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Posts tagged ‘Measurements’

Dec 4

Understanding the Influence of Coastal Fog on the Water Relations of a California Pine Forest

Forests along the California coast and offshore islands experience coastal fog in summer, when conditions are otherwise warm and dry. Since fog-water inputs directly augment water availability to forests during the dry season, a potential reduction of fog due to climate change would place trees at a higher risk of water stress and drought-induced mortality.  Dr. Sara Baguskas completed her Ph.D. research in the geography department at UC Santa Barbara on how variation in fog-water inputs impact the water relations of a rare, endemic tree species, Bishop pine, located on Santa Cruz Island in Channel Islands National Park. The goal of her study was to enhance our ability to predict how coastal forests may respond to climate change by better understanding how fog-water inputs influence the water budget of coastal forests.

Fog on Trees

Dr. Baguskas’ study seeks a better understanding of how fog-water inputs influence the water budget of coastal forests.

Fog Manipulation

Santa Cruz Island supports the southern extent of the species range in California, thus it is where we would expect to see a reduction in the species range in a warmer, drier, and possibly less foggy future. To advance our mechanistic understanding of how coastal fog influences the physiological function of Bishop pines, Dr. Baguskas conducted a controlled greenhouse experiment where she manipulated fog-water inputs to potted Bishop pine saplings during a three-week drydown period. She installed soil moisture (VWC) sensors horizontally into the side of several pots of sapling trees at two different depths (2 cm and 10 cm) and exposed the pines to simulated fog events with a fog machine.

In one group of plants, Baguskas let fog drip down to the soil, and in another treatment, she prevented fog drip to the soil so that only the canopies were immersed in fog.  She adds, “Leaf wetness sensors were an important complement to soil moisture probes in the second treatment because I needed to demonstrate that during fog events, the leaves were wet and soil moisture did not change.” Additionally, Baguskas used a photosynthesis and fluorescence system to measure photosynthetic rates in each group.

Fog in pine trees from the ground

The fog events had a significant, positive effect on the photosynthetic rate and capacity of the pines.

Results

Dr. Baguskas found that the fog events had a significant, positive effect on the photosynthetic rate and capacity of the pines.  The combination of fog immersion and fog drip had the greatest effect on photosynthetic rates during the drydown period, so, in essence, she determined that fog drip to the soil slows the impact of drydown.  

“But,” she says, “when I looked at fog immersion alone, when the plant canopies were wet by fog with no drip to the soil, I also saw a significant improvement in the photosynthetic rates of these plants compared to the trees that received no fog at all, suggesting that there could have been indirect foliar uptake of water through these leaves which enhanced performance.”  An alternative interpretation of that, Baguskas adds, is that nighttime fog events reduced soil evaporation rates, resulting in less evaporative loss of soil moisture.

Dr. Baguskas says her “canopy immersion alone” data are consistent with other research: Todd Dawson, Gregory Goldsmith, Kevin Simmonin, Carter Berry, and Emily Limm have all found that when you wet plant leaves, it has a physiological effect, suggesting the plants are taking water up through their leaves and not relying as much on soil moisture.  (These authors performed different types of experiments, but their papers serve as reference studies). Baguskas says, “My results suggest that is what’s going on, but it’s not as definitive as other studies that have actually worked on tracking the water through leaves using a stable isotope approach.”  

Lessons Learned

Though Dr. Baguskas did not monitor soil temperature in this study, she says that in the future, she will always combine temperature data with soil moisture data.  She comments, “Consistently, the soil moisture in the “canopy-immersed only” plants was slightly elevated over the soil moisture in the control plants.  It made me wonder if this was a biologically meaningful result. Does it support the fact that if plants are taking up water through their leaves, they don’t rely on as much soil moisture?  Or did my treatment change soil temperature, and is that having a confounding effect on my results?  What I’ve learned from this, is that in the future I will always use soil probes with temperature sensors because you may not know until you see your results if temperature might be important.”

Future Fog Studies

Baguskas is a USDA-NIFA postdoctoral Research Fellow working with Dr. Michael Loik in the Environmental Studies Department at UC Santa Cruz. She continues to study coastal fog, but now in strawberry fields. Her current research questions are focused on integrating coastal fog into water-use decisions in coastal California agriculture. She loves the work and continues to rely on soil moisture sensors to make meaningful and reliable environmental measurements in the field and greenhouse.

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Nov 13

Will Sample Disturbance Lead to Lower Accuracy?

Sampling soil for laboratory analysis of water potential is done for two basic reasons.  The simplest is to determine the current water potential of the soil.  The other is to determine the moisture release curve of the soil.  Regardless of the reason for measurement, the question of sample disturbance is important to ensure an accurate result.  Dr. Colin Campbell explains why:

researcher hand holding soil

Soil is disturbed when it’s removed from its natural structure.

Water Potential and Pore Size:

In soil samples, the void spaces (pores) in 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 going to be held because of the surface association.  

In a clay, water will be held more tightly than in a sand at the same water content because clay contains smaller pores and thus 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.

Sample Disturbance

Sample disturbance (disturbing soil pores when you remove a sample from the ground) becomes an issue depending on the water potential of your sample. Typically, the less negative (wetter) the water potential, the larger impact sample disturbance will have on the measurement.  We can do a calculation that shows there are specific pore sizes associated with specific water potentials (see table 1).

Water potential units diagram

If you disturb a sample with low water potential, permanent wilting point (-1.5 MPa) for example, the pores that are still filled with water would be approximately 0.2 um in diameter, far too small to be broken apart by scooping up a sample.  Thus, we could reasonably assume that your WP readings won’t be affected much.  But if you disturb soil with higher water potential, say field capacity (-0.033 MPa), it’s much more likely that water will be disturbed, as it fills pores to approximately 9 um.    

Hygrometers

Still, this is only an issue if you are attempting to measure in a high WP range.  If your chilled-mirror hygrometer only measures up to -1000 kPa, sample disturbance will not be an issue because those pores that will have broken will likely be larger than the sub-micrometer that are holding water, which is beyond the accuracy of your instrument.   However, some hygrometers can now measure to an upper limit of -100 kPa, which approaches the point where sample disturbance will make a difference.  

Tensiometers

If you are sampling to measure with a tensiometer (measures 0 kPa to -80kPa), it’s extremely important to keep your samples intact because tensiometers cover the emptying range of the largest pores found in soil.  A soil collar (sample ring) pounded into the ground will yield the most intact soil core.  It’s the best method to use if you need make sure soil pores remain undisturbed to yield an accurate water potential measurement.

For a more in-depth examination of the magnitude of the effects of sample disturbance, read this chilled-mirror hygrometer App Note detailing the subject.

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Nov 9

Screening for Drought Tolerance

Screening for drought tolerance in wheat species is harder than it seems.  Many greenhouse drought screenings suffer from confounding issues such as soil type and the resulting soil moisture content, bulk density, and genetic differences for traits like root mass, rooting depth, and plant size. In addition, because it’s so hard to isolate drought stress, some scientists think finding a repeatable screening method is next to impossible. However, a recent pilot study done by researcher Andrew Green may prove them wrong.

An automatic irrigation setup with green plants sticking out

Automatic Irrigation Setup

The Quest for Repeatability

Green says, “There have been attempts before of intensively studying drought stress, but it’s hard to isolate drought stress from heat, diseases, and other things.”  Green and his advisors, Dr. Gerard Kluitenberg and Dr. Allan Fritz, think monitoring water potential in the soil is the only quantifiable way to impose a consistent and repeatable treatment. With the development of a soil-moisture retention curve for a homogeneous growth media, they feel the moisture treatment could be maintained in order to isolate drought stress.  Green says, “Our goal is to develop a repeatable screening system that will allow us to be confident that what we’re seeing is an actual drought response before the work of integrating those genes takes place, since that’s a very long and tedious process.”

Why Hasn’t This Been Done Before?

Andrew Green, as a plant breeder, thinks the problem lies in the fact that most geneticists aren’t soil scientists. He says, “In past experiments, the most sophisticated drought screening was to grow the plants up to a certain point, stop watering them, and see which ones lived the longest. There’s never been a collaborative approach where physiologists and soil scientists have been involved.  So researchers have imposed this harsh, biologically irrelevant stress where it’s basically been an attrition study.” Green says he hopes in his research to use the soil as a feedback mechanism to maintain a stress level that mimics what exists in nature.

Data acquisition a cabinet setup for green's expanded experiment

Data Acquisition Cabinet setup for Green’s expanded experiment.

The Pilot Study

Green used volumetric water content sensors, matric potential sensors, as well as column tensiometers to monitor soil moisture conditions in a greenhouse experiment using 182 cm tall polyvinyl chloride (PVC) growth tubes and homogenous growth media. Measurements were taken four times a day to determine volumetric water content, soil water potential, senescence, biomass, shoot, root ratio, rooting traits, yield components, leaf water potential, leaf relative water content, and other physiological observations between moisture limited and control treatments.  

Soil Media:  Advantages and Disadvantages

To solve the problem of differing soil types, Andrew and his team chose a homogeneous soil amendment media called Profile Greens Grade, which has been extensively studied for use in space and other applications.  Green says, “It’s a very porous material with a large particle size.  It’s a great growth media because at the end of the experiment you can separate the roots of the plant from the soil media, and those roots can be measured, imaged, and studied in conjunction with the data that is collected.”   Green adds, however, that working with soil media isn’t perfect: there have been hydraulic conductivity issues, and the media must be closely monitored.

What’s Unique About this Study?

Green believes that because the substrate was very specific and his water content and water potential sensors were co-located, it allowed him to determine if all of his moisture release curves were consistent.  He says, “We try to pack these columns to a uniform bulk density and keep an eye on things when we’re watering, hoping it’s going to stay consistent at every depth.  So far it’s been working pretty well: the water content and the water potential are repeatable in the different columns.”

Irrigation setup for the expanded study with research data cabinet

Entire Irrigation setup for the expanded study.

Plans for the Future

Green’s pilot study was completed in the spring, and he’s getting ready for the expanded version of the project:  a replicated trial with wild relatives of wheat. He’s hoping to use soil moisture sensors to make automatic irrigation decisions: i.e. the water potential of the columns will activate twelve solenoid valves which will disperse water to keep the materials in their target stress zone, or ideal water potential.

The Ultimate Goal

The ultimate goal of Green’s research is to breed wild species of wheat into productive forms that can be used as farmer-grown varieties. He is optimistic about the results of his pilot study.  He says, “Based on the very small unreplicated data that we have so far, I think it is going to be possible to develop a repeatable method to screen these materials.  With the data that we’re seeing now, and the information that we’re capturing about what’s going on below ground, I think being able to hold these things in a biologically relevant stress zone is going to be possible.”

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.

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Nov 5

Accurate Field Saturated Hydraulic Conductivity—Why is it so difficult?

Inaccurate saturated hydraulic conductivity (Kfs) measurements are common due to errors in soil specific alpha estimation and inadequate 3D-flow buffering.  Leo Rivera, METER research scientist, explains why getting an accurate saturated hydraulic conductivity (Kfs) measurement is so difficult.

Farming driving tractor spraying his field

Water infiltrates the soil in three dimensions; it spreads laterally, as well as downward.

“Sorptivity, or the ability of soil to absorb water, has traditionally been a complex measurement for scientists to make.  This is because water infiltrates the soil in three dimensions; it spreads laterally, as well as downward.  The problem is, the value which represents sorptivity, Kfs, is a one-dimensional value.  Scientists use Kfs in modeling as the basis of their decision-making, but they have to remove the effects of the three-dimensional flow to get that value.  

“The traditional method for removing those effects is to look at a table of alphas or the soil macroscopic capillary length.  But since alpha is an estimate of the sorptivity effect, or how much the soil is going to pull the water laterally, if you use the wrong value, your estimate is going to be significantly off.

“The other problem with making this measurement is that most researchers have found the double ring infiltrometer does not buffer three-dimensional flow perfectly. Thus, if you are operating on the assumption that you’re getting one-dimensional flow in the center ring, you will overestimate your field saturated conductivity (Kfs) values.  This can be disastrous, particularly if you’re working with a soil that has been engineered to have a very low permeability.  If you overestimate Kfs, you could incorrectly assume your cover is ineffective (Ks is over 10-5 cm s-1).  But really, you’ve overestimated Kfs, and the cover may actually be compliant.”

Leo discusses solutions to these and other infiltrometer difficulties the webinar “Advances in Lysimeter Technology“. 

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Oct 31

Water Potential/Water Content:  When to Use Dual Measurements

In a previous post, we discussed water potential as a better indicator of plant stress than water content.  However, in most situations, it’s useful to take dual measurements and measure both water content and water potential.  In a recent email, one of our scientist colleagues explains why: “The earlier article on water potential was excellent.  But what should be added is an explanation that the intensity measurement doesn’t translate directly into the quantity of water stored or needed. That information is also required when managing water through irrigation.  This is why I really like the dual measurement approach. I am excited about the possibilities of information that can be gleaned from the combined set of water content, water potential, and spectral reflectance data.”

Field plantation with a sprinkler in the middle of it

Potato field irrigation

Managing Irrigation

The value of combined data can be illustrated by what’s been happening at the Brigham Young University Turf Farm, where we’ve been trying to optimize irrigation of turfgrass (read about it here). As we were thinking about how to control irrigation, we decided the best way was to measure water potential.  However, because we were in a sandy soil where water was freely available, we also guessed we might need water content. Figure 1 illustrates why.

Turf farm data concerning water potential diagram

Figure 1: Turf farm data: water potential only

Early water potential data looks uninteresting; it tells us there’s plenty of water most of the time, but doesn’t indicate if we’re applying too much.  In addition, if we zoom in to times when water potential begins to change, we see that it reaches a stress condition quickly.  Within a couple of days, it is into the stress region and in danger of causing our grass to go into dormancy.  Water potential data is critical to be able to understand when we absolutely need to water again, but because the data doesn’t change until it’s almost too late, we don’t have everything we need.

Turf farm data dual measurements data diagram

Figure 2: Turf farm data, volumetric water content only

Unlike water potential, the water content data (Figure 2) are much more dynamic. The sensors not only show the subtle changes due to daily water uptake but also indicate how much water needs to be applied to maintain the root zone at an optimal level. However, with water content data alone, we don’t know where that optimal level is. For example, early in the season, we observe large changes in water content over four or five days and may assume, based upon onsite observations, that it’s time to irrigate. But, in reality, we know little about the availability of water to the plant. Thus, we need to put the two graphs together (Figure 3).

Water potential and vol. water content diagram

Figure 3: Turfgrass data: both water potential and volumetric water content together.

In Figure 3, we have the total picture of what’s going on in the soil at the BYU turf farm. We see the water content going down and can tell at what percentage the plants begin to stress.  We also see when we’ve got too much water: when the water content is well above where our water potential sensors start to sense plant stress. With this information, we can tell that the turfgrass has an optimal range of 12% to 17% volumetric water content. Anything below or above that range will be too little or too much water.  

Soil water potential and volumetric water content diagram

Figure 4: Turfgrass soil moisture release curve (black). Other colors are examples of moisture release curves for different types of soil.

Dual measurements will also allow you to make in situ soil moisture release curves like the one above (Figure 4), which detail the relationship between water potential and water content.  Scientists can evaluate these curves and understand many things about the soil, such as hydraulic conductivity and total water availability.

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Oct 16

Measuring Frozen Water Potential: How and Why?

In China recently, a fellow scientist asked Dr. Colin Campbell if matric potential sensors work in frozen soils.  His answer? Sort of. In this blog, he explains what he meant by his enigmatic reply: When water freezes in the soil, most matric potential sensors won’t work accurately because frozen water essentially disappears to the measurement. For example, in a dielectric measurement circuit, most of the water that was polarized in the electromagnetic field solidifies in the ice matrix. Thus, because dielectric measurements determine the charge that is stored when water is polarized, ice is not measured. But, many matric potential sensors contain a component that will measure frozen water potential: the temperature sensor.

Frozen ground with horseshoe prints

Horseshoe prints in frozen soil.

How Does Temperature Measure Water Potential?  

The temperature of a frozen matrix like soil has a fundamental thermodynamic relationship to the energy state of that water. For every one degree C below freezing, the water potential decreases by 1.2 MPa. For example, if the soil drops down to -4 C, the soil water potential will be -4.8 MPa. However, one thing many people don’t understand is that there is still liquid water in frozen soils.

Where is the Liquid Water in a Frozen Soil?

Some liquid water will always be associated with soil surfaces because water, as a polar molecule, is attracted by opposite surface charges. Ice is a collection of water molecules that have slowed enough that they are arranged in a crystal-like structure. When ice arranges in that structure, it will attract and use all those water molecules that are available but will have difficulty stealing away water bound to soil surfaces. That water will remain liquid. As soil temperature drops, water layers closer and closer to soil particle surfaces will slow and join the ice structure.

Why Worry about Frozen Water Potential?

Previously, we’ve discussed the importance of water potential in determining the availability of water for plant growth. But below freezing, plants are either dormant or expired, so why measure frozen water potential?

There are a couple of reasons frozen soil water potential may be interesting to scientists. Liquid water in frozen soil still has the possibility to move. So, knowing soil temperature will allow models to predict water flow.  

Even more interesting is what could be done with a temperature sensor and a measurement of water content using dielectric permittivity. As we mentioned earlier, ice essentially disappears to a dielectric measurement.  Thus, a dielectric sensor water content measurement should provide the amount of liquid water in the soil. Using the temperature sensor to infer water potential (assuming the soil begins wet enough that its pre-frozen state has not reduced WP significantly), we can combine the WP and VWC measurements over a range of temperatures to generate an in situ moisture release curve. This idea was developed into a prototype instrument that appeared to have promise as a new laboratory technique to obtain moisture release curves.

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Oct 9

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Water Content helps Turf Growers find Water/Nutrient Balance

Many athletes don’t like artificial turf. They say it’s hot, uncomfortable to run on, causes burns when you slide or fall on it, and changes the way a ball moves.  Professional women’s soccer players even started a lawsuit over FIFA’s decision to use artificial turf in the 2015 Women’s World Cup.

Soccer players running after the soccer ball on a green field

Soccer players on natural turf.

Some universities—including Brigham Young University—have responded to athlete concerns by using natural turf fields for practice and in their stadiums. But the challenge is to develop plants and management practices for natural turf that help it stand up to frequent use and allow it to perform well even during the difficult fall months. It’s a perfect research opportunity.

BYU turf professor and manager of BYU sports turf, Bryan Hopkins and his colleagues in the Plant and Wildlife Department, have been able to set up a new state-of-the-art facility to study plants and soil in both greenhouse and natural conditions. The facility includes a large section of residential and stadium turfgrass.  

Before Soil Sensors

Initially, BYU maintained the turf farm grass on a standard, timer-based irrigation control system, but over time they realized that understanding the performance of their turf relative to moisture content and nutrient load is crucial. Last year during Memorial Day weekend their turf farm irrigation system stopped working when no one was around to notice.  During those four days temperatures rose to 40 C (100 F), and the grass in the field slipped into dormancy due to heat stress. In response, Dr. Hopkins began imagining a system of soil moisture sensors to constantly monitor the performance of the turf grass.  He wanted not only to make sure the turf never died but also to really understand the elements of stress so they could do a better job growing healthy turf.

Sensors Give a Clear Picture

Soon afterward, a team of scientists, including fellow professor Dr. Neil Hansen, installed volumetric water content (VWC) and matric potential sensors at two different sites: one in the sports turf and one in a residential turf plot.  Each plot had two installations of sensors at 6 cm and 15 cm, along with VWC only at 25 cm, to measure water moving beyond the root zone. Combining these measurements, they could clearly see when the grass was reaching stress conditions and how quickly the turf went from the beginning of stress (in terms of water content and time) to permanent wilting point. In addition, ancillary measurements of temperature and electrical conductivity provide an opportunity for modeling surface and root zone temperature as well as fertilizer concentration dynamics.

Researcher digging a dirt canal and installing sensors

Installing water content sensors at the BYU turf farm.

Errors Revealed

What the researchers learned was that they were using too much water. Dr. Colin Campbell, a METER research scientist who worked with BYU on sensor installation, comments, “We found in the first year that the plants never got stressed at all. So this year, the researchers allowed the water potential (WP) at 6 cm to drop into the stress range (~ -500 kPa) while observing WP at 15 cm (-50 kPa to -60 kPa). We hope this approach will reduce irrigation inputs while creating some stress in the grass in order to push the roots deeper.”

What’s happening with the water?

Dr. Campbell’s favorite part of the sensor data was the detailed picture it gave of what was happening with the water in the sandy soil (Figure 1). He says, “Most people believe that they have an intuitive feel for water availability in soil.  If we were only using water content sensors, seeing a typical value of 20% would lead us to believe we were comfortably in the middle of the plant available range (A).  But in this study, using our colocated soil water content and soil water potential sensors, the data showed readings over 15% VWC were too wet to affect the WP (B). However, once WP visibly changed, it quickly moved toward critical stress levels (C, -1500 kPa is permanent wilting point); it only took two days for the water potential to change from -8 kPa to -1000 kPa.  A subsequent dry period (D) shows similar behavior, but this time the 15 cm WP drops to near -1000 kPa.”

Water potential changes diagram

Figure 1

The plant stress levels were reached surprisingly quickly in this soil because its sand composition has a lot of large pores and not very many small ones (Figure 2). Campbell explains, “The large pores store water that is not held tightly due to low surface area, so the water is freely available. But at around 10% VWC all the water from the large pores is used up. As the soil dries beyond that, the water is held tightly in small pores and becomes increasingly unavailable. This is clear in the moisture release curve.  We see almost no change in water potential as the soil dried to 16% VWC, but from 10% down to 7%, the water potential reached permanent wilting point, and it happened in just over a day.”

VWC and Water potential sensors diagram

Figure 2

What the Future Holds:

The researchers wanted to make sure that if they went down to certain stress levels, they wouldn’t cause harm to the plants, so this year, they installed a weather station to monitor evapotranspiration and calculate irrigation application rates.  They also began measuring spectral reflectance to monitor changes in leaf area (NDVI) and photosynthesis (PRI).  This will enable them to see the impact on the plants as the turf is drying down.  “In the future,” says Campbell, “we hope that both commercial and residential turf growers will be able to more effectively control their irrigation and nutrients based on what we find in this study.”

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Sep 19

Assessing Erosion Risk after Forest Fires

As forest fires throughout the Northwest die down, one scientist’s work is just beginning.  An article from our archives details the important research that takes place in the aftermath of the flames:

Forest on fire with sun shining through the smoke

In 2015, over eight million acres of forest burned in the United States. Major fires burned in five northwestern states: Washington, Idaho, Montana, Oregon, and California.

Flagstaff, Arizona is typically a dry place. But in August 2010, churning rivers flowed down roadways and around—and through—homes in the Flagstaff area. The floods were caused by a fire—the 15,000 acre Shultz fire that raged around Flagstaff from April to July, 2010.

One might not ordinarily think of a fire causing a flood, but to Forest Service research engineer Dr. Peter Robichaud, the setup is classic. “After a fire, you’ve changed the hydrology of the hillside,” he says. “Normally in an unburned area, rain gets soaked up by forest floor material on the ground and then it soaks into the soil. After a fire goes through, there’s no forest floor material to soak up the water and the soil may become water repellent due to heat from the fire.”

Reduced infiltration means increased runoff and erosion. As Robichaud explains, “If you have a steep slope and high velocities, along with very erodible soil, things converge rather quickly and you can generate debris flows and mudslides.  It’s not just a 100% increase. It’s orders of magnitude increase.”

Burned trees standing in a swampy area covered in water

After a fire, soil commonly becomes hydrophobic, just one factor in increased runoff.

One of Robichaud’s research interests is in designing a model for post-fire erosion. The model helps land managers and assessment teams in the field to evaluate the risks such erosion might pose. “It lets them see what might be affected if they have an erosion event,” he says.

“Is it going to affect the municipal water supply, affect a road crossing, an interstate highway, a school that happens to be at the mouth of a canyon? Once they can estimate the amount of erosion that might occur, they can use the model to help pick treatments to reduce the risk.”

Often practitioners will use the model to establish an early warning system to areas that will be affected.

Along with developing the model, Robichaud has also looked for ways to help postfire assessment teams gauge the water repellency of the soil after a fire. Historically, soil in a burned area was evaluated using the water drop penetration time test, or WDPT. Team members would place a drop of water on the surface of the soil and time how long it took to be absorbed. This seventies-era test was easy to do in the field, but Robichaud wanted something more representative.

Trees and a street covered in a pool of water

One of Robichaud’s research interests is in designing a model for post-fire erosion to help land managers and assessment teams in the field evaluate the risks such erosion might pose.

“I’ve always felt we could do a better job of characterizing the changes in soil condition,” he says. “[The WDPT] doesn’t really represent the physical process of the water infiltrating, because you put a single drop of water on the surface… The ideal method is a rainfall simulator, but it’s not practical in the field. [You] can’t expect every assessment team after a fire to set up a rainfall simulator for a couple of weeks.”

As he looked for alternatives, Robichaud started using a Mini Disk Infiltrometer. Practitioners all over the world use infiltration measurements along with Robichaud’s model of post-fire erosion to assess the impacts of a fire, predict erosion, and make plans to manage and reduce the associated risks. Robichaud’s online Erosion Risk Management Tool allows researchers and assessment teams alike to use scientifically solid analysis. He’s currently involved in refining and validating the model, improving assessment techniques, using remote sensing technology to perform assessments, and looking at alternative post-fire treatment options to reduce erosion risk, among other things.

To see what Dr. Robichaud’s been up to recently, read his 2014 paper, The temporal evolution of wildfire ash and implications for post-fire infiltration, published in the International Journal of Wildland Fire.   Find out more about Robichaud’s research, methods for use of the Mini Disk Infiltrometer for changes in infiltration characteristics after fire, or access the Erosion Risk Management Tool, by visiting the Moscow Forest Sciences Laboratory website.

Learn more about wildfire and soil moisture

See how soil moisture information could improve assessments of wildfire probabilities and fuel conditions, resulting in better fire danger ratings here.

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Sep 11

Scientists and Greenhouse Growers Collaborate to Help the Environment

Bodies of water across the world face extreme pressure from non-point source pollution.  It’s easy to get overwhelmed by the sheer enormity of this problem, but it didn’t daunt Dr. John Lea-Cox, Research and Extension Specialist for horticulture at University of Maryland.  Dr. Lea-Cox was acutely aware that agriculturally applied fertilizers threatened serious harm to the Chesapeake Bay area near his home. Using an early version of METER’s water content sensors, he began to put together a system that could monitor water status in nursery operations. The effort was based on the work of Dr. Andrew Ristvey (now a colleague at Maryland) who showed water savings of more than 50% during his PhD work using TDR sensors in pots growing ornamental plants.  Dr. Lea-Cox and his colleagues wanted to ultimately develop a network of soil moisture and environmental sensors that would help greenhouse and nursery growers know when to turn on and off their water. Their goal was to reduce nutrient and water use through more efficient application.

Close up of a yellow flower with red tipped petals

How did Dr. John Lea-Cox, Research and Extension Specialist for horticulture at University of Maryland, convince nursery growers to reduce water and fertilizer use?

Convincing Growers

One hurdle facing Dr.Lea-Cox was that water savings didn’t resonate with all growers.  But he soon realized that better irrigation control influenced things growers did care about: higher quality crops, lower mortality rate, and less spending on pesticides.  Dr. Lea-Cox discovered that when he showed growers their moisture sensor data, they were hooked. One snapdragon grower, who found that he could use the sensors to produce a more lucrative A grade crop, said he would not like to go back to the days before sensors. “My gosh, it would be like going back ten years. It would be like trying to measure the temperature in a room without a thermometer. We are totally dependent on them.”

Pink orchids growing in a nursery super green nursery

Orchids grown in a nursery.

Finding Collaborators

Dr. Lea-Cox was not only good at convincing growers, but scientific collaborators as well.  Building on this team’s initial findings, he organized a project to develop water retention curves to tie the amount of water in pots to what was actually available to the plant for several different mixes of potting soil. He realized that moisture measurements were practically useless to growers without a mechanism for viewing them all in one place, so he began to look for collaborators who could build an integrated, wireless system to get root zone information to the nursery grower’s computer and allow them to set irrigation limits and automate their systems based on soil and weather data.  

The resulting collaboration was a group of diverse scientists and commercial growers who could study root behavior, plant-environmental interactions, the performance of the plants, and individual grower interaction with the system.  After a few years of testing, the group received $5M in funding from the Specialty Crops Research Initiative (SCRI) Program over five years to improve horticulture for ornamental plants grown in the U.S.

Lauren Crawford, METER’s soils product manager, says that the resulting collaboration was unique. “It was amazing that an instrumentation company, a research group, and commercial growers were able to work so well together. It was because of the trust we had for each other. We were very transparent about what we were doing, even when we knew that transparency would be difficult. The result was that we were able to make tremendous progress in both science and technology.”

Watch two virtual seminars highlighting SCRI research given by scientists Marc Van Iersel and John Lea-Cox.

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

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Aug 14

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The Potential of Drones in Research

Someday soon,  multi-rotors will execute pre-programmed flight paths over several hundred research plots collecting daily data and sending it back to a computer while researchers sip their morning coffee.  Researchers and growers won’t need to know anything about flying: the drones will fly themselves.  This is the dream.

One UAV (unmanned air vehicle) industry leader at the above drone demonstration commented, “The truth is that this is where agriculture (and research) is going, and I don’t mean ‘Tomorrowland’ going–I mean it’s pretty much there.  The only thing that’s holding us back is a permit from the FAA for autonomy, and that’s because the FAA is slowly backing into this UAV piece because we have the busiest general aviation sky in the world. But really, what you should have in your mind is multiple units operating with a single operator in a control vehicle.”  The above UAV was extensively tested in California’s NAPA valley with results soon to be published online.

In this blog, a METER scientist and an instrumentation engineer give their perspectives on what needs to happen before drones reach their full research potential.  

Drone hexacopter flying against a blue sky

Drone Hexacopter

What are the advantages of drones for researchers?

Dr. Colin Campbell, research scientist-

One of the biggest challenges of work in the field is variability: low spots, high spots, sandy soil, clay soil, hard pans beneath the surface in some areas and not in others.  This results in highly variable performance in crops.  In addition to that, even when you have good homogeneity in a field, you might have differences due to irrigation or rainfall. If we want to improve agriculture, one thing that we have to do is be able to come out with better tools to be able to visualize the field in more than a single dimension. In order to do this right now, students go out and take plant measurements all day, every day, all summer long. The advantage of a drone is that you could do flyovers of a field, monitoring the traits that you’re interested in using reflectance indices that would normally take days of work.

What are the obstacles to progress?

Greg Kelley, mechanical engineer, and drone hobbyist-   

Recently, the FAA has come out with a set of guidelines for the industrial use of drones:  flying machines have to stay under a certain ceiling (500 ft; 150 m), and they have to be flown in the line of sight of the operator.  The naive thing about those policies is: how much control does the operator have over the drone anyway?  It used to be that with your remote control, you were moving the control surfaces (flaps, rudder, etc) on the aircraft, but this is changing.  The onboard computer performs things like holding a stable altitude, maintaining a GPS location, or auto-stabilization (it keeps the aircraft level, even when a gust of wind comes).  Those are degrees of control that have been taken away from the operator. Thus, according to the level of automation that the operator has built into the system, he may not be in direct control at all times. In fact, these machines are being developed so that they can fly themselves. From my perspective, the FAA regulations are going to have to evolve along with the automation of drones in order to allow the development of this technology in an appropriate way.

Drone with eight rotors sitting on a landing pad

Drone with eight rotors.

What needs to happen before drones reach their full potential?

Dr. Colin Campbell–  

Even if we get the flexibility required with drones, we’ve got to get the right sensor on the drone. On the surface, this seems relatively simple.  Sensors to measure spectral reflectance are available in a package size that should easily mount on a drone platform. But, there are still many challenges.  First, current spectral reflectance sensors make a passive reflectance measurement, meaning we’re at the mercy of the reflected sunlight.  Clouds, sun angle, and leaf orientation, among other things, will all affect the measurement. There are several groups working on this (just search “drone NDVI” on the internet), but it’s a difficult problem to solve.  Second, drones create a spectral reflectance “map” of a field that needs to be geo-referenced to features on the ground to match measurements with position.  Once data are collected, the behavior of “plot A” can only be determined by matching the location and spectral reflectance of “plot A.”  Different from the first challenge, this is more related to programming than science but is still a major hurdle.

Despite these challenges, drones promise incredible benefits as an agricultural and environmental measurement tool. As one industry leader at the drone demonstration put it, “the complexity of the problems that agriculture faces and the opportunities for efficiencies are vast.  It will require ongoing engagement, next year and the year after that. There are a lot of questions to be answered and the efficacy is yet to be determined, but it’s exciting to watch the UAV helicopter and where it’s going.”  Both Campbell and Kelley agree that significant advances will be made within the next few years.

Read about an ROI calculator that’s been created to help growers quantify whether the benefits of using a drone will exceed their costs.

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

Get more information on applied environmental research in our

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