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.
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.
Learn how to identify and understand behavior of soil moisture sensors in different soil types
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.
There’s a lot to consider when collecting soil moisture measurements.
Get your soil moisture questions answered in our Office Hours series.
Join Environment Support Manager, Chris Chambers, and Director of Science Outreach, Leo Rivera, as they discuss submitted questions all about getting the best soil moisture measurements.
In the full episode, they discuss:
How difficult is the calibration of dielectric sensors?
How does soilless media affect the operation of dielectric sensors?
How much can organic soil amendments influence soil moisture?
Is it possible to determine the soil hydraulic properties from soil water content?
Why volumetric water content instead of gravimetric water content?
What is the best way to correct for the temperature sensitivity of sensors?
Listen to Dr. Colin Campbell, WSU environmental biophysics professor, as he discusses how to calculate the angle of the sun, or solar zenith angle.
Transcription
Hi, I’m Dr. Colin Campbell. And this is a METER Chalk Talk. A couple of years ago, I was heading out into the backcountry and we wanted to figure out what kind of gear we should take along. A friend suggested we should just check the wind chill factor. But when I looked into it, we found out that it doesn’t even consider solar radiation in that calculation. Our exchange of energy in the environment is highly dependent on radiation, particularly solar radiation. And today, we’re going to talk a little bit more about that. Now the first thing to know about solar radiation is where the sun is in the sky. In fact, our absorbed radiation really depends on it. Interestingly, it’s one of the few things in life you can really count on.
With a few equations, we can figure out where the sun is in the sky at any time of the day. And I’m going to take you through some of these equations, one of the things I want you to know first is, they’re a little complicated, so don’t get stressed. In fact, if you just want to stop the video at a certain point. And check out these equations for a moment and write them down. That’s just fine. Now let’s just jump into it.
So here on my screen, I’m showing a graph of where the sun might be, at any point in a day if you were standing on the equator. Now in the middle, I’m going to draw this blue line across there, that is at the equinox. Now at the two solstices the sun might be here tracking across the sky, or here. And of course, this diagram is really showing kind of a fisheye picture of where that sun might be. There are two ways to describe where the sun is. One is a zenith angle. The zenith angle has a symbol, we call psi. In fact, the angle to the Earth’s surface from the perpendicular or normal, so this would be that zenith angle. Now there’s another angle we might be interested in, it’s called the Azmuth angle. But for our purposes of today, I just want to focus on this zenith angle because it’s the most important as we consider the radiation impact in an object that we’re interested in.
So to calculate the zenith angle, we’re going to go down and discuss the equation where this right here is zenith angle. And this here is the equation that we use to calculate that. Now you recognize the sines and cosines. And there’s just a couple other things in here. Of course, we’ve got t, which is time. And then a few other variables, phi. This is the latitude. Delta, this we call the solar declination, and finally, t zero, this is solar noon. Now before we get too crazy and worried about this equation, all we have to do is put in a few things into here, and we’ll be able to calculate that. So the first thing we need to know is the time of day.
Then we need to know the day of year. Now we actually call this a special name. This is called a Julian day. And it starts counting from January 1. The other things we need to know is of course, latitude, and longitude. And I’ll get to why in just a moment. The first parameter we’re going to try to find is called the solar declination. The solar declination equation looks pretty crazy. And anytime you see an equation like this in a book or something, the first assumption you should make is this is an empirical equation. As I look out on the internet and study other materials, I find that these equations actually are fairly common out there. And this isn’t exactly the way you see it in every piece of literature. But let me talk you through it here.
Really, there’s only one thing we need to know. It is the Julian day and we can go on the internet and calculate these a lot of programs just have those hard coded in like Excel. And all we need to do is just put that Julian day in for each of these values-here into here, and then we can eventually calculate the delta value. And then we can go put it back in this equation. So as long as we know the declination here, this is just the latitude. Let’s say my latitude is about 47 degrees. We just put that right here. All we need to know now is this t zero or solar noon. So what did we do for that?
Well, solar noon is calculated like this: t zero is equal to 12. That’s solar noon, and then we change it for wherever we are with respect to entered Meridian. And we call that the LC longitudinal correction, and then we also subtract off this equation of time t. We can start with the equation of time here. That’s this equation right here. And that’s not very small. In fact, not only is it not small, but it has a whole bunch of f’s in it. You can see f, here, this two times f, this is three times f, this is four times f. And now in the cosine or sines, then we have cosines here. So what is that?
Well, f is another one of these little bit long equations it is two point, or sorry, 279.575 plus 0.98565 times the Julian day. Now, if you get that, you just plug it back in here. And you can calculate your equation of time. And this is a number much smaller than one that you can plug in to this equation right here. Now, what about the longitudinal correction?
Well, the longitudinal correction Lc, that’s pretty straightforward. It’s essentially for every degree east of this of the standard meridian, you add 115. So for example, where I live, I’m at one 117.2 degrees, longitude, our standard meridian 120 degrees. And so the difference is, we’re east of that 2.8 degrees, and therefore the longitudinal correction, LC is just 2.8 over 15, or equal to 0.19h. So essentially, what I do is take that right there, and plug it in up here for the longitudinal correction. So essentially, we take 12, and we subtract off the longitudinal correction, and then with our equation of time, we get this value and eventually have t zero.
So what does all this mean? What does it sum up to? Well, there’s a lot of numbers in here. But if we go back to our initial equation, all we’re going to need to do now is simply this. We have our solar noon, we plug our time in. And then we use our solar declination here that we calculated on the first part of this discussion, our latitude here, and then suddenly, we’re able to calculate the Zenith Angle. And I’m going to try to link to a little calculation spreadsheet I did in Excel onto the sheet or onto the this video and then you can go ahead and look at that, how it’s done, and do your own calculations. For more content like this, check out our YouTube channel or head over to metergroup.com. Thanks for watching a METER Chalk Talk.
Like a silent battle cry, plants call out to signal they are under siege as a warning to other plants and to call in reinforcements to fend off the invasion.
Listen to research on pathogen infection, water stress, and how plants communicate and defend themselves.
How does this communication work? What else are plants doing to protect themselves from disease and predators alike? In our latest podcast, Natalie Aguirre, a PhD candidate and plant physiology and chemical ecology researcher at Texas A&M University, dives into her research on pathogen infection, water stress, and how plants communicate and defend themselves.
Natalie Aguirre graduated with a degree in biology from Pepperdine University, where she completed an honors thesis conducting research on the interaction of drought stress and pathogen infection in chaparral shrubs. She then spent a year as a Fulbright scholar in Spain, studying the effect of water stress on Dutch Elm Disease. Most recently, Natalie worked for the Everglades Foundation, creating educational programs and materials about the Florida Everglades.
The views and opinions expressed in the podcast and on this posting are those of the individual speakers or authors and do not necessarily reflect or represent the views and opinions held by METER.
Abiotic stress in plants: How to assess it the right way
As a plant researcher, you need to effectively assess crop performance, whether you’re selecting the best variety, trying to understand abiotic stress tolerance, studying disease resistance, or determining climate resilience. But if you’re only measuring weather data, you might be missing key performance indicators. Water potential is underutilized by plant researchers in abiotic stress studies even though it is the only way to assess true drought conditions when determining drought tolerance in plants. Learn what water potential is and how it can improve the quality of your plant study.
Soil directly impacts plant growth via nutrient availability, disease pressure, root growth, and water availability.
Quantitative genetics in plant breeding: why you need better data
If you’ve studied plant populations, you’re probably familiar with the simplified equation in Figure 1 that represents how we think about the impact of genetics and the environment on observable phenotypes.
Figure 1. Phenotype = Genotype + Environment
This equation breaks down the observed phenotype (plant height, yield, kernel color, etc.) into the effects from the genotype (the plants underlying genetics) and the effects of the environment (rainfall, average daily temperature, etc.). You can see from this equation that the quality of your study directly depends on the kind of environmental data you collect. Thus, if you’re not measuring the right type of data, the accuracy of your entire study can be compromised.
Water potential: the secret to understanding water stress in plants
Drought studies are notoriously difficult to replicate, quantify, or even design. That’s because there is nothing predictable about drought timing, intensity, or duration, and it’s difficult to make comparisons across sites with different soil types. We also know that looking at precipitation alone, or even volumetric water content, doesn’t adequately describe the drought conditions that are occurring in the soil.
Figure 2. The TEROS 21 is a field sensor used to measure soil water potential
Soil water potential is an essential tool for quantifying drought stress in plant research because it allows you to make quantitative assessments about drought and provides an easy way to compare those results across field sites and over time. Let’s take a closer look to see why.
In our latest podcast, Dr. Bruce Bugbee, Professor of Crop Physiology and Director of the Crop Physiology Lab at Utah State University, discusses his space farming research and what we earthlings can learn from space farming techniques.
International space station
Find out what happens to plants in a zero-gravity environment and how scientists overcome the particular challenges of deploying measurement sensors in space. He also shares his research on the efficacy of LED lights for indoor growing.
Dr. Bruce Bugbee is a Professor of Crop Physiology, Director of the Crop Physiology Laboratory at Utah State University, and the President of Apogee Instruments.
His work includes collaborating with NASA to develop closed life-support systems for long-term space missions. He’s been involved with the development of crop-growing systems for future life on the Moon, in addition to in-orbit or in-space shuttles. He’s worked on projects for Mars farming, including the use of fiber optics for indoor lighting, And as a part of this research, he was involved in the creation of the NASA Space Technology Research Institute’s Center for the Utilization of Biological Engineering in Space (or CUBES).
Dr. Bugbee also has long been a critic of the use of indoor farming as a means of solving food shortages, due to the large amount of electricity needed to provide light for photosynthesis. His recent work in this area has included studies into the efficacy of LED lights for indoor growing. (Credit: Wikipedia)
The views and opinions expressed in the podcast and on this posting are those of the individual speakers or authors and do not necessarily reflect or represent the views and opinions held by METER.
Advances in sensor technology and software now make it easy to understand what’s happening in your soil, but don’t get stuck thinking that only measuring soil water content will tell you what you need to know.
Water content is only one side of a critical two-sided coin. To understand when to water, plant-water stress, or how to characterize drought, you also need to measure water potential.
Better data. Better answers.
Soil water potential is a crucial measurement for optimizing yield and stewarding the environment because it’s a direct indicator of the availability of water for biological processes. If you’re not measuring it, you’re likely getting the wrong answer to your soil moisture questions. Water potential can also help you predict if soil water will move, and where it’s going to go. Join METER soil physicist, Dr. Doug Cobos, as he teaches the basics of this critical measurement. Learn:
What is water potential?
Why water potential isn’t as confusing as it’s made out to be
Common misconceptions about soil water content and water potential
Dr. Cobos is a Research Scientist and the Director of Research and Development at METER. He also holds an adjunct appointment in the Department of Crop and Soil Sciences at Washington State University where he co-teaches Environmental Biophysics. Doug’s Masters Degree from Texas A&M and Ph.D. from the University of Minnesota focused on field-scale fluxes of CO2 and mercury, respectively. Doug was hired at METER to be the Lead Engineer in charge of designing the Thermal and Electrical Conductivity Probe (TECP) that flew to Mars aboard NASA’s 2008 Phoenix Scout Lander. His current research is centered on instrumentation development for soil and plant sciences.
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.
When you irrigate in a greenhouse or growth chamber, you need to get the most out of your substrate so you can maximize the yield and quality of your product.
But if you’re lifting a pot to gauge how much water is in the substrate, it’s going to be difficult—if not impossible—to achieve your goals. To complicate matters, soil substrates and potting mixes are some of the most challenging media in which to get the water exactly right.
Without accurate measurements or the right measurements, you’ll be blind to what your plants are really experiencing. And that’s a problem, because irrigating incorrectly will reduce yield, derail the quality of your product, deprive the roots of oxygen, and increase the risk of disease.
Supercharge yield, quality—and profit
At METER, we’ve been measuring soil moisture for over 40 years. Join Dr. Gaylon Campbell, founder, soil physicist, and one of the world’s foremost authorities on soil, plant, and atmospheric measurements, for a series of irrigation webinars designed to help you correctly control your crop environment to achieve maximum results. In this 30-minute webinar, learn:
Why substrates hold water differently than normal soil
How the properties of different substrates and potting mixes compare
Why it’s difficult if not impossible to irrigate correctly without accurately measuring the amount of water in the substrate
The fundamentals of measuring soil moisture: specifically water content and electrical conductivity
How measuring soil moisture helps you get the most out of the substrate you choose, so you can improve your product
Easy tools you can use to measure soil water in a greenhouse or growth chamber to maximize yields and minimize inputs
