Water Management: Plant-Water Relations and Atmospheric Demand

Going by soil moisture data alone? Other strategies, like plant and weather monitoring, can inform water management decisions.

Soil moisture data are useful, but they can’t tell us everything. Other strategies for growers and researchers, like plant and weather monitoring, can inform water management decisions.

In this webinar, world-renowned soil physicist, Dr. Gaylon Campbell shares his newest insights and explores options for water management beyond soil moisture. Learn the why and how of scheduling irrigation using plant or atmospheric measurements. Understand canopy temperature and its role in detecting water stress in crops. Plus, discover when plant water information is necessary and which measurement(s) to use. Find out:

  • Why the Penman-Monteith equation, with the FAO 56 procedures gives a solid, physics-based method for determining potential evapotranspiration of a crop
  • How the ATMOS 41 microenvironment monitor combined with the ZL6 logger and ZENTRA Cloud give easy access to crop ET data
  • How assimilate partitioning can be controlled by manipulating plant water potential using appropriate irrigation strategies
  • Why combining monitoring soil water potential with deficit irrigation based on ET estimates provide an efficient and precise method for controlled water stress management
  • And more

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


Dr. Gaylon S. Campbell has been a research scientist and engineer at METER for over 20 years, following nearly 30 years on faculty at Washington State University. Dr. Campbell’s first experience with environmental measurement came in the lab of Sterling Taylor at Utah State University making water potential measurements to understand plant water status.

Dr. Campbell is one of the world’s foremost authorities on physical measurements in the soil-plant-atmosphere continuum. His book written with Dr. John Norman on Environmental Biophysics provides a critical foundation for anyone interested in understanding the physics of the natural world. Dr. Campbell has written three books, over 100 refereed journal articles and book chapters, and has several patents.


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Hello, everyone, and welcome to How to Use Plant Water Relations and Atmospheric Demand for Simplified Water Management. Today’s presentation will be 30 minutes followed by 10 minutes of Q&A with our presenter Dr. Gaylon Campbell, whom I’ll introduce in just a moment. But before we start with that, 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 towards the end. Second, if you want us to go back and repeat something you missed, don’t worry, we’ll be sending a link to the recording of the webinar via email within the next three to five business days. Alright, with all that out of the way, let’s get started. Today we’ll hear from soil scientist Dr. Gaylon Campbell, who will discuss using plant water relations and atmospheric demand for water management. Dr. Campbell has been a research scientist and engineer at METER for 20 years following nearly 30 years on faculty at Washington State University. His first experience with environmental measurement came in the lab of Dr. Sterling Taylor at Utah State University, making water potential measurements to understand plant water status. Dr. Campbell is one of the world’s foremost authorities on physical measurements in the soil plant atmospheric continuum. His book written with Dr. John Norman on environmental biophysics provides a critical foundation for anyone interested in understanding the physics of the natural world. Dr. Campbell has written three books, over 100 refereed journal articles and book chapters and has several patents. So without further ado, I’ll hand it over to Dr. Campbell to get us started.

Okay, thank you. As was mentioned, about 60 years ago, I worked as an undergraduate student in the lab of Dr. Sterling Taylor. He was the soil physicist at Utah State University. Dr. Taylor was a pioneer in irrigation management. He worked out methods for scheduling irrigation by measuring soil water potential, and he established limits and guidelines for optimal irrigation that are still in use today. One of the projects that he gave me to work on related to determining the water budget of a crop. That work resulted in my first scientific publication. But we might ask why Dr. Taylor, who had already developed excellent methods for controlling irrigation, using soil water potential, why you would care about monitoring crop water budget too. And I hope to show you in our seminar today the power of combining those two approaches. The knowledge Sterling obtained and his insights into optimal irrigation management are as relevant today as they were 60 years ago. But the tools that we have today for obtaining the data could not even have been imagined 60 years ago. Sterling used homebuilt tensiometers 2ith Mercury manometers to measure the suction. You went out each day with a notebook and recorded the readings of the tensiometers with pencil and paper. I can’t even imagine what he would have thought if I had told him then that one day, we could access the data on a phone that we carried around in our pocket, or on a computer that was connected to the cloud. Over the past growing season scientists, I think few growing seasons, scientists from METER have worked with researchers from Brigham Young University, and a potato grower in southern Idaho to monitor and optimize irrigation of potato crops. You might ask why we would still be researching that if Dr. Taylor had already worked it all out 60 years ago. He did work out a lot of things. But it’s a complex subject. And we found that there are still some things to learn. We think that’s why it’s called REsearch.

And here, let’s start with some of the results from those experiments. We monitored eight locations. I’ll share data, a little bit of data at least from five of those. I show here a record of the matric potential for mid June to mid August in one of those locations. Nominal field capacity we say is about minus 33 kilopascals and when the soil is wetter than that, we expect a significant loss of water from the root zone through drainage. You can see that the matric potential exceeded field capacity on several times during this period, and so we would expect some water to have drained below the root zone. Sterling would have set the lower limit for the optimal irrigation somewhere above minus 100 kilopascals. And you can see that the water potential got below that value a couple of times during the season. But in general, the irrigation on this field we would say was pretty well managed. I want to just say word about the TEROS 21 in the picture on the right. As I said, Sterling used mercury tensiometers. There are modern versions of tensiometers today without the mercury, and those would have worked to do this. But with a tensiometer, every time the water potential drops below about minus 80 kilopascals, they fail and you have to service them before they will work again. So that would have made this a hard thing to use in this application. Sterling also tried other ways of making measurements, electrical sensors that were based on electrical resistance and those do change with water potential. But they are also strongly influenced by temperature and by salts in the soil. So they don’t give the precision that’s needed for irrigation, good irrigation management. Sterling would have given a lot for a sensor like that TEROS 21. Now with sufficient effort, we could have made a graph like this one 60 years ago.

But I want to go on now and talk about water balance measurements, and for those, we could not have done that 60 years ago. This is METER’s ZL6 data logger with an ATMOS 21 micro environment monitor. The ATMOS 41 provides measurements of the climate variables required to estimate evapotranspiration. The ZL6 records those data and transmits them to the cloud. The ZENTRA cloud software that we provide does the calculations for the evapotranspiration estimates, and soil moisture sensors are also connected to the logger. And those data also are transmitted to the cloud. It took us less than 30 minutes per site to install these sensors, to set up the logger and the microenvironment monitor, and to start receiving data from the cloud. I want to spend a little bit of time talking about measuring evapotranspiration and so we’ll start by defining some terms. We use the term evaporation to describe the amount of water that evaporates from the soil. The transpiration is the amount of water that evaporates from the plant. Having been taken up from the soil by the plant, we need to separate those two because they operate in different ways. The water that evaporates from the soil, as the soil surface dries, is reduced dramatically. The water that evaporates from the plant, since the plant is able to access water throughout the root zone, will go on evaporating for a much longer period of time.

Now for estimating the evapotranspiration of a crop, it’s useful to first estimate the evapotranspiration of a reference crop with specified characteristics. And then to compute the crop evapotranspiration from that. A widely used standard for computing evapotranspiration is the Food and Agriculture Organization’s publication, FAO 56. And that’s the one that we follow in ZENTRA Cloud. In it weather variables such as radiation, temperature, wind speed, are converted to a reference ET based on specified assumptions about the properties of the evaporating surface. And once we have that reference ET, we multiply it by a crop coefficient, KC to get the crop ET. Both calculations assume optimal agronomic conditions where water is never a limiting factor. We use the Penman Monteith equation to calculate the reference evapotranspiration. It’s the sum of of two terms, the first term is the net radiation minus soil heat flux. So that represents the energy that’s available to evaporate the water. The second term, crop conductance multiplied by a vapor deficit, represents the evaporation that the crop would have if it were kept at air temperature. The weighted sum of those two terms gives us the reference evapotranspiration. Now, to do these calculations, we need the net radiation, the vapor deficit, the vapor conductance, and apparent phycrometer constant, and the soil heat flux. The ATMOS 41 gives us solar radiation, air temperature, atmospheric vapor pressure and wind. The FAO 56 publication gives us directions on computing each of the needed variables from the ATMOS 41 data. And from the variables like the crop height and crop conductance that are specified for the reference crop so that we can do that reference ET calculation.

This is a typical crop coefficient function. You see that for most of the season, it has value near one. So the crop ET and the reference ET are similar. The data that we have is mostly for a crop with full cover. And so we’ll assume avalue of Kc of one or the measurements that we’ll show. So here I’ve plotted the cumulative components of the water budget over the time period that we showed the matric potential in the earlier slide. The gray line is the precipitation and irrigation. And that was obtained we had in each field, a rain gauge that made a measurement of the irrigation that occurred in that field. The blue line is our estimate of the evapotranspiration based on the calculations that we just went through. The orange line is an estimate of the drainage. I used a simple model to compute the drainage, I assumed a soil reservoir of 25 millimeters of water. When the daily water use brought that reservoir below zero, I reduced the transpiration, and when irrigation brought it above 25 millimeters, I assumed that the excess water drained. Now looks like the losses and inputs of water match pretty well up until about the middle of July. That point you can see that the evaporative demand decreased little bit at the same time that irrigation amounts increased a little bit. So the combination of those two resulted in a couple of drainage events but those were the main events for the whole season.

In this graph you can see the power of looking at the water balance information at once. The way Dr. Taylor wanted to the cumulative evapotranspiration irrigation information gives us an overall view that we need to steer the process. Soil matric potential data gives us the detailed picture on where we are with our steering process at present. It’s important to remember though, that each of the lines here represents a measurement for just one single sensor at one location. We see a consistent pattern with this data set, with the matric potential sensor confirming what we see in the drainage, evapotranspiration and irrigation data. And that was true for all five of the datasets that we looked at, we looked in this detail at the others. But that wouldn’t always necessarily be the case. These sensors can be improperly installed or can malfunction. And so by having redundant data the way we have with this approach, we can still do a good job of managing irrigation, even if there are some inconsistencies or problems with those.

Here are the water balance calculations for all five fields. And again, I’d remind you that the irrigation was measured just one rain gauge per field, and the evapotranspiration was from a single ATMOS 41 applied to all fields. The differences in ET that you see in our model are the result of our model reservoir running dry. And those correlate well with the low matric potential readings that we saw in the measurements. The drainage estimates also come from the model and from the irrigation and ET estimates. So of course, there’s significant uncertainty in those numbers. The leaching factor fractions that are shown here, just for the 60 days of data that we analyze, they don’t include other irrigations or rain through the rest of the season. And all in all, though, I think that these data indicate a good job of managing irrigation to achieve maximum production and, at the same time, a good stewardship of the water that was applied. The approach that we’ve taken so far has focused on everything but the plant. And some think that’s a mistake. They say that the plant should tell us if it’s stressed or not, we should monitor the plant to know when we should irrigate. Sterling Taylor was also a pioneer in this area. Besides wanting to monitor soil water potential and the crop water budget, he wanted to monitor plant water stress. The instruments we use to try to monitor water stress in Sterling’s laboratory were early versions of the ones that we use now, the same ones, but 60 years have brought a lot of improvements. I’ll talk about those improvements in a bit. But first we need a little bit of background on plant water relations.

There’s a diagram of the SPAC, the soil plant atmosphere continuum, that shows the path the water takes and the resistance it encounters. As it moves through the plant from the soil through the plant to the atmosphere, the water potential in the soil is high, in the atmosphere it’s low. Water tends to move from regions of high water potential to regions of low water potential and this is the water potential gradient that is the driving force for transpiration. Summarize those ideas here that water potential is a measure the energy state of the water in the system. Water potential gradient is the force that drives water through this soil plant atmosphere continuum, and water flows from high to low potentials. But water potential can also be used to describe the availability of water for biological processes. This is, what we may see is the daytime water potential distribution in the soil plant atmosphere continuum along the transpiration path. This represents midday summertime conditions under two cases, wet soil and dry soil, the extremes that we might encounter. Just say that these values are hypothetical. Species certainly differ remarkably in their response to water stress. But there are still some things that we can note here. One is that the water potential of the air is more negative by a couple of orders of magnitude than it is anywhere in the plant. There’s no direct way for the plant to regulate its water loss by lowering its water potential. It would die before its water potential could change significantly enough to change the the rate of water loss. So the only way that a plant can regulate its water loss is by changing its vapor conductance of its leaves. If we look at the plant at night the picture changes a lot. The plant tends to equilibrate with the soil, the stomates close, transpiration reduces dramatically, and atmospheric humidity is higher so that the water potential to the atmosphere is not so low.

Let’s look at the biochemical and physiological responses to the water potential variation in the plant due to varying soil moisture and evaporative demand. This is from a review article written by Ted Hsiao quite a few years ago, but it gives us some important information that we can apply for manipulating the partitioning through irrigation management of assimilate. We can see two groups of processes here, those that are associated with synthesis of new tissue that’s just cell growth and wall synthesis, protein synthesis, and those are active high water potentials maybe zero to minus 300 kilopascals. Those that are associated with stomatal conductance, CO2 assimilation, respiration and sugar accumulation are active over a wider range of potentials and generally at lower potentials than ones related to growth. So we can, I think, come to a set of conclusions like these, leaf water potential varies widely from night to day, every day. So a low water potential doesn’t necessarily mean that a plant’s stressed and we shouldn’t equate in our mind leaf water potential and water stress. Those are different things. High leaf water potential depends on the soil water potential, the highest leaf water potentials. The lowest depends mainly on evaporative demand and availability of water from the soil. This stomatal conductance decreases when the demand exceeds the supply. And growth is fastest when the soil is wet and the demand is low. Cell expansion’s mainly at night with plentiful soil moisture. But photosynthesis can go on until the soil dries significantly. So we could picture two different irrigation scenarios maybe, one that we might use in annual crops where we’re mainly interested in achieving the highest rates of biomass increase we can get, and with those we would want to just monitor the soil moisture and keep it in the right range so that growth can be as fast as it can be. On the other hand at times, we want to steer the plant toward less vegetative growth and more reproductive growth. And so we may want to control the vegetative growth. This is particularly true, for example, in wine grapes, where the vines are intentionally stressed to increase fruit quality. We want maximum growth, we just manage the water to maintain the highest plant water potential as possible. And we can do that just by monitoring the water potential of the soil. But if we want to irrigate in such a way that we stress the plant to the right levels, then we need more information than we can get just by monitoring soil water potential.

So talk just a little bit about ways that we can monitor water potential or can model conductance in plants. Now the theory for each of these methods that I’ll talk about was not well developed 60 years ago. The instruments that we had in Sterling’s lab were primitive, but we attempted to use all of the methods that we’ll talk about here during that time. Since then, they have all been successfully used to monitor water stress in crops and orchards and vineyards. Pressure bomb on the left is one of the methods used to directly measure leaf or stem water potential. You just put a leaf or a twig, seal it in the chamber with a piece of the xylem extending through the seal to atmospheric pressure, and the pressure that it takes in the chamber to produce free water at that part of the xylem that sticks out is equal to the negative of the water potential of the tissue. And the center picture is METER’s diffusion porometer for measuring stomatal conductance. And I’ll show you a little bit of data a little bit later on that comes out of that. Picture on the right is Apogee’s infrared thermometer that measures the difference between canopy and air temperature. We can read that otu directly with a ZL6 logger and make those data available through ZENTRA Cloud too. This shows the relationship between stomatal conductance on the vertical axis and leaf water potential on the horizontal axis. Not surprisingly, there’s a relationship between those two, that we show with the line but there’s a lot of scatter. The leaf water potential varies a lot from leaf to leaf in the canopy. And the stomatal conductance also varies a lot.

Now measurements like these could be used, and in fact have been used to manage irrigation. But you can see that you would need several measurements and identify every several measurements in order to know what you should be doing with that. The equation here tells us how canopy temperature relates to water stress of a crop. The Tc minus Ta is the canopy minus the air temperature that’s measured with the radiometer that we showed in the earlier slide. And that can be used to measure water stress. The terms in the equation should look a little bit familiar. They’re the same terms as the ones in the Penman Monteith equation, but now we’re using MDT to determine canopy temperature and canopy conductance. The gv term is the canopy conductance and you can see that that’s one term in the equation. That’s the one that represents water stress in the crop, and we can solve for that. But we would, of course need to know all the other terms in the equation.

This is an example where all of those terms were measured and brought into play. To do the calculation, it was provided to me by Mark Blomquist at Apogee, the makers of the canopy temperature sensor that we use. These were measurements on a corn, the black dots that you see in the upper graph are measurements of the difference between canopy and air temperature. The other energy balance quantities that were measured in the black dots in the upper graph are estimates of canopy minus air temperature for a canopy that wasn’t transpiring at all. The green dots in the upper graph are estimates of the canopy temperature, if the canopy were not water stressed at all. Mark computes a water status index, that water status index is the red minus the black divided by the red minus the green. And so, when the crop is not water stressed, that water status index is shown in the bottom graph, when the crop is not stressed, the index is at one and then as water stress develops, the index decreases. And then this with precipitation comes night. Now that ratio that he computes, that water status index, is the ratio of potential to actually D, and so it’ll also tell us just what the ratio of potential to actual photosynthesis is.

All of these methods work, just the way Dr. Taylor thought they would. And a little bit news to manage assimilate partitioning through irrigation time and amount, but in every case, it takes a lot of work and a lot of skill. We stop and think for a minute, we realize that the outcome of any of these methods is to reduce transpiration. That’s how we induce water stress in a plant. What if we turn that idea on its head, and instead of monitoring the plant, we estimate its well watered evapotranspiration and irrigate it some fraction of that rate, the same fraction we would get by monitoring the plant? That has been done. And this, another example of the power of combining evapotranspiration and so on with moisture measurements, I’ll show you an example from a vineyard where they applied water to meet 100% of evaporative demand and until the completion of bloom, and then reduce the irrigation rate to 70% of the total evapotranspiration, or what they estimated that to be. The reference ET was computed to determine irrigation rates. I don’t have the actual ET data from that. The graph I show here is just the same graph we had earlier for potatoes, but I just wanted to illustrate what it looks like to reduce the ET to 70%. We use the METER TEROS 21s again for this experiment. They were buried at 30, 60, and 120 centimeters or one, two, and four feet. Until around July 1, the irrigation was at 100% of ET. You can see that they did a good job of that because the water potentials are holding pretty steady. Water never did get down to the lowest level, the 120 centimeter level low, and that stays low even through that 100% time. When the 70% irrigation started, the lower sensor immediately started dropping and then just stayed dry. The 60 centimeter sensor sees some of the irrigations, but eventually, we aren’t getting any water down to that level either. But the highest sensor, the 30 centimeter one sees all of the irrigations for the rest of the season. Now each of these, the result of this management was to arrest vegetative growth and increase sugars in the fruit, just like happens when we use a pressure bomber canopy temperature to do that, if we do it right, but the method is a lot easier to apply and get right. All it takes is the microenvironment monitors and matric potential sensors, and ZENTRA cloud to provide some reference ET estimates.

So let me conclude with these ideas. The Penman Monteith equation with the FAO 56 procedure gives us a solid physics based method for determining potential evapotranspiration from a crop. And at least for a good share of the season when we have full cover, we can use that to know the amount of water that crop needs. The ATMOS 41 microenvironment monitor with ZL6 logger and ZENTRA Cloud gives us easy access to those estimates. Combining ET irrigation and matric potential provides a powerful tool for managing irrigation for maximum production and minimum waste of water. And finally, combining soil moisture monitoring with deficit irrigation based on ET estimate can be an efficient way for precise control of plant water stress. Thank you.

All right. Thank you very much Gaylon. We do have some time now for a few questions. I think we’ll see how many questions we can get in. But we appreciate that everybody has stuck around and listened and participated. We do have several great questions that have already been submitted. We do have time for you to submit more questions. So any and all questions that you have, please enter them into the Questions pane, and we will keep track of them. If Dr. Campbell cannot get to them right now during our live webinar, we do have them recorded. And somebody else, whether Dr. Campbell, or somebody else from our METER Environment team will be able to get back to directly via email and answer your questions specifically. So don’t worry if we don’t get to your question. We’ve got plenty of questions and we’ll see how many we get through. Okay, first off. So let’s go back actually, I’m going to scroll through, Gaylon, if you’re okay with this. We’ve got a lot of good questions about some of our slides here. And I want to go back to our Penman Monteith slides. We’ve got a few questions in here asking about the the equation and the parameters within the equation. And they’re wondering exactly, so what kinds of sensors will they need in order to gather these data to fill these parameters? Is it just one, or are there going to be multiple? And what’s the best way that they will be able to gather all these data?

Well, the easiest way of course, is just with the ATMOS 41 since that has the stuff on it and the ZENTRA Cloud does all of those calculations for you. You need at least a measure of solar radiation, you need a measure of air temperature, you need the measurements that will give you the vapor deficit of the air, so the relative humidity or vapor pressure, you need wind speed and then for reference ET, the canopy is already specified, so that’s a part of the standard.

Okay, great. Another one down here a little bit further. There’s a question specifically on this midday slide. And we have on this slide both the roots and the soil kPas, basically their equivalent at negative 30 kPa. Does this mean that the plants are not, there’s no diurnal uptake of water?

No, it just means that water encounters two main resistances as it enters and goes through the plant. The biggest one is as it goes from the soil into the root at the root endodermis, and and then the next biggest one is somewhere up in the leaf as it moves out from the xylum into the the leaf mesophyll. And so in this diagram, we should have shown a drop in potential as it went from the soil into the root, a significant drop that, I guess what I’m trying to show here is the soil and the root surface and at 30 kilopascals, there isn’t a significant drop, a significant enough drop in potential to show here. But in going from the root and the water at the root surface to inside the root xylem, you can see a big drop from minus 30 to minus 700. That’s the drop that occurs across the endodermis.

Alright, another question relevant to your vineyard discussion here. If you’re deficit irrigating wine grapes via an ET based approach, knowing very accurate KC values would be extremely important. We know different grape varieties respond differently to water stress, do we have accurate KC values for different grape varieties or for grapes that are grown in different climates?

Now because of our time limitations today, we didn’t spend much time on the KC part of estimating evaporation. I absolutely agree that to do that vineyard irrigation properly, we would need to do that correctly. And that needs to be the topic of another seminar not this one. It’s a fairly involved thing.

All right. And again, we will be able to get back and answer your question in more detail. So don’t worry about that. How does saline and sodic irrigation water affect soil matric potential?

Water potential in soil is the sum of matric and osmotic potentials. And so, the effect of salinity on matric potential, I mean we separate it out so that those are, so we treat them separately, there. So, matric potential would be a separate thing from from osmotic potential, but certainly osmotic potential has a big effect on plant water status and again, that needs to be the subject of a separate seminar.

Okay, I think we have time for one more question here. Again, we appreciate all of your questions that have come in and we will be able to get back to them via email here in the next few days. So, final question just in general, on water potential in general, why is water potential important or relevant or why is it preferred in these studies? And are there other sensors you could use, capacitance, FDR sensor that would work in the same way?

Okay. To to specify the state of water and soil or in the plant, we really need two variables. One is the amount of water, the water content, and the other is the energy state of the water, the water potential. And those two are related for a specific soil but differ from one soil to another. And we really need to know both things to understand the state of the water in the soil. The water potential works well for things like we were talking about today, because for any soil, we can specify the upper and lower limits that we want to set. And they’ll be the same for every soil. Whereas if we did that in terms of water content, we would have to specify each of those according to the soil that we were dealing with. So for the things we’re talking about today, I could say, you need to keep the water potential below minus 30 kilopascals, so that, you know, the water doesn’t run out the bottom of the soil, you need to keep it above minus 100 kilopascals, so that you don’t stress that crop. And those guidelines would apply wherever you were applying this, where if we did it in terms of water content, we’d have to be specific for the soil you are dealing with.

Great. Thank you again, Dr. Campbell. That’s going to wrap it up for us today. Thanks again for joining us everybody. We hoped you enjoyed this discussion as much as we did. And thanks again for all of your great questions. We had dozens of questions come in. We’ll be able to get back to you via email here in the next little bit. Please consider answering the short survey that will appear after this webinar is finished, just to tell us 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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