Interpreting Soil Moisture Data Panel Discussion

A two-part series focused on interpreting soil moisture data. Both parts of the series were run as panel discussions.

How to Interpret Soil Moisture Data Part 1

This webinar (above) is Part 1 of a two-part series focused on interpreting soil moisture data. Both parts of the series were run as panel discussions.

In Part 1, panel members review several data sets. The discussion is primarily geared toward irrigation management, covering citrus, corn, wine grapes, and turf grass.

How to Interpret Soil Moisture Data Part 2

In part 2 (above) of “How to Interpret Soil Moisture Data”, the panel members consider data variability arising from horizontal and vertical heterogeneity in various soil characteristics, identifying the influence of hardpans and interpreting diurnal fluctuations. They also share some of their thoughts on how to approach the design and installation of a soil moisture sensor network.

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Soil Moisture: Why Water Content Can’t Tell You Everything you Need to Know

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Part 1 Transcript

Good morning, everybody. Thanks for joining us this morning or this evening, wherever you may be in the world. This is kind of a unique seminar, and we’re excited to have you guys join us. I’m here with Colin Campbell and Chris Chambers, and my name is Lauren Crawford.



So I want to say thank you as well to the folks that are sharing their datasets with us today with the ones we’re going to talk about. We get requests every day to help interpret data, and I’m fairly certain that this is one of our favorite parts of our jobs. That being said, if we can empower you to better understand your data, we’re going to try to do that, in addition to talking to you. So that’s kind of the reasons that we’re doing this today.

Anytime we do things live, you know we’ll get something. So I’m pretty certain that the best person to discuss a soil moisture dataset is the person collecting the data because you know so much more about what’s going on with the site. That being said, it never hurts to have a third party kind of give you an opinion on what’s going on with the data. In our case, we kind of bring some unique expertise and experience to the table. And that’s kind of what we’re hoping to share with you today. So Colin and Chambers and I can sit around and talk about soil moisture data all day. And we’re certainly happy to do that. But this is a pretty informal seminar, and we really want you guys to participate. So please speak up, please join the conversation. Tell us when you think we’re wrong. And speak up when you have a question, use the chat feature. If that’s not clear how to do that, maybe you can use the raise your hand feature, and we’ll try to address that at the same time that we’re giving this presentation. But please use the chat feature. And we’re going to answer these questions and bring in your comments as we’re talking. We’re not going to wait until the end.

So one of the things that we’re doing today, we’re going to be asking poll questions such as this one up here. This question is particular to help us gauge where we should linger in the presentation, and areas that we might want to skip over. So a poll question is gonna pop up. And we really want you guys to give us your feedback here so we know who we’re talking to today. So we’ll just take a couple of seconds to give you guys a chance to answer this poll question.

All right, thanks. So just to give you guys an idea of who’s watching with us today, we’ve got the majority of the people are doing irrigation with soil moisture sensors. And then we’ve got some forestry and range land, a little bit of nursery and greenhouse. Some people who are doing other work with soil moisture sensors, and we’re certainly curious to figure out what that other work is. So we’ll get started and we’ll try to linger around that field irrigation work a little more since that’s the majority of the audience.

Okay, so this is our first data set today. I’m going to ask Colin to talk about this one a little bit since he out of the three of us is the most familiar with this dataset.

So these data were collected actually several years ago now by a couple of our good friends down in Florida. They’re working in orange groves in a really sandy situation, 97% sand is quite a unique soil type that we work with, especially down there in that Florida area. We had precipitation as well as irrigation in this area, both of which we were measuring. And we added in the meteorological data to our data set just to kind of learn a little bit more about what was going on. The sensors they were using were EC-5s, and they tried to bury these down in the root zone. And here are the data they collected. So we have sensors at 15, 30, 45, and 90 centimeters. And those are the sensors we’re going to focus on, just during our discussion. We have some rain coming in, as well as irrigation events. And one of the things that we’re looking at in this particular data set is some very fast movement of water down through the soil. It’s quite an interesting data set because you can see that immediately on a rain event or an irrigation event, we see almost all sensors responding. In fact, when I was down looking at this site, they were actually using some of the area of this orchard to apply wastewater, treated wastewater, and dumping it directly on the sand, and it disappeared almost instantly from view into the sand. So it’s not surprising that we’d see this water just, if it got at the top sensor, that it would be moving down through the profile.

So one of the things that we’re going to want to do here is try to figure out where is our field capacity point, and then our permanent wilting point in this sand. When Lauren put together this presentation, she went and accessed a site that we really like. It was put together by WSU and USDA, Washington State University Biological Systems Engineering Department and the USDA. And what they have here is a way that you can try to look up your soil type, and from models that they put into this program, estimate your field capacity and your permanent wilting point. And it’s interesting, we’ll show on the graph now as we go forward, we estimate a field capacity, kind of full point, somewhere around 10% to 12% volumetric water content, permanent wilting point around 4%, and then we estimated based on some rules of thumb that we have that somewhere about halfway in between is our refill point. We also looked at these data over time to try to get that information, and we’ve marked up the graph here to show that. Now because it’s a sandy soil, some of our rules of thumb don’t work very well. For example, we like the idea of making measurements of water content about one to two days after applying a heavy soaking event, either rain or irrigation, and then say from there, that’s about our field capacity point. In the sand with it moving so quickly down through, we had to make some little adjustments to that. I think we’re taking about one day here to say well, that’s kind of our full point, beyond which we’re really washing water down through the profile.

Remind me, I feel like that with this data set, they were hoping to flush some stuff through the profiles, is that right?

So with that wastewater treatment, and they were using reclaimed water out here on the site, as I recall, it’s been a few years, this was almost 10 years ago, but they were trying to do that in some place. And I believe here they were trying to make sure they didn’t get salt buildup in the upper profile from any impurities in that water.

So one of the things we get asked a lot by people who write, you know, email us is, hey, how can I tell if there’s water moving past my root zone, water that really isn’t accessible by the plant? So we’ve circled that a little bit. What you’re seeing there if you look at the 90 centimeter sensor that’s in red, and what you’re seeing there is increases in water content. Now according to the soil physics of the situation, we want to be measuring maybe within lysimeter, certainly with water potential sensors if we weren’t doing with a lysimeter to get an idea of deep drainage, but the fact that we see water content change so rapidly and over such a wide range down there at 90 centimeters means that we can be fairly confident that the water that’s moving down though the profile is getting below the root zone. And I think we were, at least our guess was that at 90 centimeters, most of the water that was going to be taken up by the plant would be above that. So, we have there a line there at 4% volumetric water content. That was the estimate of permanent wilting point by that Saxton program.

We could go back and look at that just, there we go.

So if you look there, the wilting point was about 4.4%. That line I drew on there actually was before I looked at the Saxton model, again, this was something that Larry Parsons and Vijay said to me, Hey, that 4% line is what we’ve always assumed to be permanent wilting point. So interesting, the kind of conventional wisdom matched up fairly well with what Saxon put together. That stress point is something that that we never want to get to. It exists on our graph there just to give us an idea of if we’re approaching that point, we’re going to be stressing our citrus beyond what we’d like to.

And to add to that a little bit. In talking to Gaylon Campbell about this, he says that it’s the Saxton and Rawls model and other models out there do a really good job of estimating permanent wilting point. And with that field capacity, that’s a little bit harder to use a model to estimate because of the effects of density and other organic matter, other parameters in the soil that are more difficult to incorporate in the models. So having those two used in conjunction, this is the, I feel like the — sorry — I feel like models like this are a great place to start when you’re starting to look at the data. And then once you’re in actually collecting data, trying to pick those points using the real data is usually pretty effective. Did we want to say anything about this one?

Let’s move on. I mean think we’re reiterating some points.

Okay, great. So we’ve got two additional data sets from citrus irrigation monitoring that we’re going to look at today. In this case, the grower was using soil moisture sensors below the root zone to help them determine whether or not they’re over watering. So in the case we just looked at, they wanted to push a little bit of that water below the root zone because it was reclaimed water. And in this case, they’re using a sensor below the root zone to help them see how well they’re doing with their irrigation strategy. So they’re using the full and refill points, but they’re refining those with that deep sensor. And these data sets are from an irrigation consultant in Florida, who spends his days helping citrus and other growers schedule irrigation. So thanks for Kyle Kirkner with Water and Earth Sciences for sending these along. So I love this dataset because it’s just so nice, and it illustrates the point so well, how well you can do irrigation, once you start to mentally learn the soil moisture and what the values mean. So let’s just talk through this value. I don’t know if you could see my mouse on here. But this blue line here is the soil moisture sensor in the root zone. And they’re actually using the sensor to trigger an irrigation event. So you can see right when the sensor gets below this target zone, an irrigation event shown here in the green comes on. And this line down here, this darker blue line, this is a sensor that was placed further below the root zone. So any water going down here is water that’s lost for use by the plant. And you can see they’re doing an incredible job of keeping their water in the root zone where the plant can use it. And it’s only these times where they have a precip event, so these blue bars are precipitation events compared to the irrigation events. Those are the only time where they get flow through the root zone down below and that’s a really interesting point for me to see that. So anytime you’re doing really precise irrigation like this, you do have to be aware of salts building up in the soil. In this case, where they’re monitoring precip and the irrigation and monitoring below, they’re able to see that when they are getting a buildup of salts, it is getting pushed down during this irrigation event.

Lauren, do you know how they’re triggering irrigation on this one, are they using a — ?

I don’t. That’s a great question for Kyle. And I don’t know that. I didn’t have a chance to ask him, but I wondered the same thing. So this is a another data set that Water and Earth Sciences sent over. And this is a case where the goal was to decrease the amount of time that the irrigation pump was on. And Kyle did a great job of of showing here how many minutes the pump was on and the changes they made over time. So again, they use that sensor below the root zone to help them gauge how much water they were keeping in the root zone. So you can see here, the pump is on for 534 minutes, they get a little spike below the root zone, so they keep dialing that back a little. And that spike in the root zone continues to go down until finally down here, they’ve cut the time that the pump was on in over half, and that they’re not getting any spikes down there below the root zone, which to me suggests that they’re keeping the majority of their irrigation water in the root zone for the plants to use. And they’re minimizing the amount of time that that pump is on.

Now we have a dataset from Spain where a drip irrigation system is used to irrigate a corn crop. So the reason we brought in these drip irrigation datasets is because they really create some unique challenges for using soil moisture sensors to interpret your irrigation and to manage your irrigation. When you have a homogenous front of water coming through like you would with a flood irrigation, you have a little more flexibility on where you put the sensors. And so if you have three sensors at 10 centimeters depth in a flood irrigated regime, you have a really good chance of those sensors reading similar to each other. When you have a drip system, the response of the sensors is going to be really dependent upon where they are with respect to that bulb of water under the dripper. And so we’re going to see in here a little bit how the location spatially, with regards to depth, plays a really important role in interpreting the data. So this data set we’re going to look at a total of nine sensors. They have three different profiles in this system. And in each of those profiles, we have a sensor at 15, 30, and 45 45 centimeters. So right here we’re looking at the data displayed where all the sensors at the same depth are graphed together. So the next slide we’ll look at is where the sensors in the same profile are together. But what I wanted to show here was this effect of, we get calls a lot essentially saying, Look, my sensors are installed at the same depth, and they’re reading differently, so the sensor must be broken. And a lot of times, really all that is is spatial variability. So if we just look at this top graph here, we can see one of the sensors, the sensor in blue, has some major spikes where the other two don’t. The other two might just be in a different location with respect to the dripper. Alternatively, the dripper that was above this blue profile might have been malfunctioning, giving these weird spikes. We don’t know, but what this is meant to convey is that you really need to understand where your sensors are with respect to this dripper to interpret the data. And the other point is that we can also learn a lot more from having these sensors spatially located with respect to where the dripper is.

Okay, and now we regrouped those same data sets to look at the profile, the individual profiles. And you can see they make a little more sense where you have a spike at the 15 centimeters, and then down to the 30, and then down to the 45. And that’s what we’d expect. It looks like also, we had a sensor up here in the blue profile that looks like it got unplugged at some point and maybe got plugged back in, that we often have questions about, hey, I don’t know if my sensor is working or not. And this is a really great example to show. With the sensors, if it’s not working, it’s really not working, typically. It doesn’t typically look like real data when the sensor isn’t working properly. So that’s one thing to keep in mind. If it looks at all like real data the sensor is probably working fine.

Have you talked about the kind of orangish line there? What’s that all about, the orangish shade?

Yep, so these orange, this is from the consultant, our consultant in Spain LabFerrer, they also do irrigation consulting, and they’re also our rep in Spain. And what they provide to their growers is a target zone. That’s what you’re seeing with orange, a target zone that they recommend the grower keep their soil moisture sensor, the data from their soil moisture sensor in, to have the optimum irrigation so that you’re not wasting water, if that’s the goal, and you’re optimizing growth.

So essentially, this was something similar to when we talked about the citrus that they actually put just a shade in there to show where they’re trying to hit kind of the full and refill points.

Right. Yep. And so they were— I haven’t worked with LabFerrer with this group in particular. But typically, they’ll provide these and then the grower, depending on what their strategy is, they try to stay in that target range, or they don’t. Sometimes they make a decision that is better for them to get out of that target range. And that might be what we’re seeing here early on in 2014.

So if you’ve watched any of the seminars that I’ve done, there’s a good chance that you’ve seen the data that we’ll be presenting next before. This is a dataset that I collected with permission from the vineyard, of course, and so I feel like I have a good understanding of what’s going on because I put the sensors in there. I looked at it on a weekly basis to try to understand what was going on. And so from a data perspective, I feel like I know this one pretty well. This vineyard in particular does deficit irrigation, which is a really good segue, when we’ve been talking about having a target zone with a full and refill point. With this dataset, they’ll change their target points over the span of the growing season to account for that deficit irrigation. So after bloom is complete, they go from 100% evapotranspiration demand to 80%. So that was how they schedule irrigation the first year. I was just wanting to learn about vineyard irrigation. They were just willing to let me come in and play. So this first year, they didn’t look at the data at all, and we would just discuss it when both they and I had time to go down and talk about it. And because I didn’t know anything about vineyard irrigation, and they hadn’t used soil moisture sensors, we were both learning what the data meant for the first time. So unlike the last datasets we saw where these are well-trained consultants, we were new to the game with vineyard irrigation. And so I’ll probably spend a little more time with this dataset again, because it’s mine, and I know more about it. But let me tell you a little bit about what we’re looking at here. So this blue line is your water content value at two feet. And this purple line is your water content value at four feet. These black and green lines are your water potential at those same depths, respectively. So the black is water potential at four feet. The green is water potential at two feet.

What is that in SI? 60 and 120 centimeters maybe?

What’s that?

You’re giving it in English units, so 60 and 120.

Right, here we go.

Trying to make sure —

I did present this in Spain, and so I had done all the conversions, but in my head, it’s still in feet.

It’s in a vineyard here in the US. They’re going to talk about in feet. So yeah, just to make sure everybody knows what we’re talking about, that’s 60 and 120 centimeters down buried in the soil.

And we were trying to target the areas where the roots were. We have this is a really thick layer of silt loam soil. You can see the cover crop underneath here. This is Coco Umiker in this picture. They make great wines. Okay, so back to this data set. This is a long time ago. This is 2009. And we’re still collecting data from the site today. But let’s talk about early on. So we’ll just talk about the water content here at the 60 and 120 centimeters, or two and four feet, whatever units you feel like you want to use. What Karl Umiker’s goal was was to, like I said, irrigate at 100% evaporative demand until bloom was complete, which happened right about here, where you could see these drop off. And then he just went to 80%. And so what I thought was really interesting, when I called Karl the first time, I said, Hey, Karl, what what happened here? I thought you were deficit irrigating after the fourth of July, but you’ve got these huge spikes that actually went above field capacity, what’s going on there? And he said, Oh, well, I didn’t mean to do that. I was just trying to get water down to four feet. And so this is a really great illustration of when having these sensors can give you an idea of what was going on. It was hotter these days, we talked about why he was trying to get water down, why he deviated from that 80% ET. He said, Look, it was it was hotter that day, I just wanted to ensure that the plants didn’t get too stressed more than I wanted them to, so I applied a little more water. And so like I said, his goal was to get down to four feet — he never got down to four feet. If you look at this purple line, we see these spikes at two feet, we never had any water hit that four foot sensor.

This is something I see all the time, in particular. Customers will call in and, I’ve poured water on and poured water on but I just can’t get it down to the sensor. That sensor can’t be working. Particularly on hot days with a high transpiration in unsaturated soils, you know, it takes a lot of water to get past that root zone in particular and hit your deeper sensors.

And it may not happen. I mean, in a silt loam soil, like we have here locally, you know, during a hot summer, once you lose that water down deeper in the profile from roots down there, what’s happening is that water gets taken up there, there’s no, the hydraulic conductivity just is not going to be there compared to exactly what you’re saying. Especially evaporative demand is there. So the water is coming out. And so if you’re expecting to be able to replace that water down low, you got to start before the deficit becomes so high that you’re just not going to be able to balance that.

That’s right. And bear in mind that water is going to move laterally too, so it’s basically going to be pulling it left, right — and left, right and up.

Yeah. So you might be applying it there and it’s running.

Good point.

You just don’t have the ability to move it through.

All right. I want to talk a little more about this dataset. Again, remember, that Karl didn’t look at the data the first year. He was using a standard ET method. And that’s what we wanted. That’s what everybody wanted, because no one knew what the data was going to tell us. So I think in February or so, Karl and I got together again and looked at what had happened over the winter. And what was shocking to Karl, who’s the viticulturalist, was that these stress levels up here, the water potential levels at these deep zones, continued to increase. The water became less and less available through December. And that got him a little bit, that was a little frustrating to him because he did not want his vines going into winter under that stress.

Are those MPS-2s, Lauren?

These are MPS-1s. These predated MPS-2s.

By the way, we had a question on that first year’s data set before we jump into the second, which just said, Hey, why can we see the spikes on those particular days? And where are the sensors installed with respect to the tree? And you can answer this maybe Lauren, but my guess is that I mean, they’re down at 60 centimeters. So any nominal irrigation is just going to kind of be all lost in the smoothing of that curve. But Karl there, as I understand it, was actually hitting the irrigation really hard to try to get water back in. And so you’re gonna see the spikes because he really poured on a lot of water during those days.

That’s right. So again, like Colin said, because of these depths being the way they were, if we had had a sensor at one foot, you probably would see more the spikes in irrigation. I think these were so deep that you just didn’t see, he did. And he was irrigating once a week during 2009. And we never saw it when he was keeping at 80% ET. We did see it when he over irrigated over that 80%.

Sorry, yeah, sorry to interrupt.

Yeah, keep the questions coming. We’re happy to stop and address things as we as they come up.

Thank you. Thank you for that question.

So you were actually talking about the second year, though, where we were starting to see the flattening out of that water potential trend. Now, one thing, by the way, as you’re talking about that, one thing to mention is the actual water potential we’re seeing there, which is during, you know, starting early to mid July, we’re seeing a negative 200 to 250 kPa water potential for those grapes. Now, can you talk a little bit about that? So typically, the plant optimal is around, you know, if you’re below about negative 100 kPa, the plants are feeling a little stressed. Why would we expect to see this in a vineyard at about negative 250? Because they’re trying to stress the grapevines. Right?

They are. And this is another point with where the sensor is with respect to the dripper. Where these water potential sensors are, I tried to get these near where the dripper was, but this might not — what the root sees is not necessarily what the sensor is seeing. That’s our hope, but it’s not always the case. But yeah, in this case, you could see down here, when they weren’t trying to stress the plant, they were at about field capacity, negative 33 down here, and it was only when they were intentionally stressing the plant, and that’s the choice of a grower. Not everybody does this. It’s difficult, and it’s risky. And so not everybody does this. This is a smaller vineyard.

So and once a week do you know how many liters they were putting on during that time?

I don’t remember. I know he would — I can’t remember the emitter rates. I know he would irrigate for about 24 hours once a week during

Maybe that’s something we can post after the seminar, just to give a little feedback. So a great question. We’ll try to get that that information.

I think it was, I don’t know, maybe it was four liters an hour. But he would irrigate, this first year he would irrigate a lot of 24 hours. And he was using other things. He was out there with the vines all the time. And so he just hadn’t incorporated this into his regime for scheduling irrigation. I just want to point out another thing here with this — you could see this water content finally started to recover in December, but the water and the water potential, but that four foot took a really long time to recover. This is all just recovery from precipitation. When they stopped irrigating when they harvested, the only recovery was from precipitation. Okay, so after that first year, when Karl and I both learned a little more about it, we decided to put some additional sensors in to install or to just look and see. We had the question before about well, we never saw irrigation events with those deeper sensors. So we put one at 30 centimeters to see if we could start seeing what was going on. And now that Karl understood better what he was seeing, he was invested in learning more about it.

So one of the questions that came up here is, so we see some of the water potential and the water content sensors kind of doing their thing here in the graph. How to decide which one to use? Well, it really depends on the approach that we’re trying to take in the field. What I would say, I loved, and in my experiments right now, I’m doing this, I love to colocate water content and water potential sensors because with the water content, can get a lot more detail, especially when we have a high amount of water in the soil. As that starts to decrease, we get that fine tuning that’s given by the water potential sensor. In this case, if we’re really trying to deficit irrigate and trying to hit that negative 200 to 300 kPa range, of course, your sensor you’re going to depend on is going to be the water potential sensor. And yet, you know, water potential can change so quickly, especially something like a sand where you’re doing great, you have plenty, the water potential is high, nothing’s changing, and suddenly, boom, changes so quickly, because of this, the water release curve suddenly drops. So I love both.

Yeah, water potential really gives you a great measure of what the plant is actually experiencing and what it’s dealing with. But I think a lot of people, water content is such an intuitive measure, I think a lot of people just have an easier time understanding that measurement and wrapping their head around what it is, what it means.

Yeah, that’s really true.

And this is a case where, since we manufacture these and I had a little play time, I could install both and see what happened. And someone commented that it is really interesting to see these sensors colocated and watch what the data are doing. Let me get this.

So I think the main point was that the challenge is, of course, that many matric potential sensors can’t read below a certain water potential. So if we were reaching the negative 150, negative 200 kPa, a lot of things are dropping, and that’s absolutely true. My favorite water potential sensor for accuracy is the tensiometer. And of course, that’s going to run out at negative 80 to 100 kPa, and so we really couldn’t see the stress condition. The MPS-6 that I’m working out in the field with right now does a good job well below permanent wilting point. So, you know, we’re happy about that. But the downside is that it doesn’t work between zero and negative nine kPa. So in a really well watered situation, we may not be able to see anything here, so.

Well, for any given soil, too, there’s a relationship between water potential and water content. So if you understand your system well, it’s whatever you’re more comfortable dealing with or whatever you have the funds to install. The trick, the real trick, and this is in every irrigation application, is understanding how your plants are responding to what’s there, whether you get water content or water potential. What does your level need to be to turn the water on, and when do you need to turn it off?


I would say I agree with you partially. But I think this data set is a perfect case to show when water potential is better used. So I love to have both in deficit irrigation examples because when we’re at field capacity, even with a tensiometer, there’s just not a lot changing with water potential at that high of water potential. It’s hard to get changes, it’s hard to see changes and interpret the changes. We were looking at those citrus datasets where you saw really fast changes, and that’s around field capacity of water content. But you can see down here, once we start deficit irrigating, you don’t get huge changes in water content, but you’re getting giant changes in water potential. And that’s because of that relationship.

That’s a great point, Lauren.

Okay, let’s see, what’s next. Maybe I’ll let you look at those too. So again, Karl was excited about the data, I was excited about the data, we installed more for the next year, and Karl started looking at the data more frequently. And this is a data set from the following year. This is a really confusing data set, and someone made the point that I didn’t put a legend on the graph, and that’s a good point. I totally forgot. You know, you get into these datasets, and you start to know them, and then you forget that no one else knows them as well as you do. So once — the point with this one essentially is that we put a sensor at one foot — we’re just going to be looking at water potential here — we put a water potential sensor at one foot or 30 centimeters, and had another one at two feet and four feet again, so 60 centimeters and 120 centimeters. And I got busy this year, Karl was happy, and then I’d check in every once in a while and look at the data, and I’d call and say, what’s going on? You’re irrigating so much. You’re doing a lot more than once a week. And this is a great case where the grower started to learn what the data meant. He was at the vineyard. He’s seeing what the vines look like every day, and what he recognized was that if he could keep that one foot sensor pretty wet, that he could maintain — these squiggly lines that are kind of difficult to see in this, but these are those two foot and four foot water potential lines, these two purple lines. And what he realized is if he irrigated more frequently, but with less water, he could keep the stress levels of where the grapevines roots were, the stress level very consistent, and that’s what he did here. Another thing to notice is after they harvested, he did a major irrigation event here to kind of bring his roots back to field capacity to get ready to go into the winter. So Karl was really happy with, granted, there’s a lot that goes into grapes, and Karl just did a great presentation about all of the things that make a good grape, and this was just one of I think his top 10 list. But what he called me with at the end of the season with and said he was really happy about was his levels, his sugar levels had gotten to where they were, they were just kind of sitting around waiting for everything to get even better with the grapes, whereas the year before they’d had extra growth, they’d had extra vine growth because of those, he thought because of those spikes in the year where he didn’t intend to give so much water. But he did.

So there are a couple of questions and then we probably ought to move on to the WSU dataset.


So a question on measurement interval, how often were you reading up here, do you remember?

Probably every hour?

And so one of the comments, another comment was related to water potential and water content and it’s that people have little more experience with the water content, so are we going to see kind of the stepwise drops in water potential across the day like a water content? And one of the reason I say well, I love both in this situation is because water content really tells you how much water is being used in a crop. And when you’re an irrigation specialist, one of the big pieces you have to offer is tell people how much you’ve got to water, you know, replace during a day or during a week. And that’s what a lot of the reports are going out to the growers that they’re actually saying. So and water potential doesn’t say that. It says how close you are to the line, but a lot of people want to put up those shaded regions and say stay in here, and then watch the amount of water changing. So that’s a great question. And water potential is great. It acts like a thermometer in your house telling you what range the plants are happy in, kind of like how happy you are with the temperature in your house. But they’re not going to tell you how long to turn on your heater to heat your house up to be more comfortable again. You can’t tell how long turn on your sprinkler with water potential. You can with water content. Another point was made that actually is a really good one, we do see a little more lag in the water potential measurement compared to the water content, certainly in that in the first graph we were looking at. That’s not typical, and that really may be a part of what the soil was doing. You saw in the wet up, van you go back up to that graph really quick?

Well, there is also an equilibrium process at work there.

Right, yeah, exactly.

So I mean, that’s a function of the sensor, a function of the sensor and a function of the soil, whereas the water content sensors get in instantaneous, it’s like a flash photo, where the water potential was kind of more of an integrated measure that requires equilibration of the sensor with the soil.

Yeah, no, that’s exactly right. And I don’t know in this graph where we got the green line coming down, that’s the water potential. The blue line on the bottom coming up, that’s the water content. And obviously, the water content jumps up. You know, I mean, we’re talking a full month really before the content jumps up compared to the water potential. That’s not a sensor related thing.

And this is also maybe just, this was a drip system. So if the sensors are five inches apart from each other.

Well, but this case, we’re talking a winter though. This was just precipitation recharge.So you know, I would say that you’re exactly right, Chris, it’s a function of soil more than it’s a function of sensor. But what would you expect for a water potential and water content sensor response? Water content, instantaneous; water potential, probably one hour in the wet situation to, you know, five or six hours if you were coming, you know, if you’re very dry and stuff like that. So.

We’re actually going to skip to another data set. We’re going to skip around a little because Colin has a class to teach. Well, we talked about this pretty extensively. Let’s talk about this turf one real quick.

So last year, a couple of friends of mine were working in turf grass, and over a weekend, the Memorial Day holiday weekend here in the US, something went wrong with their irrigation system. It shut down and killed all a very important grass. It was all dead. They said, Hey, we don’t want this to happen again, hey, can we work together with you to. I was all excited, thought it was fun to go in a new environment. I hadn’t worked in turf grass before. We installed sensors at 6 and 12 centimeters. That was the colocated water content sensor and the new MPS-6 sensor we just came out with. And so that was at 6 and 12 centimeters and down at sorry, 15. And then 25 centimeters, we put a water content sensor that was well below the root zone of this grass. And we just wanted to see, basically wanted to make sure there was still water coming into the system at all times. And then beyond that, what is the behavior of water in the turfgrass system. So just to take you through this dataset quickly. You can see all the labels down there. And the water potential basically from mid June all the way through the end of September really did almost nothing. So the lowest we reached was actually when they shut off the system for a couple of days. We approached negative 50 kPa. You can see the water content changes. And basically I suggested to them after looking at those purple and blue grass and saying, Hey, we’re going up and down quite nicely, but we never touched the water potential, we never get to the point where they’re stressed. And again, we’re in a pretty sandy situation, said we’re washing a lot of water below the root zone. That’s not a goal. So let’s see if we can back off a little. So that’s what we tried starting in August. You can see the irrigation pattern changed to a less frequent irrigation with a mid day application to try to keep the plants from going into stress. Still, we didn’t have any water potential stress in the root zone of the plant, and we’re still getting water moving through down at that 25 centimeter level. And just kind of to round out the discussion, we’re still trying to figure out how to play with this better, even when we slowed down more on the irrigation between August and September. You can see those water content spikes, delaying out to more like four or five days. Still, nothing changed terms of water potential. And finally, when we shut the system off in October, you can see then we started to apply some stress, which is actually what you’re trying to do in turf grass. And we have a great dataset. In that sand, things are jumping up and down, kind of like we would suspect, but you can see from discussion of the vineyards that we’ve got a lot of work to do to try to improve how we water the grass, how we limit the water. And that kind of light blue line showing a lot of spikes, water moving beyond the root zone, we’ve got to figure out how to limit that. So that’s a little bit about about that.

Well, Colin has gotta get to class. Thanks for joining us. It looks like we’re about halfway through with about five minutes left. So we’re probably going to have a part two of this at one point, but so that we can get out of the irrigation a little, I’m going to skip around to another data set here to close this out. So you’ll see a lot of stuff. If you’re interested in the stuff that I’m skipping through, join us for the next iteration of this. This was another dataset at Cook Farm. We love this dataset, but we’re going to skip over this.

I like that one. It has a big point.

I love that data set.

All right, part two.

We’ll see if we could get a non agriculture application in. I do want to ask people this, though, because I am really interested in this. We talked a lot about this water potential, water content comparison. And I really want to know from you guys, if you have a preference. And I know that not all the time, you’ll have the ability to choose. In a lot of applications, you need water content, and in a lot of applications, you need water potential. But in scenarios where you have the choice, I’d love to know what you choose. Again, with me, I always choose both. I have that advantage, I guess.

And we did have someone apologize for too many questions. Do not apologize for that. I think it really helped us get a discussion going a little bit better, and hopefully made it a more interesting presentation. And I think we’ll maybe try this again in the future. And by the way, we did overload this presentation with data. So it’s totally fine.

No we enjoy it. So please keep asking questions. We’re — if you work for a company that makes soil moisture sensors, you’re always going to have too much data. Okay. Let’s move down here to another data set. Okay, this I talked James Leary about this dataset yesterday for — no, I guess it was two days now — for over an hour. And this is actually a site that I’ve visited. And it’s really beautiful. So aside from Hawaii being beautiful, these datasets that James has collected are just phenomenal. So what we’re going to look at are these andisols, these sites where — James manages invasive plant species on Maui and the kikuyu grass there is an invasive species, but it also outcompetes a more destructive invasive species called fireweed. And so kikuyu grass may be able to be managed by adjusting stocking rates and grazing intensity of the grazing animals, which can then manage fireweed without herbicides. And so the reason that James is monitoring soil moisture and temperature is to try to better understand kikuyu productivity so that he can guide ranchers towards more sustainable grazing practices. Give me just a second here.

Okay, we’re gonna move down to talk about the site a little bit. So we’re going to look at four of James’s nine weather station sites. We’re going to be looking at the top pair, which has the most substantial difference in elevation between the two sites. And essentially what this gives us is an incredible temperature gradient in between the sites. So they have approximately the same precipitation regime but vastly different temperatures. And then we’re going to look at this middle pair a little closer to kind of look at — these are large, longer term datasets, and this is going to show us what a drought in a natural system might look like for the soil moisture in an area, which we just thought was really interesting. So again, these are andisols in the lower elevations. And so these are volcanic soils, and they don’t have a lot of structure. And the higher elevation side is a hydric soil, so it’s essentially staying saturated all the time. Okay, so this is James’s dataset from the two pairs that we talked about. This lower soil moisture line here is that lower elevation site, and the higher one is from the higher elevation. And what James told me about this, and if I misrepresent this, this is this is all me, this is not at all James. I was typing feverishly to try to capture everything that he said, but it’s still really fun to look at and to talk about, even if I get it wrong. So I was really curious as to why these were so different. There’s about double the water content in these two soils. And James said at this higher elevation site, the temperature is really what’s controlling productivity so that there’s not as much growth, and so there’s a lot more water in the soil. And what James really likes about these datasets is that he can use these, he says these are more useful for him, to help him understand the timing of precip events than a tipping bucket. He said specifically at these higher elevation sites that there’s a lot of fog contribution to the water coming into the system, and the tipping buckets just don’t capture that really well. And what he’s started to be able to do is look at an event within the hour, and he can essentially estimate from how large that event is in the soil moisture data, he can estimate how many days he’s going to have water available for plant growth. And he wanted me to point out here, and we’ll see this more distinctly in the next data set that we look at, that when this soil moisture flatlines here, he said this is absolutely real. Once he gets about a little below this 20% value, productivity just shuts down.

Okay, we have a couple of questions here that we’re going to address. So one question was, what’s the, and this was for the previous work, what’s the difference between water potential and matric potential? Matric potential is a component of the soil water potential, which is made up of gravimetric, matric, and osmotic.

Osmotic, yeah. So, yeah, you’ll have a water potential, we kind of use the term a little bit interchangeably. And the,

We kind of assume a minimum osmotic and a minimum gravimetric.

And matric potential sensor, our MPS sensors do measure matric potential.

Okay, so we’re gonna go to another data set here. And this is such a fun illustration of — well, it was not fun at the time, I’m sure it was very stressful to be going through this drought. But you could see this drought of 2012 down here. And you can see the soil moisture starting to recover in 2013 and 2014. And James said this was strongly, as you would expect, strongly reflected in the productivity. One of the things he said was most interesting, again, I’d said we’d see this more here, we see these, you get these spikes in water content when there’s a precip event. Again, these are unstructured soil, so you see it almost immediately. And then you’ve got this nice exponential decay down to a flatline, and he said, nothing is going — this is real data, nothing is going on at this flatline that there’s no more available water. And he’s using these exponential decay curves, essentially, to help him forecast into the future, how many days of productivity he’ll have. And that’s one of the ways he’s using this to help these ranchers schedule when they bring any of their grazing animals up to graze. So we’re a little past nine o’clock. And that’s the end of our time. As you could see through all of these other datasets, we have a lot that we still want to talk about, and we’d love to do a follow up if you guys enjoyed today’s seminar. We’d love to do this again and talk about those other datasets. If you have any questions, please, you can email us after at [email protected], or like we said you could just type them in here. Again, we’d love to do this again. Again, I wanted to thank the people that were willing to share their datasets with us today and to talk me through some of the conditions at their site. As you can imagine, when you’re looking at someone else’s data, we had a lot of questions today that we just hadn’t asked people, and it’s very telling when you don’t have all of the additional information with the soil.

And one more note, we’ve had a couple people asking if they could send in their data and get our opinion on it. And we’re always happy to check out people’s data to see what they’re doing, particularly if there’s some unknown or if you’re not quite sure what’s going on in it. We don’t have all of the answers. All the data sets today we’ve had time to mull over and get some more information about. But we’re happy to help out as much as we can and at least try to help you come up with the right questions to move forward. And you can send those, feel free to send any datasets to [email protected].

And I really enjoyed this today. So if you’re willing to let us get up and talk about your data in front of a couple hundred people, please tell that to us in your email. Certainly there’s people that we talk to and they said, No, I don’t want you to share my data. Please, you can certainly still ask us about your data. And we’re more than happy to go through it with you. But we also love sharing data if you’re willing to share the data with us. So again, thanks for joining us today. Have a great day and we hope to hear from you guys soon.

Part 2 Transcript

Thanks for joining us this morning for a somewhat unplanned second virtual seminar to follow up with our first virtual seminar, What is my soil moisture sensor trying to tell me?

Spontaneous, not unplanned.

Spontaneous. So if this is the first seminar you’ve joined us for, let me catch you up to what this is about. We did a seminar about a month ago where we just went through some soil moisture datasets and talked about what we thought the datasets meant, with the goal of giving you tools to interpret your own datasets. And so if you missed that seminar, we’ll send you the first one. But just know that we’re continuing on where we left off with the last one. So just to talk about what we did last time, we talked about how soil moisture sensors are used to make irrigation more efficient, and how they’re used in vineyards for deficit irrigation. We talked about a range land study. And we talked about using water content sensors versus water potential sensors. So last time, we had a lot of folks that were joining us to talk about irrigation, so we’ve got some more irrigation datasets today. We’re also going to talk about looking at soil water content data that are spatially distributed across the landscape. And I’ll also — over a depth. And we’re also going to look at the effects, trying to decipher treatment effects from soil moisture. It sounds like we’ve got some technical difficulties with the sound right now that we’re working on. So if you’ll just hold tight, we’re working on fixing those.

Okay, so you’re probably the one that knows your soil moisture data set the best, but we’ve got a unique skill set here that we’d like to lend to what you’re doing. So just to tell you a little bit about who you’re hearing today, hopefully with better sound in a few minutes. We’ve got Colin Campbell here. He’s one of our R&D scientists, and he’s the one that’s developed most of our soil moisture sensors. He’s also got extensive experience installing these sensors in his own research and with existing research that’s going on right now. We’ve got Chris Chambers here. Chris is the one that receives all of your questions when you write to [email protected].


And I’m here too. My unique skill set comes because I am not a very good planner when it comes to collecting soil moisture data. And I make a lot of mistakes. And I’ve learned from all of those mistakes. So my hope is that I could share some of that hard learned experience today.

Okay, this seminar is different than most of our seminars, in that we really want you to interrupt us and ask questions through the questions box on the GoToWebinar. We’re going to ask these questions as we go. And so we’re going to bring up your questions when you ask them, and we’re going to talk about the answers. So please ask questions. If you don’t agree with what we’re saying, please say that too. Any questions, any comments, we more than welcome those. This is an open discussion about soil moisture data. We’re giving you one perspective, but there there are many perspectives out there, and we’re excited to hear from you. So we’re gonna do a quick poll question just to get an idea of who was with us in the first seminar, and this will kind of help us as we go if we covered a lot of a topic with the first seminar, we won’t cover it as much here, but if there are a lot of people that weren’t with us before, we’ll probably try to revisit that.

We’ll give you a few more seconds here to answer.

Okay, well, thanks for answering. Just to give you some feedback, the majority of you weren’t with us for last month’s seminar, which is fine. We’re going to be discussing some of the same stuff again, and we’ll go into more detail, and we will try to repeat some of what we talked about last time, since the majority of the viewers today weren’t here. But I encourage you, if you missed the first one, it was a really fun seminar. It’s the reason we did this again. And so I encourage you to come back and watch that too. Okay, I’m gonna hand the time over to Colin Campbell here. This was one of his field sites. It still is one of his field sites where he’s a collaborator. We’ll talk about this application first.

So this is a dryland wheat farm that we set up — I think we’ve been running this experiment continuously for the last seven years. I believe we started this around 2008. And this is actually done in a dryland wheat farm that’s not too far from Decagon located here, in the Palouse region of eastern Washington state. This is a 37 hectare dryland wheat farm. Like I said, it’s a Palouse silt loam soil type. We weren’t really aware of it when we started this experiment, but going out and studying the soil profile out there, we have a hardpan around 130 to 140 centimeters in some locations of this farm. We have about 500 millimeters of rain. This year is a really dry year, and we’re getting most of that just in today, it started raining. We have a continuous rotation out there of three different crop types of wheat, typically a barley, and then something else that they decide to put in there, typically a legume to try to do some nitrogen fixation. These are, this farm is located, just like the Palouse that we live in here is a rolling hills area where we have about a 40 meter elevation change across there.

So what our goal was to install several systems, I think we’re up to about 42 different sites across this farm. Initially, we started with 12. And these 12 sites were all created similarly, where we dug a kind of a square trench, installed a at that time an ECHO-TE sensor, which was a precursor to our current 5-TE sensor at 30 centimeters and then installed other sensors at 60, 90, 120, and 150 centimeters. There in the picture on the right hand side, you can see actually Lauren installing a sensor at the 60 centimeter depth, I think. And you can see what kind of trench we dug. And what we would do is just pull the topsoil out and lay it to the side so we could repack it in that top 30 centimeter layer very carefully. And then we’d auger holes down through from that 30 centimeter depth down to the other depths to install the sensor. Now, some from the first seminar have asked a little bit on, Hey, can you give a little bit more information about some of the data in terms of precipitation and other things. We did collect that. I’m not going to show any here in this particular presentation.

But as you look here, this is just one site that you’re seeing. It is on a hilltop site. So as I said those are, these were rolling hills. On the top of one of these hills, we located one of our measurement sites. And I’ve labeled here on the graph water content on the y-axis and then time on the x. I was wrong. We started this in 2007, so we actually have eight years of data now. But we’re only going to look at the first year of data. Why? Because there’s a lot of similarity in data over time, and so differentiating between the years isn’t particularly useful. But these are data that came out of the first four months of the season. We installed the sensors at the beginning of May almost exactly this time 8 years ago. And I’ve labeled there so that you can see the 30, 60, 90, 120, and 150 centimeter sensors, and see generally what those look like in terms of their time series. So we know that the wheat crop that was there had already begun taking up water at the 30 centimeter level, and we can see that dropping in terms of its water content. The 60 centimeter followed soon afterward, and we can see the general expected trends downward in the rest of the depths. But as you look at this dataset, the thing that starts confusing you is probably, why do we see this wide spread in volumetric water content at the start of the experiment? If we were truly in a profile that had refilled after the winter season, why wouldn’t we see all the water contents basically start at the same point? And this was a bit of a difficult question for us to answer initially.

And I’m gonna give everybody a chance to look at this because we’re going to ask you why you think that 120 centimeters sensor started so high and then ended lower than everybody else. So just kind of form that, take a mental picture of this image in your mind, and we’re going to ask you your opinion on, again, why that 120 centimeter value started so much higher than everything else, and then dropped so much lower.

So take a second and give us your opinion on this. This is to make sure you’re paying attention. It’s a pop quiz.

The interesting thing about this is, these were the three answers that we listed — lower bulk density, perched water table, or a bad installation. We didn’t know what the answer was. We actually thought that these were three possibilities, and we went out and dug down to that sensor to try to get a better idea of what was going on. We had originally for that install, for that really deep sensor, there’s two deep sensors, we had just augered a hole down, and so we didn’t have a lot of good views of what we were digging into, except for the soil that was coming up in the auger. Okay, we’re gonna close the poll. And I’m gonna actually share the results with you here and talk about — and I’ll let Colin talk about what we found here.

Lauren, I think it’s nice that most people didn’t think you’d installed the sensor. There’s always that possibility. So it’s interesting, we’ve got about 70% talking about the perched water table. Let’s close the poll and look at exactly what we found out there. Was that you can see on the right hand side now kind of a diagram of what things looked like out in the field. I did mention early on that we’d seen a hardpan out there. And it does turn out that the hardpan layer there at about 130 centimeters was creating kind of a perched water table there in both the 120 centimeter and the 90 centimeter sensor. We’re basically not able to drain to field capacity initially, and so they had a higher water content. But interestingly enough, this year, we’ve gone out and done — well, last year and this year, we’ve gone out and done a lot more sampling of the bulk density to see how the bulk density changes with depth. And not surprisingly, for those of you who suggested it was lower bulk density, comparatively, that we do see quite a range of bulk densities, I mean within the kind of bookends of what we would expect in a mineral soil. But there is a range of bulk densities that doesn’t make this soil profile as homogeneous is maybe you might expect

Well bulk density does affect the output of the probe, but it’s fairly robust to bulk density compared to other, compared to some of the other factors, like I think Doug’s analysis was that you can get a what 16% difference in bulk density before you see a 1% change in water content. So bulk density is a factor, but you really have to get a big difference for it to have that large of an effect on water content. Air gaps certainly, can certainly pull that reading down. But that hardpan, and this is this is something I get a lot from customers is trying to interpret something like this, and frequently you’ll see a dataset that looks beautiful. 30 centimeters, 60 centimeters, a meter, and it does the exact same pattern that you would think. And then a site right next door, the middle sensor would just do something, it just breaks the pattern. And without knowing what’s in that soil or what you have in the soil, it’s difficult to interpret what you’re seeing in the water content. So sometimes it’s very important to get in there and know what kind of soil you have these in. And you could tell with his hardpan. I did, I helped install a few of those, and I don’t know if any of you have worked with Palouse loam here, but it’s great for boreholes. You can just bore bore bore, and you’re making tons of progress and then at the site, you hit that hardpan, and then it’s a grind to get that borehole through it.

In our defense you did auger those holes after, that was the second installation, right?

Yeah, that was the second installation.

Yeah, that was a couple of years later. So really, we should have known this. But we’re a little young in our —

I think Lauren wasn’t paying attention.

Maybe Colin and Chris, can you talk a little bit more about why there’s such a difference between the 120 and 150? I know we talked about the 120 versus everything else, but specifically, that 120 centimeters versus 150. Can you specifically —

So, I don’t know what Chris thinks here, but from my experience what happened here, without really knowing much about wheat, maybe it doesn’t make sense that you’d see roots down into 120 centimeters, but after, certainly after this first year, we learned where the wheat is taking up the water. And those roots, of course — the summers here on the Palouse are very, very dry. We have high vapor deficits, vapor pressure deficits and we have relatively low daytime humidities, that those roots are reaching quite deep in the soil profile, it’s very nice soil they’re getting down there. And what’s happening there is there’s a reservoir of water available for the soil for the sensors — or sensor — the roots. And what the sensors are picking up is that the roots are going down there and gathering all the water. And what we do see on that 120 centimeters, it’s basically depleting that water down to about the level that we would expect from the 30 and 60 centimeter. Now, physiologically, this is quite interesting because it almost looks in this graph, like the 90 centimeter water was almost passed up by the roots as they kind of found a little reservoir down at down 120 centimeters. Now we shouldn’t get too crazy about this interpretation because of course, we’ve got to do quite a bit more science there to really understand that. Even to start with, we should be measuring in terms of water potential and saying okay, what is the actual water potential of soil down at 90 centimeters? Did it become unavailable, so to speak, at 35? I don’t think so, but there’s only so far that this interpretation can go.

And at 150 centimeters, there’s really no place for the water to go there. The water table there is not that deep, but I don’t believe it was up to 150 centimeters. It was just kind of sealed, that sensor was just kind of sealed in there.

It’s sealed from the hardpan. So the roots aren’t getting through the hardpan just like your auger didn’t get to.

Well and just at the beginning here, the sensor at 120 centimeters, this was probably closer to saturation, where down at 150, we probably started more at field capacity since that was able to more easily drain.

No, exactly. That’s why the 90 and the 120 are up so high initially is because the idea of field capacity is a dynamic idea. If you don’t have drainage, you’re not going to have quote unquote field capacity. So let’s maybe move on and this is an interesting graph. When we tried to look at the water depletion over time, and it’s not perfect, it’s this one and you’ll see on the next one that we made like this that because of timing and being out in a active commercial field, we had to bury cables so that the farm implements didn’t break them all off, so we don’t have a perfect time series here. But what you can see is volumetric water content now on the x-axis, with depth on the y-axis. And just give you an idea of the depletion of water at the various depths by the wheat in versus time. And so it probably comes as no surprise that from the last graph, that we can see that the wheat was taking up water by depth is, first, of course at the upper depths, and then as time went on, they were depleting the lower depths of water. And then of course, right there toward the end, toward harvest time, between 7/21, 8/2, so late July, early August, when they did go out and just were preparing to harvest, we’ve got senescence of the leaves, etc, that very little water is taken out.

So one of the other interesting things that we collected, or one of the information that we collected was the wet up. So this is the same data set. Now we’re looking at what happened in the fall and winter season. We’re missing a 90 centimeter sensor that didn’t get connected up, I don’t think, when we put it out there. So you can see some of the things that we really expect to see, we see there, the 30 centimeter sensor is showing the several rain events. And again, as someone suggested, certainly would be nice to put precipitation on here, but with all these sites, we actually were collecting precipitation from a meteorological stand that was out in the field, and we just didn’t match those things up. So there at the 30 centimeters sensors, again, we can see the precipitation. And then we see the wet up coming from the 90 centimeter sensor. That was pretty much full, but the water didn’t get down to the 120 centimeter sensor till fairly late in the season, we see an event there around the first part of February that really bumps the 120 centimeter. And what we’re seeing there is really that perching of the water table, again, that it dried out over the summer, takes a fair amount of water to really perch that water table and then it spikes up. And the thing that I was looking for in this data set was, if that sensor is behaving like it probably should, that that 120 goes back to about the same level we saw in the first graph, and it did. We also see that the 150 centimeter sensor, again, it didn’t move down much and it didn’t move up much, but when we finally hit kind of saturation above the hardpan, that we do see the 150 bump, as water just kind of forced us through that hardpan down to the lower levels. Not a lot of change, though.


And I want to continue to encourage people to ask questions. We’ve got a couple of questions that have come in. But please give us your comments and ask questions and we’ll answer them. And we’d love to debate the stuff. This is what we do every day.

Are you picking a fight, Lauren? Not on my data set — you gotta wait till your data set. So here’s the general wet up here. And again, the bulk of the wet up in this situation was down at the 120 centimeter. Interestingly enough, we’re not seeing a lot at the top. And again, just because of the dynamics of being in an actively managed field, we couldn’t get the data loggers back in the field till quite a while after harvest. And you know that first year we weren’t very good with this. But in fact, in subsequent years, we have been able to get sensors back in the field and get almost complete data sets out there. In fact, Chris I think they’re now, against maybe our recommendation, burying loggers in their own watertight boxes.

I think they talked to me about that before they did it and picked up a bunch of cables. People like to bury loggers. Sometimes it works; sometimes, you know, it doesn’t work out so good.

In this situation,

It’s a fun adventure. [laughter]

they’re putting a lot of deskit in there, they’re sealing, they’re putting a lot of RTV silicone on the connectors. I don’t know what their success rate is.

I’ll have to check on that.

The connectors where the batteries are located are the only places that we don’t cover with epoxy, so it’s not the end of the world. We do try to waterproof this stuff as much as we can.

Don’t bury your stuff.

I recommend against burying unless you’re gonna really get them waterproof. But hopefully we’ll see how those go.

So collecting a full dataset is more possible for them but not easy.

This is a really good data set to really look at this. So Colin and Chris, why do you think that the 90 centimeter sensor readings remained so constant here? We don’t — everything else, we see a big change except at 90.

So that’s really interesting. I mean, this is a great question related to — can you back that slide up just a little bit?

That’s going the wrong way.

Let’s see if we can. So what I think we’re seeing, even if we go to the one before, let’s go all the way back. So we see a 90 that does drop some, not a lot. So we didn’t get, this is a wet up. But we didn’t get a full picture of the 90 because of course, we didn’t get in the field far enough? I think, so I think that relates to the physiological nature of what’s going on out there. My guess is that the wheat was honing in on where the easiest water to get was. And that was down at the 120 centimeter level right above the hardpan and basically bypassed the 90 centimeter level and went straight to the hardpan, and we just don’t see that wet up in the 90.

Am I going the wrong way? Probably. So the 90 it winds up down at, down around 35.

35, something like that. And then it bumps up a little bit. So we’re back to 40 to 45. So we do have wet up.

Yeah, we missed the refill in this part of the season. But yeah, that’s a great point for, that Colin’s made about the root variability. They’ve got— have to allocate for nutrient uptake and for water uptake.

And sometimes I mean, you’ll get questions, right, Chris, that say, Hey, I don’t think that sensor is working.

That’s always a, you know, that’s always, it is kind of one of the first things people jump to when they see something they don’t, that, you know, is difficult to explain. Is a sensor working? I’m a big advocate of finding out whether your sensor is working. If you’re not sure and you can, dig it up. Read it in air and water, make sure it changes. If it does change, then what you’re seeing down there is real. But without knowing what’s happening with your soil, you know, if you don’t realize that there’s that hardpan there or a texture change, then it’s hard to explain when the sensor does something that you don’t expect.

I’ll ask one more question about this dataset. And we’ll move on since it’s 8:30 and we’ve gotten through one data set. Colin, you were part of the design of the study. And the deep — the most shallow sensor that you guys installed was 30 centimeters, is that the shallowest? What was the reasoning behind installing at that depth versus, say, starting at 10 centimeters, and would you expect to see more variability at 10 centimeters?

So we get a good idea of really the early crop growth in terms of water uptake, and also just a really good connection, of course, to atmospheric conditions at 10 centimeters. But there’s a really easy reason why we didn’t put one to 10 centimeters, and that’s simply because the plow would have eaten it up. So at times, they’re taking implements over this at some depth. And we wanted to make sure that our sensors weren’t wrapped up in somebody’s plow somewhere.

And when they took the loggers out, they left the cables in but buried them below the plow.

Exactly. So we could have gone a little bit shallower, we probably could have gotten say to 20 centimeters. I don’t think I would have gotten any less than 15 centimeters out there because I think we could have had a problem. You never know. So this this stuff is variable. They don’t plow very deep out there, as the minimum till operation as I understand it, and they’re doing quite a bit of work trying to really use environmentally friendly approaches, but we couldn’t put them any less.

Okay, we will look at this data set one more time. This is really cool.

So this is a different site. So this is a toe slope site where we get quite a bit more water because the water is running downhill. And so this was just something I noticed as I was going through this rather large data set, and this is something we get questions about all the time. Generally when your soil moisture wiggles up and down and does it on a diurnal basis, what Chris and I would tell you is you’ve got some temperature sensitivity and your measurement, and by their nature, so water content sensors are temperature — there is a temperature,

Dielectric changes by temperature.

Yeah, there’s sensitivity there. And if you’re really interested in that question, contact us particularly, we’ve got some things that you might be able to do. And there are papers coming out on that. Generally dielectric sensors, the output can be improved by temperature. But this is not a temperature sensitivity.

150 centimeters deep, no way.

So look at this a little bit and I don’t think we put in a poll here, but I’d love to get you to kind of study this little square here. And notice a few things about this, as the wheat is taking up water over time, we’re seeing this diurnal signal in the 30, the 60, the 90, and even down at the 150 centimeters, and people when they look at this, say, hey, you know, I think what’s going on here is just you’ve got that dielectric with temperature signal. But I counter with the fact that if you look at the 150, it is completely flat for the first three months of signal coming out there. And it’s only as the roots begin to take up water that we actually see this change. And one thing to note here is really there was no evident hardpan down here on the bottom.

This is a different site than we were looking at earlier.

All right. And so what’s happening here, I think, is hydraulic redistribution. I’ve graphed really carefully up there, kind of the time on the x-axis from that little box there with volumetric water content on the y-axis. And the relative change in water content with time is much more indicative of an overnight building up of water pressure in the roots and an increase in water content. I think what we’re seeing is just —

And then being pulled out in those drier soil layers that had been depleted already.

And even, you know, we’re just we’re getting roots growing around the sensors, probably. And we’re sensing that. Now it’s easy to say, hey, what about 120? And my guess is there are just not roots near that sensor that they’re depleting water at that level, but the roots aren’t so close. And this was only one out of the 12 sites we saw it. So maybe we had some prolific root growth. Chances are we had heavy wheat growth there. So generally, I think what Chris and I would say is, if you see these things, you probably have temperature sensitivity. But if you got it going on down at 150 centimeters, it’s very likely a true response to the plant. And people say, wait, what about lager response or heating up of wires or anything like that? We tested all these things. We don’t have a temperature graph in here, but I think that there was nothing going on with temperature.

And some people have reported that there are sound issues. We’re going to start moving away from using dial up here at Decagon, and hopefully that’ll resolve that. But I think this is on our end that we’re having internet connectivity issues. And we’ve got some technical folks working to resolve that. So I really do apologize. We’re gonna keep going. But ask questions, and we’ll continue to work on this. Hopefully, it will just get better.

We don’t actually use dial up, Lauren.

We don’t use dial up. That was a joke. It feels like it right now.

Okay, I did the second data set.

Okay, this is another one of Colin’s. That was another technical difficulty. This is another one of Colin’s data sets that he installed with Dr. Richard Gill at Brigham Young University.

So here we’re doing something quite different. We’re in a natural ecosystem up at about 3000 meters on the Wasatch Plateau. What we’re studying here is how changing precipitation environments affect the plant species dynamics. And we’re trying to understand those from the perspective of soil water content, and also plant canopy dynamics. So we actually had measurements of water content in the soil while we’re measuring with the prototype of the SRS sensor, NDVI. So we’re not going to spend a whole lot of time here on these things. What I’m going to show is just a couple of different plots of data from this experiment. And what we’re doing here is we used a prototype, GS-3 water content temperature and electrical conductivity sensor. And then in his lab, Rick had students create the soil moisture release curve. So what I’m showing first is going to be the water potential. And on the next graph, we have water content from another year. The reason we’re showing water content is that we hadn’t had time to convert this over at the point that we produced a paper on this. And so one of the things it does is just illustrate the difference between water content and water potential in our measurements. So this is just a water content — or water potential with time graph. And what we’re seeing now is the water content changing in a control plot where we left the natural amount of rain coming into the system. And then we have a minus 30, which drops 30% of the rain, and then a 70% of the rain, kind of taken off plot. And what we can easily see here is just the trends in water over time. We did have a small rainstorm right at the beginning of August there, you can see a small blip, but in general, we had very little precipitation over time. As you can see, we have great separation between the ungrazed 30% and the ungrazed 70%. So as we might expect these differentiate themselves quite well.

As we move to the next year, again, I mentioned this in 2011, we hadn’t quite had the opportunity to change them over to water potential. But the other thing that you can see is that there’s just not much separation between the data in the ungrazed control where we had, like I say, the standard amount of rain and then in the two other cases, we actually used rain out shelters, which just took off, caused the rain to only come through at about 70% level and then down only 30% of the rain. This was in one of the wettest years that occurred there on the Wasatch Plateau. And the 70% level just didn’t change that much from the control. So we have two problems here. One, we’re doing it in water content and in the range that we’re interested in, so we can’t separate the different controls very much. And on the other side, the other issue is that this was one of the wettest years and there was so much rain that it just didn’t separate between the plots so. So we might might be a little bit concerned about what is happening here. If we didn’t realize, if we weren’t taking ancillary measurements, we did have a precipitation gauge up there and knew that we basically were in one of the wettest years in history up there.

So this is another data set that illustrates that point really well that it’s hard to look at one year’s worth of data and make any really solid conclusions about the site in terms of a long term study of what’s happening. So it’s difficult to look at one year and determine that there’s going to be the same effect the following year. So this is an irrigated site where we had water content sensors, these were 10-HS sensors, at four depths, and we had eight sensors at each depth, and those were averaged. And we also had water potential sensors only at one depth, but we had six sensors, averaging those. So this is a Pinot Noir crop in California. This was in a loam, transitioning to a sandy loam and then a sand and this is a long data set.

That’s with depth, right, so the upper portion is a loam and then the deeper in the profile, right?

That’s right. Sorry. Sorry.

Just wanted to make sure they weren’t kind of imagining it spacially, you know, over the green.

Oh, yeah, sorry about that. One of the comments we got from last month’s seminar was that we talked a lot about soil moisture, but we didn’t show a lot of the other environmental data, like Colin talked about initially, and we’re trying to do that a little bit better. And having this precipitation data set is gonna lend so much to understanding the data that we’re going to be looking at. So I just want to talk through this a little bit. We’ve got 2011, and that was an above average precipitation year. And then we have 2012, which was somewhat of an average. 2013, this was the start of the drought in California, and you can see how low it is. You can see that this dataset was stopped in 2014. So you could see a little bit of where we’re starting in 2014, in terms of precipitation, and it’s not looking good. And we know now that that was a really difficult drought year as well. So understanding the dynamics of year to year is a really good place to start when looking at your soil moisture data.

And where in California is this, Lauren?

I don’t remember.

Central valley?

So it’s up on the coast, toward the north.

This is another data set, just to look at growing degree days is a way to look at the temperature across the years. So that first year 2011, it was warmer, so warmer and wetter. 2012 it was fairly average. 2013, it was drier, but cooler. And the trend that we had for 2014, which was detrimental to the crops, as we know now, was it was warmer than normal and drier than normal. And you can see that trend starting there in January.

Okay, now we can look at the water content at these sites. Again, this is a deficit irrigated site and so on about May, they started doing a little bit of deficit irrigation. And the water content values really reflect those precipitation values that we saw with 2011 having a lot of water. 2012, we had a similar effect, we didn’t start out with as much, and you can see here at the end, though, we’ve got some major storms at the end of 2012 that give us a really good start of going into 2013. So drier — average year, drier. But we come in and we’ve got, we’re going into 2013 wetter. So even though it’s a dry year, we started with a good base of soil moisture. And this is where it’s really interesting with this data set is you could see in 2014 at the beginning, we’re starting out as dry as we are in mid season. And that’s a really scary thing to start watching.

So let’s look at the water potential data here. This grower was not using the data. This was monitoring standard practices for this irrigated crop. You can see and I wouldn’t say there’s too many things here, except what we see is there were events here. I think that, and I talked to the person that gave me this dataset, but they wanted to remain anonymous, that these peaks were when the grower, again, we talked about this last month, the grower didn’t realize that they were adding this much water and reducing the amount of stress in the crop as much as they were. So these were days that they wanted to stay a little drier, but you can see they got almost back up to field capacity. So when you’re trying to stress a plant and you’re sending it back up to these dramatic changes from water potential, that causes some interesting things with the plants. Okay. This is just an example of the water content data over the depth. The water content data we were looking at before was the sum of the depths. And this is looking at an example of those individual depths. The other thing about this data set here is the sensors that are graphed with the blue lines, this was right under the emitter, and the sensors graphed with the pink lines, these were in the rows between the — these were in those inner spacings between the rows of grapes. And what this is really showing is, is even though this was in the inner spacing, so not where it’s irrigated, the plants are pulling some water from that space as well. So this is an area that’s often neglected with water balances, but you can see here that this is an important source of water for the plants. And you can see, I’ll say one more comment about this data set. You can see it’s really difficult for us to get watered down to these lower depths, you don’t see a lot of water getting down to that 48.

You’re talking about the summertime, right? When the vapor deficit is high, it’s hard to push water down through, even when they’re irrigating pretty heavily, but the first 40, 50 centimeters, and then we don’t see it down at 100 centimeters or something like that.

And one thing I want to point out, too, these are a lot of different ways to look at soil moisture data. So right here, this is the summary of the different — where are we? Here we go — This is the summary of across that entire depth. And that’s made up from these individual datasets. So these are two different ways of looking at almost the exact same, well, the exact same data. There’s some averaging because there’s so many sensors. This was a really well instrumented site. And that’s pretty rare and quite unique.

Okay, this is one more irrigated crop here. The goal of this instrumentation, this installation, was to do a water balance on an irrigated garlic. So precipitation was monitored, irrigation was monitored, I’m pretty sure these were monitored with just a standard rain gauge. Drainage under the root zone was monitored with what’s called a Decagon Drain Gauge. There were two of these at the site. The installation of the drain gauge is pretty substantial. It’s large instrument, and it’s really, is it?, one of the only ways to get deep drainage. So when you are doing a water balance, we do have a lot of people call and say, I’ve got soil moisture sensor data across depths, and I want to show what’s going below the root zone and what’s contributing to the groundwater. Really, the only accurate way to do that is to use a drain gauge, to physically measure the water that’s going below the root zone. And we’ll look at the data there. The 10-HS sensors were used to monitor soil water storage. And we had MPS-2 sensors at different locations used to monitor water potential.

So this is a great example of what happens when a tractor comes by. You could see this gap here.

I love the drainage data. It’s just such a missing piece in so many data sets.

That is really true. So just to explain this a little, these dark blue bars are precipitation, the light blue bars are irrigation, and the red are drainage that are of water moving past the root zone. And so you can see that these are co located with irrigation events. So what that tells us is that there’s irrigation water that’s going past the root zone. And this is deep enough that once it’s passed, it’s not accessible by the plants anymore. There are some times that you would want to do this, especially if you’re trying to manage salts in your system. And there are times that you don’t want any water moving past the root zone because again, that water is not being accessed by the plant at that point. So the drain gauge data here are just beautiful. And to get that soil water storage component for the water balance, we had the 10-HS sensors and that’s graphed with the green line there until the tractor came.

Okay, this these are the same data set but we’re looking at soil moisture across the depth. And the reason we’ve got some weird things going on, I think — this is kind of the reverse with what we saw at the Cook Farm data, where we have much drier at the most shallow depths, and then wetter, and then wettest at the deepest. I’m pretty certain that this was a soil type difference. And we did have a clay and I don’t remember what else we had here. But there was a pretty substantial variation in soil type at this site. We didn’t get to confirm this, we think that this was about when they were doing harvest. And you can see we kept our water content pretty high. But once irrigation stopped is indicated by here, our soil moisture value started to really flatten out. And this is pretty fast. It went from drainage to just flat. And we see that a lot with these clay soils.

So we also might be seeing just, like that wheat crop, the senescence of — I don’t know anything about garlic except that when I eat it, my breath smells bad — but we probably are seeing this senescence of leaves right before the harvest. And probably a drop in uptake. We’re seeing less irrigation. But then we do see a precipitation event right there at the end that spikes the moisture at the zero to four inch level, so up to 10 centimeters. But we don’t see it much at those other depths, probably because we’re so dry up on top in those conditions. Where were these taken, these data? Were they California?

It was another California.

Okay, so we’re seeing fairly dry earlier, comparatively to what we might see at the Cook Farm in northern.

This is our last slide. I’m so amazed that we’re going to be done on time. And I’m very happy.

You’re much faster than I am, Lauren.

I don’t have as much to say I guess. So this is our last slide. This is looking at that same site. But looking at soil water potential as measured with MPS-two sensors. The really interesting thing about this is we had the sensors, they were all installed at the same depth, six sensors installed at the same depth. They were also installed exactly, or as close as the installer could to right below the emitter. So the idea was to get as much of a replication of data as possible. With those MPS-2 sensors, we did have a lot of variability with those. I’d say that the newer sensors, the MPS-6, we were able now to rule out variability between sensor to sensor. I think with the MPS-2s here, I don’t know if we could rule that out between spatial variability and sensor to sensor variability. So I’d love to redo this with MPS-6 sensors where we don’t, where we don’t have to worry about the sensor to sensor variability anymore. But this is what I’ve seen with drip irrigated soil moisture data is traditionally, when people monitored irrigation with soil moisture sensors, there was a lot of assumptions that there was a homogeneous wetting front going down, and so it didn’t matter so much where the sensors were located with respect to sprayers, or especially with flood irrigation. But when we have have drip irrigation, even very small changes in soil type that you would expect in a normal field can have really dramatic changes on how the water is distributed. And if anyone’s ever used a drip irrigation system, they’ve come a long way, but they’re still, it’s really difficult to get consistent wetting bulbs from every single dripper. That’s an assumption that a lot of people make, and I think it’s a little bit scary that that physical mechanical system is going to distribute water exactly the same way from every dripper. There’s just limitations with pressure, and, that are just too difficult to overcome financially, really. So it’s a challenge.

So are you saying here in this data set that the variability might be caused because some sensors are in the onion of the dripper and others are kind of out in dry areas where the dripper is not reaching? Is that kind of what your conclusion is?

I think that with a drip, I think with a drip, anytime you’re monitoring soil moisture in a drip system, you have to assume that that is going to cause some of your variability.

So there’s a question here, how many sensors per treatment as a replica are necessary to get reliable values out of this thing? These aren’t your data set, so it’s a little hard to say, but there are six sensors here, right? Out here.

And the error bars are plus or minus one standard error. Is that right?

Oh, that’s a good question. I think so.

So I mean, it just depends. So in an experiment that I might be running here, you know, we’ll typically put, I mean, just for financial reasons, we’ll put one sensor out in kind of every area that we’re interested in and try to depend on good calibrations going in. With a dripper type system, I think your discussion of how variable those drippers are is pretty relevant here. And trying to get around that is a real challenge because in those conditions, the idea of a dripping system, a drip system is to get water right where the roots are, and not put it in soil that no roots are going to be in. That’s I think the goal.

This comes up in every single customer I talk about that’s monitoring irrigation, where even in a greenhouse or very tightly controlled growth setting is that, you know, how many sensors do you need? You can’t put a sensor in every tray or sensor in every bed.

Well, it reminds me, I asked Colin’s dad about this years ago now, probably when I just started at Decagon, because it’s such a common question, how many sensors do I need? And I said, Gaylon, I’d love to really be able to help people determine this value, can you help me write something up about this? And he said, Oh, that’s easy. And I got my pad and pen out and ready to take some notes about calculating power analyses. And I said, Okay, tell us how to do it. And he said, the number of sensors you need is just more than you can afford. And he said that that’s the case in every site. And it’s going to be a challenge anytime, like Chambers said, even in a really well controlled greenhouse setting.

So did you write that down?

No. I think we tried to write it in a different way. But it was really.

But the trick and this comes up in discussion after discussion, the real trick is finding the representative place to put your sensors. And if you can find those spots that are representative of your sample population, then that’s where you can put the minimum amount of sensors. And I don’t think that these data, you know, the error bars here are pretty scary in certain parts, and then not too bad in other parts.

And you know, what I would say about this dataset is I’d like to see what the individual sensors are doing. Because it’s really hard to know exactly. I mean, so just so we know what we’re talking about, the y-axis here is bars, so about negative one bar, that’s about the lower plant optimal level, so we can see that the garlic is kept there, it looks like toward the harvest time. So it’s all between that plant optimal range. But I’d like to see what sensor’s watering.

I just want to clarify, because we’ve done everything except answer the question here. We don’t know exactly how many sensors you need. But there are some some things to consider when you’re —

And I just don’t ever average data. I just never average data. And I never try to use soil moisture data averages to characterize a site. I’ll maybe put 10 out, but I’ll look at all 10 and maybe throw out the highest and throw out the lowest and then use those eight in between to make a decision.

You’re just throwing them out. I mean, but so there are a lot of approaches here, and we’ve actually seen there are published articles, if people are interested in, how many soil moisture sensors do I need to get a certain amount of accuracy, but we shouldn’t lose sight of the fact that in general, our goal is to understand how much water is available to the plant. And if we start averaging water content sensors, that may not help us because we’re averaging in soil textural differences that relate to water potential. I’m much more comfortable averaging water potential sensors or converting water content to water potential first, and then, you know, the possibility of averaging them. But here, I’d love to see a little more granularity to this data set. Because based on this, I’d say, Wow, you’d have to use quite a few of them. But I don’t think that’s generally true. My water potential data are all pretty consistent.

Yeah, but it looks possibly, partly that it’s the most important time of the year, when they’ve got some of the biggest growing demand when they need the best data. And that’s

They are in the right place, though, in terms of their optimal water level.

I’d love to redo this with MPS-6 sensors that don’t have that higher.

That’s a great point.

Yeah, I’d like to see what it said.

Well, again, thank you, everybody, for joining us today. We’ll thank these — some of the people that are listed in this thank you, we discussed their data last month as well. But as you can see because we’ve run over twice doing these seminars, we love looking at this data, we love talking about these datasets. So I really would love if you would share your datasets with us and allow us to discuss them here. We can make them anonymous. If you don’t feel comfortable with that, but still have questions about your data, please still send them to us, and we can discuss them offline. Also, if you ever have questions about your soil moisture data or anything else in life —

Not life, just soil moisture.

You can just contact us at [email protected] or our numbers 509-332-2756 or is it 332 5604 directly to the support line. Again, thanks for joining us today. If you’d like these seminars, let me know where we’re just talking off the cuff. These are uncommon for us, but we enjoy them, and if you do too, we’d love to keep doing them.

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