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Posts from the ‘Ecology’ Category

Nov 11

Using Pedotransfer Functions to Predict Soil Properties

In this latest chalk talk video, METER soil scientist and application expert, Leo Rivera, discusses the use of pedotransfer functions (PTFs) for predicting soil properties such as hydraulic conductivity and field capacity.

He explains that while direct measurements are ideal, PTFs can provide rapid, cost-effective alternatives. PTFs use soil texture, particle size distribution, and bulk density as inputs, with accuracy depending on the quality of the database and the input data. There are limitations, such as PTFs not accounting for soil structure and organic matter, which can significantly impact hydraulic conductivity, thus, Rivera recommends using PTFs judiciously. PTFs can be especially helpful for large-scale assessments, and he suggests seeking tools that incorporate more parameters for improved accuracy.

Learn more:

Watch our Soil Moisture Master class→

See METER environmental sensors→

Video transcript

0:00
Hi. My name is Leo Rivera, and this is a METER Chalk Talk.

0:11
Today I want to talk about a topic that I get asked a lot about, and it has to do with how to predict some of these soil properties that we typically measure. So we often have used tools to measure things like soil hydraulic conductivity, retention curves, predicting field capacity and permanent wilting point. And ideally, that’s the best way we can do it, is by making those measurements. But that’s not always an option. So there are times where we need to be able to more rapidly assess soil properties, where we don’t have the budget to assess some of these more expensive soil properties to measure. And there are tools available to make these predictions rather than making the measurements. And I want to talk about those today to make sure that you understand the capabilities of these tools, that they’re out there, but also understand the limitations. And so the primary tool I’m going to talk about is a pedotransfer function, or a PTF.

So typically, with a pedotransfer function, we’re using something like soil texture. So for example, I have here an image of the soil triangle, and I can say I have a clay loam soil. So I’ve got my clay loam right here. And my goal is to predict field capacity. My goal is to predict field capacity for minus 33 kPa, and permanent wilting point, which is minus 1500 kPa, typically to make those measurements, it’s going to take several days to several months, depending on how you choose to make that measurement. But that’s not always an option. So we can use a petal transfer function to take that property, like soil texture, and predict those values. And pedotransfer functions also can be used to predict things like hydraulic conductivity. So you can see an example of a hydraulic conductivity graph here as well. So a pedotransfer function can be a really powerful tool to predict some of these properties that are typically take more time or more expensive to measure, and maybe you don’t have the time to make those measurements. So it’s really important to understand how a pedotransfer function works before utilizing this tool.

So pedotransfer functions utilize databases, whether it’s soil survey or other generated databases, where you have a lot of soil data, and you have all of these data, like texture, density, where they’ve already measured, hydraulic conductivity and some of these other soil properties. It then takes your input and utilizes that database to best predict what those values are that you’re trying to assess. So if I’m trying to predict, for example, field capacity, you can input parameters like soil texture, particle size distribution and bulk density. And you can do this in various orders. You can use soil texture on its own. You can use particle size distribution on its own, or you can combine particle size distribution and soil bulk density together to make these predictions. And it’s going to go into that database and try and make its best prediction based on the data available on that database.

Your pedotransfer function is only going to be as strong as the data that’s in the database, but it’s also only going to be as powerful as how good of an input you give it to predict these these values. So if we’re using soil texture on its own, as you can imagine, if I was predicting a clay as a soil texture, we’ll just use that as our example. If we look at our clay soil on the on the soil texture triangle, that is a huge range of combinations of sand, silt and clay fraction. So as you can imagine, that’s a pretty broad area that you’re trying to predict from, and there’s a higher potential for error in that prediction. Now, if we were to refine that and use the particle size distribution, so if we knew our exact sand, silt and clay fraction that we were trying to predict, we could then refine our predictions. We’re going to get rid of that circle, and we’re going to refine our prediction, saying, our soil has exactly this amount of sand, silt and clay. And that’s going to refine how the pedotransfer function is pulling those data from in the database to predict those values.

But as we know, soil texture on its own and particle size on its own only tells part of the story. So we can further refine that by adding our bulk density into that prediction, which is going to help improve the prediction of either field capacity or hydraulic conductivity. And in some areas, that should be fine. And so as long as you’re happy with that level of error, that’s fine. But, especially when we’re looking at things like hydraulic conductivity, we know there are other factors that play a big role in hydraulic conductivity, such as soil structure. So ideally, we would be including structure in our prediction, and organic matter in our prediction, because we all we know that these play a significant role in how soil transmits water, but most pedotransfer function, tools like Rosetta and Soil View don’t really take these into account.

So when you’re looking at these values, especially trying to assess measurements like hydraulic conductivity, you need to understand these limitations when using these tools. Now, there are other databases and pedotransfer function tools out there that are doing a better job of taking some of these into account. And if you’re going to use those tools, you want to try to make sure, if you’re if you’re really concerned with the accuracy of your values that you’re using pedotransfer function models that take more of these parameters into account. So the more inputs you can have into your prediction, the more accurate you’re likely going to come out with your predictions of these factors.

I just wanted to cover some of the basics on pedotransfer functions, and if you’re going to use them. They’re really powerful tools if we need to use them, especially when we’re trying to characterize large areas. It’s not always feasible to make measurements across these large areas, and they can help give us a little more data to work off of, rather than just the measurements on their own to try and characterize what’s happening across the large watershed, for example. But we need to understand how our inputs can affect the accuracy of those predictions. If you want to learn more about this or other topics, please visit us on our website www.metergroup.com or on our YouTube channel under meter talk talks and thank you for watching.

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Aug 1

The 70,000-landslide storm

In 2017, Hurricane Maria ravaged the island of Puerto Rico, with category 5 winds topping out at 174 mph (282 km/h).

In this mountainous nation with the 9th highest road density in the world, thousands of landslides wreaked havoc on the large number of rural communities that became cut off from supplies and travel. Dr. Stephen Hughes, a professor in the Department of Geology at the University of Puerto Rico Mayagüez, has turned this catastrophe into a lesson by harnessing before and after data to develop a landslide susceptibility map with resolution down to every 5 m. Join us as we discuss with him the process of developing landslide prediction across the entire island nation.

Listen to the podcast here→

Notes

Stephen is a professor in the department of geology at the University of Puerto Rico-Mayagüez. He obtained his bachelors in geology and earth science from the University of North Carolina at Chapel Hill and his PhD in geology from North Carolina State University. He teaches classes in structural geology, geomorphology, and field geology, and his research projects have focused mostly on tropical landslides and landscape evolution, with the funding of such organizations as the NSF, USGS, USDA-NRCS, and NOAA.

Links to learn more about Dr. Stephen Hughes

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The views and opinions expressed in the podcast and on this posting are those of the individual speakers or authors and do not necessarily reflect or represent the views and opinions held by METER.

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Jun 10

How to analyze soil moisture data

CONTRIBUTORS

You’ve buried soil water content and water potential sensors in the ground, installed an ATMOS 41 in the field, and set up your ZL6 data logger. Your network of instruments has been collecting data for days, weeks, or even all season. Now what? Performing soil moisture data analysis for your research location is one thing. Knowing how to extrapolate meaningful inferences and conclusions to understand what is happening and troubleshoot issues is completely different.

In this article, we will step through multiple data sets to understand how soil water content, soil temperature, soil water potential, and atmospheric measurements can be used to discover the meaning behind the traces. Within this article you will learn how to identify the following events in your data:

  • Behavior of soil moisture sensors in different soil types
  • Infiltration
  • Flooding
  • Soil cracking
  • Freezing
  • Spatial variability
  • Temperature effects
  • Diurnal patterns due to hydraulic redistribution
  • Broken sensors
  • Installation problems

Each example will be represented by a graph. It is not necessary to understand every aspect of information within these graphs. Each one is used as an illustration of common soil moisture data patterns you might run into and how to extrapolate the most useful information possible from the patterns seen. Each graph will have a box in the upper right-hand side corner with the soil type and crop type so you have a better understanding of the variables at play.

All of the data provided was collected by data loggers, such as our ZL6 series, and uploaded to ZENTRA Cloud for remote viewing at the convenience of the user. All data sets are either from METER’s own instrumentation or are supplied by the data owner and are included with their permission.

A photograph of a ZL6 next to a tablet showing ZENTRA Cloud data
Figure 1. ZL6 Basic data logger with data collected and stored within the ZENTRA Cloud platform
Effects of soil types
A graph showing water content and water potential measurements for a turf grass in loamy sand in wet conditions
Figure 2. Water content and water potential measurements for a turf grass in loamy sand in wet conditions

In Figure 2 we see the data from an engineered loamy sand with a cover crop of turf grass. Our goal when executing our experiments in this example was to improve irrigation in turf grass. This grass had a fairly shallow root zone, the middle of which was about six cm deep and the bottom at about 10 cm. Over time, this example showed first relatively wet conditions to start through June and July, a fixed drying period condition in July and August, and drying until the cessation of water uptake in August and September.

This graph shows two soil moisture data types: volumetric water content on the left y-axis and matric potential, or water potential, on the right y-axis. Time is on the x-axis ranging from early summer to the start of fall. To understand what these data clusters can tell us, we must look at each data set individually.

Read the full article→

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Apr 10

Office Hours Episode 11: Soil Moisture

There’s a lot to consider when collecting soil moisture measurements.

Get your soil moisture questions answered in our Office Hours series.

Join Environment Support Manager, Chris Chambers, and Director of Science Outreach, Leo Rivera, as they discuss submitted questions all about getting the best soil moisture measurements.

In the full episode, they discuss: 

  1. How difficult is the calibration of dielectric sensors? 
  2. How does soilless media affect the operation of dielectric sensors? 
  3. How much can organic soil amendments influence soil moisture? 
  4. Is it possible to determine the soil hydraulic properties from soil water content? 
  5. Why volumetric water content instead of gravimetric water content? 
  6. What is the best way to correct for the temperature sensitivity of sensors? 
  7. And more. 

Watch the full episode now: https://metergroup.com/office-hours-qa/office-hours-11-soil-moisture-measurements/

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Feb 16

A different approach to stream restoration

University of Idaho graduate student, Adrianne Zuckerman, is taking a different approach to stream restoration than the traditional approach, channel manipulation, which often requires heavy equipment and major disruption to the riparian area.

Zuckerman set out to understand how vegetation lining the stream bank impacts habitat quality for anadromous salmon and steelhead in Washington’s Methow River, which flows through the eastern Cascades. Zuckerman wanted to know how tree species composition affects the amount of nutrients available to the benthic insect community, since they are a critical food source for young salmonid fish.

When Zuckerman began investigating methods for measuring leaf contribution to the stream, she found that leaf litter traps were the standard equipment. Leaf litter traps are time-consuming to set and maintain, and data analysis consists of frequent visits to the field followed by extensive time in the lab processing leaf material.

Stream with rocks and trees

Looking for an alternative method, she discovered the LP-80 ceptometer: a lightweight, field-portable instrument for measuring leaf area index. Using the LP-80, Zuckerman was able to rapidly assess the leaf area contribution of each tree species along the riparian corridor. Using this information, it was relatively straightforward for her to estimate the contribution of each tree species to the stream food web.

Zuckerman’s research will help land managers and other researchers understand the importance of riparian vegetation for maximizing the food available to salmonid fish species. Improvement and maintenance of optimal stream-side vegetation composition should ultimately help to enhance salmon populations in the Pacific Northwest.

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Jul 25

1 Comment

How To Estimate the Impact of Radiation Sources in the Environment

What impact does direct solar radiation have on the overall radiation balance? Dr. Colin Campbell, WSU Environmental Biophysics professor and METER scientist, shows you how to do the calculations in our latest chalk talk.

Transcript

Hi, I’m Dr. Colin Campbell. And this is a METER Chalk Talk.

Have you ever been outside on a hot day walking in the full sun and then stepped into the shade? The relief is almost immediate. And I was thinking about that a lot when I was looking at this graph here, the estimated crop water loss on one of my experiments.

So this is an ET zero, meaning a reference ET. But since I was working grass, that was actually the estimated water loss from this grass crop. And what I noticed was that the shape of this curve kind of went up, and then went down. And it kind of matched right here, the solstice, the summer solstice. And in my mind, I thought, you know, what impact is direct solar radiation have on the overall radiation balance? Well, we can quickly just jump down and look at the equation that talks about how we might estimate the evapotranspiration from a crop. I’m not going to be able to have time here to get into what each of these variables mean.

But as you see, solar absorbed radiation, R abs is a strong component of that overall calculation. Now, when we talk about absorbed radiation, we need to understand that it’s not just all direct sunlight. In fact, if you assumed that, you’d be off in the weeds quite a bit, because it contains components of both longwave radiation, which is radiation that’s coming from your terrestrial surroundings, and shortwave radiation, that which is coming from predominantly the sun.

So let’s talk about that for a minute. With absorbed radiation, we have shortwave radiation. This is radiation that’s less than four micrometers. And we have longwave radiation. This is not surprisingly, from wavelengths greater than four micrometers. Now, this shortwave radiation, this comes from the sun longwave radiation comes from other sources, like trees, the sky, ground, just other objects that are around the temperature that we expect in the natural environment. Now, the truth of the matter is to get R abs, we need to combine both of these things into a single number. And it actually gets even more complex than that. So bear with us as we go on to the next equation.

R abs is a function of both shortwave radiation and long wave radiation. And when we calculate our radiation balance to get absorbed radiation, we have to actually take all of this into account. Now, you might be wondering, what are the other pieces in this equation, we’re going to spend a little time going over that. So you might understand how we can get from all of these numbers, all of these potential sources of radiation to a final number of R abs.

This portion of the equation here is shortwave radiation. And we’re going to talk about the variables in that equation. The first one we see is alpha s. It’s a number between zero and one. It signifies the percentage of shortwave radiation that the object can absorb. The other parameters in the equation include some F’s and some S’s. The F’s we call view factors, we’ll discuss view factors in more detail in another chalk talk. But suffice it to say that these essentially are parameters to estimate the amount of radiation that our object can see in its surroundings.

S stands for shortwave radiation. And this comes from several different sources. They include p: this is radiation that’s coming directly from the sun. That’s the one I mentioned earlier, that we feel if we’re standing in the direct sun, versus if we walk into the shade. But there are a couple of others. One is diffuse. This is the radiation that’s scattered as light comes into our atmosphere and it’s scattered by the atmosphere.

Finally, there’s R. This is reflected radiation, radiation that when it comes in, hits a surface, it reflects off that surface and comes and impinges on our object. Think about snow. If you’ve ever been skiing or out on the snow, you know, on a sunny day, you’re getting a lot of radiation that’s being reflected back. This portion of the equation over here is our longwave portion. Similar to our shortwave, it contains many of the same symbols, but they’re a little bit different.

The alpha L is the absorbed radiation. Now in the long wave that also goes from zero to one. The F is our view factor again, but now the view factor of longwave radiation, and L stands for that longwave radiation. This time, the subscripts A, that stands for atmosphere, and G stands for ground. If we put together all components in this equation, we’ll be able to solve for absorbed radiation. But that’s going to take a little bit of work. First, we need to understand the absorptivity of our surface both in the shortwave and the longwave.

The shortwave typically is calculated just from tables from looking out on the internet. For example, if I wanted to look at the absorptivity of a maple leaf, that’s typically around 50%. But it’s something that’s probably been calculated in literature. For our longwave radiation, almost all objects absorb long wave radiation at about 97 to 98% of the possible total.

So it’s pretty easy to estimate these absorptivities for objects that are fairly common. Calculating solar radiation and long wave radiation take a little bit more time. And especially understanding the view factors or how much of a particular surface our object sees, is going to take a whole chalk talk on its own. We’re going to leave this discussion here and leave for next time an opportunity to talk about how to calculate our shortwave radiation, or long wave radiation, and then get to the complicated discussion of view factors.

For more content like this, head over to our YouTube channel, or go to metergroup.com. Thanks for watching METER chalk talks.

See METER environmental sensors → 

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Jun 8

How to calculate the angle of the sun

Listen to Dr. Colin Campbell, WSU environmental biophysics professor, as he discusses how to calculate the angle of the sun, or solar zenith angle.

Transcription

Hi, I’m Dr. Colin Campbell. And this is a METER Chalk Talk. A couple of years ago, I was heading out into the backcountry and we wanted to figure out what kind of gear we should take along. A friend suggested we should just check the wind chill factor. But when I looked into it, we found out that it doesn’t even consider solar radiation in that calculation. Our exchange of energy in the environment is highly dependent on radiation, particularly solar radiation. And today, we’re going to talk a little bit more about that. Now the first thing to know about solar radiation is where the sun is in the sky. In fact, our absorbed radiation really depends on it. Interestingly, it’s one of the few things in life you can really count on.

With a few equations, we can figure out where the sun is in the sky at any time of the day. And I’m going to take you through some of these equations, one of the things I want you to know first is, they’re a little complicated, so don’t get stressed. In fact, if you just want to stop the video at a certain point. And check out these equations for a moment and write them down. That’s just fine. Now let’s just jump into it.

So here on my screen, I’m showing a graph of where the sun might be, at any point in a day if you were standing on the equator. Now in the middle, I’m going to draw this blue line across there, that is at the equinox. Now at the two solstices the sun might be here tracking across the sky, or here. And of course, this diagram is really showing kind of a fisheye picture of where that sun might be. There are two ways to describe where the sun is. One is a zenith angle. The zenith angle has a symbol, we call psi. In fact, the angle to the Earth’s surface from the perpendicular or normal, so this would be that zenith angle. Now there’s another angle we might be interested in, it’s called the Azmuth angle. But for our purposes of today, I just want to focus on this zenith angle because it’s the most important as we consider the radiation impact in an object that we’re interested in.

So to calculate the zenith angle, we’re going to go down and discuss the equation where this right here is zenith angle. And this here is the equation that we use to calculate that. Now you recognize the sines and cosines. And there’s just a couple other things in here. Of course, we’ve got t, which is time. And then a few other variables, phi. This is the latitude. Delta, this we call the solar declination, and finally, t zero, this is solar noon. Now before we get too crazy and worried about this equation, all we have to do is put in a few things into here, and we’ll be able to calculate that. So the first thing we need to know is the time of day.

Then we need to know the day of year. Now we actually call this a special name. This is called a Julian day. And it starts counting from January 1. The other things we need to know is of course, latitude, and longitude. And I’ll get to why in just a moment. The first parameter we’re going to try to find is called the solar declination. The solar declination equation looks pretty crazy. And anytime you see an equation like this in a book or something, the first assumption you should make is this is an empirical equation. As I look out on the internet and study other materials, I find that these equations actually are fairly common out there. And this isn’t exactly the way you see it in every piece of literature. But let me talk you through it here.

Really, there’s only one thing we need to know. It is the Julian day and we can go on the internet and calculate these a lot of programs just have those hard coded in like Excel. And all we need to do is just put that Julian day in for each of these values-here into here, and then we can eventually calculate the delta value. And then we can go put it back in this equation. So as long as we know the declination here, this is just the latitude. Let’s say my latitude is about 47 degrees. We just put that right here. All we need to know now is this t zero or solar noon. So what did we do for that?

Well, solar noon is calculated like this: t zero is equal to 12. That’s solar noon, and then we change it for wherever we are with respect to entered Meridian. And we call that the LC longitudinal correction, and then we also subtract off this equation of time t. We can start with the equation of time here. That’s this equation right here. And that’s not very small. In fact, not only is it not small, but it has a whole bunch of f’s in it. You can see f, here, this two times f, this is three times f, this is four times f. And now in the cosine or sines, then we have cosines here. So what is that?

Well, f is another one of these little bit long equations it is two point, or sorry, 279.575 plus 0.98565 times the Julian day. Now, if you get that, you just plug it back in here. And you can calculate your equation of time. And this is a number much smaller than one that you can plug in to this equation right here. Now, what about the longitudinal correction?

Well, the longitudinal correction Lc, that’s pretty straightforward. It’s essentially for every degree east of this of the standard meridian, you add 115. So for example, where I live, I’m at one 117.2 degrees, longitude, our standard meridian 120 degrees. And so the difference is, we’re east of that 2.8 degrees, and therefore the longitudinal correction, LC is just 2.8 over 15, or equal to 0.19h. So essentially, what I do is take that right there, and plug it in up here for the longitudinal correction. So essentially, we take 12, and we subtract off the longitudinal correction, and then with our equation of time, we get this value and eventually have t zero.

So what does all this mean? What does it sum up to? Well, there’s a lot of numbers in here. But if we go back to our initial equation, all we’re going to need to do now is simply this. We have our solar noon, we plug our time in. And then we use our solar declination here that we calculated on the first part of this discussion, our latitude here, and then suddenly, we’re able to calculate the Zenith Angle. And I’m going to try to link to a little calculation spreadsheet I did in Excel onto the sheet or onto the this video and then you can go ahead and look at that, how it’s done, and do your own calculations. For more content like this, check out our YouTube channel or head over to metergroup.com. Thanks for watching a METER Chalk Talk.

Learn more:

Watch our Weather Monitoring Master class→

See METER environmental sensors→

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Apr 21

Building a National Water Potential Network

Champions of water potential

Drs. Kim Novick and Jessica Guo team up to discuss the vital role water potential measurement plays in both plant and soil sciences and the work they are doing to establish the first-of-its-kind nationwide water potential network. Join their discussion to understand how a communal knowledge of these measurements could impact what we know about climate change and ecology as a whole.

A water potential measurement network could increase our understanding of climate change and ecology.

Listen to the podcast→

Notes

Dr. Kim Novick is a professor, Paul H. O’Neill Chair, Fischer Faculty Fellow, and director of the Ph.D. Program in Environmental Sciences at Indiana University.  She earned her bachelor’s and Ph.D. in environmental science at Duke University’s Nicholas School of the Environment. Her research areas span ecology and conservation, hydrology and water resources, and sustainability and sustainable development, with specific interests in land-atmosphere interactions, terrestrial carbon cycling, plant ecophysiology, and nature-based climate solutions.

Dr. Jessica Guo is a plant ecophysiologist and data scientist who studies plant-environment interactions under extreme climate conditions. She earned her bachelor’s in environmental biology from Columbia University and her Ph.D. in biological sciences from Northern Arizona University.  She is currently at the University of Arizona, where she blends her passion for reproducible workflows, interactive visualizations, and hierarchical Bayesian models with her expertise in plant water relations.
 

Links to learn more about Dr. Kim Novick

Links to learn more about Dr. Jessica Guo

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Our scientists have decades of experience helping researchers and growers measure the soil-plant-atmosphere continuum. 

Disclaimer

The views and opinions expressed in the podcast and on this posting are those of the individual speakers or authors and do not necessarily reflect or represent the views and opinions held by METER.

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Mar 1

Understanding the Language of Plants

Like a silent battle cry, plants call out to signal they are under siege as a warning to other plants and to call in reinforcements to fend off the invasion.

Listen to research on pathogen infection, water stress, and how plants communicate and defend themselves.

How does this communication work? What else are plants doing to protect themselves from disease and predators alike? In our latest podcast, Natalie Aguirre, a PhD candidate and plant physiology and chemical ecology researcher at Texas A&M University, dives into her research on pathogen infection, water stress, and how plants communicate and defend themselves.

LISTEN NOW→

Notes

Natalie Aguirre graduated with a degree in biology from Pepperdine University, where she completed an honors thesis conducting research on the interaction of drought stress and pathogen infection in chaparral shrubs. She then spent a year as a Fulbright scholar in Spain, studying the effect of water stress on Dutch Elm Disease. Most recently, Natalie worked for the Everglades Foundation, creating educational programs and materials about the Florida Everglades.

Links to learn more about Natalie Aguirre

Publications by Natalie Aguirre

Natalie Aguirre on Loop Open Science Research Network

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Questions?

Our scientists have decades of experience helping researchers and growers measure the soil-plant-atmosphere continuum. 

Disclaimer

The views and opinions expressed in the podcast and on this posting are those of the individual speakers or authors and do not necessarily reflect or represent the views and opinions held by METER.

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Oct 24

Determining drought tolerance in plants: How to do it right.

CONTRIBUTORS

Abiotic stress in plants: How to assess it the right way

As a plant researcher, you need to effectively assess crop performance, whether you’re  selecting the best variety, trying to understand abiotic stress tolerance, studying disease resistance, or determining climate resilience. But if you’re only measuring weather data, you might be missing key performance indicators. Water potential is underutilized by plant researchers in abiotic stress studies even though it is the only way to assess true drought conditions when determining drought tolerance in plants. Learn what water potential is and how it can improve the quality of your plant study.

Soil directly impacts plant growth via nutrient availability, disease pressure, root growth, and water availability.

Quantitative genetics in plant breeding: why you need better data

If you’ve studied plant populations, you’re probably familiar with the simplified equation in Figure 1 that represents how we think about the impact of genetics and the environment on observable phenotypes.

Figure 1. Phenotype = Genotype + Environment

This equation breaks down the observed phenotype (plant height, yield, kernel color, etc.) into the effects from the genotype (the plants underlying genetics) and the effects of the environment (rainfall, average daily temperature, etc.). You can see from this equation that the quality of your study directly depends on the kind of environmental data you collect. Thus, if you’re not measuring the right type of data, the accuracy of your entire study can be compromised.

Water potential: the secret to understanding water stress in plants

Drought studies are notoriously difficult to replicate, quantify, or even design. That’s because there is nothing predictable about drought timing, intensity, or duration, and it’s difficult to make comparisons across sites with different soil types. We also know that looking at precipitation alone, or even volumetric water content, doesn’t adequately describe the drought conditions that are occurring in the soil.

Figure 2. The TEROS 21 is a field sensor used to measure soil water potential

Soil water potential is an essential tool for quantifying drought stress in plant research because it allows you to make quantitative assessments about drought and provides an easy way to compare those results across field sites and over time. Let’s take a closer look to see why.

Read the full article→

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