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Hello everyone and welcome to the Agronomist. I am your host, Lindsay Smith. I decided that if spring wasn't going to arrive, I was going to shame the weather into warming up by doing an entire show about weather data. Just kidding. This one. Actually, this topic that we're going to tackle tonight was suggested by Jonathan Zettler, who's going to join us in a moment. But of course, before we do, a quick reminder. If you collect those CU credits, head on over to realagriculture.com agronomist let us know you took in the show. We'll get you those credits. And while you're there, sign up for the newsletter. It's free, goes out on Wednesday. I fill it personally with all the things I think you might want to read, including this show. Thanks for everybody joining us in the chat already. Let us know where you're joining from. Maybe your weather is better than mine, but it's still winter and it's going to be for a very long time. But that's okay because, well, there's not much to do in the field just yet. However, that is going to change very quickly. Tonight's conversation, we'll bring in our guests. We are talking weather data and what to do with it because that is of course the most important part. Let's start with so Jonathan, this is your second time on the show. We'll start with you. Where are you joining us from? It does look like a hotel room, so I feel like you're maybe off on some adventures. Where are you joining us from? What's keeping you busy this week?
So this week we have a booth at Grain Farmers of Ontario's March Classic event and this year it's in Niagara Falls. So outside my window here in the hotel room, we can literally look right over where the water falls. Over the falls. That's an excellent view.
It is. It's pretty cool. I won't lie. Nighttime Niagara Falls. It's pretty neat. So there you go. Okay, Guy, joining us from Winnipeg, Manitoba, close to my hometown. There's still sunshine there, it looks like, but introduce yourselves to the agronomists, to the listeners, to the show. What is it that you do? What keeps you busy?
Oh, thank you, Lindsay. I'm Guy Ash, vice president for metos Canada. Work as a global training manager for Pestle Instruments. So heavily involved in digital agriculture, Internet of things or IoT. And my background is heavily in meteorology and agricultural meteorology and data science. So in Winnipeg in the wintertime, what keeps you busy is a lot of snow shovelling, plugging your car in and ice fishing.
Yes, ice fishing. Okay. I have gone like a couple times. I'll be honest, it terrifies me to this day. Anyway, there you go. Okay, great to see everybody in the chat. Looks like I'm not alone on the terrible weather. Let's maybe start there. From both of you, the question of course is, well, I can't control the weather, so why do I need to know so much about it? But of course everything depends on the weather, so we need to know everything about it. Jonathan, give us your perspective. Set up this conversation for us. How important or why is it so important that we collect this data and then do something with it?
So quite frequently, many farmers or agronomists are collecting that data and unfortunately a lot of the times it ends up living in a notebook and if they're manually collecting it using a rain gauge. But it's probably the main piece that drives the temporal variability in the weather condition or in the crop conditions from year to year. So this afternoon's conversation, for example, before we went out for supper was how much snow mould are we having this year and winter wheat and is it due to how much snow we had and lack of ground cover and different things to allow that particular disease to take some of this crop. So as far as what happens on a day to day basis, if you're out on the farm, that's naturally where the conversation gravitates to. It is weather conditions and in terms of how it's going to impact the crop. And I think Guy had a really good point earlier, before we started this conversation in terms of the impact he thought that has in his area.
So yes, Guy, this is, it's a great stat and it's one of the ones that I think we sort of come back to is that, you know, we are just so dependent on the weather, but it is of course a variable we cannot control. So Guy, from your perspective, how big of, of course A risk is weather. And. And why is it so important to have tools that can help us assess maybe some of those data points for making decisions?
Yeah, it's a loaded question. I mean, I could go on for quite a while talking about being a weather guy, no pun here, but you know, I said at the beginning, and I've seen the stat that up to 70% of the uncontrollable risk that farmers face is based on weather. And we know today that we talk about climate change or climate variability, whatever you want to call it, it's increasing, we know that by the magnitude of events that we have. So that variability is widening over time. You know, it used to be much thinner. So now, as Jonathan says, the variability that occurs temporally but also spatially. Right. So if you think about this, you know, over a very small geographic area, you can have tremendous change in what's occurring in one field to another field because of very randomised, localised heavy showers as an example, and that drives then this risk so high. And you need to be on top of that to manage that. How do I manage that? Is have actionable tools and solutions that provide me heads up of what's going on now and into the future. So really today, you know all the discussions about digital tools that can be used for weather to help mitigate some of the risk that you face as a producer, it's all about the risk. So if we shave off percentages of risks by using these tools, that's a lot of money in the pocket. When you look at what the cost today of production, especially when you multiply
it over thousands upon thousands of acres. Right. It just adds up, it multiplies. Now, Jonathan, you mentioned before we went live a storey of a little project you had that seemed like a good idea at the time, but it was sort of an aha moment. Tell me about your project.
So at the time, and I think about this quite frequently when I'm at scouting fields, so it was either grade 10 or a grade 11 geography project. And one of the requirements is that we record the weather data every day, the high and the low kind of thing, for several weeks or a month. I can't remember the length of time at this point, but at that time Pioneer Seeds was giving out this nice little digital weather station that would keep track of that. So I kept it outside my bedroom window, through the sensor outside the window and was able to write that down every single day. Seemed like fairly useful information to make the report. But as an agronomist, like, how do I derive value out of that. And yes, we have growing degree day calculations and some of these things, but there's only so many hours in a day, so it'd be nice to automate some of this. And since going away to school and being involved in the industry over the last number of years, well, there's disease models and different things that we can model off some of this data. And at that time we wouldn't have had precipitation on it because it literally just measured temperature at the time. So you start integrating these different sensors together and with some of these validated models that we have, all of a sudden we can start to predict the risk of management decisions that we maybe need to go look on. And I feel like based on the number of tools that we have available to us as an industry at times, that the adoption maybe hasn't been as significant as I would have expected at this point. So that's part of the reason why I suggested the topic, Lindsay, is just that the tools are there to make more informed decisions. And sometimes the actual inputs that we're applying might be the same, but the outcome might actually end up being different because we do a better job of timing based on risk or the ability to detect things maybe a little bit earlier than maybe what they had occurred.
So, producer J, can you go To Jonathan, slide 2 on the gadget side, and Guy, in a moment I'll ask you to sort of lay out some of the things that we can capture. But Jonathan, to your point, I mean, I think we all love after a rainfall to compare. So how much rain did you get? Right? And that's important. And I mean, realistically, if it rains and how much it rains and when is a key driver of yield, that's all there is to it. But yield isn't everything, right? And there are a lot of things, a lot of decisions we have to make to potentially protect that yield or enhance that yield, et cetera. But when it comes to. And Guy mentioned it, you know, the Internet of things, the capturing of data, the so many. We can measure so many things, but we have to be able to have an actionable item that comes out of it. So what is your take on gadget versus tool? What differentiates between the two?
So both of these could be considered gadgets, depending on how you're going to use them, or they could both be considered tools. I own both the devices in those pictures. One I took earlier this week, one I took a few years ago out of field when I was doing an update to it. The rain gauge is Great. But for example, I do a bit of hobby farming on the side. That weather station is located probably 30 kilometres from my house, 35 kilometres. And when I want to go to the field to make maybe a harvest decision or a spray decision, I don't have half an hour to go there and cheque, just to see maybe if the conditions are right to go do that. I can look on my phone without having to do that. I can see if we had rainfall on some of those things. So the piece, I think that sometimes when we have this conversation, if we're still using maybe more manual methods, is like, what is your time worth? And how do you get some visibility into some of that data to make better choices and have it with you at kind of all times? So I have other stations as well out there that I can go and cheque in on that either customers have or I have. And from a work planning standpoint, man, does it make me so much more efficient versus manually having to record some of that? So, but that's kind of where sometimes when we're having this conversation as far as whether it's a gadget or tool, at times when I had looked at some of these in the past, Lindsay, it was like, man, I don't know how you justify that. But then once you have it on the farm, it's like, I don't know how I would work without it.
So I think there's a lot of tech that falls into that. Guy, go ahead.
Yeah, I was going to make a comment. I mean, we all have a device, right? A piece of hardware. Do you spend much time thinking about the hardware? Not a lot. It's the solutions, what we're talking about here, the actual solution. So that's a piece of hardware and the means to the end is the solution, right? Whether it's apply fertiliser or spray or time or whatever. So the hardware allows that to happen, you know, and as discussed here, you know, you've got to have that data at your field. If it is not at your field, you are not getting the correct data. Especially for things that change rapidly over short distances. Things like rainfall, things like soil moisture, things like leaf wetness, relative humidity. All those variables drive processes that are occurring within the crop, whether it's an orchard like you see here, or grapes, or a canola field, wheat, corn, whatever. So the variability in the field is quite unique and that's what you'll see when you have that data. So if I'm going to be making a yield estimate on that field, I Literally need to know how much rainfall occurred and that needs to be worked into a yield equation with the development of the crop. So I can't do that from something that's, you know, as indicated 30 kilometres away and manual, it just is impossible. So field level data is the key. And as a meteorologist, I'll say that everybody says, well, how can you even do this? You know, you're a meteorologist, you guys are always wrong. How are you, how are you even employed? Right? So the reality, I always say, is, look, we don't have, we didn't have data in the past, we have data now. Now we can give you informed decisions on things happening within a field if the proper information is there, the information flow. So the hardware is like your phone, it provides the collection and data to your phone, which you view it on in a solution. And the solution could be one of many, many different things. Maybe I'm taking too much time there, I'll let you know.
No, it's all good because, and we have several examples and things that we're going to tie together here in some of the decisions that perhaps are more informed. You know, maybe it is about the spray days or pulling the trigger on certain things or not. And certainly we saw some examples of this in the 2025 season as far as conditions that could potentially have been, let's say, for disease development. And then you know, sort of petered out. So we are starting to use some of these tools. Peter Johnson asks Jonathan, do you use all the tools and do you have faith in their prognostications? He just likes to use big words to sound smart. But Jonathan, from your perspective of using some of these tools, how accurate is the sort of the advice that you can sort of glean out of it? Or is it only as good as the data that goes in?
It's definitely only as good as the data goes in. And I'd like to point out that like these tools do need maintenance. So the ring gauge I had pictured there earlier, that's the new one for this season because the one previous to that got left out in some cold weather and developed a hole in it. So they do need some maintenance occasionally and, or there's been a number of situations where the device gets filled with either bird poop or, or different things. So occasionally throughout the season you need, you do need to do some maintenance on them to ensure that they're able to record rainfall when it happens. It doesn't have to be that frequent at times, but it certainly needs to happen occasionally. So how does, to answer Peter's question, how do I use it? So when I'm looking to use weather data, I don't just have kind of like the base sensor. I'm paying for a weather forecasting or work planning tool and I'm also paying for disease modelling and crop staging and some of it is for Jonathan Farmer hat and some of it is for Jonathan Agronomous hat. The crop staging and disease modelling tends to be for Jonathan Agronomous hat to help pace the crop as we go through the season, to kind of have an idea on when to go time things. And especially for maybe those fields that aren't maybe as close to the regular skating route, it just helps you stay on top of weather. Now is a good time to go and cheque. Or from my experience with fazarium head blight timing, for example, you're timing timing hard because the weather's hot, cold front comes in, cools the crop down for a week. All of a sudden, all of that timing that you had done based on days, because you're doing the timing based on days in the staging guides and not degree days, it kind of goes out the window a little bit and you have to add a bit of time to it. So you almost have to look at the weather forecast to some extent when you're doing some of that. From a work planning standpoint, using weather data can help considerably to maybe understand, for example, with fertiliser applications, is today a good day to go put that on and have it washed into the soil so the plant can take it up? And I can think of a number of years ago looking at kind of the two week forecast and the second pass of nitrogen and it was going to remain kind of prolonged cold and wet. It was maybe a little early to go put it on based on crop stage, but by the time that we were going to come out of that cycle, corn planting was going to be in full swing. And guys were not going to be thinking about trying to get wheat with the second pass of nitrogen on. And the long term forecast after that also looked quite dry after that period kind of went through. And then you. So the customers that had maybe waited past that period of wet weather to to go put some of that on, ended up putting the nitrogen fertiliser on that crop kind of at a time when probably should have maybe been planting corn or doing different things. And then we didn't have any weather to maybe wash that material into the root zone. So the outcomes that I see by having those tools you can make trade offs in terms of decisions based on your workload to make things happen. And that's kind of been my experience and trying to do a better job.
Now Guy, a few points here and I think it's an important one in that, you know, if we depend on say a public system for radar, et cetera, and for some reason Canada really struggles with this, we maybe don't have a lot of data, but if we're going to go fully, you know, have our own weather stations, etc. That potentially drives up cost pretty significantly. So is there a like magic or a rule of thumb as to how many data points I need or how many weather stations I need or what kind of network can I draw from? Is there an ideal and then is there a realistic side to that?
Yeah, that has good question. Lots of factors involved, right? So one is geography, what type of area you live in, Is there a lot of elevation change? Is there low spots, drainage, Is there changes in weather patterns? So, you know, dependent upon the variability and weather, you need to have more devices in a smaller area. If it's a homogeneous area of flat prairie where I can watch my dog run away for three days in Winnipeg, I don't need as many that often, right? Other is the crop type, of course, what type of crop are we talking about here? If we're into celery or carrots or grapes, you know, or high cash value crops, you may need more devices because you have frost mitigation tools you want to use, right? So you might need two or three frost type sensors. If you're talking about, you know, a broad plane looking at a corn over a large area, or wheat, whatever it is, maybe you don't need as many. Maybe every six kilometres you could use a station. So when we're doing this, we really talk to the customer and say, what are you trying to do here? What's the problem you're trying to solve, right? And what crop are you looking at? And really design a network or devices to fit that customer's needs. And the other thing is you can tap into networks. Some people put out networks that provide coverage over an area and you just tap into the device, you don't really own it, you tap into the information flow and it's managed by your retailer or whoever's doing your agronomy for you. So there's different ways to do the business model to provide it cost effective.
So Tom mentions North Dakota. So the N don, which is fantastic acronym and also I'm not sure why everybody's Handles comes up as some sort of nonsense as opposed to Tom Wolfe, but we know it's Tom Wolf. We can tell by the, by the picture. And also Warren here in eastern Ontario says that his district soil and crop just invested in 19 stations to spread around, plus 13 that they already had should have good county coverage. So Jonathan, from your perspective and Producer J, I know we need to go to a read here right away, but is there an opportunity for. Exactly, as Guy explained of potentially working together on some of these things, is that sort of you have your own and so you work it for your own business or what does that look like out on the landscape?
As far as what I've seen in the landscape, there hasn't in the past been a lot of software options to maybe allow the sharing out of it. And that's kind of been a really big issue in being able to develop maybe a network and do some cost sharing with some of these tools. I would say in the last few years here that's changed considerably. Sometimes there's maybe been tools out there that have allowed the sharing of data to see, okay, Warren got this much rain and I got this much, but you couldn't turn that into like an agronomic output that I could make a decision on. And that I would say was the issue in a lot of ways. But yeah, curious to see what Guy has to say.
I was going to say like the Endon Network, North Dakota Weather Network is fantastic. You know, I use it as well. We do work all over North America. So, you know, even in that I have work with growers down there that have their own devices. Why? Because they're too far away. They want to know what's happening in their fields. So a regional mesonet is a fantastic thing. It provides great data, good quality data and that data can be ingested into the tools. As Jonathan say, you know, we could pull data in and use it to do other things and fill in the gaps. So yes, data should be shared and it should be accessible, especially if it's provincial, state or federal type data.
Okay, we have to take a quick pause. We're going to go to our clip with Albert Tunuta and Allison Robertson, titled aptly, that the weather calls the shots because. Well, yes, it does. Producer J. If you would,
What happened with southern rust in Iowa this year?
So in Iowa this year we had historic levels of southern rust and we would walk into a field and walk out looking like a Cheetos. There was so much spores and so much southern rust there. I'd say Think that there were a number of factors that contributed to that. First of all, increased acreage, corn acreage in the South. The farmers down there didn't spray fungicides because of the corn prices. We had stronger and more southerly winds bringing that inoculum into Iowa. We had a lot of susceptible hybrids on the landscape. And then once those spores arrived in Iowa, the beginning the of of July we had there like we had three times as much precipitation from July until the middle of August than normal. And coupled with that we had very warm temperatures, so higher than average temperatures, which it was just, I mean, tropical conditions, perfect conditions for southern ice.
And you like to talk about the disease triangle. Yes, yes, exactly.
What about a yield loss? I mean I heard some extreme losses,
pretty much some tough losses.
Yeah, I mean I would say that, you know, depending on whether you use the fungicide or not, but. 25 to 80 bushels per acre.
Yeah, yeah, 25 to 80. Albert, that sounds familiar.
Car spot. You've been wrestling with that for a number of years.
On a bad year, 25 to 80 sounds like car spot.
Yeah, no, it ditto for everything she just said in terms of that southern rust experience and tar spot experience. You know, for us in 2021 we had that spore load in the Midwest, we had environmental conditions, we had the storm fronts coming in, we had the leaf wetness, we had that infection window and then that extended window right through that were ideal leaf wetness. Exact similar situation in Ontario and ultimately the unfortunately the same result.
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Okay, so this of course, or that that example in that discussion between Albert and Allison really is somewhat of a look back, right? And that, that we do quite a bit of. We look back and we look at what does the data tell us and, and we have the result. What we're talking about here tonight is more about, you know, shrinking that timeline down to actually informing decisions in season. And we do have some things like some apps that will tell you if conditions are favourable or not and give us that sort of risk profile. But maybe we'll go to slide three of Jonathan's. I believe it is. And we'll talk about how because Pete asks here what about leaf wetness. How do we get that data? Can we get that data? So that sort of dovetails into this conversation about disease management and using weather data. So, Jonathan, take it away.
So kind of when I look at crop planning and how I use weather data, you kind of have a set number of decisions that you've picked throughout the season. And in this specific case, it doesn't say at the top there, but we're talking about corn. So when it comes to corn, you kind of sit down, lay out all the management decisions at the start of the season, pick the ones that the grower is most likely to run into issues with from a probability standpoint. And then the way I kind of look at weather data is it's pacing us as we go through the season in terms of are we going to do option A or are we going to do option B when it comes to herbicide or side dress, in terms of do we need a nitrogen inhibitor? Maybe we don't. It acts as a guide in terms of if you're reading a book in the past, I read those choose your own adventure books, while the weather data just helps you pick which adventure we're going on next and you continue down the garden path and enjoy things as they go. So as as far as management decisions go, that's kind of how I look at weather data. So I have this crop calendar that I've made. I was trying to think of where I kind of got the idea from it at the time I was working in an area that dealt in both the Holland marsh and with potatoes. So it's kind of in that Alliston area and within the horticulture Omath publications, they have these crop calendars that they build out for the crops in terms of when you're supposed to spray or do different management decisions. And I think that's kind of where I maybe got some of the idea behind this chart at the time, I can't remember for sure, but we know what needs to be done. And depending on where you started in that book, in terms of the path that you've picked and your management abilities is kind of going to determine what's going to happen. And the weather data just provides some level of confidence. So one thing that Lindsey had asked before the show is like, what did. What's the main point? I wanted to hone in on and I feel like some of the best farmers and agronomists out there have this level of intuition in terms of what it comes to with weather and what to do. And they seem to have a sense in terms of being able to act on it. For the rest of us at times, what weather data does when we have numbers in front of us is just a. It helps back test some of our theories in terms of well I think this is going to happen and it gives you a number in order to put that to and it gives you kind of a degree of confidence. So reading the chat a little bit here up further Peter had mentioned about what do you do when the forecast is wrong and how many times does that happen? Well, some of the wetter modelling that I'm working with kind of puts a degree of confidence to some of that in terms of whether it's a good decision or not. So when we're looking at the spray forecast so this it's showing all red at the moment because obviously it's not a suitable time to do spraying. But it gives you a degree of confidence red, green, yellow in terms of whether to go and do a spraying decision and whether it's good weather. This spring was brutal to try and get spraying done and the closer you are to the time that you need to make that decision in terms of spraying, obviously the degree of confidence in terms of being able to predict that weather is much more accurate. And I certainly leaned up fairly heavily on that tool to try and get pre emerge on for the soybeans came up because it's bad news bears in that crop, especially when you're growing IP soybeans in terms of being able to keep them clean if we're having to resort to other chemistry. So it having these tools and taking the data and simplifying it into kind of like a stoplight system certainly makes that decision a lot easier when you have so many other things going on
now. So some good questions here as well some questions about deciding on say a weather station and also different technologies etc. So we can pick away at some of these depending on if you've got some thoughts on some of them. But I wanted to Producer J. Can you go to. I called it slide 11 the how of guys because I do want to give Guy a chance to sort of work through from your company's perspective how it works for through you. So what either retailer or farmer accesses if they're using your company.
Okay. Yeah. You know again we can talk about the network type and the hardware type which it does in here. So you know on the left side you can see the how there's an agronomy team typically that will put out a set of devices over an area. Now again, all devices don't need to be the same, right? Number one thing when you get hardware is you need to build the hardware for the applications. And let's say for example, you don't need wind speed direction all the time, so you don't need to put that on the device. Some devices you buy come pretty much fixed with what they have and that's what you get. So depending upon the type of hardware, you may want flexibility in your design. So in here you can see different colours on there of devices which represent different types of stations for different applications. One may be for strictly for irrigation, soil moisture, simply looking at a device that measures soil moisture, soil temperature, volumetric ion content down below the crop, which you can use for irrigation management. Others may want the full wind speed package, temperature, humidity, leaf wetness, solar radiation, all the gizmos that are available on a full station because they need all those different sensors for their application, so you need a full one. Others may simply want something in field that's measuring temp, humidity, leaf wetness and precip, which is for really uses for the disease cycle. So most of the disease models you see drive off those type of variables. Some use soil temperature, moisture and solar radiation as well. So you can have a network of devices that can be multifaceted and multi types, you can have different providers. So in our situation, you know, we have metals, we can also integrate Davis stations in there. We do connect different types of probes on the device so you can have whatever type of probe you're comfortable with on that particular device. So many of the devices today have a lot of flexibility in ingestion and then go further. You could ingest government data. So if there's an API of data from governments, you can ingest them. That then goes to the second pane where you know, a farmer joins a network and they get access to data off the stations in their area. So they could access multiple devices over their area so that they can, you know, if their fields are spread out over 50, 80 kilometres, they can see that data. And the business model is you pay a fee for that information, not for the hardware in this case. So you're paying for the fee for the solution. As we talked about, the actual solution for your problem, whether it's, you know, notification on spraying or disease models or yield forecasting, you see that and you then get your own private stations that you look at and your own field metrics that are your own information nobody else sees. But the basic weather data you can see for the entire network so now you can start to customise, where are my fields, what are my planting dates, what type of soil? You don't want that input, field capacity or wilting point, you know, spray cycles, all that is in your account, nobody else's account. But you still have access to all that data and reports for the entire area. And then you can start to do fancy things like in here where you have heat map showing you the temperature over an area or the precip over an area, you know, 48 hours, seven days. Okay, now my disease, I can look at my growth stage, as Jonathan said, in relation to my disease pressure. Am I synchronised? Right. And what does my spray window look like? So all those become part of the application. Now, as a retailer, Jonathan sees that all in his network, this was his network. And he can then make recommendations and calls to farmers because he has intelligence over the area. What it does allow him to do is to cover more area because he's got a feed of information coming to him for his geographic area, more coverage for the area. So any questions on that? That's kind of just a quick run through of the model, but it's multifaceted in hardware and sensors and tools that come out of it. So the tools are extensive diseases. You know, there's 85 different disease models, insect models, spray windows, harvest windows, seeding, windows forecast, field level forecasting, not from a town, but for your field, for that device, yield forecasting, prediction, all those kind of things. Irrigation management.
So now, so the one question, and Jonathan, you sort of touched on it, so I do want to what can or what are you using for leaf wetness? Because that certainly seems to be one of those key things that drives tar spot development and of course potentially for other diseases as well. Um, so Jonathan, what, what have you used for leaf wetness and what does that look like? Sort of in practise.
So at the basic level, I think you could walk into a field 11 o' clock in the morning and with your pants and determine if the crops wet or not. The challenge is you can't do that on every single field necessarily. Right. And you don't know how long. Like Peter makes a comment about it being critical for tar spot, for example. Well, how long has my pants been wet? Well, I'm not going to stand there all day to determine that. So at a base level, well, yeah, you could do the wet pant test. The second best one would be to go put what's actually called a leaf wetness sensor out there to measure that. And it Measures how many minutes that that crop has been wet at that temperature or at that moisture or whatever. And so number of different sensors out there that can do that. Some the big piece is that you just need a station that's measuring that. So ideally it's in the crop when it's doing that. From what I've seen in order to provide an accurate prediction, sometimes it gets a bit tricky, but there's ways around it, I guess. So kind of the next best thing would be to have some modelling to predict, okay, it's been wet and this is a high risk situation. But it doesn't necessarily maybe always have crop stage. Kind of the best in class is it tells you that you're at high risk and you are at the stage that you're at risk of infection, that you should maybe consider spraying from a yield impact standpoint. So you kind of got different levels maybe of being able to measure this and turn it into actionable insight. So you can go from the wet pant test to the leaf sensor to maybe the disease model to the disease model that has the crop stage included and being able to measure some of that. So that's kind of the various options I guess on the market.
So one of the things that I'm hearing and totally makes sense to me is that we also especially let's say if you're a one person show or maybe only a two person show and you're covering a pretty wide area, you can't necessarily do the 11am pants test in every field because it won't be 11am by the time you get to the third or the fourth or the fifth. Right. There is a certain level of, you
know, you get one chance to the wet pant test per day.
That's right. And then who knows. So exactly that like, I mean it really does in my mind. It's, it's thinking about what are, what is useful information I could be gathering remotely and from a wide area that could actually inform the decisions I need to make or when I need to make them. Obviously, as you pointed out Jonathan, it is, you still have to line it up with, okay, crop stage, pathogen presence, whatever, if it's disease, whatever. So there's still that part to it. But I mean it does really mean that you sort of expand, you know, the amount of actual data you're gathering without physically being there, which is just a huge potential time saver, which is huge. Steve shares. I use my weather station to back up my decisions just to start raking hay. Very important. Or when to turn the key in the combine. I like that. Okay, we just, very quickly we're going to go to our last read of the night and then we've got a couple other key questions I want to touch on specifically, of course, in the yield prediction side of things. So, Producer J, if you would.
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Speaking of crops that have some disease problems, I present to you pulses. Okay, let's talk about. Of course. So we, we have gone through a bit of, at least very quickly, you know, whether it's leaf wetness or some of the other humidity or temperature things that we might need to know. We've also got a question though about yield potential, and this is I think, an important one. And Producer Jay, let's go to, I think slide 21 of guys on yield predictions. And there's another question about crop intelligence as well. But guy, walk me through how this works. What all goes into this and sort of what we can learn from walking through this example.
All right, so yield prediction, in this case, we're looking at environmental yield. We're not looking at the, you know, we're assuming here that you're doing things right with your crop. You've got somebody like Jonathan, who's the expert providing you the nutrient and the disease management and herbicide management seed. So what is the environmental conditions for yield which really is driven off of two main factors on the Canadian prairies, which have been well documented for years and years. And that's the water that's there as well as the thermal development with water being number one, number two being thermal development. So a long season, generally you get higher yields with good water. So is if you have a device that's located at the field that's measuring the amount of water that has occurred, you can make a prediction with a model. So you can model out the amount of yield that occurred in the field or within zones in that field, and you can define the zones how you want as well. The other way to do it is to put a probe in the ground in different zones and measure the soil moisture and calculate the amount of moisture and make an estimate of yield. So what we do in the yield model today for us is look at the water that's occurred in the field from Seeding to date so to today. And then that gives us an estimate of and the thermal development of the crop, where we're at in terms of the yield for that field. And then we use the prediction into the future, so physiological maturity. So as you get closer to physiological maturity, you zero in on the real number. But that prediction is made with a seasonally adjusted daily forecast. So the forecast is designed for your field based on the station data from the field. So the station data is nudging and tuning the forecast. We use AI to learn the weather at your location. And we use 25 different, up to 25 different forecast models and weight them every day. And the highest performing one gets the highest weighting. That gives you a very accurate prediction out every day to physiological maturity. And it's always updated, constantly updated. So you'll get an estimate of where you are today based on what's actually occurred in terms of the water and thermal development of the crop, and then a prediction into the future based on a seasonally daily adjusted forecast based on the water to come and the thermal development. And we've done this for all the different environments that are there, from very dry to very wet and everything in between. And like I say, the number one thing here is if you have the data at the field now, the sensors at the field, you can make a very, very accurate estimate of yield for that field or that zone in that field. And we've done this in trials, you know, at universities, colleges, with retailers, on many different crops from canola to wheat to barley to potatoes. And they are spot on with data at the field. And that's the number one storey here, is making sure you have the proper coverage for your fields and the proper instrumentation. You can do a darn good job. So as an example, you know, we did one in Olds and I think it was 96 and a half bushels was the crop for wheat, and I think we were at 94 and a half bushels on average for the field. So, you know, very close to what happened. Potatoes, I think I was, you know, 430, 100 weight. And we came in, you know, 430, 300 weight. So, you know, extremely close as to what happened. So again, this is an environmental model for yield. The next version coming out will be include the nutrient side to it as well. So the environmental with nutrient, which will then give you an agronomic yield perspective overall. So again, having the data and trusting your data and good quality data from a field makes all the difference in the world for these predictions.
I still don't know if 34 bushels an acre is going to pay the bills this year, but hey, we got to roll with what the weather hands us. Okay, Jonathan, there's a question here in the chat from Tom. What do you think of crop intelligence and what they've been able to do with water driven yield potential? We cannot stress enough of course, that water and when it shows up is really the ultimate decider of yield. But any thoughts on that, on the water driven yield potential?
So I actually tried their weather station modelling and system in Ontario a number of years ago at a few sites and great team to work with. At the time the producer, producer maybe didn't know what to do with the data in terms of like how do I make a management decision? I think in Ontario, from my experience with it, we just have very different growing conditions maybe than what they're working with because that company's based out of Saskatchewan. So. And the crop mix that we're working with may be a little bit different. So the looks like a promising system when they're trying to determine how much water is in the ground. So what Essentially what they're trying to do is measure how much water is in the ground going into freeze up and then use that to make some planning decisions over the winter, put it back out in the spring if they don't leave it out there and then adjust crop management as they kind of go through the season. Some challenges that I ran into it with our smaller field size and the fact that we basically have full groundwater recharge over the winter. So Lindsay, you also live in Ontario. How many inches of precipitation do you get? I'm not saying that we don't have some very dry ground conditions over the past two falls being fairly dry, but generally speaking going into the cropping season that's not really an issue. The other piece is it's kind of nice to be able to use maybe a metric like rainfall at times with our smaller field size to extrapolate that data over over a wider area to try and measure what the expected yield outcome could be. So I guess in Ontario anyways, what are we going to use that yield metric to make a management decision on mostly and to me that's probably nitrogen rates and corn. The biggest driving factor of that is rainfall at a very key time right before tasseling based on research that I've seen here. So probably not maybe as big of a fit in our growing environment. I'm sure it's a fit in other environments. But based on my experience. That's kind of what we saw.
So, I mean, it is a. It is a key difference and it's one that on this show, some of the biggest differences that we talk about on, on the agronomy side are, you know, some of those differences for Ontario. I mean, it is pretty rare that spring is not exactly that full soil, water recharge. If anything, we want it to be dried, seeding or planting, because usually that's. We need that to actually get a crop in the ground. And sometimes it can be the complete opposite for the prairies. And really there are times where the question is if it will rain, not when. It is an if. And that is a completely different scenario. Mark, I did want to share because it was so dry in Ontario. Mark shares 25 dry land corn average 58 bushels an acre. Just sad. That's very sad. Corn. But 25 irrigated corn average 276 bushels an acre. So the difference was that there was no rain after a late planting due to a wet May, which is exactly what we're talking about. And so that is really, that is the difference. Right. Once the crop goes in, if the tap turns off, it can have just that absolutely astronomical difference. Now, we all hope, and hope isn't a plan, I know, but we all hope that, you know, those extreme years are few and far between and if everybody had irrigation, well, look out. But that's not necessarily ever going to be a thing for everybody. So there you go. Now, I did want to, as we round out our discussion here, guy you mentioned, and this is sort of the point I want to maybe land on, is that obviously as we go through this, there is a lot of tech out there. There are several different ways and means, as you mentioned, hardware, software and then the crunching of it, really to make the decision from it. But you pointed out that obviously the quality of the data and then the modelling that we have, the continual improvement of that modelling to make sure that we're actually getting an accurate readout is so important. So from your perspective, from your company's perspective, what does that look like as far as each year updating or upgrading how those decisions get made on these platforms?
Yeah, I mean, there's, you know, in our company, we, we're a hardware company, sensor company, a telemetry telecommunications company and a software company, both desktop, Android, iOS and API. So there's a tonne of factors here and all that has to be updated all the time. And then the data itself has to be quality assured, quality control. There's got to be management systems in the background that we're not even talking about here, you know, that have to be looked after. Your equipment needs to be upgradable. You know, you need to be able to upgrade it over the airway so when changes happen in sensors, you can upgrade it. Those type of things. All that's got to be part of a system that's in place for any company that you use. If you don't, then you can't see what's happening with that company. You should be able to perform things on the sensors from afar. Part of that is maintenance, right? Part of that is looking, resetting, you know, is it working? Is the communication there? Do you have means for different types of communication? You know, in the world today, we have narrowband IOT, we have LTE, we have LoRa, we have near earth platforms, we have satellite platforms. So all those things are different ways to communicate. And a device, you should be able to swap in and out modems to do that. So those are all part of the things to look at. And what sensors can you add? You know, you should be able to add on, you know, at least a dozen or 30 sensors if you want to the device that many and then have options for other ones. Because nothing is the same as we know in farming. Everything needs to be unique for each application. If I'm talking about celery or if I'm talking about corn or grapes, it's always different, right, what we require. So, you know, my best thing is to, you know, do your research, read a little bit, look at the background of the hardware. The software itself has to be then upgraded. So if you have disease models, where are they from? Are they peer reviewed, are they government done, Are the university done? All that needs to be done as well. And that's a constant thing going on all the time as well. There's new models that come out for new problems and they have to be peer reviewed, tested and made sure they work in the environment that they're designed for. So there's always updates and always things being improved. At least in my company, every, every week there's something new that we're looking at for devices or software. Literally.
Now, producer Jay, can you bring up slide nine? So Jonathan's last slide? I think this is an excellent thought to end on the last one being trying to be less wrong. Not 100% right, but let's put that
in there specifically for Peter.
There you go, Pete. Trying to be less wrong, which Pete, I think is offended by this gut feel conversation and so, but it is true, right? Like over time I think you get better at spotting problems before their problems or recognising weather patterns or recognising conditions that you know, resulted in, you know, let's say a really bad year, whatever. I, I think there is something to be said for years of experience and, and those sorts of things and, and something, you know, bugging you that like, that doesn't seem right, whatever the case may be. But data, I mean if it's good data, sometimes that is also incredibly valuable because then you can't talk yourself out of it or take it takes the emotion out of things too. But I'll leave it to you Jonathan, your three takeaways as well as your trying to be less wrong.
So before when you're talking about data, you can get lost in the forest versus the trees. And that's why sometimes having a stoplight system to quantify some of this, so you can look at all the leaf wetness data that you want, but at what point does that become an issue? And sometimes you need that nudge from a software or maybe you have a metric that you can set that if I have many, this many hours or minutes of leaf wetness that it sends me an alert kind of thing. And some of that's certainly out there in some of the software. So from an intuition standpoint, the data just helps us ground that gut feel that Peter doesn't like at times. And it, or gives you back testing maybe to test some theories that you might have this year. For example, at that weather station that made a picture on, I shared some of that data with my brother who is seed corn agronomist for one of the companies and he was looking at it to try and back test, okay, why did we have such wet corn at harvest in Ontario? And it helped provide some of that data to maybe test that theory kind of thing. The other piece that I find when I need to make a decision, sometimes if I have incomplete data, I try and come to a decision as quick as I can with it. But when you have that metric and you're trying to plan, you can make those decisions faster so you can get through that cycle faster in terms of making those decisions. They call it the OODA loop. At times I can't remember exactly all the acronyms in it, but the first one is to orient yourself in terms of where you are and giving weather data and having that in a format that you can read and make a decision on. You're able to orient yourself and get there much Quicker. And then kind of the last one is when you're out of crop stage or window to do something, sometimes your best decision is the least of two bad outcomes. So but without having some of that data to. To make some of those decisions and, and trying to figure out which outcomes the least bad reality is. Like this fall, for example, there's still corn out in my area and a significant amount of it. There's probably, I'm going to guess, somewhere between 15 and 20%, depending on where you are, of the corn crop didn't get off. And for some people, that bad decision, depending on their drying capacity and storage capacity and marketing, was to pay that drying fee if they had the ability to get the corn off. And for others, they rolled the dice and decided to leave it out because they wanted to pay less drying fees and hope that we weren't going to have too bad of a winter. Some of it's came off already, some of it hasn't. But that's kind of an example of, well, what's the best of two bad
outcomes kind of things I was gonna say. I don't know what Steve's weather station would tell him about starting the combine right now, but if you have corn still out, maybe you do need to. It's a really good example, though, Jonathan and I appreciate that in the. It happens all the time, really, right in farming is that it's not an ideal situation. So what are my choices and which is the outcome? I can sleep at night or I'm okay with. And sometimes that means leaving it out. And sometimes it means exactly that you're going to pay the drawing and be done with it. So I just hope everybody's run the numbers when you make these decisions because that can sometimes help you. Again, it's a data point that can help you make the least bad decision, let's say. And I do like not trying to be 100% right, just trying to be less wrong at times. All right, okay, we're going to leave it there. Thank you both for joining me here on the Agronomists. Fascinating conversation. So many cool things that we can measure and we can do and turn them into action. And that's the key. The most important and key insight I think is what can we do with all this data if it's good data? So thank you both and thank you everyone in the comments for your questions, for weighing in, for making time for us in, in your evening. I really appreciate it. We'll be back next week, of course. But Jonathan Guy, thank you so much for joining me on the the agronomist. All right. Cheers, everybody. Have a great week. Sa.