Video Transcript
0:02
hello everyone its Aaron Breimer with
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Veritas forum business management in
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this video I want to talk about how we
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determine at Veritas if variable rate
0:12
prescriptions actually make money this
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accounts for both our prescriptions as
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well as other prescriptions that you
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might rate yourself or that are offered
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on the market some of you will have
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recognized this slide from other videos
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that I have done this video it
0:28
specifically focuses on the fourth
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pillar the validation so what gets
0:33
measured gets managed so a little bit of
0:36
history on variable rate prescriptions
0:38
as far as how Veritas has been working
0:41
with them when you’re looking at
0:43
variable rate prescriptions there’s a
0:45
lot of different data layers that you
0:47
can use here’s a short list there’s
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probably a lot more it completely
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depends what you want to do for your
0:54
prescription in my mind there is one
0:57
layer that is the absolute most
0:59
important and that is the data layer
1:02
between your ears
1:03
it’s the experience you have of that
1:06
field of that crop you know that field
1:09
that crop better than any computer every
1:11
well so make sure that the data layer
1:14
between your ears
1:15
what when you look at that prescription
1:17
you say you know what that makes sense
1:20
if you’ve got that coin for you then
1:22
you’ve got a good prescription at least
1:24
in my opinion once you take all that
1:27
data you’re gonna create what’s called a
1:28
management zone map now the smart kids
1:32
they call this an algorithm so if you
1:34
hear people talk about an algorithm
1:35
ultimately what they’re talking about is
1:37
they’re talking about splitting your
1:39
field into different zones and in each
1:42
zone put in different rates that’s all
1:44
an algorithm is it’s a fancy way of it
1:46
being able to say that so when you hear
1:48
algorithm this is what people are
1:49
talking about so I’ll give you an
1:52
example of a variable rate management
1:53
map remember this is what a lot of
1:55
people call an algorithm this is
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actually one of my dad’s fields this is
2:00
a corn prescription you can see we split
2:03
it up into three zones the majority of
2:05
these zones were created using satellite
2:08
imagery as well as some aerial imagery
2:10
and of course the
2:12
most important data layer my dad’s
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experience in that firm so we’ve got a
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high productivity zone medium
2:18
productivity zone and a low productivity
2:20
something like I said this is a corn
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example that’s on corn in those high
2:24
yield zones that’s where we put the high
2:26
rate in the low rate low that yields
2:30
ohms
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that’s where the lower rates go when we
2:33
switch it up for soybeans or dry beans
2:36
or lentils legumes and actually we’re
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testing it on wheat this year what we do
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is where the high yield zones we
2:44
actually back down the rates and that’s
2:47
to reduce the chance of disease whereas
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in low yield zones we’re pushing the
2:51
yields a little bit higher this is our
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formula this is our algorithm I guess if
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you want to use something different
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that’s completely up to you we’ll be
3:01
happy to work with you whatever your
3:03
philosophy is for your farm for your
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crops so on the left you can see that’s
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that management zone map on the right
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that is the resulting yield data from my
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dad’s combine it’s an older combine
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older yield monitor some maybe not
3:20
super-clean data hopefully you don’t
3:23
hold that against my dad’s data but
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here’s what you can conclude from this
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is that the highest yield came from the
3:31
best soil yeah not exactly
3:34
earth-shattering
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this does not tell you a variable rate
3:38
actually made you money this tells you
3:40
that variable rate that you got your
3:42
zones right congratulations like I said
3:45
as long as you look at that data and say
3:48
this makes sense that day layer between
3:51
your ears you’re off to the races but
3:54
ultimately what you want to know did it
3:56
make you money now Veritas we’ve been at
3:59
the variable rate prescription for a few
4:02
years now and we’ve always been trying
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to figure out does this make money and
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if it does make me how much money is it
4:08
making so we’re gonna go back all the
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way to 2011 and this is how we started
4:16
validating if variable rate was making
4:18
money
4:19
so we have our prescription and
4:21
literally what we were doing was we were
4:23
asking the farmer to turn the
4:24
prescription on and off this is probably
4:27
how most farmers are going to test their
4:30
prescriptions because it’s easy let’s
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face it this is how we’ve always done
4:34
testing in agriculture so there’s our
4:38
test strips now here’s the problem you
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might notice the over on the right hand
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side there’s an extra wide one what
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happened there was the farmer turned the
4:47
prescription off and then he forgot to
4:49
turn it back on so it got it a little
4:51
extra wide now he was willing to admit
4:53
it so we were gonna call that an oopsie
4:55
he forgot to turn it back on so it’s how
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a lot of farmers had tested but it’s not
5:01
always they easy to remember to turn it
5:03
on and off for every field you guys have
5:05
a lot going on when you are using these
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variable rate prescriptions because it’s
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the busiest time of the year it’s spring
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so there is that limitation this brings
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us to 2012 where we tried to make things
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easy and now if you go back to this the
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very first slide I shared that’s our
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number one pillar how do we make it
5:24
simple and easy for farmers to do it
5:26
because if it’s not they’re not going to
5:28
do it so we tried to make it a little
5:30
bit easier in 2012 by pre-programming
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here’s our prescription and we asked the
5:36
farmer how wide his planter was in his
5:38
case 40 feet we asked him what side he
5:40
was going to start planting he told us
5:43
there was on that upper straight angle
5:47
so what we did was we calculated over
5:49
where the planter would have a full pass
5:52
and actually to expand it to make sure
5:54
that we hit it right we pre-programmed a
5:58
strip of 120 feet in so and you can see
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we actually put two of them in and
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farmer goes plant set the variable rate
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check strips are automatically put in
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there talk about making it easy here’s a
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challenge we ran into it is if you look
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at those strips on either side of those
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strips there’s a different amount of
6:19
zone and each one of those zones we
6:22
already recognize act a little bit
6:23
differently so having those
6:26
representative representative strips
6:28
it’s next to impossible in fact what we
6:30
found
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is if you can put that check strip in
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the exact right spot every time you will
6:38
win big and you’ll show huge return on
6:41
investment however if you put it in the
6:44
wrong spot you’ll lose every time and
6:46
you’ll lose big so this is not really
6:49
giving you the answers yes you can go
6:52
and digitally cut out each one of those
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zones and compare side-by-side but that
6:56
takes a lot of work so we decided to try
6:59
to make things a little bit easier keep
7:01
that easy component for the farmer make
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it easier for us and also to be able to
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get better data so in 2013 we came up
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with this idea of air box this is what
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we came up with and this is what we call
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I’ve seen them called all kinds of
7:15
different things on the market I’ve
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heard them called learning blocks I’ve
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heard them called learning stamps
7:21
knowledge squares there’s probably a
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hundred different way of ways to call
7:26
them depending on who you that you’re
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working with this is not something
7:29
specific to Veritas what’s specific to
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Veritas is that we put these into every
7:33
one of our prescriptions so you can see
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it there’s that original script for my
7:37
dad and we’ve put in these
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pre-programmed randomized blocks
7:42
scattered throughout the field these
7:44
things are great it makes it super easy
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for the farmer because they’re
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pre-programmed right you can see lots of
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representation and not only lots of
7:54
representation but a lot of replications
7:57
this is something that the statisticians
8:00
really want to drive home because it is
8:03
very very important to make sure that
8:05
your results are validated the other
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cool thing about this is that you can
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include outlier rates so in this example
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those squares are 120 feet by 120 feet
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so about a third of an acre and the pink
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ones are 40,000 plants per acre now in
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corn that is extremely high now my dad
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he’s got good fertility 20-inch corn we
8:27
thought we could push it here’s what I’d
8:29
ended up happy wherever those pink
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squares were my dad had a perfect 120
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foot by 120 foot square of flat corn now
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if you’ve ever worked with a farmer and
8:40
he has corn goes flat he’s
8:43
gonna be happy but in this case my dad
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didn’t mind because he was able to see
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in this case just a few squares that
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40,000 was way too much I imagine how
8:53
upset he would have been if I third of
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the field if all those green zones were
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in at 40,000 or a big strip break down
9:02
the field or a couple strips or a down
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the field had 40,000 like it would have
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been horrible he would have been really
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groping so this makes a day easier to be
9:12
able to include those that outlier rates
9:14
it is easy to analyze the results pile
9:19
numbers lots of it software out there to
9:22
be able to overlay different layers to
9:25
be able to get results here’s an example
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you can see there’s a whole bunch of
9:29
numbers I’m a big numbers guy lots of
9:31
fun to be able to look at this not every
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farmer likes to look at numbers all day
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long so in 2014 I started to work with
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our data scientists to come up with a
9:43
better easier way to be able to
9:45
interpret those numbers so like I said
9:47
not everyone likes numbers maybe you’re
9:50
a little bit more of a visual learner so
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here we have our prescription we’ve got
9:54
our vera blocks pre-programmed in there
9:56
and here’s what we do we create response
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curves now if you think back to school
10:02
response curves are basically the line
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of best fit so that’s what the dark line
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is they’re the shaded line is something
10:09
we have called the confidence interval
10:12
I’ll explain this a little bit more so
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response curves basically we got the
10:16
line of best fit
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that’s that dark line up the shaded
10:19
areas that’s the confidence interval so
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the more data points you have
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at any one point along that curve the
10:27
more confident you can be if you think
10:30
about it in terms when you’re reading a
10:33
newspaper article about politics and
10:35
they’re talking about a poll and they’ll
10:37
tell you that party a is pulling at 32%
10:43
plus or minus two point one percent 19
10:46
out of 20 times that’s confidence
10:48
interval so we actually include that now
10:52
the really cool thing about response
10:53
curves you can actually eat
10:55
and what the positive or negative return
10:58
on investment yes there is a negative
11:00
return on investment at times if you
11:02
make a mistake you are going to have a
11:04
negative ROI the cool thing about this
11:06
is you get to learn you get to learn
11:09
what is the ideal rate so when you hit
11:12
that ideal rate that is actually going
11:14
to allow you to measure the potential
11:15
return on investment so a really cool
11:18
really powerful tool here in these
11:20
response curves this this is what I call
11:23
the secret sauce that Veritas is working
11:26
on this allows us to be able to validate
11:28
every prescription every prescription
11:31
did it make money did it lose money how
11:34
could we make more money
11:36
these are critical this is really really
11:39
cool stuff to be able to use on your
11:42
firm on your fields on your
11:44
prescriptions now I love response curves
11:48
man I just figured that out however you
11:51
might want to know where on my field of
11:54
mind making money so we’ve taken it one
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step further and we take those response
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curves and we integrate them back into
12:00
the field so here’s an example you can
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see that area response curves up in the
12:04
right hand corner hey that’s great but
12:06
now you can look on your field and say
12:09
okay those response curves what do they
12:11
tell me on my field where did I make
12:13
money so you can see in this case on
12:15
this field the actual profit now when we
12:18
figure out actual profit right we’re not
12:20
talking about yield we’re talking about
12:22
actual profit we take into consideration
12:24
what was the difference in yield how
12:27
much was the crop worth and how much did
12:31
the C bit cost so when you put more seed
12:34
down it’s gonna increase the cost of the
12:36
seeds so we actually take that into
12:37
consideration and then the other piece
12:39
we do is we actually remove the cost of
12:42
the prescription as well so this is the
12:44
net positive to the farm and then you
12:47
can see the there is the potential
12:49
revenue as well you can see what the
12:52
actual target rate was and you can see
12:54
what the ideal target rate was so the
12:57
actual return on investment was thirteen
13:00
dollars and four cents the potential was
13:03
another twenty four dollars all the way
13:06
up to $37
13:08
and really all we were adding was
13:10
another 500 seeds per acre and just
13:13
tweaking where we were putting that 500
13:15
seats so huge huge potential here now I
13:20
give you a CD example here’s an example
13:24
in nitrogen now interesting thing about
13:27
nitrogen when we first started working
13:29
with nitrogen I was absolutely convinced
13:32
nitrogen on corn we had to put more
13:35
nitrogen in the highest yielding parts
13:37
of the field our response curves proved
13:40
to me that I was 100% wrong and that’s
13:44
pretty tough for an agronomist to hear
13:47
that you were wrong here’s why your
13:50
highest yielding corn usually comes in
13:53
from the low parts of your field the low
13:56
parts of your field have a little higher
13:57
organic matter organic matter is going
14:00
to release nitrogen so any extra
14:03
nitrogen that that high yielding crop
14:05
needs is coming and is being supplied by
14:08
the soil so you actually need more
14:12
nitrogen on your tougher spots of the
14:15
field so this was a pretty cool learning
14:17
for us now there’s an exception to this
14:21
there’s always an exception in
14:22
agriculture the exception is if you’re
14:24
working with cover crops or high residue
14:26
situations because those low areas
14:29
they’re going to grow better crop of
14:31
crops or they’re gonna have more residue
14:33
and that residue is gonna have a high
14:36
carbon content and that high carbon
14:39
content is gonna need extra nitrogen to
14:41
break down this is why it takes a lot of
14:44
work to be putting into a prescription
14:46
and why you have to sit there and think
14:50
about if it makes sense
14:52
so algorithms are great but does it make
14:56
sense for your farm that’s what this
14:58
gives you an example with nature one
15:00
more cool example for you I thought this
15:02
one was really cool
15:04
so in Ontario we talked about the four
15:06
R’s I did it’s specifically on
15:08
phosphorus I do know that some other
15:12
areas talk about nitrogen so we talked
15:16
about adaptive management so outside of
15:19
what the four R’s tell you or
15:21
how to use the four R’s better on your
15:24
farm here’s an example of a field that
15:28
actually challenged me for a couple
15:30
years you can actually see that there’s
15:32
a very distinct difference in yield and
15:34
this line showed up almost every year
15:37
now the farmer told me that there was a
15:39
history difference of the crops the
15:45
higher yielding areas had a history of
15:47
specialty crops vegetables so there was
15:50
going to be more fertilizer and then on
15:52
the other side there was basically
15:54
continuous corn and soybeans and not a
15:57
whole lot of extra fertilizer put down
15:59
so the yields were slowly slowly getting
16:01
worse and worse to this point you can
16:03
see where the history when some
16:07
vegetables over 238 bushels for a pretty
16:09
good chunk of that field and where it
16:11
wasn’t you’re down to under 150 in some
16:16
cases under a hundred and sixteen like
16:18
wow that’s that’s painful so the
16:22
question then became from the farmer is
16:25
what could they do about it fairly large
16:27
farmer you can see that the size of the
16:29
acres they’re fairly large equipment he
16:33
wanted to know should he invest for dry
16:36
fertilizer on his corn planter so what
16:39
we did a flat rate across the whole
16:41
field with cheque blocks are very blocks
16:45
scattered throughout of different rates
16:47
of dry starter he actually had a
16:50
neighbor plant this field in order for
16:52
him to be able to check so that way he
16:55
could make a better decision on his
16:57
other fields as well as if he was going
17:00
to continue to farm this field what he
17:02
was gonna have to do so you can see we
17:04
ranged from 0 pounds per acre all the
17:06
way up to 200 pounds per acre of dry
17:09
fertilizer this is in a 2 by 2 besides
17:11
the row here’s the response curve really
17:15
cool response curve like I said we work
17:18
off of profit so when you look at the
17:21
vertical axis that’s profit that’s not
17:24
yield that’s actual profit so what was
17:26
the difference in yield and how much did
17:28
that fertilizer cost you can see the
17:31
peak of that curve before it starts to
17:34
drop down
17:34
again is somewhere around 135 to 150
17:39
pounds per acre so this actually makes
17:42
him money because well let’s face it if
17:45
he was at zero that’s 700 dollars up at
17:49
that 130 550 he’s almost at seven
17:54
hundred seventy five dollars actually
17:55
said a little over by the looks of it so
17:57
75 bucks an acre by making that
18:00
investment how he can make better
18:01
decisions yeah
18:03
fermer asked an interesting question he
18:06
said well does that also carry over on
18:10
that high yielding part of the field
18:11
versus the low part of yielding part of
18:14
the field remember that yield map I
18:15
showed you so we actually split the deal
18:18
now the data here the what you’re going
18:24
to look at is the economics the scale
18:27
changes a little bit so the curves look
18:31
a little bit different but the data is
18:32
the same in fact that that yellow line
18:34
on the bottom one is the same curve as
18:38
the one from above that’s all the data
18:40
but you can see the red curve that is
18:43
the low yielding zone and you can see
18:46
that has actually shifted from that one
18:50
35-ish
18:51
area to probably closer to 175 is the
18:55
optimum so in the low yielding areas
18:57
he’s gonna have to put a little bit more
18:59
fertilizer down now in the high yielding
19:03
parts it still pays because that’s what
19:06
he was asking he goes well this is one
19:08
of my more challenging fields maybe I
19:10
should just give this field up um and
19:12
focus without dry fertilizer on all my
19:14
other fields but even his high yielding
19:17
parts of that field was responding now
19:20
not at a hundred thirty-five the option
19:23
one was seventy-five this is the power
19:26
of working with response curves data
19:29
Vera blocks it’s easy for him to execute
19:31
and it creates incredibly deep insights
19:34
so variable rate prescriptions can make
19:37
you money but if you’re not measuring
19:40
them they could also cost you a pile
19:43
if you’re interested in knowing more on
19:46
how this works for your farm give us a
19:48
shout we’d be happy to have a
19:50
conversation thanks