Hydraulic conductivity is the ability of a porous medium (soil for instance) to transmit water in saturated or nearly saturated conditions. It’s dependent on several factors: size distribution, roughness, tortuosity, shape, and degree of interconnection of water-conducting pores. A hydraulic conductivity curve tells you, at a given water potential, the ability of the soil to conduct water.
One factor that affects hydraulic conductivity is how strong the structure is in the soil you’re measuring.
For example, as the soil dries, what is the ability of water to go from the top of a sample [or soil layer in the field] to the bottom. These curves are used in modeling to illustrate or predict what will happen to water moving in a soil system during fluctuating moisture conditions. Researchers can combine hydraulic conductivity data from two laboratory instruments, the KSAT and the HYPROP, to produce a full hydraulic conductivity curve (Figure 1).
Figure 1. Example of hydraulic conductivity curves for three different soil types. The curves go from field saturation on the right to unsaturated hydraulic conductivity on the left. They illustrate the difference between a well-structured clayey soil to a poorly structured clayey soil and the importance of structure to hydraulic conductivity especially at, or near, saturation.
In Hydrology 301, Leo Rivera, Research Scientist at METER, discusses hydraulic conductivity and the advantages and disadvantages of methods used to measure it.
Watch the webinar below.
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Dr. Yossi Osroosh, Precision Ag Engineer in the Department of Biological Systems Engineering at Washington State University, continues (see part 1) to discuss the strengths and limitations of IoT technologies for irrigation water management.
Informed irrigation decisions require real-time data from networks of soil and weather sensors at desired resolution and a reasonable cost.
LoRaWAN (a vendor-managed solution see part 1) is ideal for monitoring applications where sensors need to send data only a couple of times per day with very high battery life at very low cost. Cellular IoT, on the other hand, works best for agricultural applications where sensors are required to send data more frequently and irrigation valves need to be turned on/off. Low-Power Wide-Area Networking (LPWAN) technologies need gateways or base stations for functioning. The gateway uploads data to a cloud server through traditional cellular networks like 4G. Symphony Link has an architecture very similar to LoRaWAN with higher degree of reliability appropriate for industrial applications. The power budget of LTE Cat-M1 9 (a network operator LPWAN) is 30% higher per bit than technologies like SigFox or LoRaWAN, which means more expensive batteries are required. Some IoT technologies like LoRa and SigFox only support uplink suited for monitoring while cellular IoT allows for both monitoring and control. LTE-M is a better option for agricultural sensor applications where more data usage is expected.
NB-IoT is more popular in EU and China and LTE Cat-M1 in the U.S. and Japan. T-Mobile is planning to deploy NB-IoT network in the U.S. by mid-2018 following a pilot project in Las Vegas. Verizon and AT&T launched LTE Cat-M1 networks last year and their IoT-specific data plans are available for purchase. Verizon and AT&T IoT networks cover a much greater area than LoRa or Sigfox. An IoT device can be connected to AT&T’s network for close to $1.00 per month, and to Verizon’s for as low as $2 per month for 1MB of data. A typical sensor message generally falls into 10-200 bytes range. With the overhead associated with protocols to send the data to the cloud, this may reach to 1KB. This can be used as a general guide to determine how much data to buy from a network operator.
Studies show there is a potential for over 50% water savings using sensor-based irrigation scheduling methods.
What the future holds
Many startup companies are currently focused on the software aspect of IoT, and their products lack the sensor technology. The main problem they have is that developing good sensors is hard. Most of these companies will fail before batteries of their sensors die. Few will survive or prevail in the very competitive IoT market. Larger companies who own sensor technologies are more concerned with the compatibility and interoperability of these IoT technologies and will be hesitant to adopt them until they have a clear picture. It is going to take time to see both IoT and accurate soil/plant sensors in one package in the market.
With the rapid growth of IoT in other areas, there will be an opportunity to evaluate different IoT technologies before adopting them in agriculture. As a company, you may be forced to choose specific IoT technology. Growers and consultants should not worry about what solution is employed to transfer data from their field to the cloud and to their computer or smart phones, as long as quality data is collected and costs and services are reasonable. Currently, some companies are using traditional cellular networks. It is highly likely that they will finally switch to cellular IoT like LTE Cat-M1. This, however, may potentially increase the costs in some designs due to the higher cost of cellular IoT data plans.
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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 onsite (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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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. Read more…
Modern technology has made it possible to sample Normalized Difference Vegetation Index (NDVI) 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. Read more…
Globally, the gap between the energy production and consumption is growing wider. To promote sustainability, University of California San Diego PhD candidate and ASCE GI Sustainability in Geotechnical Engineering committee member, Tugce Baser, Dr. John McCartney, Associate Professor, and their research team, Dr. Ning Lu, Professor at Colorado School of Mines and Dr. Yi Dong, Postdoctoral Researcher at Colorado School of Mines, are working on improving methods for borehole thermal energy storage (BTES), a system which stores solar heat in the soil during the summer months for reuse in homes during the winter. Read more…
Weather data, used for flight safety, disaster relief, crop and property insurance, and emergency services, contributes over $30 billion in direct value to U.S. consumers annually. Since the 1990’s in Africa, however, there’s been a consistent decline in the availability of weather observations. Read more…
Plants require nutrients to grow, and if we fail to supply the proper nutrients in the proper concentrations, plant function is affected. Fertilizer in too high concentration can also affect plant function, and sometimes is fatal. 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…
We were inspired by this Freakonomics podcast, which highlights the book, This Idea Must Die: Scientific Problems that are Blocking Progress, to come up with our own answers to the question: Which scientific ideas are ready for retirement? We asked 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…
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 stomatal conductance varies with water potential
There is a strong correlation between stomatal conductance and plant water potential: as plant water potential becomes more negative, stomatal conductance decreases. Some species are sensitive and show a rapid decrease in stomatal conductance; 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 stomatal conductance 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 stomatal conductance 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 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 stomatal conductance and water potential is vital information for growers. A stomatal conductance 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 stomatal conductance 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 stomatal conductance 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. Stomatal conductance was measured daily, between 11am and 12pm, with an SC-1 Leaf Porometer. Soil water potential was measured by combining data from an MPS-6 (now called TEROS 21) 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 stomatal conductance 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.
Water potential is the most fundamental and essential measurement in soil physics because it describes the force that drives water movement.
Water potential helps researchers determine how much water is available to plants.
Making good water potential measurements is largely a function of choosing the right instrument and using it skillfully. In an ideal world, there would be one instrument that simply and accurately measured water potential over its entire range from wet to dry. In the real world, there is an assortment of instruments, each with its unique personality. Each has its quirks, advantages, and disadvantages. Each has a well-defined range.
Below is a comparison of water potential instruments and the ranges they measure.
A comparison of water potential instrument ranges
To learn more about measuring water potential, see the articles or videos below:
Soil moisture release curves have always had two weak areas: a span of limited data between 0 and -100 kPa and a gap around field capacity where no instrument could make accurate measurements.
Using HYPROP with the redesigned WP4C, a skilled experimenter can now make complete high-resolution moisture release curves.
Between 0 and -100 kPa, soil loses half or more of its water content. If you use pressure plates to create data points for this section of a soil moisture release curve, the curve will be based on only five data points.
And then there’s the gap. The lowest tensiometer readings cut out at -0.85 MPa, while historically the highest WP4 water potential meter range barely reached -1 MPa. That left a hole in the curve right in the middle of plant-available range.
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. (NOTE: The new TEROS Borehole Installation Tool eliminates preferential flow and reduces site disturbance while allowing you to install sensors at depths you choose.)
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.
An augered borehole disturbs the soil layers, but the relative size of the impact to the site is a fraction of what it would be with a trench installation. A trench may be about 60 to 90 cm long by 40 cm wide. A borehole installation performed using a small hand auger and the TEROS Borehole Installation Tool creates a hole only 10 cm in diameter—just 2-3% of the area of a trench. Because the scale of the site disturbance is minimized, fewer macropores, roots, and plants are disturbed, and the site can return to its natural state much faster. Additionally, when the installation tool is used inside a small borehole, good soil-to-sensor contact is ensured, and it is much easier to separate the horizon layers and repack to the correct soil density because there is less soil to separate.
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.
Salt in soil comes from the fertilizer we apply but also from irrigation water and dissolving soil minerals. If more salt is applied in the irrigation water than is leached or taken off in harvested plants, the soil becomes more saline and eventually ceases to support agricultural production (see part 1). This week, learn an effective way to measure electrical conductivity (EC) in soil.
How to Measure Electrical Conductivity of the Soil Solution
As mentioned above, the earliest measurements of solution conductivity were made on soil samples, but it was found to be more reliable to extract the soil solution and make the measurements on it. When values for unsaturated soils are needed, those are calculated based on the saturation numbers and conjecture about how the soil dried to its present state. Obviously a direct measurement of the soil solution conductivity would be better if it could be made reliably.
Two approaches have been made to this measurement. The first uses platinum electrodes embedded in ceramic with a bubbling pressure of 15 bars. Over the plant growth range the ceramic remains saturated, even though the soil is not saturated, allowing a measurement of the solution in the ceramic. As long as there is adequate exchange between the ceramic and the soil solution, this measurement will be the EC of the soil solution, pore water EC.
Salt in soil comes from the fertilizer we apply, irrigation water and dissolving soil minerals.
The other method measures the conductivity of the bulk soil and then uses empirical or theoretical equations to determine the pore water EC. The ECH2O 5TE uses the second method. It requires no exchange of salt between soil and sensor and is therefore more likely to indicate the actual solution electrical conductivity. The following analysis shows one of several methods for determining the electrical conductivity of the saturation extract from measurements of the bulk soil electrical conductivity.
Mualem and Friedman (1991) proposed a model based on soil hydraulic properties. It assumes two parallel conduction paths: one along the surface of soil particles and the other through the soil water. The model is
Here σb is the bulk conductivity which is measured by the probe, σs is the bulk surface conductivity, σw is the conductivity of the pore water, θ is the volumetric water content, θs is the saturation water content of the soil and n is an empirical parameter with a suggested value around 0.5. If, for the moment, we ignore surface conductivity, and use eq. 1 to compute the electrical conductivity of a saturated paste (assuming n = 0.5 and θs = 0.5) we obtain σb = 0.35σw. Obviously, if no soil were there, the bulk reading would equal the electrical conductivity of the water. But when soil is there, the bulk conductivity is about a third of the solution conductivity. This happens because soil particles take up some of the space, decreasing the cross section for ion flow and increasing the distance ions must travel (around particles) to move from one electrode of the probe to the other. In unsaturated soil these same concepts apply, but here both soil particles and empty pores interfere with ion transport, so the bulk conductivity becomes an even smaller fraction of pore water conductivity.
When water evaporates at the soil surface, or from leaves, it is pure, containing no salt, so evapotranspiration concentrates the salts in the soil.
Our interest, of course, is in the pore water conductivity. Inverting eq. 1 we obtain
In order to know pore water conductivity from measurements in the soil we must also know the soil water content, the saturation water content, and the surface conductivity. The 5TE measures the water content. The saturation water content can be computed from the bulk density of the soil
Where ρb is the soil bulk density and ρs is the density of the solid particles, which in mineral soils is taken to be around 2.65 Mg/m3 . The surface conductivity is assumed to be zero for coarse-textured soil. Therefore, using the 5TE allows us to quantify pore water EC through the use of the above assumptions. This knowledge has the potential to be a very useful tool in fertilizer scheduling.
Electrical Conductivity is Temperature Dependent
Electrical conductivity of solutions or soils changes by about 2% per Celsius degree. Because of this, measurements must be corrected for temperature in order to be useful. Richards (1954) provides a table for correcting the readings taken at any temperature to readings at 25 °C. The following polynomial summarizes the table
where t is the Celsius temperature. This equation is programmed into the 5TE, so temperature corrections are automatic.
Soil salinity has been measured using electrical conductivity for more than 100 years.
Units of Electrical Conductivity
The SI unit for electrical conductance is the Siemen, so electrical conductivity has units of S/m. Units used in older literature are mho/cm (mho is reciprocal ohm), which have the same value as S/cm. Soil electrical conductivities were typically reported in mmho/cm so 1 mmho/cm equals 1 mS/cm. Since SI discourages the use of submultiples in the denominator, this unit is changed to deciSiemen per meter (dS/m), which is numerically the same as mmho/cm or mS/cm. Occasionally, EC is reported as mS/m or µS/m. 1 dS/m is 100 mS/m or 105 µS/m.
Richards, L. A. (Ed.) 1954. Diagnosis and Improvement of Saline and Alkali Soils. USDA Agriculture Handbook 60, Washington D. C.
Rhoades, J. D. and J. Loveday. 1990. Salinity in irrigated agriculture. In Irrigation of Agricultural Crops. Agronomy Monograph 30:1089-1142. Americal Society of Agronomy, Madison, WI.
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