Water Management: 3 Tools You Might Be Missing

Water Management: 3 Important Tools You Might Be Missing

Dr. Colin Campbell explores the latest water management research and real world examples to answer the questions: Does water management work? What are challenges and best practices? And what should we do next?

Why overwatering is causing you problems

Just like a thermostat can be set optimally for comfort without wasting heat, the latest advances in sensor technology can do the same for plants: keep them comfortable, without wasting water. This means you can have higher quality and yield while reducing problems caused by overwatering such as disease or the need to reapply expensive nutrients that have been flushed away. If you want to understand the impact you’re having on the environment while at the same time producing better plants, you’ll need to measure the variables that drive those things. Water is inherently knowable. And if we can know it, we can manage it effectively.

Better management—better plant performance

Join Dr. Colin Campbell as he explores the latest water management research and real world examples to answer the questions: Does water management work? What are challenges and best practices? And what should we do next? Discover:

  • The role water plays in managed ecosystems
  • How using measurement technology like soil water potential, soil water content, electrical conductivity, and temperature can show impacts of management
  • How to deploy these sensors effectively in high-dollar ecosystems
  • What the interplay is between environmental variables like evapotranspiration and soil water
  • How combining these variables can inform water management
  • How overwatering impacts disease and critical nutrients in the root zone

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

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Hello everyone and welcome to Water Management: Three Tools You Might Be Missing. Today’s presentation will be about 40 minutes, followed by about 10 minutes of Q&A with our presenter, Dr. Colin Campbell here, whom I will introduce in just a moment. But before we start, we’ve got a couple of housekeeping items. First, we want this webinar to be interactive. So we encourage you to submit any and all questions in the Questions pane. And we’ll be keeping track of these for the Q&A session toward the end. Second, if you want us to go back or repeat something you missed, don’t worry. We’ll be sending around a recording of the webinar via email within the next three to five business days. All right, with all of that out of the way, let’s get started. Today we’ll hear from Dr. Colin Campbell, who will discuss the latest research in water management. Colin Campbell has been a research scientist at METER for 20 years following his PhD at Texas A&M University in soil physics. He is currently serving as Vice President of METER Environment. And he is also adjunct faculty with the Department of Crop and Soil Sciences at Washington State University, where he co teaches environmental biophysics, a class he took over from his father Gaylon over 20 years ago. Colin’s early research focused on field scale measurements of CO2 and water vapor flux, but has shifted toward moisture and heat flow instrumentation for the soil plant atmosphere continuum. And he was recently named a fellow of the American Society of Agronomy. So without further ado, I’ll hand it over to Colin to get us started.

Thanks so much, Brad. And it’s great to be with you, all of you here today. Talk about a topic that I think a lot about and certainly has challenged me personally to learn and grow quite a bit more. So in today’s seminar, what I’m going to do is just talk about a lot of the things that we’ve been learning lately, and try to put it together in a total picture of where we’re going and thinking about water management, and highlighting some of these tools that maybe you’re missing. I think we were too. But before I get started, I want to tell a little story about Ignaz Semmelweis, and puerperal fever. Now, I was thinking about this a little bit and read a story in a book called The Outward Mindset by the Arbinger Institute that talked about Dr. Semmelweis. He worked in Vienna Hospital back in the 1840s. And these hospitals, were birthing clinics that they put together, so women could come in and have babies, essentially, for free. And the clinic that the doctors ran in this Vienna hospital was called the first clinic. And you can see in a graph, that with the pink line there, that first clinic, and its death rate, percent death rate of the patients coming in. So these are perfectly healthy women going in. And their intention is to have a baby, and there is a 1 in 10 chance, a 10% chance, that if you go in there healthy to have a baby, you won’t come out again. And the second clinic, so the first clinic was actually used to train doctors, if you went in there, you agree to allow doctors to train and they go out into the world, presumably, Semmelweis kind of ran this thing in the hospital. And also midwives were trained in this hospital as well. They were trained in the second clinic. And you can see in that graph, in the blue line, the death rate of the second clinic was about 4% or so. You see from this graph, that clearly, if you went into the first clinic and got seen by doctors, you had a two times greater chance to die before you came out. And this was shocking to Semmelweis. And he looked all over for the problem, I don’t have time to detail this, I’d encourage you to go read the story, because it’s so interesting. But what he ended up finding after a lot of work, that it was actually the doctors that were causing the problem, it was him. He was the problem. And he discovered that those doctors that went around, down and worked with the cadavers in the cadaver lab, were coming up and helping these women have babies. And it turned out that they didn’t know anything about washing hands. And that he produced this chloride wash that you kind of wash your hands with, they didn’t know exactly what it was. They didn’t have germ theory at that time. That came along later. But he discovered this handwash, this chlorine handwash, would essentially make it so the death rate in these hospitals, as you see in the second graph on the right there about 1847. The death rate dropped from over 10% down to much less than 5% and down to 1%, eventually, which was just amazing. They essentially found that the doctors were the problem.

And so I wanted to relate that to our irrigation managers and those of us who try to control irrigation. The intention of irrigation systems is to get healthy crops and optimum yield or performance, depending on what we’re working with. Maybe we’re growing crops, maybe we’re just growing turf grass, but whatever that is, we’re focused on trying to get healthy crops, optimal performance. But what we often get out of that situation is higher water use, lower nutrient availability, higher weed pressure, more labor, all the things that ostensibly this irrigation system is supposed to stop. So the goal, obviously, for us in this case, is to minimize water, fertilizer, labor, herbicide, all these things. And it ends up that us as irrigation managers typically get the very thing we’re trying to avoid. We get all these problems. And so I wanted to consider this in the context of some of the things that we’ve been working on. So today’s seminar is just going to be a lot of pictures, a lot of data, me talking about some of the projects we’re working on. And, and some of these, I won’t be able to describe in detail. In fact, if you go back to some of our older virtual seminars, you can see see some of these data that i’ll present here. And you may need to go back to get a little context. I’ll do my best. but if you need a little context, that’s where you might go.

So let’s talk about some systems that are not in balance. This is a sports turf field. It’s got 12 inches of ASTM spec soil and what it really is, is a sand, and its goal is to make sure that the players can play on this, even if they get a heavy rainstorm. I think about this a lot right now—my son plays soccer at the local high school. And right now a soccer field essentially has puddles of water all over on it. And honestly, that’s not that great. So thinking about a field where we could actually drain that water out and get playing same day is pretty desirable. Now, the goal of the management of this turf grass is, one, to keep the grass beautiful for game day. If that goes on national TV, you want your grass looking beautiful, that’s not surprising. There are some other goals though, things like the playing surface being optimal for the big football players, these these beautiful, strong individuals to be able to not break through and tear up the turf and actually have a good surface to run on. We want to optimize inputs, we don’t want to put a lot of water on here, we don’t want to put a lot of fertilizer, it costs money and it wastes critical natural resources. And finally reduce in base weed pressure. Talk all the time and turf grass about annual Bluegrass or Poa. And I just been learning a lot about that lately. That word just strikes fear into people, this idea of Poa.

Now, here’s some data from that field. This is water content, this is the amount of water on the y axis. And on the x axis, just time. We’re going from the first of July through about the 18th of July. And I just took a snapshot of last year. And what we see is descending water contents in here. But we also see that all the water contents according to our optimal range there on the bottom, all the water contents are quite a bit above our optimal level for sand. This is another look at that same field now we’re looking down in the south part of that field. And we’re still really up high in terms of our water content, above that the optimal range, although the six inch range, by the way, where there are no roots, that’s actually coming almost into the range, but the two inch range that where we have the bulk of our roots is way way high. Now as you look at this, and I’m not getting into this particular slide too much but the north and the south electrical conductivity are also really low. Electrical conductivity is an indicator of nutrient status in the soil. And while in another field nearby, we have nutrients in a pretty good range the pore water electrical conductivity on our turf grass field is really low. In fact, one of the situations, it’s close to tap water, so essentially there’s no fertility in that water that the root zone is experiencing. So here are the problems. We got low nutrient availability, limiting that canopy greenness, we have excess water that actually favors this annual bluegrass, the Poa that everybody hates, because it forms really weak soil structure. So when you’ve got players running on that surface, they’re going to break through they’re going to tear it up, they may be injured and it certainly takes a lot of effort to try to make that beautiful after a game. And it also develops a shallow root system because the roots don’t have to go deep to find the water and so we kind of developing a system we don’t want which is roots that really can’t grab the water when they need to.

So here’s another situation that we were working in last summer. It’s an outdoor cannabis setup, where we’re irrigating under these great conditions, I don’t have time to really run down each in detail. But basically we got wide row spacing. Got this beautiful drip tape irrigation system controlled by open sprinkler, it’s a silt loam soil and we buried sensors at several locations in the soil. Now we did have a hot dry spring and summer at this location and they had limited water starting the year. But even though they had a little bit limited water we irrigated that such as there was plenty of water when we transplanted the cannabis into that location. Now, here’s what happened. If we look at water content, now on the y axis again and time on the x axis now we’re actually going from the first of July all the way into October, and it doesn’t look like that much happened here. We have the starting water contents around 35% kind of descended over that first month down to around 20 to 30%.

But here’s what we found in the field. When we went out and looked at this, the cannabis crop was not impressive. In fact, it worked out very poorly on the year I did some calculations on the irrigation and started to compare it with evapotranspiration. The irrigation system applied about point four millimeters per hour. And they put on two to three cycles of water per day. And they even looked under this this black plastic mulch that they had out there and said, Oh, the soil is completely wet, it’s all muddy, in fact. When we started looking at how much water that surface needed, we realized how bad this was, that in fact, we were putting less than two millimeters of water on per day. And that cannabis crop needed four, six, and even more millimeters of water per day. And so we just diverged, because of the very, very warm dry system, they’d never—weather—they’d never experienced that before. So that got me thinking about tools that you might be missing that we were missing to try to make sure our irrigation goes well, that we aren’t the problem. Right that we talked about Semmelweis’s problem. So here’s a vision that we have is we’re talking about irrigation as METER Group, we’re thinking about a vision for this that’s really simple. Now we can complicate this a lot. And we might add to this things like modeling and other things. But as I think about this, here are some of my thoughts.

We need to know when to turn the water on and when to turn the water off. That was actually put into paper by some relatives of mine years ago, into published paper. My Great Uncle Mel Campbell, kind of stated that. That’s all you need to know. But the answer to that question is actually the hard part. So to answer this irrigation question, you must know three things, how much water the crop used, the current availability of water in the soil, and the total water available to you, how soon are you going to run out? And I’m going to talk you through like the idea behind these and some actual situations where we’re working on this at various experiments right now. So what are the tools we’re going to use? How much water are my plants using daily? Well, the tool we’d use there is evapotranspiration, the amount of water loss due to evaporation from the soil and transpiration from the plant canopy. Now other things is the soil optimally, is the soil water optimally available for plant growth? That would be water potential. Now, if you’ve watched any of our other seminars, you probably think, Oh my gosh, we’re back to water potential. Yeah, we are because it’s an important tool. But here we’re actually putting this in context of other tools we might use in some of the stories we’ve seen as we’ve worked on this. So we’re going to talk about this again, but water potential, like temperature, shows a comfort range for plants. And we’re gonna get into that in that section. And finally, the last question, How much water is freely available to the plants? We learn about this through what’s called a moisture release curve. This is a relationship between water potential and water content that defines the available water envelope that we’re working in. Now that may not have occurred to you, it may have occurred to you. But I want to show you a little case where we’re actually using that with some of these other tools we have and combining them together.

So tool number one we’re going to talk about is evapotranspiration. Now we gave a similar seminar earlier with Campbell Scientific going through evapotranspiration, how its measured, what are some of the errors, and I don’t want to get into that and some of you are looking at this slide going, Oh my gosh, is he really going to jump into these equations? I’m not. All I’m going to say is that that there is a relationship that exchanges heat and energy, mass and energy, and systems, where we have solar radiation coming in, we have heat coming out in the form of temperature and the form of water vapor loss. And we have other exchanges in the system. And we can make we can make that, we can use an equation actually to solve this. I’m gonna see if I can bring up a pointer. This is a new system for me. So up here is an equation lambda e. That is our Penman Monteith equation or evapotranspiration equation that uses senses, other energy exchanged in the system to figure out how much water is being lost both in evaporation and transpiration. Now that equation in and of itself doesn’t help us. But that’s this is actually this right here. That’s supposed to be a lambda, not a not a small l, lambda EO. That is our our evapotranspiration here, but we can’t directly measure it very well. So we’re going to compute that from a crop coefficient, we’re not going to go deep into there, we can talk about that in another virtual seminar. And this lambda e zero, that’s our reference evapotranspiration. So we get our evapotranspiration through a couple of steps here. And then we actually can use that to schedule irrigation.

So how might we do that? That’s one of the biggest questions we get asked. So that’s a big long word, it has a big long equation, what do I need for it? Well, the reality is you only need solar radiation coming in, you need wind speed, you need temperature, and you need relative humidity measured at your site. And so here, for example, right on the left side, this is our ATMOS 41, all in one weather station that you can use. And then we use that and it measures all those parameters. To calculate the evapotranspiration of a system by knowing that crop coefficient. There’s long pieces of literature on calculating crop coefficient. If you want to look at something to read the FAO 56 manuals out there online, you can go look it up and start learning about crop coefficients. Okay, so out of a system like that, what do we get? Well, here is ETO write right up here. This is a graph of reference evapotranspiration on the y axis, and on the x axis, we have time. And that goes, this is actually over that same time period as our outdoor cannabis experiment I showed you earlier, there are a few things I want you to notice about this graph. We’ve got great numbers every day of how much water would be lost by a reference crop. This reference is a 12 centimeter high grass well watered that completely covers the ground. Now our cannabis was not that. But over the season, as it grew and started to cover the soil surface, it’s actually mimicked this grass canopy. So when they, here in the early season, you see that six millimeters per day. That was what reference ETO there was. But it wasn’t actually what the crop was using. Because the crop was still actually very small, we needed to multiply that by the crop coefficient KC, to get our actual value. So at that time, it was around one millimeter per day, but it was quickly growing. So by the end of July, our canopy was nearly close. So we were actually needing that full five millimeters per day. And we were only putting on that one to two millimeters a day. And that’s where we got that discrepancy where water content dropped. And we’ll show some data from there later.

The thing that you also should matter notice is that during the summertime, where we had these hot, dry conditions, we thought that down toward August, we’d need more and more of this irrigation because it seemed hotter and drier to us. But the actual poem of water from the system wasn’t nearly that, as we see later in the summer, and even in late August here, we dropped from six millimeters per day on average down to around four millimeters per day. This is something maybe you didn’t realize, again, would lead to overirrigation. I certainly hadn’t kind of conceptualized this in the same way. Someone asked me why does this happen that way? And of course, we know that solar radiation is one of the main, is the input of energy into the system. And that peaks in June. And so it shouldn’t surprise us completely that this is in a very hot, dry summer, we just almost never had clouds in the sky during the summer, that that would look like that. Okay, so reference ET is pretty awesome. But here’s the problem. I was thinking about this in the context of this, see if I can get this graphic to go, and you know what I need to do, I need to go turn that pointer off. Let’s see if this works.

So reference ET essentially gets us to drive the car in the right direction. Now, if I hit that, that’s it, Brad. I’m new to the system, so I’m just learning on it. So it’s like driving car down the road, referencing ET tells you that you’re going in the right direction. But if you’re not going in between the lines, it won’t help you there, maybe you’re on the wrong side of the road, maybe you’re driving off in the weeds on the side of the road, it will help you be super consistent about that, which is great. If you’re in the weeds stay in the weeds. I guess that’s not how we how we go. But it will keep you very consistent. And maybe you want to see an illustration of that. And I saw one last summer in our data. So we’re going to jump over here to our practice field. This is another field that we were we were measuring in, again, we’re back to this grass growing beautiful grass, I think you’ll agree, this field is the picture of health and vitality. This is actually grown not on that sand I was talking about. But this is in a native silt loam soil. And it’s under reference ET control only. So it’s a grass. So it’s that 12 centimeter high grass, it’s perfect for our ETO condition. So here’s what we got out of there, that practice field water content, so the water content is on the y axis, and the time from July 1 to July 18, that’s the same time as that other turfgrass data was showing you, that’s on the x axis. And what we see is some phenomenal consistency, every single day they irrigate, and they come to exactly the same value and the ET for each day was different. And so getting to stay this consistent just as an illustration of what I was showing you in the last slide, which was that it’s keeping you on some side of the road. But we are getting, if you look really closely, where I’m showing you some of these pointers, you can see back behind there, the deepest sensor, which is now 12 inches, where there’s, I promise you, there’s no roots down there, that’s still getting increases in water content. And that suggests we’re using a little bit too much water, we really don’t have any indication of what, how much that might be if that’s a real problem, etc. And so with ET control, we’re doing great on there, but we’re not doing good enough.

Which leads us to our second tool, which we’re going to talk about, which is water content. So soil water potential that answers the question of is water available. And if you’ve seen another virtual seminar of mine or others here at METER Group, you’ll already know the answer to these things. But if you’re new to this, let me tell you a little bit about water potential. Measuring water potential and soil is essential to good management. It’s, ideally we’re going to keep as I mentioned right at the start, keep watering the soil at an optimum level for green healthy plants. Now to measure water in the soil, there are two ways to do that, water content, the amount of water in soil usually we talked about it as a percent, like 23% water content. Or water potential, that’s an energy level. It’s how easy it is for a plant to pull that water from the soil. Now we’re going to dig a little deeper here. What defines when crops have optimal water? I want to connect a couple of dots in your mind together that I think about. Let’s create an analogy to temperature. If I had a friend visiting yesterday, and I asked him, hey, Derek, tell me when you’re in a room, how do you know if you’re comfortable? said well, I just if I know the temperature, then I know that it’s going to be comfortable, right? I feel that— I’m sweating, it’s too hot. If I’m kind of shaking, it’s too cold. And I said if I could tell you the temperature of a room would you know if you’re going to be comfortable? He said of course. And I said how about if I told you the heat content of the room? Would you know if you’d be alright? And he’s like I, you know, no, what is that anyway? And I said right, you never heard of that. Well, the same thing goes for soil water. Water potential defines plant comfort range, not water content, you can tell me a water content value, how much water there is, but we don’t know just by 23% water content, if a plant can get the water.

Now water potential, water potential is the water thermometer of that plant root zone. We can know if a plant can access water in any soil. So you might be wondering, Well, where did this come from exactly? And it’s a really old concept. So this is a table out of a book called physical at edaphology published in 1972. Some of you listening were not even born then. So this is a concept that’s been around for a long, long time. Now if we were growing potatoes, and that’s a project I worked on for several years now, we want an optimal water potential to be around negative 30 to negative 50 kPa. Now you may wonder what the heck is a kPa? And I would tell you back, does it really matter? Do you know what a Fahrenheit is? Probably not. It’s actually based on the third law of thermodynamics. But you don’t need to know that, you just know the comfort range that you need. Now, plants have a comfort range too. And it’s a little bit different for each plant. But we can know like a thermometer for temperature, we can make kind of a quasi thermometer for water potential that shows between about negative 20 and negative 100 kilopascals, plants are comfortable. As we get way down to negative 1000 kPa we get into a serious problem with plants. And that goes for any soil. Now people will say, Well, I think it is soil dependent. And we’ll get to that, yes, soils do have an envelope that they operate in. But water potential defines half of that and we’ll talk about why.

Now combining ETO and water potential actually may be the killer app that we’re looking for. So we get the the evapotranspiration from our all in one weather station. And here’s a TEROS 21 water potential sensor. When we combine these two, it actually contains some power that wasn’t there before. And let’s talk about that. So I kind of put together analogy, right I showed you before this car kind of driving on the wrong side of the road or off the road. When we have water potential and reference evapotranspiration, we actually know how much we’ve got to replace that the plants use and we know where we are in the road. So we’re not consistently driving on the other side of the road or consistently driving through the weeds. We’re actually on the right side of the road. Sorry for those of you who drive on the other side of the road, right? This is I put that together, I’ll make another seminar to go to England and Japan and other. So let’s go back to this practice field concept. So we have the ETO doing the control, the reference ET, again we’re in the native silt loam soil, the water potential goal is is from negative 20 to negative 100 kPa. So let’s just look if we add water potential to this. So here’s our water content going along very consistently. But where are water potential, so water potential here, matric potential there, on the bottom we have time. And our water potential showing, you might say, well, where’s the lines on those graphs. Where they’re right up here. You can’t see them. Because on this grass, it’s so wet, that it’s well above the needs for the water. And essentially, we got this situation over here, where we’re just filling that glass full to overflow all the time. And it’s just going out, going through the roots zone which obviously has challenges.

So we can talk about available soil water again, this is a TEROS 21 Gen 2, that’s a sensor that METER sells to measure that. Here’s us installing these in a potato field. This is one of the first experiments we did on this. Some of you may have seen this before. But the cool thing was that that summer, we really got educated on how important it is to dial this water potential in. So we installed those sensors in that picture down in Grace, Idaho. Now here’s the water content. Here’s time that goes through the whole summer. The bottom graph, sorry, that shows water content. This bottom graph is water potential, again over the same time period, the water content in this graph really isn’t showing very much change over the season. In fact, I wouldn’t really have an idea what you should do for the water watering potatoes. Here in the water potential graph, we could sometimes call that matric potential, three sites are staying in the optimum range above negative 100 kPa, we kind of opened that up just a little bit for this field. It was in a silt loam type soil. But three of those sites, these are all in the same field, they wandered into the stressed and the wilting range. They actually talked to the farmer during the year and I’m like, Hey, Ryan, I think it would be really good if you put a little more water on these locations. And he said, Well, I went to the end of the field with my shovel I dug down and there’s water there. Okay, said okay. He said it’s your sensors. Okay. You know, sometimes it is sensors, so maybe I did we installed them poorly. Then after the season I showed Ryan these data from the stress and the yield. So here’s the yield on the y axis and here is days below negative 100 kPa, so days outside the optimal range of water potential. And I said, to my way of thinking, as I looked at this graph, we can see a pretty clear relationship of when you’re low on your water, potentially outside that optimal range, and for longer periods of time, you get much lower yield.

And he said, Oh, okay. And his next step was well let’s just start measuring this in all his fields. And we’ll probably do another virtual seminar on this. He sent me some data this year on ROI from doing that, just out of the blue and said, Oh, my gosh, life is so good measuring water potential, which is great. Here’s another situation, measuring ETO. This is in potatoes again. It’s a different location near Rexburg, Idaho. This is evapotranspiration, reference evapotranspiration, and precipitation irrigation. So what I want you to notice here is how close starting kind of just a little after the first of July and going to the end of August, we didn’t get our system in very early into this situation. So we don’t know a lot about what happened earlier. We do know it was a hot and dry summer, we know that the temperature in the soil kind of went up and caused challenges for the potato set. But what we see here, because they were trying to put on a lot of irrigation water to make sure that the potatoes didn’t run out of water, we’re matching precipitation and ETO, right on the dot, to 200 millimeters each. That was interesting, it may be good, it may be bad, I just don’t know, because we need more data. And so we did get more data, this is soil water potential at 15 and 30 centimeters. So this is matric potential or water potential here, this is same time period mid July, all the way to late August. And what we see is water running just a little bit on the top side of optimal at 15 centimeters right there. At 30 centimeters, we’re actually doing pretty good. We’re on the upper bit of that range. But still in the range. Interestingly talking to the growers out there, they actually saw too much water in the canopy during this time. And it set on some challenges growth that was actually in the canopy that caused them to lose a little bit of yield. So according to our data here, maybe we could have backed off just a little bit and done some percentage, some a little bit smaller percentage of ET, but you can see how those things could come together for measuring ET and we’re measuring water potentials, we can do little small tweaks in our irrigation to try to bring these lines just a little bit more into the optimal zone.

Okay, tool number three, moisture release curves, how much water is freely available to the plants that we’re working with? Now, I’ve shown you a lot of water potential. And you might be thinking, Well, why would we ever use water content to do this? And by the way, you’ve been selling water content sensors for last 20 years, are you nuts? So I’m going to say hey, there is a big reason why you might want to do water content as well. So when we look at soils, they’re not all created equally. And I put together this graph. I don’t know. When I was working on this presentation, maybe I got lost on driving and roads. I don’t know because of the current car shortage or something right. So when silt loam soil, I liken this to a rather wide road where you can just drive down that road, it doesn’t really matter. I wouldn’t suggest this by the way, but you’ve got pretty wide boundaries, staying in your lane, there’s a lot of movement. Silt loam holds a lot of water. So if you make mistakes, it’s really forgiving. Sand is a little bit different. Sand has a pretty tight range. And we’ll show an example of this. But when you’re driving down the road, this sand road, shall we say, you got to be pretty careful to keep it between the lines. There’s just not a lot of movement there.

So I talked about this moisture release curve, but I want to now say, what is that exactly? Well, the moisture release curve compares the amount of water with what’s available for plants to use. And I’ve illustrated this on the right hand side. This is water content. This is water potential or matric potential, actually tried to cover up as you can see just a little bit beneath there. joules per kilogram. That’s an old one we used to use. I got this out of a book that’s now 25 years old. We now use kPa. They’re exactly the same number, but since I’ve used it throughout, I’ve tried to cover it up so you can see my little mistake there. But you can see there’s a very different relationship between sand, loam, and clay. It turns to the water content and its energy state or the matric potential, so at 20% water content, there’ll be absolutely no water available in the clay. Down at negative 200 kPa at 2000 kPa that’s beyond wilting point. So if you’re at 20%, your plants would all be dead. If you’re in a loam soil, you’re probably right in the optimal range. Right there. So sorry, I forget this pointer isn’t there, this 20% clay. That’s dry. Over here, silt loam, you’re at 20%, about negative 50 kPa, something like that, just eyeballing it. If you’re in a sand at 20% water content, you’re just washing water out of the sand. So this is the great thing about a moisture release curve is suddenly we understand that as water, soil dries, water becomes harder to grab out of there. And these moisture release curves describe that relationship for these different soil types.

So suddenly, we’ve got a giant picture of what this looks like. And several years ago, I did this in a sand. I mean, it was kind of a loamy sand, shall we say? It was a challenging soil to irrigate. The irrigation system, why we got into doing this, irrigation system actually quit. And my friends called me up and they’re like, hey, you know, after four days, we all went home for Memorial Day weekend, and suddenly we came back and all the grass is dead, we didn’t know. And so the problem was right here, we installed this irrigation system. And what we had was a relatively small envelope for water. So this is the upper side, this is the lower side. I did another virtual seminar on this in the past that you may want to refer back to if you’re interested more in how we came about this upper and lower ranges. That’s essentially where the water was washing through the profile. And then where the plants, this bottom side where it stopped taking up water. So way over here that I should be pointing this is where it stopped taking up water. This over here was where it was kind of the maximum where we were washing water down through. And essentially, we have a full bucket down to an empty bucket, then.

So why am I talking about this? Well, this is the cool thing about that we think about, this little gray area creates this envelope. This is our working envelope. We have to be in this envelope to optimally grow our plants. So we’re not wasting water where they’re getting optimum water. And this working envelope, if we know about it, it helps us know what we need to do in terms of water application. So if we in our root zone, let’s say it was 15 centimeters deep for that grass, right here. And if we do a little calculation, we find that we need to provide a maximum of 12 millimeters of irrigation water to fill up that profile. Now you remember back earlier, when I was talking about ET in the outdoor cannabis, it needed six millimeters a day. So not surprisingly, if we’re talking about this grass, they went home for a three day weekend and irrigated before, they came back. Guess what happened? Well, they only have an envelope of 12 millimeter day, it’s hot, it’s dry, and suddenly, there’s no water on day three, if they’re using six millimeters a day. So how did I do this, and I don’t want to get too lost in there. How to apply a moisture release curve. So determine the upper and lower limits of water potential.

And this actually is important for individual crop type that you’re using. I showed this for that turf grass in sand, we’ve got to do that for whatever crop and soil we’re in because we have to find these upper and lower ranges, like I talked about earlier. Two, find the water content values for the upper and lower water potential limits. Three, determine the plant root zone depth, I call this the Z the depth of the root zone. And then four, take the difference in water content and multiply by the rooting depth. So our maximum irrigation is very simple calculations down here, upper and lower limits of water content times the root zone depth. And we come out with this 12 millimeters of irrigation that we need to go from all the way dry, and we don’t ever want to get down there, to all the way wet, but we can certainly see why that might happen. Okay, so let’s just look at that picture we talked about before. Again, that six millimeters of day, going down to five millimeters of day. This is how much we’re estimating from reference ET is being lost from the crop. And in that root zone, we just did a great calculation where we know how much that root zone will hold, based on the upper and lower limits driven by water potential and derived out of water content.

And then we get to this cannabis irrigation I mentioned Hey, I’m gonna get back to this example. So I showed you only water content in that example but here’s the roots for that, the water potential here for the cannabis irrigation. So, again matric potential here all the way down to permanent wilting point negative 1500 kPa. And because they were putting on so little water because they weren’t matching their irrigation system with ETO and they weren’t paying attention to the matric potential here, they got all the way down to wilting point. Now, the incredible thing is that cannabis as a plant, not been studied a ton, but apparently it pulls out water all the way down to the permanent wilting point, which is amazing. But it also is not that great because it has to work really hard instead of putting on biomass and preparing for the yield, it’s just fighting to stay alive. And so we’re reducing biomass, we may be bulking up flowers, potentially. So that could be a good thing. But if you’re not managing it right, that’s a problem. And here, the grower is like, Ah, I see that you’re right. We are way way below, I’m going to irrigate like crazy. So he did for one day, but didn’t keep on it. So that’s the kind of thing, that’s the system out of balance. We can overwater but we can also way underwater. That’s why we need these tools, essentially.

So in summary, irrigation may be hurting the very plants were trying to help like Semmelweis. They thought they were the Dodgers were there, they’d be the optimal way to help these these women have children, but they were killing the very people they were trying to help. It’s just so sad. Three potential tools that could help improve plant health and resource constraints are what I talked about today, first, evapo transpiration, it’s a great start, but it’s not enough. It’ll keep us on the road. But we don’t know where it’s on the road, I guess, near the road, somewhere around the road. Combining water potential with ET can keep irrigation between the lines, like I talked about. It’s a really cool concept. And finally, moisture release curves can show that envelope we’re needing to apply, so we always stay in that optimal zone. So when we’re planning irrigation, we’re not doing dumb things like Hey, we don’t mind if the irrigation system goes off, for a while, it won’t matter. It might matter depending on if you’re in a silt loam, or in a sand or something like that. And that’s all I’ve got.

All right. Thank you, Colin, let’s do this right here. All right. So yeah, we’ve got, we’ll probably take about 10 minutes for questions now, and pull up the Q&A pane here. And thank you to those who have already submitted questions, we’ve got a few that have come in already, there’s plenty of time to submit more questions. We’ll try to take as many as we can, in the next 10 minutes or so. If we do not get to your questions, we do have them recorded. And Colin or somebody else from our METER Environment team will be able to get back to you directly to answer your questions. We’ll try to get to as many as we can before we finish, but there’s always questions that we we do not get to. Let’s say so Colin let’s start with this first one here. So Colin, can you speak to the possible future arid zones, so they mentioned here, like put Portugal and Spain and the lack of available water for irrigation for vegetable crops and big areas, such as tomato for processing and other vegetables?

Yeah, I think you’re you’re asking the right question here. We’ve got a challenge with freshwater, I think you see it in the news, you see it all over the place. And I think we need to consider how to move forward to maintain those resources. I was talking to this farmer friend of mine. And what he was telling me is like, you know, fertilizers doubled in price or more, maybe tripled in price in the last year. We have challenged with our inputs, water, fertilizer, the pesticides, all those things. And, you know, this isn’t just a giant hammer that’s going to squash all those things, right. But we need if we can create water in balance in these systems, like they’re becoming more arid because of climate change, like Portugal, Spain, here in the West Coast of the United States. We, if we understand our systems better, and if we make better measurements, pull them into, kind of, our understanding of how to irrigate and improve those things, we can continue to irrigate zones that have all this water pressure, where they’re saying, Hey, we got to stop irrigating. We need to feed the people the world. I mean, that’s, you know, we need the vegetables. I want my tomatoes. I think Brad does too. You know, we got to have those things. And so we need to figure out ways of doing this. I don’t think we can go along with a mentality that says, Hey, if plants are looking a little stressed, let’s just pour on more water. We need to take the tools to tell us more about the plants and how they’re performing, and then use them effectively to improve how we water. We don’t always need to just overwater.

This next question, Is it possible to develop moisture release curves in the field?

Yeah, great question. This is actually one thing that I was thinking a lot about. So the data I actually showed you from that grass, that where I actually developed that water envelope, the water envelope information from was actually something we did in the field. So we co located some TEROS 12 water content sensors and some TEROS 21 water potential sensors, put them together and said, Hey, can we develop this envelope in the field. And you know, what we came up with the data I showed you there didn’t go into it. But, of course, METER Group also has some pretty cool instrumentation for developing the water potential water content moisture release curve in the lab. And so I wanted to see, Hey, how do these compare. And so we actually did go into the into the lab, and put together some data. I didn’t mention it in that example itself, but there were some lab data together, and they actually match pretty well. And so I started talking to a lot of different people. And interestingly enough, a lot of people have had this idea, Hey, let’s see what we can do out in the field and in the lab. And by and large things are matching up. Now might they not match up? Yes. Why? Because we’ve got roots in the soil. If you bring it into the lab, those might dry out, we might be compacting our samples, there are many things that we might not have those sensors close enough together, they might not be responding at the same time, which is true—water potential sensors are slower to respond than water content sensor. So might they be different? Yes. What are we seeing so far? They’re pretty, you know, they run nicely together.

All right. Okay, this, this next question I want to hit, it’s pretty substantial. So I’m going to try to summarize as best as I can, but basically, they’re talking about their their work in the Mekong Delta in Vietnam, and just trying to figure out the best ways to irrigate there. And they said that their current process is they first estimate the water balance equation for their soil and crop and climate condition, they then estimate the crop water requirement, or evapotranspiration, for that season, then they make a soil water characteristic curve, and determine the total readily available water. And then they irrigate based on certain thresholds. Again, and this individual says this, this does bring in quite a bit of uncertainty into this situation. So what are some of the, I guess, what are some of the pros and cons to modeling versus direct measurements? And how can the two work together?

You know, it’s funny, my career has mostly been spent on measurement and designing instrumentation for measurement, and in doing field measurement. My father spent a lot of time modeling, and I do teach modeling, but he actually implemented some of the some of the models that some of you might be working with. And it’s been interesting to talk to him—I came in with a bunch of field data not so long ago and was like, Oh, man, I gotta figure out how to do something with this. I don’t remember what it was. And Dad said, Hey, have you modeled it before? Have you done any modeling? And I’m like why would I ever do that, I’m measuring? There’s an obvious need to model and to match modeling with measurements. And so the approach that I’ve talked about today is consistent with pairing, things like evapotranspiration and soil water measurements in the field, with modeling and what we can predict from other measurements. Because one thing that’s challenging is we’re measuring at one location in the field, or maybe in multiple locations. But we’re not measuring every spot in the field. And we really can’t do it enough to, well, on one side to drive something like a variable rate irrigation system that could put down exactly the right amount of water any location, at field. And on the other side, where you only have one switch, I turn to the center pivot, for example, on, and it just runs at a certain speed around the field. And that’s all I’ve got, how fast the center pivot is turning. So what I would say is the use of a sensor, like a water potential sensor in the field, even at one location, at a couple depths, can greatly enhance this modeling effort you’re talking about to go through the estimating the water balance, you know, coming in with your climactic conditions, all that kind of stuff, creating a moisture release curve. All of these things tend to be out in the field efforts, we can create kind of a model of the system with a moisture release curve. But I believe that going into the field and directly measuring water potential is something we’ve really missed up to this point. And something we need to think about for the future. Because it’s great to model, it’s great to do it through a moisture release curve and say, Hey, I’ve got water content, I’ll just predict water potential. But there’s, it was a great article just recently published in Nature Geosciences, talking about measuring water potential in the fields was one of the great gaps that we’re experiencing right now. And we need to really consider that deeply. In the past, we might have gotten away with a statement that said, Hey, there’s really no great way to measure water potential in the field. This was one of our great life’s work here at METER Group is to change that particular thing. Sorry. We got other questions, so I can move on.

All right. Let’s see. Here’s one. When do you measure water potential? At maximum heat time? Do you make an average between hourly measures? Average between pre-dawn and midday? Any thoughts on that?

Yeah, so that’s a great question. It kind of relates to a couple of different things. And I want to do a virtual seminar in this other topic, it’s kind of referring to two different water potentials, there’s a water potential of the plant, and there’s a water potential in the soil. Water potential in the soil is really heavily buffered, you know, day and night. So we don’t see a lot of swings, except when it gets really, really dry, there’s a little bit, but I was showing you water potential for a lot of the stuff we’re working on, and you just don’t see day night swings. You do see day night swings in plant water potential. And we did some measurements with a new sensor that’s out there by a company called FloraPulse, and looked at some of their data in trees, for example. And the FloraPulse measurements that you know, in the stem of a plant, those were highly connected, especially in the day with the evapotranspirational demand, you know, the vapor demand and so there, you’re going to, if you really want to understand what the water potential is in the soil, what’s available to the plant, it’s best to make predawn measurements and now there’s a sensor out there that you can monitor that kind of day and night, you can see some of those things. It does come with challenges, what I really wanted to see was a connection between the water potential in the stem and the water potential in the soil, that didn’t come out of this experiment. And hopefully, that’s this summer’s experimentation. But that’s what I learned about that.

Alright, okay, I think we’ve got time for one or two more here. This next one is asking, Do we need ETO if we have water content loss? Alright.

So I was waiting for that. And so the answer to that is kind of no. But kind of yes. I certainly can get away with just putting water potential sensors out there and ETO. That’s why I presented those as tool one and tool two. The thing that you don’t, with ETO, you still have this crop coefficient in there that’s still not that easy. And some people were arguing very persuasively is like, I don’t need ETO at all. I’m just getting measure water content, water potential in the soil. It’s should be the same. The water use out of the soil should be the same as the water use, you know, that ET is picking up. And what I would say is, Yeah, I understand. But what I’ve seen so far is that I really like to bury a water content sensor out there with water potential so I can see the moisture release curve. And typically, in our studies, we don’t see a need for an ATMOS 41 in every field. We see a need of ATMOS 41, let’s say, in a, you know, 15 kilometer radius. And so what I would suggest is putting a single ATMOS 41 out there to get your ETO for several fields, for many fields in the area, and then use your water content in that specific field to fine tune some of these things. And especially as we talked about modeling, which I said I never do, this is where the modeling probably is going to start helping out to pick out those inconsistencies. Just a lot of the stuff on analyzing just me, and we need to pick out some of those inconsistencies.

All right. Okay. I think I think we’re out of time for now. For any of those who want to squeeze in a question and get it put in. Again for those questions that we did not get to, and we do have them recorded and again, Colin or somebody else from our METER Environment team will be able to get back to you to answer your question directly. Thank you again to everybody for all your great questions. That’s going to wrap it up for us today. Thank you for joining us, and we hope that you enjoyed this discussion, I think as much as we did here. Please consider answering the short survey that will appear after the webinar is finished, just to let us know what types of webinars you’d like to see in the future. And for more information on what you’ve seen today, visit us at metergroup.com. Finally, look for the recording of today’s presentation in your email, and stay tuned for future METER webinars. Thanks again, stay safe, and have a great day.

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