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-: I appreciate the opportunity to visit
with everybody this morning.
This has been a effort that really,
we started back, I think almost seven years ago
when Laura kind of started working heavily
with on-farm research network,
but trying to figure out
how we can leverage precision Ag technologies more
for a lot of our studies.
And so I just want to recognize Laura and also Sarah Sivits,
some of the data that you'll see in the presentation
this morning kind of comes from some of the studies
that they were instrumental in help us carry out.
But really the question is, you know,
how do we leverage these new systems
to improve our, our studies that we're doing?
And really the, the thing that I would like to point out
is just a couple of barriers
that I've seen over the past few years
with on-farm research, just visiting with people
that have private, maybe take part in or interpret studies.
And, and one is obviously the time it takes
to put the field trials out.
So if you're doing all this manually and,
and you're having to go out in the field and,
and put flags out and measure
and all these different things
that takes a lot of time.
So I think there's an opportunity there.
The other thing comes down to how we interpret the results
of these, these studies.
So a lot of times, you know, it's confusing.
So you can see at the bottom of this image here
with the circles in red, you know,
why is , why is five bushels an acre not significant?
You know, why is, you know, $20, $30, $40 an acre,
not significant in some of these studies?
And what does that mean?
Well, a lot of times it comes down to two things
and these studies well, one could be, well,
the treatment really had no effect,
but with those wide differences, it could come down
to the number of treatment replicates
we had out in the field.
So if we only had three full field land strips
of one treatment compared to another,
that's not a lot of data points.
The second thing that can,
that can really play a major role here
is the field variability.
So oftentimes we're kind of relegated
to doing these field trials and field link strips.
So if you look at this image here on the right, you know,
we had these field link strips of different seeding rates.
You know, this entire strip gets average into one point.
And along with these other five,
so six total replicates here, you know,
that six data points that we have,
they go into this statistical analysis.
Well, there could be a lot of field variability
within those strips, and we need to be able
to try and tease that out.
I think these are some of the opportunities
that we have with precision Ag tech.
We really can't,
we can't skip the part about successful trial design.
So just a couple of highlights here.
When we, when we start off
with these studies is really identifying the treatment
that we want to go after.
And so one of the, one of the examples I will give
that's always been challenging is,
you know, for using blended products like 10-34-O
well, if you, if you're going to vary the rate of that,
maybe to, you know, impact your phosphorous levels
while you're also varying the nitrogen levels.
So focusing on the element that we wanna,
we wanna study, there's really important.
And then doing rate studies with enough data
to really generate response curves
within our, our results is really key.
And we'll talk a little bit more about
that later, how we space those out.
Checks are always really important,
especially in a lot of these product tests.
You know, what would have happened if,
if I had done nothing, you know?
Yeah. The crop might not have looked as, as good, but,
but could, could that have been something I had avoided and,
and really not taken a yield penalty
instead of spraying the whole field for instance.
But the other thing I would mention is,
I'll use the term block or rep interchangeably here.
But if you notice, like in this top study,
we've organized this study into four blocks
and each block contains our two treatments.
And the bottom study, you can see
that these reps or blocks are organized again
with our three treatments.
This time we're testing two different products
and a check or do nothing, you know, strip it's important
to have those across the field.
And we have each one of the treatments in those
you can see they're represented there.
And then really the third thing
is we try and randomize those,
and that helps some take out that field variability impact.
But you can imagine if I had
high yielding areas on the east side here
and really low yielding areas moving consistently
to the west, if I keep the order of my treatments,
the same across that field.
So we basically, I'm biasing all those results in
the same direction of that yield variability.
So just a couple of basic things we really can't forget.
And in terms of the data and how the number of reps
and the importance there,
let's look at it kind of a scenario here
of what could happen.
So here's this field study,
we'll look at a couple of times,
again, we've got six blocks or six reps across the field
of each one of these treatments.
And you can see this is the typical grower rate,
I think 36,000 in this case.
And we're going above that , and below that,
to just look at the optimal seeding rate for this field.
Well, if you look down in this chart,
basically, we're looking at the partial profit
for each one of those blocks,
and you can see the rates here
and what you notice is four out of six of those ended up
having a lowest eating marae
and the highest partial profit compared to the others.
You look down here in the, in the results summary chart,
you can notice, that we actually did see significance there
in terms of partial profit with a $12/acre advantage
of going to the lowest seeding rate in that case.
So having the reps allows us to run
the statistical analysis, increasing the number of reps,
lets us detect these differences at a,
at a more fine or higher resolution.
Well, what would have happened in that study?
So let's just have, for instance,
two of those blocks and say, well,
I just went out and threw these two blocks out,
this is just how they happen to fall using
that previous study.
I'm only looking at these two sets of block data.
And these two cases actually are our typical C536000
in both cases would have indicated
the highest a partial profit.
In that case, we can't do statistical analysis
because we don't have a minimum of free reps.
And, and really, if you think that back,
that first set of data,
that basically , that decision could cost us $12/acre,
if we make that choice moving forward.
So again, the power of replications and,
you know, for frankly where these,
where these plots get placed is, is pretty critical.
And here's kind of the worst example I would say
from this is what if I go out
and say, hey, you know,
this is a new, new seed I've got for ya.
You've been applying 36,000 seeds/acre.
I promise you if you plant 40,000 seeds breaker,
you're going to get a higher partial profit
or marginal, never turn off that.
And if we that just by happenstance, put that, that study,
you know, one strip of that study here
and block number six, this is the result,
we would have gotten in that case.
We would have actually seen by about $15 an acre
or a higher partial profit with that higher seeding rate.
So now, if you think about that,
compared to what my typical practice was,
that costs me $15/acre.
If you think back to the original,
the balanced well-designed study at tech
nearly could cost me $27 per acre if I make that choice.
So this, this issue of data quality and,
and how we design these studies
can really impact operations.
And this is something we really had an opportunity again,
for this type of,
so how can we use precision Ag technology to help us out?
Well, this is an example of a field trial.
Basically we get AB lines equipment widths,
from our co-operators.
We create these prescription maps that have,
you can see in this case are blocks
that are randomly within those blocks.
We have those different treatments that we're working with.
And in this case,
on this pivot irrigated field for soybeans,
we're able to get 16 blocks out of that study.
And in this, in this study,
we were actually able to detect yield differences,
statistically down to two bushels per acre,
which is pretty impressive given that we weren't even able
to use all the blocks that we designed.
And I'll talk about that in a second, but,
but the ability to design these in software upload
those to the control monitor in the cab
and do a product or a rate study with soybeans, corn, or,
or maybe like the application rate of nitrogen,
things like that.
This is something that can be done
and I'll show you some resources
for that here toward the end.
Just a quick note, typically for the studies
that we we've been planning when
it comes to target seeding rates,
usually we try and have three or 4,000 seeds per acre
between those.
So, you know, if you see that,
that initial study of 36,000, and we want to test
above that and below that by 4,000 seeds per acre,
3000, I think would be the minimum.
You'd want to go there on soybeans.
Usually 30,000 seeds per acre is kind of the difference
we like to see so that we know
we've really putting a response out there from that.
And then in terms of nitrogen,
usually around 30 pounds per acre, that's the,
the difference we like to see among those treatments
to really try and generate that curve.
And of course, with nitrogen,
I know folks don't like to do that,
but if you can go with a really low rate
or zero in some cases out there,
sometimes that can really help you define
that nitrogen response curve.
It's a great idea.
I know it doesn't look pretty,
but a couple other quick notes think really
about your harvester widths just kind of the
that's the data collection methods.
So we really have to base our studies off that,
usually for corn it's much easier
because we always have that common denominator,
a little tougher in soybeans.
So we have to think about that when we design the plots
and then really be careful with dry spreader.
So if you're doing some type of a product rate study
or something like that,
dry spreaders' inconsistent patterns,
you really have to be careful
where you harvest in those studies.
Just be really careful with that.
The second thing is that once we designed the study,
put them out of fuel.
You really have to get the as applied data
back from these studies.
This is an example, that's that study we just saw,
we've got our blocks out in the field.
We didn't, didn't know that this would be planted
at an angle, the versus what we designed the studies,
but as applied data, it lets us know
where those rates for hopefully successfully planted.
And you even see here, I mentioned
before the prescription wasn't successfully loaded
until we got up to this past here, you know,
about a quarter of the way up the field.
So all these blocks and the bottom really were,
had to be eliminated
because there was no rate change in those blocks.
So that's really set to, in my opinion,
get the as applied data.
So you can validate what the machine intended
and tried to carry out in terms of that field operation.
Just another example, here is a study
that we designed with our prescriptions for nitrogen rates,
got a text from the operator when they loaded that up
into the machine, I said,
Hey, look at these blocks, they don't look anything like
what you sent your prescription map, is this okay?
And this is actually an artifact
of a, of the GreenStar 3 systems.
They regrade these, these polygons.
You don't notice this in large field areas,
but when you come down to these small blocks,
we're trying to do where we're only talking about,
you know, 16 rows or, or 24 rows.
Luckily we knew that and we designed this into the study.
So we would still get a couple of harvester passes
within each one of these.
So, but again, you really have to validate
what's going on out in the field.
I unfortunately have I think two lectures
on yield monitor data collection and quality check.
I'm going to try and get away with it in one slide here.
But you know, this is our baseline of data
that we're trying to collect, assess what we do at a field.
So we have some best management practices out there.
And as you get towards the fall, you know,
we always try and get those out to folks
that are using these systems, but calibration is key.
You know, calibration affects every single data point,
really trying to hone that in,
if you can't right at the time of the harvest of the plot
is, is really a good idea.
And of course, if you can do a multi-point calibration,
so four more loads to kind of span that range of yields
you might see in the field that's critical.
And just recall that those,
those individual calibration loads are very small
because, you know, we're talking about a high-flow condition
moving down to low-flow condition.
You know, you only need
60 or 90 bushels per calibration load
to generate that curve. And actually, you know,
if you're collecting way more than that,
you're probably negatively impacting your results
just because you want to maintain
that flow condition throughout that load.
And then when we're harvesting the plots
and you're out in the field,
speed changes are the worst enemy of a yield monitor,
data quality.
Anytime the operator speeds up or slows down,
basically that's going to impact the flow
through the machine, the calculation of area covered.
So it's just really a double whammy in terms of,
you know, collecting good data.
So try and be consistent there.
The, the yield data itself really has to be,
it has to go through some hyper quality control check,
and we, we typically use a fairly standard way of doing that
through a software called Yield Editor.
It's available from the USDA,
and I've argued with a lot of statisticians, not this,
but it's the bottom line is the machinery itself.
We know can generate errors as we're collecting yield data.
And again, if you think about the acceleration-deceleration,
why do I have a yield data point out in that field
that says 3000 bushels per acre?
You know, that's just not reality.
And if that data point gets left in here, and again,
we know it's a physical issue that was generated
that we have to eliminate that,
or else our start studies are going to be,
you know, biased horribly for that.
So just a, a point we'll make
that software is actually free.
It's a little tough to get the data in and out of that,
but we're working on hopefully an option to help with that.
You can look and see that it makes a difference.
So when we go in with a raw yield data,
the same study we looked at before,
I'm not seeing a significant difference in my,
in my partial profit in this case
when we actually post-process that.
And then generally in this case,
it was a lot of lead time going into harvesting the blocks,
eliminated some low yield data points
and basically shifted our yield estimates up
within those blocks.
You start to see statistically, we saw differences there,
sweeping impact, certainly impact the study.
A couple other quick points I would make,
in terms of in-season observations.
Really, I think critical here, you know,
imagery's coming a long way and it's going
to give us the opportunity for some of these plots
to ever fly a field, get emergence counts,
and counts at a high resolution,
the cost on that, you know, continues to come down,
but even just going out into these blocks and,
and doing, you know, a few staying counts for population,
making sure that the, you know,
the, the rates and the ramp rate of that,
you know, is pretty consistent. I think it's critical.
We've, you know, we've had a lot of studies where,
you know, plots have just been wiped out by some issue,
you know, rain erosion, too much pressure on that.
And so, anyway, being able to go in and document,
you know, this was my seeding rate study,
you know, here were kind of the emergence rates
that we saw to go back and even put that into the study,
I think is very important.
And it's getting much easier to go out
and do these geo reference stand counts
with some of the apps and software
and, and things like that that we have.
But I mean, thinking about the future,
you know, the imagery, again,
the resolution I think is, is really key,
but, you know, we, for instance, have studies like this,
where we noted wind damage,
you know, scouting out in the field,
noticing wind damage out,
and then getting that imagery to look at, you know,
what was the scale and scope of all that wind damage?
How did that affect our study that we have?
That field in particular has a ridge or two,
and you can see that wind damage really hit that area.
You can even see some, what looked more consistent here.
You know, this is some previous crop management issues,
but on this issue on the right, you know,
this is a much higher resolution image.
And, and we're looking at the,
the red edge and near infrared,
but you're seeing a ground squirrel damage,
you know, throughout some of the plots
in this particular study.
So being able to tie that back to
what you're actually seeing out in the field,
I think is pretty critical.
So we talked, we talked a little bit about,
you know, the rep issue,
the Ag technology's going to let us increase
the replications we have on our field.
Being able to put the blocks in areas of the field
can help us identify how the field variability
impacted the results.
This is another example of,
of how we can do this.
This is a little bit different because we don't have
that nice block design.
This is where you and I have to start looking
at geospatial statistics.
But you know, this was a split planner study
for offense and defense of hybrids.
What you see is the, you know, those,
those varying across the field of those two hybrids.
And, and if you averaged all that data out
from, from, you know, each strip,
basically you would have gotten a two bushel difference
in the yield, you know, no significance,
while it's the lever.
What really the key here was, what was the soil moisture
and water holding capacity.
So this high sloping area
move it down to the low sloping area.
What you can see,
we quantified this versus in our CS slope,
basically you saw all of a sudden, and in these,
these areas where there was high water-holding capacity,
that offensive hybrid really out-yielded
the, the defensive hybrid.
And then as you move up the slope
where we have less, less availability.
So again, that's just helping us understand
how that variability impacts the results of that,
or trying to average all that data in there
was clearly an underlying factor here
that we had to account for in that.
So that's most of what I have for the day,
what I would leave you with is if you're interested in this,
we're glad to support anyone that wants to try and do this,
any type of study, where you want to try
and leverage your technology.
If you're interested in learning how to do this,
for yourself, our team, Laura and others have created
this learning modules.
We've taken some of our data management workshops
with these online. So you can,
you can learn how to design these studies with this,
going through these modules, and these are gonna be free.
And we'll, we're
just gonna probably a highlight this a little bit more
at the end of the day. But again,
I think there's a real opportunity out there.
And if I were just to summarize these, you know,
these five critical steps, you have to design it correctly.
Once you deploy the field study,
you really have to do that as applied data check ,
in-season scouting is really critical
to make sure that things, you know,
were progressing appropriately through the field and,
and there's not some external factor affecting the study.
And then we get down to the yield data quality,
the collection of that data ,extremely important.
And then finally,
we can go through that statistical analysis,
maybe have to incorporate some spatial issues into that,
but, but that's kind of the process by
which I think we can really improve the information
that we get out of these studies.
And hopefully that's something that,
that might be of interest to you in the future.
And again, we're glad to support
and you'll have contact information
for several people here, but I'm always glad to visit.
If, if there's anything I can help with, just let me know.
So.
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