Dr. Yossi Osroosh, Precision Ag Engineer in the Department of Biological Systems Engineering at Washington State University, discusses where and why IoT fits into irrigation water management. In addition, he explores possible price, range, power, and infrastructure road blocks.
Wireless sensor networks collect detailed data on plants in areas of the field that behave differently.
Studies show there is a potential for water savings of over 50% with sensor-based irrigation scheduling methods. Informed irrigation decisions require real-time data from networks of soil and weather sensors at desired resolution and a reasonable cost. Wireless sensor networks can collect data on plants in a lot of detail in areas of the field that behave differently. The need for wireless sensors and actuators has led to the development of IoT (Internet of Things) solutions referred to as Low-Power Wide-Area Networking or LPWAN. IoT simply means wireless communication and connecting to some data management system for further analysis. LPWAN technologies are intended to connect low-cost, low-power sensors to cloud-based services. Today, there are a wide range of wireless and IoT connectivity solutions available raising the question of which LPWAN technology best suits the application?
IoT Irrigation Management Scenarios
The following are scenarios for implementing IoT:
buying a sensor that is going to connect to a wireless network that you own (i.e., customer supplied like Wi-Fi, Bluetooth),
buying the infrastructure or at least pieces of it to install on site (i.e., vendor managed LPWAN such as LoRaWAN, Symphony Link), and
relying on the infrastructure from a network operator LPWAN (e.g., LTE Cat-M1, NB-IOT, Sigfox, Ingenu, LoRWAN).
This is how cellular network operators or cellular IoT works. LPWAN technology fits well into agricultural settings where sensors need to send small data over a wide area while relying on batteries for many years. This distinguishes LPWAN from Bluetooth, ZigBee, or traditional cellular networks with limited range and higher power requirements. However, like any emerging technology, certain limitations still exist with LPWAN.
Individual sensor subscription fees in cellular IoT may add up and make it very expensive where many sensors are needed.
IoT Strengths and Limitations
The average data rate in cellular IoT can be 20 times faster than LoRa or Symphony Link, making it ideal for applications that require higher data rates. LTE Cat-M1 (aka LTE-M), for example, is like a Ferrari in terms of speed compared to other IoT technologies. At the same time, sensor data usage is the most important driver of the cost in using cellular IoT. Individual sensor subscription fee in cellular IoT may add up and make it very expensive where many sensors are needed. This means using existing wireless technologies like traditional cellular or ZigBee to complement LPWAN. One-to-many architecture is a common approach with respect to wireless communication and can help save the most money. Existing wireless technologies like Bluetooth LE, WiFi or ZigBee can be exploited to collect in-field data. In this case, data could be transmitted in-and-out of the field through existing communication infrastructure like a traditional cellular network (e.g., 3G, 4G) or LAN. Alternatively, private or public LPWAN solutions such as LoRaWAN gateways or cellular IoT can be used to push data to the cloud. Combination of Bluetooth, radio or WiFi with cellular IoT means you will have fewer bills to pay. It is anticipated that, with more integrations, the IoT market will mature, and costs will drop further.
Many of LPWAN technologies currently have a very limited network coverage in the U.S. LTE Cat-M1 by far has the largest coverage. Ingenu, which is a legacy technology, Sigfox and NB-IOT have very limited U.S. coverage. Some private companies are currently using subscription-free, crowd-funded LoRaWAN networks to provide service to U.S. growers: however, with a very limited network footprint. Currently, cellular IoT does not perform well in rural areas without strong cellular data coverage.
In two weeks: Dr. Osroosh continues to discuss IoT strengths and limitations in part 2.
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Whether researchers measure soil hydraulic properties in the lab or in the field, they’re only getting part of the picture. Laboratory systems are highly accurate due to controlled conditions, but lab measurements don’t take into account site variability such as roots, cracks, or wormholes that might affect soil hydrology. In addition, when researchers take a sample from the field to the lab, they often compress soil macropores during the sampling process, altering the hydraulic properties of the soil.
Roots, cracks, and wormholes all affect soil hydrology
Field experiments help researchers understand variability and real time conditions, but they have the opposite set of problems. The field is an uncontrolled system. Water moves through the soil profile by evaporation, plant uptake, capillary rise, or deep drainage, requiring many measurements at different depths and locations. Field researchers also have to deal with the unpredictability of the weather. Precipitation may cause a field drydown experiment to take an entire summer, whereas in the lab it takes only a week.
The big picture—supersized
Researchers who use both lab and field techniques while understanding each method’s strengths and limitations can exponentially increase their understanding of what’s happening in the soil profile. For example, in the laboratory, a researcher might use the PARIO soil texture analyzer to obtain accurate soil texture data, including a complete particle size distribution. They could then combine those data with a HYPROP-generated soil moisture release curve to understand the hydraulic properties of that soil type. If that researcher then adds high-quality field data in order to understand real world field conditions, then suddenly they’re seeing the larger picture.
Table 1. Lab and field instrument strengths and limitations
Below is an exploration of lab versus field instrumentation and how researchers can combine these instruments for an increased understanding of their soil profile. Click the links for more in-depth information about each topic.
Particle size distribution and why it matters
Soil type and particle size analysis are the first window into the soil and its unique characteristics. Every researcher should identify the type of soil that they’re working with in order to benchmark their data.
Particle size analysis defines the percentage of coarse to fine material that makes up a soil
If researchers don’t understand their soil type, they can’t make assumptions about the state of soil water based on water content (i.e., if they work with plants, they won’t be able to predict whether there will be plant available water). In addition, differing soil types in the soil’s horizons may influence a researcher’s measurement selection, sensor choice, and sensor placement.
Different readings in soil moisture sensors are caused by spatial variation in water content (see part 1). These readings provide researchers valuable information about soil texture, watering patterns, and water use. This week, learn two more strategies to keep in mind when trying to understand the varying patterns of soil moisture at your research or irrigation site.
In some crop studies, it may be important to account for horizontal variation.
Strategy #2: Crop Studies—Representing Variation in a Homogeneous Environment
In some research projects, it will be important to account for horizontal variation. How variable is the water content across a field? We did an experiment in which we set out a transect across a field of bare, tilled soil. Using a METER EC-5 soil moisture probe connected to a Procheck meter, we sampled water content at one meter intervals over a 58 meter distance. The individual readings are shown in Figure 1.
Figure 1. You can determine how many samples are necessary to characterize a homogeneous area in about an hour using and EC-5 soil moisture sensor and a ProCheck.
In this data set the samples are not spatially correlated. The variation is apparent. The mean water content of the data set is 0.198 m3m-3. The standard deviation is 0.023 m3m-3. The coefficient of variation is 12%. Using some simple geostatistics, we determined that three carefully placed sites would adequately represent the variation present in this very homogeneous environment. Of course, in some environments, samples will not be independent. If a semivariogram indicates that some underlying spatial factor influences soil moisture variability, you will have to consider that in your experimental design.
By taking into account the major relevant sources of soil moisture variation, you can plan enough sampling locations to draw conclusions from your data.
On a forested hillside, horizontal variation in soil moisture will obviously be significant. Determining how many sensors to use and where to place them is not at all trivial. Stratified sampling—systematically sampling from more uniform subgroups of a heterogeneous population—may be a better way to deal with this kind of variety. The researcher classifies the site into strata (eg., forested canopy, brush, hillside, valley) and evaluates the number of samples needed to statistically represent the variation present within each stratum.
Many people allow for the variation in soil moisture values that come from slope, orientation, vegetation, and canopy cover. Some fail to consider the important soil-level variations that come from soil type and density. By taking into account the major relevant sources of soil moisture variation, you can plan enough sampling locations to draw reasonable conclusions from your data. Choose too few locations, and you run the risk of missing the patterns that will lead to higher level understanding. Choose too many, and not only will you be unable to afford your experiment, you may miss the patterns altogether as your experiment overflows with random abundance.
Sometimes researchers want to compare dissimilar sites.
Comparing Data from Different Sites or Strata
Comparing absolute water content numbers can give confusing results. Both measurements are volumetric water content, but 35% here vs. 15% there actually tells us very little. Was the site in sand or clay, or something in between? If conditions at the two sites are virtually identical, the comparison may make some sense. But often, researchers want to compare dissimilar sites.
Figure 2. Changes in VWC with depth (convention: negative values indicate depths below soil surface) for the same time period at Site 1.
Water potential measurements determined by converting absolute volumetric water content to soil water potential using a moisture characteristic curve specific to each soil type can be used to compare results across sites. Comparing relative values—quantities of water used in centimeters for example—can also be both useful and valid.
Figure 3 below illustrates an experiment we performed in a dryland field where water content measurements were made over a growing season at 30, 60, 90, 120, and 150 cm below a wheat crop. The graph of soil moisture data shows how water is taken up from successively deeper layers. By subtracting one profile from another and summing over the layers where change occurs (for instance, in Figure 2 above, subtract the far left line from the far right line to see how much water was used from May 10th to August 21st), you can determine the amount of water used by the plants over a particular period. If similar data were taken at different sites or in different strata, these relative values, in terms of quantified water use, could form the basis of solid comparison studies.
Figure 3. Soil water content in winter wheat measured at 30 cm increments
Read more about accurate soil moisture: Can you sample the profile without a profile probe? Find out.
How Do you Know You’re Getting Accurate Soil Moisture?
Researchers and irrigators may wonder if their soil moisture sensors are accurate because probes at different locations in the same field have different water content readings. Different readings in soil moisture sensors are caused by spatial variation in water content. These readings provide researchers valuable information about soil texture, watering patterns, and water use. Here are some ideas and strategies to keep in mind when trying to understand the varying patterns of soil moisture at your research or irrigation site. Click the links for more in-depth information about accurate soil moisture.
One irrigator noticed a few sensors indicating low water content after a heavy rain that had uniformly wetted his vineyard.
Horizontal vs. Vertical Variation
It’s helpful to distinguish variation in the vertical from variation in the horizontal. Most people expect strong vertical variation due to wetting and drying patterns, soil horizonation, and compaction. Water content can vary drastically over distances of only a few centimeters, especially near the soil surface. Horizontal variation is typically less pronounced-in a bare or uniformly planted field, and at a given depth, it might be quite small. But surprisingly large variations can exist, indicating isolated patches of sand or clay or differences in topography. One irrigator noticed a few sensors indicating low water content after a heavy rain that had uniformly wetted his vineyard. Knowing that sand has a low field capacity water content, he surmised (correctly) that he had found the sandy areas in the vineyard.
Soil moisture sensors sometimes measure unexpected things.
Because properly installed dielectric soil moisture sensors lie in undisturbed (and therefore unanalyzed) soil, they sometimes measure unexpected things. One researcher buried a probe in what appeared to be a very dry location and was startled to measure 25 to 30% volumetric water content. Those readings made the soil appear saturated, but obviously it wasn’t. She dug down to the sensor and found a pocket of clay. As she discovered, it is impossible to get much information from an absolute water content measurement without knowing what type of soil the sensor is in.
Since we expect variation, how do we account for it? How many probes are needed to adequately characterize the water content in an application or experiment? There is no simple answer to this question. The answer will be affected by your site, your goals, and how you plan to analyze your data. Here are some things you might consider as you plan.
If a field will be irrigated as a unit, it should be monitored as a unit at one representative spot.
Strategy #1: Irrigation—Use Soil Moisture as an Indicator
What information do you have when you know a field’s volumetric water content? That number independently tells an irrigator very little. Soil moisture can be used like a gauge to show when a field is full and when it needs to be refilled, but the “full” and “empty” are only meaningful in context.
The goals of irrigation are to keep root zone water within prescribed limits and to minimize deep drainage. Understanding and monitoring the vertical variation lets you correlate a real time graph of water use data with above-ground field conditions and plant water needs. It makes sense to place probes both within and below the root zone.
By contrast, measuring horizontal variation—placing sensors at different spots in the field—is not very helpful. If a field will be irrigated as a unit, it should be monitored as a unit at one representative spot. Because there’s no way to adjust water application in specific spots, there’s no benefit to quantifying spatial variation in the horizontal. Like a float in a gas tank, a set of soil moisture sensors in the right spot will adequately represent the changing soil moisture condition of the whole field.
We recommend a single probe location in each irrigation zone with a minimum of one probe in the root zone and one probe below it. Additional probes at that site, within and below the root zone, will increase the reliability of the information for the irrigation manager, at minimal additional cost.
In two weeks: Learn two more techniques researchers use in crop studies and ecology studies to account for variability in order to obtain an accurate soil moisture picture.
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This week, guest author Dr. Michael Forster, of Edaphic Scientific Pty Ltd & The University of Queensland, writes about new research using irrigation curves as a novel technique for irrigation scheduling.
Growers do not have the time or resources to investigate optimal hydration for their crop. Thus, a new, rapid assessment is needed.
Measuring the hydration level of plants is a significant challenge for growers. Hydration is directly quantified via plant water potential or indirectly inferred via soil water potential. However, there is no universal point of dehydration with species and crop varieties showing varying tolerance to dryness. What is tolerable to one plant can be detrimental to another. Therefore, growers will benefit from any simple and rapid technique that can determine the dehydration point of their crop.
New research by scientists at Edaphic Scientific, an Australian-based scientific instrumentation company, and the University of Queensland, Australia, has found a technique that can simply and rapidly determine when a plant requires irrigation. The technique builds on the strong correlation between transpiration and plant water potential that is found across all plant species. However, new research applied this knowledge into a technique that is simple, rapid, and cost-effective, for growers to implement.
Current textbook knowledge of plant dehydration
The classic textbook values of plant hydration are field capacity and permanent wilting point, defined as -33 kPa (1/3 Bar) and -1500 kPa (15 Bar) respectively. It is widely recognized that there are considerable limitations with these general values. For example, the dehydration point for many crops is significantly less than 15 Bar.
Furthermore, values are only available for a limited number of widely planted crops. New crop varieties are constantly developed, and these may have varying dehydration points. There are also many crops that have no, or limited, research into their optimal hydration level. Lastly, textbook values are generated following years of intensive scientific research. Growers do not have the time, or resources, to completely investigate optimal hydration for their crop. Therefore, a new technique that provides a rapid assessment is required.
How transpiration varies with water potential
There is a strong correlation between transpiration and plant water potential: as plant water potential becomes more negative, transpiration decreases. Some species are sensitive and show a rapid decrease in transpiration; other species exhibit a slower decrease.
Plant physiologist refer to P50 as a value that clearly defines a species’ tolerance to dehydration. One definition of P50 is the plant water potential value at which transpiration is 50% of its maximum rate. P50 is also defined as the point at which hydraulic conductance is 50% of its maximum rate. Klein (2014) summarized the relationship between transpiration and plant water potential for 70 plant species (Figure 1). Klein’s research found that there is not a single P50 for all species, rather there is a broad spectrum of P50 values (Figure 1).
Figure 1. The relationship between transpiration (stomatal conductance) and leaf water potential for 70 plant species. The dashed red lines indicate the P80 and P50 values. The irrigation refill point can be determined where the dashed red lines intersect with the data on the graph. Image has been adapted from Klein (2014), Figure 1b.
Taking advantage of P50
The strong, and universal, relationship between transpiration and water potential is vital information for growers. A transpiration versus water potential relationship can be quickly, and easily, established by any grower for their specific crop. However, as growers need to maintain optimum plant hydration levels for growth and yield, the P50 value should not be used as this is too dry. Rather, research has shown a more appropriate value is possibly the P80 value. That is, the water potential value at the point that transpiration is 80% of its maximum.
Irrigation Curves – a rapid assessment of plant hydration
Research by Edaphic Scientific and University of Queensland has established a technique that can rapidly determine the P80 value for plants. This is called an “Irrigation Curve” which is the relationship between transpiration and hydration that indicates an optimal hydration point for a specific species or variety.
Once P80 is known, this becomes the set point at which plant hydration should not go beyond. For example, a P80 for leaf water potential may be -250 kPa. Therefore, when a plant approaches, or reaches, -250 kPa, then irrigation should commence.
P80 is also strongly correlated with soil water potential and, even, soil volumetric water content. Soil water potential and/or content sensors are affordable, easy to install and maintain, and can connect to automated irrigation systems. Therefore, establishing an Irrigation Curve with soil hydration levels, rather than plant water potential, may be more practical for growers.
Example irrigation curves
Irrigation curves were created for a citrus (Citrus sinensis) and macadamia (Macadamia integrifolia). Approximately 1.5m tall saplings were grown in pots with a potting mixture substrate. Transpiration was measured daily, between 11am and 12pm, with a SC-1 Leaf Porometer. Soil water potential was measured by combining data from an MPS-6 Matric Potential Sensor and WP4 Dewpoint Potentiometer. Soil water content was measured with a GS3 Water Content, Temperature and EC Sensor. Data from the GS3 and MPS-6 sensors were recorded continuously at 15-minute intervals on an Em50 Data Logger. When transpiration was measured, soil water content and potential were noted. At the start of the measurement period, plants were watered beyond field capacity. No further irrigation was applied, and the plants were left to reach wilting point over subsequent days.
Figure 2. Irrigation Curves for citrus and macadamia based on soil water potential measurements. The dashed red line indicates P80 value for citrus (-386 kPa) and macadamia (-58 kPa).
Figure 2 displays the soil water potential Irrigation Curves, with a fitted regression line, for citrus and macadamia. The P80 values are highlighted in Figure 2 by a dashed red line. P80 was -386 kPa and -58 kPa for citrus and macadamia, respectively. Figure 3 shows the results for the soil water content Irrigation Curves where P80 was 13.2 % and 21.7 % for citrus and macadamia, respectively.
Figure 3. Irrigation Curves for citrus and macadamia based on soil volumetric water content measurements. The dashed red line indicates P80 value for citrus (13.2 %) and macadamia (21.7 %).
From these results, a grower should consider maintaining soil moisture (i.e. hydration) above these values as they can be considered the refill points for irrigation scheduling.
Further research is required
Preliminary research has shown that an Irrigation Curve can be successfully established for any plant species with soil water content and water potential sensors. Ongoing research is currently determining the variability of generating an Irrigation Curve with soil water potential or content. Other ongoing research includes determining the effect of using a P80 value on growth and yield versus other methods of establishing a refill point. At this stage, it is unclear whether there is a single P80 value for the entire growing season, or whether P80 shifts depending on growth or fruiting stage. Further research is also required to determine how P80 affects plants during extreme weather events such as heatwaves. Other ideas are also being investigated.
For more information on Irrigation Curves, or to become involved, please contact Dr. Michael Forster: firstname.lastname@example.org
Klein, T. (2014). The variability of stomatal sensitivity to leaf water potential across tree species indicates a continuum between isohydric and anisohydric behaviours. Functional Ecology, 28, 1313-1320. doi: 10.1111/1365-2435.12289
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Dr. Colin Campbell, METER soil scientist, explains soil sensor differences, pros, cons, and things to consider when choosing which sensor will best accomplish your research goals. Use the following considerations to help identify the perfect sensor for your research. Explore the links for a more in-depth look at each topic.
Scientists often measure soil moisture at different depths to understand the effects of soil variability and to observe how water is moving through the soil profile.
CHOOSE THE RIGHT MEASUREMENT
Volumetric Water Content: If a researcher wants to measure the rise and fall of the amount (or percentage) of water in the soil, they will need soil moisture sensors. Soil is made up of water, air, minerals, organic matter, and sometimes ice. As a component, water makes up a percentage of the total. To directly measure soil water content, one can calculate the percentage on a mass basis (gravimetric water content) by comparing the amount of water, as a mass, to the total mass of everything else. However, since this method is labor-intensive, most researchers use soil moisture sensors to make an automated volume-based measurement called Volumetric Water Content (VWC). METER soil moisture sensors use high-frequency capacitance technology to measure the Volumetric Water Content of the soil, meaning they measure the quantity of water on a volume basis compared to the total volume of the soil. Applications that typically need soil moisture sensors are watershed characterization, irrigation scheduling, greenhouse management, fertigation management, plant ecology, water balance studies, microbial ecology, plant disease forecasting, soil respiration, hydrology, and soil health monitoring.
Water potential: If you need an understanding of plant-available water, plant water stress, or water movement (if water will move and where it will go), a water potential measurement is required in addition to soil moisture. Water potential is a measure of the energy state of the water in the soil, or in other words, how tightly water is bound to soil surfaces. This tension determines whether or not water is available for uptake by roots and provides a range that tells whether or not water will be available for plant growth. In addition, water always moves from a high water potential to a low water potential, thus researchers can use water potential to understand and predict the dynamics of water movement.
Understand your soil type and texture
In soil, the void spaces (pores) 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 held because of the surface association.
Clay holds water more tightly than a sand at the same water content because clay contains smaller pores and thus has 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. Because water is loosely bound in a sandy soil, the amount of water will deplete and replenish quickly. Clay soils hold water so tightly that water movement is slow. However, there is still available water.
Note: Use the PARIO soil texture analyzer to automate soil texture identification.
Two measurements are better than one
In all soil types and textures, soil moisture sensors are effective at measuring the percentage of water. Dual measurements—using a water potential sensor in addition to a soil moisture sensor—gives researchers the total soil moisture picture and are much more effective at determining when, and how much, to water. Water content data show subtle changes due to daily water uptake and also indicate how much water needs to be applied to maintain the root zone at an optimal level. Water potential data determine what that optimal level is for a particular soil type and texture.
The secondary products of a lightning strike include electromagnetic pulses, electrostatic pulses, and earth current transients.
Surge suppression components typically perform their suppression function by temporarily short circuiting the voltage between two wires, several devices, or ground.
Electromagnetic pulses are created by the strong magnetic field that is formed by the short term current flow taking place in the lightning strike. With current flows as high as 510kA per microsecond, these currents create very large magnetic fields. These short term magnetic fields then induce voltages onto wires and cables.
Electrostatic pulses are created by electrostatic fields that accompany a thunderstorm. Any cable suspended above the earth during a thunderstorm is immersed in the electrostatic field and will be electrically charged. Quick changes in the charges stored in both the clouds and earth take place whenever there is a lightning strike. The charge on the cable must now be discharged or neutralized. Unable to find a path to ground (earth), it breaks down insulation and component in its efforts to return to earth.
Earth current transients are the direct result of the neutralization process that immediately follows the end of lightning strike. Neutralization is accomplished by the movement or redistribution of charge along or near the earth’s surface from all the points where the charge had been initially induced to the point where the lightning strike has just terminated. Earth current transients create a shift in potential across a ground plan, often called a “ground bounce”.
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.
This week, we continue highlighting the second of two current research projects (see part one) which use soil moisture sensors to measure volumetric water content in tree stems and why this previously difficult to obtain measurement will change how we look at tree water use.
Tamarisk tree: an invasive species dominant in Sudan and arid parts of the United States. (Photo credit: biolib.cz)
Determining Tree Stem Water Content in Drought Tolerant Species
Tadaomi Saito and his research team were interested in using dielectric soil moisture sensors to measure the tree stem volumetric water content of mesquite trees and tamarisk, two invasive species dominant in Sudan and arid parts of the United States. Mesquite is a species that can access deep groundwater sources using their taproots which is how they compete with native species. Tamarisk, on the other hand, uses shallow, saline groundwater to survive. The team wanted to see if dielectric probes were useful for real-time measurement of plant water stress in these drought tolerant species and if these measurements could illuminate differing tree water-use patterns. These sensors could then potentially be used for precision irrigation strategies to assist in agricultural water management.
Temperature Calibration Was Essential
After calibrating the soil moisture sensors to the wood types in a lab, the team inserted probes into the stems of both trees. They also monitored groundwater and soil moisture content to try and infer whether or not the trees were plugged into a deep source of water. Interestingly, Saito found that, unlike soil, where temperature fluctuation is buffered, tree stems are subject to large variations in temperature throughout the course of the day. This temperature fluctuation interfered with the soil moisture probes’ ability to accurately measure VWC. The team came up with a simple method for accounting for temperature variability and were then able to obtain accurate VWC measurements.
Saito’s results were similar to Ashley Matheny’s study (see part 1), in that they found a lot of different patterns, even in trees of the same species. Water-use depended on where the trees were on the landscape. Some of them were tapped into groundwater, and the stem water storage didn’t change no matter how dry the soil became. Whereas others, depending on their position in the landscape, were very dependent on soil moisture conditions.
Saito’s study illustrates that we see everything about a tree that’s above ground, but we may have no sense of what’s going on below ground. We can put a soil moisture sensor in the ground and decide there’s plenty of moisture available. Or if conditions are dry, we may decide the tree is under drought stress, but we don’t know if that tree is tapped into a more permanent source of groundwater.
Other researchers have put soil moisture sensors in orchards looking at stem water storage from a practical standpoint for irrigation management. Their data didn’t work out so well because of cable sensitivity where water on the cable created false readings. However, the data they were able to obtain showed that some of the trees were plugged into water sources that were independent of the soil. Those trees were able to withstand drought and needed less irrigation, whereas other trees were much more sensitive to soil moisture.
If we had an inexpensive, easy to deploy measurement device plugged into every tree in an orchard, we could irrigate tree by tree, give them precisely what they needed, and account for their unique situation.
What Does it All Mean?
The interesting thing about using soil moisture sensors in a tree is that stem water content is a difficult to obtain piece of information that has now been made easier. Historically, we’ve focused on measuring sap flow, but that’s just how much water is flowing past the sensor. We’ve measured what’s in the soil: a pool of moisture that’s available to the tree. But some trees are huge in size, such as ones along the coast of California. They’re able to store vast amounts of water above-ground in their tissue. Understanding how a tree can use that water to buffer or get through periods of drought is a unique research topic that has had very little attention. With these kinds of sensors, we can start to investigate those questions.
Reference: Saito T., H. Yasuda, M. Sakurai, K. Acharya, S. Sueki, K. Inosako, K. Yoda, H. Fujimaki, M. Abd Elbasit, A. Eldoma and H. Nawata , Monitoring of stem water content of native/invasive trees in arid environments using GS3 soil moisture sensor , Vadose Zone Journal , vol.15 (0) (p.1 – 9) , 2016.03
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In an update to our previous blog, “Soil Moisture Sensors in a Tree?”, we highlight two current research projects using soil moisture sensors to measure volumetric water content (VWC) in tree stems and share why this previously difficult-to-obtain measurement will change how we look at tree water usage.
Researchers explore the feasibility of inserting capacitance soil sensors in tree stems as a real-time measurement.
Soil Moisture Sensors in Tree Stems?
In a recent research project, Ph.D. candidate Ashley Matheny of the University of Michigan used soil sensors to measure volumetric water content in the stems of two species of hardwood trees in a northern Michigan forest: mature red oak and red maple. Though both tree types are classified as deciduous, they have different strategies for how they use water. Oak is anisohydric, meaning the species doesn’t control their stomata to reduce transpiration, even in drought conditions. Isohydric maples are more conservative. If the soil starts to dry out, maple trees will maintain their leaf water potential by closing their stomata to conserve water. Ashley and her research team wanted to understand the different ways these two types of trees use stem water in various soil moisture scenarios.
Historically, tree water storage has been measured using dendrometers and sap flow data, but Ashley’s team wanted to explore the feasibility of inserting a capacitance-type soil sensor in the tree stems as a real-time measurement. They hoped for a practical way to make this measurement to provide more accurate estimations of transpiration for use in global models.
Scientists measured volumetric water content in the stems of two species of hardwood trees in a northern Michigan forest: mature red oak and red maple.
Ashley and her team used meteorological, sap flux, and stem water content measurements to test the effectiveness of capacitance sensors for measuring tree water storage and water use dynamics in one red maple and one red oak tree of similar size, height, canopy position and proximity to one another (Matheny et al. 2015). They installed both long and short soil moisture probes in the top and the bottom of the maple and oak tree stems, taking continuous measurements for two months. They calibrated the sensors to the density of the maple and oak woods and then inserted the sensors into drilled pilot holes. They also measured soil moisture and temperature for reference, eventually converting soil moisture measurements to water potential values.
Results Varied According to Species
The research team found that the VWC measurements in the stems described tree storage dynamics which correlated well with average sap flux dynamics. They observed exactly what they assumed would be the anisohydric and isohydric characteristics in both trees. When soil water decreased, they saw that red oak used up everything that was stored in the stem, even though there wasn’t much available soil moisture. Whereas in maple, the water in the stem was more closely tied to the amount of soil water. After precipitation, maple trees used the water stored in their stem and replaced it with more soil water. But, when soil moisture declined, they held onto that water and used it at a slower rate.
Researchers want to figure out the appropriate level of detail for tree water-use strategy in a global model.
Trees use different strategies at the species level
The ability to make a stem water content measurement was important to these researchers because much of their work deals with global models representing forests in the broadest sense possible. They want to figure out the appropriate level of detail for tree water-use strategy in a global model. Both oak and the maple are classified as broadleaf deciduous, and in a global model, they’re lumped into the same category. But this study illustrates that if you’re interested in hydrodynamics (the way that trees use water), deciduous trees use different strategies at the species level. Thus, there is a need to treat them differently to produce accurate models.
Reference: Matheny, A. M., G. Bohrer, S. R. Garrity, T. H. Morin, C. J. Howard, and C. S. Vogel. 2015. Observations of stem water storage in trees of opposing hydraulic strategies. Ecosphere 6(9):165. http://dx.doi.org/10.1890/ES15-00170.1
Next week: Learn about more research being done using soil moisture sensors to measure volumetric water content in tree stems.
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