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Posts from the ‘Water Potential sensors’ Category

Apr 16

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Do the Standards for Field Capacity and Permanent Wilting Point Need to Be Reexamined?

We were inspired by this Freakonomics podcast, which highlights the book, This Idea Must Die: Scientific Problems that are Blocking Progress, to come up with our own answers to the question:  Which scientific ideas are ready for retirement?  We asked METER scientist, Dr. Gaylon S. Campbell, which scientific idea he thinks impedes progress.  Here’s what he had to say about the standards for field capacity and permanent wilting point:

Canola Field right next to an eroded soil cliff

A layered soil, a soil that has a fine-textured horizon on top of a coarse-textured soil, will hold twice as much water as you’ll predict from the -⅓ bar value.

Idea:

The phrase, “this idea must die,” is probably too strong a phrase, but certainly some scientific ideas need to be reexamined, for instance the standard of -⅓ bar (-33 kPa) water potential for field capacity and -15 bars (-1500 kPa or -1.5 MPa) for permanent wilting point.

Where it came from:

In the early days of soil physics, a lot of work was done in order to establish the upper and lower limit for plant available water.  The earliest publication on the lower limit experiments was by Briggs and Shantz in 1913. They planted sunflowers in small pots under greenhouse conditions, letting the plants use the water until they couldn’t recover overnight, after which they carefully measured the water content (WC).  The ability to measure water potential came along quite a bit later in the 1930s using pressure plates.  As those measurements started to become available, a correlation was found between the 15 bar pressure plate WCs and the WCs that were determined by Briggs and Shantz’s earlier work.  Thus -15 bars (-1.5 MPa) was established as the lower limit of plant available water.  The source of the field capacity WC data that established a fixed water potential for the upper limit is less clear, but the process, apparently, was similar to that for the lower limit, and -⅓ bar was established as the drained upper limit water potential in soil.

Sunflowers against a blue sky

Briggs and Shantz planted sunflowers in small pots under greenhouse conditions, letting the plants use the water until they couldn’t recover overnight, after which they carefully measured the water content (WC).

Damage it does:  

In practice, using -15 bars to calculate permanent wilting point probably isn’t a bad starting point, but in principle, it’s horrible. Over the years we have set up experiments like Briggs and Shantz did and measured water potential. We have also measured field soils after plants have extracted all the water they can.  Permanent wilting point never once came out at -15 bars or -1.5 MPa.  For things like potatoes, it was approximately -10 bars (-1 MPa), and for wheat it was approximately -30 bars (-3 MPa).  We found that the permanent wilting point varies with the species and even with soil texture to some extent.

Of course, in the end it doesn’t matter much as the moisture release curve is pretty steep on the dry end, and whether you predict it to be 10 or 12% WC, it doesn’t make a huge difference in the size of the soil water reservoir that you compute.

However, on the field capacity end of the scale, it matters a lot.  If you went out and made measurements of the water potentials in soils a few days after a rain, it would be an absolute accident if any of them were ever -⅓ bar (-33 kPa).  I’ve never seen it.  A layered soil, a soil that has a fine-textured horizon on top of a coarse-textured soil, will hold twice as much water as you’ll predict from the -⅓ bar value.  On the other hand, if you’re getting pretty frequent rains or irrigation, that field capacity number becomes irrelevant. Thus, -⅓ bar may be a useful starting point for determining field capacity, but it’s only a starting point.

Why it’s wrong:

Field capacity and permanent wilting point are dynamic properties.  They depend on the rate at which the water is being extracted or the rate at which it’s being applied.  They also depend on the time you wait to sample after irrigation. Think of the soil as a leaky bucket.  If you were trying to carry water in a leaky bucket and you walked slowly, the bucket would be empty by the time you get the water where you want it. However, if you run fast, there will still be some water left in the bucket.  Similarly, if a plant can use water up rapidly, most of it will be intercepted, but if a plant is using water slowly, the water will move down past the root zone and out the bottom of the soil profile before the plant can use it.  These are dynamic phenomena that you are trying to describe with static variables.  And that’s where part of the problem comes.  We need a number to do our calculations with, but it’s important to understand the factors that affect that number.

Rye Field

Rye field

What do we do now:

What I hope we can do is better educate people. We should teach that we need a value we call field capacity or permanent wilting point, but it’s going to be a dynamic property.  We can start out by saying: our best guess is that it will be -⅓ bar for finer-textured soils and -1/10 bar (-10 kPa) for coarser-textured soils. But when we dig a hole and find out there is layering in the profile or textural discontinuities, we’d better adjust our number.  If we’re dealing with irrigated farmland, the adjustment will always be up, and if we’re dealing with dryland or rain-fed agriculture where the time between water additions is longer, we’ll use a lower number.

Some Ideas Never Die:

One of the contributors to the book, This Idea Must Die, Dr. Steve Levitt, had this to say about outdated scientific ideas, and we agree:  “I love the idea of killing off bad ideas because if there’s one thing that I know in my own life, it’s that ideas that I’ve been told a long time ago stick with me,  and you often forget whether they have good sources or whether they’re real. You just live by them. They make sense. The worst kind of old ideas are the ones that are intuitive. The ones that fit with your worldview, and so, unless you have something really strong to challenge them, you hang on to them forever.”

Harness the power of soil moisture

Researchers measure evapotranspiration and precipitation to understand the fate of water—how much moisture is deposited, used, and leaving the system. But if you only measure withdrawals and deposits, you’re missing out on water that is (or is not) available in the soil moisture savings account. Soil moisture is a powerful tool you can use to predict how much water is available to plants, if water will move, and where it’s going to go.

In this 20-minute webinar, discover:

  • Why soil moisture is more than just an amount
  • Water content: what it is, how it’s measured, and why you need it
  • Water potential: what it is, how it’s different from water content, and why you need it
  • Whether you should measure water content, water potential, or both
  • Which sensors measure each type of parameter

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 soil moisture”—>

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

Apr 12

Thoughts on Soil Sensor Installation from a German Precisionist

Many researchers carefully choose the right instrumentation for their projects, but when it comes to installing the soil sensor into the soil, they are less than careful about the process. Researchers need to know how to install sensors in a way that will allow them to get the most accurate data the instruments are capable of.

Georg Von Unold

Georg von Unold

Georg von Unold has almost two decades of experience installing all types of soil sensors and a German eye for precision that is unmatched in our experience. As the president and founder of UMS (now METER Ag), a German company that develops and manufactures precision soils instrumentation, and a close friend, we thought there would be no one better to share a couple of ideas on careful installation.  Here’s what he had to say:

Pick the Right Place to Install your Sensors

When we develop research instrumentation we look at the accuracy and the resolution of our instruments from a technical point of view.  However, the heterogeneity of research sites can be so vast that we have to take care to select a research site that is representative from a scientific point of view of the results we would like to publish.  We do this first by analyzing the biosphere above the soil that is visible to us, and then perhaps doing some auguring into the soil at various sites to investigate what might be going on in different areas of the field.  If you are researching on a farm, it is important to ask the grower where he’s had good and bad harvest results, where he’s needed to irrigate, and where he’s had problems with erosion.  Always interview people who know the history and specifics of the sites first, because if the sites are flooded or at risk for landslides, it will be a bad choice for long-term monitoring.  Investigating the right place for your sensors before you install will save you time and help you obtain the most applicable and accurate data for your research.

Flat Gravel

We knew that gravel would have bad capillary contact because the stones would have holes between them.

Be Careful with the Way you Install Sensors

One of our research projects used tensiometers to try and determine how water flowed through gravel.  We knew that gravel would have bad capillary contact because the stones would have holes between them. So we decided to make a slurry of fine material from this gravel soil and put it in the installation hole so that the tensiometer would have better capillary contact.  It was a good idea, but it led to misleading results.  What we ended up with was a kind of water reservoir with fine material around the tensiometer which had nothing to do with the true moisture situation in the gravel.  The tensiometer gave us wonderful readings: very constant but with no dynamics that would have been typical for a gravel soil.  When we took it into the lab to investigate, we realized we’d built an artificial soil around our tensiometer.  We weren’t measuring the gravel but were measuring our artificial error which we had created so carefully.  The other thing we found is that over the course of time our slurry would move away from the tensiometer, and within a few years, the tensiometer would be simply hanging in a big gap.  This project also contained fine, heavy soils. Eventually, we realized that we needed an auguring tool that would not push the soil away or compact the soil where we placed the tensiometer because compaction would mean different hydraulic behavior.  So we asked our friends at a Dutch company to make us an auger that was shaped in a form that wouldn’t change the natural soil density that we wanted to measure.

It is important to be careful when you install sensors. For example, if you have a clay soil and you auger a bigger hole than your tensiometer, you will have a water tube around your sensor.  If your soil flooded, the water would flow down your shaft to where your tensiometer is placed, and then what are you measuring?  Thus it is necessary to seal the shaft or to prevent access of surface water to a deeper horizon.

Researcher squatting letting sand fall through his fingers

You need to remember that if you want to measure temperature at a depth of one meter below the surface, the thermal conductivity is strongly dependent on the kind of soil and the moisture of the soil.

Beware of Simple Mistakes

You can also make simple mistakes with other types of soil sensors, such as temperature probes.  You need to remember that if you want to measure temperature at a depth of one meter below the surface, the thermal conductivity is strongly dependent on the kind of soil and the moisture of the soil.  If, for example, you put a temperature probe wired with copper wires in a dry sand or gravel, you will get an average value of the temperature of the sunlight exposed hot cable. The reason is that the copper is leading the temperature down to where you measure and has a much higher conductivity compared to dry, coarse soil.  Thus it is important to think through your installation processes because it is likely you will have a different installation method in a clay soil versus a gravel soil.

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

Get more information on applied environmental research in our

Mar 16

The History and Future of Water Potential

I often hear researchers complain about the accuracy of our TEROS 21 water potential sensors.  We still have room to improve, but we’ve certainly come a long way! People have been attempting to make water potential measurement in the field for over 100 years. The following is a brief overview of the evolution and history of water potential measurements over that time.

Pre-MPS-1 Prototype

Pre-MPS-1 prototype.

Livingston Discs

The Livingston disc, developed in 1908, was one of the first attempts at determining water potential in the field.  The Livingston Disc was actually a primitive, manual version of the technology used in our MPS6 ceramic disc.  Here is how it worked:  first, you’d weigh the dry disk, then put it in the soil and let it equilibrate.  After that, you would dig it up and weigh it again.  Using the water retention curve of the disc, you could then determine the water potential.

Gypsum block

In the 1940s gypsum block sensors were invented as the first solid matrix equilibrium technique for water potential.  This method tried to continuously sense water potential with a simple electrical conductivity measurement in a solid porous (and naturally occurring) gypsum matrix.  However, because naturally occurring gypsum doesn’t have a consistent pore size distribution and it degrades over time, the instrument was not very accurate.

1940's Gypsum Block Sensors

In the 1940’s gypsum block sensors were invented as the first solid matrix equilibrium technique for water potential. Image: www.soilmoisture.com

Tensiometers

In the 1960’s a liquid equilibration technique called tensiometry was discovered that allowed water potential measurement with good accuracy even in the presence of positive pore water pressures.   Tensiometers work extremely well in wetter soils with water potentials between 0 and -80 kPa and should be the choice for all wet soil applications, especially above -9 kPa where the MPS6 will not work (the air entry value for its ceramic is -9 kPa).  However, when the soil dries out the water under tension in the tensiometer eventually cavitates, causing the output to be useless until they are refilled.  Thus solid equilibrium techniques like the TEROS 21 are the best choice across the dry range.

1960 Tensiometer

Tensiometers are the most accurate way to measure water potential in the field in the wet range, but are limited to the plant optimal range of about -100 kPa and above.

The Evolution of Ceramic Discs

We learned with the gypsum blocks that one of the challenges in solid matrix water potential measurement is finding a material that will create the same water retention curve every time. In quest of this goal, the ceramic discs in sensors like the TEROS 21 have taken years of development.  Because of the limited range of the tensiometer, we wanted to develop a water potential sensor that could measure over a larger range.  The hardest part about developing that ceramic was getting a variety of pore sizes so the instrument could read said wide range of water potentials.  This started years ago in the lab of Dr. Gaylon Campbell at Washington State University where his technician, Kees Calisendorf, experimented over a long period of time to come up with the perfect recipe.

MPS1

The MPS1 was our original matric potential sensor released in 2001. It allowed for long-term monitoring in the field because, unlike gypsum, the ceramic did not degrade over time.

Even after we found a consistent ceramic, there were still outliers.  So creating a calibration method was essential to making an accurate sensor.  The first challenge was to be able to store calibration points in the sensor, which required a microprocessor.  The second, and more difficult task, was to establish a method to calibrate large numbers of sensors at once.  We tried many different approaches like pressure plate, equilibration over salt solutions, and even centrifugal force, but nothing worked.  Finally, in a discussion with our partner, UMS, we discovered the key.  We now can accurately calibrate 50 sensors at a time in only 12 hours.  Still, even with these advanced techniques, we only have a sensor with an accuracy of plus or minus 10%, but considering the history of how hard it’s been to develop consistent ceramic, this accuracy is exciting for the range that we can get.

MPS 2

The MPS 2 was our second matric potential sensor which offered two-point calibration and a temperature sensor, improving accuracy.

What’s Next?

Now that we’ve created a reliable calibration method, we can turn our attention toward further improving the sensor measurement range as well as its accuracy.  Testing different ceramics, or other porous media, may hold the key to a solid equilibrium technique sensor reading all the way to 0 kPa, eventually replacing the need for tensiometers in the field.

TEROS 21

The two key innovations in the MPS6 (now called TEROS 21), released in 2014, are the addition of a microprocessor to the sensor and fast, accurate equilibration at multiple points.

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”—>

Get more information on applied environmental research in our

Dec 15

Despite Drawbacks, Scientific Collaboration Pays Off

Though collaboration can fuel innovation and increase the relevance and complexity of the scientific questions we study, I’ve noticed it does have its ups and downs.  The highs and lows we’ve run into on our research projects may help others avoid some of the pitfalls we experienced as many diverse groups tried to learn how to work together.

collaboration

Researchers discussing science at the Lytle Ranch Preserve, a remarkable desert laboratory located at the convergence of the Great Basin, Colorado Plateau, and Mojave Desert biogeographical regions.

There can be bumps in the road when collaborating with companies who want to test their product. Being at the forefront of innovation means that untested sensors may require patience as you work out all the bugs together. But from my perspective, this is part of the fun.  If we are late adopters of technology, we wouldn’t get to have a say in creating the sensors that will best fit our projects as scientists.

Collaborating scientists can also sometimes run into problems in terms of the stress of setting up an experiment in the time frame that is best for everyone.  During our experiment on the Wasatch Plateau, we had six weeks to get together soil moisture and water potential sensors, but our new GS3 water content, temperature, and EC sensors had never been outside of the lab. In addition, we planned to use an NDVI sensor concept that came out of a workshop idea my father Gaylon had.  We’d made ONE, and it seemed to work, but that is a long way from the 20 we needed for a long-term experiment in a remote location at 3000 meters elevation. In the end, it all worked out, but not without several late nights and a bit of luck.  I remember students holding jackets over me to protect me from the rain as I raced to get the last sensor working.  Then we shut the laptop and ran down the hill, trying to beat a huge thunderstorm that started to pelt the area.

collaboration

Desert-FMP Researchers at the Lytle Ranch Preserve

Other challenges of scientific collaboration present organizational hardships.  One of the interesting things about the interdisciplinary science in the Desert FMP project is the complexity of the logistics, and maybe that’s a reason why some people don’t do interdisciplinary projects.  We are finding in order to get good data on the effects of small mammals and plants you need to coordinate when you are sampling small mammals and when you’re sampling plants.  Communicating between four different labs is complicated.  Each of the rainout shelters we use cover an area of approximately 1.5 m2 .  That’s not a lot of space when we have two people interested in soil processes and two people interested in plants who all need to know what’s going on underneath the shelter.  Deciding who gets to take a destructive sample and who can only make measurements that don’t change the system is really hard.  The interesting part of the project where we’re making connections between processes has required a lot of coordination, collaboration, and forward-thinking.

In spite of the headaches, my colleague and I continue to think of ways we can help each other in our research.  Maybe we’re gluttons for punishment, but I think the benefits far outweigh the trouble we’ve had.  For instance, in the above-mentioned Desert FMP project we’ve been able to discover that small mammals are influential in rangeland fire recovery (read about it here).  We only discovered that piece of the puzzle because scientists from differing disciplines are working together.  In our Wasatch Plateau project, my scientist colleague said it was extremely helpful for him to be working with an instrumentation expert who could help him with setup and technical issues.  Also, we’ve been able to secure some significant grants in our Cook Farm Project (you can read about it in an upcoming post) and answer some important questions that wouldn’t have occurred to either one of us, if we hadn’t been working together.  In addition, solving problems that have cropped up in our projects has spurred us on to a new idea for analyzing enormous streams of data in near-real time.  (read about it here).

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

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

Get more information on applied environmental research in our

Nov 17

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Near Real-Time Data Analysis

We are entering an era of cheap data.  Sensor technology has advanced to the point where it has become easy to collect large amounts of measurement data at high spatiotemporal resolution.

real-time data analysis

Hydroserver map screen: Using an off-the-shelf open source informatics system like Hydroserver kept us from reinventing what’s already out there, but allowed flexibility to program to our own needs.

We are now to the point where we have gigabytes worth of data on soil moisture, plant canopy processes, precipitation, wind speed, and temperature, but the amount of data is so overwhelming that we are having a difficult time dealing with it. The cost of measurement data is dropping so quickly, people are forced to change from a historical mindset where they analyzed individual data points to the mindset of turning gigabytes of data into knowledge.

real-time data analysis

Because Bioinformatics students are used to working with DNA data, they understand how to write computer programs that analyze large amounts of data in near real-time.

One approach suggested by my colleague Rick Gill, a BYU Ecologist, is to collaborate with bioinformatics students.  Because they are used to working with DNA data, these students understand how to write computer programs that analyze large amounts of data in near real-time.  Rick came up with the idea to tap these students’ expertise in order to analyze the considerable information he anticipates collecting in our Desert FMP Project, an experiment which will use TEROS 21 and SRS sensors to determine the role of varying environmental and biological factors involved in rangeland fire recovery.

Rick and I are predicting that near real-time data analysis will give us several advantages. First, we need readily available information so we can tell that sensors and systems are working at the remote site.  Large gaps in data are common for sites that aren’t visited often, and sensor failures are missed when data are collected but never analyzed.  With our new approach, all data are databased instantly, and the results are visualized as we go.  Not only that, we’ll be able to control what’s being analyzed as we see what’s happening.  We can tell the bioinformatics students what we need as we begin to see the results come in.  If we see important trends, we can assign them to analyze new data that may be relevant right away.

These techniques have the potential to help scientists from all disciplines become more efficient at collection and analysis of large data streams. Although we’ve started the process, we have yet to determine its effectiveness.  I will post more information as we see how well it is working and as new developments arise.

Watch Dr. Gill’s data analysis webinar: Finding Insights in Big Data Sets

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

Get more information on applied environmental research in our

Nov 1

Spectral Reflectance and Water Content in the Wasatch Plateau Experiment

We chose to collaborate with Brigham Young University in an experiment on the Wasatch Plateau in 2009 because a scientist friend of ours had been working in that area the previous five years, and he noticed there were big grazing responses.  The plants growing in the long-term grazed areas were all drought tolerant, while ungrazed plots had plants that were often found only in wetter areas.  The only difference was the fence that kept sheep on one side and not on the other.   The big question was: how does water influence plants in this ecosystem that we understand relatively well? The story had always been the influence of grazers, when in fact, maybe the indirect consequence of grazing was mediated by water.

water

The Wasatch Plateau above Ephraim Canyon, UT, USA.

METER donated some sensors in order to set up an experiment where we changed the amount of water in various plots of land. We had rain exclusion plots, and we had treatments where we collected all incoming rainfall and reapplied it either once a week or every three weeks.   This allowed us to say to what extent this system was controlled by water during the growing season.  To do this, we took measurements with our prototype NDVI Spectral Reflectance Sensor to measure canopy greenness. We also used our prototype volumetric water content sensors to measure soil moisture (this was a few years ago and the sensors were prototypes at the time).  Using these sensors, we found that water is critical in a system people have dismissed as being climate-controlled because it’s at the top of a mountain.

water

A very early prototype of a NDVI sensor measuring canopy greenness in experimental plots on the Wasatch Plateau.

It turns out the amount and timing of precipitation makes a big difference.  We were able to directly connect plant survival, not just to the grazing treatment, but to the actual amount of water that was in the soil. Also, using continuous NDVI data, we were able to look closely at the role of grazing on plant canopies.  When we looked at our NDVI data, we were able to see a seasonal signal, not just a single snapshot sample in time.  So by having the richer data from the data loggers, we obtained a more nuanced understanding of the impact of land use on these important ecological processes.

One of the mistakes we made was failure to include redundancy in the system.  We only had two replicates, so when one of them went down we ended up having just one little case study.  However, that mistake gave us new ideas on how to set up a better system using the right sensors for the job, and it generated a new idea on how to get real-time analysis of data.  In our new Desert FMP project, we have a much better-replicated system where more is invested in the number of sensors that we’re putting out. Each treatment combination will have five to ten water potential sensors.  We are also developing a system where we can analyze data in real-time, so this time we will know when a sensor goes out if a student accidentally kicks it.

 For more details on the Wasatch Plateau Experiment, watch for our published paper that we’ll link to when it comes out.

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

Get more information on applied environmental research in our

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