Episode 16: Why Overwatering Invites Disease and Removes Critical Nutrients

Episode 16: Why overwatering invites disease and removes critical nutrients

Dr. Colin Campbell discusses his collaborative research efforts controlling water on potato farms in southern Idaho. In potatoes, overwatering impacts disease and reduces critical nutrients in the root zone. He and other researchers discovered that combining measurements helped them better understand the impacts of management and the interplay between variables like evapotranspiration and soil water.


Dr. Colin Campbell, PhD, is research scientist and head of research and development at METER Group. Learn more about Dr. Colin Campbell:



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Hello, everybody, and welcome to We Measure the World, a podcast produced by scientists, for scientists.

One of the luminaries of irrigation, said to me one day when I was complaining about this, he said, using ET is just like looking at the outgoings and incomings to your bank account, but never looking at your bank balance. If you don’t know your bank balance, you just know what’s coming in going out. You don’t know if you’re close to the line of some someday you’ve got a big withdrawal. You might overdraft the account. And if you’re not paying attention to the soil, that is a problem.

That’s a small taste of what we have in store for you today. We measure the world explores interesting environmental research trends, how scientists are solving research issues, and what tools are helping them better understand measurements across the entire soil plant atmospheric continuum. Today’s guest Dr. Colin Campbell has been a research scientist at meter for over 20 years following his PhD at Texas a&m University in soil physics. He is currently serving as Vice President of Meter environment, and is also adjunct faculty with the Department of Crop and Soil Sciences at Washington State University, where he co teaches environmental biophysics, a class he took over from his father Gaylon over 20 years ago. Dr. Campbell is early research focused on field scale measurements of co2 and water vapor flux, but has shifted toward moisture and heat flow instrumentation for the soil plant atmosphere continuum. And just last year, he was made a fellow of the American Society of Agronomy, and recently elected to their board of directors. And today, he’s here to talk to us about his potato Irrigation Research in collaboration with Brian Hopkins, and Neil Hansen at Brigham Young University. So Colin, thanks so much for being here.

It’s great to be here. Thank you.

All right. So as with most, if not all of our guests, we want to start off by getting into your background a bit. How did you get into potato Irrigation Research?

You know, I don’t know. But some of my earliest memory my father, Gaylon, who’s been on this podcast. And as on some of several of our webinar series, he was a field scientist, and I still have the opportunity to work with him every day, which is one of the most wonderful things about the career I have. Back then, for reasons that I don’t know it used to load us children up in, in our old valiant it was a Ford valiant or something, and we’d drive out to the center of the state, and we go study potatoes back then we just go out and I’d mess around doing stuff while dad would do research. But at some point, when we got hungry, I can remember him going and digging a hill of potatoes and taking a potato out washing it off with one of those kinds of lab, you know, water bottles, kind of rubbing it off dumping a little salt on it, and we’d eat them. And, and even to this day, I enjoy a raw potato. They’re surprisingly good at least to me, maybe district evoking these memories, but they but but even though people are like that’s really weird. I think that was my first introduction to potato research. Now. Since then, I really haven’t really haven’t connected with potatoes. My graduate research work was in irrigated rice, not super close because it’s a different different system. Your this irrigated rice is flood irrigated. Most of the potatoes we work with are our center pivot irrigated. But working through commercial agriculture, especially in grad school meeting these growers, one of my good friends, LG Ron, is this great innovative rice farmer and in South Texas, near El Campo connecting to him made me realize that there were some key things that we could do from the research side to help him after my, my graduate research, I actually took all my data down and sat down with him. And a few of his grower friends, and showed them where their nitrogen application could be most efficacious. Oddly enough, they were dumping a very large nitrogen application on at the end of of a certain time period in their the rice growing process that wasn’t effective at all. Essentially, the plant had shut down, it’s already it was filling its its fruiting bodies, the the heads of the rice, and it wasn’t useful. And he was like, This is so great, you’re saving me, you know, 10s of 1000s of dollars just sitting down here and talking about it. So there I got a feel for how much fun it would be to connect in the process of a growers life, you know, what we could do for them and for the environment, too. Of course, an extra nitrogen application that isn’t being used by the rice crop is also going to end up somewhere we don’t want it right. Yeah. The Gulf of Mexico in that case. Now, when I got to METER Group, I was, you know, when I think back to those days, I was young, I didn’t know that much about what we were doing. And I immediately dropped into this project where a cousin of mine had developed a business plan at Washington State who was at a student who come back years later, who wanted to get a business degree and never finished college, he came back to get a business degree and his business plan was to try to control irrigation at one of these large farms in the center of the state of Washington. And so he made a whole business plan. And the big thing that it centered on was a measurement of water content. And, and so he had been running around in his youth, kind of measuring water content and fields using the, what they call a neutron probe, it has a little radioactive source in it in, they can measure the water content in the field, but you can’t leave it anywhere. And so it was a constant driving around with these little Honda 120 fives or something with this probe on the back and they go around and measure the water content in these potato fields. And he said, you know, that was bad, what we need to do is put a radio with a water content sensor that’s not radioactive into the soil there. And then we can let them know what their irrigation is doing. And so that was my first project. That’s how I got into it was was I was out at, at this farm, measuring the water content of this the soil and trying to help these growers control irrigation.

So what were some of the results of those those early forays into sensing and irrigation?

Yeah, so the results were really mixed. So I put together a system based on my graduate school knowledge that could get the data back to this was in the center of state, we live on the east side of the state, and we could get data back here to the business. But what we learned was, we didn’t know a ton about making sensors as a first year that we made the sensor to go in the soil, and many of them broke because of generally the design that we put together in the sensor, it just, it had some flaws. But what we did see was this, this little nugget, this little vision that this could be done, and that growers routinely overwater in potatoes. And that’s still the same is it’s 22 years since we first threw instruments into this field. And I started learning about potatoes. And that hasn’t changed.

So moving from the Columbia basin over into to Idaho. Can you tell us a little bit about your next project into potato irrigation? They’re in grace, Idaho? What were some of the questions you’re trying to address? I assume that similar to your previous projects there, what were the questions were the problems? What were you trying to work out there?

So I guess I took like a 15 year hiatus, you know, worked in potatoes, and then just moved on doing a lot of other projects. And Dr. Neil Hansen and Dr. Bryan Hopkins from Brigham Young University, contacted me and said, Hey, we’ve been working in this field and in grace, Idaho for the last three years, and we’ve seen some important trends in the soil moisture. And we’re wanting to extend our work such that we can understand better the dynamics of water in the field, what they’ve been doing before is actually going in hand sampling across the field, three, maybe four times during a season, they’d use it to get soil type the water content, a few other you know, maybe get it the nutrients in the field. But that didn’t give them a dynamic picture of what was happening with the water in the field. And they said, Hey, we’ve just concluded this turfgrass research that that we put together in 2015, do you want to join up with this project, and see what we can understand about irrigation, water, in potatoes. I’m a sucker for joining these projects. So I said, Certainly those guys are awesome to work with two of my favorite individuals anywhere. And so when they suggest a project, I have yet to say no on one. So we jumped in together.

Right, is there anything unique about that field or that research area in particular?

You know, there are a few new unique pieces about the grace operation. This is where with a group specifically overseen by a man named Brian Christianson, an actual graduate of Brigham Young University of this program. And so they I believe, reached out to Ryan when he’s back on his family farm. Christiansen farms, or as they call it BKR farms. They reached out to Ryan and said, Hey, can we start some of this research on this particular field? And one of the things that was cool about working with Ryan Christiansen is you He’s an innovative farmer. And he’s always pushing the limits. He has drones that are measuring nitrogen in the leaves using spectral spectral imaging. He’s got sensors, he already had sensors in the soil and was already trying to interpret them. And when I got there, he’d already fine tuned his irrigation, such that I don’t think he was over watering. If anything, I think he was underwater sightly, because he had fine tuned it so well, a guy who really uses innovation, he to push his operation forward and try to generate profits.

You talked about some of the things that he was doing that what were some of the other measurements that you were taking, or really watching out for

our original goal on the project was to establish some sites in some statistically interesting areas of the field. They had observe certain trends in some of the areas in the field. And they’ve actually marked some of the sites in the field saying, hey, here are six locations that we believe might be indicative of some of the different trends that occur in this field. Now, to give you a size of the field, I think it was something like around 40 hectares is that 60 acres, I can’t remember something like that. And it’s a really fun field, you said, what are some of the unique aspects of this field, one of the very unique aspects is this is a center pivot irrigated field, the northern part, the top half of the pie essentially grows potatoes, then wheat, then wheat and then back to potato. So it’s on a three year rotation, the southern half of the center pivot is a golf course. The Christiansen family loves golf, which I think is just wonderful. And so they center pivot, irrigate this golf course. And the reason we wanted to go out there is that the golf course makes just enough money to be able to support a variable rate irrigation system, which means that they can control water in any square foot, right, basically using a prescription. And the problem with with the situation was that the south half, Ryan had a really good idea of where you know how he wanted to apply the water, we got some greens, we got some fairways, we got some rough, we’ve got sand traps. And so Ryan was able to easily go in and program that that system to tell it where he wanted to put the water down. Then he was like, Well, what about the potatoes. And interestingly enough, the potatoes were harder. Hmm, interesting. And so the goal of the project was to really understand how to use the variable rate irrigation system to irrigate the potatoes because we already knew how to irrigate the, the turf grass on the Golf course one of the funniest things and I’ve got, one of the funniest things I’ve got to say about this is that, not surprisingly, there are no trees, or no tall trees on this golf course. Usually, when you go on a golf course, there are some trees. And I didn’t think about this, but it’s center pivot irrigated. So and so that’s, that’s one of the funny stories about there. So we wanted to try to make a prescription that Ryan could use on this variable rate irrigation system by by first 100 sampling sites. And then we stepped into this saying, well, those sampling sites aren’t really are doing a great job for what they’re intended to try to understand the variability in the field. But we want to understand the dynamics the dynamics of the water in the field. So that’s, that’s what we initially got into,

if you’re trying to get at a variable rate, irrigation plan, the potato field, again, just from from viewing it with the sensors, depending on where you’re planting the sensors, that might give you a much different view with a lot more variability, I would assume. So how do you deal with with with that kind of variability that you’re getting from these measurements?

Dealing with variability is a complicated problem. And to a certain extent, we can’t deal with all the variability so we have to kind of pick our battles. Our first battle was really understanding does this variability that we see out there impact potato yield, or water use or any of these kinds of things, because if it doesn’t, then we can walk away and feel good about what Ryan’s doing, I already told you it was a pretty good irrigator going in, so maybe there’s no point of going out there. And what we found was they actually made kind of a matrix to understand how to play water, like this has, you know, in the top of the matrix, you can use more water and this has plenty of growth potential. And down at the bottom corner of the matrix, the bottom left essentially was this has little fertility and can’t use any water, you know, more water isn’t going to help it and it kind of had the different cases in the other two quadrants and that we are trying to understand And the field in the context of something like that, where it could we effectively use more water where there’s additional growth potential. And we’re trying to tie that into the available water in the root zone. So when I got there, and on the first day, I met Ryan Christiansen and he came in, and I said, Hey, Ryan, you know, tell me about the field. And he said, Well, just a minute, because I’ve got one thing I’ve been dying to talk to you about, like, Okay, what is it? And he said, I’ve been using these water content sensors here in this field for a couple years now. And I wonder if over time they stopped working. And I said, that’s interesting. I’m always willing to admit that a sensor might break. But the sensors you’re actually using, in my experience, and I’ve been doing a lot of research with them typically don’t break. So tell me what your you’re seeing. And he said, Well, I’ve installed them in the field. And over time, they’re just flat. They just don’t change. And I said, Well, tell me about the water content you’re reading? Well, it’s around 30%, plus or minus a few percent, and it starts, you know, say 30-34%. And by the end of the year, it’s about 30%. Said, that’s really curious. I don’t think that’s because the sensors aren’t working. He’s like, Well, how about if they’re kind of imbibing water, they get saturated or something I’m like, I think that actually happens. So that’s kind of what started provided a backdrop of what we’re going to do. And luckily, because we’ve been experimenting in this turfgrass, Brian and Neil and I, when we went to this field, we already had a plan where we were going to use water content sensors, which was the traditional way that that Ryan would have been doing it over the last few years. But this was really my first big foray. I guess it turfgrass was one but really in field agriculture, using water potential. And so we call it located the water potential and water content sensors out there in the field. And then we learned amazing stuff.

Can you tell us about or at least at the very beginning, because we’re we’re the data that you were getting there the beginning? Did that jive with what Ryan Christiansen was seeing? And then secondly, what were some of the new or surprising things that you learned there at the beginning of that project?

One of the key pieces you already mentioned was this question of variability. And we had kind of a serendipitous meeting that year with a friend of mine son who just finished up a PhD at Stanford, and he was kind of poking around for projects looking for, for a permanent position. And he reached out to me it was just talking about, Hey, are you interested in in satellite data? I said, I think there’s a lot of use for I just don’t know a ton about it. And he said, well, for my PhD, I’ve been using certain satellite satellite bands, to look at groundwater in the Central Valley of California, the same bands, I think we could use are some bands from the satellite we could use in an agriculture field to maybe get a sense for how much water is available. And I said, Well, that’s interesting. I’d love to kind of connect the dots, we’ve got a lot of intense sampling. We’ve got, you know, point sampling, we’ve got these dynamic locations, six sites where we got multiple depths of water content, and water potential, would you be interested in just kind of throwing your hat in the ring here, too, and investigating this? Dr. Ryan Smith said, Yeah, I’d love to see what we can do, then kind of had this unique combination of pretty intense field sampling, locational understanding and remote sensing that we could bring to bear on this problem. And in the end, it was the combination of all those things that that helped us really investigate and finally come to some interesting conclusions about what has happened in the field in that year and subsequent years changing learning, understanding and implementing for rank Christiansen some of these practices in his actual operations. So first of all, the question of of this variability, we decided to use some of these satellite remote sensing data to try to understand the variability in the field due to differences in water. So initially, we didn’t know or really understand some of the extended bands now we use something called the Normalized Difference Water Index. And we didn’t use that the first year, at least the the least initially understand that that could provide some understanding of what was going on the soil but those kind of developed over time. We put these sensors in the field just after had emergence of the potato plants in the field. And then we started to watch nothing happened that year. That was astounding since then, we’ve seen maybe more dynamics in the water just because we know how to manage it better. Ryan manages more closely to kind of that line. And so we do see the water potential drop out more than we did that year that the water potential started high, and just slowly went down over the season. But immediately, I started to notice that the water content didn’t change. It was just like Ryan had said, he said, it doesn’t change. And I’m watching it. And midway through the season, we’re still at 30%. And we started the season at 30%, we had six sites that were all reading between 31 and 37% water content. But I turned to the water potential data. And I started to see a very specific trend. Optimal water potential is from about negative 20 to negative 60, kilopascals in potatoes. That’s what the literature says. And so I was watching the water potential of these sites and three sites were cooking along negative 20 to negative 30. Consistently, three other sites were dropping down below this negative 60. They traveled beyond negative 100 kPa and just kept going down. So I texted Ryan, I said, Ryan, I’m not going to tell you how to run your field. But three of those sensors are saying you got dryness in the field. And Ryan texted back and said, Hey, I went out with my shovel dug in the soil there. And things are looking great. It’s probably your sensor. And again, I am perfectly willing to, to accept that one of my sensors isn’t performing well, because things don’t perform well in the field. Sometimes, when we go out and do an experiment, though, we should have all these possibilities in mind. It could be the sensor, it could be the system, or it could be real, you know, the system. When I say the system, I mean, maybe installed the sensor on, you know, maybe your water’s running down the cable of a sensor, maybe you did any number of things. Maybe for some reason your digital to analog converter isn’t working, I don’t know, could be any one of those things. But I really asked Ryan, I said, Can you just maybe accept for a moment that this is possibly true. And he said, I can’t a little bit. But you know, I’ve been going and shoveling the soil out in that field for a very long time. And I know, I can feel with my hands when we have optimum moisture, which by the way, I do believe that Ryan’s pretty good at that. So we just watched it. And at three sites that water kept going down and down and down. And if three other sites have stayed flat. And luckily, during that the year I’d actually installed just on a whim, we put Atmos 41 all in one weather stations out there. So I could get the E T, and at three other sites, we put canopy temperature sensors. And it just so happened that those two of those sites were at these dry sites. And one of them was was it a well watered site. If I had designed this, it couldn’t have been better. And it’s weird, because I’ll freely admit that some of my research is just dumb luck. You know, I put the sensors out there that I think would work. And then sometimes you kind of hit a jackpot. And sometimes you don’t, we’d hit the jackpot with the canopy temperature sensors, because we did have the measurements in the soil. So that’s one clear identification of a problem. But I needed a second measurement to be able to say, hey, I have now very good confidence there’s a problem these six sites. And so I compare this well watered site to the two dry sites and said if there’s a considerable difference between the canopy temperatures at these three different sites all know that the well watered site has enough water and these canopy temperatures that are high means that the stalemates are closing and that these sites are truly stress for one. And when on my computer. I did an analysis of it. And not surprisingly, it came back that these canopies are popped. In fact, one of them was five degrees Celsius above this, this will water canopy temperature which suggests we’ve got this tomato closure. We’ve got strep plant stress, and I texted Ryan and said Hey Ryan, I’ve got a second data point to suggest we’ve got a problem out there. By this time, there wasn’t a lot we can do about it and and we just let the system ride down for the rest of the summer. And I collected all the data and put together kind of analysis of what’s going on. But then we had kind of the coup d’etat. It was Ryan because he’s an innovative grower He has a yield monitor on his combine. And I said, Hey, Brian, when you’re done, can you just send me the yield data from those sites? Yeah, just and they can’t be exact. But you know, neither are is the stress just happening at that location. So he sent me the data for the whole field, I extracted the the sites where the yield was taken from and mash them up with the six sites that we’d seen either good water availability or poor water availability. And and compare it. And the data were very clear at those stress sites, we lost 25% of you. And I sent that down to Ryan, he’s like, I’m a convert. I know that that is exactly what I was looking for. At the end of the day, that was really important. I could tell canopy stress, I could tell soil moisture stress, I could tell all these things. But that wasn’t really important unless we understood that it was impacting you. And once Ryan saw that was impacting yield, he knew he could he needed to make choices in his operation to try to get that water at the optimum.

Taking this example and others that you’ve that you’ve seen, just from a broader perspective, how can we use measurements such as you know, soil water content, water potential, electrical conductivity, temperature, just to better understand the impacts of water management,

trying to understand the impacts of water measurement. Using sensing is a complicated effort. And sometimes what I try to do is simplify that into some some of the key parts of that. For example, you mentioned electrical conductivity, which we haven’t talked about. Now, electrical conductivity can be really important, especially if you irrigate irrigating with poor water quality. Or maybe you already have a field that’s affected by salt, understanding our electrical conductivity and managing it with leaching fraction, which we’ve done a virtual seminar seminar on, that can be really important. So measuring that, in some cases, important for Ryan, that wasn’t important. What was important for Ryan was to try to connect what the what we were seeing in the root zone in terms of water with what the plant was using, and then applying the right amount of irrigation water. So after that year, where we saw the value of the system, Ryan decided to buy sensors for all the potato fields he was working on. And so he couldn’t buy six sites for every potato field. In fact, trying to install them, maintain them drive tractors, around and above them, etc, would have just been too much of an effort. He just bought one site per field. But that left us a big challenge. What would we do about where to place that site. And so again, this work with Ryan Smith, now at Missouri, A and T was critical, because Ryan said, Hey, I think I can put together a little program that if, if you’ll tell me where your fields are, then I can look at some of these satellite indices, and try to figure out where might be an average site where we can put these sensors, so that you can get a feel for what your field is doing without putting sensors everywhere. Now, as a sensor manufacturer, I think you should have 100 site 100 sensor sites, and you’re just getting, I thought it was great. I thought it was a great idea. So Ryan Smith worked out a program had Ryan Christiansen, send in all his field parameters, you know, the edges of his field. And Ryan Smith then pinpointed location, each one of those fields that we should deploy sensors. And instead of me going down and installing this time, Ryan Christensen went and installed in all his fields himself. And I just monitored the data through the whole season. And we found something really interesting, because he had the sensor data. And because he knew to irrigate to that point, we no longer had any stress in the field. So I couldn’t prove again, Hey, see the year yields dropped. But he also just nailed that through the whole year. And when we analyze the yield data from each one of those sites, it was pretty much dead on the average yield for that field. And in fact, we did the statistics, and each of those sites with it was within one standard deviation of the mean, for the the field production. I don’t know if that, you know, maybe anecdotal a little bit, maybe we’d have to do a bunch more fields to feel statistically This is a relevant number. But it did mean for us that we were on the right track using satellite remote sensing to be able to give an idea of where we might place the sensors in the field. That was one of the things that that really jumps out at me as a key finding of this project is that we can have a good idea of where we can put sensors in the field. The answer to a question that so many people have asked me for so many years where I put in, I just have one site, where to put it. And using Normalized Difference Water Index, Normalized Difference Vegetation Index, and a couple of other indices. We know where to put them,

right. I doubt that this is a one size fits all solution, right. But at the same time, it seems that this is a generally applicable plan of action.

So is this a plan by which we can address irrigation challenges in potatoes? And I would say, yes, it is we’ve kind of codified that a little bit, what we’ve done is say, Okay, first send us your fields, we’ll tell you where to put the this sensors. And when you put sensors out there, we’d like to see at least one water content, one water potential sensor, we’d love to see a couple of different depths. So we’re getting an idea in the root zone, what is the moisture availability, that’s going to represent the average situation in your field. And you’re simply going to extrapolate that to get an understanding of how to apply your your irrigation water. Now, this is complicated by topography, by the sprinkler performance, one field last year, we had one of the growers that was unhappy, so he put it out there, he’s running low, like, Hey, you’re running pretty low at the spot. And we pinpointed a place to put it. And he said, No, I feel like I’m doing great. And we analyzed where we’d put it, and it maybe on a slight slope could have impacted there. But what we did notice was that that one of his sprinklers on his center pivot may not have been working super well, for a while, they did change that, but they were already kind of in arrears in their water. And that may have contributed to one of the problems. But generally speaking, when we locate these kinds of things in the field, that that works quite well. Right. And and Ryan’s been doing this now for I think we’re starting our fourth year, fifth year, I can’t remember and, and really happy with with how that’s proceeded,

right. So how then in these situations, can we account for the, I guess, the interplay between environmental variables, like we’re dealing with evapo transpiration, that’s something that we haven’t really discussed yet. But the interplay, then between evapotranspiration, soil, water and other things like that.

Glad you asked that question. Because the evapo transpiration is just telling us how much water was required from the crop, we can calculate the actual crop loss of water by using a crop coefficient. And that should tell us something about how much water needs to be replaced. That’s a great start for irrigation. And for a lot of years, that’s been the kind of bedrock of how you apply water in the field. And I remember when I started into this irrigation area, people said to me, Hey, I don’t think we need to measure things in the soil. We’ve got E T. Now one of the luminaries of irrigation, said to me one day when I was complaining about this, he said, using E T is just like looking at the outgoings and incomings to your bank account, but never looking at your bank balance. If you don’t know your bank balance, you just know what’s coming in going out, you don’t know if you’re close to the line of some someday you’ve got a big withdrawal, you might overdraft the account. And if you’re not paying attention to the soil, that is a problem. And I’ve taken that to heart over the years as we talked about measuring water content in the field. But it’s been really amazing. As we measured water content and water potential, we now can make what I sometimes call a moisture requirements envelope. We can see how much water is in the soil and the energy state or availability of the water and combining those two tells you okay, I’m can go from 16% in the sand down to 8%. That’s my range from from upper optimal to lower optimal. And if we know that rooting depth, you can tell how much water you need to apply to fill that back up. That’s extremely powerful because if you’re getting you’re at, okay, I’m losing six millimeters of you tea every day or whatever it is that a quarter inch, a little more than a quarter inch. If I’m losing that per day and I know I have basically three times that storage in the root zone I know how often I need to reapply that Water. Now with different soil types, the regularity upon which you reapply the water is important. If you’ve got a finer texture soil, you may do it less often, if you got to sand, you may do it more often. But still knowing how much water it takes to refill that, essentially that zone that the roots are taking water up from his critical

How does over and I guess even under watering affects other issues such as disease and nutrient intake and other things in the root zone.

I’ve often said I’m not a potato expert, I like to eat potatoes, but but I’m not in the know of all the ins and outs of disease and, and other things in potatoes. But what I do know is that their optimal zone of water potential is kind of more rigorous than most plants, potatoes are particularly sensitive to the availability of water in their root zone. If you get too much water, we can get disease, we can get rot. That’s something that when you go talk to growers out in the field, they’re telling all kinds of stories, well, it’s too wet this season, we got a lot of rain right at the end, and suddenly, our potatoes were just all black. And so we had to squirt them off, you know, with high pressure of high pressure wash to even save these potatoes for some kind of production. To dry make smaller tubers, there are all kinds of problems on that side, I kind of look at potatoes as sitting on a knife edge. In terms of optimal water were too high or too low, you’re going to have problems. That’s not necessarily the same with with other crops and potatoes are particularly susceptible to this. So optimizing the water and that root zone ends up being a really critical factor. And so being able to measure that water availability is crucial for success for potato growers. In my experience, yeah.

You mentioned other field crops, is there any applications or things that you have learned that then we can take across to other growers or other crops.

Potatoes aren’t the only crop that is hypersensitive to water. And as we go around and look at these opportunities, things like wine grapes, they are really sensitive to too much water. It’s great to stress wine grapes, get them down into a pretty low water potential. It fills the grapes with sugars makes them taste better and excetera. So wine grapes are critical one. Hops, for example, another one that that really needs good irrigation. And we can go on tree fruit, especially some of the new varieties like honey, crisp and cosmic, crisp and others. They depend on a certain stress level to build a sugars in in their apples. And we can go on with with a variety of things taught to growers of onions, for example, and onion growers. They depend a lot on the water potential in near the onion to produce cannabis, the perfect onion as well. It’s not something I’ve worked in a lot. But I was surprised when I talked to them that they’ve been using water potential for a lot of years, led by a researcher that that’s recent really recently retired Clint shock, who did a lot of this work early on and one of the kind of luminaries in the the water potential world, especially for onions.

Interesting. What are then some of the major challenges that you faced in implementing a better water management within within potatoes and elsewhere.

So I want to go back to talking about water content, because people have used water content to measure irrigation in different crops for a lot of years. One of the challenges with water potential is it’s not that easy to measure. So a long time ago, people kind of assumed the Tensiometer, which was the way you could measure water potential and went to an electro magnetic field method for water content, which was quite a bit easier. There was very clear understanding of how much water was in the soil. And that makes a lot of sense to the mind rather than Hey, this is the energy state of the water. And and so for many years, people have kind of developed ideas around the water content. But the variability of water content field to field and even over the year discourages a lot of people from actually measuring water in the soil. So one of the major challenges that we’re fighting is, hey, I went and put in this this profile probe. We took three weeks the beginning of season to try to say what was full and what was the refill point. That took us a ton of time on labor. And now I think it’s switched. You know, I think it’s lower than I thought it was and now where does that setpoint How can I effectively irrigate, super easy to put in, don’t know what to do with it now. Getting water potential into this equation, because it’s not super easy to understand and trying to start creating this vision around a water envelope has been slow to adopt. So as you talk about challenges here, that’s one of our big ones is to say, hey, like a thermostat in your house, we can measure a similar quantity like temperature in the soil to say, I’m comfortable or uncomfortable. And in how much water to apply there. This is not a concept that is going to catch on Super simply, I imagined when they invented the thermostat, it took a while for people to really engage, like, you know, I’m just building a fire, I’ve got a furnace and what you know, I know when it’s too hot, and I just go and, you know, slow down the burner, I know it’s too cold, and I increase it. Why do I need this thing that, you know, expensive thing that sits on the wall? In a similar way, I think we’re working out that same hill with water potential. The idea is fundamental to be able to be successful with irrigation. And yet, it’s just going to take time for people to realize, hey, there’s a thermostat for the bill. I don’t need to go out with my shovel like Ryan did and kind of feel the soil generations of people feeling the soil and saying if there’s water availability, can, you know we can set that aside and actually measure it

right you you’ve talked about the this idea of evangelism or spreading by word of mouth, the importance of working with water potential, or working with water potential in concert with water contents to help with water management. What specifically or maybe not specifically, but maybe generally can be improved upon within the measurement itself of water potential, the sensing the systems and other things like that.

There’s always room to improve our measurements of water potential or anything else we do. And one of the things I think is funny is that when I was studying how to make a water potential sensor in 2001, I came up with a design for the sensor that was based on one of the materials we were using to try to measure the water potential. And the sensor we use today is the exact same design, even though that material never worked out. So we actually designed the ceramic because we’d already designed the circuit board to fit the same footprint of that original design original material. And we’re still using it today. And admittedly, it’s not that easy to install. So yeah, there’s there’s great opportunities to improve these things. Why haven’t we done it yet? Well, I think first we wanted to improve the performance of the sensor such that today, I’m absolutely confident in making a water potential measurement field, our TEROS 21 gen two is amazing, in my opinion. And I, you know, I didn’t do all the work on I want to say up front, I’m not not trying to toot my own horn, I didn’t. Leo Rivera, Colby Thrash, and some of our other research scientists here. Put a lot of work into designing this, this second generation sensor so that it can cover all the way up to nearly saturation, which I never dreamed would be possible back in the day. Right now we’re working on a new sensor that will be easily installable. People keep asking me, Hey, When’s that going to come out? And I’d like that one. I’m like, I’d like it to. But it’s not ready yet. But we are working on it. And certainly that is one thing that that I would like to produce is something that we could go out in the square fields and be able to maybe take a drill and drill it in, shove the sensor in and beyond. Right. And that’s, that’s probably the next thing on the horizon. I’d love to make sensors that we could make a profile measurement of water potential profile measurements of water content, you’re a little bit troubling, because we always have air gap issues. Profile measurements of water potential wouldn’t be suffering the same problem. Because if we have a connection between the sensor and the soil somehow, then we can reach equilibration, even if we have disturbed soil or anything like that. So you know is that on the horizon? Maybe something that that we’ll work on. So those are the kinds of improvements, I’d love to see. We’ve done a lot of work up to this point and figuring out how to install, install a calibration on each sensor. So every sensor we make is individually calibrated. I don’t think people actually know that. And that’s one of the cool things that that we’ve been able to do. And that produces a high level of confidence in the performance of the sensors on the field. So I think that’s great. Now,

you mentioned with Ryan Christensen’s farm, that he was seeing these discrepancies or at least even even in the data that you’re receiving between water content and water potential and how his water content was staying pretty much the same all throughout the season around you know, it’s a 30% or so and you had the water potential is dropping? Can you explain a little bit what is going on within that system there?

It’s interesting that this kind of phenomenon actually existed because I looked at the data from the whole year of water potential water content from those six sites. And I guess this will tally one for Ryan Christiansen because I got the other side where, where he was measuring with a shovel, it wasn’t really indicative of what’s happening in that whole field. It was at that site, he happened to be digging a shovel right next to one of our, our well watered sites in the end, and so he didn’t really get a feel for those other sites that were actually being stressed to some degree. But on his side, I doubted his story about these water content centers, remaining consistent across the season, when we looked at the data from that field, those six sites, each of them only changed 3% or less, and over the whole season, even the ones that were stressed. I was pretty shocked by that. So did that mean that the sensors weren’t working, and I scratched my head over that for a while. When we put together, we combine the water content and the water potential data, we create what we call a moisture release curve. That moisture release curve is specific to any soil that you’re in and it changes. So it could be different with depth. You know, if you have a little bit of a different soil to go deeper, it could be certainly different field to field. I know it is I’ve seen it consistently even in on Ryan Christensen’s farm. What were what was happening in that particular field is that we were just in an interesting area on the moisture release curve, which meant that, that we had a lot of change in the water potential with not as much change in the water content. Essentially, the water content could not help Brian in that field to irrigate? Well, because if you started out at 33% water content and ended the season at 30%, and went from negative 30, kilopascals, to negative 1000, kilopascals, which did happen in one site. I don’t know what to do with the water content data, I would have been like, Well, I’m not sure why that caused a problem. But the water potential information was clear. It was simply stressed at that those sites that happened in that specific field, that moisture release curve, I admittedly have never seen that in any other field that we’ve worked in. So I don’t want people to run out and you know, buy water potential sensors for that purpose or say water content sensors won’t work, because usually, they’ll both change relatively to one another, you know, a little bit more. But in this case didn’t happen.

All right. And so we’d like to finish with any any funny stories that you’ve had from this potato project or anything along those lines that you’d like to share with us. One story

wasn’t about me, but but we’re working on a golf course. Right? So when you turn out, this is the greatest thing ever, that you load all your field gear into a golf cart off, and then you can dry out to the field. And so this was great. We had a bunch of heavy equipment, we’re installing it. So we got there the first day and Ryan said, Hey, feel free to use the golf cart. Apparently that got maybe overdone at some point, because the next year I was back, there was a rule that graduate students were not allowed to drive a golf cart into the potato field. Maybe potato fields are very bumpy. They’re they’re got a lot up and down based on the big piles that they plant the potatoes in so they could you know, they’re loose soil so you can harvest them easily. And I don’t think going in a golf cart in there would be very fun, but potentially that happened. So no more golf carts out in the potato field.

All right. Well, our time is up for today. Thank you so much, Colin for him stopping by and sharing your your passion your projects with us here. And if you in the audience have any questions about what you have heard today on this topic or would like to hear more, feel free to contact us at metergroup.com. You can also reach out to us on Twitter @meter_env. And you can also view the full transcript from today in the podcast description. Stay safe, and we’ll see you next time on we measure the world.

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