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-: Okay. Well, since we already solved the technical part.
Good morning, everyone.
Thanks for joining.
So today I'm gonna try to give a brief introduction
to one of the technologies that we are testing
through our precision nitrogen project.
And so we are gonna dive into crop model based tools here.
So just to give you an idea
of how these tools might become very useful and helpful is,
and I wanna try to link it to some of the questions
that you might have during your growing season,
like for example,
how much water you have left in the soil, right?
If we wanna try to predict
how many days can I go without rain,
crop model based tools
kind of allow you to make that prediction into the future,
or also things like,
well, how much nitrate am I having in the soil
in that particular season?
So crop model based tools kind of integrate all the factors
to kind of know how much nitrate you have.
And then also, are helpful
because they combine all these things
and try to give you an assessment
of how much nitrogen you might need for the crop
during that growing season.
So on that graphic, on the right,
what I'm showing you
is kind of all the factors that interact
to define that optimal N rate.
And for those that are not familiar with that term,
it means the nitrogen rate that maximize your profit
for that particular year
and so that particular soil in your field.
And so as you can see,
it's a balance between all the nitrogen
that's coming from your soil.
So the soil supply and the crop demand.
So how much nitrogen you need in order to satisfy the yield.
So crop model based tools integrate all those buckets
that you see see in that figure
and try to allow you to have a prediction.
So what is in a crop model?
So imagine like it has different boxes
that have multiple equations that run every single day
to kind of come up with an estimation
of how much your plant is growing
and how much nitrogen it's gonna need.
So for running some of those tools,
you might need some management data.
So like planting day, the fertilizer day,
what kind of, sort of, so fertilizer are you using
and things like that.
And then we'll also incorporate things like
all your weather variables.
So temperature, radiation, and precipitation.
And as you know,
those are key to guide
how much nitrogen I will have in mineralization in the soil
or how much leaching I'm gonna have.
So all important factors.
And then as well,
we will have all the soil characteristics.
So this is another key point of these tools
that will allow us to account for all the organic matter,
values, our texture
and different nitrate conditions in the soil
when we start the season.
Another thing that these tools allowed us to do
is to account for the spatial variability.
So here in this picture,
what I'm showing you
is like two contrasting soils in a field.
So we have an upper hill and like a more low in lane area.
So crop model based tools allowed us to put
an input of different organic matters
at the top of the hill
and maybe a different organic matter value at the bottom.
That way, when we run that model,
then we can predict two target N rates
for the two parts of the field.
And another key point,
and I think that's one of the maintenance strengths
that we have for these tools
is that they account for weather.
So the more closer we get
to when we are gonna do the decision in the growing system,
the more weather data we can incorporate
to know either if we lose nitrogen in the soil,
if my crop is really good that season,
and if I'm gonna need more or less nitrogen.
So those are kind of the benefits.
And in this graph,
what I'm showing you is sort of like the predictions
of one of the models that we have run in the Midwest,
and in the top panel,
I'm showing you the prediction of soil nitrate
in the growing season,
and in the bottom panel,
this is yield as a function of the date.
And the distance between that green line and the red line
is sort of the uncertainty that you have
predicting the yield when you start your growing season.
And you can see that that panel when you move to the right,
so closer to your harvest date, it's getting smaller.
So that means that the more information we know,
the less uncertainty we have to predict our yield.
The same happen with how much nitrate we have in the soil.
So what model is the best?
And here I'm just giving you some idea
about what are the commercial models
that we are currently using or testing
in our On-Farm Research Network.
And so we have Maize-N, Adapt-N from (mumbles)
Granular, FarmersEdge, Flurosat.
And I'm sure that more and more of these softwares
and platforms are gonna come up popular,
but all in the background,
they're starting to run a crop model.
So you will have a simulation of your cropping system
in the background.
So what is the best model?
I always make this joke in terms of like,
well, we kind of pick the bagel
that best suit our purpose, right?
So this is the same.
You might drive the truck for going to the farm, right?
And then your car for going to the city.
So this is kind of what we're trying to learn is like,
what model is better for,
depending on the logistics of your farm
and depending what environment are we at.
So that's kind of what we're testing.
So a great opportunity to test these models
via On-Farm Research Network.
So today I'm gonna share two of the pilot studies
that we did last year,
and that we are gonna plan to conduct
for the next three years.
So in this study,
we have two pilot sites in Southern Nebraska,
and we have one dryland popcorn,
and one irrigated corn,
so very contrasting situations.
And this is sort of
how the layout of the experiment looks like.
You might see a lot of colors here.
I will walk you through.
So what you see the strip that is pink,
that is the model Adapt-N from (mumbles)
that the grower wanted to test against Granular software,
that they have their own proprietary crop model.
And then the blocks that you see in different colors,
those are different nitrogen rates.
And we call it nitrogen ramps or blocks.
Basically, increments of nitrogen
until you saturate the response of yield to nitrogen.
That is key here
because that will allow us to calculate
what was your optimal N rate.
So the N rate that gives you the maximum profit
at each part of the field.
And then at the end of the system,
we can compare what Granular or Adapt-N give us
as optimal N rate against our kind of true
for that particular year.
So this is kind of for giving you an overview
of what the kind of data
that we are getting for those blocks.
So what I'm showing in this picture is yield on the Y-axis
and in the X-axis, nitrogen.
So this is the curve that we are expecting to have
in different parts of the field.
And in this example,
that is coming from another field
that we also run some pilot studies,
we have a different EONR maximizing your profit
in each of the soils that we have in that field.
So this is the kind of information that we are gathering
with these studies.
So now let's jump into the prescriptions.
So for this field,
we gather all the managements, soil bubbles, yield history,
and we input everything into the two software platforms.
We work with Granular,
and they show us this prescription on the left,
and then in Adapt-N,
we got the prescription on the right.
Two things to notice.
Total amount of nitrogen spent in the field
was pretty similar.
Although the distribution of fertilizer
across the two fields was different.
So what we are starting to learn here
is that the way that they configure soils
and the way that they attribute different N rates
across the field are different.
But now we are diving into the reasons of why
and what are the impacts of those different recommendations.
So when we look at all the data aggregated,
so this is after we harvest the field,
we view and monitor.
We analyze the total nitrogen rate used,
the corn yield, nitrogen use efficiency
and partial profit.
So imagine that you summarize all those strips
that I show you at the beginning,
and now we calculate all the different inputs and outputs
for each of the treatments.
So what we found in this case
is that there were no significant difference
between the two models.
So they've overalled the field,
they yield the same,
they spent pretty much the same amount of nitrogen,
and we had a really high nitrogen use efficiency
that was around .82, .86.
So that was one example.
Now, jumping into the second pilot study that we had.
This was in dry land,
and keep in mind that this was popcorn,
one of the crops that is not quite calibrated
within these platforms,
but what we can see is that Adapt-N recommended
a little bit lower N rate across the field.
And again, the distribution of fertilizer
is contrasting as well.
So when we look at the results of this trial,
we found that the total amount of nitrogen
that you can see it in the top panel on the left,
the total amount recommended by Granular in this case
was about 50 pounds (mumbles)
And we think that this was related with the fact that
we targeted 100 bushel yield for this popcorn,
and it ended up being a super windy season, super dry,
so really our yield goal was pretty lower.
And then for the rest of the thing,
we did not find either for the yield or the partial profit,
we did not find any significant difference.
So a lot to dive into this data.
I just wanna like share with you
kind of the first opportunities here that we're finding.
And if you're interested on comparing
some of these softwares,
we will have more opportunities for you in a little bit,
but please don't hesitate to ask and contact me.
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