Jackson: Welcome to the FarmBits podcast, product of Nebraska extension digital agriculture,
I'm Jackson Stansell, and I'm Samantha Teten and we come to you each week to discuss the trends,
the realities and the value of digital agriculture.
Through interviews and panels with experts, producers and innovators from all sectors of
digital technology- we hope that you step away from each episode with new practical knowledge
of digital agriculture technology. Sam: Welcome back to the FarmBits
podcast for our fourth episode in the nitrogen management technology series
and our 35th episode of the FarmBits podcast. Jack: Capturing soil soil mineralization
and early season nitrogen losses is often the missing piece of nitrogen
management decisions that farmers have to make, Sam: And so using responsive nitrogen management
techniques such as active crop canopy sensors as described in this episode can help fill this gap.
Jack: So, our guest for this episode is Dr. Jim Schepers, an emeritus professor
in agronomy at the University of Nebraska who has spent a career working with sensor-based
nitrogen management through the USDA ARS. Sam: Jim helped to develop this efficiency
index concept which contributed to the development of active crop canopy sensors
offered commercially today. Jack: This episode provides a
really good broad orientation to sensor-based nitrogen management
and it'll set up our next few episodes so let's dive right into our interview with Jim.
Jim:
I'm a farm boy from platte river valley out between Kearney and Grand Island. I grew up with
4-H and then FFA and came off to the university and in agronomy and member of agronomy club and
those kind of things. But, on the farm we grew up with corn, alfalfa soybeans a few cattle,
few pigs all that kind of stuff. So, I got exposed to lots and lots of farming activities,
and I still have that in my blood I think. Jack: Yeah, and so can you tell us a little
bit about your career and kind of how you got into the nitrogen management
area that we're focusing on today? Jim: I've always been intrigued by
fertilizers because we would see how the crops responded to fertilizer and
one of the teachers back in agronomy he said the soil is a lousy place to store nitrogen,
and he was just right and so when I went off to to graduate school at Illinois that got me
I'll say in a position, had some plant physiology and things like that.
I taught soil physics at the University of Georgia for two years and did irrigation research with
corn so in 1975 the USDA job opened up here in Nebraska, and I was solicited to come here,
but at that time they were just starting to have problems with nitrate in the groundwater of the
platte river valley, and as we talked to farmers and said you know you guys are going to have
problems here and an old professor Olsen who told us about lousy place to store nitrogen he said
you boys are going to have trouble out here if you keep putting on this much fertilizer,
and so as we talked to farmers about this they said well if you want us to cut back
on the amount of fertilizer we put on you're going to have to find a way to identify the
problem before it reduces yield and then you have to find a way for us to fix the problem right. So,
that sent me back to Illinois on a sabbatical leave to talk to the plant physiology people
and the professor Hegeman says you're just going to have to measure chlorophyll.
That's the driving factor sure and he said if you want to measure nitrogen that's okay
but be careful because corn plants have luxury consumption and so you never know
how much extra is sitting around there in the corn plant, and you don't want to come up short. But,
you'd also like to know when you're at the peak right and if you monitor chlorophyll,
you're going to be able to associate that with nitrogen, so that's what got us into this.
Sam: So, that's what we really want to focus on today is that sensor-based nitrogen management,
but before we kind of get into that can you talk about some of the other methods to determine
nitrogen rates, so such as models or yield goal based methods why is this sensor based
so different and why is that important? Jim: We'll need to go back just a little
bit from our guidance from Illinois he said you're going to have to measure chlorophyll
and at that time the instrumentation was not there to do this conveniently, and so we made some leaf
punches to punch little holes in the leaves we would send one off for chlorophyll one off for
nitrogen analysis and after about three years of this we had a stack of yield response functions,
and we look at them and say well the shape is about the same, but how do we make sense of
this and this is where we came up with the I'll say the sufficiency index concept of
laying one graph over the other one and shifting them around to say oh there's something here and
it's all tied to that nitrogen rich or the high nitrogen rate. So, in 1990 chlorophyll meters
came on to the market from Minolta and we had a large water project at the time so that's how we
started using this efficiency index to monitor the crop and schedule fertigation, but that's a good
research tool but it's not very good for practical purposes on the farmer's field. So, I told Tracy
Blackburn, our graduate student, I said what we need is a mobile version of the chlorophyll meter,
and so he went to work bothering people in all the different departments who had some kind of
a sensor, and that's what led us to Lycore who built us some preliminary sensors. And then Kyle
Holland- he would watch us and he said you guys are always out there calibrating these things
to a spectrolon panel something that is known reflectance he says that's a pain in the butt,
and he said I can build you an active sensor okay we're ready go to it and so that's
the birth of the crop circle sensors just out of the the old chlorophyll meters.
Jackson: So, what is the information that these chlorophyll meters are really capturing that
allows you to measure chlorophyll like what sort of wavelengths and what are we looking at there
what the chlorophyll meter is is doing? Jim: I'll tell you first it's basically a
potential photosynthesis meter in that it generates extra red light
okay, and it says to the plant leaf it says use as much as you want, but what you can't use I'm
going to measure, and so it cranks the leaf up to saying do everything you possibly can,
and whatever's left over I'm going to measure it and then we're going to use that
to quantify how much photosynthesis is possible. Sam: But, crop production and that photosynthesis
is impacted by lots of other things such as water stress so can you explain a little bit of how
you're differentiating that to nitrogen stress? Jim: Water stress comes first and what we found
is that if you well as a plant grows it gets bigger and bigger and the sensors are monitoring
and measuring near infrared reflectance plants can't use that wave band, and we can't see it
but insects can so that's where they know to go harvest/chew on leaves that sort of thing. But,
the more vegetation you have out there the more reflectance you get with near-infrared light,
and so that's one of the components. The other one is the chlorophyll,
so if you have lots of chlorophyll it's going to use as much red light as it can and so there will
not be as much red light reflected, so one's up NIR's up red reflectance is down, and so it
becomes a very sensitive measure of crop figure. Jim: Sure, is that where the NDVI then and some
of these other vegetation indices come from? Jim: NDVI was really developed for forestry
purposes to develop and understand how much vegetation is in a forest,
but it works also for many other crops. Jack: And so we've always heard I
guess on our end that the NDVI tends to saturate right, so if you've got a lot of
crop canopy out there a lot of biomass then you really don't have any red reflectance,
and so your measurement basically always becomes close to one and, so the NDRE I guess is an
improvement on that particular measurement? Jim: As soon as the canopy closes on corn
NDVI becomes a random number generator in that there's shadows and all kinds of other things
going on there, but if you switch to another wave band that doesn't go to very low levels right it
can be a constant level like a near-infrared reflectance or like a red edge reflectance.
It doesn't change much and many people say well red edge is more sensitive that wave band itself
is not, it's very stable and it has about the same reflectance as bare soil right, but in combination
the NDRE weight band or vegetation index is considerably more sensitive than NDVI except at
the very low levels where you still see some soil. Jackson: Sure, which I guess what you have to get
rid of soil pixels so you're actually going to use imagery too do NDRE.
Sam: That's interesting and then you guys have also taken that one step further to four nitrogen
recommendations with a nitrogen hydrogen reference strip or a virtual reference to help make sure
that you're detecting nitrogen stress. Could you describe that a little bit for our listeners?
Jim: The high nitrogen strip idea goes back to the plot studies we had with
the leaf punches and tissue testing. We said if you have a nitrogen rate that's
above what you need that's essentially a high nitrogen strip in the field. That works well but
what we found over time is that there are some situations North Dakota we found it in Missouri
where if you put on too much nitrogen, create that nitrogen strip the plants appear to be stunted
and what's happening here is that sulfur is also leachable just like nitrate, and so in
those situations the nitrogen to sulfur ratio in the plant was out of balance. And so, that's why
we say you got to be careful of a high nitrogen strip, now the other part of the story is that in
Europe they're not allowed to over fertilize. They usually fertilize their wheat three or four times
because if you give it too much at once it's likely to lodge and go down,
so they spoon feed their wheat several times in the year
and they're not allowed to over fertilize so no nitrogen, no high nitrogen strips and so this
is where Kyle Holland came up with the idea of the virtual strip because early in the season
when you're sensing that plant doesn't need the full dose of nitrogen right. It can get by with a
whole lot less and be better for it to boot so. Jack: Sure absolutely, and so you know we know
from working in the space that sufficiency index comes from basically measuring that reflectance
value right or that vegetation index value within the high nitrogen and then comparing
it to some other area in the crop you know just maybe normal crop areas receive a normal nitrogen
rate to measure the sufficiency at those locations now, so that's our sufficiency index there's also
a response index right that was come up with at Oklahoma State can you tell us a little bit
about some of the differences in these methods? Jim: Well, it turns out the sufficiency index
concept is patented okay it was developed as part of the activities with lycore
okay and since it was I'll say my idea they had to turn the patent over to the USDA
and because it was patented people could not use it for commercial purposes. Research was fine but
commercial was forbidden sure and the Oklahoma people didn't want to get caught up legally,
and so what they did, they flipped it over inverted it and said well the farmers find
it easier to understand how much more they could gain rather than how much
short you are, how much deficient you are. We like to talk about how much more we can gain.
Now the the other deal is that the denominator of the sufficiency index is always 100
that's the enrich right so you have a nice linear relationship
the sufficiency, or the response index has got a denominator that's floating around right,
and so it's not a nice linear relationship. Jack: Sure interesting and so it's still
using kind of that N-rich concept but it's just flipped through the ratio where the reflectance
from that enrich is now in the numerator? Jim: That's correct, but there's also somewhere
you have like a zero nitrogen strip right where you put no nitrogen and then you're using that.
We mostly do that to understand mineralization that one of the hardest things to identify and
quantify is mineralization not only the amount, but when it becomes available relative to
that growing plant and how much residue you've incorporated in the soil
in the previous crop. So, it's really helpful to have a check strip a zero nitrogen out there
farmers don't like it at least if it's a very big area right for good reason yeah they put
their hand out and say put some money in there. Jack: So, kind of getting to that right we've
brought up mineralization now that's really the advantage of sensors versus a lot of other forms
of nitrogen management is that they're in real time capturing how that nitrogen mineralization is
affecting the plant am I correct in saying that? Jim: You're correct that the plant is a really
good biological indicator of nitrogen supply be it from fertilizer nitrate in the water whatever,
so the plant's got roots out there that are sensing what's available to it and by using
the plant we at least have a good indication of what's been available up to that point.
Now, as you look down the road further you sort of have to say well we would expect mineralization to
continue throughout the rest of the growing season provided you have water right water comes first
water and temperature are pretty much your prerequisites for that right and actually
temperature is really important early in the spring when the soils are warming up.
If you have a warm spring, mineralization is going to be ahead of schedule
okay and so that plant's going to be have access to more nitrogen, and if you monitor the crop
you say well I've got plenty of nitrogen out here when in fact it's really a temperature response,
and that's why it's important not to get in the field too early with this sensing.
Sam: So, now you have the crops current status, measurement how are you converting that into
a nitrogen recommendation rate, so can you talk about that algorithm
a little bit and how that works? Jim: Certainly, there's really two
approaches to this and here in Nebraska my friend who designed the sensors Kyle Holland
he's quite accomplished with mathematics and he would hear us talk about nitrogen response
functions quadratic or quadratic plateau response functions and he must have sat around for a long
time thinking about these things but he said you know there's a way to tear that thing apart,
understand what's going on and so that's what he did that paper that we published
he was the mathematics behind it, and I tell people my job was to keep him honest
in that make sure that we have all the components in there that are going to be important to a
farmer. And the last one we put in there was that management zone factor because right
we knew that this model wouldn't be and wouldn't contain everything and management zone
is essentially mineralization, soil type, water holding capacity things that you can't expect
that sensor to look down the road three months or two months and say this is what's going to
happen. But, the farmer he could put that in there as a coefficient and adjust the rate accordingly,
and so when we're thinking about this algorithm which we refer to as the holland- shepard.
So, if we're thinking about this algorithm it's taking in the SI that comes in and
the farmers set parameters such as that management zone factor and then kind of an
optimal nitrogen rate for that field. Jack: and what are some of the other
things that go into that algorithm? Jim: Well, there's a term in there
that it's important to know when you're sampling with a sensor because
if you sample at v8 versus v12 that plant has already taken up more nitrogen at v12,
and so you don't want it making the same recommendation at v8 as it does at v12 right
you need it to correct for that. So, that's one of the things that's taken into account.
The other thing I should not to sell short the other kind of algorithms the
Oklahoma algorithm was developed for wheat and they're sensing early in the year to determine
how much winter kill they had and what the yield potential is going to be,
so their idea was let's predict yield determine how much extra nitrogen we're going to need to
bring that yield up right and so they're back calculating. It's a mass balance approach,
but what they're not doing is taking into account the fact that not only do you want to bring up
the the grain the bushels right but you got to grow that plant first, and they weren't putting
that in to their equation so they tried to fix that now but little things like that. Yeah just
would tend to have it under predict the amount of fertilizer you needed especially when you went
to corn and also they were locked into using the green seeker which would saturate and so
you had a random number generator out there with an algorithm that was developed for wheat,
and so there were some disillusioned farmers. Jack: Yeah, I can imagine
so you know thinking about these algorithms and you talked about the green seeker there
and you've mentioned that Kyle Holland was you know partially responsible or mainly responsible
for developing the crop circle sensor that came out of these early chlorophyll meters?
Jim: Absolutely yes, all the way. Jack: And so though the crop circle
sensor kind of developed into the optics sensors that are produced right can you
tell us a little bit more about those? Jim: The crop circle sensor was totally
developed by Holland the ARS part of it that I worked for I'll say we were responsible for
doing the field testing okay and we would in the summertime we would test them here in Nebraska,
in the wintertime we would go to Brazil. We had colleagues there that we would go down there
for several weeks and further test and evaluate what's going on. The sensors have migrated now
because of improvements in electronics. Originally Holland sensor used a we call it an amber led it
was kind of an orange light that also had NIR in it well since that time they've come up with
full range leds that are very white right contains all the wavelengths so then the trick is to put
filters in there that give you the wave bands that you want sure so so that's what he's done
with his sensors. The Oklahoma sensor the green seeker sensor, they operate differently in that
they don't continuously monitor all the wave bands it only has one detector so it would monitor
red for a while and then near infrared for a while jumping back and forth back and forth, so it was
never really monitoring the same target or plant area short time it wasn't a lot different but
just part of the design right trade-offs. Sam: So, also speaking about some of these
different sensors can you describe like the difference between a passive and an active
sensor but also like the proximal sensors that we have been talking about, but also some remote
sensing technologies that farmers might know? Jim: Yeah, the passive sensors are what we started
with using natural sunlight. The problems we had was cloud cover, time of day shadows
and then on top of that different cultivars or varieties of the crop and this is when Kyle
Holland said this is really a pain for you to put this standard calibration panel under the sensor.
Every five minutes or something like this to look up at the clouds, come on get out of the way
and so this is when he said I know how to build the electronics to separate out
natural sunlight from the very small extra signal that's coming from the light that
I would generate. It takes some really fancy electronics to do that right because
it's taking a sample about 40,000 times a second. (Wow wow it is smoking.) Yeah,
so that's the secret and why these things work at night as well as a daytime.
Jack: Which is kind of fascinating, it'd be nice to put that on a drone
and actually have the active sensor strong enough to work on the drone right.
Jim: You have to get a special license for your drone to do that. But,
they've done this with tractor mounted sensors right with cotton in Greece, it works well.
Jack: Interesting, and so those active sensors you know we work with drone imagery quite a bit and
now they have kind of the downwelling sensors that are supposed to prevent you from having to do all
of that you know calibration all the time. Jim: So, that's helpful except that
there's still cloud cover right there. The angle of the sun and the clouds
may not affect the sensor, but they do affect the brightness of the soil the pattern's on the soil,
so you just have to be careful. Jack: Absolutely yeah,
so when we think about how we're able to create an interface right we have these sensors that
are measuring you know like you said 40,000 times a second that kind of gets distilled
down I assume into kind of a one second reading that when these commercials, you know sensors
get used to inform a target rate how are we able to integrate these sensors with complex machinery
in a field to make nitrogen applications? Jim: Part of the problem is that the sensor system
and the recording system has to be very high speed but the computers in John Deere, anything else,
Raven you name it. It's pretty slow speed right, Kyle says they're about 286 computer
speed pretty slow, so what he's had to do is to put a 50 millisecond delay in there between
each signal to slow down his system to send and receive to be compatible with the other systems.
Sam: There's also a lot of because it's so much data so quickly, there's a lot
of noise there's a lot of jumping in the values is there a way to kind of
even that out a rolling average type of thing? Jim: Well, what what Holland does is he summarizes
the data and for research purposes, he'll let you record it 10 times a second if you want to but you
end up with box cars full of information and so for fertilizer purposes he summarizes it every
one second and we'll make a fertilizer adjustment accordingly. And in the process that takes out
a lot of the the variability, if you think about going through the field I'll say four miles an
hour that's about six feet per second. So, you're getting a picture of probably eight to ten plants.
Jack: Yeah, it's really interesting I mean thinking about the system
response to how quickly some of those SI values can change is it the thing?
Jim: The SI values can change and one of the reasons for having more than one sensor
is if you have three or four out there you could actually write algorithms that take out
anything that's got a standard deviation or a cv that's either high or obviously low
because of weeds that can center it really high or a we call a collivator blight. So,
something else missing plants that would send it the other way and so
that could give you a wrong fertilizer rate but for now that hasn't been done it's just
an average. If you have four sensors, it's going to give you the average of the four.
Sam: Can you talk about some of the other challenges associated with some of these
measurements. So, yeah I guess you can kind of take that whichever direction you want but
well one of the problems is that we've tried to to marry a high-speed sensor system
with a sprayer system that was designed for on and off operations. Am I at the end of the field
or do I turn off the right boom and the left boom, this sort of thing right and those valves
they might have been solenoid or they might have been a motor controlled ball valve, but it takes
two three maybe one second. It's slow, and so if you're moving through the field now
farmers like to go through the field maybe at eight or ten miles an hour so now you're 15 to
20 feet down there per second, and you've got two seconds for that valve to control
you're down the field 50 feet or so well beyond but you say well maybe that isn't so bad. I've
got 120 foot boom out here that's another problem the sensor is monitoring where it is, but they're
usually not out at the extremes of the boom right so I think one of the situations is that
it's nice to have those big booms for spraying weeds and this sort of thing right for applying
fertilizer. They're probably better to drop it back to 60 feet or something like that.
Jack: Just tough from a logistics standpoint right as far as covering a lot of ground and trying to
get all the deals that need to be side-dressed. Jim: That's right and that's why we say farmers
shouldn't plan to use these kind of sensors on all of their fields they should be used on the
most variable field or the fields that have had grandpa put manure on, but golly we don't know
where he did it or there was an old farmstead here, feedlot whatever swine barn or something
everything like that can really. You can see that in the imagery for years to come,
and so the sensor is able to pick up some of that. Jack: Yeah and so, we've talked
about this real time sensing and some of the maybe some of the benefits with dealing with
variability and some of the challenges right of the real-time response and systems that we have,
can you compare that versus more passive sensors and kind of the
approach to sensing that requires that you process the data and look at it
before you go out and make an application? Jim: There are some advantages to I'll say
pre-processing the data, you have to collect it first and some of the data you could pre-process
might be organic matter content, water holding capacity or slope of the land
things that aren't going to change very much over time. But, there are other types of indicators
like if you have drone information or aircraft even satellite information that's pretty close
to being real-time, and that's what you'd like to process and get it back quickly and ideally you'd
like to be able to merge that with the common sense information that the farmer has from the
soil and this is what you can do in the computer and then you can feed that information into a John
Deere or Raven or some kind of controller in the form of a shapefile and go make your application.
Probably what you're sacrificing is the reliability of the imagery how good it is if
you get satellite imagery, most of the time it'll tell you two percent light cover of clouds right
this sort of thing right and then you a little common sense to say this pattern looks strange
out here it's clouded, it's a shadow exactly. Sam: So, there tends to be kind of that trade-off
of we can integrate more data but it may take more processing or longer time versus you know these
on-the-go sensors. It's kind of a plug-and-play and you're out from the field and you're applying
at the same time, so there's that trade-off. Jim: Yeah, what we've seen is that drones have
allowed people to have fun and collect data yeah and take pictures and part of the problem
is it's a challenge for them to say how do we we pick out the high nitrogen reference strip here.
We've got to go in and we call it cookie cutter trim out the high nitrogen strip. It's going to
take somebody to do that right. The other way if you use the virtual reference strip,
the computer can do this for you, but you say well that reference value might not be right
for the whole field and we're pretty sure it's not right, but it's one of these things give and take.
I think that there's a lot of potential for using remote sensing of some kind maybe it isn't drones
because you have to pre-process or process the data, but drones become a really good scouting
tool right that you can fly a transector across the field identify sprinkler package,
nozzles or problems that can fix those or get somebody out there to look at them.
If you have problem areas of the field get your butt out there and look at it,
see what's causing it. It may not be nitrogen, it could be ph or sulfur something else could be
compaction limiting something who knows and open your eyes think about your your tillage equipment,
your traffic patterns, the way wagons all those kind of things just do something
that challenges you a little bit. Jack: Yeah, there's so many given
with sensor-based management there's always a trade-off right they're trying to get everything
right and you kind of brought the idea that this efficiency index is going to change as you go
through a field. I mean so do you think this management zone concept whether it's
the management zone indicator or adjusting for management zones with sufficiency is kind of the
next step that needs to be taken with sensor based management to get where it needs to be?
Jim: I think the next step is for for somebody to build a controller that will let the user
put in two kinds of information, one from the sensors or it could be a a second shape file
just two kinds of information merge it together to improve the sensitivity or take into account
those things that the sensor just possibly can't know about. But, that's the next step and then
to do that right you either have to build in a management zone factor or you say
you know we need to adjust that reference value, maybe it comes from the high nitrogen strip or
from the virtual strip that we know that the yield potential for the entire field is not the same.
And so that's what we're trying to compensate for is to maybe you've got two or three zones in the
field, and you'd like to have that sensor be able to say oh we're in a different management zone
now we need to be changing the reference value because the yield potential just isn't there.
So, there's a couple ways of doing this one is to say in the shepher's holland/shephers algorithm
we have the optimum nitrogen rate in there. Well, that's really a farmer value we put that in there
to say we want something that you're comfortable with to start with. After the first year, you
realize that you're putting on too much nitrogen he'll tailor it down the second year and after a
couple three years he's probably zeroed in pretty good about what the appropriate nitrogen rate is,
but the other I guess a different way to skin the cat is to say we're going to adjust the
reference value, which we're going to tinker with the management zone value. We're going to
play with the the optimum nitrogen rate, so there's two or three ways of getting at it
sure and it it'll depend on I think what it comes down to is that dual controller.
So, for a young electrical engineer there's your target maybe we have a few listening
to the podcast yeah that's right. Sam: Is there anything else that
you would like to see done you know in the near future when it comes to sensor-based
nitrogen whether that's maybe integrating weather data or integrating anything else?
Jim: What else I think that there's opportunities to use models they can look into the future
with some degree of of accuracy, they can probably identify extreme cases like if it's going to get
dry as heck, you need to be careful here or if you're counting on rainfall to incorporate this
nitrogen into the soil it's risky. What are your chances of having rain within the next seven days
things like that and you can never predict the large catastrophic events
that might result in excess nitrate leaching especially on sandy soils. In those cases,
this is where drones or aircraft can come in and say oh my gosh we're running into trouble here. We
had a major loss event we better get out there and fix it. Either with a pivot or with a high
clearance sprayer, if you don't have the entire field needs to be fixed this may be an opportunity
for the sprayer version, but if it looks like 70 or 80 percent of the field needs attention
turn the pivot on. Jack: Which makes sense, so all this sensor technology obviously still needs some
development but it has been shown to be useful right I mean there have been a lot of studies that
have shown that it makes people more efficient, it can be more profitable can you talk a little
bit about those studies and maybe where we do see those positive effects from using sensors?
Jim: The most positive effect or the most beneficial is probably on soils
that have the greatest variability, the rolling land, the manured fields,
fields that have been terraced in the past where they've done some earth moving
which created differences in organic matter and things like that. And so
this is where these sensors really have a good place to be used. I don't look for a whole lot of
improvements or changes in the sensors, what they're monitoring there's always people out
there who have hyperspectral imagery and this sort of thing, and I say they're out there to develop a
better mousetrap but in reality we have 30 or 40 years of science. It says we're already locked in
right on what's really important right and so let's figure out how to use that,
so I think that if you've got fertigation you can come pretty close to spoon feeding the crop
and make a good improvement that way whether you use the sensors or
hell there might even be ways to put sensors on a pivot as it goes around
right. Lycore talked about this 30 years ago, but the problem at the time was getting
water on the leaves that changes the reflectance of NIR and visible
in opposite ways right. So, that's a problem. Jack: What have you seen farmers getting most
excited about in terms of sensor based management and where are we with adoption right now?
Jim: The adoption is is not very high and it's because farmers some of them are intimidated
by the technology some of them don't have high clearance sprayers and most of them do not,
but at the same time they only want to go into the field one time they're looking for the easy button
right and they're willing to pay a little extra for that convenience of having that easy button
nobody is after them or charging them for over application right and until
farmers are only able to purchase let's say 80 percent of the normal fertilizer you're
not going to get their attention right but the the farmers who we work with they have
high clearance sprayer and we started out by loaning them my set of sensors, and I would
ride with him this is on your own right well it's a farm south of Lincoln. But, he's also
a pilot himself he has a small airplane, so he'd already been up and said I'm ready for first we're
sure but we put the sensors on and he's done this now for four years and he was telling somebody
last year he said this is worth about $50 an acre to me to be able to go in and fix
those spots in the field, and it was two years ago in 2019 we had a a lot of extra rainfall,
had some some leaching going on sure and he put on quite a bit of nitrogen with the sensors
but he had excellent yields and he'd gone up with his airplane again and showed his pictures of his
fields that were quite uniform, compared to the neighbors that looked bad. He said they
wouldn't tell me what their yields were. Sam: So, feel free to reach out to us
and know or Dr. Schepers with questions. Jack: And so, we always like to end our episodes
with asking for a piece of advice that you want to leave our listeners with
and in this case on the topic of N management and specifically using technology for nitrogen
management, if you'd rather tell a story instead of a piece of advice I think we
can also open up that opportunity as well here. Jim: Well, the the advice is don't be complacent,
look around and and think about that corn plant there are other tests like the stock nitrate test
people say oh dang, it's late in the year I can't do anything about it,
but you can fix it for next year right. You can do something better next year and the other thing is
people get these yield maps and they go in a drawer
they don't get used efficiently to understand what is driving the differences in yield
farmers can also be their own little experiment station nothing major but
pick out a problem or two and maybe even two or three farmers get together and talk and say you
know this is a common problem maybe we have two of those, but let's think about who can do this
who can do that right. Let's look around and try to understand what these systems are doing to us
or how we can improve, but that takes a commitment and many people are afraid to say I made a mistake
they don't want the neighbor to know what they're doing good or bad right and so they they keep all
their information very private. So, it seems like there almost has to be a paradigm shift in terms
of how we approach improving operations out there in Argentina they have a system that basically is
an extension service and so they have groups of farmers maybe 10 or 12 that have a little club
so to speak and so they get together once a month to talk about these things and share
information. They will even bring in specialists to talk to them about this or that, but they've
provided the or generated the information either among themselves or from somebody
else to get this done almost but it brings a whole new meaning to cooperative in a way. I just think
about all the coffee shops across America that kind of serve that purpose for farmers
that's in a way what's what's happening most of the coffee shops are bragging times
and not I shot myself in the foot twice or otherwise did you see that over there.
Dick Wiese, one of the former extension people here he would go to coffee shops in the morning,
Saturday mornings and talk to farmers and one time he said to farmers what
are you going to do if you can only buy half as much fertilizer
look around the room at each other oh that'll never happen. Well, it might be because some
of the NRDs now are really cracking down on no fall fertilizer half the rate before planting
that sort of thing and pushing people into being more spoon-feeding or systematic balancing and
synchronizing nitrogen needs of nitrogen supply. Jack: Thank you to Dr. Jim Schepers for joining
us today on this episode of the FarmBits podcast. Sam: It was great to talk to him he's been very
instrumental in our research, and so it's always great to hear his stories, which is
really what my favorite part was is hearing the stories and the history behind this technology.
It's not very often you get to hear you know from the decades ago how it all got started and why we
are where we are today that was awesome. Jack: Yeah, so much context and he has
so much experience and you really kind of heard that shine through through interview
and one of the things that I thought he said that was really poignant was that not all growers
should use this on all of their acres. It's really built for those really variable fields
and ultimately adoption this technology is kind of low and it may continue to be low until there's
really kind of a motivating factor to reduce overall nitrogen applications, which is just
interesting to think of in the overall context. Sam: Yeah, that methodology or thought behind
that is so different than we've heard from a lot of other guests and so it's always great to hear
a new perspective for sure. So yeah, thanks for joining us today we hope you'll listen
again next week as we continue our nitrogen management series. Thank you for taking the
time to join us today on the FarmBits podcast, if you enjoyed this episode please subscribe to the
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about the latest content each week. Jack: We welcome your feedback so
if you have comments or questions for us please reach out to us over email,
on Twitter or in the reviews section of your favorite podcast platform. Our contact
information can be found in the show notes. Sam: We'd like to thank Nebraska Extension
for their support of this podcast and their commitment to providing high
quality informational material to the members of the agricultural community
in Nebraska and beyond. The opinions expressed by the hosts and guests on this podcast are
solely their own and do not reflect reviews of Nebraska extension or the University of Nebraska.
Sam: We look forward to joining us next week for another episode of FarmBits.