How to Improve Irrigation Scheduling Using Soil Moisture

Every irrigator wishes for tools that answer the questions: when do I turn the water on and when do I turn the water off? The challenge is figuring out the right tools and implementing them effectively.

Every irrigator wishes for tools that answer the fundamental questions: when do I turn the water on and when do I turn the water off? The challenge is figuring out the right tools and implementing them effectively. New sensor technology and cloud computing offer new opportunities to growers, but it is often unclear how to put these into practice.

In this webinar, Dr. Gaylon Campbell covers the different methods irrigators can use to schedule irrigation and the pros and cons of each. Get to know why we schedule irrigation, best practices for soil moisture monitoring, and the results of good management.

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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 Improve Irrigation Scheduling Using Soil Moisture. Today’s presentation will be 30 minutes, followed by 10 minutes of Q&A with Dr. Gaylon Campbell, senior scientist here at METER Group. Dr. Gaylon Campbell’s involvement in agriculture and environmental research goes back to the early 1960s. If the right measurement device did not exist, he invented it. In 1983, Dr. Campbell founded Decagon Devices, which merged with Munich based UMS in 2016 to become METER Group. Dr. Campbell has written three books and more than 100 articles and has several patents. If you have a question for Dr. Campbell, type it into the Questions pane at any time during the webinar. We’ll be keeping track of these to answer during the question and answer period. So please don’t be shy and submit those questions. We’ll also be sending out a link to the on-demand webinar, as well as the slides for you to review as soon as they’re available. Without further ado, I’ll hand it over to Dr. Campbell.

Thank you. And thank you for being with us today to learn more about soil moisture based methods for irrigation management. In considering this topic, it can be useful to think about our goals in managing irrigation. Our primary purpose in applying water to a crop is to increase production, and that’s been the case for thousands of years. But the increased production can be temporary if irrigation water is managed incorrectly. Salinization from application of too much water can finally make crop production impossible. There are other environmental impacts from overirrigation. Those can be costly, too, as well as the costs from unde irrigation. Now, if we look at irrigation management at a deeper level, crops respond to water deficits in complex ways. We understand some of those ways now. And we can use those to control deficit irrigation to have an effect on assimilate partitioning and other things in the plant. And by doing that, we can not only maximize yields, but also, in many cases, maximize the value of the harvested product. Finally, we should consider other applications of irrigation. It’s not always just for supplying water to crops. It can be used for frost control. It can be used to manage nutrients. Sometimes irrigation is even used to dispose of waste. And so we need to consider those also.

Now there are a lot of different ways to look at irrigation management. But one way is to think about the questions that we need to answer. We can simplify those down to two. When do I need to turn the water on and when do I need to turn the water off? For a period of time I was a member of an irrigation association. I had a water right from an irrigation canal. The water master told me when I could irrigate, and my turn was from 6pm on Friday to 6am on Saturday. So every Friday at 6pm, assuming the guy before me had remembered to turn his water off, I had water in my irrigation ditch. I turned it on to the property and let it run until 6am on Saturday, and then I turned it off so the next person could use it. So the answer to those key questions was pretty simple. The water master gave them to me. Did I ever over-irrigate? I certainly did. Did I ever under-irrigate? I’m sure that I did that too. How could I have done better? Maybe I couldn’t have in that system. But with a little bit more flexibility, there are many ways that I could have improved on the system. I could have checked the soil to see when it was dry. I could have checked the plant to see if it was stressed and apply irrigation water if it was. I could have monitored the water loss from the crop and added water when a given amount had been used up.

So let’s look at those options in a little bit more detail. The calendar clock method is certainly the simplest, and if it were done right, I imagine it could even meet the average irrigation demand of a crop. But when we over-irrigate with that way, we not only waste water, we waste nutrients as well. And we always will over-irrigate when evaporative demand is low. On the other hand, when the evaporative demand is high, why we’ll always stress the crop with that method. If we look at the soil moisture based irrigation management, we need sensors to tell us what the moisture status of the soil is. We then need to determine the sensor reading when the profile is full and the sensor reading when it’s empty. We monitor to see what the soil moisture is. If the profile is empty, we irrigate. If it’s full, we turn the water off. Being able to irrigate in this way would help a lot in our ability to not over- or under-irrigate. But what about the soil variability? And what about the the actual need of the plants?

Many people think that the ideal way to manage irrigation would be to ask the plant. If the plant isn’t stressed, we shouldn’t need to irrigate. Now while monitoring the plant is essential for some types of irrigation, especially deficit or controlled stress irrigation, it isn’t very useful as the primary method for irrigation management. For one thing, we can stress the crop either with too little water or with too much water, or it can be stressed by disease or high evaporative demand. And we can only fix one of those things by adding more water to the soil. Now if we schedule irrigation by atmospheric data or atmospheric demand, that’s like balancing your checkbook. Measurements tell us how much water the crop has used each day. If we know how much water is in the soil, we irrigate when that quantity of water that’s stored in the soil has been removed. And then an amount of water is added by irrigation to replace the amount of water that was lost. That all seems pretty straightforward. But the shortcomings of this method come down to the uncertainties. We can’t know exactly how much water is applied without measuring it. We also deal with uncertainties in our estimates of the amount of water loss by evaporation and draining. We’d be pretty happy if we could measure any of those things within 10%. But if you apply 10% too much water or 10% too little, that effect accumulates over the season. This kind of a scheme is certainly better than irrigating by a calendar, but there are better ways to do it.

So which of these methods is best? Again, we need to consider our goals. What are our objectives and our constraints? Now hopefully the constraints wouldn’t force us to use the calendar method. That would lead to problems pretty quickly. Soil moisture monitoring gives us the best understanding and control of the system. It’s therefore a good place to start. But we can go beyond that. Reference evapotranspiration can give us insights and make our system more reliable. And measurements on the plants are useful for controlled stress or deficit irrigation. In this discussion though, we’ll just focus on the soil moisture based irrigation. Now as we do that, it’s useful to remember a statement from the architect to the soil moisture based system that we’ll describe here, Dr. Mel Campbell. Since the soil is the primary recipient of the irrigation water, it seems reasonable that the answers to these two questions, when to turn the water on and when to turn it off, should come from monitoring the soil. Mel worked out these methods more than 40 years ago. He published on that in 1982. And he and many others have used it to manage irrigation on tens of thousands of acres. About 20 years ago, Decagon, that since has become METER, worked with Dr. Campbell and his sons to design sensors and automated data collection for management. The system we started to design them was a big step forward from the manual methods that Mel and others were using. But even 20 years ago, we had no concept of the things modern technology would bring to the table. The so called Internet of Things has revolutionized irrigation management, as it has so many other areas. So as I talk about the principles Mel articulated more than 40 years ago, I’ll also talk about the implementation of those principles in ways he couldn’t have dreamed of. I want to mention the system first, and then we’ll go back and talk about Mel’s approach to irrigation management using these new tools.

So first of all, where does the data come from that we’ll use? Well the ZL6 logger is the center of the system. It has a cell modem that transfers the data that it collects directly to the cloud. It comes with a SIM, so you install it, register it, and can immediately start receiving data at your computer or at your mobile device. The solar charger provides the power so you don’t need to charge the batteries even. The logger is shown here with the ATMOS 41 microenvironment monitor that provides the solar radiation, temperature, humidity, and wind data that are needed to estimate reference evapotranspiration. The rain gauge monitors, the applied, soil moisture sensors that we’ll discuss later also plug into the logger. You can do the whole installation in as little as 30 minutes. Now the companion to the logger we call ZENTRA Cloud. This is the software that runs on your computer or your mobile device that organizes and keeps track of the data from all of the fields and locations that you have, does all the needed analysis, and displays the data and information that you need. Here we showed just the daily and the cumulative reference evapotranspiration that’s calculated from the microenvironment monitor sensors.

So if we get back now to the problem of managing irrigation using soil moisture data, here are the four things that we need to do. We need to select the right site for monitoring. We need to select a method for measuring soil moisture. We need to set our full and refill points and record and display the results. Of course the record keeping scheme should also indicate to the irrigator the answer to our two questions, when we turn the water on and when we turn it off. When considering the problem of site selection, some people get hung up on field variability. Certainly crops, soils, and irrigation systems are variable. Most of the time, the field is irrigated as a unit, so we need to pick the monitoring site that best represents the field that we’re going to irrigate. Now as technology advances, we’re seeing more and more variable rate irrigation systems, and successful irrigation with the VRI system will obviously require more monitoring spots, but for our discussion today, let’s assume that we’re irrigating a field as a unit.

So these are the things that we need to pay attention to in selecting the site. Edges of a field aren’t representative of the field, so we need to avoid them. We want a place with healthy plants. We want to know how much water is being used by the crops, so our site needs to reflect that. We also don’t want to mess up the vegetation when we’re installing the monitoring sites. Of course, we want the soil water to be representative too, so we avoid places that have run-on or runoff. We want an average exposure to the irrigation and to the climate. And we want an above average water holding capacity in the soil at that site, again that’s so that the plants in that site won’t stress and give us unusually low value for the evapotranspiration layer. Now we could use technology to help out here, drones or satellite data can be helpful, yield data that we might have from previous crops could be used, and other things that we might know about the field.

The ZL6 has a built in GPS, so it always knows where it is. That makes it easy for ZENTRA Cloud to show the monitoring locations on the map. Like I’ve shown here, the little blue squares, that each square is monitoring location, shown directly on the map of the site that you’re monitoring. The next step is to choose a soil moisture monitoring method. We can measure either water content or water potential, and both are important for irrigation management. The water content tells us how much water is in the soil and how much has gone out of it. The water potential tells us whether the water is available to the crop and whether water will run out the bottom of the soil. Whichever one we have, we usually need to infer the other one for our overall measurements. Now Mel Campbell originally used manually operated neutron probes to monitor water content, and this is still sometimes done, but reliable dielectric sensors are now available that will measure both water content and water potential in situ and will report that directly through the ZL6 to the cloud. This saves an enormous amount of work and expense. The TEROS 12 that I’ve shown here measures just the water content. The TEROS 11 gives water content and temperature. The TEROS 12 gives water content, temperature, and electrical conductivity. We’ll spend some time in a later talk on the uses of electrical conductivity and irrigation management. The TEROS 21 shown on top here is the water potential sensor. A reliable sensor like that has only recently become available, but we’ll show a little later how important the information from that sensor can be.

Let’s say with whichever quantity we measure, it’s usually necessary to infer the other. This is a relationship between water content and water potential. You can see that it’s unique for each soil, but it differs from soil to soil. The relationship between the two is called a moisture characteristic or moisture release curve. And we show here typical ones for a loamy sand, a silt loam and a clay soil. Water potential has negative values because the water in soil is less available or we have to do more work, do work, to get it out to a state of pure free water. But if we look at the water potential, say at minus 100 kilopascals with the mouse here on this line, we can see that in the sandy soil, it’s something below 10%. In the silt loam, it’s maybe 25%. And in the clay soil, it’s something like 40%. We’ll talk later about terms field capacity and permanent wilting point. The field capacity is typically between minus 10 and minus 30 kilopascals, and permanent wilting point is around minus 1500 kilopascals. So soil that’s drier than this permanent wilting point, farther over here, wouldn’t supply water to a plant, and soil that’s wetter than field capacity, that the water would drain out of that soil.

How many spots do we need to install? How many sensors do we need to install at a location? A minimum I think is two — one in the most active part of the root zone to indicate the availability of water to the plant, the other below the root zone to show over- or under-irrigation. More sensors give a clearer picture, and generally we install three at a location, at least three. We don’t always know exactly where the roots are. These pictures are of our installation tool. It’s a key part of our ability to install probes accurately and quickly, at least the water content probes. The installation mechanism is shown here on the right side. The probe is here, the installation scissor mechanism pushes it into the soil, and you can see a picture here of that being used in the field. The matric potential sensors are easier to install because soil disturbance isn’t so critical for those, and so you can just usually push them in by hand and cover them up. The next step is to set the full and refill points of the soil profile. This can be an iterative process, but you need some place to start, and the starting point is arrived at using what we’ve talked about before, field capacity and permanent wilting point. Field capacity is defined as the water content of the soil profile two or three days after a heavy rain or irrigation. It corresponds to a water potential between minus 10 and minus 30 kilopascals — minus 10 for a coarser soil, minus 30 for a finer soil.

Permanent wilting point is defined as the soil water content when dwarf sunflowers growing in it wilt and don’t recover if we place them in a humid environment overnight. Now that isn’t a lethal water potential. Plants still can get water from the soil, but it can’t get it fast enough to sustain normal transpiration. We can treat that as a lower limit for water availability in soil, but obviously, we wouldn’t want most crops to ever get to that water potential or anywhere near it. A lot of research has been done on this, and there are crop specific limits in the literature for many crops. The value around minus 100 kilopascals is about right for many crops. If we’re monitoring water potential, that’s about right for many crops. If we’re monitoring water content, then we need to know the water content that corresponds to those water potentials. And so we’ll need a moisture characteristic for the soil that we’re dealing with. Now one thing to remember is that these probes in the field measure volumetric water content. Quite often the laboratory measurements that give the permanent wilting point and field capacity are gravimetric water content, so need to be multiplied by the bulk density.

Here are those moisture release curves that we talked about earlier showing the field capacity range, where the green lines are here, the lower limit that we normally would set for an irrigated crop, and then permanent wilting point down here. That upper and lower limit can be set in ZENTRA Cloud, and then we get a band showing up on the graph, showing the range that we want to stay within, showing the upper and lower limit for that. The last part is keeping a record. This used to be the hard part because all of the field data that were collected with the neutron probes had to be plotted on graphs. And then we would paper the walls of the irrigation office with those graphs. But now ZENTRA Cloud does it all for us. The detailed record is kept, the limits are plotted, and warnings are even automatically sent out when those limits are approached or exceeded. Now this is a pre-ZENTRA graph of water content variations over time in a field. It’s useful for making a couple of irrigation management observations. The point of irrigation here was the disposal of food processing waste, so they were applying a lot of water on the sandy loam soil. Each water application shows an immediate spike in the water content at the shallowest depth, the blue line that you see here. And then a little bit later you see that the response at the deeper depth — the shallow one is at 15 centimeters, the deeper one is at 30 centimeters. Finally sometime later you see those irrigations showing up at the deepest depth, and the blue line here, that’s at 60 centimeters.

Finally, we should ask, what’s the bottom line? Does irrigation management improve yields and save water? We didn’t try to quantify the water saving. If we do it right, we should apply exactly the amount of water the crop needs. So we shouldn’t be wasting any water, so we can assume that we’re improving that. But since we had yield maps in this field where the potatoes were being grown, we were able to determine the yield in these areas where we were monitoring water potential. And we collect the data at six sites for potato yield, for those six sites where we have water potential measurements. We calculated the number of days with water potentials below minus 100 kilopascals, and then we correlated those with the yield data around that instrumentation. And it should be clear from the graph that I show here, that yields that are associated with the areas that were not stressed during the growing season had a lot better yields than the other areas. The signs of stress were not obvious at all. We didn’t see that in the crop. The only indicators that we had from plants were measurements of increased canopy temperature that we’ll talk about another time. But I think we can say for sure that if we can avoid that kind of yield loss that that will go a long way toward paying for the work that we need to do to manage the irrigation correctly.

Now in our last slide, we showed one of the ways that good irrigation management pays with better crop production. And we’ve talked about others. Over the long term, our mismanagement accumulates as a kind of technical debt in the form of salinized soil and polluted water supplies that we might not have to pay, but certainly our grandchildren and great grandchildren will. We now have the technology to avoid some of that debt, and we should be working toward that end. As we do, a good place to start is with monitoring soil moisture. Correctly done, that’ll give us the best chance I think of managing irrigation properly. Sensor technology that we have now at our disposal, linked with the Internet of Things makes for a practical solution to the irrigation management problem. Our discussion today is focused on just a few aspects of irrigation management. In the future, we’ll talk in more detail about monitoring plants to schedule irrigation, and about measuring atmospheric demand and using it to improve irrigation scheduling.

Okay, so we’re just past the half hour but we’d like to use the next few minutes to take some questions from the attendees. We’ll get to as many as we can. If we’re unable to get to yours, someone, either Dr. Campbell or I, will get back to you via email. So our first question is about the first slide and asked, what is meant by “assimilate partitioning” in slide one?

Okay, assimilate partitioning. When the plant carries on photosynthesis, it turns carbon dioxide into sugars, and those sugars can go to leaves or to roots or to the harvestable part of the plant. And by adjusting or by controlling the water properly, we have some control over where those things go. So for example, we can get, even after we have fruit on a plant, we can get a lot of sugars going into vegetative growth. If we can stress the plants a little bit, those sugars will go to the fruit production rather than the vegetative growth.

Alright, so our second question asks, what is most accurate in the wet end of monitoring, tensiometers or the TEROS 21 matric potential sensor?

Okay, the tensiometers are certainly the most accurate in that range. But the problem with tensiometers is that, and METER sells some of the finest tensiometers that are made in the world, so we do we sell both kinds of instruments. But for irrigation management, the maintenance on tensiometers is pretty difficult to manage and adds a lot to the expense. And the additional accuracy of a tensiometer is not really useful in scheduling irrigation. So the TEROS 21 takes no maintenance and gives good enough measurements for irrigation management purposes.

Third question asks, agronomists generally claim to take into account more crop calendar characteristics in addition to simpler ET soil moisture based scheduling, which are more mechanistic, empirical, and hence more uncertain as well. How can these phenomenon be incorporated into these more mechanistic, soil moisture ET stress for example, based irrigation scheduling without deep agronomical knowledge?

If I understand the question right, that’s where ZENTRA Cloud comes in. A lot of the agronomic knowledge can be programmed into the computer, into ZENTRA Cloud, and is available to the irrigator without the irrigator needing to know it in that depth. And so with cloud based systems, like we have available now, we can bring the best knowledge and best models that are available to us to bear on the problems in the field and consult them in that way.

Okay, we’re about out of time. So we’ll take one more question here before we close. Is one water potential sensor and three volumetric water content sensors a good way of going for corn or alfalfa or really any crop monitoring? So there are definitely a lot of advantages. And we have, there’s an article on the METER Group website that actually talks about dual measurements of water content and water potential in the soils and the information that you can get from that. It really, in my opinion, increases your understanding of how water is moving through the soil and what’s available, but also how much to then return or apply or irrigate back into the soil to replenish. So I would encourage you to go and read through that article that we have online. I believe it’s titled dual measurements.

All right. So that’s going to wrap it up for us today. As I said, there’s a whole bunch more questions here which we will attempt to answer via email for you. I hope you enjoyed this discussion as much as we did, and thanks for your great questions. If you would like more information on soil moisture monitoring for irrigation scheduling, or would like to talk with Dr. Campbell or the crops team further, please consider answering this short survey that will appear after this webinar. Stay tuned for future METER crops webinars. Have a great day.

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