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Posts from the ‘Plant Genetics’ Category

Soil Moisture Sensors: Why TDR vs. Capacitance May Be Missing the Point

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 vs. Capacitance

TDR began as a technology the power industry used to determine the distance to a break in broken power lines.

Clarke Topp

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 vs. Capacitance

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.  

TDR vs. Capacitance

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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Get More From Your NDVI Sensor (Part 2)

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.

NDVI Sensor

Limitations of the Normalized Difference Vegetation Index tend to occur at the extremes of the spectrum.

NDVI Sensor

NDVI Applications

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.

NDVI Sensor

Figure 4

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.

NDVI Limitations

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.

NDVI Sensor

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.

NDVI Senso

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.  

NDVI Sensor

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.

NDVI Sensor

MSAVI compares well with SAVI in terms of dynamic range and noise level (Qi et al. (1994) Rem. Sens. Env.)

Next week:  Learn about solutions for high LAI.

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Top Five Blog Posts in 2016

In case you missed them the first time around, here are the most popular Environmental Biophysics.org blog posts in 2016.

Lysimeters Determine if Human Waste Composting can be More Efficient

Top five blog posts Environmental biophysics

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 Relative Humidity in Soil: How to Stop Doing it Wrong

Top five blog posts Environmental biophysics

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 You Need?

Top five blog posts Environmental biophysics

“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.

Data loggers: To Bury, or Not To Bury

Top five blog posts Environmental biophysics

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

Founders of Environmental Biophysics:  Champ Tanner

Top five blog posts Environmental biophysics

Image: http://soils.wisc.edu/people/history/champ-tanner/

We interviewed Gaylon Campbell, Ph.D. about his association with one of the founders of environmental biophysics, Champ Tanner.  Read more

And our three most popular blogs of all time:

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

Top five blog posts Environmental biophysics

We asked 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.  Read more

Environmental Biophysics Lectures

Top five blog posts Environmental biophysics

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 In a Tree?

Top five blog posts Environmental biophysics

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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How to Measure Water Potential

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.

How to measure water potential

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.

How to Measure Water Potential

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.

Advantages

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.

Limitations

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.

Secondary Methods

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.

download

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.

Limited Range

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.

Tensiometers and Traditional Methods

Read about the strengths and limitations of tensiometers and other traditional methods such as gypsum blocks, pressure plates, and filter paper at waterpotential.com

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Water Potential: The Science Behind the Measurement (Part 2)

In the second part of this month’s water potential  series (see part 1), we discuss the separate components of a water potential measurementThe 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

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.

matric potential

Osmotic Potential

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.

Ψ_0=CΦVRT    

 

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

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:

Ψ_G=GH

 

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).

matric potential

You can feel positive pressure as you swim down into a lake or pool.

Pressure Potential

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:

Ψ_P=P/Ρ_W

 

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.

Next Week: Learn the different methods for measuring water potential and their strengths and limitations.

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Secrets of Water Potential: Learn the Science Behind the Measurement

This month in a 3 part series, we will explore water potential –the science behind it and how to measure it effectively.

water potential

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.

water potential

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.

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.

Next week learn about the four components of water potential– osmotic potential, gravitational potential, matric potential, and pressure potential.

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How Many Soil Moisture Sensors Do You Need?

“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.

soil moisture sensors

In the spatial domain, soil moisture variability arises from differences in soil texture.

Other than the fact that most situations call for more than a single sensor if you are working in the field, it turns out that there are few hard and fast rules that can be applied universally. In fact, one study that attempted to answer this question found that the optimum number of samples ranged from four to 250 (Loescher et al., 2014). Obviously, study objectives, accuracy requirements, scale, and site-specific characteristics must be taken into account on a case-by-case basis. Although no single answer can capture all scenarios, there are some generalities that you can rely on for guidance.

Keep in mind that soil moisture is dynamic in both temporal and spatial domains. Having an understanding of the driving forces of variability in both of these domains provides insight into how to go about sampling.

Spatial Variability

In the spatial domain, soil moisture variability arises from differences in soil texture (Baroni et al., 2013; Vereecken et al. 2014), amount and type of vegetation cover (Baroni et al., 2013; Loescher et al., 2014; Tueling & Troch, 2005), topography (Brocca et al., 2010; Jacobs et al., 2004; Tueling & Troch, 2005), precipitation and other meteorological factors (Vereecken et al., 2014), management practices (Bogena et al., 2010; Korres et al., 2015; Vereecken et al., 2014), and soil hydraulic properties (García et al., 2014). As you plan your study, consider the variability in these landscape features to get a sense of how many sample locations you will need to capture the heterogeneity in soil moisture across your study domain.

soil moisture sensors

Soil moisture changes in predictable patterns associated with seasonal weather and vegetation dynamics.

Temporal Variability

Soil water content can be highly variable in the temporal domain as well. This is no big surprise since we expect soil moisture to change with precipitation, drought, irrigation, and evapotranspiration, and in predictable patterns associated with seasonal weather and vegetation dynamics (Wilson et al., 2004). While this is an easy concept to grasp for any given location, it becomes more complex when we consider the variability that arises from the interaction between temporal and spatial dynamics.

Although studies have found conflicting results (primarily due to differences in spatial and temporal sampling scales), there is growing consensus that spatiotemporal variability in soil moisture content behaves in the following predictable manners. The standard deviation of soil moisture is lowest under extreme wet and dry conditions and highest under intermediate soil moisture conditions (Famiglietti et al., 2008). At the same time, the coefficient of variation (CV) is negatively related to soil moisture (Bogena et al., 2010; Brocca et al., 2007; Famiglietti et al., 2008; Korres et al., 2015). In other words, soil moisture CV is highest under dry conditions and lowest under wet conditions. Finally, the probability distribution of soil moisture content values is negatively skewed under wet conditions and positively skewed under dry conditions (Bogena et al., 2010; Famiglietti et al., 2008). All of the above characteristics appear to be scale-independent (see Fig. 10 in Famiglietti et al., 2008).

soil moisture sensors

The standard deviation of soil moisture is lowest under extreme wet and dry conditions.

Two Examples

The following examples use simulated data to help illustrate the effects of spatial and temporal heterogeneity on soil moisture content. In the first example, we simulated soil moisture content for the same study site under wet and dry conditions and calculated the probability density functions (PDF). Under wet conditions (blue line in Fig. 1) the standard deviation was low and the PDF was negatively skewed. In contrast, dry conditions resulted in a larger standard deviation and a positively skewed PDF. This example demonstrates that the parameters describing the soil moisture PDFs are not static, but instead change through time depending on soil moisture conditions.

soil moisture sensors

Figure 1. Probability density function (PDF) of soil moisture content from the same field under dry (red) and wet (blue) conditions.

In the second example, we simulated soil water content for a single point in time when conditions were neither wet or dry. The resulting PDF is bimodal, indicating that there is more than one “population” of soil moisture content within the study site (Fig. 2). There are several reasons that soil moisture content can exhibit this type of multimodal distribution. It may be that there are areas with different soil textures (e.g., drier sandy and wetter silt loam areas), that the study area includes low-lying topography and adjacent hillslopes, or that the study area has heterogeneous vegetation cover.

soil moisture sensors

Figure 2. PDF for a snapshot in time at a location that has a heterogenous landscape.

The two simple examples above demonstrate the complex nature of soil moisture across time and space. Both examples suggest that parametric statistics and an assumption of normality may not always be valid when working with soil water content in field conditions (Brocca et al., 2007; Vereecken et al., 2014).

How Many Soil Moisture Sensors?

If your objective is to determine the “true” mean soil water content for your study area, then your sampling scheme will need to account for the sources of variability described above. If your study area has substantial topographical relief, heterogeneous canopy cover, and strong seasonality in precipitation, then you are likely going to need sensors located in areas that represent the major sources of heterogeneity. If instead, your study site is fairly homogenous or you are simply interested in the temporal pattern of soil water content (e.g., for irrigation scheduling), then you can likely get away with fewer soil moisture sensors due to temporal autocorrelation in the data (Brocca et al. 2010; Loescher et al., 2014).

It is labor intensive and difficult to capture all soil moisture dynamics using spot sampling.

It is clear that soil water content is highly dynamic in time and space. It is labor intensive and difficult to capture all of these dynamics using spot sampling, although some people do choose to go this route. Like so many other areas of environmental science, some of the deepest insights into soil moisture behavior are emerging from studies using networks of in-situ sensors (Bogena et al., 2010; Brocca et al., 2010). We believe that for most applications, the use of in-situ, continuous measurements will provide you with a superior understanding of soil water content.

For a more in-depth treatment of this topic, read the articles listed below. We recommend the review by Vereecken et al. (2014) as a good place to start.

REFERENCES

Baroni G, Ortuani B, Facchi A, Gandolfi C. (2013) The role of vegetation and soil properties on the spatio-temporal variability of the surface soil moisture in a maize-cropped field. Journal of Hydrology, 489:148-159.

Brocca L, Melone F, Moramarco T, Morbidelli R. (2010) Spatial‐temporal variability of soil moisture and its estimation across scales. Water Resources Research, 46, doi:10.1029/2009WR008016.

Brocca L, Morbidelli R, Melone F, Maramarco T. (2007) Soil moisture spatial variability in experimental areas of central Italy. Journal of Hydrology, 333:356-373.

Bogena HR, Herbst M, Huisman JA, Rosenbaum U, Weuthen A, Vereecken H. (2010) Potential of wireless sensor networks for measuring soil water content variability. Vadose Zone Journal, 9:1002-1013.

Famiglietti JS, Dongryeol R, Berg AA, Rodell M, Jackson TJ. (2008) Field observations of soil moisture variability across scales. Water Resources Research, 44, doi:10.1029/2006WR005804.

García GM, Pachepsky YA, Vereecken H. (2014) Effect of soil hydraulic properties on the relationship between the spatial mean and variability of soil moisture. Journal of Hydrology, 516:154-160.

Korres W, Reichenau TG, Fiener P, Koyama CN, Bogena HR, Cornelissen T, Baatz R, Herbst M, Diekkrüger B, Vereecken H, Schneider K. (2015) Spatio-temporal soil moisture patterns – A meta-analysis using plot to catchment scale data. Journal of Hydrology 520:326-341.

Loescher H, Ayres E, Duffy P, Luo H, Brunke M. (2014) Spatial variation in soil properties among North American Ecosystems and Guidelines for Sampling Designs. PLoS ONE 9, doi:10.1371/journal.pone.0083216

Tueling AJ, Troch PA. (2005) Improved understanding of soil moisture variability dynamics. Geophysical Research Letters, 32, doi:10.1029/2004GL021935

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Improving Drought Tolerance in Soybean

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.

drought tolerance

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.

drought tolerance

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.”

drought tolerance

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.”

Future Plans

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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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.

drought tolerance

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 homogenous 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.

drought tolerance

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.”

drought tolerance

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.”

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