's your grain moisture sensor. Normally, speaking, it's going to be on the side. Here's an example of John Deere's moisture sensor. But all it's used, and most all of them use that sensor on the left there is just a DMI basic sensor. And you're measuring the capacitance or dielectric material as the moisture content goes up. Guess what, you know, you got more ions there. And so we can measure that there's a direct correlation with moisture being being able to capacitance or what we call a dielectric capability or dielectric properties change. I can measure that basically with a voltage out. And I know what the very accurately with the moisture content of that grain is. I'll take your word for John. But that's a it's this this type of sensing has been around for for some time. Does great in grain crops. And let's like most most of the companies use it. The big thing is keeping it clean.   Would this be like every every morning when you're blowing out your filters to wipe that down? I would definitely most of them in the it's maybe a little difficult to see there in the John Deere picture. But you'll see on the left side of kind of that black, that black box on the left side that that but basically, you there's a little wing nut, you take it off and you can look at it very quickly and wipe it down. Especially when you get into soybeans and wet soybeans or green soybeans, you will tend to get a little buildup. So every morning, you it's early season problem. You just check on it. Yeah. So so we're just going to go through some of these more just again, what we're trying to do is quality yield data. That's a real emphasis today in the in the industry is getting quality yield data. There's a lot of things. If I change my swath widths, if I if I put in my display that I'm taking a full swath with, you know, I'm taking eight rows. It thinks I'm taking eight rows, but I'm at that last row and I'm doing four. Guess what? All of a sudden, I've doubled my yield potentially. I got a time lag too. So we I think everyone recognizes when the grain hits the header to when it actually sees the impact plate or volumetric sensor, there's a timeline there. So you have to account for that. Most of the companies are doing a very good job of really getting that built in. So we know where that is. We got GPS area. That's really not a problem today. Back in the early mid 90s, we had a lot of error. And, you know, you get these maps and you couldn't tell what passes what. And so there's some other things there, pitch and roll, we'll talk a little bit about that's mainly an issue with with some of these, but not a big one. But again, companies are accounting for that, just like we're talking about the pitch and roll for GPS, we've got to if we can measure it, just like in here that we've got internal sensors, we can we can we can ensure that we're getting quality data out. And finally, calibration, I'll make a couple comments. But again, remember, you know, yield is volume per area. As simple as that. If you're taking a full header and you've told the display, you're taking a full header, then you're doing the best you can. If you're not taking a full header with guess what, all sudden that yield calculation is inflated. Yeah. Okay. And so right now, there are some things clause in particular, they can they have some sensors that monitor that. But here in the US, it's generally speaking on the operator's responsibility to tell you what the width is. If I don't change it, also new yield estimates are incorrect. So it's just something to note very quickly that that can have a huge, huge impact. Time delay. Again, as a as a user, or an operator, this isn't something necessarily ideal, that you're going to have to deal with. But most the companies but just think about it, it takes time from the header that goes through the whole heart, the threshing aspect up to clean grain elevator. Okay, and that can be sometimes eight to 11 seconds normally, it could be different for different combines. But again, most of the manufacturers are accounting for that. But the other thing to remember is the start and end delays. So when my header goes down mark, is it's starting to collect data, but you might not be already engaged in the crop.  I might turn around and be putting my header down at a different time than you. So just so now I can adjust those delays and we want those delays, I'll show you some examples of of errors when we talk about mapping, but we got to get those start time and end delays set. And I can adjust that based on the way I behave in that combine. train for impact style. Okay, if I'm rolling on the combine. So that's a left to right type movement. Very little influence on the impact style. This was some research that was conducted, that we reported on but basically very little influence on the measurement. But when I pitch uphill, downhill all sudden gravity starts to have an influence on on that, and definitely needs to be accounted for. Again, this really isn't an operation. An operator issue, this is what needs to be done internal to the mat to the yield monitor itself.  So just think about planting and what you're trying to figure out. So again, going up and down the hill, there's definitely going to be an influence on mass flow sensors. Here's just the effect of train on a photoelectric. Again, a lot of them are starting to work on this or have solutions. But think about that grain in the middle, they're going up. But also, if I get and pitch that, or start to roll that combine, excuse me, also that green slides to one side, covering up more of that light. Yeah. But yeah, I still had the same green as in as if I was flat. So that's been an issue for you for several years that most of the companies providing the photo electric type sensors have tried to build in account for. But that is a problem. Because that grain tends to get in gravity is working against you. So at the end of the day, photoelectric roll is a problem for photoelectrics on the mass flow or impact style pitch is an issue to consider. So again, if I'm going to go do and do some do some strip trials, these are just some things you might consider to make sure that you're getting data out that's best represented. The last thing we'll talk about is calibration. I already mentioned this, most of these sensors on these grain style yield monitors, or these grain yield monitors are nonlinear. So if I do one calibration, mark, we all assume linearity and look at what's happened as I as long as I'm working within that kind of normal operating conditions around that point where our calibrator is a flow of grain through that clean grain elevator, I'm doing pretty good. But if I get in the low flow, okay, a bad spot in the field, or a high spot in the field, all sudden, I want to deviate and have air in that yield measurement or mass flows measurement. Most all are doing a multi point calibration. This is pioneered by Al Meyer at Ag Leader back in the day when he developed really the first primary impact style yield monitor, where we're doing six points. And also if I can do some low flows up to high flows to kind of my normal flow rate that I expect out in the field, all sudden, no matter if I'm low or high, my data even relatively my data is going to be much more accurate. So this is why it's so important to do some low and high flow measurements in normal flow measurements as instructed in the operators manuals. Because even at a relative yield, even if we're talking about magnitude, it might not be we want that relative yield to show that the real variability out there in the field, I have to calibrate to these grain yield monitors, we're going to have errors. So in summary, you know, we just went through a lot of components today, kind of giving a kind of an overview of yield monitoring. It's the tool it's the data set that most everyone's going to want when we talk about precision ag site specific management errors do us do exist. Just be aware of that. And just think about either maybe how I plan or as I do some of my trials on farm, just just be aware of that. Okay. And today, that's what we're really driving to the focus today, and all these companies that are trying to provide services back to the farmers that are going to require yield monitor data, but the key is today quality yield data. So we're making the right decisions and helping our farmers do the right things out there. So a lot there. But one of the really important tools in the toolbox for when we talk about precision ag mark. John, this this caught my attention first yield monitors. We're just wow, we can get that data. And this is this is a exciting stuff. It's big. It's a big deal that change how you manage your farm. So thank you for this lesson. And we'll be back.