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Ep. 45: Dry Fertilizer Flow Sensors with John Fulton

Ep. 45: Dry Fertilizer Flow Sensors with John Fulton

With fertilizer markets reaching levels many of us have never seen before, growers may be scrutinizing their soil tests and yield data to understand the true need for fertilizer inputs across their acres this year. If you have decided to invest in dry fertility this year, how do you know that investment is landing where you need it most? In episode 45 of Precision Points Podcast, John Fulton from Ohio State tackles that question and shares the research being done to help growers get the most out of their applications.

The research John walks us through is focused on creating an accurate as-applied map of dry fertilizer applications. This allows for real-time feedback for the operator in the field, resulting in more accurate application. Which means, gone are the days of needing to empty your first load to see if you had it calibrated correctly, or doing a few extra laps to empty the hopper.

“So we're actually measuring the material flowing through the tubes and giving about a one-second update of the mass flow, the pounds per second flow,” started John. “We see a lot more strip-till units pulling buggies and placing fertilizer in those strips, subsurface with that technology. And so our sensor actually measures that amount of material flow in each of those tubes on a piece of application equipment. Just like we measure the population today on every row of a planner, it's the same type of concept.”

These sensors have also been used to determine the change rate of prescriptions in fields. By identifying the change in flow, growers can better tune their hydraulic valve to make those rate changes happen quicker and more accurately. There are blockage sensors on the market today but the flow sensors are collecting more data and adding more value to the operation in motion.

“I can get a blockage sensor, but the difficulty is actually measuring little particles flowing through a tube, and doing that accurately in a noisy and dirty environment,” said John. “Whether it's a strip-till doing three to five miles an hour or a larger machine that could cover 15 plus miles an hour, there's a lot of vibration. There's a lot of, as you know, dust and things that could be included in that product… and that's what we have been focused on, is being able to measure fairly accurately, mass flow. Not only at low flow rates, but at even high flow rates on a row by row basis on those machines.”

John shared that the data they have collected so far may just be the starting point. They have the goal to monitor two different types of fertilizer in the same tube, ultimately creating two different as-applied maps. Rarely do fields’ phosphorus and potash requirements line up perfectly and often the application is a compromise of the true needs. However, there are more rigs set up with two bins to carry those products at their own rate and the sensors could track the products movement through the tubes to create as-applied maps.

“That's been unique, that's pie in the sky, that is not easy to do,” said John. “We've spent a lot of time; but, with some of the color and some of the artificial intelligence, we've been able to at least show that there's an opportunity, I'll put it that way.”

To learn more about the work John is doing visit or follow him on Twitter. You can check out our full conversation at Precision Points Ep. 45.

Want to hear more from John? He is our most frequent podcast guest and has been on to discuss Decision Ag, Planter Technology Preparation, On-The-Go Manure Sensors, Research Trends, and On-Farm Research. John is a wealth of knowledge and has been such a wonderful asset to our podcast!


Speaker 1 (00:03):

Welcome to Precision Points, an Ag Tech podcast, where we plant seeds of innovation to inspire informed decisions about precision technology and its impact for growers like you. We explore Precision Ag tools and technology from the soil to the sky, with your host Morgan Seger.

Morgan Seger (00:22):

Welcome back to precision points, an ag tech podcast from Precision Ag I'm your host Morgan Seger. And in each episode, we strive to bring you unbiased ag tech information and ideas. Today on episode 45, I am joined once again by Dr. John Fulton from the Ohio State University. And we spend a lot of time talking about these dry flow fertilizer sensors. And the implications for dry fertilizer sensors were a little bit broader than what I initially anticipated.

Morgan Seger (00:56):

It was a really interesting conversation about making decisions on the go, using information to get more accurate data, and the impact that it can have for growers, especially in times where fertilizer prices are extraordinarily high, the markets have all been a little volatile and crazy. This is one way to really hone in on the application and make sure that you are getting accurate information and accurate applications.

Morgan Seger (01:21):

In addition to the conversation we had around those fertilizer sensors, we also talked through the 2021 research program and what the key learnings they had out of their eFields program. The eFields book with all of their data and results is available at And John spends a little bit of time highlighting what those key learnings were for him. So I hope you enjoy this conversation with John Fulton.

Morgan Seger (01:48):

Today on the show I'm joined again by Dr. John Fulton from Ohio State. And John, I think it was, episode 27 was the last time you were on, and we talked about prepping our planters. So here we are in episode 45. It's been a while, thanks for coming back on.

Dr. John Fulton (02:03):

Yeah, yeah. Thanks, Morgan. Appreciate the opportunity to be here.

Morgan Seger (02:06):

So I hope that you had a great growing season and you were able to really dive into some of those research projects and things that we had talked about before. What I was hoping we could start with today is just diving into the research that you've been working on, and looking at the granular fertilizer sensors. Could you tell our audience more about that work?

Dr. John Fulton (02:27):

You bet. You bet. Well I guess I would say upfront that considering fertilizer prices and making decisions about, do I apply or don't, and if I am applying, get it right. I might not be as willing to spend what I normally do, especially in the realm of P&K and possibly some other products this coming year. But the point I want to make is that we want to go out, and typically we're thinking about prescriptions, and even fixed rates, and say that my target application rates are this. And when we go out with some of the broadcast to strip-till, to some of these newer fertilizer application equipment and tools that are options today in Ohio, we want to verify that the performance and that that field operation, in this case fertilizer application, was done properly.

Dr. John Fulton (03:35):

I would say that we see today, and farmers evaluating timing of application, placement of application. And so not only with the planner, but we've got some strip-till or subsurface placement that has become an interest, not only for potentially saving or putting it right adjacent to the seed, or for that root to intersect and be more efficient. But there's also, there's an environmental aspect there that we want to maximize uptake and limit or eliminate any runoff concerns in the water bodies or filtering down in the groundwater. So I just say all that, that's our premise that really is, we go out with some of these different pieces of equipment and technology when we want to verify what we're doing. And so that brought about some of the research we have been doing in recent years of developing a sensor to measure fertilizer flow and ultimately create an applied map.

Morgan Seger (04:43):

Okay, so that makes a lot of sense. And we had emailed about that, the markets have just been crazy. So your work then is going to basically help make sure that those applications are accurate to get the most out of the applications you're making?

Dr. John Fulton (04:59):

That's right. And the questions at hand because of the high fertilizer prices are, do I apply or not? Do I really need it or not? And that's a good discussion. I mean, if there's a year that data and soil sampling and what we've done in the past, and evaluating fields, prioritizing fields by what we're planning on growing this growing season, this is the year, because of these high prices. And you look at the investment we want to put in as a crop, fertilizer is one of those important costs and we need to evaluate that. But when I want to, if we pull back there's folks looking at some new, I don't know if I'd say new, but techniques to maybe reduce application rates.

Dr. John Fulton (05:53):

And my point in all that is, we want to make sure that if we're doing that and we're seeing impact or no impact as it relates to yield, that we want to measure what goes out there. I use planting technology as probably the leading technology. If we think about what specifically a company like Precision Planting has done in terms of feedback to the operator and creating what we normally call as-planted maps, that has become a value proposition for growers. Right? I mean, when we look across Ohio, having not only a high-end display, but being able to monitor things like population, seed spacing, singulation, those important things that influence performance and placement, we don't have that kind of technology as it relates to feedback around fertilizer. For some of the liquids, maybe, but when we look at dry fertilizer application, we don't have something that measures the distribution, where particles end up and what that amount is reflected in, we'll say a pound per acre.

Dr. John Fulton (07:08):

And that's where we've been focused on some of this research on sensors to estimate, and then use that as feedback and ultimately creating a quality as-applied map to verify, yes, I planned this and that got accomplished, and this is how the material got distributed. And oh, by the way, yes or no, I saw... If I'm doing some strip trials, and that's becoming popular especially for a rate trial, then we want to make sure we understand what actually got applied in order to properly evaluate those results, too.

Morgan Seger (07:50):

Okay. That makes a lot of sense. For us on the farm, it usually took one load to see if we were about where we wanted to be. So this is something that is going to give you feedback on the go, then?

Dr. John Fulton (08:01):

That's correct. This will be on the go feedback to the operator. And so to your point, think about this, a lot of times, it'll just take a bulk, any of this, a spinner disc spreader that has six to 10 ton bed, to a cart carrying the same amount, if not more material. We put X amount in, and we go out and apply over so many acres. And we say, well, we should have applied for this amount. And yeah, it looks pretty good, but that does not tell you how that material got distributed across the machine and across that field. That just tells me, Hey, I loaded 10 tons, I covered 10 acres, and so I applied a ton per acre on average. But that doesn't give me any feedback about how that was distributed.

Dr. John Fulton (08:57):

Common example of that is I got 10 tons, I got a 10 acre field, I can go out in the middle of the field and dump all those 10 tons. And guess what my average application rate across those 10 fields is? Well, it's a ton per acre, but the reality is it was all placed in the center of that field, and that's not... I'm not saying that's what happens. We're just trying to make sure that we got some feedback to the operator, similar to what planters provide today, that tells us we hit our target, or, Hey, we need to make some adjustments, to your point, prior to emptying it. But we can do that all on the go and improve the performance, if not maintain the performance more better for the materials that we're trying, or in this case, fertilizers were trying to apply.

Morgan Seger (09:41):

Okay. So would this replace... I remember in ag retail, we would do pan tests, where we put things out to see how it spread. Or is that still going to be an important part of seeing where in the field it goes?

Dr. John Fulton (09:54):

So for spinner spreaders or pneumatic applicators commonly used in Ohio, AirMax would be an example from AGCO, that's a pneumatic, it has the aluminum pipes that, 70 plus feet that will spread. But the point is, no, you would not replace the pan test. What this gives you is if you have a change, maybe in the equality of the fertilizer as you apply, or you got a new source of the fertilizer, or you change the source. So I move from MAP to potash to, potentially even a blend, or some other type of product out there that I'm applying.

Dr. John Fulton (10:34):

This gives you real time feedback because to your point, pan tests can be tedious, takes a lot of time. And most frequently we may pan test for one product, but we don't quite get all products that may go through that machine in the fall or spring tested in that pan test. And so we say it works good, we calibrate it for one source and we assume it works for all. My only thing is this would give the capabilities of giving that real time feedback. And if you see an adjustment needed that the operator can make that right then versus probably that would be unknown to them.

Morgan Seger (11:13):

Yeah. And if they are running a script with the sensors, the script would give it that feedback?

Dr. John Fulton (11:21):

This would tell me that if I've got a script, so let's take MAP, I'm going to go out and I'm going to apply MAP. This would say, well, I plan for that. Right? That prescription represents what my target or I'm planning to apply spatially across that field. Normally from zero to, we'll say three to 500, depending on what the recommendations are. This would then create the map that shows where material was applied, how much in a distribution of that across that field. And then you can match that back and say, yes I planned for this as far as my prescription, and that was met for this particular pass across the field. Or, Hey, we found some errors in this and we need to make some adjustments. And so our sensor in particular was, we've really focused on pneumatic applicators.

Dr. John Fulton (12:19):

So talking about those tubes, so we're actually measuring the material flowing through those tubes and giving about a one second update of the mass flow, the pounds per second flow. We see more strip-till units being used, especially in Northwest Ohio where subsurface placements being promoted. So we see a lot more strip-till units pulling buggies and placing fertilizer in those strips, subsurfaced with that technology. And so our sensor actually measures that amount of material flow in each of those tubes on a piece of application equipment. Just like we measure the population today on every row of a planner, it's the same type of concept.

Morgan Seger (13:07):

And it's all through one sensor, or do you have one set up on each row?

Dr. John Fulton (13:11):

The idea would be one sensor for each row. So you actually get a row by row feedback to the operator, and then create an as-applied map from that feedback that you get, roughly... Well, in our case, we were given it every second.

Morgan Seger (13:30):

Okay. And I mean, I could see the benefits being twofold, that one, you can make a change on the go, if you're having an issue, you catch it sooner. But then two, you're getting more accurate data to accurately reflect your harvest results and that kind of thing. Is there something else I'm missing?

Dr. John Fulton (13:52):

No, no. And so a lot of times we'll calibrate and we will get the machine implemented, and we'll be uniform across that machine and for that instance. But unfortunately sometimes with products, something occurs, we get a little bit of build up for example, and that impacts the amount flowing down a particular tube.

Dr. John Fulton (14:19):

Sure, we can put, in many cases... There's been sensors out there for a while that show blockage, so I can get a blockage sensor. But the difficulty is actually measuring little particles flowing through a tube, and doing that accurately in a noisy and dirty environment, essentially. I mean, when we take these machines, and whether it's a strip-till doing three, five miles an hour or to a larger machine that could cover 15 plus miles an hour, there's a lot of vibration. There's a lot of, as you know, dust and things that could be included in that product. That's a little bit more tricky, and that's what we were really trying to, or have been focused on, is being able to measure fairly accurately, mass flow. Not only at low flow rates, but at even high flow rates on a row by row basis on those machines.

Morgan Seger (15:21):

Okay. Yeah. I mean, wouldn't even humidity and the weather play a difference in how it's flowing?

Dr. John Fulton (15:27):

That's correct. You get moisture, take potash, it's essentially a solid. But my point is, you throw a little bit of moisture, that just creates buildup, and all of a sudden, you potentially could have an issue in your pattern. And sure, there's material flowing, so that's not an issue. The issue is we're just not getting the proper amount flowing down that particular tube or pipe and that affects the distribution. So what we found, for example, we went out and with our sensor sensing technology, I'll put it that way. And we were using, in this case, we did some work with a strip-till unit, just to proof of concept and evaluate what we got. But not only could we instantly know that there was blockage out there, but we found as the day wore on it, like I mentioned, whether it was just a metering issue to some build up in a tube, but we found some pattern issues that occurred, that normally would not have been identified in real time, or been present in as-applied map.

Dr. John Fulton (16:37):

And another unique thing that we found in all this was when we're doing scripts, it takes time we know today to make a change, a rate change. And so we were actually able to measure that rate change using a sensor. And so a lot of times we set up our valves, our hydraulic valves. Most of this today is hydraulically powered. So we have a hydraulic valve that adjusts the speed of the conveyor or whatever the metering device is, the controls that rate based on the prescription map. And you can actually look at tuning that valve better for the machine, the ground speed and conditions that you're operating in.

Dr. John Fulton (17:23):

And so that was something we came up with that you could actually measure the response and use that as a mechanism to fine tune the hydraulic valve and do even a better job of making those rate changes quicker and at the location we prefer that to happen as it relates to the prescription map.

Morgan Seger (17:42):

Oh, that's really cool. It's exciting that you were able to catch something to just immediately understand, okay, this is going to have value. Is this something you plan on continuing to look at in 2022?

Dr. John Fulton (17:56):

Yeah. We continue to look at it. Like I said, this is not easy, what's our normal thing? If it was easy, it would have already been done. I mean we've learned a lot, but we all also learn it's going to take a little bit more work to fine tune it and make sure that it can work in a variety of environments. So, like I said, there's a lot of different, whether it's a tube, a solid tube, or some kind of poly tube or similar type tube that's used to carry that material, but also how to mount that and be able to get the sensor to evaluate not only the fertilizer flow, but look at just some particle characteristics too.

Dr. John Fulton (18:42):

And this is a little bit more pie in the sky, our goal is if we're actually applying two rates, it's starting to be common. You probably remember back, Morgan, if I take a field and I do some kind of precision soil sampling, my phosphorus or map prescriptions are going to not be similar spatially to my potash prescription, I mean where my zeros and highs are never aligned perfectly. And so for years, we've compromised because a lot of times we had to create, we want to make one pass with two products, my potash and map. Today, we're seeing more dual bin type setups, whether that's on a spinner spreader or on some of these carts, there's actually two bins. So I can split my products and then I can put both prescription maps into a display in the cab and then independently meter those.

Dr. John Fulton (19:37):

Okay? And so now I've really improved my accuracy. I don't have to compromise based on P or K in order to meet the recommendations, I can independently meter and maintain what the target is for each of those maps independently. And so now what we're interested in is because of the type of technology we're using within a sensor, we've been using it to hopefully say, well, how much map is in the tube versus in this case, an example of how much potash? And so we can actually create two independent as-applied maps, though whether there's two products flowing in that tube or not.

Dr. John Fulton (20:20):

If I got one shut off and the other ones running, I actually can verify that per-se, in my maps as well. And so that's been unique, that's pie in the sky, that is not easy to do. We've spent a lot of time, but with some of the color and some of the artificial intelligence, we've been able to at least show that there's an opportunity, I'll put it that way. But a little ways to say we could be accurate in our measurements, independently of those two flowing in a tube today.

Morgan Seger (20:52):

That's really cool. And then to think that you can take all of that information and almost instantaneously give it to the operator so they can make decisions, is just crazy to me.

Dr. John Fulton (21:02):

Yeah. And if my target rate's 100 on map here, and we'll say 200 on potash, are we meeting that there? And if that's not being met, then we need to look at either making an adjustment, or maybe we do have, like you said, blockage or whatever it may be, but we know that. And today, basically what happens on most of these applicators is we base that as-applied map on the conveyor speed. So we calibrate it, going back to your comment about pan testing, but we calibrate based on the conveyor speed, that's what controls the amount of material going out. And so technically if that's conveying but I don't have any material actually flowing out of the bin, then the as-applied map might say, hey, we're perfect. We're right on target for that, but the reality is we're out of product. But with these type sensors you at least know what's actually being applied and verifying that. And as we keep hammering on, you could somewhat in real-time, stop and make some adjustments.

Morgan Seger (22:13):

Yeah. And you know what, this might actually help reduce compaction too, because I know anytime you have a little bit left in the bin and it was supposed to be gone, you just went back over it.

Dr. John Fulton (22:22):

That's right. That's right.

Morgan Seger (22:24):

At least that's what we do, I won't speak for everyone. But you use it all up because you know it was supposed to be on the field.

Dr. John Fulton (22:30):

That's right.

Morgan Seger (22:31):

Well, it's interesting. Thanks for the work you're doing on that. I'm excited to see if you can master the pie in the sky, that seems pretty advanced, but it's exciting.

Dr. John Fulton (22:42):

Well, one thing we are trying to work into... So doing it in that environment where you actually have fertilizer particle flow through a tube is, like I said, it's not an easy task to do that fairly accurately. But what we have been trying to work on is, we go back to our pan test, and if you think back to your days of pan test, no one likes a pan test. It's a time consuming process to go out and lay out a bunch of pans, drive over the pans essentially with the applicator, and then go pick them up and put them in tubes or weigh that amount. Right? That could be a 15 to 30 minute commitment just to run one test if you got a lot of pans. But our idea is actually creating an app.

Dr. John Fulton (23:28):

So using the same kind of concepts rather than being fluent, we've basically taken our models and actually put it into an app. And rather than having to dump that pan out into a test tube or measure it, our concept is I have an app. Cameras today are pretty, 20 megapixels, and most new smartphones today, you take a picture of the pan and it calculates the amount of material in that and gives you a little bit of particle characteristics in terms of size. What's my average size? And so you could look at some things about that, that influence the pattern. But we've been working on taking the same kind of... Well, our same models, and using it more in a static condition, that's a little bit easier, it's not moving.

Dr. John Fulton (24:18):

But very similar concepts that you go down and you take pictures of all your pans, it creates a pattern for you right there in the app and you say, we got to make this adjustment. And hopefully the idea would be to really shorten time, make it a lot easier for whoever's responsible for saying, yep, we're calibrated and bring the next machine and we'll calibrate it. But making that very efficient and being able to maintain that information for that applicator as well, because we may apply in the spring, but we're not back to doing it until the fall, but what was our calibration setups, or cal numbers, and those kind of things, have that all right there for the operators for that specific machine.

Morgan Seger (25:00):

Okay. Is that something, you said you guys are working on building out right now?

Dr. John Fulton (25:04):

Yes. Yeah.

Morgan Seger (25:05):


Dr. John Fulton (25:06):


Morgan Seger (25:07):

Any idea when that might be live?

Dr. John Fulton (25:10):

Well, we're working at it, so I don't have it-

Morgan Seger (25:16):

No promises for spring applications.

Dr. John Fulton (25:17):

No, no. No promises for the spring here, but we're working at it. In fact, just a note for everyone. I mean if you haven't heard me talk lately on fertilizer application in particular, some of the newer technology and different machines that are available, but in Europe there's already a couple of apps that companies offer for calibration purposes that are, in general, what I just described. And so we've already seen some movement there to provide end users a tool, in this case apps. Hey, I'm trying to apply this fertilizer. This should be the setup. If you run essentially a pan test, you take some pictures and it'll do a very similar type scenario of what's your pattern and here's the adjusted suggested set up for that particular product.

Morgan Seger (26:08):

So what things are you and the rest of the team at Ohio State most excited to be looking at this year as we start preparing for the 2022 planting season?

Dr. John Fulton (26:20):

Pretty excited, like I mentioned, fertility type work continues to be of interest to growers. And so our eFields, our on farm research, already have interest in continuing on, whether that's rate, timing, placement type options, farmers are really interested in having some answers on that front. And so we'll keep working on that. I continue to be interested that we're looking at some other technologies that may bring value to growers. I mean, we take the SmartFirmer, for example, the precision planting, there's a lot of those that are being used today on planners that are fully equipped or are with precision planting technology. The SmartFirmer gives you your, in the furrow, your moisture, your organic matter, temperature, there's a B3 primary feedback, but that continues to be of interest. And how do you take that data, in particular the organic matter data, and does that have some influence on maybe how we inform seeding prescriptions?

Dr. John Fulton (27:28):

And so we continue to work on some soybean and corn seeding script type work. There's a lot of good products out there today that create scripts. This is just fine tuning it for a specific field. That's what we've been focused on is we can get pretty close, if not spot on a lot of times, but is the script really capturing? And are we giving, since we're creating more data it seems like for every pass across the field, the SmartFirmer being one sensor that may be able to inform some of the seeding scripts even more, and be more detailed in what population goes where for corn and soybeans. So I continue to be excited about that.

Dr. John Fulton (28:18):

We've seen some real value here recently in some of our seeding, and lot of that value in some cases that we push populations in some areas it's really just been kind of the, especially in soybeans, the ability to really drop the overall average seeding rate for our fields, and so there's a value there on corn. And just recently last year, of course, we had a bumper crop last year, but not only is there some savings on average across the field, but we saw some areas of fields see an advantage of planting more and getting even more yield.

Dr. John Fulton (28:57):

And so optimal seeding rate by, however, there's some different ways of creating zones, but basically production zones that are created that get assigned a seeding rate, in the case of corn, saw opportunities to increase yields. And so bumped those seeding rates and actually saw a response out of that in some areas too. So I guess I find that exciting because like I said, at least for the areas that were, and it's very site specific, I'm not here advocating everyone go out and do prescription seeding. But what I am saying is that with some good research, you can really tune those prescription maps in, and it'll lead to some savings on average for corn and soybeans that we've seen here in recent years.

Morgan Seger (29:45):

Sure. With the potential for more yield in addition to the savings?

Dr. John Fulton (29:49):

That's right.

Morgan Seger (29:50):

So when you're looking at those maps, have you noticed any certain variability that you see from the prescription map to the organic matter map that you're collecting? Because I know most of the prescriptions, if not all the prescriptions, are all using some form of historical data, whereas when you're collecting real time data. What's the difference you're noticing?

Dr. John Fulton (30:14):

Yeah. So I mean, in general, what I would say is traditionally using yield maps and other field level data, maybe you go way back, as a start, a lot of people use the soil type, the SSURGO soil type, but we know a lot of times those aren't probably spatially accurate to the field. I mean, they're a little coarser in how they were defined, but they're also good to look at and maybe a starting spot. But I think to your point, the question is, with something like the SmartFirmer and some of these other sensors that are coming on the market, should you use the sensor to inform versus creating a prescription map so we're actually doing it on the go?

Dr. John Fulton (31:01):

And so I think there is potentially, in some fields based on the characteristics, and in particular, the soil and things like organic matter, CEC, drainage. I mean, we know drainage is a big factor, or water movement, however you want to put some of that. But when we look at all that, some fields potentially, I would say that there might be an opportunity to make adjustments on the go. The question is, what happens in one year versus the next year? Is it similar? Does it change?

Dr. John Fulton (31:37):

And in some cases, there's some similarities for sure, as it relates to things like organic matter, that's not going to change over time. Right? Once we have a good organic matter map that should be there and not change over time. And so I think there is some opportunity, but we yet probably have a little bit more learning to do on that front, at least from my view and bringing a little bit more research to verify some of that. So we can transcribe our learnings there more broadly to others out there that are interested in providing them the proper guidance to go out and at least get started.

Morgan Seger (32:17):

Nope, that makes sense. I'm just sitting there thinking, I wonder how closely the maps that they're creating as they go across the field would correlate to just bare soil satellite imagery, and then compared to soil sampled organic maps. I just think it'd be interesting to see the patterns and start developing just a more accurate way of farming.

Dr. John Fulton (32:41):

Yeah. And we've got a couple fields that we've been spending a lot of time doing some of this work in, definitely there's some consistency, to your point, I can take that organic matter map. We actually created high density organic matter maps based on going out and sampling those fields too. And so there's a little more intent in our research to verify some of that in terms of what we collect at the resolution. I mean, some of that's done, I think we did a quarter acre on some of that. So that's a pretty dense sampling regimen to come up with some of these maps to verify. But I guess my point is that, yeah, we can see some similarities spatially. Now there may be some differences between the two data sets in terms of magnitude, but relatively they are very similar spatially.

Dr. John Fulton (33:36):

And so that's an encouragement, at least in those fields, those couples I'm thinking of that we've seen. And so I could show you a bare soil map in the spring, that's dry, versus the organic matter map, or we've actually created some other organic matter maps. You line them all up and there is spatially for those fields, some similarities spatially amongst all those. And so we're catching that potential zoning out of those maps that create zones. And then the question then gets is how do I take that knowledge and tie a nominal seeding rate to that zone? What should be on my mind? Where's my optimal, when you start looking at the seeding, that population and the curve, does that change over those zones in this field? We've proven that yes, it can. It does.

Dr. John Fulton (34:30):

And so I'd have to look back, I think for one example, we were anywhere on corn from, we were down in the 30s, 30,000. And in a couple areas that are really high productivity areas of corn, we were up, I think at 40,000. So that's a pretty wide range, and that takes some work to get to that range and have... What do I want to say? A comfort level. I mean, we're not geared to be planting 30,000 a lot of times with folks that are really pushing and trying to create a margin, but also push yield. And then on soybeans, the other field that we did, I'd have to look it up, but I want to say we're everywhere from, even down to 120 to 180 in some of the harder ground. But my point is Morgan, go out and tell someone to plant 120,000 seeds per acre and soybeans, you're going to get some questions about that, I think, from a pretty good number of people.

Morgan Seger (35:38):

Yeah. Well, and you hear people going ultra low on beans, but I mean, at the end of the day, you still have a 60,000 plants per acre swing. So it's going to give you a good feel for how it's representative across the field, performance wise.

Dr. John Fulton (35:53):

Yeah. Yeah. And to that we've spent, I think this is about our fifth year on a corn/soybean rotation in those fields. And so for example, we started with strip trials, that was of interest to the grower first, and then all of a sudden you start to see some variability. And I understand the variability, but my point is that, like for the soybeans, we were from 80,000 to 200,000 on our strip trials. So we got a 120,000 swing to try and evaluate what potentially could work best for the zones once we got it zoned and we looked into the prescription maps, or created those prescription maps.

Dr. John Fulton (36:33):

And I think on the corn, we were everywhere from 26 or 28,000 on the low end, all the way to 44,000 on the upper end to try and create that curve. Again, when you start splicing those yield maps over all those years and looking by zone, that's going to give you that response curve and begin to let you know that, are they different amongst those zones? And if yes, you start putting some figures to the math and then verifying those. And like I said, we've come out of it and found that there's some opportunities for savings in both crops. And in particular for corn, we're able to push some areas that probably were maybe a 35-36 anomaly. We were up at 38, if not a little more sometimes, and pushed those areas and improved or increased that yield as well. And so that economic optimum had increased in those areas based on what we learned out of those strip trials.

Morgan Seger (37:33):

Sure. When you get to those really high populations in corn, obviously it's going to be a curve and you're going to get to the point where it starts to fall off, but do you see it fall off drastically yield wise? Or is it more of an economic thing because your seed cost is up plus maybe yield doesn't accelerate? Or do you see the corn suffer from those populations and really drop back on yield?

Dr. John Fulton (38:00):

So these are observations we've made. So it's not only important in those studies to go out and not only make sure you get an... Or at least from my perspective, Morgan, get a good range of rates to put in for a strip trial. I mean doing 32,000 and 35,000 seeds per acre, you only got a 3,000 swing, are you really going to see a response? Probably not. You know? So we've tried to stretch that out, we use corn, a minimum of 40,000 seeds... Or 4,000 seeds, excuse me. So if we're 30 on the low, our next one would be 34, our next one would be 38 at minimum. Or I could do 30, 35, 40 as an example, picking three treatments, but you're trying to stretch that out to create that curve. But if you get too tight, if I want to go from 33 to 36/37, we're so tight we're just going to see almost a flat line in that case.

Dr. John Fulton (38:59):

But my point in all that is that I will tell you when you get in, particular to the corn, and you start pushing something like 40 or 44,000 it is very important at harvest time, not only to collect yield data, but basically look at observations during the harvest, because what happens is that you'll find that your stock diameter is going to be less, or decrease for those 40, 44,000 compared to something like a 36, 38,000 stand out there. And so I say all that is, we saw in the case of last year, we had a really high yielding potential year. It's easy to push, and in some cases, 250ish last year, if not more. Right? I mean, we saw areas of 300 because of the year, but when you got a smaller diameter stalk, we saw some lodging in some of those higher seeding areas, or those 44,000 even to 40 a little bit, and then you get a little bit more leaning on that corn.

Dr. John Fulton (40:06):

And so I know we had some scenarios where we had some wins in the fall last year. My point in all that is make sure you're collecting observations out there when you go out to harvest those. And similar things, the things we see on the lower end, so when we get down to low populations, especially in soybeans, do we see more weed pressure, because potentially we have, depending on what comes up, maybe some canopy closure a little bit later than we'd see in the higher seeding rates, seeded treatments. But in those higher populations, I would tell you, when you get lodging, of course you're going to get a lower potential yield. And so that's not only a headache in terms of slowing you down at harvest, but secondly, potentially leaving some ears and actually making it a little bit more difficult getting it through the combine, and so there's some loss there that you need to consider.

Dr. John Fulton (41:01):

And so I would say that those are important things, those observations, to make when you do that kind of work. And you might decide, well, I'm not going to do another 44,000, and so be it, but you gotta make those observations and note those when you go, especially at harvest. And how did that crop progress? Hopefully you got some scouting information. But at harvest, is that yield loss, to your question, is that due to, because there's more competition and we didn't get the years filled out as much? And that could happen, typically if we count rings around, they're somewhat equivalent when we look at 38, 40, 44. But it's the competition and how big does that ear get that ultimately sets a lot of that yield. If, in the 44,000 I get some lodging, well, but then I got that risk too, of it being lost.

Morgan Seger (41:54):

Okay. That makes a lot of sense, because obviously the higher you climb, you're going to hit a breaking point where your yield starts to go the other way. But yeah, that makes a lot of sense. Make those observations to understand where the yield loss is coming from, that'll help you be more informed going into the next year.

Dr. John Fulton (42:08):

Absolutely. Absolutely. Those are very important as you interpret those results. And I would say we tend to go out in those strips... And again it's try and pull as many ears, but try and pull some ears from those different strips to make a transect across, if not two if you have some time, pre-harvest and pull some ears and think about what to expect out of those strips. But secondly, compare those ears and you'll find some differences in size and even rows and kernels, number of kernels per row and stuff. So that's a good way to evaluate pre-harvest to do some of that kind of work.

Morgan Seger (42:50):

So as we are wrapping up here, I noticed that the eFields book for 2021 is available. Any key learnings, or anything that jumps out at you that you think our listeners would like to know about?

Dr. John Fulton (43:05):

Yeah. So our 2021 report just came out, and the first thing I would say is that it's our fifth anniversary and we've noted it as a special edition this year. And so I just want to say, in 2021, we actually had field trials in 45 counties across Ohio this year. And so that's over half the counties in Ohio had a project in them that got reported in eFields. And so we're extremely excited about that, and we want to, all the farmers and even some of their consultants that help support that, our educators that are in all the counties are working hard to try and expand what they're doing with growers. But this special edition is not only our fifth year. It's hard to believe we're already at five years on something like this. But really we're contributing that to all the farmer collaborators.

Dr. John Fulton (44:01):

I mean, it's a lot to ask. Morgan, if I came over and worked on your farm, it's going to slow you down a little bit, not all the time, but there's some work to it. And that's where we try to supply as much of that labor and support through our educators. But at the end of the time, we understand, from a farmer's perspective, we're asking a lot of them to participate. But at the end of this we talked about a few of the results, like on the seeding, we got technology results in this issue that really talk to some of the planters and even some of the other type technology that's out there that people are looking to invest in and use on the farm.

Dr. John Fulton (44:41):

And so that information is out there. We've got a really good soil health project that's ongoing and looking at sampling by death, but do things like practices, whether it's manure, no-till, conventional till, we do some comparisons across those practices that are really interesting. And starting to inform some of them, what does soil health mean? And can we sequester? What can we sequester from carbon? So there's some excitement around that, what we're learning the last couple years out of that.

Dr. John Fulton (45:14):

But I think I'd just end on, again, I just want to thank all the collaborators and consultants, and even our industry partners that make the eFields report what it is. I mean, if it wasn't for all the farmers and stuff, and in particular, we call this out up front to, not only is this a, we're talking about appreciation to all them, but there've been some real key figures that helped us along the way. I mean, this stuff doesn't happen for free, but a gentleman who's a longtime farmer and advocate of on farm research and the land grant mission, and what we do of taking research, converting that into extension information, his name's EJ Miller, and we really appreciate what EJ... He not only challenged us up front to come up with something that would speak to growers across Ohio, but he's also supported us in many ways to lift this thing off the ground and get it to going, and we really appreciate EJ.

Dr. John Fulton (46:15):

And also there's some other people I probably should mention, Brian Thompson out of Clark County. Brian was one of those people, a little younger than I, but Brian's again, sitting in offices and challenging us to think about questions they have. But coming up with a program that converts those questions into research that not only answers questions for them, but also can be used by other farmers, not only in that county, but more broadly in the state of Ohio. So a lot is packaged there, but we just want to say, thanks to everyone that's made this a real successful program. And in particular, we're excited about the 2021 report. We have 249 unique studies reported this year in the book.

Morgan Seger (47:05):

That's awesome. Awesome. Well, thank you for sharing that. If anyone listening wants to grab a copy, you can get a digital copy at digitalag.OSU.EDU.

Dr. John Fulton (47:16):

Yeah. And don't feel free to either, we got an email on that page as well. If you want a hard copy, either see your local extension office or send our digital ag team a note. Or look me up and say, Hey, I'd like a hard copy, and we're happy to send it right out to you. And so you can browse that as, not only online, but also have a hard copy there to look through as well.

Morgan Seger (47:41):

Awesome. Well, thank you for everything today. It's always a pleasure catching up.

Dr. John Fulton (47:46):

So thank you, Morgan, and wish everyone success here for the upcoming 2022 season.

Morgan Seger (47:52):

Sounds great, thanks, John.

Dr. John Fulton (47:53):

Thank you.

Morgan Seger (47:55):

As always, we want to say thank you so much for spending this time with us to listen to the Precision Points Podcast. Thank you, John, for coming on and spending that time with us and sharing some of those key learnings and the research that you're doing. I really think that when I think about the advancements that we are making in Precision Ag right now, the detail and precision that we are able to bring to farm operations and spend this time researching, it just has me very excited about the future of agriculture. All of these little progressions that we are making have so many implications, whether that's increased yield, increased soil health, more sustainable and environmentally friendly practices, that really are all coming together in a very positive way. And so I'm really excited about the work that they are doing, the research that they are doing to try and write these things out so growers like you and I can pursue these things at our own operations with confidence.

Morgan Seger (48:56):

As always our show notes for this episode, including a link to where you can get the eFields's data, will be available at While you're there, check out our grower source reviews, we have growers who come onto our site and enter their genuine feedback on Precision Ag products and services, so as other growers are trying to consider making a change on their operation. They have this data set of information and experiences to rely on. And until next time, this has been the Precision Points Podcast, let's grow together.

Speaker 1 (49:27):

Thanks for tuning in to today's episode. To hear more podcasts like this, please rate, review, and subscribe to Precision Points. Visit for show notes from this episode and read expert advice on the blog, share your experience with the Precision Ag products you use, and check out our network of farmer reviews. Let's grow together.

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