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?
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.
In his latest chalk talk, Dr. Colin Campbell, environmental scientist at METER Group, teaches how to model vertical variation in temperature and how to estimate sensible heat flux.
Video transcript
Hello, everyone. My name is Dr. Colin Campbell, and I’m a senior research scientist here at METER Group. For today’s chalk talk, we’ll be talking about modeling vertical variation in temperature. In Figure 1, I’ve put together a graph that shows the maximum and minimum temperature with height and depth in the soil at some snapshot in time at a particular place.
Figure 1. Maximum and minimum temperature with height and depth at a snapshot in time, in a particular place
It’s interesting to note that the change in temperature with depth in the soil is much faster than the change in temperature with height, whether we’re talking about a maximum or minimum. And the reason is that even though air is a good insulator, it also mixes really well. And that mixing is caused by eddies. And there’s a little more to that story. It depends specifically on surface heating by the sun through radiation and the cover type, whether it’s plants, rocks, boulders, straight soil, snow, or wind.
Equation 1
If we were going to model that, we would start by writing an equation (Equation 1) where a temperature at sun height, Z, above the surface (see variables noted in Figure 1), is equal to an aerodynamic surface temperature, T0, minus the sensible heat flux, divided by 0.4 times rho, CP, which is the volume specific heat of the air, times a variable called u*, which is the friction velocity. We multiply all that by the logarithm of z, the height above the surface minus d, which is the zero plane displacement, divided by z h, which is a roughness parameter. You might notice up here in the list of variables, that the zero plane displacement is 0.6 times H. H is the canopy height in meters. The rough roughness parameter can be estimated as 0.02 times the canopy height or times H. Now we have an equation that will help us model temperature with height.
However, often we don’t know things like H, our sensible heat flux, and u*, our friction velocity. One of the things that we notice about this equation is that it’s set up somewhat like a linear equation. As you know, an example of a linear equation is something like Equation 2.
Equation 2
Figure 1 isn’t written quite that way, but if we look closely at the example below (Equation 3), this value could be our b, and this value our m, and this value could be our x. And if we do that, we actually can get some use out of graphing temperature with height.
Equation 3
So we went out one day and measured this with a METER Group set of environmental sensors set up at certain heights above the surface. Here we placed sensors at 0.2 m, 0.4 m, 0.8 m, and 1.6 m above the ground.
Table 1
To visualize this, in Figure 2 we graphed height on the y axis and temperature on the x axis, similar to the graph in Figure 1.
Figure 2. Graph showing the relationship between height and temperature
We know from Equation 1 that the axes for temperature and height should be switched because temperature is the dependent variable, and height is the independent variable. So if we switch axes it would look like the graph in Figure 3.
Figure 3. Graph showing the relationship between height and temperature where temperature is the dependent variable and height is the independent variable.
Figure 3 is graphed with the independent variable on the x axis and height on the y axis. If we fit this curve with today’s calculators, it would be fairly easy to get a curve that would fit that. But since it’s a linear equation, we can take the temperature data from Table 1 and the In ((Z-d)/ZH) data from Table 1 and graph them together.
Figure 4. Relationship between temperature and In Z-d/zh
Figure 4 is a graph that shows what happens when we do that. Notice that, just like we suggested, it creates a linear equation (Equation 4).
Equation 4
We learned in Figure 1 the B value was equal to t0 (our aerodynamic surface temperature). Since we know our surface temperature is 34.5 degrees, we can estimate what the temperature is down here at the surface, even though we only measured down 0.2 m.
We also know from Equation 4 that our M value is equal to -2.01. And if we look at Equation 1, our slope value is below.
Equation 5 (the slope value from Equation 1)
So we can write
Equation 6
How to estimate sensible heat flux
Now, if we were interested in the sensible heat flux, which we often are, we can simply rearrange this equation to be
Equation 7
And in Figure 1, I forgot to give you this value, but for an air temperature of 20 degrees celsius,
Equation 8
And then finally, a typical unit for friction velocity, which should be measured in the field over the specific canopy you are in, is about 0.2 meters per second.
Equation 9
So if we did this calculation, we would learned that there’s about 193 watts per meter squared of sensible heat flux coming off that surface.
Equation 10
So if we can measure temperature at a few heights, we can estimate what the heat flux is coming off the surface assuming we know something about our canopy. Learn more about measuring and modeling environmental parameters at metergroup.com/environment. If you have any questions feel free to email Dr. Campbell at [email protected].
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
Irrigation management: Why it’s easier than you think
Years ago, we received an irrigation management call from a couple of scientists, Drs. Bryan Hopkins and Neil Hansen, about the sports turfgrass they were growing in cooperation with the Certified Sports Field Managers at Brigham Young University (BYU) and their turfgrass research and education programs. They wanted to optimize performance through challenging situations, such as irrigation controller failure and more. Together, we began intensively examining the water in the root zone.
BYU researchers are zeroing in on irrigation management best practices leading to better outcomes that are easier to achieve.
As we gathered irrigation and performance data over time, we discovered new critical best practices for managing irrigation in turfgrass and other crops, including measuring “soil water potential”. We combined soil water potential sensors with traditional soil water content sensors to reduce the effort it took to keep the grass performance high while saving water costs and reducing disease potential and poor aeration. We also reduced fertilization costs by minimizing leaching losses out of the root zone due to overwatering.
Supercharge yield, quality and profit in any crop with soil moisture-led irrigation management
This article uses turfgrass and potatoes to show how to irrigate using both water potential and water content sensors, but these best practices apply to any type of crop grown by irrigation scientists, agronomists, crop consultants, outdoor growers, or greenhouse growers. By adding water potential sensors to his water content sensors, one Idaho potato grower cut his water use by 38%. This reduced his cost of water (pumping costs) per 100 lbs. of potatoes, saving him $13,000 in one year.But that’s not even the best part. His yield increased by 8% and he improved his crop quality—the rot he typically sees virtually disappeared.
What is soil water potential?
In simple terms, soil water potential is a measure of the energy state of water in the soil. It has a complicated scientific definition, but you don’t have to understand what soil water potential is to use it effectively. Think of it as a type of plant thermometer that indicates “plant comfort”—just as a human thermometer indicates human comfort (and health). Here’s an analogy that explains the concept of soil water potential in terms of optimizing irrigation.
