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Posts from the ‘Irrigation’ Category

How to Use Plant-Water Relations and Atmospheric Demand for Simplified Water Management

Going by soil moisture data alone?

Soil moisture data are useful, but they can’t tell you everything. Other strategies for growers and researchers, like plant and weather monitoring, can inform water management decisions.

Researcher using the SC-1 leaf porometer to measure stomatal conductance
Researcher using the SC-1 leaf porometer to measure stomatal conductance

In this webinar, world-renowned soil physicist, Dr. Gaylon Campbell shares his newest insights and explores options for water management beyond soil moisture. Learn the why and how of scheduling irrigation using plant or atmospheric measurements. Understand canopy temperature and its role in detecting water stress in crops. Plus, discover when plant water information is necessary and which measurement(s) to use. Find out:

  • Why the Penman-Monteith equation, with the FAO 56 procedures, gives a solid, physics-based method for determining potential evapotranspiration of a crop
  • How the ATMOS 41 microenvironment monitor combined with the ZL6 logger and ZENTRA Cloud give easy access to crop ET data
  • How assimilate partitioning can be controlled by manipulating plant water potential using appropriate irrigation strategies
  • Why combining monitoring soil water potential with deficit irrigation based on ET estimates provide an efficient and precise method for controlled water stress management
  • And more…

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Presenter

Dr. Gaylon S. Campbell has been a research scientist and engineer at METER for over 20 years, following nearly 30 years on faculty at Washington State University. Dr. Campbell’s first experience with environmental measurement came in the lab of Sterling Taylor at Utah State University making water potential measurements to understand plant water status.

Dr. Campbell is one of the world’s foremost authorities on physical measurements in the soil-plant-atmosphere continuum. His book written with Dr. John Norman on Environmental Biophysics provides a critical foundation for anyone interested in understanding the physics of the natural world. Dr. Campbell has written three books, over 100 refereed journal articles and book chapters, and has several patents.

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Download the “Complete guide to irrigation management”—>

How to calculate growing degree days (or thermal time)

If you’re not using an accurate weather station at your field site to gather data for growing degree day (GDD) or thermal time calculations, you should start now. 

Image of increased yield

GDD predictions save you hours of scouting time and can increase yield because they’re a scientific way to know the best time for insect/disease control measures. In this chalk talk, Dr. Colin Campbell explains the concept of thermal time (or growing degree days) and shows two different ways to calculate it.

Watch the video

 

Video transcript

Hello, my name is Dr. Colin Campbell. I’m a senior research scientist here at METER Group. Today, we’re going to give a brief primer on thermal time. When I talked to some of my colleagues about that, they mentioned that thermal time (or growing degree days) is really just a way to match a plant’s clock with our clock. It helps us understand what’s happening with the plant, and we can predict things like emergence, maturity, etc. And the way we do this is through this equation that is pretty simple (Equation 1). 

Image of the equation for calculating thermal time
Equation 1

We can sum thermal time (Tn) by taking the summation of day one to day n of the average temperature (meaning T max plus T min divided by two), minus a base temperature (Tbase), and then multiply by time step (delta t). And in this case, our time step is just one day. 

So the whole analysis is simply the average temperature minus a base temperature. We get that value each day. And then we keep summing until it reaches a value that tells us that we’ve progressed from one stage to another stage. 

A good example of this is wheat. When I was young, I did an experiment on this in biology. The idea was that for emergence, the wheat plant needs 78 day degrees from planting to emergence. So I used Equation 1, and when I had summed enough day degrees, I knew the wheat was moving from the planting stage to a post emergent stage. I went out and measured the wheat and it actually matched up well. Not every wheat plant emerged at that point, but the average was quite close. 

So what does that mean in terms of graphical data? I wanted to show you what this equation actually looked like and then plant a seed in your mind for our next discussion, which will be, how good is this analysis? If you think about modern technology, like the ATMOS 41 weather station, you can get temperature measurements that are every five minutes or even every one minute. So wouldn’t it be better if we collected our thermal time information with this equation (Equation 2)?

Image of equation number two for finding thermal time
Equation 2

We can take the sum over each day, like we did in Equation 1, but instead we take the integral of temperature at a small time step T(t) (like five minutes) minus the base temperature (Tb) and then just integrate this across the day. We’re going to learn about that in my next chalk talk. But for now, let’s go to this graph (Figure 1). 

Image showing temperature vs. time over twenty four hours
Figure 1. Temperature vs. time over 24 hours

In Figure 1, we have temperature on the y axis and time on the x axis. The total time is 24 hours. This is our daily step, where we’re collecting this information about thermal time. And here are all the parameters from the equation: the maximum temperature (Tmax), the minimum temperature (Tmin), and the average temperature (Tave). And then this is a base temperature (Tbase). And to familiarize you with what we’re talking about, Tbase is the temperature below which progress is not made in the development of this plant. The progress is not reversed, meaning if it’s below the base temperature, the plant is not reversing its development, but it just doesn’t progress. 

The black line is what I’ve drawn as a typical diurnal temperature swing. So it’s going from a minimum in the early morning up to a maximum sometime in the afternoon. And I’ve tried to compare these two approaches. One one side, we have the average temperature and the base temperature. This rectangle is our thermal time for that day. But the question is, with all our temperature data (like from the ATMOS 41) where we have pretty small time increments, could we instead just integrate over the day and then collect all the information on thermal time that is below this black line (the actual temperature, and of course, we subtract out the base temperature). How much difference does that make compared to this here? And what are the implications of not being able to measure our temperature terribly accurately? We’re going to talk about that in our next discussion, and I look forward to seeing you then.

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Six short videos teach you everything you need to know about soil water content and soil water potential—and why you should measure them together.  Plus, master the basics of soil hydraulic conductivity.

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Automate your growing degree day (GDD) models using the ATMOS 41 weather station and ZENTRA Cloud software.

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Soil Hydraulic Properties—8 Ways You Can Unknowingly Compromise Your Data

Avoid costly surprises

Measuring soil hydraulic properties like hydraulic conductivity and soil water retention curves is difficult to do correctly. Measurements are affected by spatial variability, land use, sample prep, and more.

Image of a research using the SATURO infiltrometer in the field
Leo Rivera teaches soil hydraulic properties measurement best practices

Getting the right number is like building a house of cards. If one thing goes wrong—you wind up with measurements that don’t truly represent field conditions. Once your data are skewed in the wrong direction, your predictions are off, and erroneous recommendations or decisions could end up costing you a ton of time and money. 

Get the right numbers—every time

For 10 years, METER research scientist, Leo Rivera, has helped thousands of customers make saturated and unsaturated hydraulic conductivity measurements and retention curves to accurately understand their unique soil hydraulic properties. In this 30-minute webinar, he’ll explain common mistakes to avoid and best practices that will save you time, increase your accuracy, and prevent problems that could reduce the quality of your data. Learn:

  • Sample collection best practices
  • Where to make your measurements
  • How many measurements you need
  • Field mapping tools
  • How to get more out of your instruments
  • How to use the LABROS suite to fully characterize soils (i.e., full retention curves and hydraulic conductivity curves)
  • Best practices for measuring field hydraulic conductivity using SATURO

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Chalk talk: How to calculate vapor pressure from wet bulb temperature

In this chalk talk, METER Group research scientist, Dr. Colin Campbell, extends his discussion on humidity by discussing how to calculate vapor pressure from wet bulb temperature. Today’s researchers usually measure vapor pressure or relative humidity from a capacitance-based relative humidity sensor.

Image of an ATMOS 14 capacitance-based relative humidity sensor
ATMOS 14 capacitance-based relative humidity sensor

However, scientists still talk in terms of wet bulb and dew point temperature. Thus, it’s important to understand how to calculate vapor pressure from those variables.

Watch it now

 

Video transcript


Hello, my name is Dr. Colin Campbell. I’m a research scientist here at METER group, and also an adjunct professor at Washington State University where I teach a class on environmental biophysics. And today we’re going to be extending our discussion on humidity by talking about how using a couple of common terms related to humidity, we can calculate vapor pressure. The first term we’re going to talk about is dew point temperature. I’ve drawn a couple of figures below that illustrate a test I performed when I was a graduate student in a class related to biophysics.

Illustration of a dew point temperature test preformed by Colin Campbell
Dew Point Temperature Test Illustration

The professor had us take a beaker of water and a thermometer and put ice in the beaker and start to stir it. The thermometers were rotating around in the glass, and our job was to look carefully and find out when a thin film of dew began to form around on the glass. So we watched the temperature go down, and at some point, we observed a thin film form onto that glass. At the point the film began to form, we looked at the temperature to get the dew point temperature, which means exactly what it says: the point at which dew begins to form. 

This experiment wasn’t perfect because there is certainly a temperature difference between the inside of our glass where we’re stirring with the thermometer and the outer surface of the glass. But it was a good approximation and a great way to demonstrate what dew point temperature is. So we can say that the dew point temperature is the point at which the air is saturated and water begins to condense out. We call this Td or dew point temperature. The beautiful thing about dew point temperature is that if you know this value, you can easily calculate vapor pressure and even go on to calculate relative humidity, as I talked about in another lecture

To calculate vapor pressure from our dew point temperature, we’ll call vapor pressure of the air, ea which is equal to the saturation vapor pressure (es) at the dew point temperature (Td) (Equation 1).

Vapor pressure equation
Equation 1

And as I discussed in my other lecture, the saturation vapor pressure is a function of the temperature (not multiplied by the temperature). It’s pretty simple to get the saturation vapor pressure at the dew point temperature. We simply use Tetons formula (Equation 2 discussed here), which says that the saturation vapor pressure at the dew point is equal to 0.611 kilopascals times the exponential of b Td over C plus Td (Td being the dewpoint temperature).

Tetons formula for the saturation vapor pressure at the dew point temperature
Equation 2

So let’s assume our dew point temperature is five degrees C. This is something you can find in many weather reports. If you look down the list of measurements carefully, it’s usually there. So the vapor pressure of the air (ea) is calculated by the formula I showed (Equation 1). Our first constant b is 17.502 and our second constant C, is just 240.97 degrees C. If we plug all the values into that equation, it ends up that our vapor pressure is 0.87 kilopascals. 

Accumulative vapor pressure calculation
Equations 3 a, b, and c

Now there might be a variety of reasons we want this value. We might want to use it to calculate the relative humidity. If so, we’d simply divide that by the saturation vapor pressure at the air temperature. Then we’d have our relative humidity. More commonly we use the ea and the saturation vapor pressure at the air temperature to calculate the vapor deficit. So possibly in some agronomic application that might be interesting to us. So that is dew point temperature. 

Now we’ll talk about another common measurement, our wet bulb temperature. This was much more common in past years where there weren’t electronic means to measure things like dew point or humidity sensors. And we used to have to make a measurement of humidity by hand. And what they did was to collect a dry bulb temperature or a standard air temperature. And that dry bulb temperature (or the temperature of the air) was compared to what we call a wet bulb temperature.

Wet bulb temperature measurements preformed by hand illustration
Wet Bulb Temperature

Researchers made this wet bulb temperature by putting a cotton wick around the bulb of the thermometer. This was just a fabric with water dripped onto it. Once that wick is saturated with water, the water begins to evaporate, and they would use wind to enhance that evaporation. For example, some instruments had a small fan inside that would blow water across this wick, or more commonly, two temperature sensors were attached on a rotating handle, so they could spin them in the air at about one meter per second (or two miles an hour). I don’t know how you’d ever estimate that speed, but that was the goal. This would help the water evaporate at an optimum level. 

You can imagine what happens during this evaporation by thinking about climbing out of the pool. You feel some cooling on your skin as water begins to evaporate when you climb out of a pool on a dry, warm summer day. That’s water as it changes from liquid into water vapor, and it actually takes energy for this to happen (44 kilojoules per mole). That’s actually quite a bit of energy used for changing liquid water into water vapor. When that happens, it decreases the temperature of this bulb. If we wait till we’ve reached that maximum temperature decrease, we can take that as our wet bulb temperature, or Tw.

This wet bulb temperature is not quite as simple as our dew point temperature to use in a calculation. Here’s the calculation we need to estimate vapor pressure from the wet bulb temperature. 

Wet bulb temperature equation
Equation 4

We take the saturation vapor pressure (es) at the wet bulb temperature (Tw) and subtract, the gamma (Ɣ), which is the psychrometer constant 6.66 times 10-4-1 times the pressure of the air (Pa), multiplied by the difference between the air temperature (Ta) or that dry bulb that I mentioned earlier, and the wet bulb temperature (Tw). 

Gamma is an interesting number. It’s actually the specific heat of air divided by the latent heat of vaporization, or that 44 kilojoules per mole that I mentioned before. We can simply take it as a constant for our purposes here as 6.66 times 10-4-1. So let’s actually put it into a calculation. 
Our example problem says find the vapor pressure of the air. If air temperature (Ta) is 20 degrees Celsius, the wet bulb temperature (Tw) is 11 degrees Celsius, and air pressure (Pa) is 100 kilopascals (basically at sea level). And just to remind us, this is the constant gamma (6.66 times 10-4-1). Air pressure is 100 kilopascals. We take this standard equation (Equation 4) and insert all these numbers.

Equation to find the vapor pressure of air and gamma
Equation 5

So our vapor pressure is going to be this calculation from Tetons formula (Equation 2) and if you plug all those numbers into your calculator (notice our degrees C will cancel) we’re left with kilopascals. So our vapor pressure is about 0.71 kilopascals. So that is how we calculate the vapor pressure from the wet bulb temperature. 

I hope this has been interesting. These are values that you may hear about. It’s less common today since we usually get our relative humidity from a capacitance-based relative humidity sensor, but still scientists talk in terms of wet bulb and dew point temperature. So it’s important to understand how we actually calculate our vapor pressure from those variables. If you’d like to know more about this, please visit our website, metergroup.com, and look at some of the instruments that are there to make measurements. Or you can email me if you want to know more at [email protected]. I hope you have a great day.

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Water potential sensors improve peanuts, cotton, and corn irrigation

Ron Sorensen, a researcher at the National Peanut Research Laboratory in Georgia, is working to help small-scale peanut, corn, and cotton farmers in Georgia optimize irrigation.

Image of a cotton field in southern U.S

Cotton field in southern U.S.

Shallow subsurface drip irrigation is a very economical alternative for these farmers. “If you can put in a pivot, most of those are already in,” says Sorensen. What he’s working with are “small, irregularly shaped fields that go around swamps or in trees or backwoods where they might have 10 to 15 acres that used to be an old farmstead.”

Sorensen helps revitalize these old farmsteads by revamping old wells and plowing in drip tape. In many cases, farmers can have water running the same day they start the project.

Subsurface drip offers significant benefits to these farmers. “What I like about drip is that…I can fill up the soil profile, and I know I can fill it up. With the pivot, I’m putting out water today, and I may be coming back two days later and doing it again,” Sorensen says. “And I’m putting all this water on the leaves, creating the incidence for disease… With cotton, you can actually wash the pollen out of the flowers, and you won’t set a boule. There you’re losing yield. Whereas with drip, you can turn it on, you’re never wetting leaves, you’re getting full pollination… I’m an advocate for drip on small irrigated fields.

Image of a tractor plowing a field

“Irrigating down here [using diesel-powered irrigation pumps] is upwards of $11 an acre any time the farmer turns it on. So if he can wait a day, he’s saved that irrigation.”

“I love pivots on big fields, but we have to manage the water correctly. Farmers are starting to see that. We can save the farmer money, save him time, save him labor. Those…are all side benefits of irrigating correctly,” says Sorensen.

As farmers begin to see the benefits of efficient irrigation, Sorensen’s challenge is to help them know when to water and how much water to put on.

Sorensen is at the end of his third year gathering data with METER water potential sensors. He buries sensors at 10- and 20-inch depths in three separate plots, then irrigates when the average water potential reaches -40, -60, and -80 kPa respectively.

“We started in corn, because we know it has a shallow root system, and when you don’t irrigate corn, you don’t get any yield.” Initially, they allowed the profile to dry out to -120 kPa, but “we discovered it doesn’t work. We weren’t ever irrigating, and we had really bad yields. -120 was much too dry, and we cut back to -40, -60, and -80.”

The researchers used water potential readings with moisture release curves to determine how much water to add to bring the profile to field capacity.

Image of a researcher using a TEROS 21 sensor

METER TEROS 21 water potential sensor

They found that allowing the soil to dry to -60 allowed them to save water without impacting yields in corn. Cotton and peanuts are a different—and more complex—story.  “Both cotton and peanut, if you don’t get any rain or water, they just hunker down, and when you do get a rainstorm, they flourish. When the rainfall comes at a funny time, it changes everything,” Sorensen explains.

Take this year, for example. Until the first of June, Sorensen’s plots got very little rain. Then from June to the end of July, he got 24 – 26 inches. “All the differences between our plots are just gone. Irrigated is the same as the non-irrigated because by the time we started to irrigate, it started raining.”

Despite this setback, Sorensen is confident that the data will ultimately help produce a reliable irrigation tool for farmers. His goal is to add a drip irrigation module to Irrigator Pro, a computer program currently used by pivot irrigators.

He is working with several farmers who already use sensors in conjunction with the Irrigator Pro model. “They can use the computer model as a guess to get close, and then they can use a sensor to really get down to the exact day,” he says. And in Georgia, the exact day can matter quite a bit.

“What it comes down to is: Do I need to turn the pump on or not?” he explains. “Irrigating down here [using diesel-powered irrigation pumps] is upwards of $11 an acre any time the farmer turns it on. So if he can wait a day, and if we have one of these gulf storms come through, and the farmer gets 3/4 inch of rain, he’s saved that irrigation.

“And if you can save two, three, maybe four irrigations a year, we’re conserving water, we’re making the farmer more sustainable, and he can take that money and reinvest it into his farm, or into his children, or wherever he wants to put it. And that makes it so we have food on the table, clothes on our backs, cotton, corn, or peanuts, we’ve got food to eat.”

Learn more

Download the “Complete guide to irrigation management”—>

Discover METER water potential sensors

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Soil Moisture—6 Common Oversights That Might Be Killing Your Accuracy

Your decisions are only as good as your data

If you rely on soil moisture data to make decisions, understand treatment effects, or make predictions, then you need that data to be accurate and reliable. But even one small oversight, such as poor installation, can compromise accuracy by up to +/-10%. How can you ensure your data represent what’s really happening at your site?

Image of a researcher digging an installation site for a sensor
Chris Chambers discusses how people unknowingly compromise their soil moisture data.

Best practices you need to know

Over the past 10 years, METER soil moisture expert Chris Chambers has pretty much seen it all. In this 30-minute webinar, he’ll discuss 6 common ways people unknowingly compromise their data and important best practices for higher-quality data that won’t cause you future headaches. Learn:

  • Are you choosing the right type of sensor or measurement for your particular needs?
  • Are you sampling in the right place?
  • Why you must understand your soil type
  • How to choose the right number of sensors to deal with variability
  • At what depths you should install sensors 
  • Common installation mistakes and best practices
  • Soil-specific calibration considerations
  • How cable management can make or break a study
  • Factors impacting soil moisture you should always record as metadata
  • Choosing the right data management platform for your unique application

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7 Weather Station Installation Mistakes to Avoid

Rookie mistakes that ruin your research

Ever spent hours carefully installing your weather station in the field and then come back only to discover you made mistakes that compromised the installation? Or worse, find out months later that you can’t be confident in the quality of your data?

Image of a researcher installing an ATMOS 41 all-in-one weather station
Installing ATMOS 41 Weather Stations

Our scientists have over 100 years of combined experience installing sensors in the field, and we’ve learned a lot about what to do and what not to do during an installation.

Best practices for higher accuracy

Join Dr. Doug Cobos in this 40-minute webinar as he discusses weather station installation considerations and best practices you don’t want to miss. Learn:

  • General siting and installation best practices
  • Installation recommendations from WMO and other standards organizations
  • Common installation mistakes
  • How to identify installation mistakes in your data
  • Microclimate variability and how to pick a representative location
  • Troubleshooting at the site
  • Types of metadata you should always collect

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More resources

Explore which weather station is right for you.

Learn more about measuring the soil-plant-atmosphere continuum.

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Chalk talk: How to measure leaf transpiration

In his latest chalk talk video, Dr. Colin Campbell discusses why you can’t measure leaf transpiration with only a leaf porometer.

Image of the SC-1 Leaf Porometer which measures stomatal conductance
The SC-1 Leaf Porometer measures stomatal conducance

He teaches the correct way to estimate the transpiration from a single leaf and how a leaf porometer can be used to obtain one of the needed variables.

Watch the video

 

Video transcript

Hello, my name is Colin Campbell. I’m a senior research scientist here at METER Group. And today we’ll talk about how to estimate the transpiration from a single leaf. Occasionally we get this question: Can I estimate the transpiration from a leaf by measuring its stomatal conductance? Unfortunately, you can’t. And I want to show you why that’s true and what you’ll need to do to estimate the total conductance, and therefore, the evaporation of a leaf.

Image of a researcher Measuring stomatal conductance With an SC-1 Leaf Porometer
Researcher Measuring Stomatal
Conductance With an sc-1 Leaf Porometer

The calculation of transpiration (E) from a leaf is given by Equation 1 

Image of the equation used for the calculation of transpiration from a leaf
Equation 1

where gv is the total conductance of vapor from inside the leaf into the air, Cvs is the concentration of vapor inside the leaf and Cva is the concentration of vapor in the air.

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Learn more about canopy measurements

Download the researcher’s complete guide to leaf area index—>

Questions?

Our scientists have decades of experience helping researchers measure the soil-plant-atmosphere continuum. Contact us for answers to questions about your unique application.

How to interpret soil moisture data

Surprises that leave you stumped

Soil moisture data analysis is often straightforward, but it can leave you scratching your head with more questions than answers. There’s no substitute for a little experience when looking at surprising soil moisture behavior. 

Image of orange, yellow, and white flowers in a green house
Join Dr. Colin Campbell April 21st, 9am PDT as he looks at problematic and surprising soil moisture data.

Understand what’s happening at your site

METER soil scientist, Dr. Colin Campbell has spent nearly 20 years looking at problematic and surprising soil moisture data. In this 30-minute webinar, he discusses what to expect in different soil, environmental, and site situations and how to interpret that data effectively. Learn about:

  • Telltale sensor behavior in different soil types (coarse vs. fine, clay vs. sand)
  • Possible causes of smaller than expected changes in water content 
  • Factors that may cause unexpected jumps and drops in the data
  • What happens to dielectric sensors when soil freezes and other odd phenomena
  • Surprising situations and how to interpret them
  • Undiagnosed problems that affect plant-available water or water movement
  • Why sensors in the same field or same profile don’t agree
  • Problems you might see in surface installations

Watch it now

Learn more

Download the “Complete guide to irrigation management”—>

Degradation of soil-applied herbicides under limited irrigation

Soil-applied herbicides are important for controlling weeds in many crops because they offer a broadened control spectrum and chemical diversity. But if soil-applied herbicides persist in the soil too long, there is risk for damage to susceptible rotational crops in succeeding years. Since herbicide degradation in the soil is highly dependent on water, imminent needs to reduce agricultural water use in the future could lead to limited herbicide degradation and a greater risk for carryover.

Image of a sunflower in a sunflower field facing the sun
Some crops don’t have a wide variety of post-emergent herbicide options, so growers are dependent on soil-applied herbicides for weed control.

Recently Daniel Adamson and a research team at the University of Wyoming, under the guidance of Dr. Gustavo Sbatella, investigated the effects of soil-applied herbicides under limited irrigation conditions. They wanted to understand how limited irrigation affects the efficacy and carryover of soil-applied herbicides in Wyoming’s irrigated crop rotations. A two-part field study was undertaken by applying four soil-applied herbicides to dry beans and four soil-applied herbicides to corn. 

Soil microbe activity matters

Describing his research site, Adamson says, “Wyoming is not a huge farming state but there’s a pocket of farm ground near the Powell/Cody area with a unique rotation. The main crop is sugar beets, and they also grow dry, edible beans, sunflowers and malt barley. Some of these crops don’t have a wide variety of post-emergent herbicide options, so growers are dependent on soil-applied herbicides for weed control. However, they need to balance weed control with timely dissipation so sensitive rotational crops won’t be injured.

Adamson says that soil-applied herbicides tend to be fairly long-lived in the soil, which is advantageous for weed control. Importantly, the herbicides dissipate through degradation by soil microbes, and soil microbes are highly influenced by how much water is in soil. When the soil is moist and warm, microbes are more active, and they degrade the herbicides faster. Thus, his team hypothesized that if future climate change effects led to limited availability of surface water for irrigation, these herbicides may not degrade as quickly and possibly injure crops planted successionally.

Assessing herbicide damage

During the first year, the research team applied three irrigation treatments to each crop: 100%, 85%, and 70% of crop evapotranspiration. Both crops and soil moisture were monitored using METER data loggers and soil moisture sensors. Adamson recalls, “The sensors were our means of tracking what was happening in the soil in terms of volumetric water content. Some of the areas were chronically dry, so the sensors enabled us to confirm that the treatments were applied correctly and should theoretically affect how the herbicides were performing. The volumetric soil water content of the three irrigation treatments averaged 24%, 18%, and 16% throughout the growing season, and crop yield decreased as irrigation was reduced.” 

Over the course of the second year, the team collected soil samples at regular intervals following herbicide application. They analyzed the samples for herbicide level and used them to perform a greenhouse bioassay to determine crop response to residual herbicide. Also during the second year, crop response was evaluated in the field when sugar beet, sunflower, and dry bean or corn was planted over the original plots and assessed for herbicide damage.

Crops planted in a field assessed for herbicide damage
The results of the experiment were surprising.

Hurdles and challenges

Adamson said timing was the major difficulty in terms of applying irrigation treatments. He said, “There were no differences in irrigation timing for the various treatments. The way we irrigated was not representative of a typical deficit irrigation strategy because we were tied to a sprinkler with other projects on it. So we irrigated based on when the full water treatment would normally be irrigated. Other treatments had smaller nozzles so the amount of water was physically reduced.”

Adamson said they also weren’t prepared to track how some of the herbicides would behave in the soil. “Some of the herbicides degrade into metabolites that are phytotoxic in the soil, and it was hard to analyze for all molecules that were plant active. So that was challenging.”

Surprising results

Adamson said the results of the experiment were surprising. He says, “It was a good result for growers because we found there were no differences in the fields, statistically or visually, between how the herbicides carried over in the really dry soil versus the normally irrigated soil. So that was surprising, but from a practical standpoint for farmers, it was important information. They now know if they do have to start applying less water, it isn’t something to be overly concerned about.”

More research is needed

Adamson says more work is needed in this area of research. He adds, “There’s a tremendous amount of information within the weed science community about what herbicides do in the soil and things that influence that. But relatively few studies look at changing irrigation rates in a practical sense. A lot of the current studies are done in rain-fed systems where the amount of rain changes (i.e., a normal year vs. a drought year). In irrigated systems, you might reduce the amount of water, but it’s not a drastic reduction like a rain-fed system might experience. There’s not a huge amount of research looking at how different irrigation rates affect herbicide management, so I do think it would be worth exploring in the future.”

Download the researcher’s complete guide to soil moisture—>

Download the researcher’s complete guide to water potential—>