Get better air temperature accuracy with this new method
Accurate air temperature is crucial for microclimate monitoring
The accuracy of air temperature measurement in microclimate monitoring is crucial because the quality of so many other measurements depend on it. But accurate air temperature is more complicated than it looks, and higher accuracy costs money. Most people know if you expose an air temperature sensor to the sun, the resulting radiative heating will introduce large errors. So how can the economical ATMOS 41’s new, non-radiation-shielded air temperature sensor technology be more accurate than typical radiation-shielded sensors?
We performed a series of tests to see how the ATMOS 41’s air temperature measurement compared to other sensors, and the results were surprising, even to us. Learn the results of our experiments and the new science behind the extraordinary accuracy of the ATMOS 41’s breakthrough air temperature sensor technology.
In this brief 30-minute webinar, find out:
Why you should care about air temperature accuracy
Where errors in air temperature measurement originate
The first principles energy balance equation and why it matters
Results of experiments comparing shielded sensor accuracy against the ATMOS 41
The science behind the ATMOS 41 and why its unshielded measurement actually works
Patterns of water replenishment and use give rise to large spatial variations in soil moisture over the depth of the soil profile. Accurate measurements of profile water content are therefore the basis of any water budget study. When monitored accurately, profile measurements show the rates of water use, amounts of deep percolation, and amounts of water stored for plant use.
Three common challenges to making high-quality volumetric water content measurements are:
making sure the probe is installed in undisturbed soil,
minimizing disturbance to roots and biopores in the measurement volume, and
eliminating preferential water flow to, and around, the probe.
All dielectric probes are most sensitive at the surface of the probe. Any loss of contact between the probe and the soil or compaction of soil at the probe surface can result in large measurement errors. Water ponding on the surface and running in preferential paths down probe installation holes can also cause large measurement errors.
Installing soil moisture sensors will always involve some digging. How do you accurately sample the profile while disturbing the soil as little as possible? Consider the pros and cons of five different profile sampling strategies.
Preferential flow is a common issue with commercial profile probes
Profile probes are a one-stop solution for profile water content measurements. One probe installed in a single hole can give readings at many depths. Profile probes can work well, but proper installation can be tricky, and the tolerances are tight. It’s hard to drill a single, deep hole precisely enough to ensure contact along the entire surface of the probe. Backfilling to improve contact results in repacking and measurement errors. The profile probe is also especially susceptible to preferential-flow problems down the long surface of the access tube.
Trench installation is arduous
Installing sensors at different depths through the side wall of a trench is an easy and precise method, but the actual digging of the trench is a lot of work. This method puts the probes in undisturbed soil without packing or preferential water-flow problems, but because it involves excavation, it’s typically only used when the trench is dug for other reasons or when the soil is so stony or full of gravel that no other method will work. The excavated area should be filled and repacked to about the same density as the original soil to avoid undue edge effects.
Digging a trench is a lot of work.
Augur side-wall installation is less work
Installing probes through the side wall of a single augur hole has many of the advantages of the trench method without the heavy equipment. This method was used by Bogena et al. with EC-5 probes. They made an apparatus to install probes at several depths simultaneously. As with trench installation, the hole should be filled and repacked to approximately the pre-sampling density to avoid edge effects.
Multiple-hole installation protects against failures
Digging a separate access hole for each depth ensures that each probe is installed into undisturbed soil at the bottom of its own hole. As with all methods, take care to assure that there is no preferential water flow into the refilled augur holes, but a failure on a single hole doesn’t jeopardize all the data, as it would if all the measurements were made in a single hole.
The main drawback to this method is that a hole must be dug for each depth in the profile. The holes are small, however, so they are usually easy to dig.
Single-hole installation is least desirable
It is possible to measure profile moisture by auguring a single hole, installing one sensor at the bottom, then repacking the hole, while installing sensors into the repacked soil at the desired depths as you go. However, because the repacked soil can have a different bulk density than it had in its undisturbed state and because the profile has been completely altered as the soil is excavated, mixed, and repacked, this is the least desirable of the methods discussed. Still, single-hole installation may be entirely satisfactory for some purposes. If the installation is allowed to equilibrate with the surrounding soil and roots are allowed to grow into the soil, relative changes in the disturbed soil should mirror those in the surroundings.
Bogena, H. R., A. Weuthen, U. Rosenbaum, J. A. Huisman, and H. Vereecken. “SoilNet-A Zigbee-based soil moisture sensor network.” In AGU Fall Meeting Abstracts. 2007. Article link.
Dr. Colin S. Campbell discusses whether TDR vs. capacitance (see part 1) is the right question, the challenges facing soil moisture sensor technology, and the correct questions to ask before investing in a sensor system.
It’s easy to overlook the obvious question: what is being measured?
What are You Trying to Measure?
When considering which soil water content sensor will work best for any application, it’s easy to overlook the obvious question: what is being measured? Time Domain Reflectometry (TDR) vs. capacitance is the right question for a researcher who is looking at the dielectric permittivity across a wide measurement frequency spectrum (called dielectric spectroscopy). There is important information in these data, like the ability to measure bulk density along with water content and electrical conductivity. If this is the desired measurement, currently only one technology will do: TDR. The reflectance of the electrical pulse that moves down the conducting rods contains a wide range of frequencies. When digitized, these frequencies can be separated by fast fourier transform and analyzed for additional information.
The objective for the majority of scientists, however, is to simply monitor soil water content instantaneously or over time, with good accuracy. There are more options if this is the goal, yet there are still pitfalls to consider.
Considerable research has been devoted to determining which soil moisture sensors meet expectation.
Each Technology Has Challenges
Why would a scientist pay $100+ for a soil volumetric water content (VWC) sensor, when there are hundreds of soil moisture sensors online costing between $5 and $15? This is where knowing HOW water content is measured by a sensor is critical.
Most sensors on home and garden websites work based on electrical resistivity or conductivity. The principle is simple: more water will allow more electrons to flow. So conductivity will change with soil water content. But, while it’s possible to determine whether water content has changed with this method, absolute calibration is impossible to achieve as salts in the soil water will change as the water content changes. A careful reading of sensor specs will sometimes uncover the measurement method, but sometimes, price is the only indication.
Somewhere between dielectric spectroscopy and electrical resistance are the sensors that provide simple, accurate water content measurement. Considerable research has been devoted to determining which of these meet expectation, and the results suggest that Campbell Scientific, Delta-T, Stevens, Acclima, Sentek, and METER (formerly Decagon Devices), provide accurate sensors vetted by soil scientists. The real challenge is installing the sensors correctly and connecting them to a system that meets data-collection and analysis needs.
Installation Techniques Affect Accuracy
Studies show there is a difference between mid-priced sensor accuracy when tested in laboratory conditions. But, in the field, sensor accuracy is shown to be similar for all good quality probes, and all sensors benefit from site specific soil calibration. Why? The reason is associated with the principle upon which they function. The electromagnetic field these sensors produce falls off exponentially with distance from the sensor surface because the majority of the field is near the electrodes. So, in the lab, where test solutions form easily around sensor rods, there are differences in probe performance. In a natural medium like soil, air gaps, rocks, and other detritus reduce the electrode-to-soil contact and tend to reduce sensor to sensor differences. Thus, picking an accurate sensor is important, but a high quality installation is even more critical.
Improper installation is the largest barrier to accuracy.
Which Capacitance Sensor Works Best?
Sensor choice should be based on how sensors will be installed, the nature of the research site, and the intended collection method. Some researchers prefer a profile sensor, which allows instruments to be placed at multiple depths in a single hole. This may facilitate fast installation, but air gaps in the auger pilot hole can occur, especially in rocky soils. Fixing this problem requires filling the hole with a slurry, resulting in disturbed soil measurements. Still, profile sensor installation must be evaluated against the typical method of digging a pit and installing sensors into a side-wall. This method is time consuming and makes it more difficult to retrieve sensors.
New technology that allows sensor installation in the side of a 10 cm borehole may give the best of both worlds, but still requires backfill and has the challenge of probe removal at the end of the experiment.
The research site must also be a consideration. If the installation is close to main power or easily reached with batteries and solar panels, your options are open: all sensors will work. But, if the site is remote, picking a sensor and logging system with low power requirements will save time hauling in solar panels or the frustration of data loggers running out of batteries.
Often times it comes down to convenience.
Data Loggers Can Be a Limitation
Many manufacturers design data loggers that only connect to the sensors they make. This can cause problems if the logging system doesn’t meet site needs. All manufacturers mentioned above have sensors that will connect to general data loggers such as Campbell Scientific’s CR series. It often comes down to convenience: the types of sensor needed to monitor a site, the resources needed to collect and analyze the data, and site maintenance. Cost is an issue too, as sensors range from $100 to more than $3000.
Successfully Measure Water Content
The challenge of setting up and monitoring soil water content is not trivial, with many choices and little explanation of how each type of sensor will affect the final results. There are a wealth of papers that review the critical performance aspects of all the sensors discussed, and we encourage you to read them. But, if soil water content is the goal, using one of the sensors from the manufacturers named above, a careful installation, and a soil-specific calibration, will ensure a successful, accurate water content measurement.
Time Domain Reflectometry (TDR) vs. capacitance is a common question for scientists who want to measure volumetric water content (VWC) of soil, but is it the right question? Dr. Colin S. Campbell, soil scientist, explains some of the history and technology behind TDR vs. capacitance and the most important questions scientists need to ask before investing in a sensor system.
TDR began as a technology the power industry used to determine the distance to a break in broken power lines.
In the late 1970s, Clarke Topp and two colleagues began working with a technology the power industry used to determine the distance to a break in broken power lines. Time Domain Reflectometers (TDR) generated a voltage pulse which traveled down a cable, reflected from the end, and returned to the transmitter. The time required for the pulse to travel to the end of the cable directed repair crews to the correct trouble spot. The travel time depended on the distance to the break where the voltage was reflected, but also on the dielectric constant of the cable environment. Topp realized that water has a high dielectric constant (80) compared to soil minerals (4) and air (1). If bare conductors were buried in soil and the travel time measured with the TDR, he could determine the dielectric constant of the soil, and from that, its water content. He was thus able to correlate the time it took for an electromagnetic pulse to travel the length of steel sensor rods inserted into the soil to volumetric water content. Despite his colleagues’ skepticism, he proved that the measurement was consistent for several soil types.
TDR sensors consume a lot of power. They may require solar panels and larger batteries for permanent installations.
TDR Technology is Accurate, but Costly
In the years since Topp et al.’s (1980) seminal paper, TDR probes have proven to be accurate for measuring water content in many soils. So why doesn’t everyone use them? The main reason is that these systems are expensive, limiting the number of measurements that can be made across a field. In addition, TDR systems can be complex, and setting them up and maintaining them can be difficult. Finally, TDR sensors consume a lot of power. They may require solar panels and larger batteries for permanent installations. Still TDR has great qualities that make these types of sensors a good choice. For one thing, the reading is almost independent of electrical conductivity (EC) until the soil becomes salty enough to absorb the reflection. For another, the probes themselves contain no electronics and are therefore good for long-term monitoring installations since the electronics are not buried and can be accessed for servicing, as needed. Probes can be multiplexed, so several relatively inexpensive probes can be read by one set of expensive electronics, reducing cost for installations requiring multiple probes.
Many modern capacitance sensors use high frequencies to minimize effects of soil salinity on readings.
Advances in Electronics Enable Capacitance Technology
Dielectric constant of soil can also be measured by making the soil the dielectric in a capacitor. One could use parallel plates, as in a conventional capacitor, but the measurement can also be made in the fringe field around steel sensor rods, similar to those used for TDR. The fact that capacitance of soil varies with water content was known well before Topp and colleagues did their experiments with TDR. So, why did the first attempt at capacitance technology fail, while TDR technology succeeded? It all comes down to the frequency at which the measurements are made. The voltage pulse used for TDR has a very fast rise time. It contains a range of frequencies, but the main ones are around 500 MHz to 1 GHz. At this high frequency, the salinity of the soil does not affect the measurement in soils capable of growing most plants.
Like TDR, capacitance sensors use a voltage source to produce an electromagnetic field between metal electrodes (usually stainless steel), but instead of a pulse traveling down the rods, positive and negative charges are briefly applied to them. The charge stored is measured and related to volumetric water content. Scientists soon realized that how quickly the electromagnetic field was charged and discharged was critical to success. Low frequencies led to large soil salinity effects on the readings. This new understanding, combined with advances in the speed of electronics, meant the original capacitance approach could be resurrected. Many modern capacitance sensors use high frequencies to minimize effects of soil salinity on readings.
NASA used capacitance technology to measure water content on Mars.
Capacitance Today is Highly Accurate
With this frequency increase, most capacitance sensors available on the market show good accuracy. In addition, the circuitry in them can be designed to resolve extremely small changes in volumetric water content, so much so, that NASA used capacitance technology to measure water content on Mars. Capacitance sensors are lower cost because they don’t require a lot of circuitry, allowing more measurements per dollar. Like TDR, capacitance sensors are reasonably easy to install. The measurement prongs tend to be shorter than TDR probes so they can be less difficult to insert into a hole. Capacitance sensors also tend to have lower energy requirements and may last for years in the field powered by a small battery pack in a data logger.
In two weeks: Learn about challenges facing both types of technology and why the question of TDR vs. Capacitance may not be the right question.
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Last week we discussed Normalized Difference Vegetation Index (NDVI) sampling across a range of scales both in space and in time, from satellites sampling the entire earth’s surface to handheld small sensors that measure individual plants or even leaves (see part 1). This week, learn about NDVI applications, limitations, and how to correct for those limitations.
Limitations of the Normalized Difference Vegetation Index tend to occur at the extremes of the spectrum.
People use NDVI to infer things like leaf area index (LAI) or fractional light interception (FPAR) of a canopy. Some scientists also associate NDVI with biomass or yield of a crop. People also use NDVI to get a sense of phenology (general temporal patterns of greenness), as well as where vegetation occurs or how much vegetation is in a particular location.
In Figure 4, you can see how the reflectance spectrum at a given canopy LAI changes with leaf area index, decreasing in the visible range while increasing in the near infrared.
At very low LAI’s, the reflectance spectrum is relatively undifferentiated between red and NIR (black line), but when LAI is high, there’s a strong absorption of red light by chlorophyll with a strong reflectance in the NIR. If fact, as LAI increases, there’s an ever-increasing reflectance in the near infrared around 800 nm.
Limitations of the Normalized Difference Vegetation Index tend to occur at the extremes of the spectrum. Any time there’s very low vegetation cover (majority of the scene is soil), NDVI will be sensitive to that soil. This can confound measurements. On the other extreme, where there’s a large amount of vegetation, NDVI tends to saturate. Notice the negligible difference between spectra at an leaf area index (LAI) of 3 (purple) versus 6 (green). Indeed, in a tropical forest, NDVI will not be sensitive to small changes in the LAI because LAI is already very high. However, several solutions exist.
Solution 1-Soil Adjusted Vegetation Index
Figure 5 shows the results of a study taking spectral measurements of different vegetation indices across a transect of bare soil. Moving from dry clay loam to wet clay loam, we see a very strong response of NDVI due to the wetness of the soil; undesirable if we’re measuring vegetation. We’re not interested in an index that’s sensitive to changes in soil or soil moisture. However, there are a few other indices plotted in figure 5 with much lower sensitivities to variations in the soil across the transect.
Figure 5: Qi et al. (1994) Rem. Sens. Env.
The first one of those indices is the Soil Adjusted Vegetation Index (SAVI). The equation for SAVI is similar to NDVI. It incorporates the same two bands as the NDVI–the near infrared and the red.
Soil Adjusted Vegetation Index (Huete (1988) Rem. Sens. Env.)
The only thing that’s different, is the L parameter. L is a soil adjustment factor with values that range anywhere from 0 to 1. When vegetation cover is 100%, L is 0 because there’s no need for a soil background adjustment. However, when vegetation cover is very low, that L parameter will approach one. Because it is difficult to measure exactly how much vegetation cover you have without using NDVI, we can modify the NDVI so it’s not sensitive to soil by guessing beforehand what L should be. It’s common practice to set L to an intermediate value of 0.5. You can see in Figure 5 the Soil Adjusted Vegetation Index or SAVI has a much lower sensitivity to the soil background.
Solution 2- Modified SAVI
The next vegetation index is the modified SAVI (MSAVI). The SAVI equation contains an L parameter that we have to estimate–not an accurate way of handling things. So a scientist named Key developed a universal optimum for L. We won’t get into the math, but he was able to simplify the SAVI equation to where there’s no longer a need for the L parameter, and the only inputs required are the reflectances in the near infrared and the red.
Modified SAVI (Qi et al. (1994) Rem. Sens. Env.)
This was a pretty significant advance as it circumvented the need to estimate or independently measure L. When Key compared SAVI to MSAVI, there was virtually no difference between the two indices in terms of their sensitivity to the amount of vegetation and their response to the soil background.
MSAVI compares well with SAVI in terms of dynamic range and noise level (Qi et al. (1994) Rem. Sens. Env.)
In Haiti, untreated human waste contaminating urban areas and water sources has led to widespread waterborne illness. Sustainable Organic Integrated Livelihoods (SOIL) has been working to turn human waste into a resource for nutrient management by turning solid waste into compost. Read more…
Estimating the relative humidity in soil? Most people do it wrong…every time. Dr. Gaylon S. Campbell shares a lesson on how to correctly estimate soil relative humidity from his new book, Soil Physics with Python, which he recently co-authored with Dr. Marco Bittelli. Read more.…
“How many soil moisture sensors do I need?” is a question that we get from time to time. Fortunately, this is a topic that has received substantial attention by the research community over the past several years. So, we decided to consult the recent literature for insights. Here is what we learned.
Globally, the number one reason for data loggers to fail is flooding. Yet, scientists continue to try to find ways to bury their data loggers to avoid constantly removing them for cultivation, spraying, and harvest. Chris Chambers, head of Sales and Support at Decagon Devices always advises against it. Read more…
During a recent semester at Washington State University a film crew recorded all of the lectures given in the Environmental Biophysics course. The videos from each Environmental Biophysics lecture are posted here for your viewing and educational pleasure. Read more…
Soil moisture sensors belong in the soil. Unless, of course you are feeling creative, curious, or bored. Then maybe the crazy idea strikes you that if soil moisture sensors measure water content in the soil, why couldn’t they be used to measure water content in a tree? Read more…
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In the conclusion of our 3-part water potential series (see part 1), we discuss how to measure water potential–different methods, their strengths, and their limitations.
Vapor pressure methods work in the dry range.
How to measure water potential
Essentially, there are only two primary measurement methods for water potential—tensiometers and vapor pressure methods. Tensiometers work in the wet range—special tensiometers that retard the boiling point of water (UMS) have a range from 0 to about -0.2 MPa. Vapor pressure methods work in the dry range—from about -0.1 MPa to -300 MPa (0.1 MPa is 99.93% RH; -300 MPa is 11%).
Historically, these ranges did not overlap, but recent advances in tensiometer and temperature sensing technology have changed that. Now, a skilled user with excellent methods and the best equipment can measure the full water potential range in the lab.
There are reasons to look at secondary measurement methods, though. Vapor pressure methods are not useful in situ, and the accuracy of the tensiometer must be paid for with constant, careful maintenance (although a self-filling version of the tensiometer is available).
Here, we briefly cover the strengths and limitations of each method.
Vapor Pressure Methods:
The WP4C Dew Point Hygrometer is one of the few commercially available instruments that currently uses this technique. Like traditional thermocouple psychrometers, the dew point hygrometer equilibrates a sample in a sealed chamber.
WP4C Dew Point Hygrometer
A small mirror in the chamber is chilled until dew just starts to form on it. At the dew point, the WP4C measures both mirror and sample temperatures with 0.001◦C accuracy to determine the relative humidity of the vapor above the sample.
The most current version of this dew point hygrometer has an accuracy of ±1% from -5 to -300 MPa and is also relatively easy to use. Many sample types can be analyzed in five to ten minutes, although wet samples take longer.
At high water potentials, the temperature differences between saturated vapor pressure and the vapor pressure inside the sample chamber become vanishingly small.
Limitations to the resolution of the temperature measurement mean that vapor pressure methods will probably never supplant tensiometers.
The dew point hygrometer has a range of -0.1 to -300 MPa, though readings can be made beyond -0.1 MPa using special techniques. Tensiometers remain the best option for readings in the 0 to-0.1 MPa range.
Water content tends to be easier to measure than water potential, and since the two values are related, it’s possible to use a water content measurement to find water potential.
A graph showing how water potential changes as water is adsorbed into and desorbed from a specific soil matrix is called a moisture characteristic or a moisture release curve.
Example of a moisture release curve.
Every matrix that can hold water has a unique moisture characteristic, as unique and distinctive as a fingerprint. In soils, even small differences in composition and texture have a significant effect on the moisture characteristic.
Some researchers develop a moisture characteristic for a specific soil type and use that characteristic to determine water potential from water content readings. Matric potential sensors take a simpler approach by taking advantage of the second law of thermodynamics.
Matric Potential Sensors
Matric potential sensors use a porous material with known moisture characteristic. Because all energy systems tend toward equilibrium, the porous material will come to water potential equilibrium with the soil around it.
Using the moisture characteristic for the porous material, you can then measure the water content of the porous material and determine the water potential of both the porous material and the surrounding soil. Matric potential sensors use a variety of porous materials and several different methods for determining water content.
Accuracy Depends on Custom Calibration
At its best, matric potential sensors have good but not excellent accuracy. At its worst, the method can only tell you whether the soil is getting wetter or drier. A sensor’s accuracy depends on the quality of the moisture characteristic developed for the porous material and the uniformity of the material used. For good accuracy, the specific material used should be calibrated using a primary measurement method. The sensitivity of this method depends on how fast water content changes as water potential changes. Precision is determined by the quality of the moisture content measurement.
Accuracy can also be affected by temperature sensitivity. This method relies on isothermal conditions, which can be difficult to achieve. Differences in temperature between the sensor and the soil can cause significant errors.
All matric potential sensors are limited by hydraulic conductivity: as the soil gets drier, the porous material takes longer to equilibrate. The change in water content also becomes small and difficult to measure. On the wet end, the sensor’s range is limited by the air entry potential of the porous material being used.
In the second part of this month’s water potential series (see part 1), we discuss the separate components of a water potential measurement. The total water potential is the sum of four components: matric potential, osmotic potential, gravitational potential, and pressure potential. Below is a description of each component.
Matric potential arises because water is attracted to most surfaces through hydrogen bonding and van der Waals forces. This water droplet is pure but no longer free. The matric forces that bind it to the plastic have lowered its potential and you would have to use some energy to remove it from the surface and take it to a pool of pure, free water.
Soil is made up of small particles, providing lots of surfaces that will bind water. This binding is highly dependent on soil type. For example, sandy soil has large particles which provide less surface binding sites, while a silt loam has smaller particles and more surface binding sites.
The following figure showing moisture release curves for three different types of soil demonstrates the effect of surface area. Sand containing 10% water has a high matric potential, and the water is readily available to organisms and plants. Silt loam containing 10% water will have a much lower matric potential, and the water will be significantly less available.
Matric potential is always negative or zero, and is the most significant component of soil water potential in unsaturated conditions.
Osmotic potential describes the dilution and binding of water by solutes that are dissolved in the water. This potential is also always negative.
Osmotic potential only affects the system if there is a semi-permeable barrier that blocks the passage of solutes. This is actually quite common in nature. For example, plant roots allow water to pass but block most solutes. Cell membranes also form a semi-permeable barrier. A less-obvious example is the air-water interface, where water can pass into air in the vapor phase, but salts are left behind.
You can calculate osmotic potential from the following equation if you know the concentration of solute in the water.
Where C is the concentration of solute (mol/kg), ɸ is the osmotic coefficient (-0.9 to 1 for most solutes), v is the number of ions per mol (NaCl = 2, CaCl2 = 3, sucrose = 1), R is the gas constant, and T is the Kelvin temperature.
Osmotic potential is always negative or zero, and is significant in plants and some salt-affected soils.
Gravitational potential arises because of water’s location in a gravitational field. It can be positive or negative depending on where you are in relation to the specified reference of pure, free water at the soil surface. Gravitational potential is then:
Where G is the gravitational constant (9.8 m s-2) and H is the vertical distance from the reference height to the soil surface (the specified height).
You can feel positive pressure as you swim down into a lake or pool.
Pressure potential is a hydrostatic or pneumatic pressure being applied to or pulled on the water. It is a more macroscopic effect acting throughout a larger region of the system.
There are several examples of positive pressure potential in the natural environment.
For example, there is a positive pressure present below the surface of any groundwater. You can feel this pressure yourself as you swim down into a lake or pool. Similarly, a pressure head or positive pressure potential develops as you move below the water table.
Turgor pressure in plants and blood pressure in animals are two more examples of positive pressure potential.
Pressure potential can be calculated from:
Where P is the pressure (Pa) and P_W is the density of water.
Though pressure potential is usually positive, there are important cases where it is not. One is found in plants, where a negative pressure potential in the xylem draws water from the soil up through the roots and into the leaves.
This month in a 3 part series, we will explore water potential –the science behind it and how to measure it effectively.
To understand water potential, compare the water in a soil sample to water in a drinking glass.
Definition of Water Potential
Water potential is the energy required, per quantity of water, to transport an infinitesimal quantity of water from the sample to a reference pool of pure free water. To understand what that means, compare the water in a soil sample to water in a drinking glass. The water in the glass is relatively free and available; the water in the soil is bound to surfaces, diluted by solutes, and under pressure or tension. In fact, the soil water has a different energy state from “free” water. The free water can be accessed without exerting any energy. The soil water can only be extracted by expending energy. Water potential expresses how much energy you would need to expend to pull that water out of the soil sample.
Water potential is a differential property. For the measurement to have meaning, a reference must be specified. The reference typically specified is pure, free water at the soil surface. The water potential of this reference is zero. Water potential in the environment is almost always less than zero, because you have to add energy to get the water out.
You can’t tell by measuring heat content whether or not heat will be transferred to another object if the two touch each other.
Extensive vs. Intensive Variables
Water movement in the environment is really a physics problem, and to understand it, we have to distinguish between intensive and extensive variables. The extensive variable describes the extent or amount of matter or energy. The intensive variable describes the intensity or quality of matter or energy. For example, the thermal state of a substance can be described in terms of both heat content and temperature.
The two variables are related, but they are not the same. Heat content depends on mass, specific heat, and temperature. You can’t tell by measuring heat content whether or not heat will be transferred to another object if the two touch each other. So you also don’t know if the object is hot or cold, or whether it will be safe to touch.
These questions are much easier to answer if you know the intensive variable–temperature. In fact, though it can be important to measure both intensive and extensive variables, often the intensive variable gives you more useful information.
In terms of water, the extensive variable is water content, and it tells you the extent, or amount, of water in plant tissue or soil. The intensive variable is water potential, and it describes the intensity or quality of water in plant tissue or soil. Water content can only tell you how much water you have. If you want to know how fast it can move, you need to measure hydraulic conductivity. If you want to know whether it will move and where it’s going to go, you need water potential.
If you want to know whether water will move and where it’s going to go, you need water potential.
Two Key Water Potential Questions:
1. Where will water move? Water will always flow from high potential to low potential. This is the second law of thermodynamics—energy flows along the gradient of the intensive variable.
2. What is the availability of water to plants? Liquid water moves from soil to and through roots, through the xylem of plants, to the leaves, and eventually evaporates in the substomatal cavities of the leaf. The driving force for this flow is a water potential gradient. In order for water to flow, therefore, the leaf water potential must be lower than the soil water potential.
Limited water availability is a significant issue threatening the agricultural productivity of soybean, reducing yields by as much as 40 percent. Due to climate change, varieties with improved drought tolerance are needed, but phenotyping drought tolerance in the field is challenging, mainly because field drought conditions are unpredictable both spatially and temporally. This has led to the genetic mechanisms governing drought tolerance traits to be poorly understood. Researcher Clinton Steketee at the University of Georgia is trying to improve soybean drought tolerance by using improved screening techniques for drought tolerance traits, identifying new drought tolerant soybean germplasm, and clarifying which genomic regions are responsible for traits that help soybeans cope with water deficit.
Researchers are trying to improve soybean drought tolerance by using better screening techniques for drought tolerance traits.
Which Traits Are Important?
Clinton and his colleagues are evaluating a genetically diverse panel of 211 soybean lines in two different states, Kansas and Georgia, for over two years to help him accomplish his research objectives. These 211 lines come from 30 countries and were selected from geographical areas with low annual precipitation and newly developed soybean lines with enhanced drought-related traits, along with drought susceptible checks. The researchers are looking at traits such as canopy wilting. Some plants will take a few days longer to wilt, allowing these plants to continue their photosynthetic ability to produce biomass for seed production. Other traits that he is interested in evaluating are stomatal conductance, canopy temperature with thermal imaging, relative water content, and carbon isotope discrimination.
The scientists want to monitor traits such as canopy wilting.
Use of Microclimate Stations to Monitor Environmental Conditions
Clinton says to make selection of drought tolerant lines easier and more predictable, knowledge of field environmental conditions is critical. He says, “You can phenotype all you want, but you need the true phenotype of the plant to be observed under real drought conditions so you can discover the genes for drought tolerance and improve resistance down the line in a breeding program.”
In addition to soil moisture sensors, the team used microclimate weather stations to help monitor water inputs at their two field research sites and determine ideal time periods for phenotyping drought-related traits. Steketee says, “We put microenvironment monitors in the field next to where we were growing our experimental materials. Both locations use those monitors to keep an eye on weather conditions throughout the growing season, measuring temperature, humidity, and precipitation. Since we could access the data remotely, we used that information to help us determine when it was time to go out to the field and look at the plots. We wanted to see big differences between soybean plants if possible, especially in drought conditions. By monitoring the conditions we could just go back to our weather data to show we didn’t get rain for 3 weeks before we took this measurement, proving that we were actually experiencing drought conditions.”
The team identified some lines that performed well.
Results So Far
Though 2015 wasn’t a great year for drought in Georgia, Clinton says there was a period in late July when he was able to measure canopy wilting, and they identified some lines that performed well. He says, “We compared our data to the data collected by our collaborator in Kansas, and there are a few lines that did well in both locations. Hopefully another year of data will confirm that these plants have advantageous drought tolerance traits, and we’ll be able to probe the advantageous traits out of those lines and integrate them into our breeding program.”
The team will use what’s called a genome-wide association study approach to identify genomic regions responsible for drought tolerance traits of interest. This approach uses phenotypic information collected from the field experiments along with DNA markers throughout the soybean genome to see if that marker is associated with the trait they are interested in. If the scientists find the spot in the genome that is associated with the desired trait, they will then develop genomic tools to be used for selection, integrate that trait into elite germplasm, and ultimately improve the drought tolerance of soybeans.
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