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Episode transcription:
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Drew Lyon: Hello, welcome to the WSU Wheat Beat podcast. I’m your host, Drew Lyon, and I want to thank you for joining me as we explore the world of small grains production and research at Washington State University. In each episode, I speak with researchers from WSU and the USDA-ARS to provide you with insights into the latest research on wheat and barley production.
If you enjoy the WSU Wheat Beat podcast, do us a favor and subscribe on iTunes or your favorite podcast app and leave us a review so others can find the show too.
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My guest today is Dr. Joaquin Casanova. Joaquin received his M.S. in agricultural engineering and Ph.D. in electrical engineering from the University of Florida. As part of the USDA’s Northwest Sustainable Agroecosystems Research Unit, he studies remote and proximal sensing approaches for precision agriculture. He focuses on the problems of soil acidity, weeds, and nitrogen. Hello, Joaquin.
Dr. Joaquin Casanova: Hi, Drew.
Drew Lyon: So, what are some of the latest developments in your work on mapping and treating soil acidification?
Dr. Joaquin Casanova: Well, you may be aware that soil acidity is a major problem in this region and due largely to over-fertilization and removal of crop residues. And one way of treating this is with applying lime to the soil to raise the pH. But lime is expensive, so you would want to apply it in a very precise manner to save money.
So, my work has been focusing on how can we generate prescription maps for lime application. Now, making prescription maps to map the acidity, that can take a lot of soil samples–it’s very labor intensive to do it at any good resolution. So, I’ve been working in this on-farm trial of a technique using a combination of remote sensing and minimal soil sampling to generate maps of soil acidity and lime requirements. And the big thing I’ve found so far–in two years so far on the project–is that the rate of senescence, the timing of when senescence occurs in the crop, that is a big indicator of where the acidity is highest.
So, in this ongoing project, every fall I’ve been taking soil samples then comparing that to the remote sensing data [and] updating a model. And it’s going to continue for the next two years. This fall, I’ll map the acidity again. And then the farmer is going to apply lime using a prescription map I develop from that sampling. And then in the following fall, we’ll map it again and see what the impact is.
Another problem with this lime application is that it’s fairly imprecise. So, I’m currently studying a trial right now of some precision lime application we did at Cook Research Farm with a subsurface lime liquid application, and this allows us to inject lime at a specified depth and at a specified rate, which is a little more precise than the surface application of, like, pelletized lime or something like that.
So, that’s still a work in progress, and we’re studying the effects on the soil acidity profile and the soil microbiome over the next several years.
Drew Lyon: Okay. So, you fly a drone, I take it, over this area at senescence?
Dr. Joaquin Casanova: So, what I’ve been doing is using remote sensing data. So far, I haven’t really used a drone on this field yet. I may do that this fall because once I have a better drone with a better camera that would actually be useful for this task. But, I’ve been relying on data from the Sentinel satellite, Sentinel-2, and I take a series of images over the course of the season and so I can track, you know, when is senescence occurring and how rapidly is senescence occurring.
Drew Lyon: Okay. And the more rapid the lower the pH or the greater the acidity is at?
Dr. Joaquin Casanova: Yeah. So, you would think, you know, if the crop is under greater stress, it’s going to senesce sooner. It’s going to senesce faster. And that seems, you know, that intuition matches up with what the remote sensing data tells you.
Drew Lyon: Oh, neat. So, there’s lots of different lime sources and some of them react faster than others. So, how long do you think–is tracking, I guess, for one year going to give you enough information or do you have to track it longer because it takes longer for the lime to…?
Dr. Joaquin Casanova: That’s a good point. Right now, we just planned ahead for the next two years and we’ll just kind of reevaluate at that point. I just was told this morning–the farmer just let me know–he just decided to do another lime trial on his property, just on his own. He set up different lime rates on a different field. So, I’m probably going to end up starting to study that, too.
Drew Lyon: Okay. Yeah, I think, when we talk soil health, a lot of times we talk about carbon, but soil pH is a pretty big factor in soil health and what can grow, so trying to figure out how to deal with it, I think, is on a lot of peoples’ minds.
Dr. Joaquin Casanova: Oh, yeah. Yeah. And not to mention, we have this historical data set from the Palouse Conservation Field Research Station and it’s been a liming study that was started in 2002 and it’s just been continuously cropped since the lime was applied in 2002. And there were some soil samples taken along the way, but none of it has been analyzed.
So, we conducted a final soil sampling on that this spring. And right now, the samples are waiting to go out to be tested from the entire history. So, we’ll have a good record of different lime rates and application types and what the effects were over 20+ years.
Drew Lyon: That would be really interesting to see.
Dr. Joaquin Casanova: Yeah.
Drew Lyon: So, another area that I know you’re working in is herbicide resistant weeds or mapping weeds as they relate to herbicide resistance. What can remote sensing do to address that problem?
Dr. Joaquin Casanova: Yeah. Well, for researchers or for growers, it can be challenging to map out weeds, especially over a large field. And when you look at the research methods on this and the methods for mapping, there really isn’t anything consistent and there isn’t really anything very practical. You see different recommendations for how high to fly your drone, for instance, or how do you actually sample for weed density–the recommendations are all over the map.
So, I’ve got a few sites that I’m looking at, on-farm trials–I think, five in total at this point– where I’m studying the weeds in a few different ways. One is the ground truth, which is I go around the field and at random points I take pictures with a camera on a pole at a fixed height, and then I count up the weeds in the images so I get an idea of the weed density.
Then I fly over the field with, just like a very inexpensive drone with a consumer level camera, a GoPro, and I make a map of the field that way. Then I’m doing another flight at a slightly higher altitude with a more advanced drone and a multispectral camera. And then finally, I’m also looking at satellite data from high resolution satellites.
So, this way I can get an idea with these different sites, different weeds, different crops, what is actually the best way to map out weeds in terms of time, in terms of money and accuracy. And, hopefully I can, at the end of the season or a season or two, I can have an idea about what methods I would recommend using in the future, because right now there isn’t really very much guidance on how do you map weeds for research or for prescription application of herbicides.
Drew Lyon: I know I’ve had some discussions with Dr. Ian Burke, my colleague, and you and others, and it’s trying to nail down just how you go about doing it is an open question. So, it’d be nice to figure out a consistent way of doing it because weeds, a lot of weeds tend to be patchy, so you’d think doing some kind of prescription-type map for applying herbicides and figuring out maybe what herbicides are resistant to in this patch versus that patch and adjusting herbicides could be really useful. So, I think remote sensing has a role. We just have to figure it out.
Dr. Joaquin Casanova: Yeah, exactly. And, I know you’re going to talk to Dr. Burke later, so he may talk more about this project, but this is also part of the project that he’s working on with testing out post-harvest weed seed control with seed destructors. So, at the same time, we’re hoping to use this data to monitor the effect of that post-harvest weed seed control.
Drew Lyon: Okay. Yeah. The impact mills and combines. Yep. Yeah. Seeing whether those help reduce the spread or the patchiness of these different weeds. Yeah. I’m really looking forward to seeing the results of that.
But another area you work on is nitrogen. So, how are you using remote sensing to manage nitrogen?
Dr. Joaquin Casanova: So, one thing I’ve encountered in talking to growers is that when there are prescription maps made for nitrogen fertilizer in wheat, it’s typically based on maximizing yield without any consideration for protein levels or how efficient the nitrogen is being used. So, I’ve developed a method which is using data from the Cook Agronomy Farm [and] it classifies the wheat performance based on whether or not you achieved your protein goals–you know, depending on the type of wheat, the protein goals are going to be different–but, whether you achieved your protein goals and how efficient was the wheat in using the nitrogen just with the simple ratio of the nitrogen in the grain compared to the nitrogen that was applied. And using those two metrics, I classify the wheat into one of six performance grades.
And each performance grade, you might say, has a specific action that would be recommended in terms of increasing fertilizer, decreasing fertilizer, maybe switching to a different cultivar class of wheat. So, for instance, if it’s in the way I have the classes specified, one class would be “Okay, you hit your protein targets, but the wheat is inefficient in using nitrogen.” So, how could you improve that? Well, maybe you could decrease the fertilizer and improve your efficiency. Well, that’s all well and good, but if you don’t have a map of how your field performed in terms of these protein classes, in terms of the efficiency classes, it’s really not going to be very useful.
So, I’m working on an online tool where you can draw out, you know, the boundaries of your field, enter some information about how much fertilizer you applied, what cultivar class you planted, when you planted, when you harvested, and then it will use remote sensing data to generate a map of what the different classes of performance were over the field and give some recommendations. And I’ve been testing this with data from four different farmers who had yield monitors and protein monitors, so I knew, like, what the actual performance of the wheat was. And then I used that to build the models–and test them–that I’m putting in the online tool.
Drew Lyon: Okay. I just find this remote stuff really fascinating because our soils, our fields are very diverse in this part of the world and we have tended to treat them all the same, and that’s not very efficient. And this remote sensing seems to be an excellent tool for helping us farm our variable land in a much more efficient and hopefully cost-effective way.
Dr. Joaquin Casanova: Yeah, it’s the simple things that vary over the landscape, like even a slope or the aspect, they have a tremendous impact on how your crop performs. And if you’re not taking that into account, you could be just throwing away nitrogen.
Drew Lyon: So, if growers want to come and see some of the things you’re doing or learn more about some of the things you’re doing, is there a website they can come to or someplace they can go to find the information? Because my guess is there’s going to be a lot of growers interested in what you are doing.
Dr. Joaquin Casanova: Well, I’m in Clark Hall; I guess that’s the best way to reach me–is try and catch me in my office. But, there’s the USDA website, USDA-ARS. You can just look up the Northwest Sustainable Agroecosystems Research Unit and my contact information is there.
Drew Lyon: Okay. And we can put that in our show notes so people can find it easier because, yeah, I do think there’s a lot of interest in this. The technology’s coming along, getting a little cheaper to get access to some of these things and so, I think, people will be looking for how they can utilize this.
Joaquin, thanks for being my guest today.
Dr. Joaquin Casanova: It was my pleasure.
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Thanks for joining us and listening to the WSU Wheat Beat podcast. If you like what you hear don’t forget to subscribe and leave a review on iTunes or your favorite podcast app. If you have questions or topics you’d like to hear on future episodes, please email me at drew.lyon — that’s lyon@wsu.edu — (drew.lyon@wsu.edu). You can find us online at smallgrains.wsu.edu and on Facebook and Twitter [X] @WSUSmallGrains. The WSU Wheat Beat podcast is a production of CAHNRS Communications and the College of Agricultural, Human, and Natural Resource Sciences at Washington State University.
I’m Drew Lyon, we’ll see you next time.
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The views, thoughts, and opinions expressed by guests of this podcast are their own and does not imply Washington State University’s endorsement.