Predictable Yields Using Remote and Field Monitoring

What happens when you take satellite products and add soil water potential data? Dr. Colin Campbell explains the formula for prescribing irrigation events that will get you the yields you want.

New data sources offer tools for growers to optimize production in the field. But the task of implementing them is often difficult. Research work is underway and offers a guide on how data from soil and space can work together to make the job of irrigation scheduling easier.

Join us for this webinar with METER’s Dr. Colin Campbell as he explains the formula for prescribing irrigation events that will get you the yields you want.

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Our scientists have decades of experience helping researchers and growers measure the soil-plant-atmosphere continuum.


Dr. Colin Campbell has been a research scientist at METER for 19 years following his Ph.D. at Texas A&M University in Soil Physics.  He is currently serving as Vice President of METER Environment. He is also adjunct faculty with the Dept. of Crop and Soil Sciences at Washington State University where he co-teaches Environmental Biophysics, a class he took over from his father, Gaylon, nearly 20 years ago.  Dr. Campbell’s early research focused on field-scale measurements of CO2 and water vapor flux but has shifted toward moisture and heat flow instrumentation for the soil-plant-atmosphere continuum.


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Hello, everyone, and welcome to Predictable Yields Using Remote and Field Monitoring. Today’s presentation will be 30 minutes followed by 10 minutes of Q&A with Dr. Colin Campbell, Senior Research Scientist and Vice President of METER Environment. If you have a question for Dr. Campbell, type it into the Questions pane at any time during the webinar, and we’ll be keeping track of those to answer during the Q&A. So please don’t be shy and ask those questions. We’ll also be sending out a link to the on-demand webinar, as well as the slides for you to review as soon as they’re available. So without further ado, I’ll hand it over to Dr. Campbell.

Thanks, Brad. It’s a pleasure to be with you here today. And I hope that I have the opportunity to share some things that will be interesting to you and we’ll have the opportunity to discuss this in more depth and get to questions at the end. So today we’re going to be talking about predictable yields using remote and field monitoring. This is a ongoing project and effort that I’m working on with a lot of other people. Some people at Brigham Young University as well as Missouri S&T University, and a grower Ryan Christiansen who is a part of BKR farms, located in Grace in southern Idaho here in the United States. And I’ll be mentioning some of these people as we go along, especially Ryan Smith, at Missouri S&T University, who did a lot of the work on the satellite data.

When I was young, I couldn’t wait for summer to come because that was a time I got to go work with my grandfather on his dry farm, growing wheat in southern Idaho. And it was interesting that I was so excited about it because mostly what I did was ride a Caterpillar Tractor, a D5, up and down in the field, somewhat like this one pictured here on the left, staring backwards at the implement I was pulling as I weeded fields and tried to make sure that the track I made on the pass before was exactly where I put the weeder on this pass. And so hour after hour, day after day, I’d be out on that tractor. And when things got boring, as they sometimes did, I thought about what I’d love in the future, which would be me sitting at home in an air conditioned room with a joystick and a TV screen, where I would be able to just guide that tractor along and not have to be hot, baking in the sun, and worried about a crick in my neck turning around and looking to implement I was pulling. On the right hand side, this is one of the new Caterpillar challenger tractors pulling another implement relative to our day now. And I was talking to my cousin who now runs this southern Idaho dry farm. And he told me that now their tractor just goes up and down in the field guided by GPS, and the person who’s actually inside the cab can just read a book the entire time, while the GPS guides it and puts the the implement exactly in the furrow that was made when the implement passed on the last pass. And the only thing that that he needs to do is turn the tractor around at the end of the field. And so, you know, the truth of the matter is that things have really changed in this area. Now, they didn’t change exactly how I thought they would. But we’ve made some amazing progress with technology. But to answer some of the other questions that challenge growers today, I think they give us a little bit more pause. And one of the things I think about often is the challenge of managing irrigation. The question has always been pretty simple, when to turn the water on and when to turn it off. And I have read papers by my father who spent some time working in this area, and that was the same question he was grappling with more than 30 years ago when he wrote this paper. What we need is fairly straightforward. We need a knowledge of if and how much water is available to plants. And we need this information at the same scale as our ability to control the water. So this depends on the number of valves. If we only if we only have one valve per field, it makes sense that our control should be limited to that. But if we can control water at a really fine level, we need much more knowledge. Now, the tools that we have to do this are things like measurements of water in the soil and the field. Those are usually at a specific location, over a number of depths. But tools are becoming available that can help us with those in situ measurements, things like drone data, where we can do flyovers of the field and look at, for example, spectral reflectance information to tell us something about plants and possibly soil, and also satellite products that also deliver information on a large spatial scale. So, as we sit down and think about where we need to go in the future, it seems like combining these kinds of pieces of data, or large piles of data together to extract key information from each, will help us drive the overall knowledge. But our challenge is, right now, that we don’t understand completely how these scales connect, and whether combining them will provide more meaningful results. And again, looking back at this key, which is, hey, when do I turn the water on, and when do I turn it off. So I’m going to break up this discussion in the case study of year one and year two. And so the first year, we’re talking about an individual field, the second year that I’ll eventually talk about, we actually tried to extrapolate some of what we learned over multiple fields. So the first discussion we’re having is in a single field, we had the opportunity to work with a variable rate irrigation system and some yield mapping that the grower did to try to make a better prescription for him to know where to put his water. So our initial goal on this project was really to try to understand the spatial variation and water holding capacity, and then create a prescription map. This was before year one that we tried to do this. And ostensibly, it didn’t go super well. So it was more complicated than we expected. So here’s a graph showing yield on the y axis and the predicted crop stress from the year 2016. So this was before this year one of our study. And essentially what came out was not a great correlation between what we predicted without any measurements in the soil as crop stress and this yield. On the right hand side, I’m actually showing you the prescription map of the variable rate irrigation system. So here is a center pivot field, this distance across the diameter is roughly 700 meters, kind of a half a mile or so. And on the bottom, there are a lot of changes in this color. And the color is indicative of how the grower is changing his prescription for irrigation. The reason there are a lot of changes is this is the local golf course in Grace, Idaho. There are no tall trees on it because it’s irrigated with a center pivot, which I think is pretty funny. But there are lots of nice long fairways and some rough areas and some tee boxes. And the grower is able to irrigate all of these differently because he understands what the different areas are going to need in terms of water. This north part of the field, however, you don’t see much color variation here. And the problem is that the grower doesn’t really understand how he might distribute his water any more effectively than just applying approximately the same amount across the whole field. And so even after this study in 2016, we didn’t know any more than when we started essentially to try to give them this prescription. And so we’re going to fast forward to 2018, where we actually started on the project that I was part of. So here’s our site, it’s located near the very small town of Grace in Idaho in the United States. It’s under center pivot irrigation, as I mentioned, and in the year 2018, two years ago, they planted seed potatoes, and then in 2019 was irrigated wheat. And I won’t show you any data from this 2019. It’s in a three year rotation, so this year, they’re again growing wheat, and then they’ll be back to potatoes in 2021. The cool thing about this study was that we got to work with this really innovative grower who has a potato yield monitor and a wheat yield monitor and has this variable rate irrigation system only on this single field. He told me that essentially he could justify this variable rate irrigation because it was paid for essentially by the golf course on the southern half. But then he could kind of work with this variable rate irrigation and see if he could improve his yields on the northern half. And that was kind of his goal. One special note, we did have telemetry in the field, we put out a total of six systems. But just so you know what we’re talking about, sometimes I label these 906, because that was the data logger number or sometimes we just shortened it to a site six, or even just the number six. They’re all referring back to this telemetry site, of which we had six in this field.

I don’t want to go too in depth on the satellite system that we were using. I’m not an expert in satellite, I was so happy to have Ryan Smith working together with me on the project who is an expert, and he put together all of these data. But if you’re someone who really wants to know the details of the satellite sources that we use, I just put together this slide, you’ll be able to see it later when we send out the slides. So if you want more information, it’s all here. And the first year we used three satellites, the Normalized Difference Vegetation Index, or NDVI for canopy greenness, and it sensed the red and near infrared light being reflected from the surface. We also measured a thermal image, this gave us canopy temperature and this was emitted surface infrared radiation. We also use the radar backscatter from Sentinel-1, this gave us an idea of the plant soil moisture, and it’s difficult to separate those two out but so we got some measure of this moisture on the surface from the radar backscatter. Over here we’re showing the repeat times, we’ve got 12 days for the Landsat 8, and eight days on Sentinel-1, that’s assuming that it wasn’t cloudy there and it wasn’t cloudy very much. The resolution I don’t have here and I don’t remember off the top of my head. But if you’re interested, I can tell you that or you can go out and search for it. The second year, we found a different index that seemed to be working a little bit better for what we were wanting to do. It’s called the Normalized Difference Water Index. From the satellite Sentinel-2. It measures plant moisture from reflected infrared light, and this is on a five day repeat. And here I do have the resolution. It’s about 10 to 60 meters. And of course, the cloudy scenes needed to be removed.

Let me tell you a little bit about what we actually did in the field. And this was pretty exciting to me, because it was the first time that we could go out and deploy METER’s new ZENTRA system, which involves a lot of things that I’ve just dreamed about in my career, being able to go and use in the field to try to make setup easy and more precise. So one of the things that I love about the the new, the ZL6 ZENTRA logger is that it has auto recognition. So instead of worrying about carrying a computer out to set up all the sensors, I just plugged all the sensors in, and the system auto recognized them. It also has bluetooth smartphone application. So all I needed to do is if I wanted to make sure everything’s reading right, I simply connected via bluetooth to my smartphone. The bluetooth is just located inside this data logger. And I could see what all the sensors were reading, which for me is critical. I never want to walk out of a field not knowing if the sensor is reading right because chances are, I’m going to have to go back. It also has an integrated solar panel so that we got continuous solar charging, of course during the day, and we never had any power to worry about. It has plenty of power, even in winter months. In some of my other research, we never go dry on the power, which is great. The other thing is that it has GPS location, which was great for making a quick map of all the sites, and also is pretty important in other projects I have talked to people about who end up doing things like tall corn canopies, because then the logger disappears, and you have to know where to go and find it. It’s great to have GPS on there to know where to go. And finally getting the data out of the field. Once I put these out there, I never really want to go back and have to deal with them. Especially since this site is more than 11 hours by car from where I live. And so having a cloud enabled data logger that just connected using a cellular network was just awesome. These are the instruments that we put out in the field, we use the all-in-one weather station, the ATMOS 41. The reason for that is that I wanted to be able to calculate the evapotranspiration based on the measurements from the weather station and then of course I had to have some measurements of canopy as well. And that was key to try to understand how much water we thought was leaving and to anticipate stress. We also use the TEROS 21. This is our water potential sensor. And this is the one that we’re going to feature in our discussion today because it ended up being the piece or the measurement device that really helped us understand a lot about the potatoes and their stress level. Also connected to that was an infrared thermometer that’s right here. We looked at the canopy from a height of two meters or about six feet. And what we’re doing there is trying to understand how stressed the canopy was. As the temperature gets higher than air temperature, or especially if you can, comparing it to a well watered site, as stressed canopies increase in temperature over well watered, it indicates a level of stress. We also installed these TEROS 12 water content, temperature, and electrical conductivity sensors, which were going to help us learn how much water was being used at depth. And originally it was my plan to kind of look at these sensors, the output of the water content, and try to figure out if we were actually, if we need to irrigate because that’s what I’ve done in projects where we’ve been in potatoes. But you’ll see that that ended up not being the case here. And finally you already heard that we use the ZL6. Here’s just a table showing all the measurements we did and what depths they came in, I’m not going to spend much time because I already described these. What I wanted you to look at is this north area of the fields. So this is just a diagram showing that north part of that center pivot. And this shows you all the locations of our sensors. So 906 or site six here, 7, 12, 9, 10 and 11. You can see how they were spread out across this field. And again, this is kind of a half a mile or 700 meters in diameter. So there was quite a bit of distance between each site. We picked these sites in the first year just by looking at sampled soil data and trying to get them to be placed in a location that was statistically different from other locations. And we just did that through an analysis. Here’s us installing the system. The coolest thing about this was that we actually installed this entire system, on average 35 minutes per site, which is just unheard of for me. Usually I spend time digging a trench and trying to figure out how to set up the logger with my computer kind of balanced under my hand and making sure the sensor wires are all taken care of. Now we did those type of things, but we did it way more efficiently using some of the new METER gear that’s that’s come out lately. This is one of my favorite things. This is a sensor installation tool. So we auger to four foot deep hole. It was about four inches wide in this kind of soil, wasn’t rocky really, didn’t take us long, now in a rockier soil that will take a little bit longer. And we just pushed these TEROS 12s into the side of this borehole using this installation tool. And we installed those at 6, 18, and 30 inches. And let me show you how this is done. Because I think this is pretty cool. This is not in the field. This is one of my colleagues here, Leo Rivera, and he’s dug a borehole here with just an auger. And he’s using that install tool here to set the depth at which he’s going to install the sensor. And then he’s putting the TEROS 12 in the carriage that he’s then going to lower down into the hole. And it’s going to stop right at this install depth that he’s set before putting it down the hole. When it’s got down to, when it’s kind of reached that depth, he just turns this handle — you see him turning here — and it just inserts this sensor into the side of the borehole. And this is so fun to play with. It gets them installed quickly. Every single time in my experience when I’ve been out here in the field doing it we’re getting a good installation. And when that happens, we can expect the data to be very, very consistent. And I’ve seen that here in the potato field. I’ve seen it several other installations I’ve done, it just starts the experiment out right or the project. And then before I left the field, I was actually looking on my smartphone to make sure the data was going up to the cloud. And here are just some visualizations of this experiment here, the VRI experiment. And this is water content and water potential over time from one of those six sites. And I’ve just sitting here watching it and making sure that things are all looking good. Of course we wouldn’t have this kind of data over just the two hours that we spent installing them and then me looking at my phone. But over time we get to see this, but the little little data that I got to look at was really useful to know that everything worked well.

So I wanted to talk a little bit about some of the results that we saw in the field. And I’ve given other presentations on this. And if you’re interested in going more in depth, I’m perfectly happy to share some of those things with you. But I just wanted to talk about a couple of the things that we learned there. So on this figure, we have water content on the y axis and time, from the beginning of experiment, in early June to when the potatoes were all killed at the end of August. And here we’re looking at the water content from these six sites over time. And these are all placed to 18 inches with that borehole installation tool that I just talked about. One of the things that the grower mentioned when we started working on this project was, hey, I’ve been trying to control my irrigation with these sensors for a long time. And what I notice is, there’s just not a lot happening with the water content. So I want you to tell me, in your estimation, when to turn the water on and off. I said, Ah, no problem. I’ll just look at the data. It probably seems to you like it seemed to me after looking at this data over several weeks that there’s just not a lot going on with these data. What we see is he’s irrigating. And because he’s irrigating, he’s keeping the water fairly constant, which I think is a good thing. And we see some change over time. But there are no visual cues to say, hey, we’ve got a problem here. This was in contrast to— now I’m going to show you the exact same depth but instead of water content, I’m going to show you water potential or matric potential, and this is for the exact same time and all of those same sites, we have 906 through to 912, we didn’t have a 908. And these are all the water potential or matric potential lines over the same period that I showed you in the last graph. And immediately what you see is much more differentiation between the different sites. And you may be wondering, well, what does that actually tell me? Well, here’s a little useful table for you just to remember something about matric potential. This says that between maybe negative 5 and negative 100, that the matric potential is in the optimal range. Now think about this in the same way you might think about your home thermostat, where you know that the temperature range in which you’re most comfortable. The plants, I mean, when they think of water, they don’t think of the amount of water there, they think they’re — they don’t think at all but — they kind of key off the water potential in the soil, because if it’s in this range, they can grow optimally, they have plenty of water. But when you drop below negative 100 kilopascals, plants become more and more in stress, down to kind of a lower limit that will cause them to permanently wilt. Now, I’ve written a hard number here, negative 1500. That’s actually not a hard number, it came out of an experiment almost 100 years ago. But that’s close to what that value is. And for us, it’s going to be pretty useful, because I just put it over here on our graph. And we can get a relative scale of, at the each one of these locations, how much stress that these potato plants were in. So we all started the season different from our water content, we’ll just flip back to that slide, just briefly, we can look we have a wide range of water contents, that each location started at. But we have a really narrow range of water potentials. And what this tells me as a soil physicist is that there are some different soil types here in the field. And in fact, we’ve done some of that, that’s a part of some of this research, but I’m not going to present it here. But that actually is true. Now as we look at these trends over time, what we see is location 912 followed by 909 and 910, we’re starting to get well into the stress range, while these other three really didn’t stress. The grower called me one day, he said, You know, I’ve been looking at these data, and I don’t think your sensors are working, which is always a concern to me. And so I went I was looking at the data as well and I started to get a little bit stressed but then looked at some canopy temperature data and it was also showing a high canopy temperature, also indicative of stress. And it turned out that when he went to the field to check the moisture, he walked out to the nearest location that was in the field, here at 906 and was looking at the water there, and it actually was doing just fine. And we didn’t know for sure if there was actually stress in other locations. But let’s see what happened to the yield at these different locations. So what I did was just say, Okay, we need to somehow compare these locations, what we’re going to do is we’re going to assign anything any day, that on average was below negative 100 kilopascals, we’re going to say that that’s going to add one day to the tota,l the sum of days below 100 kilopascals at that particular site. And then we compared that, days below 100 kilopascals, to the yield in the field. And what we see here in this graph was the locations where we saw very little days in stress had a much higher yield than those locations that had significant days and stress, in the 40 and 50 day region. And immediately when I looked at that graph, I said, Oh my gosh, yes, I think our sensors are actually telling us something about water limiting in those exact locations, and the grower was quite surprised and excited that we could actually see this. Now working with my collaborator Ryan Smith, we were able to extrapolate this change in matric potential with yield using satellite products. So we took our matric potential, these sites, used the three satellite products, the NDVI, the canopy temperature, and the moisture, looked at those correlations and then tried to extrapolate a relationship between our extrapolated moisture across the field and yield. And it is weak, there’s a lot of scatter in these data. But on the other side, it’s extremely exciting because we’re showing correlation between these measurement sites and the whole field that we just haven’t seen before. So we were excited by this. And the grower was so excited to see this, he immediately said, hey, you know what, I just want to be able to take what we’ve learned so far, and expand this into all of my fields that were growing potatoes in this year.

So now we’re stepping into year two, he said, Look, can we do this, I’m gonna have seven fields out there, none of which use variable rate irrigation. So these other seven fields are not VRI fields, they just have one valve, you know, controlling the center pivot. They’re all similar size, you’ll see them in a satellite picture in a little while. They’re similar size, some bigger, some smaller, and he had names for these fields like Cemetery, Max’s Pivot, etc, that he knew them by, so I just used them because year two’s data was generally data created by him. He bought the systems. He installed the systems. He invited me to look at the data. But he then was going to do the analysis with a little of my help. So what he did was install water content sensors at 6 inches and 12 inches, and also a matric potential sensor at 12 inches. He also put a rain gauge out there and then connected them just like I talked about, onto the cloud with with a ZL6 Data Logger. Because he had limited funds to do this and because I just left it up to him because I was interested in to see what he do, that’s what he decided, and I was just kind of a spectator to it. So he went out and installed all these sensors. But before he installed them, he asked this age old question, which is, hey, wait a second, where do you think I should put these systems out in the field if I only am putting one location? And Ryan Smith and I sat down and wondered and started asking ourselves the question, now what we do if we needed to guess at a location to put these? And what we decided was we would select them by the satellite estimation of seasonal wetness across each field. So what you’re seeing is six out of the seven fields here. You might recognize this shape here. That’s our VRI field. We didn’t put any of these systems, we did have systems out there this year, but we’re not going to talk about those because they’re in wheat. These are all potato sites. And there’s one that I’m not showing here. But what Ryan Smith did was say okay, based on my analysis, the red dots that we’re seeing, these are going to be the driest locations. And the green dots are really what I can see as the average moisture across the season. And then we left it up to the grower to decide whether or not he wanted to do the driest or the average. And it was interesting, because he came back and very forcefully said, I need to go for the average, I think this is the best way to do it, which ended up being a good choice, which I’ll show you in a moment. So I’m going to show you some data, these are coming straight out of ZENTRA cloud, in the dashboard page. So here, we can actually put all the data at a specific depth, this is at the 12 inches, the 30 centimeters depth. And this is the water content over the whole season. I’m even showing here what happened after vine kill, although I’m covering it up. And then these are the various fields that I mentioned to start with. We see a very similar pattern to what we saw in year one in that single field, the VRI, that the water contents are spread out quite a bit. And this again, is I mean, all the fields were at their full point, essentially, we’re starting in the spring, it was a nice wet spring. But all the water contents are different, because they’re all a little bit different soil types. But, again, we had the challenge that when we looked at these data, where do we turn the water on and where do we turn it off? Now I could go to each one of these individual traces and mark the full and refill point. I know how to do that. And this can be done fairly easily. But one of the things we learned in year one is that we could do that more easily with our water potential sensors. Because now I gave you this nice table here. Now you’re used to it, you’re probably looking quickly here at our matric potential. Now we don’t go to negative 1500. He didn’t even come close to that, because he knew what the range was in year two. We’re just showing 500. This is the table or the chart that we looked at through the year. And again, we see this very, very consistent starting point of between negative 30 and negative 50 kPa, well in the plant optimal range. And so everybody’s starting the same place. That made a lot of sense. And then Ryan said, Well, I just want to make sure I know what I’m doing. What should I be irrigating to, the grower said. And so I used a little tab up here that we have, an alert system and a range system and put a range on there, and I decided, well, let’s not get too wet. So let’s say the upper range is negative 20. And the lower range is negative 100. And our goal is just to keep these lines within there. And that’s what Ryan worked on these seven fields, the grower during year two. And you can see from the data that occasionally we went outside those bars, but what happened was once it went outside that we recovered fairly quickly. So there was no, like in year one, no consistent trend outside the optimal range, that when it went out, we put it back in. Now our goal in year two was also to try to figure out if we could connect satellite data and yield so that even though we were measuring at a single point, we could still be able to extrapolate across it, the field, and talk about moisture.

So I’m going to talk about what we learned here in the context of kind of sensor placement, as we use the satellite to figure out where we should put the single sites. And then we’re going to talk about how well it worked to be able to judge the yield. So here are the seven fields. We only got data back on the yield from five of them so far. And just like I did on the other fields, I put days below negative 100 kPa in this column. And just for reference, I’m showing you the 2018 potato data here on the right hand side from the variable irrigation field. What we, just remember, we had many days and stress at those individual sites in the VRI. And we compare to the sites that didn’t have basically any stress, and we didn’t see any real change in yield even in this site 907 or site seven, that only had 16 days. Now if we look over here, days below negative 100 kPa, what we’re able to do is essentially keep the days in stress to about what we saw as the optimal, as the lower end of the optimal here in 2018. So not surprisingly, we struggled seeing any differentiation in terms of yield loss. And most of the yield differences between what we’re seeing here in this column, in the yield at the sensor column, were difference in varietal planting. So these, all of these were different varieties. This was the the lowest yielding, a different variety, but it also had zero days in stress, it never went outside the stress level. One thing to notice here is that our average yield for the field and our yield at the sensor location was amazing. They’re amazingly close. In fact the difference here in this column was almost always below one standard deviation of the average yield for the field. And in some cases, it was quite close, like negative 16 here, positive 6.4 here, and even 33. This is pretty amazing, that it was that good. And I’ll talk about this as probably the most important outcome of year two’s analysis. Here was one of the disappointing parts, that we were hoping that end NDWI, the Normalized Difference Water Index, or what we were taking as plant stress would correlate somewhat with potato yield. And what we saw is not super well. There is some correlation there. It wasn’t great. But the take home, I think on this this slide is because he was irrigating based on our sensor data, I guess on the other hand, even though we didn’t see the correlation that we wanted to here, on the other hand, he really kept it at optimal level. So if we do see this, we may be missing things. So the take home point here: overall yield, connections to the satellite data, there is not much correlation between the yield and satellite derived NDWI, the water index. The yield data from the seven fields also shows no clear patterns, when we investigate it, even with our evapotranspiration satellite data from a new satellite called ecostress. Now, this was kind of on our first effort. And I think there’s a lot of things that we can do to really get more information about this, hopefully, I’ll give you a virtual seminar in the coming months that talks about, really getting deeper into the data and figuring this out. But so far, we weren’t able to derive any connections. But when we actually talked to the grower, his feedback was interesting. He said he looked at the data on ZENTRA Cloud every single day, multiple times a day. And that was great. I mean, I love seeing these data come through. And I’m really excited that he also enjoyed it. He also said that he had much lower water use, at least his feeling for that, in the fields this year than in previous years. Even though it was a cooler year that generally puts 20 to 25 inches of water on these fields, in this year, he was down in the 17.5 to 19 range of water. So he felt that was a really great success. And he was extremely impressed that the average sites we picked by satellite were right on his average, because he went out just prior to harvest and dug some lines of potatoes, as he typically does. And he dug in a dry spot, a typically wet spot, and then at our sensor location. And always when he dug at the sensor location, it was between the dry and wet spot, the yield of potatoes, and for every single field that he actually went and tested. And so it matched up our yield mapping finding. And he said he took his dad out and did this and his dad was was extremely surprised that it worked that well. So in summary, picking a single measurement site when we could only make one measurement really worked well. And it’s probably the most important finding from this project so far. And something I would like to continue to do to more effectively pick where we put our single in situ measurements. The grower irrigating to in situ water potential produced great potato yields and seemed to use less water. He was really excited about this after the season, and scaling up using a single season’s single point measurement in a field using satellite data was not successful. And as I mentioned, we just simply need more data. And that’s not surprising. I can imagine an opportunity for machine learning, some artificial intelligence doing this. But to do that successfully, needa multi years of infield data, you need multi year satellite data. And things like the ecostress satellite, it’s only a year and a half old. So we’re gonna have to be a little patient. But maybe like the challenger tractor that now drives, like drives using GPS, we’ll be able to look back on this time and say, hey, yeah, we were making progress. And we finally figured out when to turn the water on and when to turn it off. So with that, I’ll take questions.

Great, thank you. We’re past our estimated time of a half hour, but we still want to take a couple minutes to answer some questions. So please enter your questions into that questions pane. If we do not get to your questions, it’s still good to have them there. We’ll have them recorded. And then either Dr. Campbell or somebody from our METER Crops team will be able to get back to you and answer your questions in more detail later on. So let’s see. One question that we have is, and I’m going to paraphrase this. But if we’re not seeing stress in water content measurements, should we be measuring water content at all?

Yeah, so that’s a great question. And that was one we were talking about with the grower quite a bit, what is the value of water content, if we’re not, if it’s not able to tell us stress, the value of a water content measurement is it’s actually telling you how much water is there, and how much is being used by the crop. And while we looked at those water content measurements, you may have noticed that there was a daily squiggle in those lines. And you may have thought to yourself, I think those sensors are temperature sensitive. Well, the reality is that we’ve done quite a bit of testing on this and the data are suggesting that those wiggles ,as they kind of drop every day, are the plant uptake of water during that day. And if we start projecting those over depths in the soil, we can tell how much water is being taken up from the soil. And so in my opinion, how much water is there, and the availability of water, given by the water content and the water potential sensors respectively, is critical knowledge to have together. So in all of my work, and out, you saw the grower did as well, he’s still putting in water content sensors, that’s helping us essentially fingerprint each field in terms of the soil type. And so I would say use both.

Okay. And a follow up question as well, from another listener is, Will you be refining the target limits for the potatoes in year two?

So this is something that I haven’t had a chance to sit down and talk to the grower about, we usually have our meetings in February, where we’ve got the holidays out of the way, and all the work that kind of came post season. And we kind of review what we learned and then see how we did, because it’s not just, so you saw them drop below the optimal limit in some fields a few times. And this is not always because he decided to do that or just forgot to turn on the pivot. There’s always challenges: is there water available to use at that point? Or, you know, other things like, Well, we had to go out and do an operation in the field, we couldn’t water that day. And so we’re going to be talking about those limits. You know, he’s pretty concerned that he gets adequate water on the field, would we extend those two maybe negative 200 for example, go the other way. And I don’t think we’re confident enough to say we can make changes like that without being a little bit worried that we wouldn’t get enough water. So we’ll talk about that, I think is the answer.

Okay. I think we’ll have time for one more. Maybe I’ll squish a couple of these together. What was the satellite used and the band to determine average and dry conditions?

So I’m gonna have to— all satellite questions I’m gonna push to my collaborator Ryan. I think that’s on the question list and we’ll email you out about that. We’ll get him to answer that question.

How about NDWI, you want to answer questions about NDWI?

NDWI is as I understand it, it bans on the satellite, a short infrared and a mid infrared. I don’t know this, I looked it up. I was actually studying up on this a little bit. I don’t know the exact bands that they’re looking at there. But the question is out there and I’ll get an answer.

Okay. And I think that’s all we’ve got time for right now. But again, those of you who have asked questions and there are several of you. We do have those recorded. And again, Dr. Campbell, or someone else from our METER Crops team will be able to get back to and answer your questions fully and in more detail via email. So we’re going to close it up there. Again, thank you for joining us today. We hope you’ve enjoyed our discussion here as much as we did. Thank you again for your great questions. Again, if you would like more information on this project, or would like to talk further with Dr. Campbell or with our METER Crops team, please consider answering the short survey that will appear after this webinar, and also look for links to the recording and the slides via email. And again, stay tuned for future METER Crops webinars. Thanks, have a great day.

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