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Nov 11

Using Pedotransfer Functions to Predict Soil Properties

In this latest chalk talk video, METER soil scientist and application expert, Leo Rivera, discusses the use of pedotransfer functions (PTFs) for predicting soil properties such as hydraulic conductivity and field capacity.

He explains that while direct measurements are ideal, PTFs can provide rapid, cost-effective alternatives. PTFs use soil texture, particle size distribution, and bulk density as inputs, with accuracy depending on the quality of the database and the input data. There are limitations, such as PTFs not accounting for soil structure and organic matter, which can significantly impact hydraulic conductivity, thus, Rivera recommends using PTFs judiciously. PTFs can be especially helpful for large-scale assessments, and he suggests seeking tools that incorporate more parameters for improved accuracy.

Learn more:

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Video transcript

0:00
Hi. My name is Leo Rivera, and this is a METER Chalk Talk.

0:11
Today I want to talk about a topic that I get asked a lot about, and it has to do with how to predict some of these soil properties that we typically measure. So we often have used tools to measure things like soil hydraulic conductivity, retention curves, predicting field capacity and permanent wilting point. And ideally, that’s the best way we can do it, is by making those measurements. But that’s not always an option. So there are times where we need to be able to more rapidly assess soil properties, where we don’t have the budget to assess some of these more expensive soil properties to measure. And there are tools available to make these predictions rather than making the measurements. And I want to talk about those today to make sure that you understand the capabilities of these tools, that they’re out there, but also understand the limitations. And so the primary tool I’m going to talk about is a pedotransfer function, or a PTF.

So typically, with a pedotransfer function, we’re using something like soil texture. So for example, I have here an image of the soil triangle, and I can say I have a clay loam soil. So I’ve got my clay loam right here. And my goal is to predict field capacity. My goal is to predict field capacity for minus 33 kPa, and permanent wilting point, which is minus 1500 kPa, typically to make those measurements, it’s going to take several days to several months, depending on how you choose to make that measurement. But that’s not always an option. So we can use a petal transfer function to take that property, like soil texture, and predict those values. And pedotransfer functions also can be used to predict things like hydraulic conductivity. So you can see an example of a hydraulic conductivity graph here as well. So a pedotransfer function can be a really powerful tool to predict some of these properties that are typically take more time or more expensive to measure, and maybe you don’t have the time to make those measurements. So it’s really important to understand how a pedotransfer function works before utilizing this tool.

So pedotransfer functions utilize databases, whether it’s soil survey or other generated databases, where you have a lot of soil data, and you have all of these data, like texture, density, where they’ve already measured, hydraulic conductivity and some of these other soil properties. It then takes your input and utilizes that database to best predict what those values are that you’re trying to assess. So if I’m trying to predict, for example, field capacity, you can input parameters like soil texture, particle size distribution and bulk density. And you can do this in various orders. You can use soil texture on its own. You can use particle size distribution on its own, or you can combine particle size distribution and soil bulk density together to make these predictions. And it’s going to go into that database and try and make its best prediction based on the data available on that database.

Your pedotransfer function is only going to be as strong as the data that’s in the database, but it’s also only going to be as powerful as how good of an input you give it to predict these these values. So if we’re using soil texture on its own, as you can imagine, if I was predicting a clay as a soil texture, we’ll just use that as our example. If we look at our clay soil on the on the soil texture triangle, that is a huge range of combinations of sand, silt and clay fraction. So as you can imagine, that’s a pretty broad area that you’re trying to predict from, and there’s a higher potential for error in that prediction. Now, if we were to refine that and use the particle size distribution, so if we knew our exact sand, silt and clay fraction that we were trying to predict, we could then refine our predictions. We’re going to get rid of that circle, and we’re going to refine our prediction, saying, our soil has exactly this amount of sand, silt and clay. And that’s going to refine how the pedotransfer function is pulling those data from in the database to predict those values.

But as we know, soil texture on its own and particle size on its own only tells part of the story. So we can further refine that by adding our bulk density into that prediction, which is going to help improve the prediction of either field capacity or hydraulic conductivity. And in some areas, that should be fine. And so as long as you’re happy with that level of error, that’s fine. But, especially when we’re looking at things like hydraulic conductivity, we know there are other factors that play a big role in hydraulic conductivity, such as soil structure. So ideally, we would be including structure in our prediction, and organic matter in our prediction, because we all we know that these play a significant role in how soil transmits water, but most pedotransfer function, tools like Rosetta and Soil View don’t really take these into account.

So when you’re looking at these values, especially trying to assess measurements like hydraulic conductivity, you need to understand these limitations when using these tools. Now, there are other databases and pedotransfer function tools out there that are doing a better job of taking some of these into account. And if you’re going to use those tools, you want to try to make sure, if you’re if you’re really concerned with the accuracy of your values that you’re using pedotransfer function models that take more of these parameters into account. So the more inputs you can have into your prediction, the more accurate you’re likely going to come out with your predictions of these factors.

I just wanted to cover some of the basics on pedotransfer functions, and if you’re going to use them. They’re really powerful tools if we need to use them, especially when we’re trying to characterize large areas. It’s not always feasible to make measurements across these large areas, and they can help give us a little more data to work off of, rather than just the measurements on their own to try and characterize what’s happening across the large watershed, for example. But we need to understand how our inputs can affect the accuracy of those predictions. If you want to learn more about this or other topics, please visit us on our website www.metergroup.com or on our YouTube channel under meter talk talks and thank you for watching.

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Jun 3

Why We Live Or Die By Soil Health

In our latest podcast, Dr. Cristine Morgan, one of the US’s premier soil scientists and Chief Scientific Officer at the Soil Health Institute shares her views on soil health: what it is, how to quantify it, what’s the payoff, and why it’s so critical to our success as a society.

“Our soils support 95 percent of all food production, and by 2060, our soils will be asked to give us as much food as we have consumed in the last 500 years.” (Credit: https://livingsoilfilm.com/)

Her thoughts? “We all live or die by soil, literally. We just have to remind people that it’s about quality of life. It’s about the food that you eat. It’s about the safety and welfare of your children.” 

LISTEN NOW—>

Notes

Dr. Cristine Morgan is the Chief Scientific Officer at the Soil Health Institute in North Carolina. Learn more about the Soil Health Institute on their website. 

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

Disclaimer

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.

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Oct 19

Water content + water potential—better together

New soil moisture master class

Everybody measures soil water content because it’s easy. But if you’re only measuring water content, you may be blind to what your plants are really experiencing. To understand when to water or plant water stress, you need to measure both water content AND water potential.

Learn more in our soil moisture master class, “Secrets of water in soil“. Six short videos teach you everything you need to know about soil water content and soil water potential—and why you should measure them together. Plus, master the basics of soil hydraulic conductivity.

View master class—>

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Sep 8

How to calculate growing degree days (or thermal time)

If you’re not using an accurate weather station at your field site to gather data for growing degree day (GDD) or thermal time calculations, you should start now. 

Image of increased yield

GDD predictions save you hours of scouting time and can increase yield because they’re a scientific way to know the best time for insect/disease control measures. In this chalk talk, Dr. Colin Campbell explains the concept of thermal time (or growing degree days) and shows two different ways to calculate it.

Watch the video

 

Video transcript

Hello, my name is Dr. Colin Campbell. I’m a senior research scientist here at METER Group. Today, we’re going to give a brief primer on thermal time. When I talked to some of my colleagues about that, they mentioned that thermal time (or growing degree days) is really just a way to match a plant’s clock with our clock. It helps us understand what’s happening with the plant, and we can predict things like emergence, maturity, etc. And the way we do this is through this equation that is pretty simple (Equation 1). 

Image of the equation for calculating thermal time
Equation 1

We can sum thermal time (Tn) by taking the summation of day one to day n of the average temperature (meaning T max plus T min divided by two), minus a base temperature (Tbase), and then multiply by time step (delta t). And in this case, our time step is just one day. 

So the whole analysis is simply the average temperature minus a base temperature. We get that value each day. And then we keep summing until it reaches a value that tells us that we’ve progressed from one stage to another stage. 

A good example of this is wheat. When I was young, I did an experiment on this in biology. The idea was that for emergence, the wheat plant needs 78 day degrees from planting to emergence. So I used Equation 1, and when I had summed enough day degrees, I knew the wheat was moving from the planting stage to a post emergent stage. I went out and measured the wheat and it actually matched up well. Not every wheat plant emerged at that point, but the average was quite close. 

So what does that mean in terms of graphical data? I wanted to show you what this equation actually looked like and then plant a seed in your mind for our next discussion, which will be, how good is this analysis? If you think about modern technology, like the ATMOS 41 weather station, you can get temperature measurements that are every five minutes or even every one minute. So wouldn’t it be better if we collected our thermal time information with this equation (Equation 2)?

Image of equation number two for finding thermal time
Equation 2

We can take the sum over each day, like we did in Equation 1, but instead we take the integral of temperature at a small time step T(t) (like five minutes) minus the base temperature (Tb) and then just integrate this across the day. We’re going to learn about that in my next chalk talk. But for now, let’s go to this graph (Figure 1). 

Image showing temperature vs. time over twenty four hours
Figure 1. Temperature vs. time over 24 hours

In Figure 1, we have temperature on the y axis and time on the x axis. The total time is 24 hours. This is our daily step, where we’re collecting this information about thermal time. And here are all the parameters from the equation: the maximum temperature (Tmax), the minimum temperature (Tmin), and the average temperature (Tave). And then this is a base temperature (Tbase). And to familiarize you with what we’re talking about, Tbase is the temperature below which progress is not made in the development of this plant. The progress is not reversed, meaning if it’s below the base temperature, the plant is not reversing its development, but it just doesn’t progress. 

The black line is what I’ve drawn as a typical diurnal temperature swing. So it’s going from a minimum in the early morning up to a maximum sometime in the afternoon. And I’ve tried to compare these two approaches. One one side, we have the average temperature and the base temperature. This rectangle is our thermal time for that day. But the question is, with all our temperature data (like from the ATMOS 41) where we have pretty small time increments, could we instead just integrate over the day and then collect all the information on thermal time that is below this black line (the actual temperature, and of course, we subtract out the base temperature). How much difference does that make compared to this here? And what are the implications of not being able to measure our temperature terribly accurately? We’re going to talk about that in our next discussion, and I look forward to seeing you then.

Take our Soil Moisture Master Class

Six short videos teach you everything you need to know about soil water content and soil water potential—and why you should measure them together.  Plus, master the basics of soil hydraulic conductivity.

Watch it now—>

Learn more

Automate your growing degree day (GDD) models using the ATMOS 41 weather station and ZENTRA Cloud software.

See ATMOS 41 weather station performance data.

Request a ZENTRA Cloud demo—>

Download the researcher’s complete guide to soil moisture—>

Download the “Complete guide to irrigation management”—>

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Jan 8

Best of 2019: Environmental Biophysics

In case you missed them, here are our most popular educational webinars of 2019. Watch any or all of them at your convenience.

Lab vs. In Situ Water Characteristic Curves

Image of a researcher running hand across wheat

Researcher Running A Hand Across Wheat

Lab-produced soil water retention curves can be paired with information from in situ moisture release curves for deeper insight into real-world variability.

Watch it here—>

Hydrology 101: The Science Behind the SATURO Infiltrometer

Image of a fallen tree being supported off the ground by many other trees

A Forest With Fallen Trees

Dr. Gaylon S. Campbell teaches the basics of hydraulic conductivity and the science behind the SATURO automated dual head infiltrometer.

Watch it here—>

Publish More. Work Less. Introducing ZENTRA Cloud

Image of a researcher collecting information from a ZL6 data logger

Researcher is Collecting Data from the ZL6 Data Logger

METER research scientist Dr. Colin Campbell discusses how ZENTRA Cloud data management software simplifies the research process and why researchers can’t afford to live without it.

Watch it here—>

Soil Moisture 101: Need-to-Know Basics

Soil moisture is more than just knowing the amount of water in soil. Learn basic principles you need to know before deciding how to measure it.

Watch it here—>

Soil Moisture 201: Moisture Release Curves—Revealed

Image of rolling hills of farm land

Rolling Hills of Farm Land

A soil moisture release curve is a powerful tool used to predict plant water uptake, deep drainage, runoff, and more.

Watch it here—>

Soil Moisture 301: Hydraulic Conductivity—Why You Need It. How to Measure it.

Image of a researcher measuring with the HYPROP balance

Researcher measuring with the HYPROP balance

If you want to predict how water will move within your soil system, you need to understand hydraulic conductivity because it governs water flow.

Watch it here—>

Soil Moisture 102: Water Content Methods—Demystified

Image of a researcher holding a TEROS 12 in front of a field

Modern Sensing is more than just a Sensor

Dr. Colin Campbell compares measurement theory, the pros and cons of each method, and why modern sensing is about more than just the sensor.

Watch it here—>

Soil Moisture 202: Choosing the Right Water Potential Sensor

Image of a dirt plowed field being used for electrical conductivity

Electrical Conductivity

METER research scientist Leo Rivera discusses how to choose the right field water potential sensor for your application.

Watch it here—>

Water Management: Plant-Water Relations and Atmospheric Demand

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

Watch it here—>

How to Improve Irrigation Scheduling Using Soil Moisture

Image of a crop field

Capacitance

Dr. Gaylon Campbell covers the different methods irrigators can use to schedule irrigation and the pros and cons of each.

Watch it here—>

Next up:

Soil Moisture 302: Hydraulic Conductivity—Which Instrument is Right for You?

Image of plants growing out of the sand

Leo Rivera, research scientist at METER teaches which situations require saturated or unsaturated hydraulic conductivity and the pros and cons of common methods.

Watch it here—>

Image of grapes growing off of a tree

Predictable Yields using Remote and Field Monitoring

New data sources offer tools for growers to optimize production in the field. But the task of implementing them is often difficult. Learn how data from soil and space can work together to make the job of irrigation scheduling easier.

Watch it here—>

Learn more

Download “The researcher’s complete guide to soil moisture”

Download “The researcher’s complete guide to water potential”

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Dec 19

Chalk talk: How to calculate absolute humidity

In this video, Dr. Colin Campbell discusses how to use air temperature and relative humidity to calculate absolute humidity, a value you can use to compare different sites, calculate fluxes, or calculate how much water is actually in the air.

Depicting vapor and humidity coming off of the ground

Vapor density tells you how much water is actually in the air.

Watch the video to find out how to calculate absolute humidity and how to avoid a common error in the calculation.

 

Video transcript: Absolute humidity

Hello, I’m Dr. Colin Campbell, a senior research scientist here at METER Group, and also an adjunct faculty at Washington State University where I teach a class in environmental physics. Today we’re going to talk about absolute humidity. In a previous lecture, we discussed how relative humidity was a challenging variable to use in environmental studies. So, I’m going to show the right variable to use as we talk about humidity. 

Absolute humidity can be talked about in terms of vapor pressure, which is what I’m used to, or in terms of vapor density. Whatever we use, we usually start by calculating this from a relative humidity value and a knowledge of air temperature. In my relative humidity lecture, I said that Hr (or the relative humidity) was equal to the vapor pressure divided by the saturation vapor pressure. And in most field studies, we’d typically get a report of the air temperature and the relative humidity. So how do we take those two values and turn them into something we could use to compare different sites, calculate fluxes, or calculate how much water is actually in the air? We’ll need to work through some equations to get there. I’m going to take you through it and give you an example so that you know how to do that calculation.

Vapor pressure

First, we’ll talk about absolute humidity in terms of vapor pressure or Ea. If we rearrange this equation here (very simple math), the relative humidity times the saturation vapor pressure will give us our vapor pressure. And that vapor pressure is now an absolute humidity. How would we do this? Well, let’s first talk about an example in terms of vapor pressure. 

Let’s say a weather report said the air temperature was 25 degrees Celsius and the relative humidity was 28% or 0.28. First, we’d use Teten’s formula which I talked about in the previous lecture. We’d say the saturation vapor pressure at the air temperature is equal 0.611 kPa times the exponential of a constant times the air temperature divided by another constant plus the air temperature. So in our case, the air temperature is 25 degrees, which we’ll add here. Remember saturation vapor pressure is a function of 25. So 0.611 kPa times the exponential of 17.502. In the previous lecture, I showed you that b value times 25 degrees divided by the c value 240.97. And then we add to that 25 degrees (this is for liquid water, of course, it’s 25 degrees Celsius because nothing’s frozen). If you were working over ice, these constants would be different. So we put this into our calculator or into a spreadsheet, and we easily calculate the saturation vapor pressure at 25 degrees C is 3.17 kPa. But we’re not done yet. 

We have to go back to this equation that says the vapor pressure is equal to the relative humidity times the saturation vapor pressure. When we plug our data in, the relative humidity 0.28 times the saturation vapor pressure that we calculated right here, we get a vapor pressure of 0.89 kPa. And if we were calculating fluxes (we’ll talk about that in another lecture), this is typically the value we would use. But there are other things we can do with the absolute humidity values that might be useful.

Vapor density

So let’s talk about vapor density. If we had a certain volume of air, and we wanted to know how much water was in that volume of air (for example, if we were going to try to condense it out) we’d more typically use this vapor density value. But how do we get from a vapor pressure that we can easily calculate from a weather report to a vapor density that would allow me to know how much water was actually in the air? 

This is our equation that says the vapor pressure times the molecular weight of water divided by the universal gas constant times the kelvin temperature of the air will give us the vapor density. So I’ll take you through an example here, just continue on the one we’ve already done, just so you can see how to calculate it and to avoid a pretty common misstep. 

How to avoid a common error

Again, molecular weight of water is 18.02 g/mol. The universal gas constant R is 8.31 J/mol K. And here’s the kelvin temperature of the air. I’ve scribbled this in a little bit. That’s how I note the difference between something like this, which would be air temperature in Celsius and this air temperature in kelvin. So let’s go ahead and plug all these into our equation. There’s our vapor pressure. We’re just dragging that over here. There’s our molecular weight of water. There’s our universal gas constant. And here is the kelvin temperature of the air. So as we look at this, you immediately say, how do I cancel these units? The kilopascals and the joules are certainly not going to cancel as they are. But there are conversions we can use. A Pascal is equal to an Nm-2, and a joule is equal to an Nm.

So if we change this joule to an Nm, we change this Pascal to Nm-2, we have to pay attention here as we’re doing it that the kilo right there, don’t forget that because that can mess you up. So I’m circling that to make sure that we’ve got this. Now we cancel our N’s, and combining together we get a m-3. That’s what we’re hoping for on the bottom. The grams come out on top, they don’t cancel, but everything else does. The mols cancel mols, the kelvin cancels the kelvin there, and the N cancels the N. 

And we come out with just what we were looking for, save one thing, which is a kg/m-3. And this calculation gives us point 0065. But since we actually want to do this in grams, because that’s more typical of what you find in how much water there is in air. It’s not a kg of water, but more in terms of g/m-3 of water, we get 6.5 g/m-3. 

Check your calculation

One way you can check this calculation (just as a rule of thumb), is if we had a pressure of the air of 100 kPa and a temperature of 20 degrees Celsius, the multiplier to get from your vapor pressure to your vapor density is about 7.4 or so. We’ll just say around 7.0. And we’ll do a quick mental calculation, 0.89 times 7, that should give us something around 6.0. So our answer should be around 6.0, and it is. It’s certainly no orders of magnitude off. So we’ve got at least close to the right answer, by doing a mental check, and we can say this conversion works. 

If you want to learn more about instrumentation to measure all kinds of atmospheric parameters, please come to our website, www.metergroup.com or you can email me to chat more about this: colin.campbell@metergroup. com. 

Download “The researcher’s complete guide to LAI”

Download “The researcher’s complete guide to soil moisture”

Download “The researcher’s complete guide to water potential” 

Take our Soil Moisture Master Class

Six short videos teach you everything you need to know about soil water content and soil water potential—and why you should measure them together.  Plus, master the basics of soil hydraulic conductivity.

Watch it now—>

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Sep 17

Chalk Talk: Intensive vs. Extensive Variables

Learn the difference between intensive and extensive variables and how they relate to soil water potential vs. soil water content in our new Chalk Talk whiteboard series. In this video series, Dr. Colin S. Campbell teaches basic principles of environmental biophysics and how they relate to measuring different parameters of the soil-plant-atmosphere continuum.

Watch the video

 

Learn more

To learn more about measuring water potential vs. water content read: Why soil moisture sensors can’t tell you everything.

Download the “Researcher’s complete guide to water potential”—>

Download the “Researcher’s complete guide to soil moisture”—>

Video transcript

Hello, my name is Colin Campbell. I’m a senior research scientist here at METER group. And I teach a class on environmental biophysics. Today I wanted to talk about something we teach in the class: the difference between extensive and intensive variables. I’d like to do this with the goal of relating it to the difference between volumetric water content and water potential. 

Here, I have a picture of a ship moving through the ice and some metal that’s been heated in a furnace. I think we would agree the ship has the highest amount of heat in it compared to this very small piece of metal. And if we placed that piece of metal onto the outside of the ship, despite the fact that there is more heat in the ship, we know the heat would not move from the high amount of heat (ship) to the low amount of heat (metal). It would actually move from the highest temperature to the lowest temperature. Why is that?

The reason is that heat moves because of temperature and not because of heat content or the amount of heat in something. Heat content defines an amount or an extent. And we generally term something that defines an extent or an amount as an extensive variable.An extensive variable depends broadly on the size of something or how much of something there is. 

This differs for temperature. Temperature doesn’t depend on size. The temperature could be the same in a very small room or a very large room, but the amount of heat or heat content in those rooms would be quite different. When we describe temperature, we talk about intensity, which is why we call these types of variables intensive variables. This is because they don’t depend on size or amount. 

Let’s talk about another example. Here’s your heating bill. Maybe it’s natural gas. What you’re paying for is the amount of heat you put into the house. But the question is, are you comfortable in the house? And from this bill, we can’t tell. Maybe you put in 200 heat units, whatever those might be. We can’t tell if that’s comfortable because we don’t know the size of the house or the type of insulation. All those things would influence whether you were comfortable. 

Alternatively, if the temperature is 71 F that’s quite comfortable. That’s equivalent to about 22 degrees Celsius. So the intensive variable, temperature, is different than the extensive variable, heat content, that tells us how much heat we put in. And that’s important because at the end of the day, that leads to cost. 

On this side, we don’t know how much we paid to keep it at 22 C because heat content doesn’t tell us anything about that. But the intensive variable temperature does tell us something about comfort. So both of these variables are critical to really understanding something about our comfort in the house. 

Now let’s talk about the natural environment. Specifically, we’re going to talk about soils. We’ll start with the extensive variable. When we talk about water in soil, the extensive variable is, of course, water content. Water content defines the amount of water. Why would we care about water content? Well, for irrigation or a water balance.

The intensive variable is called water potential. What does water potential tell us? It tells us if soil water is available and also predicts water movement. If this soil had a water content of 25% VWC and another soil was at 20% VWC, would the water move from the higher water content to the lower water content? Well, that would be like our example of the ship and the heated piece of metal. We don’t know if it would move. It may move. And if the soil on either side was exactly the same, we might presume that it would move from the higher water content to the lower water content, but we actually don’t know. Because the water content is an extensive variable, it only tells us how much there is. It won’t tell us if it will move. 

Now, if we knew that this soil water potential was -20 kPa and this soil water potential over here was -15 kPa, we would know something about where the water would move, and it would do something different than we might think. It would move from the higher water potential to the lower water potential against the gradient in water content, which is pretty interesting but nonetheless true. Water always moves from the highest water potential to the lowest water potential.

This helps us understand these variables in terms of plant comfort. We talked about the temperature being related to human comfort. We know at what temperatures we are most comfortable. With plants, we know exactly the same thing, and we always turn to the intensive variable, water potential, to define plant comfort.

For example, if we have an absolute scale like water potential for a particular plant, let’s say -15 kPa is the upper level for plant comfort, and -100 kPa is the lower level of comfort, we could keep our water potential in this range. And the plant would be happy all the time. Just like if we kept our temperature between 21 and 23 Celsius, that would be comfortable for humans. But of course, we humans are different. Some people think that temperature is warm, and some think it’s cold. And it’s the same for plants. So this isn’t a hard and fast rule. But we can’t say the same thing with water content. There’s no scale where we can say at 15% water content up to 25% water content you’ll have a happy plant That’s not true.If we know something about the soil, we can infer it. But soil is unique. And we’d have to derive this relationship between the water content and the water potential to know that. 

So why would we ever think about using water content when we measure water in the soil? One reason is it’s the most familiar to people. And it’s the simplest to understand. It’s easy to understand an amount. But more importantly, when we talk about things like how much we’re going to irrigate, we might need to put on 10 millimeters of water to make the plants happy. And we’d need to measure that. Also if we want to know the fate of the water in the system, how much precipitation and irrigation we put on versus how much is moving down through the soil into the groundwater, that also relates to an amount.  

But when we want to understand more about plant happiness or how water moves, it’s going to be this intensive variable, water potential that makes the biggest difference. And so with that, I’ll close. I’d love for you to go check out our website www.metergroup.com to learn a little bit more about these measurements in our knowledge base. And you’re also welcome to email me about this at colin.campbell@meter group.com.

Take our Soil Moisture Master Class

Six short videos teach you everything you need to know about soil water content and soil water potential—and why you should measure them together.  Plus, master the basics of soil hydraulic conductivity.

Watch it now—>

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Feb 19

Why does my soil moisture sensor read negative?

How is a negative number possible?

METER soil moisture sensors measure the dielectric constant of the substrate in which they are installed. They are designed to measure soil, which has a dielectric constant of around 5.

Researcher holding a TEROS 12 soil sensor in front of a wheat field

METERTEROS 12 soil moisture sensor

Water has a dielectric of approximately 80, so if we assume that a dry soil has a dielectric of 5 (VWC = 0.00 m3/m3), then changes to the bulk dielectric read by the soil moisture sensor will be attributable to changes in water content. If you read a METER sensor in air, which has a dielectric constant of 1, you will quite naturally get a negative number.   

Improving accuracy of dielectric soil moisture sensors

There are two common causes for negative readings on a METER soil moisture sensor:  

1) Poor contact with the soil resulting from improper installation or disturbance

Air gaps next to a sensor will contribute the lower dielectric of air to the measurement resulting in an underestimation of VWC. Air gaps can arise if enough care is not taken to pack soil around the sensor body to approximate native bulk density. Sensors that have been disturbed, such as having a cable tripped over, can also develop air gaps that can result in negative results in dry soils. (To reduce the possibility of air gaps when installing METER sensors, use the new TEROS borehole installation tool) 

2) A calibration that is inappropriate for the soil in which the sensor is installed

If the standard mineral calibration is used, an error of ~ 3-4% can be expected in METER sensor readings. Negative numbers can be observed in oven-dry soils (by definition a VWC of 0.0 m3/m3) down to ~ – 0.02 m3/m3 with no malfunction of the sensor. The dielectric constant of the soil is assumed to be 5 and this is a valid assumption in the majority of soils of primarily mineral composition. If your soil has a different dielectric constant, such as can occur in soils with high organic matter content, then the uncertainty in your measurements will increase. This is not a large problem because METER sensors can be calibrated to match a given soil with very little investment in resources.

Want more details?  

Watch our webinar titled Why Does My Sensor Read Negative below. This webinar is designed for those who use electromagnetic sensors (capacitance/TDR/FDR) to measure soil water content. Learn about the theory behind the measurements. Dr. Doug Cobos discusses:

  • What is volumetric water content?
  • Dielectric measurement theory basics
  • Dielectric mixing models
  • Why might a sensor read a negative VWC?
  • Can a sensor really have 2% VWC accuracy for all soils?
  • Sources of error in dielectric measurement methods
  • Improving accuracy of dielectric measurements

 

Take our Soil Moisture Master Class

Six short videos teach you everything you need to know about soil water content and soil water potential—and why you should measure them together.  Plus, master the basics of soil hydraulic conductivity.

Watch it now—>

Download the “Researcher’s complete guide to water potential”—>

Download the “Researcher’s complete guide to soil moisture”—>

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Nov 13

Which Soil Sensor Should I Choose?

Dr. Colin Campbell, METER soil scientist, explains soil sensor differences, pros, cons, and things to consider when choosing which sensor will best accomplish your research goals. Use the following considerations to help identify the perfect sensor for your research.  Explore the links for a more in-depth look at each topic.

Researcher Holding a TEROS 12

Scientists often measure soil moisture at different depths to understand the effects of soil variability and to observe how water is moving through the soil profile.

CHOOSE THE RIGHT MEASUREMENT

  • Volumetric Water Content:  If a researcher wants to measure the rise and fall of the amount (or percentage) of water in the soil, they will need soil moisture sensors. Soil is made up of water, air, minerals, organic matter, and sometimes ice.  As a component, water makes up a percentage of the total.  To directly measure soil water content, one can calculate the percentage on a mass basis (gravimetric water content) by comparing the amount of water, as a mass, to the total mass of everything else.  However, since this method is labor-intensive, most researchers use soil moisture sensors to make an automated volume-based measurement called Volumetric Water Content (VWC). METER soil moisture sensors use high-frequency capacitance technology to measure the Volumetric Water Content of the soil, meaning they measure the quantity of water on a volume basis compared to the total volume of the soil.  Applications that typically need soil moisture sensors are watershed characterization, irrigation scheduling, greenhouse management, fertigation management, plant ecology, water balance studies, microbial ecology, plant disease forecasting, soil respiration, hydrology, and soil health monitoring.
  • Water potential:  If you need an understanding of plant-available water, plant water stress, or water movement (if water will move and where it will go), a water potential measurement is required in addition to soil moisture. Water potential is a measure of the energy state of the water in the soil, or in other words, how tightly water is bound to soil surfaces. This tension determines whether or not water is available for uptake by roots and provides a range that tells whether or not water will be available for plant growth. In addition, water always moves from a high water potential to a low water potential, thus researchers can use water potential to understand and predict the dynamics of water movement.

Understand your soil type and texture

In soil, the void spaces (pores) between soil particles can be simplistically thought of as a system of capillary tubes, with a diameter determined by the size of the associated particles and their spatial association.  The smaller the size of those tubes, the more tightly water is held because of the surface association.

Clay holds water more tightly than a sand at the same water content because clay contains smaller pores and thus has more surface area for the water to bind to. But even sand can eventually dry to a point where there is only a thin film of water on its surfaces, and water will be bound tightly.  In principle, the closer water is to a surface, the tighter it will be bound. Because water is loosely bound in a sandy soil, the amount of water will deplete and replenish quickly.  Clay soils hold water so tightly that water movement is slow. However, there is still available water.

Note: Use the PARIO soil texture analyzer to automate soil texture identification.

Two measurements are better than one

In all soil types and textures, soil moisture sensors are effective at measuring the percentage of water. Dual measurements—using a water potential sensor in addition to a soil moisture sensor—gives researchers the total soil moisture picture and are much more effective at determining when, and how much, to water.  Water content data show subtle changes due to daily water uptake and also indicate how much water needs to be applied to maintain the root zone at an optimal level.  Water potential data determine what that optimal level is for a particular soil type and texture.

Get the big picture with moisture release curves  

Dual measurements of both water content and water potential also enable the creation of in situ soil moisture release curves (or soil water characteristic curves) like the one below (Figure 1), which detail the relationship between water potential and water content.  Scientists and engineers can evaluate these curves in the lab or the field and understand many things about the soil, such as hydraulic conductivity and total water availability.

Turf-grass Soil Moisture Release Curve

Figure 1. Turfgrass soil moisture release curve (black). Other colors are examples of moisture release curves for different types of soil.

Read the full article

Download the “Researcher’s complete guide to soil moisture”—>

Download the “Researcher’s complete guide to water potential”—>

Get more information on applied environmental research in our

Take our Soil Moisture Master Class

Six short videos teach you everything you need to know about soil water content and soil water potential—and why you should measure them together.  Plus, master the basics of soil hydraulic conductivity.

Watch it now—>

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Jul 10

Soil Moisture Sensors: Which Installation Method is Best?

Patterns of water replenishment and use give rise to large spatial variations in soil moisture over the depth of the soil profile. Accurate measurements of profile water content are therefore the basis of any water budget study. When monitored accurately, profile measurements show the rates of water use, amounts of deep percolation, and amounts of water stored for plant use.

How to avoid measurement errors

Three common challenges to making high-quality volumetric water content measurements are:

  1. making sure the probe is installed in undisturbed soil,
  2. minimizing disturbance to roots and biopores in the measurement volume, and
  3. eliminating preferential water flow to, and around, the probe.

All dielectric probes are most sensitive at the surface of the probe. Any loss of contact between the probe and the soil or compaction of soil at the probe surface can result in large measurement errors. Water ponding on the surface and running in preferential paths down probe installation holes can also cause large measurement errors.

Installing soil moisture sensors will always involve some digging. How do you accurately sample the profile while disturbing the soil as little as possible?  Consider the pros and cons of five different profile sampling strategies.

Preferential flow is a common issue with commercial profile probes

Profile probes are a one-stop solution for profile water content measurements. One probe installed in a single hole can give readings at many depths. Profile probes can work well, but proper installation can be tricky, and the tolerances are tight. It’s hard to drill a single, deep hole precisely enough to ensure contact along the entire surface of the probe. Backfilling to improve contact results in repacking and measurement errors. The profile probe is also especially susceptible to preferential-flow problems down the long surface of the access tube.  (NOTE: The new TEROS Borehole Installation Tool eliminates preferential flow and reduces site disturbance while allowing you to install sensors at depths you choose.)

Trench installation is arduous

Installing sensors at different depths through the side wall of a trench is an easy and precise method, but the actual digging of the trench is a lot of work. This method puts the probes in undisturbed soil without packing or preferential water-flow problems, but because it involves excavation, it’s typically only used when the trench is dug for other reasons or when the soil is so stony or full of gravel that no other method will work. The excavated area should be filled and repacked to about the same density as the original soil to avoid undue edge effects.

Researcher is holding an ECHO EC-5 in front of soil

Digging a trench is a lot of work.

Augur side-wall installation is less work

Installing probes through the side wall of a single augur hole has many of the advantages of the trench method without the heavy equipment. This method was used by Bogena et al. with EC-5 probes. They made an apparatus to install probes at several depths simultaneously. As with trench installation, the hole should be filled and repacked to approximately the pre-sampling density to avoid edge effects.

An augered borehole disturbs the soil layers, but the relative size of the impact to the site is a fraction of what it would be with a trench installation. A trench may be about 60 to 90 cm long by 40 cm wide. A borehole installation performed using a small hand auger and the TEROS Borehole Installation Tool creates a hole only 10 cm in diameter—just 2-3% of the area of a trench. Because the scale of the site disturbance is minimized, fewer macropores, roots, and plants are disturbed, and the site can return to its natural state much faster. Additionally, when the installation tool is used inside a small borehole, good soil-to-sensor contact is ensured, and it is much easier to separate the horizon layers and repack to the correct soil density because there is less soil to separate.

Multiple-hole installation protects against failures

Digging a separate access hole for each depth ensures that each probe is installed into undisturbed soil at the bottom of its own hole. As with all methods, take care to assure that there is no preferential water flow into the refilled augur holes, but a failure on a single hole doesn’t jeopardize all the data, as it would if all the measurements were made in a single hole.

The main drawback to this method is that a hole must be dug for each depth in the profile. The holes are small, however, so they are usually easy to dig.

Single-hole installation is least desirable

It is possible to measure profile moisture by auguring a single hole, installing one sensor at the bottom, then repacking the hole, while installing sensors into the repacked soil at the desired depths as you go. However, because the repacked soil can have a different bulk density than it had in its undisturbed state and because the profile has been completely altered as the soil is excavated, mixed, and repacked, this is the least desirable of the methods discussed. Still, single-hole installation may be entirely satisfactory for some purposes. If the installation is allowed to equilibrate with the surrounding soil and roots are allowed to grow into the soil, relative changes in the disturbed soil should mirror those in the surroundings.

Reference

Bogena, H. R., A. Weuthen, U. Rosenbaum, J. A. Huisman, and H. Vereecken. “SoilNet-A Zigbee-based soil moisture sensor network.” In AGU Fall Meeting Abstracts. 2007. Article link.

Read more soil moisture sensor installation tips.

Download the “Researcher’s complete guide to soil moisture”—>

Download the “Researcher’s complete guide to water potential”—>

Take our Soil Moisture Master Class

Six short videos teach you everything you need to know about soil water content and soil water potential—and why you should measure them together.  Plus, master the basics of soil hydraulic conductivity.

Watch it now—>

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