# Posts from the ‘Horticulture’ Category

## Chalk Talk: Why is Humidity Relative?

Dr. Colin Campbell, a senior research scientist at METER Group, as well as adjunct faculty at Washington State University teaches about relative humidity.

Watch the video to find out why we use the term relative humidity and why comparing RH at different research sites can be a challenge.

## Why is humidity relative?

Hi, I’m Dr. Colin Campbell. I’m a senior research scientist here at METER Group, as well as adjunct faculty up at Washington State University. And I teach a class in environmental biophysics. And today, we’re going to be talking about relative humidity. Have you ever looked at a weather report and wondered, what do they mean by the term relative? Why aren’t we talking about absolute things? And so today I’m going to talk about what is relative humidity? Well, relative humidity we’re going to define here as just hr. And hr is equal to the partial pressure of water vapor in air divided by the saturation vapor pressure or the maximum possible partial pressure of water in air as a function of temperature. So this is relative because anytime we have a partial pressure of water vapor, we’re always dividing it by the maximum possible water vapor that could be in the air at any point.

## Comparing RH at different sites is a challenge

So, why would relative humidity be such a challenge for us as scientists to use in comparing different sites? I wanted to talk about that so we can focus in here on this saturation vapor pressure. Over here we have Tetens equation. This says that the saturation vapor pressure, which is a function of air temperature is equal to 0.611 kPa times the exponential of a constant “b” times the air temperature divided by another constant “c” plus the air temperature. So at any point, depending on the air temperature, we can calculate the saturation vapor pressure, and then we can put it back into this equation and get our relative humidity. There are two situations we might think about for calculating our saturation vapor pressure. The most typical is this one: where that constant “b” is 17.502 degrees C. And the constant “c” is 240.97 degrees C (the units on this are degrees C, so these will cancel). If we’re over ice, those constants will be different: “b” would be 21.87 degrees C and “c” would be 265.5 degrees C.

So as I mentioned, relative humidity is a challenging variable to use in research because while vapor pressure (ea) (the vapor pressure of the air) is somewhat conservative across a day, the saturation vapor pressure (with respect to air temperature), this changes slowly with temperature across the day. So if we graphed temperature on one axis and the relative humidity on the other axis, we might during a typical day have a temperature range that looks somewhat like this. And even if the actual vapor pressure “ea” wasn’t changing, we’d see a relative humidity trend that looked like this: only changing because of air temperature. And because of that, if we wondered how do I compare the water in the air at one research site, for example, with the water in the air at another research site? We might be inclined to average them. But because of this trend, the average of the relative humidity at any site tends to be around 0.60 to 0.65 and therefore will be totally irrelevant in the literature.

So we need to speak in absolutes, and in my next lecture, I’m going to go into what we can do to calculate that absolute relative humidity. If you want to know more about making measurements in the atmosphere, go to metergroup.com, look at our atmospheric instrumentation, and you can learn more from there.

## Data deep dive: why am I seeing diurnal changes in soil moisture?

In the video below, METER soil scientist Dr. Colin Campbell discusses an often-misdiagnosed water content signal that looks like typical diurnal temperature cycling but is actually due to a phenomenon called hydraulic redistribution. He shows how easily these patterns can be seen in ZENTRA Cloud data management software.

## Watch the video

To understand how soil moisture and soil water potential work together, download “The researcher’s complete guide to water potential.”

## Video transcript

Hello, my name is Colin Campbell. I’m a research scientist here at METER Group. And today we’re going to be digging into some water content data that I collected over the last summer. This is a field that’s planted in spring wheat, it’s about 700 meters across. And we’ve set up six measurement sites. At each one of these sites, we’re making several measurements, but the ones we’re going to talk about today are just water content. And while we’ve installed water content sensors at 15, 45, and 65 centimeters, we’re just going to focus on the 65-centimeter water content sensors. These sensors are the METER TEROS 12 soil moisture sensors, so they also measure electrical conductivity and temperature, and we’re going to look at temperature as well because that figures into this discussion.

So this field was planted in April of 2019. And not a lot interesting goes on at the 65-centimeter depth through April, May, and June. But as we get into July, the wheat is reaching maturity, and they essentially are going to cut off the irrigation water here on July 22. So up to July 22, there’s really not a lot of movement in the water content. One of the sites decreases a little bit, but each line is flat. What I noticed as I was looking at this particular graph is after this long period of very flat data, after June 22 when the irrigation was cut off, we start to see some movement in the water content at this depth Not only is there movement down, but there’s a daily movement of the actual water content signals, all but this top light green line. And it made me wonder, what’s going on?

Diurnal water content fluctuations are not always due to temperature.

Initially, whenever you see a diurnal movement, you suspect that it’s caused by temperature. It’s been said that every sensor is probably a temperature sensor first, and a sensor of whatever we’re really interested in second. In this case, we can look to see what the temperature is doing at that depth. Here’s soil temperature, at 65 centimeters, and even though there’s just a little bobble in the line, the line is almost completely flat. We see the seasonal trends in temperature, but really no diurnal temperature cycling. And this scale is also fairly small. So back to our 65-centimeter water content. If it’s not temperature that’s affecting these lines, then what is it?

I’ve seen this before in an experiment that I did years ago in a non-irrigated wheat field. We were measuring down at  150 centimeters, and when the water had been used up in the upper levels of the soil profile, the roots of the wheat plant just simply went down to 150 centimeters and started taking water up. So this is what I assume is also happening here. The wheat has extended its roots down to 65 centimeters, since its irrigated wheat. That’s not too deep, but wheat doesn’t necessarily need to get its roots down super deep. And as the wheat accesses that water, we’re seeing these daily drops in water. But then we’re seeing just a slight increase in water. Here on July 28, we’re seeing that water go up slightly. And so why is this happening? We might understand how the water is being taken out of the soil, but why do we see a slight increase in the water content (just a few tenths of a percent)?

What I think is happening, in this case, is that it’s not temperature, but actually, roots are growing down into this area, and they’re probably growing around the sensor. As we change from day to night, we see a release in the elasticity of the water in the xylem, and maybe just a little bit more water down in the roots as they’re the transpiration pull of the day is lessened and stops overnight. The stomates are closed, and we see just a little bit of water coming back into the roots and possibly into the soil.

Now there was a big discussion many years ago about whether this was something called hydraulic lift where trees could take up water from deep in the soil profile and essentially give it back to plants near the surface. And although it was a great debate, it was never proven that this actually happened: water being spread from deeper locations to more shallow locations by roots. But this is probably hydraulic redistribution where we just have roots filling with water, and when they are filled, we see a little bit in the water content sensor.

## Chalk Talk: Intensive vs. Extensive Variables

Learn the difference between intensive and extensive variables and how they relate to soil water potential vs. soil water content in our new Chalk Talk whiteboard series. In this video series, Dr. Colin S. Campbell teaches basic principles of environmental biophysics and how they relate to measuring different parameters of the soil-plant-atmosphere continuum.

## Video transcript

Hello, my name is Colin Campbell. I’m a senior research scientist here at METER group. And I teach a class on environmental biophysics. Today I wanted to talk about something we teach in the class: the difference between extensive and intensive variables. I’d like to do this with the goal of relating it to the difference between volumetric water content and water potential.

Here, I have a picture of a ship moving through the ice and some metal that’s been heated in a furnace. I think we would agree the ship has the highest amount of heat in it compared to this very small piece of metal. And if we placed that piece of metal onto the outside of the ship, despite the fact that there is more heat in the metal, we know the heat would not move from the high amount of heat (ship) to the low amount of heat (metal). It would actually move from the highest temperature to the lowest temperature. Why is that?

The reason is that heat moves because of temperature and not because of heat content or the amount of heat in something. Heat content defines an amount or an extent. And we generally term something that defines an extent or an amount as an extensive variable.An extensive variable depends broadly on the size of something or how much of something there is.

This differs for temperature. Temperature doesn’t depend on size. The temperature could be the same in a very small room or a very large room, but the amount of heat or heat content in those rooms would be quite different. When we describe temperature, we talk about intensity, which is why we call these types of variables intensive variables. This is because they don’t depend on size or amount.

Let’s talk about another example. Here’s your heating bill. Maybe it’s natural gas. What you’re paying for is the amount of heat you put into the house. But the question is, are you comfortable in the house? And from this bill, we can’t tell. Maybe you put in 200 heat units, whatever those might be. We can’t tell if that’s comfortable because we don’t know the size of the house or the type of insulation. All those things would influence whether you were comfortable.

Alternatively, if the temperature is 71 F that’s quite comfortable. That’s equivalent to about 22 degrees Celsius. So the intensive variable, temperature, is different than the extensive variable, heat content, that tells us how much heat we put in. And that’s important because at the end of the day, that leads to cost.

On this side, we don’t know how much we paid to keep it at 22 C because heat content doesn’t tell us anything about that. But the intensive variable temperature does tell us something about comfort. So both of these variables are critical to really understanding something about our comfort in the house.

Now let’s talk about the natural environment. Specifically, we’re going to talk about soils. We’ll start with the extensive variable. When we talk about water in soil, the extensive variable is, of course, water content. Water content defines the amount of water. Why would we care about water content? Well, for irrigation or a water balance.

The intensive variable is called water potential. What does water potential tell us? It tells us if soil water is available and also predicts water movement. If this soil had a water content of 25% VWC and another soil was at 20% VWC, would the water move from the higher water content to the lower water content? Well, that would be like our example of the ship and the heated piece of metal. We don’t know if it would move. It may move. And if the soil on either side was exactly the same, we might presume that it would move from the higher water content to the lower water content, but we actually don’t know. Because the water content is an extensive variable, it only tells us how much there is. It won’t tell us if it will move.

Now, if we knew that this soil water potential was -20 kPa and this soil water potential over here was -15 kPa, we would know something about where the water would move, and it would do something different than we might think. It would move from the higher water potential to the lower water potential against the gradient in water content, which is pretty interesting but nonetheless true. Water always moves from the highest water potential to the lowest water potential.

This helps us understand these variables in terms of plant comfort. We talked about the temperature being related to human comfort. We know at what temperatures we are most comfortable. With plants, we know exactly the same thing, and we always turn to the intensive variable, water potential, to define plant comfort.

For example, if we have an absolute scale like water potential for a particular plant, let’s say -15 kPa is the upper level for plant comfort, and -100 kPa is the lower level of comfort, we could keep our water potential in this range. And the plant would be happy all the time. Just like if we kept our temperature between 21 and 23 Celsius, that would be comfortable for humans. But of course, we humans are different. Some people think that temperature is warm, and some think it’s cold. And it’s the same for plants. So this isn’t a hard and fast rule. But we can’t say the same thing with water content. There’s no scale where we can say at 15% water content up to 25% water content you’ll have a happy plant That’s not true.If we know something about the soil, we can infer it. But soil is unique. And we’d have to derive this relationship between the water content and the water potential to know that.

So why would we ever think about using water content when we measure water in the soil? One reason is it’s the most familiar to people. And it’s the simplest to understand. It’s easy to understand an amount. But more importantly, when we talk about things like how much we’re going to irrigate, we might need to put on 10 millimeters of water to make the plants happy. And we’d need to measure that. Also if we want to know the fate of the water in the system, how much precipitation and irrigation we put on versus how much is moving down through the soil into the groundwater, that also relates to an amount.

But when we want to understand more about plant happiness or how water moves, it’s going to be this intensive variable, water potential that makes the biggest difference. And so with that, I’ll close. I’d love for you to go check out our website www.metergroup.com to learn a little bit more about these measurements in our knowledge base. And you’re also welcome to email me about this at colin.campbell@meter group.com.

## Soil moisture: ECH20 vs. TEROS, which is better?

See how the new TEROS soil moisture sensor line compares with METER’s trusted ECH20 sensor line.

## Volumetric water content—defined

To evaluate the performance of any water content sensor, you need to first understand its technology. In order to do this, it’s necessary to understand how volumetric water content (VWC) is measured. Volumetric water content is the volume of water divided by the volume of soil (Equation 1) which gives the percentage of water in a soil sample.

So, for instance, if a volume of soil (Figure 1) was made up the following constituents: 50% soil minerals, 35% water, and 15% air, that soil would have a 35% volumetric water content.

The percentage of water by mass (wm) can be measured directly using the gravimetric method, which involves subtracting the oven-dry soil mass (md) from the mass of moist soil (giving the mass of water, mw) and dividing by md (Equation 2).

The resulting gravimetric water content can be converted to volumetric by multiplying by the dry bulk density of the soil (b) (Equation 3).

## Why capacitance technology works

Volumetric water content can also be measured indirectly: meaning a parameter related to VWC is measured, and a calibration is used to convert that amount to VWC. All METER soil moisture sensors use an indirect method called capacitance technology. In simple terms, capacitance technology uses two metal electrodes (probes or needles) to measure the charge-storing capacity (or apparent dielectric permittivity) of whatever is between them.

Table 1 illustrates that every common soil constituent has a different charge-storing capacity. In a soil, the volume of most of these constituents will stay constant over time, but the volume of air and water will fluctuate.

Since air stores almost no charge and water stores a large charge, it is possible to measure the change in the charge-storing ability of a soil and relate it to the amount of water (or VWC) in that soil. (For a more detailed explanation of capacitance technology watch our Soil Moisture: methods/applications webinar.

## Capacitance today is highly accurate

When capacitance technology was first used to measure soil moisture in the 1970s, 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. Over time, this new understanding, combined with advances in the speed of electronics, enabled the original capacitance approach to be adjusted for success. Modern capacitance sensors, such as METER sensors, use high frequencies (70 MHz) to minimize effects of soil salinity on readings.

The circuitry in capacitance sensors can be designed to resolve extremely small changes in volumetric water content, so much so, that NASA used METER’s capacitance technology to measure water content on Mars. Capacitance soil moisture sensors are easy to install and tend to have low power requirements. They may last for years in the field powered by a small battery pack in a data logger.

## TEROS and ECH20: same trusted technology

Both TEROS and ECH20 soil moisture sensors use the same trusted, high-frequency (70 MHz) capacitance technology that is published in thousands of peer-reviewed papers. Figure 3 shows the calibration data for the ECH20 5TE and TEROS 12.

## Just released: ATMOS 41 comparison testing data

Climate parameters such as precipitation, air temperature, and wind speed can change considerably across short distances in the natural environment. However, most weather observations either sacrifice spatial resolution for scientific accuracy or research-grade accuracy for spatial resolution.

ATMOS 41 all-in-one weather station

The ATMOS 41 represents an optimization of both. It was carefully engineered to maximize accuracy at a price point that allows for spatially distributed observations. Additionally, because many researchers need to avoid frequent maintenance and long setup times, the ATMOS 41 weather station was designed to reduce complexity and withstand long-term deployment in harsh environments. To eliminate breakage, it contains no moving parts, and it only requires recalibration every two years. Since all 14 measurements are combined in a single unit, it can be deployed quickly and with almost no effort. Its only requirement is to be mounted and leveled on top of a pole with an unobstructed view of the sky.

## Comparison testing and sensor-to-sensor variability data

METER released the ATMOS 41 in January 2017 after extensive development and testing with partnerships across the world, in Africa, Europe, and the US. We performed comparison testing with high-quality, research-grade non-METER sensors and conducted time-series testing for sensor-to-sensor variability.

## Data collection: 8 best practices to avoid costly surprises

Every researcher’s goal is to obtain usable field data for the entire duration of a study. A good data set is one a scientist can use to draw conclusions or learn something about the behavior of environmental factors in a particular application. However, as many researchers have painfully discovered, getting good data is not as simple as installing sensors, leaving them in the field, and returning to find an accurate record. Those who don’t plan ahead, check the data often, and troubleshoot regularly often come back to find unpleasant surprises such as unplugged data logger cables, soil moisture sensor cables damaged by rodents, or worse: that they don’t have enough data to interpret their results. Fortunately, most data collection mishaps are avoidable with quality equipment, some careful forethought, and a small amount of preparation.

Before selecting a site, scientists should clearly define their goals for gathering data.

## Make no mistake, it will cost you

Below are some common mistakes people make when designing a study that cost them time and money and may prevent their data from being usable.

• Site characterization: Not enough is known about the site, its variability, or other influential environmental factors that guide data interpretation
• Sensor location: Sensors are installed in a location that doesn’t address the goals of the study (i.e., in soils, both the geographic location of the sensors and the location in the soil profile must be applicable to the research question)
• Sensor installation: Sensors are not installed correctly, causing inaccurate readings
• Data collection: Sensors and logger are not protected, and data are not checked regularly to maintain a continuous and accurate data record
• Data dissemination: Data cannot be understood or replicated by other scientists

When designing a study, use the following best practices to simplify data collection and avoid oversights that keep data from being usable and ultimately, publishable.

## Hydrology 301: What a Hydraulic Conductivity Curve Tells You & More

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.

## IoT Technologies for Irrigation Water Management (Part 2)

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 weather and soil moisture 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.

## IoT Technologies for Irrigation Water Management

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:

1. buying a sensor that is going to connect to a wireless network that you own (i.e., customer supplied like Wi-Fi, Bluetooth),
2. buying the infrastructure or at least pieces of it to install onsite (i.e., vendor managed LPWAN such as LoRaWAN, Symphony Link), and
3. 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 weather and soil moisture 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.

## Lab vs. field instruments—when to use both

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 soil moisture (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.