In this 30-minute webinar, research scientist and METER’s Director of Scientific Outreach, Leo Rivera, dives deeper into soil water potential, applications of the measurement, and how to make sure you are making the most of the tools available. Within this webinar he will discuss:

• Using water potential data to infer other processes and properties in soil
• The resources available to determine the right water potential ranges for your plants
• How to choose the right water potential sensor for particular applications
• How to minimize the possibility of preferential flow
• The difference between soil water potential and plant water potential and when to measure each
• And more

Transcript:

BRAD NEWBOLD 0:10
Hello everyone, and welcome to water potential 201: getting the most from each measurement. Today’s presentation will be about 30 minutes followed by about 10 minutes of Q&A with our presenter Leo Rivera, whom I will introduce in just a moment. But before we get started, we have 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 these for the Q&A session toward 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 Leo Rivera, who will break down the multitude of applications for water potential. Leo is a research scientist and director of science outreach at METER Group. He earned his undergraduate degree in agriculture systems management and Master’s in soil science at Texas A&M University, where he helped to develop an infiltration system for measuring hydraulic conductivity, which is used by the NRCS in Texas. Lee was the force behind application development in meters hydrology instrumentation, including the SATURO, HYPROP and WP4C. He also works in R&D to explore new instrumentation for water and nutrient movement in the soil. So without further ado, I’ll hand it over to Leo to get us started.

LEO RIVERA 1:27
Hi, everyone. Thank you for joining today’s webinar. And thank you for bearing with us. Well, we worked through a couple of technical difficulties. But today, we are here to present water potential 201, which is a build on from a past webinar done by Doug Cobos water potential 101. And today’s focus is getting the most from each measurement. And so there’s a few things that we’re going to try and touch on within that. There we go. But before I dive into that, because again, we’re talking about water potential. I always like to start with these two slides, because water potential is one of those more complex topics to understand. And I always feel like this is a good intro as we start talking about water potential. So in order to describe matter, in the environment, there are two variables that we need to understand. And that those are extensive variables, which describe the extent or the amount of of matter, or energy within the environment in the intensive variable, which describes the intensity or quality of matter or energy. So here are some examples, which just kind of help kind of paint the picture of what that what those mean. So here, we have two examples, we have volume, which is our extensive variable, everybody knows what volume is. And then we have density, which is the intensive variable. And we need both to really understand different different types of matter, water, air solid materials, things like that. Another good example of this, and I think this is the one that paints the picture, the best is the difference between heat content, which is our extensive variable, the amount of heat that’s actually contained within an object or within a room or whatever. Or, and then the intense variable, which is temperature, we all relate to temperature a lot better than we relate to heat content, because temperature actually influences how we feel whereas heat content can vary depending on many different factors. And, and so it’s really hard to understand what he content means for us. That is the same as we when we think look at things like water content, which is our extensive variable. And water potential, which is our intensive variable. water content tells us how much water is there. But it doesn’t tell us how it’s impacting the things around it like how it’s available. And what the likeliness is, it’s just going to move things like that. Whereas water potential or intensive variable tells us all of those things. And so we need to understand both areas to really understand water and soil. So I clicked on the wrong thing. So just to dive a little deeper, just to define what water potential is. Water potential is the energy required per quantity of water to transport an infinitesimal quantity of water from a sample to a reference pool of pure free water. So really, the thing to take away here is our reference for water potential is a pool of free pool of pure free water. So that’s our zero point. And then from there we go negative as it becomes less available or more tightly bound.

So again, today our focus is talking about how to get the most out of that measurement. And I think one of the things that we wanted to focus on first is applications of water potential data. So when we’re thinking about applications of water potential data and how we can best utilize this measurement, of course, the first thing that always comes to mind is that we can use water potential to directly measure the water availability, and how and, and, and how the plants gonna feel and how likely the water is to move, things like that. And one of those questions, of course, is how do I know what the ideal range is for my plant. And we’ll actually dive into that here in just a little bit. But first, I really want to focus actually, to other applications of water potential, that that I think are maybe less known or just less utilized, when, especially when it comes to field measurements. So one of those applications is actually just generating soil moisture release curves, which gives us that direct relationship of water potential versus water content. And we’re all familiar with doing that in the lab. We have many tools available to do that. But I don’t know how many people have actually generated soil moisture release curves in the field. I know, there’s a few I’ve seen. I’ve seen, you know, a handful of publications out there and worked with some researchers who are doing this. We’re going to dive a little bit into that. And the other question is, well, what about water movement in soil? Can I use water potential to actually monitor which way the water is moving in the soil? And another bigger question is, can I actually use it to measure hydraulic conductivity in the field as well. And so we’ll dive a little bit into that as well. Not quite as much, but we’ll touch on both subjects. So first, let’s talk about soil moisture release curves. Soil moisture release curves are typically generated in the lab. Here we have some good examples of soil moisture release curves that regenerating the lab for three different soil types of silt loam, and let me find sand and find sandy loam. Everybody’s very familiar with this, this is the typical approach that we take. But how well do these curves apply to the field, and one of the things we know is that these data don’t always represent what we actually see in the field, especially like oftentimes, when we take moisture release curves in the lab, we see much higher saturated water contents than what we ever see in the field. There are other things that we that we see that sometimes create differences. So. So how well do these actually match up? And can we use field data to better characterize these things? So yeah, can we can we use, generate good curves using field data? So let’s dive into that. So here, I’m gonna talk about a project that we’ve been working on. So we actually have a research site where we’re working on an irrigation project. And so at this site, we have two sets of sensors at two different depths, eight centimeters and 15 centimeters where we’re monitoring irrigation for turf. That’s why those are such shallow depths because the typical reading dirt depth for this turf is less than 15 centimeters. And so at each depth, we have TEROS, 21, matric potential sensor and a TEROS 12 water content sensor. And we’re using that along with ET data to inform our irrigation decisions. We’re not talking about that. What we’re talking about is can we use these two sensors together, if they’re co located to generate in situ soil moisture release curves. So a good example is if we had a profile of sensors, like we see an example on the right, very close to each other, of course, not too close to where they’re going to interfere. Can we get generate in situ soil moisture release curves from these data. So let’s take a look at some of the data and see what that looks like. So here, we have a good example of or so here we have two graphs showing the water content data and the water potential data for one of the locations at this research site. And in the top you see the water content data, the dark blue is our water content at eight centimeters in the light blue is our water content at 15 centimeters. And the the chart on the bottom is our matric potential data that’s co located with both of those. So again, the dark blue is the eight centimeters and the light blue is the 15 centimeter data. So what’s really cool is maybe not so cool is that we forgot to turn the irrigation on in April. So we had a really nice drying period. That gave us a good range of data that we actually could use to generate a soil moisture release curve. One of the things that to keep in mind here is that if we’re going to generate a soil moisture release curve, using field data, ideally, we want to do it on a drying trend, because on wetting cycles, sometimes you’re gonna see some differences in the time response of the two sensors. It’s not a lot, but it’s enough where if you’re trying to do an in situ soil moisture release curve, it could generate some issues. We also have when with soil moisture release curves, we know that we have hysteresis and we have scanning curves that cause things to be different. So just focusing on the drying period. So we have that drying period here that we decided to focus on to use for our, our, our soil m- our in situ soil moisture release curves that we’re going to try and generate. So if we focus on that period of data right there, we can then actually pull those data out. And how do I go back one? Here, Brad, can you take me back one, sorry. There we go. Thank you. So here, if we actually pull the data for both those sites, and plot the water content versus water potential, here, we see what looks exactly like a typical soil moisture release curve for these sites. And another thing that I think is really cool is one, we had a really good section of data here. And we were actually able to plot this and generate some Van Genuchten functions off of this and get our field capacity in a permanent wilting point. But what’s really interesting is if you look at these, you can see the differences at the eight centimeter and the 15 centimeter depths how that water is retained a little bit differently. And we know that the soil is different at those two different depths, we’ve looked at it, we know and we know that soil properties change. And you see that reflected in the soil moisture release curve that’s generated from the from the in situ data. So really excited to see that I think there’s a lot more potential for this, I see a lot of people doing more work in this area, it’s just really a fun area to explore. So that brings to light our next question that we talked about is can we also use that type of data to better understand water movement movement in soils, to characterize water fluxes, and, and even maybe looking at unsaturated hydraulic conductivity, we know that water potential is the driving factor in water movement in soil, water moves from high potentials to low potentials. So can we, we know we can use these data to actually see which way the water is moving in the soil. But can we also use these data to actually measure unsaturated hydraulic conductivity in the field. And I think the theory is there. Because we know that with Darcy’s law, if we have the changes in water potential, so actually, if we have the changes in the the fluxes of water content, so if we have our water content changes, over time across those two distances, we can characterize our fluxes, then we have our changes in water potential at those same locations. And if we have both of that, those together, we can actually plug that in to this equation here to calculate hydraulic conductivity at those at those given time periods. Now, having said that, there are a lot of assumptions that go into this, we use this theory in the lab into with tools like the HYPROP and regenerating soil moisture release curves to also measuring unsaturated hydraulic conductivity that way. That works a lot better, because it’s a confined sample of water, the water is only evaporating from the surface, so there’s only one way it can go. So it’s a lot easier to do that with a controlled sample versus trying to do that in the field. But I believe that if we use our drying trend data, we can actually characterize these these fluxes and get a rough estimate of hydraulic conductivity in the field as well. And this is something we plan to explore a little more with some of our field data as well. So we talked about some applications. The other piece that we really wanted to want- another piece that we wanted to focus on in today’s webinar is identifying ideal water potential ranges for different plants. And I decided to mess around with chat GPT been doing a little bit more of that just because I’m curious about the capability of the tool and what it can do. And I was just curious if I put into chat GPT “How do I identify what the ideal range of water potential is, for my plant”, this is the response I got. It was this kind of long series of response, but it all essentially sum, summed down to one researcher plant species to consider the native habitat three observed the plant behavior for soil moisture testing, which, which of course you can do that consult experts, experiment and adjust and understand water potential. So actually understand what that means and the factors that impact it. But of course, we know when we know, one, we can measure water potential, and we have the measurements that we can make with it. And also, the experts have already done a lot of work in this area for us. And so, if we go beyond chat GPT but actually look back at what the experts have done…

Of course, we know different plant species have different ideal water, water potential ranges. We have plants that have higher drought tolerance, and we have some plants that actually prefer to be stressed at certain periods of growth to, to, to induce ideal traits or quality in those plants, for example, wine grapes, and there are other plant species that like this as well. And if you go back and look at the literature, Dr. Sterling Taylor, also known as one of the founders of environmental biophysics, actually published ideal ranges for a wide range of crops in, in the physics of irrigated and non irrigated soils journal. And I think it’s called physical adophology in 1972. So this has been in the literature for a long a long time. And there’s more as you go deeper into literature, there’s more information on this. There’s another good publication on cool season turfgrasses response to drought. And what you see there from Aronson, Gold and Hull. And so there’s, there’s tools out there, and you just have to dive into the literature to see what you can find. And so if we go and actually look at the work that was done by Dr. Taylor, here, we have a nice chart showing many of those plants that show the ideal ranges for all these different plant species. Here you can see ideal ranges for strawberries, which are really, particular. They like a really tight white range. Potatoes, it’s a little bit broader. And then of course, when you look at things like carrots and onions, I prefer different what one like, they actually like to be a little more stressed. And they also prefer rain, different ranges at different times have their growth stages. So things like that. So there’s resources available to help us identify what the ideal ranges are for these plants. And there’s more work being done in this area as well. So we’ve talked a lot about the applications of water potential data. But another piece that we need to focus on is actually how to choose the right sensor for your application. Because when it comes to choosing water, potential sensors, there are many options out there, they all have different advantages that make one better over the other for different applications, and different different uses. So let’s think here, for example, okay, sorry. So when we’re thinking about what water potential sensor you want to use, there’s some things that you really want to think about one, what is your application? Am I trying to measure water fluxes? Or am I more focused on plant stress? Or am I looking at things like slope stability? And you see this chart on the right here that shows the different ranges for the different types of sensors. And where their accuracy ranges. And where are they on this chart, actually a little outdated, but but you can see where some of their ideal ranges are, and in what they work in. So you want to address this, but also think about what is your soil and media? And what are the expected water potential ranges that you’re going to see. And another important piece to think about is maintenance needs. How accessible is the site? Will you need to be going out and refilling things like you would need to in tensiometers? Or do you need something that’s really low maintenance that can just be deployed, and not something you have to worry about? So let’s talk I’m going to use two different applications just to kind of hone in on this a little bit. So first, we’re gonna start with this turf irrigation application. We know that with turf, our ideal water potential ranges are minus 30 to minus 100 kilopascals. However, as you’ve seen with our example, there are often times where you might go beyond that minus 100 kPa range. And also typically, in many turf applications, the user needs are a low maintenance sensor. And they need the sensors to be fully buried, especially if it’s turf or like a sports field or things like that, you need that stuff out of the way, out of sight out of mind type things. So in an example like this with today’s calibrated solid matrix sensors, we know that they’re accurate enough to meet the needs of these users. And they’re low maintenance. So there, that’s going to be more of an ideal type sensor for this application. Then let’s think about this other another application. So let’s say we’re looking at slope stability, which has very different needs from that of irrigation. And some of these other studies. Typical water potential ranges can range anywhere from plus 100, which is going to be saturated and a perched water table down to minus 80 kPa. In some cases, you might go well beyond that, depending on the site and those things. But typically, when we’re thinking about slope stability, we’re thinking about that wet range, which is where we’re typically at a higher risk of slope failure. These users need the ability to measure positive pore pressures because positive pore pressures often are a much higher risk of triggering landslides and and shifting of soils and things like that. And because of that accuracy near saturation is actually really critical in these applications in most cases. So tools like a tensiometer, are actually typically are typically better for this application, one because they can measure those positive pore pressures. And two, because when it comes to accuracy in that near saturation range, there’s nothing that really beats the accuracy of a tensiometer. There are some cases where a solid matrix center might actually be better if it’s a site that really has poor access. And there are some sites where they can see landslides triggered well, well before well before saturation, there are other factors that and that, that impact that. And so in some of those, some of those cases are solid matrix sensor might actually be a better tool. So talked about application, talked about how to choose right, right sensor. But I actually decided to add this part to the webinar, just because I think it’s a really interesting thing to think about. And that is, how can an installation impact the accuracy of my measurements, and not going to actually, in this example, I’m actually going to focus on temperature measurements, but but I think a lot of this applies to some of these other applications. And in some cases, we need accurate temperature measurements to go along with this as well. So one of the things I was that we’ve been curious about, and we did a research project on this last summer, is how does cable routing impact our temperature measurements and the accuracy of those temperature measurements. So we decided to take four different routing options where we run the cables fully at 10 centimeters below ground from the logger to the measurement site. We also had cables run fully along the surface, so they’re completely exposed at the surface. And then we also had an installation where we looped the cables at a desired depth. So we did two loops to help kind of dissipate some of that heat that comes in from the cables. And then the other, the last option is where the cables were fully run along the desired depth. So at 10, 20 and 30 centimeters, the cables were trenched at those depths from the measurement site to the data logger. And I believe this was about four feet from our four meters sorry, from the measurement site to where the data logger was, which is not uncommon to see those types of distances. So how did these insulation approaches impact the accuracy of those temperature measurements. And so here, we’ll just quickly touch on this. Here, we we’ve looked at two different things, we looked at the actual peak temperatures. And you see that on the left here. And I think just the important part to point out right here is that from kind of one of our ideals, installations, we saw as much as a one as a .6 degrees Celsius difference in our daily peak temperatures. And as we saw went throughout the season, we saw that that average that went went higher or lower, depending on how much how intense the solar radiation was that day. And so what that tells us is that the heating of the cables from the radiation from the Sun was actually impacting the measurement, the temperature measurement. And the other thing that impacted actually when our temperature peaked during the day. And so here, you can see that our some of our installations had as much as a 30 minute difference in peak temperature throughout the day, so just a brief touch on this. We’ll talk more about this later, probably in our chalk talk. But just wanted to point out that these things that we when we these things that we do with our installation can impact the accuracy of our measurements. Oh I touched the wrong thing again. I got it. There we go. Okay, so just to conclude things. water potential data has many applications beyond just knowing how available water is to plants, we can use it to generate soil moisture release curves in situ or in the lab. The both are great ways to go. We can also use it to measure water fluxes and understand water movement and soils, which is really critical in many applications. And there are many resources in the existing literature for identity that identifying ideal water potential ranges, we just need to go out and search them. There’s a lot of tools out there already. And when it comes to choosing the right water potential sensor, really it’s you need to know the needs of your application to help you best identify which sensor to use. So with that

BRAD NEWBOLD 24:53
All right. Thank you, Leo. So we’d like to use the next 10 minutes or so to take some questions from the eye hence, thank you to everybody who sent in questions already. And there’s still plenty of time to submit your questions if you’d like. And we’ll try to get to as many as we can before we finish. If we don’t get to your question live, we do have them recorded. So don’t worry, Leo, or one of our other METER experts will respond directly to, to your question via email. So our first question here is asking, Leo, what is the utility of saturated hydraulic conductivity for crop irrigation management?

LEO RIVERA 25:35
Yeah, that’s a really good question. You know, when I think of of using hydraulic conductivity and irrigated applications, especially when you think of saturated hydraulic conductivity, it more so just comes to irrigation rates and and how fast you can apply water without inducing runoff. I think beyond that, I don’t know of many other applications of saturated hydraulic conductivity, I do think unsaturated hydraulic conductivity actually can become a little more important. It’s especially more important in soilless media applications, because we’ve actually found that hydraulic conductivity beyond can also be a limiting factor beyond water potential. So we’ve seen that in research that if hydraulic conductivity is low, it limits the ability of water to trend to migrate back towards the area of the root and can actually cause stress at water potentials that you wouldn’t typically expect to see plants being stressed.

BRAD NEWBOLD 26:39
Next question here. What is your opinion on comparing water released curves generated in the field by the sensors with curves generated in the lab with pressure plates and core samples?

LEO RIVERA 26:51
Yeah, that’s that I love that question. That’s something that we spent some area some time working in. And we have one other project where this was in a, again, in another turf application. But this was an engineered soil. So typical turf fields have your drainage layer, and then an engineered sand that goes above that. So we did a comparison with the core samples from the field versus the in situ data that we were seeing there. And we actually saw really good agreement between those two sites. And I think so I think what that tells us is, in some applications, you can get really good agreement between the lab generated data and the field generated data. I know for the example that I showed earlier with our site, that we would see differences between the lab generated data and the field data, we never see it when you measure the soils in the lab, we see as much as a 48 to 50% saturated water content. We almost never see that in the fields. So there are differences just due to trapped air. And also just due to probably just due to spatial variability as well that are causing some of those differences between the field and the lab generated data. So I think I think there’s a lot to explore, you also need to think about, is it an intact core versus a repacked core? That’s definitely going to cause differences between the two. Yeah, so I hope I hope that answer that.

BRAD NEWBOLD 28:24
All right, next one here. So this is dealing with installation. When installing the sensors at different depths with different sedimentology, for example, sand, or mixed, or clay, do you need to be careful in refilling the borehole trying to cover it with the same type of sediment? Or leave the borehole open in sand, for example, trying to avoid the collapse and creating a preferential path? Is it recommended to use bentonite, for example?

LEO RIVERA 28:53
Yeah, that is definitely there. I think there are some things to think about when it comes to that question. That’s, which is it’s a really good question. I do think in some cases, how you repack the core can impact the quality of the measurement, especially where you do see some of those differences in the same ontology and the makeup of the soil across those layers. Ideally, we want to try and repack in the same way that the soil came back out. And so we can try and maintain some of that continuity. That’s not always possible. And so my biggest concern always with a with the borehole after the installation, is that it becomes a preferential pathway for water. So we always want to avoid that. And if that’s ever a concern, that that might be the case. I would probably use something I don’t know if I’d use bentonite maybe at the surface to cap it just to keep it from being a potential source for preferential flow. Oftentimes, what I typically recommend is when you do pack that you make it a mound So that way, because it there is likely going to settle a little bit. But that way it doesn’t become a depression and allow water to pond there and then have it become a, you know, infiltrate more water through the borehole than what you’d normally see through the soil.

BRAD NEWBOLD 30:17
Alright, this next one going back to that chart of crop varieties with their comfortable water potential ranges, this person was just asking, Do you think so? Do you think that chart? Is that chart out of date? Do you think that chart would would apply to current varieties? Or has there been? You know, I don’t want to put your your farmer hat on or or anything like that. But what do you think about that?

LEO RIVERA 30:43
Yeah, you know, I do think there certainly need to make updates, there’s been a lot of sorry, excuse me, there has been a lot of advancements in plant breeding, and improving the drought tolerance of these different types of species. And so there’s definitely that impact. The other piece I really think about is I’m amazed that they were able to do that work and get identify those ranges, in the 60s, in the 50s, and 60s, and especially with the tools that were available, but also I know, what Dr. Taylor was capable of, and the things that came out of his lab. So I’m not, you know, when you think about it, again, I’m not surprised he was able to do that. But there, there have also been improvements in, in our ability to make those measurements and and identify what that actually looks like. And one other piece to just add to that. And I’ve had some discussions with other researchers on this is, you know, we’ve seen cases where we’re seeing plants stressing in ranges where we wouldn’t expect them to stress. And this goes back to that same question, or that same point I hit on with the soilless media is that sometimes there can be other factors that limit availability of water beyond water potential. And one of those factors is is hydraulic conductivity and limiting the ability of water to migrate back towards the the air interface around the root? So yeah do you think for sure, there’s, there’s needs for improvement in that area to better define what those actually are for plants.

BRAD NEWBOLD 32:23
Okay, this next individuals asking about soil salinity, and they’re asking about soil moisture, but wanting to know the impacts of soil salinity on soil moisture, I want to throw in water potential as well. And if there’s any impact when it comes to soil salinity on water potential, and how can they measure actual soil moisture, and I’ll say water potential as well. And to avoid a any kind of effect from soil salinity.

LEO RIVERA 32:48
Yeah, there that’s quite a bit to think about there. The salinity for sure impacts several things. Of course, we know it impacts water content measurements, especially depending on the type of sensor that you’re using. And really high saline environments can be really difficult to make good water content measurements. I think this is an area that we definitely plan to touch on more in the future with some future things coming and how we can improve our abilities to measure in high saline environments. But to kind of address what what Brad was touching on is how does salinity impact water potential and our water potential measurement? Well, one important piece is we know that salinity impacts water potential because of osmotic potential. So when we think of water potential, there are many factors that go into it. We have matric potential, osmotic potential, pressure potential, gravitational potential, those things. Osmotic and matric potential are the two primary water potentials and saturated conditions. Oftentimes, we just ignore the osmotic potential because normally, it’s negligible, but it can become a limiting factor to avail because it does add additive. So if as a salt increase that decreases availability of the water, especially and can stress plants, so it does actually become something that we need to understand when we’re trying to understand water potential and soil and, and how that’s impacting availability of water. So yeah, and just to touch on that a little bit in terms of measuring it. There aren’t actually many tools that measure osmotic potential well, in situ, most things like tensiometers, and solid matrix sensors only measure matric potential. And so what I would recommend is actually tying in things like an EC measurement to get that poor water EC to try and get at pore water EC, then we can use that unsaturated extract EC, and then we can use that to try and quantify what our osmotic potential is as well, but it is it’s not it’s not an easy thing to do.

BRAD NEWBOLD 34:56
All right, I think we’re gonna do a couple more. Here. We’ve got plenty of questions, and we definitely will not be getting to to all of them. But I want to kind of combine, there’s a couple of installation questions here that kind of want to combine. One is asking again, and we’ve heard this before, does it matter if you installed the TEROS 21, vertically or horizontally, and then the other is just a location? Usually it’s within the root zone, if you’re dealing with, for instance, they’re asking about about vineyards and you want to get it in the root zone to see where that uptake of water is. And that water availability, right?

LEO RIVERA 35:30
Yeah, no, that’s exactly right. And so did, to touch on the installation. Orientation question. First, I do think it’s the orientation impacts, especially with the 21 impacts the measurement. And hopefully, if you can see my camera, sometimes we see people install the tensiom- or the TEROS 21 with the plates, horizontal. And what happens there is that actually creates a little bit of a, a shadow to the water because the water and it blocks the water as it’s moving down. And so you can have one plate that’s reading wetter and one plate that’s reading drier. So I always recommend installing the, the TEROS 21s with the plates oriented vertically to limit that, its impact on water movement. Yeah, hopefully, that helps. And then if you’re thinking about co-locating it with other sensors, ideally, it’s within, you know, within five centimeters, we can’t, we don’t want it to be much tighter than that. Because they can impact each other’s measurements if you get them too close to each other. But then thinking about insulation within the root zone, of course, that is absolutely critical. And you need to understand the routing depths of your plants. So like, for example, our our turf application, you know, we know that those that turf species doesn’t root beyond deeper than 15 centimeters. If we were dealing with some more drought tolerant native species, we would need to go deeper than that. Because they’re going to root down deeper. But vineyards in those areas, we know that the wine grapes, especially because they’re well established have much deeper rooting profiles. And so you would need to measure down in those areas. Because if you’re measuring only near the surface, it’s going to look like things are a lot drier to the plant than what they actually are. And so you need to know that whole rooting profile and what that gradient actually looks like.

BRAD NEWBOLD 37:23
All right, final question here. And this is again, kind of a combo question, but just the effectiveness of using water potential to assess or work with, you know, soil health in general, soil management and conservation practices. Can you touch on a little bit about that kind of application?

LEO RIVERA 37:42
Yeah, well, you know, I think what’s a really cool use of water potential data, when, when we think about soil health, of course, there’s a lot of pieces that go into soil health, it’s not just the physical properties of soil. There’s the course the improvements in the microbial populations and other things. But one of the main goals in in soil health is to improve the retention properties of soil and improve the hydraulic properties of soil so that they can hold on to more water, they can infiltrate more water, and they can provide more water to the plant, especially as as, as we see areas become more droughty. And what’s really cool about using these measurements in situ is we could actually look at those impacts over time. And see if over time, the management and management changes are actually improving these retention properties of soil. Typically, what they’re doing right now is taking samples and doing these things, these measurements in the lab, but that’s really time consuming. And is is a single period. If we can continuously monitor these data ordered over time, we can actually see over that period of time how things are actually impacting those those those dynamic soil properties.

BRAD NEWBOLD 39:02
All right. I think that’s going to wrap it up for us today. Thank you very much, everybody for all those great questions. Again, if we did not get your question, we do have them recorded, and somebody from METER might even be Leo, who will respond directly to your to your question via email. Also, please consider answering the short survey that will appear after the webinar is done 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 and heard today, please visit us at METER group.com. Finally, look for the recording of today’s presentation, your email, and stay tuned for future METER webinars. Thanks again, stay safe and have a great day.