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Episode 44 | Cover Crop Decision Support Tool by Illinois Extension
The Illinois Nutrient Loss Reduction podcast explores efforts to reduce nutrients in Illinois waterways from agricultural runoff to municipal wastewater with host Todd Gleason and producers Rachel Curry, Nicole Haverback and Luke Zwilling with University of Illinois Extension.
Read the blog at extension.illinois.edu/nlr/blog.
Episode 44 | The Cover Crop Decision Support Tool
00:00:06:08 - 00:00:31:17
Todd Gleason
This is the Illinois Nutrient Loss Reduction Strategy podcast, episode 44. The cover Crop decision support tool. I'm University of Illinois Extension's Todd Gleason. There's a brand new cover crop tool that's been released by the farm doc team. Actually, a series of folks here on the Urbana-Champaign campus of the University of Illinois. Jonathan Coppess now joins us from the Department of Agricultural and Consumer Economics.
00:00:31:20 - 00:00:45:04
Todd Gleason
Hi, Jonathan. Thanks for being with, as I said, brand new tool, but this is actually a tool, that is a year old I guess now. And it was updated. So first, why was this cover crops tool created. What is it supposed to do.
00:00:45:04 - 00:00:48:26
Jonathan Coppess
Yeah. So thanks, Todd. This is, it's good to talk to you. And it is a year old.
00:00:48:26 - 00:01:12:11
Jonathan Coppess
We released it last October. The first iteration of it, we updated it, going into the new year. And then this is our second update. So a little bit of background on this. We've, we've been working together with kind of a multidisciplinary team for being a trend, with Shalamar Armstrong at Purdue and others around and then working very closely with our National Center for Supercomputing Applications here on campus.
00:01:12:17 - 00:01:31:07
Jonathan Coppess
And the idea was we have this, it sort of comes out of a nutrient loss reduction strategy and the need for farmers to reduce the loss of nitrogen, in particular in Illinois. And we know that cover crops is arguably the most effective in field practice to cut that nutrient loss, because you're growing it in the fallow season.
00:01:31:09 - 00:01:52:20
Jonathan Coppess
So it's going to absorb or scavenge that extra nitrogen. It's going to hold it in that in the crop biomass. And so it's not going to be leaching into the tile and from the tile or the waterways and all the way down to that dead zone in the Gulf of Mexico. So we kind of began with that. We, we've gotten, some very generous funding from NREC. the Nutrient Research and Education Council here in the state.
00:01:52:22 - 00:02:13:29
Jonathan Coppess
We've got some generous funding from the Walton Family Foundation to build this tool. And the idea was, look, we understand, you know, cover crops have these benefits, but for the farmer, you know, there's there's added cost, there's added management needs, there's added risk. If you're putting a crop in what would normally be fallow. And you're you've got to deal with that crop, particularly, in that planting window in the spring.
00:02:14:02 - 00:02:25:19
Jonathan Coppess
So we started working with Rabin and with, with, NCSA to try to design a way to just provide this sort of best estimate, modeled outcomes of what's going on in that field. If you put a cereal rye cover, crop it.
00:02:25:22 - 00:02:36:05
Todd Gleason
We'll hear more about that in a moment. But just to insert here, NCSA is the National Center for Supercomputing Applications here on the Urbana Champaign campus of the University of Illinois.
00:02:36:08 - 00:02:44:03
Todd Gleason
And the tool is online and it runs out of those supercomputers. Again, here's Jonathan Coppess.
00:02:44:06 - 00:02:55:24
Jonathan Coppess
And and as Tran will talk about the yeah, the big time frame is in that spring spring stretch to now you know what kind of biomass is out there. And we also want to know what kind of nitrogen uptake, what have we prevented from being lost.
00:02:55:24 - 00:03:15:26
Jonathan Coppess
And so that's where it began. And then as we've developed and work through this, we keep looking for new ways to, improve the tool, some of it from feedback we get from farmers and working with different groups of farmers, some of it just trying to improve, you know, around the science and around the modeling and get a better, a better estimate of, of that cover crop.
00:03:15:26 - 00:03:51:25
Jonathan Coppess
And so this latest iteration is kind of a combination of both of those attempts. We've added some functionality that we think is really important, and we've improved some of the modeling capabilities. So we've probably extended the that the amount of my understanding of this and some of the experts that we have out here with us this afternoon, we can talk about more specifically what we've done with this release, but it it comes out of that nutrient loss reduction strategy and the, the, the idea we want to help farmers as they try to manage through the decision process around adopting this, incredibly important practice for nutrient loss reduction.
00:03:51:27 - 00:04:16:25
Todd Gleason
So I'd like you to join me in asking questions of the other two, because I bet you're able, to target some of the places I'm headed. So do do that. I, want to bring a couple of folks into the conversation. They include Trent Ford. He's the Illinois state climatologist at the Illinois State Water Survey, the Prairie Research Institute here on campus and in the College of Aces, and ABE, the Department of Agricultural and Biological Engineering.
00:04:16:25 - 00:04:46:13
Todd Gleason
Rabin Bhattarai is with us. And he's a modeler. So let's wait on the modeler for just a second, because data input is the important part, that the modeler has to work with. And, Trent, as a meteorologist, you worked with, these two and other folks on the cover crops tool, I suspect I asked you for a great deal of data as it relates to the weather, but I don't know exactly what they wanted nor why they wanted it.
00:04:46:16 - 00:05:12:23
Trent Ford
Yeah. So when Jonathan came to me and kind of presented, the tool and the problem I got was over a year ago, I don't think we were in masks at that time. He had mentioned that, you know, they, they they want farmers to have the right information in front of them when they're making decisions. And of course, a lot of these decisions, whether it be termination, how much nitrogen they're storing, those sorts of things, they need to know what how weather conditions are going to affect that.
00:05:12:25 - 00:05:30:29
Trent Ford
Not today or tomorrow, but two, three, four weeks from now. And so the original iteration of the tool was using historical data to kind of give us an outlook of, okay, if we have a wet period of time, if we have a dry period of time, if we have warm or dry period of time. And that is a really good first start.
00:05:31:01 - 00:05:49:24
Trent Ford
But one of the things that I wanted to do is bring in, forecast information. So we think about our National Weather Service forecast. The dynamic forecast and forecasts are present on your show, Todd, all the time. And use that information to give maybe a little bit of a more realistic outlook. Of course, forecasts are not reality.
00:05:49:24 - 00:06:18:08
Trent Ford
They are forecasts inherently, but it gives a little bit closer to what we're experiencing. And so therefore, if we get to, let's say, February, January, the time periods of folks are trying to start making decisions about what they're going to play in the spring and of course, what they're terminating in the field, is the models that are able to give us as forecasts, are initialized with conditions then, as opposed to being based off of historical data that may or may not take into context how warm the winter or fall has been.
00:06:18:10 - 00:06:42:23
Trent Ford
So what we've done is we've pulled in, subseasonal forecasts, subseasonal being anywhere from one to 4 to 5 weeks on time forecasts of things like temperature, precipitation, wind, solar radiation that can feed in the model, the crop models, to give a better idea of, okay, here's what the, the forecast for the weather models are seeing in the next 2 to 4 weeks.
00:06:42:26 - 00:06:49:03
Trent Ford
How's that going to affect the plants and give, again, a better, more realistic idea of, for folks to make their decisions.
00:06:49:03 - 00:06:51:15
Todd Gleason
So that model data is coming from where?
00:06:51:17 - 00:07:00:08
Trent Ford
Yes. So it's actually it's interesting. It's from, because, you know, the National Weather Service puts out a seven day forecast and that's really just not enough lead time to make decisions.
00:07:00:08 - 00:07:23:12
Trent Ford
And so what we do is we pulled in NOAA, the at the National Oceanic and Atmospheric Administration's, forecast from their sub seasonal experiment, what they call sub X. This is started from a research experiment has grown to operations to try to improve sub seasonal prediction. Historically, predicting one week timescales has been fairly fairly easy or has been improved quite by quite a bit.
00:07:23:14 - 00:07:41:28
Trent Ford
But the 2 to 4 week timescales are not. And so NOAA's got this project to, to improve those, those forecasts. So we bring those in from a multitude of models of different, climate forecasting, weather forecast models. And again, these are forecasts of temperature, precipitation. And they're, they're operational. So they're updated at least once a week.
00:07:42:01 - 00:07:52:11
Trent Ford
So we can, update them as well as update the tool, once a week or even even sooner, with, with those, you know, 2 to 4 out, week out, weeks of weather conditions.
00:07:52:11 - 00:08:01:28
Todd Gleason
So Rabin I have some questions for you. Rabin Bhattarai is of course, in the college of aces in ABE, Ag and biological engineering, section.
00:08:01:28 - 00:08:06:20
Todd Gleason
You're the modeler. First, what do you do? What is a modeler?
00:08:06:20 - 00:08:23:17
Rabin Bhattarai
So in terms of, like, talking about modeling, what what we try to do is like, we try to replicate, we try to mimic the real world phenomena through equations and numbers. So what we do is then for the model, what we try to do is like, let's say we have the input, right?
00:08:23:17 - 00:08:44:05
Rabin Bhattarai
As Trent mentioned, as you are, on a daily basis, like what happened today, how much rain happen? Okay, what was the temperature? And then if that what the water came as the rainfall. Okay. What part of that water actually run on the soil surface in your field? What fraction of that water actually went into a soil profile to increase your soil moisture content?
00:08:44:05 - 00:09:03:12
Rabin Bhattarai
Right. And then as your water went to the soil horizon. Okay. How did the other things move with that water? Okay. We can compute a layer by layer calculation, like how does your soil moisture can change as your water moves into the soil horizon? How does your nitrogen concentration in that soil can change as your water moves along the horizon?
00:09:03:18 - 00:09:24:22
Rabin Bhattarai
And what can happen to that water? That water can be sucked by the plant root. How does again, the moisture content can drop in a couple of days as your work plant start to draw the water from the soil profile. So we try to replicate those phenomena through the equations. That based on our earlier experiment, people have done this kind of experiment for many, many years.
00:09:24:22 - 00:09:54:16
Rabin Bhattarai
We have come with some equations that can replicate this phenomena, and we're trying to put that together in the system to see, okay, if you if a farmer grows a cover crop in this field for his location, right, for his weather condition, for his soil condition, how does your cover crop simulation can make a difference in terms of water dynamics, in terms of how much tile water flow can be reduced by implementing cover cropping in this field, how much nutrient loss nitrate loss to his from his field can be reduced by implementing this cover crop.
00:09:54:16 - 00:10:22:16
Rabin Bhattarai
And we are focusing just one at the moment. Cereal rye. Okay. And we plan to implement more because even with the cereal implementation, we actually had go through some glitches because the model we selected to begin with actually didn't have cereal rye, because it was a crop model which does focus more on the cash crop. It has model to simulate your corn growth, soybean growth, and lots of other 40 plus plant, which is more like a cash crop, potato and sorghum and whatnot.
00:10:22:17 - 00:10:33:16
Rabin Bhattarai
But there was no cereal rye model there. We actually had to play around with winter wheat. And then try to change some parameter for winter wheat to actually replicate your, cereal rye growth in the model.
00:10:33:18 - 00:10:44:24
Todd Gleason
Okay, so Jonathan, we have the weather data coming in. We have the modeler putting it into the models, and site specific for fields.
00:10:44:24 - 00:11:09:02
Todd Gleason
The farmer is able to tell I'm taking from this model how what the nutrient control is and where that nutrient might be going. That's kind of the ideal. However, there must be within the tool something that the that the farmer will find practical for use as it relates to managing the crop. What are those things.
00:11:09:05 - 00:11:13:16
Jonathan Coppess
Yeah. And it's something key that were being was talking about.
00:11:13:19 - 00:11:38:04
Jonathan Coppess
This is and the model we're using is an open source like you know this is been built and worked on by a lot of scientists over time. And one of the things that I think we would argue that's somewhat unique about this project is and Trent kind of alluded to this, too, we're taking this and using it. You know, we're we're sort of in the, quintessential extension mindset, right?
00:11:38:04 - 00:11:57:20
Jonathan Coppess
We're translating this into something usable. So that that we are we are sort of tapping into that research capability and pulling that into this. Exactly what you're saying. So I've got a field in Illinois, we have the SERGO, USDA soils database. So I select that field, it pulls the data into it. And now we've got this weather forecasting capability.
00:11:57:21 - 00:12:18:04
Jonathan Coppess
Pull that into a model that runs on a daily basis. This this, estimate of what's going on. Right. So what does this do for the farmer? Well, we don't expect a lot to happen in the winter, particularly in that spring month if I'm going to put a cover crop out there, particularly if I'm starting it for the first time, there is a different field.
00:12:18:07 - 00:12:35:01
Jonathan Coppess
If I put cereal rye and then burn that, terminate cereal rye. I go into plant. That's a different field than it was before, particularly if you're used to working that field in the fall or the spring. You know, for example, you might have 2,000 pounds of biomass per acre. You may have to figure out how to work through that.
00:12:35:01 - 00:13:10:08
Jonathan Coppess
And we, I'm doing very poor job compared to what Shalamar does when he usually explains this, but it is definitely the, you know, everything from the equipment needs, the timing needs to how you think about, getting into that field and planting into that cover crop. Your termination time is going to be pretty key, right? You're going to you know, we can see this at the end of our dashboard really towards that, that April May time frame as we pull in more, more heat, units over time, more, more growing degree days, that cover crop is going to take off.
00:13:10:08 - 00:13:30:12
Jonathan Coppess
So the difference in terminating, you know, April 15th versus March 15th can be pretty significant to what you're doing with the equipment you need in there, what you're going to plant, as we talked a lot with Shalamar’s team at Purdue, trying to figure out what the nitrogen outcome is, you may need starter, because that plant has taken up a lot of of nitrogen into the biomass.
00:13:30:13 - 00:13:47:07
Jonathan Coppess
You're not going to get it. If there's nitrogen, there's so there's there's a lot of decisions that come around that, that. And we were in the early stages. Right. We we want to keep building this thing out. But we you can kind of see that we get more questions. Every time we work on it, I get more questions that come up like, oh, we need to check.
00:13:47:07 - 00:14:08:20
Jonathan Coppess
How do we put that into it? How do we address that issue? So one of them, that we in the release from the winter was about decomposing the decomposition that cover crop. So Shalamar’s team has a model built into that kind of fits on the after, the crop growth model. So at termination. You know, how long do we think this is going to, take to decompose in that field?
00:14:08:23 - 00:14:27:08
Jonathan Coppess
Eventually we'd like to get estimates of the nitrogen, impacts on that in the field. So a lot of that is, back to your question about the farmer. It is particularly if you're not used to doing cover crops. Right. It's giving you some more to think about as you make the management decisions in that field. We're not going to tell you when to terminate.
00:14:27:09 - 00:14:48:15
Jonathan Coppess
We don't have nobody has those answers. A lot of this is, is trial and error as learning as you go, but this is going to give you a better set of information. Now, Trent's, weather forecasting, we have a much improved weather forecast, and you're going to be able, we think, as we go into this spring to really track what we think the growth is.
00:14:48:18 - 00:15:04:27
Jonathan Coppess
You can look out your window or you can walk through it and see if we're going to have, you know, some some numbers around that you can then use in your decision making. And that's ultimately what it comes down to. This is just trying to help. That decision is trying to help the adoption process. Many farmers have a lot of questions and concerns about how this is.
00:15:05:00 - 00:15:25:07
Jonathan Coppess
This practice works in their field. And so this isn't going to solve them all. But this is a little bit more data, a little bit more information, frankly, a whole lot more science going into that, that decision making, particularly in the spring window, ultimately hoping we get more farmers adopt the practice, more farmers that adopt practice, less nitrogen in our water.
00:15:25:09 - 00:15:28:01
Jonathan Coppess
And that's sort of the net outcome.
00:15:28:03 - 00:15:49:10
Todd Gleason
Shalamar Armstrong, of course, is an agronomist at Purdue University. He and his team have been working with the folks here at the University of Illinois, including Trent Ford and Rabin Bhattarai, who's in the Department of Agricultural and Biological Engineering. Of course, we also have with us Johnson Coppess in the Department of Agricultural and Consumer Economics.
00:15:49:10 - 00:16:00:16
Todd Gleason
Again, Shalamar Armstrong is at Purdue University and an agricultural economist. He developed some of the data that underpins the online web tool.
00:16:00:23 - 00:16:36:29
Shalamar Armstrong
It's a great question. So we're collecting, first of all, the tool, one of the goals of the tool is to help farmers visualize, cover crop growth. And so at this study site in Lexington, Illinois, we have multiple several years, since 2014 of, several years of data, that that describes and quantifies, cover crop growth, namely cereal rye growth.
00:16:37:01 - 00:17:27:02
Shalamar Armstrong
Right. And this is to cover crop biomass, cover crop nitrogen content within the biomass and cover crop carbon content within the biomass. And so, the data collected, that that describes the biomass and it's constituents, is then used to calibrate and validate the model. In addition, we also quantified the impact of the, the presence of the cover crop and the performance of the cover crop to, decrease nitrate loss in poor drainage.
00:17:27:05 - 00:18:01:18
Shalamar Armstrong
Right. And so over those same amount of years, we're collecting the amount of water in the form of tile drainage that's leaving the site. Okay. In this replicated study, we also, collecting and quantifying the, the mass and concentration of nitrate that's leaving the site in the water and the impact of coal crops on that mass, and concentration of nitrate leaving the tile drainage site.
00:18:01:21 - 00:18:41:08
Shalamar Armstrong
We also measuring the. Cash crop yield, let's say corn cash crop yield. Right. And that's also a critical component, with that helps understand the gravity and the loss of nutrients. Right. And that's, that's also a component of data from the field study that's underpinning the, the tool. So the tools, number one, it helps farmers to visualize, and conceptualize, cover crop growth on their field.
00:18:41:10 - 00:19:03:00
Shalamar Armstrong
Right. So you may have a farmer who's thinking about, hey, I want to adopt cover crops this year with the two, it's to say, before you actually put it on the ground, let's let's model this before you. You're very, that, so we you can kind of play with the idea of having cover crops on your farm.
00:19:03:00 - 00:19:36:05
Shalamar Armstrong
So number one, it says, okay, this farmer can select the site that they would, want to grow cover crops, the actual field, and then, they, they are allowed to give some input data on what their management is, you know, whether they're growing corn, soybean, or, you know, the nitrogen that they are applying to, let's say corn, when they will apply the nitrogen, when they would like to plant the corn, when they would like to terminate the cover crop.
00:19:36:07 - 00:20:09:22
Shalamar Armstrong
Right. And so, then that farmer would run the two, and the two would then generate, what the biomass would be over time. And, and it will give an output of a graph of biomass. Accumulation over time. And for aboveground biomass of the right, right. And then at the same time, it's also giving the C/N ratio of that biomass in an output graph.
00:20:09:24 - 00:20:30:03
Shalamar Armstrong
Okay. Also in in the output is a key component of, you know, the percent nutrient loss reduction. So in other words, what's the percent reduction in nitrate loss and the poor drainage because of the presence of the cover crop?
00:20:30:05 - 00:20:43:12
Todd Gleason
Interesting. So could could I go back, in time in the tool because it calculates based on, predicted weather and actual weather after it's passed.
00:20:43:14 - 00:21:16:09
Todd Gleason
Can I go back in time? Say, to the fall, on the farm. And I think what you've told me is that I can model what would be happening. How with a cover crop, should I, if I were to have sown it and gave it some inputs as related to the tool itself, and it would tell me what that crop should look like today, it would also predict what it should look like in March and April as well.
00:21:16:12 - 00:21:24:16
Todd Gleason
Correct. Right. And then as, as time passes, it fills in the weather and, and resolution of what's happening on the field gets better.
00:21:24:19 - 00:21:30:18
Shalamar Armstrong
Yes. Yeah. So it gets updated. With actual weather updates. That's one of the big improvement.
00:21:30:20 - 00:21:36:18
Todd Gleason
So I can, I can, I can model a cover crop on, on a farm that doesn't actually have a cover crop on it.
00:21:36:18 - 00:21:47:24
Shalamar Armstrong
Yes, exactly. It will perform on that field. Exactly. That's exactly what the model does. It helps farmers visualize, cover crop growth.
00:21:47:29 - 00:21:54:03
Todd Gleason
One last thing, and I'll turn back to you. Jonathan Coppess, we need to know where to find this cover crop tool.
00:21:54:07 - 00:22:04:12
Jonathan Coppess
So there's two ways to find it. The first is the direct route which is going to cover crop dot NCSA dot Illinois dot edu.
00:22:04:14 - 00:22:31:07
Jonathan Coppess
So again this is hosted by the National Center Supercomputing Applications here on campus. They have done an amazing job working with with Rabin, with Shalamar and with Trent. Like converting this into this usable, workable tool. So we're, we're pretty, ecstatic about the work that NCSA has done. So you can go that way, or you can, jump through the farm dot systems either get through farm dot daily or go straight to the cover crop dot NCSA dot Illinois dot edu website.
00:22:31:08 - 00:22:38:23
Todd Gleason
Again, the website address for the cover crop analyzer is cover crop NCSA dot Illinois dot EDU.
00:22:38:23 - 00:23:03:05
Todd Gleason
You've been listening to episode 44 of the Illinois Nutrient Loss Reduction Strategy podcast. The cover crop decision support tool. The program was produced in conjunction with the Illinois Extension Watershed Outreach. The program was produced in conjunction with Illinois Extension Watershed Outreach Associates, Jennifer Jones and Rachel Curry. I'm Illinois Extension's Todd Gleason.