(uplifting music)
-: All right, well, I'm gonna go ahead and kick things off.
My name is Jackson Stansell.
I am a Master's student
in Agricultural and Biological Systems Engineering
at the University of Nebraska
and I've been working on sensor-based fertigation
over the past couple of years during my master's program,
alongside Tyler Smith, Dr. Brian Krenke,
Dr. Daran Rudnick and Dr. Joe Luck.
Those are kind of the core members of our team.
And the first thing I really wanna address
as far as sensor-based fertigation management goes
is what our motivation is for working
on sensor-based fertigation.
So, you know, as people who are here in Nebraska
and just generally kind of in the great Plains region,
we know that efficient nitrogen use is important
for achieving acceptable water quality.
This is something that is definitely a focus
here in Nebraska,
especially with our significant groundwater stores
that we have available.
We know also know that optimal nitrogen use
results in higher profits.
If we're actually matching what that optimal nitrogen rate
should be in a field, then we're also going to achieve
the appropriate yield and actually
the economically optimal nitrogen rate,
which is really what we're trying to aim for
with using sensors to direct our fertigation management,
coupled with kind of this efficient nitrogen use
and kind of our groundwater quality issues.
We know that there's a potential for regulation
coming down from kind of local regulators,
particular entities and we need methods and technology
to mitigate the impact of these necessary regulations
on productivity of corn
to be able to retain profits, retain yields, where they are
and maybe even improve those with enhanced management.
So what we've seen in the literature
is that fertigation inherently offers
many nitrogen use efficiency benefits.
It offers the opportunity to split nitrogen applications
throughout the course of the growing season
and also allows for very timely applications of nitrogen.
Particularly once you get into the reproductive
growth stages of corn,
you can see some benefit to fertigation applications
during those stages.
We also know that prior research has demonstrated
that using sensors to guide fertigation decisions
may maximize nitrogen use efficiency benefits
and that's exactly what we are seeking to build on
with this research.
So to get into the method that we have developed
over the past few years,
I'd like to go ahead and begin with what our first step is.
And our first step is pre-seasoned data collection
and site setup.
So what this consists of is collecting yield history data
for a particular field, getting all the different
soil spatial variability data.
So soil EC, elevation slope, as many layers as we can.
And then also collecting some information
about the irrigation system itself,
it's operating parameters, as well as the fertigation pump
that we're going to be using at that particular site.
And so collectively that data,
especially that soil spatial variability and yield data
comes together and we're able to kind of characterize
the spatial variability within that field
and we make what's called an indicator block,
establishing application with the prescription
that is made according to all of that soil
spatial variability data.
And so essentially what this indicator block
and establishing application is,
is we're going out and putting rate blocks
at very specific points in the field
so that we can sample those with our imagery
throughout the course of the growing season.
And we do that by V6.
So this application can be made
with any sort of variable rate-capable ground equipment,
whether it's a dual product planter,
whether it's an anhydrous bar,
or if you even wanna use the center pivot,
we've shown this year that you can actually use
a center pivot to put these in.
But once we make that indicator block
establishing application,
we get into what I call our weekly cycle
over the course of the growing season.
So this starts at V6 and each week we go out
and we collect imagery,
whether that be from satellite, drone or manned aircraft.
We then process that imagery
and this is particularly necessary for drone imagery,
where you have a lot of small images taken
as you cover a field.
They have to be stitched together to make one cohesive image
of the entire field.
And so once we have that one cohesive image
of the entire field that's multi-spectral
and one allows us to produce the NDRE vegetation index,
which is defined in the on-farm research book.
We actually get into our analytics process
and so we have an algorithm on the back end
that is able to essentially assign sufficiency indices
to each individual sector within a field.
So this is looking at our research field setup
where we have a lot of these field sub-regions.
These are 15 degree sectors out in the field
and so you can kind of see the management zones
overlayed there
and for each one of those individual management zones
and sector intersections,
we can assign a nitrogen sufficiency index to that location.
Then using that analytics layer,
we can actually go in and make our fertigation decisions.
So these are go or no-go fertigation decisions.
Either this sector needs fertigation or does not.
And then using those decisions,
we're able to generate a fertigation prescription.
And this fertigation prescription basically assigns
30 pounds of nitrogen breaker to be applied
to all those sectors that indicate
that they need fertigation and zero to all the ones
that say that they don't need fertigation.
So, as you can see here,
the low sufficiencies from our analytics layer
translate to those sectors that are receiving fertigation.
So once that fertigation prescription is made,
we go out and actually make that fertigation application.
So this process is completed weekly,
delivering those go or no-go decisions
until R3 is observed at the time of image capture.
And I'm saying time of flight here
because generally we're using drone imagery
to make these decisions.
So overall, the objective of this method
is to match the corn nitrogen uptake curve
using on-off nitrogen application control with fertigation.
So here, each one of these stairsteps
would indicate a fertigation application
triggered by a sensor and as you can see,
we're trying to just match that uptake curve
for corn as best as possible, which should result
in the optimal amount of nitrogen being applied.
So far, I think we're seeing pretty good results here.
So this is actually a graph
showing different nitrogen uptake curves
and the one I want you to focus on is this black line.
This is basically translating if we went out and applied
the UNL recommended N,
if we were to say that that translated
directly to crop nitrogen uptake
over the course of the season,
that's what that black line would be.
And then our treatment is here in the blue
and you kind of see those stairsteps,
following that upswing in the curve
and really matching, overall,
the total rate that we need out there
but with varying timing and amounts
to match that uptake curve for the corn.
So we've looked at this method that we've developed
at 10 different sites over the past two years,
five in 2019 and five again, here in 2020
and most of our sites have been
in the central part of the state,
with a couple up in the Northeastern part of the state.
And then also one at the Eastern Nebraska
Research and Extension Center in the (audio cuts out).
Generally the way that our treatments are laid out
in these studies, we have 12 different sectors.
Each one of these sectors are 15 degrees.
We have four total reps out in the field
and generally we have a grower management treatment
and we have two sensor-based management treatments.
Generally we'll call them risk averse and risk tolerant
but I'm going to get into exactly
what those treatments are here next.
So we have three different approaches
for sensor-based fertigation management
that we have investigated as treatments
over the past two years.
We have our Risk-Tolerant Last 60 treatment,
which is implemented for only the last 60 pounds
of applied nitrogen out of a grower's total nitrogen goal.
So if a grower had a total nitrogen goal of 180 pounds,
then we would actually kick our method in
at 120 pounds of applied nitrogen
and use the sensors to manage that last 60 pounds, okay?
The reason why we call this risk tolerance
is because we're requiring more indicated deficiency,
more indicated nitrogen deficiency
to trigger a fertigation event, okay?
And goal of doing that is to prioritize nitrogen savings
over saving yield, okay?
So we're really trying to maximize
how much nitrogen we can save with this particular method.
We also have the Risk-Averse Last 60 treatment,
which similarly to the Risk-Tolerant Last 60
is implemented for only the last 60 pounds
of applied nitrogen
but we require less indicated deficiency
to trigger a fertigation event.
So this means that we're going to apply nitrogen
more frequently throughout the growing season.
And the goal here
is ultimately to prioritize our yield savings.
We still wanna save nitrogen
but we also wanna make sure that we're not losing
that yield potential.
Lastly, we have our Risk-Averse Full Season treatment,
which is only different
from this Risk-Averse Last 60 treatment
in the length of time for which it is implemented.
Okay, so we implement this treatment
all the way from V6, or post indicator
block establishment, onward.
Okay, so this is V6 to R3,
the entirety of the fertigation management
for the season is being managed using sensors, okay?
And the reason why we're wanting to do this
for the full season is that we wanna optimize
both nitrogen and yield savings at the same time.
So in 2019, we looked at the Risk-Tolerant Last 60
and Risk-Averse Last 60 treatments.
And then in 2020, we looked at the Risk-Averse Last 60
and the Risk-Averse Full Season.
So to get into some aggregate results,
I'm not gonna get too much
into individual site results here
but I'm happy to answer any questions on those.
What we have graphed here is the difference
between our sensor-based fertigation management treatments
and our grower management treatments for efficiency,
which is shown here on the x-axis
and profitability, which is shown here on the y-axis.
If we were both more profitable and more efficient
than the grower in any particular implementation,
we would be located in this upper right-hand quadrant here.
If we were less profitable but more efficient,
then we would be in this bottom right-hand quadrant here
and so on and so forth, okay?
And so what we can see from this graph
is that the vast majority of our implementations
with sensor-based fertigation management
have been more efficient than typical grower management.
What we also see is there's a lot less consistency here
in terms of profitability, right?
So overall, 94% of our implementations
have been more efficient than typical grower management
and actually, the only particular implementation
that was not more efficient is this one over here,
in the bottom-left hand corner.
And we were actually 16 days late,
just due to kind of a labor backup.
This was during late June of 2019,
so we've since corrected that issue
using some software automation.
But that is really the only site
for which we have not been more efficient
than typical grower management.
On the profitability side,
53% of our implementations have been more profitable
while 47 have been less profitable
than typical grower management.
So on average, across all of our sites,
taking those site-by-site differences
with typical grower management,
what we've seen is that our profitability
for Risk-Averse Last 60 treatment
is better than typical grower management by $1.14 per acre.
Our risk tolerance is worse
than typical growth management by only 81 cents per acre.
And then with our Risk-Averse Full Season,
which has only one year of data so far,
we had a loss on average of $12.22 per acre.
I'm gonna get back to that number in a minute
because that is extremely concerning
if you ever see this on a graph
but we do see that we've increased efficiency
with every single treatment on average versus the grower,
which corresponds to what I've already been saying.
Now, getting back to this Risk-Averse Full Season
number here, what I wanna show you
is that we had one particular site
and this is Howard County.
It's the last site in the book
if you're looking at the section
of sensor-based fertigation management studies
that had these two points here
that are significantly less profitable
than the typical grower management on that field.
And at this particular site,
what I'm showing you here is our 2019 yield map.
This is when we did not have any treatments on this field.
And then I'm showing our 2020 yield map,
which is when we did actually run this study
on the south side of this particular field.
What you can see is that there's a lot of consistency
in terms of the yield patterns on this field,
especially here in the southeastern corner
of this field, where we have a large hill,
it's very sandy, a lot of slope.
And so it's just really
a low yielding area in this field, okay?
If we take this particular rep out,
which I met with the grower
on this particular study last week to review results
And he said, "You know, you're really
not gonna do anything on that particular hill,
as far as yield goes."
And so he was comfortable with at least looking at it
without this rep in there.
If you take this single rep out, not even this entire site,
both of those points moved to being more profitable
than typical grower management.
And that actually flips our entire averages
across all our sites for both of our risk averse treatments,
both the Last 60 and the full season
to being more profitable than typical grower management
with the Risk-Averse Full Season
coming in at $1.72 an acre more profitable.
And you also see a fairly significant,
I guess it's about a pound of grain per pound of nitrogen
increase in terms of our actual efficiency
from the sensor-based fertigation management treatments.
And overall that would move us to 65% of our sites
being more profitable than typical grower management.
So the bottom line here,
I know I've already been preaching on this a little bit.
Sensor-based fertigation management
does lead to increased nitrogen use efficiency.
I'm very confident in saying that here today.
Secondly, sensor-based fertigation
can lead to increased profit
but outcomes are pretty inconsistent.
So I'm just gonna call it 53 to 65% of the time,
whether you wanna leave that rep in there or not,
we're between 53 and 65% of the time
being more profitable than typical grower management
and that's something that we need to improve.
Full-season sensor-based management
shows a lot of potential but with only one year of data,
there's a lot more tuning and a lot more data
that we need to collect
in order to determine whether or not
you can actually implement sensor management
over the course of an entire growing season for fertigation.
We have some ongoing work.
We're looking to adjust our cutoff timing,
so extending our fertigation applications
past R2 into our R3 and R4.
We're also looking at doing some rate adjustments
by growth stage so when we have that rapid uptake,
should we increase it to 40 pounds, 50 pounds, an acre
in those sectors that need fertigation?
And how should we adjust this for soil type?
We've been doing a lot of studies on sandy fields
but we kinda wanna look at how this would react
on heavier textures and so we're looking
at different soil types and then seeing
if we need to modify the method in any way.
Indicator block implementation alternatives.
As I mentioned, you can put those in with the pivot.
That's something we've shown this year,
so we're gonna keep looking at ways
to make that more effective.
And we're also automating this process through software,
which is my project right now
and it's pretty exciting to see it all come together
and hopefully that'll decrease the amount of time
it takes to actually do this process,
implement it on a week-by-week basis
throughout the growing season.
I just wanna thank our funders for this research real quick.
And then my contact info is here.
If anybody has any questions, we only have 30 seconds,
but I'll do my best to answer them quickly.
(uplifting music)