Improve Your Plant Study: 3 Types of Environmental Data You May Be Missing

You may be missing key insights about the true environment of your plant study. Application expert Holly Lane teaches how to better understand the environment your plants are growing in and the stresses they're exposed to.

What data are you missing?

As a plant researcher, you need to effectively assess crop performance, whether it’s yield or disease resistance. But if you’re only measuring weather data, you might be missing key performance indicators in your variety trials. Understanding the full picture of the environment will make it easier to select the right varieties to advance—and avoid wasting resources on advancing bad selections.

To accurately assess plant stress tolerance, you must first characterize all environmental stressors. For example, drought studies are notoriously difficult to replicate because of high weather variability. Precipitation data is not enough to assess drought. You need a tool to quantify drought at the soil level.

Get better, more accurate conclusions

It’s important for your environmental data to accurately represent the environment of your site. That means not only capturing the right parameters but choosing the right tools to capture them. In this 30-minute webinar, application expert Holly Lane discusses how to improve your current data and what data you may not be collecting that will optimize and improve the quality of your plant study. Find out:

  • How to know if you’re asking the right questions
  • Are you using the right atmospheric measurements? And are you measuring weather in the right location?
  • Which type of soil moisture data is right for the goals of your research or variety trial
  • How to improve your drought study, why precipitation data is not enough, and why you don’t need to be a soil scientist to leverage soil data
  • How to use soil water potential
  • How accurate your equipment should be for good estimates
  • Key concepts to keep in mind when designing a plant study in the field
  • What ancillary data you should be collecting to achieve your goals

Next steps


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

  • Lane, Holly M., and Seth C. Murray. “High Throughput can produce better decisions than high accuracy when phenotyping plant populations.” Crop Science. (Article link)


Holly Lane has a BS in agricultural biotechnology from Washington State University and an MS in plant breeding from Texas A&M, where she focused on phenomics work in maize. She has a broad range of experience with both fundamental and applied research in agriculture and worked in both the public and private sectors on sustainability and science advocacy projects. Through the tri-societies, she advocated for agricultural research funding in DC. Currently, Holly is an application expert and inside sales consultant with METER Environment.


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Hello, everyone, and welcome to Improve Your Plant Studies: 3 Types of Environmental Data You May Be Missing. Today’s presentation will be about 20 minutes, followed by about 10 minutes of Q&A with our presenter, Holly Lane, whom I’ll introduce in just a moment. But before we start, we got a couple of housekeeping items. First, we want this to be interactive. So we encourage you to submit any and all questions in the Questions pane. And we’ll be keeping track of those for the Q&A session toward the end. Second, if you want us to go back repeat something you missed, no worries. We’re recording the webinar, and we’ll send around a link to the recording and slides via email within the next few business days. Alright, let’s get started.

Today we’ll hear from Holly Lane, who will discuss how to get the right data to better understand the environment your plants are growing in, and the stressors they’re exposed to. Holly has a bachelor’s degree in agricultural biotechnology from Washington State University, and a master’s in plant breeding from Texas A&M, where she focused on phenomics work in maize. She has a broad range of experience with both fundamental and applied research in agriculture, and worked in both the public and private sectors on sustainability and science advocacy projects. Currently, Holly is an application expert and inside sales consultant with METER Environment. So without further ado, I’ll hand it over to Holly to get us started.

All right, thank you so much for the introduction, Brad. I’m really excited to be here today and talk to you all about different ways that you can think about the way you’re collecting environmental data in your plant research. And so I thought we could start from the basics today. Many of us who have studied plant populations are familiar with this simplified equation that really breaks down how we think about the impact of both genetics and the environment on observable phenotypes. And so for those of you who aren’t familiar, or to refresh the rest of you, this equation breaks down the observed phenotype, P, whether that’s something like plant height, yield, kernel color, etc, into the effects from the genotype, or the plant’s underlying genetics, and the effect from the environment, so whether that’s something like rainfall, average daily temperature, etc. It’s also possible that you might be considering interactions between genotype and the environment at a finer level in your study. But that’s not something we’re going to have time to touch on today. So I thought we could break down each of these pieces, and assess how you all in the audience are addressing each of these components in your research. So for many researchers, a lot of focus and resources might be put into understanding the genetics in this equation, or the G, whether that’s through a full genome sequencing, marker analysis, or even pedigree information. Even on a categorical entry level basis, you are probably accounting for this in some way as you’re analyzing your data.

And so our first poll question here, how are you assessing the genetic genetic aspects of your study? Are you looking just at entry number, a categorical variable? Are you considering pedigree information? Are you looking at marker analysis or more minor genetic sequencing? Or are you doing some sort of full genome or deep sequencing? Or are you doing none of these or something else

We’ll give it a couple more seconds here.

So it looks like we have a pretty broad range of responses. Just under half of you are doing none of these or considering something else. And I would love for you all to share that with me via Twitter. My handle is @HLplants or if you want to drop that into the questions. About a quarter of you are looking just at a categorical variable of entry number. And the smallest number of you are doing a full genome sequencing.

So additionally, there’s also been an increasing focus in breeding and other plant research programs on improving the phenotyping technology or the way that we collect the P in this equation. That might be through the use of high throughput phenotyping, which often includes the use of drones or satellite imagery, or we may be increasing both the amount and the dimension of our data by looking at phenomic information.

And so here, how are you looking at phenotyping data in your research? Are you doing manual or by hand measurements? Are you looking at high throughput technologies, whether that’s drones or satellite or something else? And are you considering phenomics data? And here selecting as many that apply to you.

Awesome, so it looks like almost all of you are still doing some measurements by hand. And that’s, I think, not uncommon. But what’s interesting is about a third of you are incorporating some sort of high throughput technology. And 17% of you it looks like are considering phenomics as an aspect of your research as well.

So what I really want to focus on today in this presentation is the E in this equation. How much focus are you putting on understanding the environment in your studies? Whether that’s looking at your own weather data, data from a local station, considering different soil parameters or something else.

So how are you accounting for the environmental impact on your study? Again, here selecting as many that apply to you. Are you looking at local weather station data? Do you have your own station set up to collect weather? Do you do some sort of soil sampling, whether that’s nutrient analysis, soil type analysis? Or do you have some sort of soil sensors implemented in your field?

Great, so it looks like many of you are considering the soil, about 70%, or three quarters. And many of you are also looking at weather data, some of which is from a local station, and much of which is also from setups that you have in your own fields, which is great.

Okay, so before we really get going today, I wanted to lay a bit of the foundation for how I think about best practices when it comes to data collection. And this is really stemming from my most recent publication that was recently accepted into Crop Science. And this is a project that we did where we’re looking at trade offs between data quality and data quantity. So oftentimes, as researchers, we have to think about these trade offs between data quality, how accurate each of our data points are, and data quantity, how much data we can feasibly collect with our chosen method. So my conclusions are based off of my, this recently accepted paper, like I said, and here we were looking at the concept of these trade offs really in the context of plant phenotyping data. So for example, treating a by hand manual measurement for height, as our a really accurate baseline, and then comparing if we had done something like plant height extraction from drone based imagery. And so what we had found was that in general, you can make up for sacrifices to accuracy with an increased throughput capacity. Or in other words, it’s often more informative to have more data points than to have fewer more accurate data points. And we can also think about how these concepts apply to capturing site variability when we’re collecting environmental data. So for example, while you might be able to afford one highly accurate station at your site, it’s likely a much better use of your budget, if you can install multiple, less expensive and slightly less accurate stations across your site to capture more of that variability. So for example, you can see in this figure how much variability we have in bulk electrical conductivity measurements, and so you can kind of get an understanding for if you only had one point of sampling or very few points of sampling, you might draw some incorrect conclusions when you extrapolate across the site.

Alright, so now that we’ve gotten some of those ideas out of the way as a foundation, let’s start talking about environmental data. And the first thing that comes to my mind when I think about the environment is weather data, and so that’s where we’re going to start today.

So there are a lot of options when it comes to how you think about collecting weather data, and what kinds of instrumentation you’re using to do that. So over there, at the peak of price, and also at the peak of performance, you have those highly accurate, fully outfitted stations, those might rend you tens of thousands, or hundreds of thousands of dollars. There on the left at the low end of performance, and also the low end of price, you also have the hobby stations that may be fairly inexpensive, but that aren’t going to give you data that’s research quality. And then you have everything in between. So the question for you as a researcher really becomes balancing performance — is something research grade?— with value — can I afford to place these everywhere I want to collect weather data? And so let’s talk a little bit about why it is so important to think about outfitting your field sites with a weather station setup. It may seem simple just to import weather data from local stations. And I know many of you in the poll responded that is something that you’re doing in your studies. But I want to present to you today that while those stations are highly accurate, they’re not necessarily really local to your field site. Unless, of course, you’re really lucky and you happen to have one set up right across the street from you. So something like a all in one weather station might be less accurate, if you’re comparing it side by side to one of those fully outfitted setups, but now if you can actually capture data right at your site, that data is going to be more accurate because it’s going to be more site specific and capture those microclimate effects. So here on this graph, we’re looking at high and low temperatures from both an infield weather station, something that’s been set up at our field site, which you can see there in the blue lines, and temperature information that’s been taken from a local weather station in the area. And so you can start to see some really large discrepancies that are occurring, especially as we move later on into the season, so looking around July 13. In those minimum temperatures, you’re seeing discrepancies of greater than five degrees Celsius. And if you’re using this information for something like a growing degree day calculation, you’re potentially introducing a pretty significant amount of air into your estimation. I do also want to note that there has been an increasing interest in using local data to create quote unquote, virtual weather stations. And I want to just note that current research is showing that unfortunately, those are just as inaccurate as using local data.

So now that I’ve hopefully convinced you that you really want to have something set up right at your field site, you might be learning that the options for getting something set up can be really overwhelming. And so choosing exactly what instrument you want at your site, you know, you might feel like you want to investigate that a little bit more. And if you do want to learn more, we have an entire webinar on this subject, I suggest you check out our Weather Monitoring 101. But in general, you’ll want to balance something like I said before, that is affordable within your budget, but that is also going to meet some minimum threshold for being research grade and robust over time. So keeping in mind that quantity over quality concept.

So why is it so important for us to ensure that we’re getting site specific, accurate weather data? And as we just discussed, you might be doing something like a growing degree day calculation, you may be looking at calculating evapotranspiration. And the more accurate your data going in is, the more accurate your model predictions are going to be on your output. You also might be trying to identify potential stresses that your plants have been exposed to. That might be high temperatures, potentially freezing temperatures. You may be wanting to track other parameters to help model if the conditions might be right for disease pressure. But most importantly, precipitation is something that’s very important for us as plant researchers, and this is also something that is highly variable spatially. And so the farther you move away from your field site and collecting that information, the more likely you are to introduce inaccuracy.

So as many of you know, based on our poll results, soil is an important aspect of the environment for plants. And that’s because soil is really the medium for plant growth. So I’m sorry to all my soil science colleagues who really get excited about soil in and of itself. But for me as a plant researcher, when I think about soil, it’s really within the context in the lens of plant growth. And we know that soil has a direct impact on plants, by imparting nutrients, there’s potential disease pressure that may be present, and also it’s directly affecting root growth. So any compaction, something like that, that might be affecting an ability for a plant to put down roots. And also a really important parameter is water availability. And that’s what I want to talk to you all today about specifically. So one really valuable, informative measurement you can make from the soil is matric potential, and matric potential really allows you to understand water availability. This is different than volumetric water content, which is what you might be thinking of when you hear about a traditional soil moisture sensor. But the main difference is while volumetric water content will tell you how much water is in the soil, it won’t tell you how accessible that water is or how much energy is needed to access that water and that’s where matric potential comes in. So this makes it a much more informative measurement in plant context. And you can think of it sort of as a water thermostat, or water thermometer for plants. So while you might be able to look at the thermostat in your house and see 70 degrees and think, okay, that’s comfortable for me as a human being, you can also look at matric potential and understand whether or not the amount of water in the soil is comfortable for your plants. Speaking for myself, I really prefer matric potential to volumetric water content. Because with volumetric water, you really need an understanding of soil type and texture to really interpolate valuable information about water availability in a plant context. But because matric potential, due to its very nature, is directly assessing water availability, it’s already accounting for soil texture. So you’re left with a measurement that’s directly comparable across sites, and across years without any need for additional analysis or any soil specific calibration. If you’re interested in learning more about the finer details, we have a couple of other webinars that are really awesome on matric potential. And when I first heard about this, I was sort of confused as to why we didn’t do this more widespread across our plant research. And really the reason for that is historically, there hasn’t really been a lot of adequate tools that have been available to us as researchers to capture this information.

So the real value I see in matric potential for plant research is that it gives you this tool that allows you to now quantify drought stress. And as many of us know, drought studies can be really hard to replicate or quantify, or even design due to the fact that drought can vary so highly in timing throughout the season, and its intensity, and its duration. And we also know that looking at precipitation alone, or even volumetric water content doesn’t adequately describe the drought that’s occurring in the soil. And it also doesn’t really allow us to make easy comparisons across sites with different soil types. And so water potential is really stepping in here to allow you to make quantitative assessments about drought and to have an easy way to compare those results across field sites and across years. So I really love this figure, because it really hits home the idea of thinking about how we can think about water potential as this thermometer for water for plants. And so the ranges in this figure are from a really nice publication by Dr. Sterling Taylor I suggest you check out. The citation is there on the right hand side of the figure. And one of the things that is often confusing about matric potential is that the kPa are always reported in a negative value. So something that helps me is I think about the more negative that the value, the more dry the soil. So zero is going to be that fully saturated range, negative 1000 and below, we’re really starting to hit that permanent wilting point. And so for example, we can look there at the very bottom, at corn. And of course, I’m a little biased because that’s what my master’s work was on. But as you can see, during the vegetative period, corn really prefers to be up in that negative 50 range, whereas during ripening and that dry down period, it actually prefers the soil to dry out quite a bit.

And we’re about to look at some sample data for potatoes. So you can see up near the top about the fourth crop down, potatoes prefer really to be in a pretty narrow window of about negative 30 to negative 50 kPa. And so here’s some volumetric water content in a potato field. And looking here, without any context about soil type, it’s really hard to make any sort of visual interpretation about the water availability or how comfortable the soil is for the potatoes in this field. But you are able to see the spikes in irrigation or precipitation events over time. However, when we add matric potential, it makes it much easier to see and have an indicator for whether or not the plants are in an optimal range for water. So here, you can see that early on in the season, they were actually over applying irrigation. And once they got to around July, they reduced the frequency, which allowed them to hit the optimal range. The soil was allowed to dry down a little bit and hit more of the optimal negative 30 to negative 50 kPa for the potatoes. However, towards the end, you can see that they actually started to let too much time pass in between irrigation events, which put significant drought stress on the potatoes. So we’re well out of that negative 30 to negative 50 range there on the right. And one thing I like to note here is visually, again, looking just at volumetric water content, you’re talking about a very minor change between what’s perfectly optimal and what’s significant drought stress, potentially reaching permanent wilting point for those crops.

So of course, matric potential, like anything else, has its limitations, the primary one being that while it can tell you when you should apply water, it can’t tell you quantitatively how much water to apply, which may or may not be an issue for you, for example, if you’re running a dryland study, if you’re not considering any water balance information, but if you do want to know how much water is entering the system or needs to be added to the system, you will need to add volumetric water content. Another limitation of much of this technology, particularly our sensors for this, is that they’re not suitable for spot checking, due to the fact that they can take up to a full day to adjust after installation. So it’ll take them a while before they’re responding at the same rate as the soil. Additionally, as we discussed, this measurement has been traditionally hard to quantify. A lot of the field sensors out there are inaccurate or expensive. And so of course, while I have you all here, I do have to make a shout out to our favorite instrument that we sell, and that’s our TEROS 21 matric potential sensor. It has a really wide measurement range in the plant available spectrum, and it has high sensor to sensor consistency. So just to wrap up for today, I want to draw your attention back to this equation, and really just bring home that the overarching goals of a lot of our plant research, whether that’s to select the best variety, to better understand disease resistance or climate resilience are reached via our ability to accurately parse out error when it comes to the way we collect these terms. And so I urge all of you to make sure that you’re paying just as much attention to your environmental data as you are to your other types of data. And the three things I hope you all consider for your next plant study are the ways that you think about weather data, you know, really balancing those data quality and quantity trade offs, getting site specific data as much as you can, considering the soil, maybe think about adding matric potential to your next study. And then of course, our team. We’re always happy to talk with you about experimental design. We’d love to hear your research objectives, you don’t have to buy anything from us. We’ll actually tell you if we think that we don’t have what you need. I’m happy to talk shop anytime. So feel free to tweet me, again my handle is @HLplants. And with that, I will thank you for your attention and take any questions.

All right. Thank you, Holly. And we’d like to take the next 10 minutes or so to take some questions from the audience. Again, thanks to everybody who sent in a question already. We’ve got a few good questions in here. There’s still plenty of time to submit questions. And we’ll try to get to as many as we can. But we’d just like to let you know just from the start here that feel free to ask as many questions as you’d like. We do have them recorded. And either Holly or somebody else from our METER Environment team will be able to get back to you and answer your question via email if we do not get to answer your question here today during the live webinar. So let’s see. So we’ve got several questions here. Just I mean, some some basic questions on matric potential, Holly, and can you reiterate just how matric potential works and does it take into account various soil media or soil types, that kind of stuff?

Yeah, so that’s a great question. Like I said, the reason I really like matric potential as a measurement is because the nature of the measurement itself is already accounting for soil type, and what type of media you have. So if you’re in a clay heavy soil, and even if the volumetric water content might be reading a little bit high, matric potential will be able to parse out that those water particles are being held very tightly by the soil. And so that’s one of the reasons I really love it is you don’t necessarily need to know anything about the soil type to to get that information.

So then, and we have a few different questions along those same lines, kind of piggybacking off of that. So then is matric potential then useful within, and so not in a field environment, but potentially in, you know, like a greenhouse or dealing with, you know, potted plants or those kinds of things?

Yeah, absolutely. I think major potential, if you’re working in any sort of soil media, is very useful, especially if you’re trying to quantify drought or run a drought study, I think it’s going to be much more quantifiable than doing something like just volumetric water content or measuring the weights of pots after you water them or something like that.

Somebody askeing also, would it be useful then to pair volumetric water content sensors with matric potential sensors? And are there any specific types of those sensors that you would recommend?

Yeah, so that’s one of the things we recommend all the time is actually pairing those two together because it will give you the most information about, one, how available the water is to your plants, but also how that water is being held in your soil over time, and building those, you know, in situ soil moisture release curves. And so our TEROS 21 is what I really love for matric potential. And then we have our TEROS line of volumetric water content sensors. So we have the 10, 11, and the 12. And which one we recommend for that is really just going to depend on your research objectives and which parameters are of interest to you.

All right. And so how would one go about maybe gauging day to day irrigation? For instance, while using matric potential.

Yeah, so kind of how we looked at that example, where the volumetric water content was insufficient to really know when to water the potatoes, matric potential is a really good indicator to let you know when to water. So even though it can’t tell you how much water to apply, it can tell you when you need to go out and apply water.

Alright, okay, we’re gonna pivot off of matric potential for a minute here. Somebody was asking about weather stations. And you had that that chart of various weather stations based on performance and price. And they were asking about AgWeatherNet stations and how those would fit into that spectrum of performance and price. Do you have any thoughts on that?

That’s a great question. So I can only speak to our ATMOS 41, which AgWeatherNet has quite a few of across the state. And I think that’s a good example of how our ATMOS 41 hits a really good mark for being research grade, but also being affordable so you can really maximize the amount of data that you’re getting across different sites.

All right, I would also plug and I know Holly did as well during her presentation, our weather monitoring 101 webinar that Dr. Doug Cobos presented on, and he went into more detail on various types of weather stations and, again, their performance and price and their usability and various research projects. So definitely go check that out. Do we have any issues with matric potential sensors being damaged by freezing conditions?

Yeah, so that’s a good question. I might have to defer to someone I know that they can tolerate freezing conditions, you just won’t necessarily have any usable data while the conditions are frozen. Maybe someone can correct me on that if I’m wrong for my team.

I believe that is correct. Yeah, depending on the conditions, it does need to have some kind of liquid water in order to get results from it. Let’s see. We will take a couple more questions here. Let’s see. Sorry we’ve got a bunch of questions that I’m trying to sort through here. So how about maybe some installation questions. Do you have any best practices when it comes to installing soil sensors in general, but specifically, matric potential sensors?

Yeah, the matric potential sensors, at least our TEROS 21s. Really, the way that they work is their reading the water that’s in the soil that is in direct contact with them. So when you’re installing them, really ensuring that you’re getting good soil to sensor contact, that the way you’ve installed the cable that’s attached to it isn’t going to give you any preferential flow, so that you’re really maximizing the naturalness of your site, while still getting that good reading.

And would you would you recommend installing it at various depths? Or is there a specific depth range, you know, within the root zone, below the root zone? If they’re dealing with drip irrigation, are there different depth installations there?

Right, well, of course, I’m always going to say, especially based on my most recent publication, that more measurements is better. So the more depths you can get, the more informative or the more information you’re going to get about water movement through your site. If you have something near the surface to capture right as irrigation is hitting it, something at the root zone, that’s gonna give you a good estimation for what your plants are really experiencing. And then also below the root zone, that’s going to allow you to understand are you over irrigating, and things are actually draining beyond the roots and being lost to your crop.

All right. Thanks, a bunch, Holly. Thank you, everybody, for attending today. That’s going to wrap it up for us. We hope you enjoyed this discussion. Again, thank you for all your great questions. We have a ton that we didn’t get to. And so again, Holly or somebody else from our METER Environment team will be getting back to you via the email that you registered with to answer your question directly. So please look for that answer coming shortly. Also, please consider answering the short survey that will appear after the webinar is finished, just to let us know what types of webinars you’d like to see in the future. And for more information on what you’ve seen today, please visit us at Finally, look for a link to the recording of today’s presentation as well as the slides in your email. And stay tuned for future METER webinars. Stay safe and have a great day.

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