Transcript:

BRAD NEWBOLD 0:10
Hello, everyone, and welcome to how to get the most accurate soil moisture data. Today’s presentation will be about 30 minutes, followed by about 10 minutes of Q&A with our presenter, Chris Chambers. And I’ll introduce in just a moment. But before we start, we’ve got a couple of housekeeping items. First, we want this webinar to be interactive. So we encourage you to submit any and all questions in the Questions pane. And we’ll be keeping track of those for the Q&A session towards the end. Second, if you want us to go back or repeat something you missed, don’t worry, we will be sending around a recording of the webinar via email within the next three to five business days. All right, with all of that out of the way, let’s get started. Today we’ll hear from ecology and plant physiology specialists, Chris chambers, who will break down the largest contributors to degradation in data. Chris operates as the environment support manager and the soil moisture sensor Product Manager here at METER Group. He specializes in ecology and plant physiology and has over 15 years of experience helping researchers measure the soil plant atmosphere continuum. So without further ado, I’ll hand it over to Chris to get us started.

CHRIS CHAMBERS 1:15
Thank you, Brad. And thank you all for joining me this morning, or evening, wherever you are. And today, I am coming at you basically from a a background of experience with soil moisture datasets. I’ve been working here at METER as a support scientist, specifically focusing on soil moisture sensors, and the data that you get from them for about 15 years. So I’m here to recap, many of the conversations I’ve had, especially some of the recurring themes, and basically tell you how METER is addressing these things to help you get better soil moisture data. So first, let’s start off with our main topic, we’re going to talk about volumetric water content pretty much exclusively today. And that’s defined as the amount of water on a per volume basis, out of an entire soil volume. So in general, you have soil mineral minerals, the air, and then some changing fraction of of soil water. So really dry soil, your fraction of minerals doesn’t change. So you’re looking at about 1%, soil water, all the way up to saturation, which is going to vary from soil to soil. But it could be you know, anywhere, depending on the soil type. But basically, it’s where most of these pore spaces are filled. So volumetric water content. And you may have just heard me talking in another seminar about how important the energy state of the water is, it is really important. But most of the soil moisture measurements taken are water content. So that’s what we’re going to focus on today. And here at METER, we like to break things down into discrete sections that then we can focus on and pick apart a little bit and try to understand better. So when we talk about soil moisture accuracy, really the subtitle of that is, how can I get the best possible dataset to test my hypotheses and or use soil moisture to drive decisions like in irrigation management. And we can break this down into four clear topics, sensor performance, the installation of the sensor, whichever sensor you choose the quality assurance and or validation of the sensors and or your data. And lastly, calibration. Now calibration is last in this section. But it’s not necessarily the least important. So let’s jump right into it and talk about sensor performance. And we’re going to talk about METER sensors today. But when you’re choosing a sensor, you’re going to have lots of different options. There’s going to be advantages and disadvantages of each one. We have a nice little microcosm here of different sensor offerings. That give us a good example of how to think about your sensors. And some of the factors to think about when you’re choosing a sensor for your project. There’s almost always a price versus performance trade off. And you can break this sensor performance down into a couple things. There’s how well it measures the electrical properties of the soil. And most of the sensors you encounter today. You will use the electrical properties of the soil to determine water content. But then there’s also how much does one sensor vary from another. And that’s what we’re going to spend quite a bit of time on today. So we’ve recently had recently, within the past five years here at METER had an innovation in soil moisture sensing. And that’s using an electronic standard to tune sensors or dial them in to a specific output across a range of water contents. Now, this lets us work with a lot of sensors, and also gives us a very repeatable process that doesn’t require that doesn’t require specific steps, or it reduces variability in both normalizing centers, and in verifying them later. And so that’s what we’re going to work a lot with today. Here, we’ve got our TEROS line of volumetric water content sensors. There’s the TEROS 10, a two pronged analog probe on the left, and the TEROS 12, a three pronged digital probe on the right. And so we’re going to take a look at these, the TEROS 10 is much less expensive than the TEROS 12. So it’s an attractive probe. But we’re going to look at the difference in performance for the extra the extra dollars that you spend on the TEROS 12. So the key about the TEROS 10. As an analog sensor, it just gives out a millivolt reading, right, so it’s very simple to read. They’re not normalized in the factory, basically, we build these and then retest them to make sure that they test and function properly within a certain range. But in contrast, with the TEROS, 12, and the TEROS 11. Both of the sensors are individually normalized before it leaves the factory. So we’re tuning these sensors, so that they all read in a similar range across a range of mimicked water contents. And this is kind of the special sauce of the of the TEROS, 11 and 12 line is that every sensor that goes out the door has been through this process for the for these two sensor models. And in addition to improving the accuracy of the center, it reduces the sensor to sensor variability. And this is a really key feature. Because in almost every soil moisture study, you’re going to be comparing different individual sensors, frequently you get a time series. And what you’ll want to look at is the differences in trends and water content between the two sensors. And with this normal normalization here we’re looking at, I can’t remember exactly a couple of 1000 TEROS twelves. All and this is an independent electronic verification that was done after they were normalized. And I want to point out these two bars right here on the y axis, we’re looking at basically the percent error. So a sensor with no error is going to be right down the middle at 0% here. And I’ve got kind of the bounds 1% error, plus 1% errors up here on the top and this blue line minus 1% errors down here on the bottom. And, uh, you can see that most of the 95% of the points fall within that 1% plus or minus 1% error. And this is one of the big advantages of the TEROS 12 is that each one is going to be tuned to this electronic this electronic standard. Now, the TEROS 12 versus the 10. The 95% confidence interval for the TEROS 12 Is is 1.12% V WC whereas the TEROS 10 has a little bit broader spread because each one of these is not tuned. Now these sensors are a lot less expensive. And just because they have a higher uncertainty does not mean that they’re not going to be extremely helpful in your project. They’ll still give you an objective measurement that can give you an accurate reading. The uncertainty when comparing sensor to sensor is going to be a little bit higher. You can see most of them fall within plus or minus 2%. So what that means for your dataset is that TEROS elevens and twelves we will be able to draw firmer conclusions from from fewer data points. And this is where the, the, the trade off between cost and performance can really make a difference in your project. And like I said, we’re using the TEROS examples today. This is an important variable when you’re comparing across sensor manufacturers across sensor models as well.

Okay, so installation, you might have heard me talk about this before, the installation is going to be one of the biggest places where you can trip up in your dataset and collecting good data. And so we’re going to spend some time on it here. And I can’t stress enough how important a good installation is. And when I think of a good installation, it’s going to your sensor is going to get good contact with the soil. This is the single most important thing is making sure that your sensor gets great contact. You ideally you want to minimize site disturbance. If you dig a giant pit, then you’re going to change the way water moves through the soil on a larger scale. So keeping that to a minimum is is ideal. And you’re going to want to include some metadata, some information about your site, that is going to be important for interpreting your data. So a borehole versus a trench. And we’ll look at a couple of a couple of photos here in a few minutes. But there’s some pros and cons of each, a borehole. And by this, I mean coring down for five inch borehole and then putting sensors in the sidewall. And you can do this in appropriate profile, it’s going to give you the minimum site disturbance as opposed to digging a trench, it’s generally going to be a quicker installation as well, because you are not moving as much soil. Some of the disadvantages of a borehole are going to be the contact, it’s going to be a little bit harder to know whether or not you’re getting good contact. Especially if you’re trying to go two meters down or three meters down, it might be difficult to see and ensure that your center is fully installed. And rocks and tree roots are could could pose a larger problem in boreholes than in a trench where you can dig them out and see them. So one of the main advantages of a trench installation is that it’s easy to take a soil sample and look at the soil profile and see where changes in your soil in your soil profile occur. Because if you get soil type differences, and we’ll talk about this a little bit later, you could come to very different conclusions about what’s happening with the soil moisture in your soil. And the negatives of a trench are of course that you’re moving a lot of soil. So it’s a larger site disturbance. And it’s it’s hard to know what the exact impact of that will be on your data in the long term and the short term. Okay, so let’s start with a borehole you generally need a specialized tool for this. And this is something that we’ve been working on. Because we think the the minimizing site disturbance is really important. And here is a TEROS 12 in the borehole tool, it will work with TEROS 10 as well. And it’s all snugly held together in here. And then you just push it into the sidewall and it has a lot of mechanical advantage to get the sensor in there and properly seated. So you’re digging out a borehole in this case, this tool uses a four inch borehole and we’ve got a nice little kind of place her at the top of the hole where you can use to make sure you get your sensors in a line or even find a pilot hole later. And here you can see this is what you want to go for. We don’t want to see any of the silver pins of the of the metal pins here. You want to get your center flush right up against here. This way you know that you’re getting the best possible contact with the soil. So this is step one. And the installation tool is really good at doing this. It’s got a little pusher adapter even so that if you don’t quite have it in there, you could get a little more leverage and get that sensor fully flush. So the Next Step. Before you backfill, check your sensor. In this case your our sensor is reading 31.8. When it’s in this position, double check it before you backfill, jot this number down. And then backfilling your hole is going to be the next step where something can really go horribly wrong. Now here we’ve got two sensors at two different depths in there. And we’ve got really good contact here, the bottom one has really good contact. So we’re in great shape, we’ve checked the readings. And so when you’re packing, you want to go sequentially, don’t just throw all the soil back in your in your hole, and then pack it down and hope for the best. Start at the bottom. Use soil that you excavated from the bottom first, put in a few inches at a time and then tamp it down. And then sequentially, add soil, tamp it down, add soil, tamp it down, work towards mimicking the bulk density of the surrounding soil. But if you get a little bit off, the sensors are fairly forgiving for that. And then at the end, you want to check your sensors. Again, I showed a screen on a zsC Bluetooth reader earlier. Check it again, make sure you have not dislodged your sensors, because that’s the worst possible thing that can happen here is that you’re throwing dirt in and packing around. You want to be really careful when you’re packing dirt around the sensors to not knock them out of the side here and then wind up with an air void here, which will be which will be difficult to explain later, or can just add extra extra noise in your dataset. And a trench installation, you can still get a good installation with a trench. There are absolutely times and places to do this. Particularly if you need larger soil samples further down. You can do soil sampling with a borehole. But it’s it’s just a little bit trickier to do. Because you don’t have as much room to move around. And here we can see a good trench installation down here. Very good contact with all of the centers, it’s a lot of soil to move back in here. So this is definitely going to take a lot longer. And you’re going to you know, it’s hard to tell what impact this is going to have on the way water moves through your soil, even these undisturbed sections of soil here on the side. So the big disadvantage of a trench like this is of course how much you’re disturbing the site and how long that’s going to take you. Okay, number one thing to remember, get good soil to center contact, The number two thing to remember, get good soil to center contact and you’re probably going to be fine. Now, I mentioned soil sampling, it is really important, I recommend grabbing, grabbing at least some soil. Even if you’re doing a borehole or a trench, grab a sample for later. That way. If some questions come up, and you’re not sure, maybe I’ve got the wrong soil type. You can go back and look at it do an analysis, even do a gravimetric water content analysis to look at the ranges that you’ll see in your water content, data or even do a custom calibration if you grab enough soil. So you’re going to want to record site specific data, especially the soil type. And getting the correct soil type is really important. So grab that soil sample. And the reason it’s important is because if you’re trying to make any inferences about the water and the water in the soil, especially in relation to its availability, a bit availability to plants, you’re going to need that soil type. Here’s just some examples of what typical water contents can be. Here we’ve got field capacity stand is only 5%. And permanent wilting point is about 1%. Right and so these two points are general benchmarks that illustrate how well a plant is going to do in a given soil. And it’s much different from a clay here on the opposite end, where field capacity is going to be closer to 42%. And plants can possibly not pull any water out of the soil at 32% water content. So especially If you’re not measuring matric potential, which you probably should, you should be, at least know the soil type.

Okay, so let’s move on to quality assurance and sensor verification. And I have all kinds of conversations about this with clients. Generally, one of my main roles is to help clients understand a at the very most basic level, is the center doing its job properly. Is it giving you? Is it giving you good data? Is it working correctly? Is it okay? So the center is working right? What’s happening in the soil? If you’re seeing something that you didn’t expect? And it’s, it’s almost always comes up to trust comes down to trust in the sensor? If you see something unexpected? Well, it’s a good question, is this sensor working correctly? Or am I am I seeing some phenomenon that that I don’t understand? Or is a problem with the dataset? So from a quality assurance and sensor verification standpoint, how do you know that your sensor is working properly? We’ll go through that basic question. And there are some standards, which is going to be The sure way to know that your sensor is doing what it’s supposed to be doing. When should I worry about my soil moisture data? And one of the questions a specific example that we’ll look at later is my soil moisture hasn’t moved in weeks, is my center working properly? So sensor verification, if something looks off, how do I know whether or not it’s the center? The surest way, nobody likes this answer, but the surest way to know is to dig the sensor up and test it in some known in some known standards, there are some primary dielectric standards, if you’re using a non METER sensor that you can use for sensor verification. They also work from METER sensors. Basically, this is a good method, regardless of the sensor type that you have. As long as you have like a fume hood, you can procure these materials and safely dispose of them. Some of them are very toxic, and have specific, specific handling information. But they can give you a known electrical property at different solutions. So if you want to see okay, my sensor should be reading about 16.8 dielectric in this solution, you can pop the sensor in here, and and get a good indication of whether your sensor is within range. The sensor or this method has a lot of flaws. It’s not something you’re going to do in the field. And you can see there’s procedural things that can affect the outcome. If you put the sensors in in different different ways too close to the side, you can get a different answer. So it’s a good method to know for sure whether or not your sensor is in range, or to compare different models of sensor to each other. But it has a lot of drawbacks that make it impractical in many, many, many, many, many situations. So with our electrical verification methods, where we’ve got a very simple clip that acts as a standard, they’re super cheap, you can keep it in your pocket, take it out to the field, you do have to get the sensor out of the ground. We’re thinking about ways where you don’t have to do that. But in the short term, if you can excavate the sensor, and sometimes it is worth it to dig it up, then you can know for sure whether your sensor is reading correctly. If it’s reading within the range given for the clip, the sensor is doing its job, and you have some other phenomenon that needs explaining. Okay, so what if you can’t dig up your sensor or you just don’t want to both reasonable cases. You know, sometimes if you’re not sure about your six foot sensor, and you don’t want to a dig a six foot hole, because we don’t have an excavation tool, or B you don’t want to disturb your entire profile above it. You’re going to have to look for some yellow flags or red flags in your data that can cause you to either suspect the sensor or the suspect the centers okay. And so this is we’ve got volumetric water content sensors here, this yellow and this green one. And typically in the winter, depending on your climate zone. Your sensors could just be In this holding pattern of just staying flat and not responding at all, generally, where we live on the police, it’s because you’ve got soil moisture building up, and you’ve got this storage pool of water in the soil that’s waiting for spring green up for when the plants will start drawing it down. And every time I think last week or the week before I had seven or eight conversations with people about this, everything’s greening up my sensor should be going my water content should be going down. What’s happening is my sensor. Okay. And your sensor, flatlining is not one of my red flags. It’s I think it’s a yellow flag. I like to keep an eye on it. You know, if you’ve got one site that’s doing this, check it every so often. But in this case, where we have a co located TEROS 21 The matric potential sensor, it’s responding more rapidly. And you can see a slight decline in the water content, which I think is appropriate as roots grow in the springtime, they work their way further and further down into the soil and start start getting progressively deeper water. Now, one question might be well, the TEROS 21 seems to be responding more quickly. This is related to the soil water retention curve. So your soil type is going to be really important here and especially your soil water retention curve, if you want to understand this difference, because you might be on a part of the curve where a small change in water content results in a correspondingly larger change in matric potential. So, this one, I’ll check back on this sensor next week. And I expect it to be quite a bit more active. And most of the conversations I’ve had in the last week, even even over a couple of days, we saw it where the roots finally got down to where the center was and started to draw water out.

So the bottom line, don’t freak out if your sensor isn’t responding, but keep an eye on it. And then there’s another case down here. Okay, what’s going on with this guy, we’ve got this 36 inch sensor down here. It’s not changing, it’s definitely much different than I would expect, given our soil types. And the water content, I would expect deeper into the profile. This one’s this one’s a good candidate to dig up and throw in a clip and see what’s happening here. Yeah, this is keep an eye on it for another week or so. Basically, because I don’t want to dig it up in the rain. But yeah, we’ll check it out. But this is this boy is borderline a red flag for me that it’s so low and not matching up at all with the other sensors in the profile. Okay, so red flags in sensors should shooting out of range high or low. Your sensors almost always have a range, whether they’re METER sensors or others, and a sensor that’s clearly failed. Like a TEROS 12, for example, will read about 800 1800 Raw counts and air and 3300 Rock Hounds in water. So if you’re outside of these ranges, either the sensor has failed. You could have high electrical conductivity. But this is a red flag that you need to take a look at what what’s happening in your soil. Possibly what’s happening with your with your sensor. Because the sensors could could have failed, or if a sensor reads erratically. There’s generally a pretty common pattern, especially in irrigation of wet up and dry down. If the sensor doesn’t fit that pattern. That’s a red flag. Okay, so what do we look at? What’s it going to look like? Here’s some TEROS elevens This is another spot in our testbed. And we’re not sure what’s happening here. It’s highly unlikely that three of these have failed. But we’re down here reading minus point 474. That’s not a bad matric potential, but it’s not a reasonable water content reading for a TEROS 11. So these guys and we did dig these up. They are bad sensors. Again, extremely rare to find this. We’ll tear apart and see what’s going on with them. But this is a clear red flag that hey, there’s no way that a properly functioning Senator Can Can, can read this. So dig it up, get some new ones in here. And this is what the new sensors are doing. This is actually a good water content right here. It could be cable damage with those others. But the point is that this is something you’re going to want to pay attention to immediately and not wait and see what happens with the data. Okay, so I hope that that helps shed some light on some things to look at when you’re evaluating the quality of your data set. Some other things to keep in mind poor contact with the soil is going to the sensor data are real. But what you’re going to see are when it’s dry, your sensor reading is going to be low, because it’s going to be reading too much air. And when the soil saturated, your readings are going to be high, because that area is going to be filled with more water than the soil can typically hold. preferential flow channels through your soil are a possible source of error. So make sure it’s a good practice to take your cables off to the side and not have them going into the soil straight above your center. So kick them off to the side before they go out in the soil. And that will keep any runoff from running right down the cables to your sensors. And an incorrect categorization of soil type can lead to interpretation errors. soil maps are not always accurate. So I think they’re a great resource. But take your soil sample along so that if something unexpected turns out in your interpretation of your data, that you can dig deeper look at your soils soil samples and see what’s happening. Okay, calibration. Well, we’ll look at the time. Okay. So calibration is extremely important. Basically, what we’re looking at is, we’ve got the electrical properties of the soil here on the x axis, and the actual water content on the y axis, the way these sensors work. In this case, it’s a sensor that measures dielectric. And there’s a function that lets you convert that dielectric to a meaningful water content here about 25, a dielectric of about 25 comes out to almost 40%, volumetric water content. And you’ll get a factory calibration with almost every center. And that’s, that’s basically what your accuracy specs on the website are based off of. So when you’re shopping from center to center, and you see two and a half percent plus or minus 3% water content, there’s almost always a caveat that calibration dependent volcanic soils might not fit a calibration very well. high organic matter contents, you might need a different calibration. So this is a really important section that we’re just going to gloss over today because it deserves a lot more time. We’re going to do a full seminar on calibrations, how to get a good calibration, when to calibrate and how to interpret your data based on based on calibration. And hopefully we’ll look at a lot of different calibrations and see and see what their impact on the data is. So stay tuned. We’ll come back and treat this topic in much more detail than we have time for today. Just be mindful of your calibration in the short term, or call me up have a chat, we can talk about whether or not you need to calibrate, we have a procedure online for the calibration as well. And so just to bring it home, this is one of the things we do is try to make improvements on every step of the process of collecting data that can impact the quality of your data. Everything from creating a high precision sensor to a high quality, repeatable installation with the borehole tool to testing and verification. If you’re not sure about some data, and you throw it on the clip, then either you know that the data that you’re currently getting is not good, or it is and you need to move on. So this is our philosophy as a company to work on every single step of the process that can that can impact the quality of your data. And like I said calibration as well. We have thought We’ll share those in an upcoming webinar. And with that, Brad is going to help me go through any questions that you’ve been adding into the chat bar. I forgot to mention typing questions. I hope you have some questions, and I hope I can answer him. But if I can’t, we’ll follow up later with our best information.

BRAD NEWBOLD 35:24
That is correct. Thanks, Chris. Yeah, so we’d like to use the next 10 minutes or so to take some questions from the audience. Thank you to everybody who’s already submitted questions, we’ve got quite a few questions that have come in. And like Chris said, we probably more than likely will not get to all of the questions that have been submitted. But we do have them recorded. So don’t worry, somebody will be able to get back to via the email that you registered with. So with that being said, let’s jump into our first question, which is asking, is the error of these TEROS? Sensors, the 1011 12, constant for different volumetric water contents? Or does it increase with increasing V, WC, ET cetera, ET cetera?

CHRIS CHAMBERS 36:07
Yeah, that’s a, that’s a really good question. For the TEROS 1112, that 1.12 a basically a confidence interval. That’s error across the range. So basically, we looked at it over, let me say, the range of mineral soils, the closer you get to water, the a little more uncertainty comes up in your data. But across the mineral soil range, that that number is really consistent. And this is also partly calibration dependent as well. You know, whether or not, you’re, whether or not your calibration fits well, at low water contents in a given soil versus high water contents in a given soil that is very much calibration dependent. So when, especially when we’re, when we’re reporting errors like this, it’s, it’s, it’s generally, it is a little bit better practice to report the raw data. Because that is not calibration dependent. But it’s easier to wrap your head around, it’s easier for most people to wrap their heads around a water content error. And so that’s what I went with today. All right.

BRAD NEWBOLD 37:30
Okay. Next question. Do you help customers choose sensor locations?

CHRIS CHAMBERS 37:37
Yes. To the best of my ability. But also, generally, it’s, it’s hard sometimes. So kind of, what we can do is is without, you know, being at your site, or, or having intimate knowledge of your experiment, we have to be careful not to not to assert too much and that we are careful not to be like, Oh, yes, puppet right here, and Bob’s your uncle. So we’ll give you recommendations, how to find representative, how to find a spot to put your sensors that’s representative of what you want to infer from the data. So if you’re looking at all bottomlands, you’re doing a riparian study or something like that, you want to measure soil moisture, you know, you’re not going to want to put your sensor at the top of the hill or too far up the side slope unless you want to compare two different land positions. So generally, we try to just use sound sampling, sound sampling advice, when we’re chatting with, with clients about positioning of sensors. All right.

BRAD NEWBOLD 38:55
This next question is, what is your recommendation for measurement of constructive growing medium in containers?

CHRIS CHAMBERS 39:02
Okay, so the TEROS 12 is actually really good for that. It has a lot of data collected, it does have its limits, and it kind of depends on the fertigation strategy, the TEROS 12 is limited to kind of has a high ceiling for how much electrical conductivity you’re you’re putting into the substrate. Generally, we have soilless calibration for most of our sensors, so, but it’s only going to fit so well. It’s largely going to be a function of calibration and positioning the sensor. What I found is most important is making sure you get a good consistent positioning of the sensor and artificial substrates. But it depends on the crop the the, you know, the Grow parameters and several different variables. All right.

BRAD NEWBOLD 40:02
These devices are doing a good job of reporting the soil moisture content for the footer to around them. But does that generalize into an entire field?

CHRIS CHAMBERS 40:10
The spatial variability question. Yeah, that’s and that point sensor might not be your best choice. If you want to know field scale, it’s going to help you understand what’s happening in a local area, it can help you I can help you understand what’s happening deeper, especially since a lot of remote sensing options don’t, don’t penetrate very far in soil, right, they’ll give you the top five centimeters. But you either need to find, find out the best sampling protocol to appropriately sample a field that at a point scale like this. Or you can use soil moisture centers as an added method for something like remote sensing, or some of the new technologies emerging that are better at field scale. So what’s your centering?

BRAD NEWBOLD 41:19
Okay, you just previously mentioned that the TEROS 12 is tuned and the TEROS 10. Is not can you please clarify that? Yeah.

CHRIS CHAMBERS 41:27
So basically, we have a range of of have electronic levels that mimic the soil properties. And so the tariffs, the tariffs, 11 and 12, are normalized. Basically, they have a best fit so that their output is, is adjusted based on those electronic standards. And the TEROS 10 is not so the TEROS 10 is verified, the TEROS, 11 and 12 are normalized. And that’s how they get better center to center variability.

BRAD NEWBOLD 42:03
Awesome. All right. We’re getting close to the end of time. I think we’ll we’ll do two more questions here. This one, they’re saying I’m thinking about CO locating water content and water potential sensors. Because the installation tool you mentioned worked for both, it

CHRIS CHAMBERS 42:16
does. So and we’ve also released a new matric potential sensor, the TEROS 22, that is easier to install. So there’s a couple of different options. There is a there is an adapter for the TEROS 21. And it works pretty good. There’s, it has some limitations. If you’re in a hard clay, it’s going to be a really rough day. So the installation tool might not be the best option. Rocks, you know, the, the insertion of the TEROS 20. One’s a little bit trickier. In in in a good soil in the right soil, a TEROS 21 is going to work with the installation tool. Otherwise, the TEROS 22. I recommend you look that up it it does not work with the tool. So it might be a little side hole. But I recommend checking that out on our website because it is much easier to install than the TEROS 21 in general. Alright,

BRAD NEWBOLD 43:18
this final question is kind of a combo of of several, when you mentioned getting your your top two or three points, were getting good soil to sensor contacts. This one is asking Do you have any sensor recommendations, or I guess installation recommendations for core soils or gravels. I will also add rocky soils when it comes to installation as well. Where VW C might be low contact with prongs may not be that good as well.

CHRIS CHAMBERS 43:50
Yeah, so there is a certain particle size. Generally if it’s classified as gravel, this measurement technique is not a good option. I don’t know of a good have a good measurement method for that. Some percentage of rocks, basically, if you can find some soil to poke the sensor into, then it’s going to work pretty well in rocks, particularly soils where the soil is derived from, from the same parent material as the rocks. But that can be tricky. And it can especially be tricky in in the borehole installation tool. You can I think there’s you can get a bit of a feel for it. Whereas you can tell if you’re poking into Iraq with a little bit of practice. But yeah, I don’t know how many bent probes you’ll have to go through for that. So there’s definitely cases where the tool is not optimal. Stands generally if it’s classified as a mineral soil. These sensors will do a good job and the factory kalob raishin will reasonably get you close. More on that in the calibration in the calibration dedicated Webinar will have coming up.

BRAD NEWBOLD 45:09
Awesome. All right, that will wrap it up for us. Thank you again for joining us today. We hope that you enjoyed this discussion. Thank you again, for all the great questions, we had a ton that we did not get to. Again, we do have them recorded and Chris chambers or another of our METER specialists and experts will be able to get back to you via your email to answer your question directly. Also, I wanted to direct your attention to our handouts pane. Here in the webinar, we have added several different PDFs, spec sheets of our soil moisture sensors, installation tool, etc. So please have a look at those before you leave. Also, please consider answering the short survey that will appear after this webinar is finished, just to let us know what types of webinars you’d like to see in the future. And for more information on what you’ve seen today, please visit us at METER group.com. Finally, look for the recording of today’s presentation in your email. And stay tuned for future METER webinars. Thanks again, stay safe and have a great day. Bye