You’ve buried soil water content and water potential sensors in the ground, installed an ATMOS 41 in the field, and set up your ZL6 data logger. Your network of instruments has been collecting data for days, weeks, or even all season. Now what? Performing soil moisture data analysis for your research location is one thing. Knowing how to extrapolate meaningful inferences and conclusions to understand what is happening and troubleshoot issues is completely different.
In this article, we will step through multiple data sets to understand how soil water content, soil temperature, soil water potential, and atmospheric measurements can be used to discover the meaning behind the traces. Within this article you will learn how to identify the following events in your data:
Behavior of soil moisture sensors in different soil types
Infiltration
Flooding
Soil cracking
Freezing
Spatial variability
Temperature effects
Diurnal patterns due to hydraulic redistribution
Broken sensors
Installation problems
Each example will be represented by a graph. It is not necessary to understand every aspect of information within these graphs. Each one is used as an illustration of common soil moisture data patterns you might run into and how to extrapolate the most useful information possible from the patterns seen. Each graph will have a box in the upper right-hand side corner with the soil type and crop type so you have a better understanding of the variables at play.
All of the data provided was collected by data loggers, such as our ZL6 series, and uploaded to ZENTRA Cloud for remote viewing at the convenience of the user. All data sets are either from METER’s own instrumentation or are supplied by the data owner and are included with their permission.
Figure 1. ZL6 Basic data logger with data collected and stored within the ZENTRA Cloud platform
Effects of soil types
Figure 2. Water content and water potential measurements for a turf grass in loamy sand in wet conditions
In Figure 2 we see the data from an engineered loamy sand with a cover crop of turf grass. Our goal when executing our experiments in this example was to improve irrigation in turf grass. This grass had a fairly shallow root zone, the middle of which was about six cm deep and the bottom at about 10 cm. Over time, this example showed first relatively wet conditions to start through June and July, a fixed drying period condition in July and August, and drying until the cessation of water uptake in August and September.
This graph shows two soil moisture data types: volumetric water content on the left y-axis and matric potential, or water potential, on the right y-axis. Time is on the x-axis ranging from early summer to the start of fall. To understand what these data clusters can tell us, we must look at each data set individually.
In this chalk talk video, world-renowned soil physicist, Dr. Gaylon Campbell, discusses how many measurements researchers and growers need to characterize soil moisture at a field or research site. He explores the question: What is the relationship between the measurements that you make and the underlying value of water content in the field?
Presenter
Dr. Gaylon S. Campbell has been a research scientist and engineer at METER for 19 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.
We quite often get a question from customers about how many measurements we need to characterize soil moisture at a site. And so that’s what I want to talk about today. A number of years ago, I knew a man who was wanting to provide a business of making soil moisture measurements for the purpose of irrigation scheduling for farmers. And he came to me wondering how many samples he should take. He figured that he wanted a fairly simple way of determining soil moisture.
So, he thought he would go into the field and he would collect soil samples from the field, he would take them back to the laboratory, he would dry them and weigh them and dry them and determine water content. And he wondered how many samples would be required to determine the water content to provide this information for a farmer.
Now, that’s not so different from the kinds of information that are often required either for practical applications like irrigation scheduling, or for research purposes. We can see the broader applications of the question of, “what’s the relationship between the measurements that we take and the underlying value of water content in the field?”
Soil water content will vary from place to place.
I think you can see that the same thing would apply whether we were taking samples and bringing them back to the laboratory, or if we were putting in soil moisture sensors, and wanting to monitor soil moisture in the field. So, the first thing we need to talk about soil moisture is a random variable, we need some vocabulary for talking about that. Two terms are important: mean and standard deviation.
If we were to collect many samples of water content from a field, and we were to plot the number of samples versus the water content of the samples, we would obtain a relationship something like this. We would get the most samples around some central value, and that central value is the mean.
The standard deviation is a measure of the dispersion around the mean. 68% of the values that we take would be within plus or minus one standard deviation of that mean value. 95% would be within plus or minus two standard deviations of the mean value.
So, let’s say that we walked out here in the field, and we took a sample and made a measurement on it. And let’s say out of that sample, we determined the water content was 27%. Now let’s say that we assume or we know from some means that the standard deviation is 3%. Then, by these ideas, we would know that the mean value – the expected value for the water content – is or at least there would be a 95% probability that the mean value of the water content would be somewhere between 21% and 33%. The mean value plus two times the standard deviation and the mean value minus two times the standard deviation.
Now we may say, “well that’s not good enough. We need better values than that. So what do we do? We need to take more samples. And so we take a number of samples and average them. And so we can know what the result of averaging several samples is, with a simple relationship. The uncertainty in the average value that we get–the standard deviation of the mean–is the standard deviation, divided by the square root of the number of samples.
So let’s say that we went out in the field, and we took 100 samples. Then the standard deviation of the mean, would be our standard deviation that we assumed before, divided by the square root of 100. The square root of 100, of course, is 10. And so that would be 0.3%. If we determined a value of 28% for that mean of the 100 samples, then with 95% confidence, we can say that the water content is between 27.4 (2 standard deviations below the mean), and 28.6.
Now we’re getting closer then to our quest of determining the number of samples that we need to take. We start out with that equation that we just had that the standard deviation of the mean is equal to the standard deviation divided by the square root of the number of samples. We can rearrange that to say that the number of samples that we need is equal to the standard deviation divided by the standard deviation of the mean, and that value squared. So, the error that we normally would talk about in the measurement–if we’re again talking about 95% confidence–the error is half of the standard deviation of the mean.
This number of samples is two times the standard deviation over the air, and that all squared. So, if we work through a little problem with that, how many samples would we need in order to know the water content within 1%? If the standard deviation is 3%, the way we’ve assumed.
So, the standard deviation is 3%. The error value that we want to get to is 1%. We want to take enough samples so that we have 95% confidence that we’re within 1%. And so the number of samples is 2 times 3%, divided by the air, 1%, and that’s all squared. And that comes out to be 36 samples. Well, when we see that number, typically we get pretty discouraged. That’s more samples than we want to take. More samples probably than we can afford to take.
To see how that relates to reality, we did a little experiment. Here we have a soccer field out behind the METER (formerly Decagon) building. We went out and took one of our sensors, the GS3, and hooked it up to our little handheld device. And we set up three transects 20 meters long, parallel with each other and spaced a meter apart. We went along and took samples every meter along these transects. And I have a little video here that shows how that sampling went. The result of that sampling is shown in this next slide.
This slide shows the result of that set of measurements that we made. And you can see it looks about like you would expect it to. We’ve got some variation, we show a mean value and some variation around it. The transects, again, showed variability but seemed to be showing about the same result for each transect. We had 60 samples there.
The average water content that we computed was 38.6%. The standard deviation was not 3%, but 5%. So, the situation is even worse than we imagined with these calculations that we just did here. With a standard deviation of 5%, if we want to know the water content within 1%, we would need 100 samples to do that. And so even with our 60 samples, here, our standard deviation of the mean is 0.65%. And so our field water content is somewhere between 37.3 and 39.9.
Well, as I say that usually is discouraging when we get to that point and see how many samples are needed to make a set of measurements, but the thing is that quite often, the thing that we need to know is not an accurate value for the average water content. Quite often, what we want to know is how much the water content is changing. And that we can know in other ways, accurately enough, so that we don’t need that many samples.
That person that I started out talking about who was wanting to schedule irrigation would need to know water content with an accuracy of 1%. Well, at least with a precision of 1% or better. But that could be achieved much more readily by installing a sensor in situ, where you’re not dealing with the spatial variability in the soil and monitoring that.
Here I’ve shown some data that we took in the field with one of our 5TE sensors hooked up to a data logger. The water content is sampled every minute, it’s averaged over hour intervals, and the plot that you see here is a plot of the water content measured each hour. Then, you can see a period of time where the soil is drying, because the plants are using water. You can see an increase in water content that results from adding water through irrigation or rain. And then again, the water content decreasing as the water is used. And you see very little variation in those data.
Now if this guy that wanted to provide the irrigation scheduling service, had wanted to do this same thing by sampling, the next slide shows the result that he would have gotten if he had gone out every hour and taken one soil sample and plotted the result.
This is what he would have gotten; the blue lines that you see. And you can see that it’s about what you would expect: that the highest values are about 10% higher than the mean value, the lowest values are about 10% lower, and the standard deviation we said is about 5. So, that’s about what we would expect. But from these kinds of data, there’s no possibility that you could ever tell when you should irrigate.
In the next slide, I show the result that you would have gotten if you went out and took 10 samples every hour. And here you can see the pattern to some extent of when the drying and wetting occur, but there’s still an awful lot of variation.
The next slide shows the result of taking 100 samples every hour, a ridiculous thought, but again, there’s still some variation in it. It still doesn’t look anywhere near as good as the in situ sample. When we’re just looking for the changes in water content, the water storage, and water use, in situ measurements make a lot more sense than soil moisture sampling.
So, let me conclude just by a few points that I hope to have made in this. First of all, the soil water content varies from place to place; that’s inherent in nature. It’s something that we expect anytime we go out to measure soil moisture. We usually need to take an average of moisture at several locations in order to know what the water content of a field is, or an experimental site. We usually can’t afford to take enough measurements to really know what it is to have it within the accuracy that we would like to have it. And so we can go through this exercise that I’ve gone through here, we can determine the number that we need, but usually, our budget won’t allow us to put in that many and so we end up compromising to some extent.
What was the life of a scientist like before modern measurement techniques? In our latest podcast, Campbell Scientific’s Ed Swiatek and METER’s Dr. Gaylon Campbell discuss their association with three pioneers of environmental measurement.
Learn what it was like to practice science on the cutting edge. Discover the creative lengths they went to and what crazy things they cobbled together to get the measurements they needed.
Hypoxic floods can be catastrophic for river ecosystems, often leading to widespread fish kills or other alterations in fish community composition and behavior. Hypoxia in rivers is uncommon due to the high rates of re-aeration in flowing waters, and when it does occur, it’s typically associated with human pollution (high nutrient loading). However, in the East African Mara River, hypoxic flooding events are not caused by humans, but by hippos.
Hippopotamia in East African Mara River
Over the past ten years, Dr. Christopher Dutton, aquatic ecologist at Yale University, and other researchers have documented frequent hypoxic floods and fish kills in the Mara river system. He says, “Our research shows these floods are caused by the flushing of hippopotamus pools. There are over 4000 hippopotami in the Kenyan portion of the Mara River bringing in over 3500 kg of organic carbon into the aquatic ecosystem each day. Hippo pools within the three tributaries of the Mara become anoxic under low discharge, while increases in discharge flush out the hippo pools and carry a hypoxic pulse of water through the river downstream.”
Hippopotamus Pool on the Mara River
Dutton and his team aim to understand the drivers of variability in these hypoxic floods and how these floods are propagated downstream in order to predict how the frequency and intensity of these events will be influenced by climate and land use change.
Unexpected patterns in dissolved oxygen
Dutton says they first noticed unusual patterns in aquatic health while working on another project. “When we started working in Kenya, we were trying to determine the environmental flows needed to maintain proper ecosystem function. We sampled from up in the forest down through the protected areas in the Masaai Mara and the Serengeti. We found the traditional indicators of water quality started to get much worse in the protected areas. This was surprising to us because we assumed water flowing through a protected area would be getting cleaner. But after we collected enough data, we could see that dissolved oxygen was crashing on average every 12 days for 8 to 12 hours and then rebounding. We hadn’t seen that in other rivers. This drew us to wonder if it was being caused by the flushing of hippo pools.”
Dutton says hippopotamus pools are slack water areas on the main river channel where hippos gather throughout the day because they don’t like fast moving water. He explains, “Every day they lounge in the water because their skin is sensitive to UV and gets desiccated in the sun. But at night and in the early morning, they leave the pools, go to the grassland, and eat tons and tons of grass. Afterward, they go back to the pool to rest, sleep, and defecate. They defecate so much organic matter into the river, it alters aquatic metabolism in ways that haven’t yet been fully understood.”
Hippopotamia Gathering in a Hippopotamus Pool
Dutton wants to understand how the organic matter and inorganic nutrients the hippos bring in are altering the ecosystem and what’s causing variability in the degree of hypoxia.
What’s causing the variability?
Dutton thinks there are two likely drivers of hypoxia: time since hippo pools were flushed and the size of the rainfall driving the event. He says, “Because rainfall in the Mara region is highly localized within and among catchments, the biogeochemistry that causes hypoxia can vary among pools and tributaries. Understanding these dynamics requires fine scale spatial and temporal data on precipitation patterns across the catchment.”
Dutton is using ATMOS 41 weather stations and METER data loggers in three Mara sub catchments to monitor the intensity and frequency of rainfall during these episodic floods where rains can be highly variable in space. He’s also documenting hippo pool biogeochemistry along with discharge and dissolved oxygen (DO) response in the main stem and tributaries. He’s using a water quality sonde to monitor DO and turbidity. He says, “We’re trying to quantify these events in the various catchments because they are different geologically. One of them has more sulfur containing rocks which causes sulfates in the water. In a reducing environment, sulfates turn into hydrogen sulfide which is toxic to fish. So we’re trying to parse out what’s really killing the fish in these different catchments.”
He says the data show there is such high biochemical oxygen demand from the bottom of these pools, that when the organic waste and reduced compounds are flushed, they continue to suck oxygen out of the river as the waste moves downstream. This often causes fish kills in the river. He adds, “We’ve seen thousands of fish dead after one of these events. But interestingly, the next day, it’s like it never happened. There are no fish anywhere on the bank. They’ve already been consumed by hyena, vultures, marabou, storks, and even lions.”
Data collection challenges
Dutton says collecting precipitation data in East Africa has unusual challenges. He says, “One of our sites is close to a hyena den. They occasionally go and unplug wires. And one of our weather stations was taken by an elephant. I concreted it in, but the elephant took it and dropped it 100 meters away.”
The team avoids losing data by locating their measurement stations near tourist camps, where locals can watch over the equipment. Dutton says, “We build fences around each of the stations, and we concrete them into the ground, but our biggest strategy is putting the site close to a camp. The Kenyans that run the camps are excited to have a weather station nearby. They enjoy seeing the data and sharing it with their guests.”
The team builds fences around installations to protect them from hyenas and other animals
What’s the future of the research?
Dutton says the team is still working on collecting data, which is not always easy. He says, “This year, a 100-year flood occurred in the Mara which destroyed our water quality sonde. The water got so high the compression on the sonde popped out all the sensors. We lost two months of data. So we haven’t yet been able to look closely at the relationships between the rainfall, the different catchments, and these crashes, but that’s something we’ll do as soon as we can get to the data.”
He says this research is important because the Mara River system is still a natural river system essentially untouched by humans with much of its megafauna intact, which is rare. He adds, “The hippos are a very natural part of this river, and these processes we’re documenting help us understand how rivers may have functioned prior to the removal of larger megafauna. In the last 50 years, there has been large scale deforestation in the upper catchment. Some people speculate that this is causing more erratic flows. So what happens when the flows become more (or less) than normal?”
Dutton recently published a peer-reviewed paper on the detailed biogeochemistry of the hippo pools in Ecosystems Journal. You can read it here. And you can read the team’s first paper documenting these events published on nature.com here.
If you rely on soil moisture data to make decisions, understand treatment effects, or make predictions, then you need that data to be accurate and reliable. But even one small oversight, such as poor installation, can compromise accuracy by up to +/-10%. How can you ensure your data represent what’s really happening at your site?
Chris Chambers discusses how people unknowingly compromise their soil moisture data.
Best practices you need to know
Over the past 10 years, METER soil moisture expert Chris Chambers has pretty much seen it all. In this 30-minute webinar, he’ll discuss 6 common ways people unknowingly compromise their data and important best practices for higher-quality data that won’t cause you future headaches. Learn:
Are you choosing the right type of sensor or measurement for your particular needs?
Are you sampling in the right place?
Why you must understand your soil type
How to choose the right number of sensors to deal with variability
At what depths you should install sensors
Common installation mistakes and best practices
Soil-specific calibration considerations
How cable management can make or break a study
Factors impacting soil moisture you should always record as metadata
Choosing the right data management platform for your unique application
Soil moisture data analysis is often straightforward, but it can leave you scratching your head with more questions than answers. There’s no substitute for a little experience when looking at surprising soil moisture behavior.
Join Dr. Colin Campbell April 21st, 9am PDT as he looks at problematic and surprising soil moisture data.
Understand what’s happening at your site
METER soil scientist, Dr. Colin Campbell has spent nearly 20 years looking at problematic and surprising soil moisture data. In this 30-minute webinar, he discusses what to expect in different soil, environmental, and site situations and how to interpret that data effectively. Learn about:
Telltale sensor behavior in different soil types (coarse vs. fine, clay vs. sand)
Possible causes of smaller than expected changes in water content
Factors that may cause unexpected jumps and drops in the data
What happens to dielectric sensors when soil freezes and other odd phenomena
Surprising situations and how to interpret them
Undiagnosed problems that affect plant-available water or water movement
Why sensors in the same field or same profile don’t agree
Soil-applied herbicides are important for controlling weeds in many crops because they offer a broadened control spectrum and chemical diversity. But if soil-applied herbicides persist in the soil too long, there is risk for damage to susceptible rotational crops in succeeding years. Since herbicide degradation in the soil is highly dependent on water, imminent needs to reduce agricultural water use in the future could lead to limited herbicide degradation and a greater risk for carryover.
Some crops don’t have a wide variety of post-emergent herbicide options, so growers are dependent on soil-applied herbicides for weed control.
Recently Daniel Adamson and a research team at the University of Wyoming, under the guidance of Dr. Gustavo Sbatella, investigated the effects of soil-applied herbicides under limited irrigation conditions. They wanted to understand how limited irrigation affects the efficacy and carryover of soil-applied herbicides in Wyoming’s irrigated crop rotations. A two-part field study was undertaken by applying four soil-applied herbicides to dry beans and four soil-applied herbicides to corn.
Soil microbe activity matters
Describing his research site, Adamson says, “Wyoming is not a huge farming state but there’s a pocket of farm ground near the Powell/Cody area with a unique rotation. The main crop is sugar beets, and they also grow dry, edible beans, sunflowers and malt barley. Some of these crops don’t have a wide variety of post-emergent herbicide options, so growers are dependent on soil-applied herbicides for weed control. However, they need to balance weed control with timely dissipation so sensitive rotational crops won’t be injured.
Adamson says that soil-applied herbicides tend to be fairly long-lived in the soil, which is advantageous for weed control. Importantly, the herbicides dissipate through degradation by soil microbes, and soil microbes are highly influenced by how much water is in soil. When the soil is moist and warm, microbes are more active, and they degrade the herbicides faster. Thus, his team hypothesized that if future climate change effects led to limited availability of surface water for irrigation, these herbicides may not degrade as quickly and possibly injure crops planted successionally.
Assessing herbicide damage
During the first year, the research team applied three irrigation treatments to each crop: 100%, 85%, and 70% of crop evapotranspiration. Both crops and soil moisture were monitored using METER data loggers and soil moisture sensors. Adamson recalls, “The sensors were our means of tracking what was happening in the soil in terms of volumetric water content. Some of the areas were chronically dry, so the sensors enabled us to confirm that the treatments were applied correctly and should theoretically affect how the herbicides were performing. The volumetric soil water content of the three irrigation treatments averaged 24%, 18%, and 16% throughout the growing season, and crop yield decreased as irrigation was reduced.”
Over the course of the second year, the team collected soil samples at regular intervals following herbicide application. They analyzed the samples for herbicide level and used them to perform a greenhouse bioassay to determine crop response to residual herbicide. Also during the second year, crop response was evaluated in the field when sugar beet, sunflower, and dry bean or corn was planted over the original plots and assessed for herbicide damage.
The results of the experiment were surprising.
Hurdles and challenges
Adamson said timing was the major difficulty in terms of applying irrigation treatments. He said, “There were no differences in irrigation timing for the various treatments. The way we irrigated was not representative of a typical deficit irrigation strategy because we were tied to a sprinkler with other projects on it. So we irrigated based on when the full water treatment would normally be irrigated. Other treatments had smaller nozzles so the amount of water was physically reduced.”
Adamson said they also weren’t prepared to track how some of the herbicides would behave in the soil. “Some of the herbicides degrade into metabolites that are phytotoxic in the soil, and it was hard to analyze for all molecules that were plant active. So that was challenging.”
Surprising results
Adamson said the results of the experiment were surprising. He says, “It was a good result for growers because we found there were no differences in the fields, statistically or visually, between how the herbicides carried over in the really dry soil versus the normally irrigated soil. So that was surprising, but from a practical standpoint for farmers, it was important information. They now know if they do have to start applying less water, it isn’t something to be overly concerned about.”
More research is needed
Adamson says more work is needed in this area of research. He adds, “There’s a tremendous amount of information within the weed science community about what herbicides do in the soil and things that influence that. But relatively few studies look at changing irrigation rates in a practical sense. A lot of the current studies are done in rain-fed systems where the amount of rain changes (i.e., a normal year vs. a drought year). In irrigated systems, you might reduce the amount of water, but it’s not a drastic reduction like a rain-fed system might experience. There’s not a huge amount of research looking at how different irrigation rates affect herbicide management, so I do think it would be worth exploring in the future.”
Improving irrigation requires smart data gathering to help growers make better choices in the field. Measuring in situ creates high-resolution, temporal data enabling us to see clearly what’s happening over time—but only at a single point. Satellites show data across a large spatial scale but are hampered by revisit frequencies, clouds, and resolution limits.
Often we see information in a silo, looking at one type of data or another. The challenge to researchers is how to connect across these scales and combine the information to make better irrigation decisions. In this webinar, Dr. Colin Campbell explores the future of irrigation and research he’s been doing with collaborators at Brigham Young University. Learn:
How researchers are combining in situ, drone, and satellite measurements to extract key information
My name is Colin Campbell, I’m a research scientist here at METER group. Today we’re going to spend time doing a data deep dive. We’ll be looking at some data coming from my research site on the Wasatch Plateau at 10,000 feet (3000 meters) in the middle of the state of Utah.
Right now, I’m interested in looking at the weather up on the plateau. And as you see from these graphs, I’m looking at the wind speeds out in the middle of three different meadows that are a part of our experiment. At 10,000 feet right now, things are not that great. This is a picture I collected today. If you look very closely, there’s an ATMOS 41 all-in-one weather station. It includes a rain gauge. And down here is our ZENTRA ZL6 logger. It’s obviously been snowing and blowing pretty hard because we’ve got rime ice on this post going out several centimeters, probably 30 to 40 cm. This is a stick that tells us how deep the snow is up on top.
One of the things we run into when we analyze data is the credibility of the data and one day someone was really excited as they talked to me and said, “At my research site, the wind speed is over 30 meters per second.” Now, 30 meters per second is an extremely strong wind speed. If it were really blowing that hard there would be issues. For those of you who like English units, that’s over 60 miles an hour. So when you look at this data, you might get confused and think: Wow, the wind speed is really high up there. And from this picture, you also see the wind speed is very high.
But the instrument that’s making those measurements is the ATMOS 41. It’s a three-season weather station, so you can’t use it in snow. It’s essentially producing an error here at 30 meters per second. So I’ll have to chop out data like this anemometer data at the summit where the weather station is often encrusted with snow and ice. This is because when snow builds up on the sonic anemometer reflection device, sometimes it simply estimates the wrong wind speed. And that’s what you’re seeing here.
This is why it’s nice to have ZENTRA cloud. It consistently helps me see if there’s a problem with one of my sensors. In this case, it’s an issue with my wind speed sensors. One of the other things I love about ZENTRA Cloud is an update about what’s going on at my site. Clearly, battery use is important because if the batteries run low, I may need to make a site visit to replace them. However, one of the coolest things about the ZL6 data logger is that if the batteries run out, it’s not a problem because even though it stops sending data over the cellular network, it will keep saving data with the batteries it has left. It can keep going for several months.
I have a mix of data loggers up here, some old EM60G data loggers which have a different voltage range than these four ZL6 data loggers. Three of these ZL6s are located in tree islands. In all of the tree islands, we’ve collected enough snow so the systems are buried and we’re not getting much solar charging. The one at the summit collects the most snow, and since late December, there’s been a slow decline in battery use. It’s down. This is the actual voltage on the batteries. The battery percentage is around 75%. The data loggers in the two other islands are also losing battery but not as much. The snow is just about to the solar charger. There’s some charging during the day and then a decrease at night.
So I have the data right at my fingertips to figure out if I need to make a site visit. Are these data important enough to make sure the data loggers call in every day? If so, then I can decide whether to send someone in to change batteries or dig the weather stations out of the snow.
I also have the option to set up target ranges on this graph to alert me whether the battery voltage is below an acceptable level. If I turn these on, it will send me an email if there’s a problem. So these are a couple of things I love about ZENTRA cloud that help me experiment better. I thought I’d share them with you today. If you have questions you want to get in contact me with me, my email is [email protected]. Happy ZENTRA clouding.
Distribution of soil water in high-sand-content putting greens is a major concern for golf course superintendents. Gravel is commonly used as a component of a sand-based root zone to increase moisture retention, but due to gravity, the contour and slope of a putting green significantly affect moisture retention. Coarse-textured soils often become too dry in higher elevations and too wet in lower elevations. This hampers performance and increases water and labor inputs.
The contour of a putting green affects moisture retention
To fix this problem, Thomas Green, a graduate student at Michigan State University, and a team of researchers are assessing the impact of gravel layer particle size and slope on soil water content in a variable-depth, high-sand content root zone. He says, “Due to lack of published research and the USGA’s wide-ranged specification for gravel selection based on the root zone material, determining the optimal bridging, filtering, permeability, and uniformity factors capable of increasing root zone soil moisture uniformity is critical.”
Validating previous turfgrass experiments
Green and his team set out to validate previous turf experiments done at MSU which showed that increasing the particle size difference between the gravel and root zone (sand) layers, in combination with a variable-depth root zone (shallower at the slope apex, deeper at the slope base) would improve soil moisture uniformity.
He says, “We wanted to retain this moisture consistently throughout the whole profile over the entire green. Our experiments decreased the root zone depth in relation to our gravel layer. So at the peak, we reduced the root zone, and in the valleys, we increased the root zone to eliminate wet spots where water accumulates.”
Water potential is the key
Green says the goal was to manipulate the “head” (or water potential) in the peaks and valleys. He explains, “We tested particle size differences between a high-sand, root-zone mix and the gravel layer. Past studies show that the greater the difference between the root zone particle size and the gravel particle size, the more water is retained at the interface. Essentially in the valleys, we increased the depth of the sand layer to create (in physics terms) a large head that forced more water to drain. At the top of the green, we did the opposite and made a thin layer of sand so more water was available. Basically, it was all about manipulating the water potential or tension on the water to retain the right level of moisture.”
The diagrams below illustrate the physics of how this works:
Figure 1. Diagram of sand and gravel layers in a putting green
In Figure 1, the gravel provides a textural barrier where pores must be saturated for water to move into the gravel.
Figure 2. Closeup of tall sand layer in the valley
Figure 2 is a closeup of the tall layer. Cohesion of water molecules together and adhesion to soil particles ties water together and exerts downward force or tension on water at the top of the profile. The larger the height from the top of the profile to the saturated surface, the more tension on the water (lower water potential).
Figure 3. Closeup of short sand layer at the peak
Figure 3 is a closeup of the short sand layer. Shorter height above the saturation zone reduces the tension in the top layer of soil (higher water potential). Thus, the high part of the green with the thinnest sand layer will have less tension and more water than the thick layer in the lower part of the green. To visualize what soil tension is like, think of people hanging on people (Figure 4). The more people there are, the more “pull” will be exerted on the top person.
Figure 4. Soil tension is like people hanging on people. The more people, the more pull exerted on the top person.
Eliminating edge effects
Green used METER soil moisture and temperature sensors at three different depths along with METER data loggers to validate that the water was in the right place. He inserted the sensors into an enormous box that mimicked a putting green. “I created a 4-ft x 4-ft module to simulate a sloping green. I had to figure out how large it should be to eliminate edge effects (water preferentially moving toward the container edges). The soil moisture sensor helped me determine just how large this box had to be to get accurate measurements.”
Green says the surface measurements were the most important, “I was interested in that top depth because in a golf setting, that’s where you need to control moisture. In a putting green, turfgrass roots aren’t very deep because the grass is so short.”
USGA has adopted the new method
Green says the results turned out as expected. “We expected that if we increased the gravel particle size difference and reduced sand depth, we would see increased water retention in our root zone profile, and that’s exactly what happened. The great thing is the USGA has now somewhat adopted these new recommendations. More and more golf courses are going to this construction method. It’s good for the industry because they’re conserving water.”
In the future, Green says he’d like to explore some research done by F.W. Taylor in the early 1900s. Taylor thought about using a vertical sand or gravel strip contoured on a slope to form a barrier to water moving downhill instead of plastic or polyethylene. This idea is illustrated beautifully in the classic 1950s era film by Dr. Walter Gardner.
