As world water demand increases and supplies decrease, how can we turn more of the water we use for agriculture into biomass? In this webinar, Dr. Campbell dives deep into the measurement and implications of making the most of every drop of water.

Crops turn sunlight, water, carbon dioxide, and nutrients into food

The availability of those resources puts limitations on the amount of food a crop can produce. A previous webinar considered the limitations of sunlight. In this 30-minute webinar, world-renown environmental biophysicist, Dr. Gaylon S. Campbell, discusses how to measure the amount of water a crop will need and how to use that value to predict the amount of biomass it will produce.

Achieve maximum biomass from every drop

Join Dr. Campbell as he discusses the measurements and calculations needed to know how much biomass a given environment can produce. Dr. Campbell will discuss:

How resource capture models work

How biomass production and water use are linked

Examples of effective uses of water resource capture models

Instrumentation needed to determine water and radiation limitations on yield

How to use soil and atmospheric measurements to quantify crop water capture

Water budgets and how they are used to get transpiration and biomass production

Dr. Campbell has been a research scientist and engineer at METER for 19 years following nearly 30 years on faculty at Washington State University. Dr. Campbell’s first experience with environmental measurement came in the lab of Sterling Taylor at Utah State University making water potential measurements to understand plant water status. Dr. Campbell is one of the world’s foremost authorities on physical measurements in the soil-plant-atmosphere continuum. His book written with Dr. John Norman on Environmental Biophysics provides a critical foundation for anyone interested in understanding the physics of the natural world. Dr. Campbell has written three books, over 100 refereed journal articles and book chapters, and has several patents.

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.

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.

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.

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 this chalk talk video, world-renowned soil physicist, Dr. Gaylon Campbell, discusses how many measurements researchers and growers need to characterize soil moisture at a field or research site. He explores the question: What is the relationship between the measurements that you make and the underlying value of water content in the field?

Presenter

Dr. Gaylon S. Campbell has been a research scientist and engineer at METER for 19 years following nearly 30 years on faculty at Washington State University. Dr. Campbell’s first experience with environmental measurement came in the lab of Sterling Taylor at Utah State University making water potential measurements to understand plant water status. Dr. Campbell is one of the world’s foremost authorities on physical measurements in the soil-plant-atmosphere continuum. His book written with Dr. John Norman on Environmental Biophysics provides a critical foundation for anyone interested in understanding the physics of the natural world. Dr. Campbell has written three books, over 100 refereed journal articles and book chapters, and has several patents.

We quite often get a question from customers about how many measurements we need to characterize soil moisture at a site. And so that’s what I want to talk about today. A number of years ago, I knew a man who was wanting to provide a business of making soil moisture measurements for the purpose of irrigation scheduling for farmers. And he came to me wondering how many samples he should take. He figured that he wanted a fairly simple way of determining soil moisture.

So, he thought he would go into the field and he would collect soil samples from the field, he would take them back to the laboratory, he would dry them and weigh them and dry them and determine water content. And he wondered how many samples would be required to determine the water content to provide this information for a farmer.

Now, that’s not so different from the kinds of information that are often required either for practical applications like irrigation scheduling, or for research purposes. We can see the broader applications of the question of, “what’s the relationship between the measurements that we take and the underlying value of water content in the field?”

I think you can see that the same thing would apply whether we were taking samples and bringing them back to the laboratory, or if we were putting in soil moisture sensors, and wanting to monitor soil moisture in the field. So, the first thing we need to talk about soil moisture is a random variable, we need some vocabulary for talking about that. Two terms are important: mean and standard deviation.

If we were to collect many samples of water content from a field, and we were to plot the number of samples versus the water content of the samples, we would obtain a relationship something like this. We would get the most samples around some central value, and that central value is the mean.

The standard deviation is a measure of the dispersion around the mean. 68% of the values that we take would be within plus or minus one standard deviation of that mean value. 95% would be within plus or minus two standard deviations of the mean value.

So, let’s say that we walked out here in the field, and we took a sample and made a measurement on it. And let’s say out of that sample, we determined the water content was 27%. Now let’s say that we assume or we know from some means that the standard deviation is 3%. Then, by these ideas, we would know that the mean value – the expected value for the water content – is or at least there would be a 95% probability that the mean value of the water content would be somewhere between 21% and 33%. The mean value plus two times the standard deviation and the mean value minus two times the standard deviation.

Now we may say, “well that’s not good enough. We need better values than that. So what do we do? We need to take more samples. And so we take a number of samples and average them. And so we can know what the result of averaging several samples is, with a simple relationship. The uncertainty in the average value that we get–the standard deviation of the mean–is the standard deviation, divided by the square root of the number of samples.

So let’s say that we went out in the field, and we took 100 samples. Then the standard deviation of the mean, would be our standard deviation that we assumed before, divided by the square root of 100. The square root of 100, of course, is 10. And so that would be 0.3%. If we determined a value of 28% for that mean of the 100 samples, then with 95% confidence, we can say that the water content is between 27.4 (2 standard deviations below the mean), and 28.6.

Now we’re getting closer then to our quest of determining the number of samples that we need to take. We start out with that equation that we just had that the standard deviation of the mean is equal to the standard deviation divided by the square root of the number of samples. We can rearrange that to say that the number of samples that we need is equal to the standard deviation divided by the standard deviation of the mean, and that value squared. So, the error that we normally would talk about in the measurement–if we’re again talking about 95% confidence–the error is half of the standard deviation of the mean.

This number of samples is two times the standard deviation over the air, and that all squared. So, if we work through a little problem with that, how many samples would we need in order to know the water content within 1%? If the standard deviation is 3%, the way we’ve assumed.

So, the standard deviation is 3%. The error value that we want to get to is 1%. We want to take enough samples so that we have 95% confidence that we’re within 1%. And so the number of samples is 2 times 3%, divided by the air, 1%, and that’s all squared. And that comes out to be 36 samples. Well, when we see that number, typically we get pretty discouraged. That’s more samples than we want to take. More samples probably than we can afford to take.

To see how that relates to reality, we did a little experiment. Here we have a soccer field out behind the METER (formerly Decagon) building. We went out and took one of our sensors, the GS3, and hooked it up to our little handheld device. And we set up three transects 20 meters long, parallel with each other and spaced a meter apart. We went along and took samples every meter along these transects. And I have a little video here that shows how that sampling went. The result of that sampling is shown in this next slide.

This slide shows the result of that set of measurements that we made. And you can see it looks about like you would expect it to. We’ve got some variation, we show a mean value and some variation around it. The transects, again, showed variability but seemed to be showing about the same result for each transect. We had 60 samples there.

The average water content that we computed was 38.6%. The standard deviation was not 3%, but 5%. So, the situation is even worse than we imagined with these calculations that we just did here. With a standard deviation of 5%, if we want to know the water content within 1%, we would need 100 samples to do that. And so even with our 60 samples, here, our standard deviation of the mean is 0.65%. And so our field water content is somewhere between 37.3 and 39.9.

Well, as I say that usually is discouraging when we get to that point and see how many samples are needed to make a set of measurements, but the thing is that quite often, the thing that we need to know is not an accurate value for the average water content. Quite often, what we want to know is how much the water content is changing. And that we can know in other ways, accurately enough, so that we don’t need that many samples.

That person that I started out talking about who was wanting to schedule irrigation would need to know water content with an accuracy of 1%. Well, at least with a precision of 1% or better. But that could be achieved much more readily by installing a sensor in situ, where you’re not dealing with the spatial variability in the soil and monitoring that.

Here I’ve shown some data that we took in the field with one of our 5TE sensors hooked up to a data logger. The water content is sampled every minute, it’s averaged over hour intervals, and the plot that you see here is a plot of the water content measured each hour. Then, you can see a period of time where the soil is drying, because the plants are using water. You can see an increase in water content that results from adding water through irrigation or rain. And then again, the water content decreasing as the water is used. And you see very little variation in those data.

Now if this guy that wanted to provide the irrigation scheduling service, had wanted to do this same thing by sampling, the next slide shows the result that he would have gotten if he had gone out every hour and taken one soil sample and plotted the result.

This is what he would have gotten; the blue lines that you see. And you can see that it’s about what you would expect: that the highest values are about 10% higher than the mean value, the lowest values are about 10% lower, and the standard deviation we said is about 5. So, that’s about what we would expect. But from these kinds of data, there’s no possibility that you could ever tell when you should irrigate.

In the next slide, I show the result that you would have gotten if you went out and took 10 samples every hour. And here you can see the pattern to some extent of when the drying and wetting occur, but there’s still an awful lot of variation.

The next slide shows the result of taking 100 samples every hour, a ridiculous thought, but again, there’s still some variation in it. It still doesn’t look anywhere near as good as the in situ sample. When we’re just looking for the changes in water content, the water storage, and water use, in situ measurements make a lot more sense than soil moisture sampling.

So, let me conclude just by a few points that I hope to have made in this. First of all, the soil water content varies from place to place; that’s inherent in nature. It’s something that we expect anytime we go out to measure soil moisture. We usually need to take an average of moisture at several locations in order to know what the water content of a field is, or an experimental site. We usually can’t afford to take enough measurements to really know what it is to have it within the accuracy that we would like to have it. And so we can go through this exercise that I’ve gone through here, we can determine the number that we need, but usually, our budget won’t allow us to put in that many and so we end up compromising to some extent.

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 CO_{2} 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.

In our latest We Measure the World podcast, we interview Oregon State University researcher Dalyn McCauley

about her experience developing a site-specific decision support tool for on-farm management of crop damaging weather events—and techniques she used to help the grower implement changes for quality and yield improvements.

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.

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.

Two researchers show easier methods conform to standards

If you’re measuring saturated hydraulic conductivity with a double ring infiltrometer, you’re lucky if you can get two tests done in a day. For most inspectors, researchers, and geotechs—that’s just not feasible. Historically, double ring methods were the standard, however the industry is now more accepting of faster single ring methods with the caveat that enough locations are tested. But how many locations are enough?

Triple the tests you run in a day

Drs. Andrea Welker and Kristin Sample-Lord, researchers at Villanova University, are changing the way infiltration measurements are captured while keeping the standards of measurement high. They ran many infiltration tests with three types of infiltrometers with a variety of sizes and soil types. In this 30-minute webinar, they’ll discuss what they found to be the acceptable statistical mean for a single rain garden. Plus, they’ll reveal the pros and cons of each infiltrometer type and which ones were the most practical to use. Learn:

What types of sites were tested

How the spot measurements compared with infiltration rates over the whole rain garden

Pros and cons of each infiltrometer and how they compared for practicality and ease of use

What is an acceptable number of measurements for an accurate assessment

Dr. Andrea Welker, PE, F.ASCE, ENV SP, is a Professor of Civil and Environmental Engineering and the Associate Dean for Academic Affairs at Villanova University. She joined Villanova after obtaining her PhD at the University of Texas at Austin. Her research focuses on the geotechnical aspects of stormwater control measures (SCMs) and the effectiveness of SCMs at the site and watershed scale.

Dr. Kristin Sample-Lord, P.E., is an Assistant Professor of geotechnical and geoenvironmental engineering in the Civil and Environmental Engineering Department at Villanova University. She received her PhD and MS from Colorado State University. Her research includes measurement of flow and transport in soils, with specific focus on green infrastructure and hydraulic containment barriers.

Mismanagement of salt applied during irrigation ultimately reduces production—drastically in many cases. Irrigating incorrectly also increases water cost and the energy used to apply it.

Understanding the salt balance in the soil and knowing the leaching fraction, or the amount of extra irrigation water that must be applied to maintain acceptable root zone salinity is critical to every irrigation manager’s success. Yet monitoring soil salinity is often poorly understood.

Measure EC for consistently high crop yields

In this webinar, world-renowned soil physicist Dr. Gaylon Campbell teaches the fundamentals of measuring soil electrical conductivity (EC) and how to use a tool that few people think about—but is absolutely essential for maintaining crop yield and profit. Learn:

The sources of salt in irrigated agriculture

How and why salt affects plants

How salt in soil is measured

How common measurements are related to the amount of salt in soil

How salt affects various plant species

How to perform the calculations needed to know how much water to apply for a given water quality

Dr. Gaylon S. Campbell has been a research scientist and engineer at METER for over 20 years, following nearly 30 years on faculty at Washington State University. Dr. Campbell’s first experience with environmental measurement came in the lab of Sterling Taylor at Utah State University making water potential measurements to understand plant water status.

Dr. Campbell is one of the world’s foremost authorities on physical measurements in the soil-plant-atmosphere continuum. His book written with Dr. John Norman on Environmental Biophysics provides a critical foundation for anyone interested in understanding the physics of the natural world. Dr. Campbell has written three books, over 100 refereed journal articles and book chapters, and has several patents.

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.

Soil moisture is more complex than estimating how much water is used by vegetation and how much needs to be replaced. If you’re thinking about it that way, you’re only seeing half the picture. You’re assuming you know what the right level of water should be—and that’s extremely difficult using only a water content sensor.

Get it right every time

Water content is only one side of a critical two-sided coin. To understand when to water or plant water stress, you need to measure both water content and water potential.

In this 30-minute webinar, METER soil physicist, Dr. Colin Campbell, discusses how and why scientists combine both types of sensors for more accurate insights. Discover:

Why the “right water level” is different for every soil type

Why soil surveys aren’t sufficient to type your soil for full and refill points

Why you can’t know what a water content “percentage” means to growing plants

How assumptions made when only measuring water content can reduce crop yield and quality

Water potential fundamentals

How water potential sensors measure “plant comfort” like a thermometer

Why water potential is the only accurate way to measure drought stress

Why visual cues happen too late to prevent plant-water problems

Case studies that show why both water content and water potential are necessary to understand the condition of soil water in your experiment or crop

Dr. Colin Campbell has been a research scientist at METER for 20 years following his Ph.D. at Texas A&M University in Soil Physics. He is currently serving as Vice President of METER Environment. He is also adjunct faculty with the Dept. of Crop and Soil Sciences at Washington State University where he co-teaches Environmental Biophysics, a class he took over from his father, Gaylon, nearly 20 years ago. Dr. Campbell’s early research focused on field-scale measurements of CO2 and water vapor flux but has shifted toward moisture and heat flow instrumentation for the soil-plant-atmosphere continuum.