Jackson: Welcome to the FarmBits Podcast: a product of Nebraska Extension Digital Agriculture.
I'm Jackson Stansell, Samantha: and I'm Samantha Teten, Jackson:
and we come to you each week to discuss the trends,
the realities, and the value of digital agriculture.
Samantha: 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.
Jackson: We're excited to welcome back Dr. Joe Luck associate professor at the
University of Nebraska-Lincoln for Episode
3 of the FarmBits podcast. Dr. Luck is a precision ag
extension specialist and has had experience working
with yield monitors and data since his
time as a graduate
student at the University of Kentucky. Samantha: For those of you who listened to
our first episode, he and Laura Thompson dove into the relevance of digital agriculture and
technology adoption.
On today's episode, we are going to be taking a look at yield data quality
and calibration, and we'll even get to a discussion of how yield data is being used,
and what to be on the lookout for in
the future. Some of you may be listening to this podcast from
your combine or while driving a grain cart, so hopefully this is timely information for you.
Jackson: And if it is, happy harvest to you and now here's our interview with Dr. Joe Luck.
Samantha:
So Dr. Luck, what do you think is the current most common practice for Nebraskans across our
state when it comes to how
they currently calibrate their yield monitor? Joe: Yeah, it's
a it's a tough question just not not being able to get out and visit with a lot of growers, but
I think most of the growers out there are trying to do a pretty good job of calibrating
you know against manufacturer specifications, but I also know that I've talked to a lot of
folks that they kind of think more is better
when it comes to calibration. So one of the key points
of good yield monitor calibration is when you do a calibration load and remember that, you know if
I've got some high moisture corn, I want to do a calibration for that crop and that moisture. I
want to try and hopefully my yield monitor allows me to do
multiple calibration loads so something
from a really high flow to a really low flow.
I only need about 3 thousand to five thousand pounds
of grain per calibration load and whether it's just access to a scale a lot of times,
I think that's part of the problem some people don't have
you know a grain cart with a scale or
a wagon something like that.
A lot of folks will harvest maybe a thousand bushels. They'll harvest
an entire truckload and take that to the when they take it to the elevator they'll weigh it,
and then they'll put that
in for their calibration load. And that is, that's a mistake when it
comes to the calibration process. You know if you think about that high flow calibration load
that I want to run, I want a quick again,
you know that you're talking 50 to 90 bushels, that's all
you need, I want that run at a very high flow rate, very
consistently high flow rate over that short
period of time. And I want to get that
weight and get it in the system. And if you can do that for
each load then technically the calibration process shouldn't take too long,
but again I think a big
part of it's just access. Do we have access to a weigh wagon or a grain cart with scales? So
Jackson: You know, follow up on that,
you know you say you want these these smaller batches, and I guess when
we're thinking about yield monitor calibration, we're
looking at ideally a three-point calibration
for yield monitors at least. And so is
the best way of getting that different amount of grain flow
in those periods of time to get those different size loads
is the best way to do that changing
your swath width, or is it changing your speed. How do you recommend doing that or both of
those equally valid methods? Joe: I think they're both
equally valid. So in an ideal situation
there's still you know still a lot of machines
out there that only let you do a two-point calibration.
So again, you know, you're trying to get the high flow and then get a lower flow rate.
The ideal situation is if you can do
four or more points per load. So again you know, if I'm doing
high moisture corn- I want to look at let's just take the the constant cut width.
I want to say I'm going to harvest it six miles an hour,
that's the fastest I anticipate traveling,
so I'm gonna run a quick calibration load, you know, three thousand pounds/six miles an hour
full header cut width. The next load down I might do you know three thousand pounds at
five or four mile an hour, and then I drop down to three
maybe, drop down to one to two miles an hour
to generate that four point
or more. So that's kind of the procedure cut width versus speed- it
really doesn't matter if you think about it.
Each one of those scenarios, I want to get that three
to five thousand pounds. So each one I should be about the same flow through the machine.
It's typically for me it's whatever's you know easiest
for the grower. The one thing I will say
is though you know when we when we say varying speed,
you want to be a constant speed while
you're harvesting that one calibration load. That's the the key, is stability. So,
that's that's the only advice I'd give there, but either way you end up with the same
result, same amount of time to to grab
that amount of grain and get it entered into the system.
Samantha: Sure and how many times should they be doing
this calibration throughout the season, or do you have
any rules of thumb for growers? Joe: Well yeah, it's
I would say a minimum: there's there's a few things
to think about. First is I have to have it for different crops. So, if I'm doing a lot,
which is fairly typical here at least in the eastern part of the state,
high moisture corn, I
need to I need to create a calibration for that.
A lot of people switch over to soybeans. They've got
to do a calibration for that. And then they'll go back to corn and it'll be lower moisture.
And we need to do that, you know, actually interestingly a little data
out of our friends over at Iowa State noted that if you have a 2.5%
swing in moisture content, that could be a 5% error in your yield data. So
that there's a lot of challenges there because you could have that much of a swing in one field.
How do you deal with that? But again in general that's the minimum. I would look at
doing and if you can do an extra calibration or two in there, it's worth it. The other thing I
would mention is test weight can also have an impact. So some of the data we collected
back when I was at University of Kentucky working in the yield monitor test facility,
we had two different hybrids of corn. One weighed about 56 pounds a bushel the other one was
62 pounds per bushel. It's a pretty big swing at the same moisture content.
Six pounds. That ended
up being a two and a half percent error, and so if we calibrated with one and switched over
to the other and didn't change anything,
it was two and a half percent error. So really you know,
keeping an eye on test weights is important
as well. So if you have hybrids of really different
test weight, I would consider that too. And so you know
you were talking about you may have different
moistures within one field and obviously you can go
back and forth between a lot of crops, I mean
is there is there any way to to kind of find this best compromise between time and accurate
calibration? Especially since harvest is, I mean there's logistics and like it's a time crunch
I think for a lot of growers.
It's a it's kind of a catch-22. You don't want to spend all your time
calibrating, you know if you're doing that.
You know we do we see some new technologies out there
like ActiveYield from John Deere that's an on it's basically on-the-go calibrating you know
on the machine you don't need a scale necessarily. So
we'll see new technologies like that coming out
that I think help out with that a lot people. I could say okay, I'm switching over a new field,
I just want to run a new calibration,
I don't have to stop and do that with the grain cart. So you
know until that time where that's widely adopted it's
you know it's just have to recognize that you
know. I've got to make that decision of you know okay
I've dropped four or five percent moisture,
and I'm still harvesting corn maybe it's time to take a new calibration just to update that.
Samantha: (Sure), so switching gears a little bit
so if let's say your combine, you're all calibrated
once, you're now harvesting the rest of your field, what are some operational best
practices to ensure that your data is coming through to the yield monitor accurately?
Yeah, that's a great question. Especially when we're doing on-farm research plots,
you want to get the best data you can. We can do a lot with post-processing
but the number one thing I tell people is once you've gotten calibrated and start running,
speed changes, try to minimize any speed change you can because even though we know
we can post-process some of that out, just taking that out of the equation is really
helpful. You know, try and run at a constant speed, let the calibration take care of
everything else. If you've done a decent you know, load of high flow rate calibration.
The second thing I would tell people to think of really about is looking at your header sensor.
Making sure that that is when you lower the header and start cutting crop that's
activating appropriately and when you pick it up it's stopping that logging. Because even if
that system is working and you're you know you're dropping 10-20 feet out from the crop,
you're telling the system,
I'm harvesting crop there. And so if we really want to line up well,
you know we need to drop that right at that last instant start engaging the crop. Pick it
up right when I'm out of the crop. Make sure that system knows that it's starting to stop
and that's especially important, you know as we as we start the passes and in the passes.
The third thing I would get people to think about is there's some great animations out there
on YouTube,
Claas, Deere, have these internal simulations of what's going through the machine.
Most of the farmers know what what goes on inside the combine,
but just recognizing that there's so
many paths. That if I'm driving along and I cut one
foot, you know, travel path of grain there's
at least three or more different passes
that grain could take through the machine before it gets to
the mass flow sensor at the top of the clean grain elevator. One of the farmers I worked with
in the central part of the state, they would actually look at their tailings
elevator sensor and try and minimize and again they're slowing down
a little bit. Because the
more crop you try and force through
the machine, typically that's when you'll see more go through
the tailings elevator. But they would actually try and slow down a little bit in their plot areas
to try and make sure that they minimize that last path.
That's the longest path material is going
to take is if it it goes all the way through the machine,
goes through the tailings elevator, and
goes right back through the machine again.
Little things like that could could make a difference,
but those are the big things. Of course moisture sensors
are important. We need to check make
sure those are operating normally
because again that's going to give you the idea of marketable
yield as you move across the field. Which is we really want to see that
in the yield maps. Jackson: Sure,
I guess you know, for the typical grower out there and you know I've talked to a few of
the growers that you know cooperate with us on research about how much they usually spend time
cleaning their yield data. You know we've said that we can do a lot post-processing wise,
so I guess for the typical grower out there in what scenarios do you think it's really worth it
to spend that time cleaning their data? You know if they have a particular use case and you
know if you do think it's worth it for
growers to spend time cleaning yield data? What tools would
you recommend to them to use for that process? Joe: Yeah it's a a great question it's
a huge, huge
answer I think for that one so I'll try and go be as brief as I can. The definitely if that
if that data is being used in the future for analysis
for prescriptive maps, nitrogen whatever,
I think it's worth it personally to clean the data. You know
if you have if you've done a really
good job those first few points we talked about, once you get
calibrated and you get in the field
speed is minimized speed changes are minimized, you know
that start at the beginning of the
harvest and you know a row and at the end really being being quick with the header,
you're going to minimize a lot of the errors we want to take out. Now if in a lot of cases if
you have point rows and you don't have some type of auto-swath monitoring on the combine.
Those point rows are going to generate errors because no
not many people are going to go in
and manually change cut width. You know in that 20 or 30 feet of field length
and and that's going to show up in
your errors. But step one is if you're using the data and
you're not looking at it,
you know if you'll take the data out and actually look at each yield map,
and say yeah I don't see a lot of errors,
you know you can you can bring that into farm management
software and quickly
do a a query and see where yield values are really high or really really low,
If you're not going to visually inspect that and you're going to use it in the future, I think
that's when we need to do the post-processing. You know you can set a lot of the farm management
software for instance, uh you know we use Ag Leader SMS, some you can go in and set filters
and and filter out some of the data internally and you can save those and reuse them.
We also use yield editor software from USDA. Once you get the data extracted out of a program
like SMS, and you can get it into the right format you can run that through yield editor,
it automatically processes a lot of those things. I
think that overlap they've got an overlap filter
for point rows in there, I think that's one of the greatest things I've seen.
You know I didn't grow
up in Nebraska and I know everybody thinks
that Nebraska has perfectly square fields except the
circular ones and that's not true. I just I'll tell people out there you know I grew up in a
in a state in an area the state where the
average field size was maybe you know 40 acres 30 acres.
Really weird shape fields um that you really see it
a lot of swing in those points. So you know if
you're out there and that's
and you have a lot of those issues a lot of point rows things like that
that's where again, if you're using that data for a further application you're going to benefit
from from removing those errors. Jackson: So and yield editor is
a freely available software right?
Growers can get that you know just from the USDA website, and
I know from using it myself it's a
pretty intuitive program to use. Joe: Yep and it's great you can
you can view what errors exist,
you know what the filters are pulling out you can look,
and see you know it's always a great step
when you're using that software is run the automatic filters.
Let it pull out what it thinks
need to be pulled out and then go in and look you can say, okay show me all of the acceleration/
deceleration errors. If they're pretty sporadic, you
know that it's probably doing its job. If you
see a particular filter that it's taking out a huge chunk of data right in one location that
probably needs a little bit more more,
uh you know visualization you look at a little bit closer.
(Sure) That's I think that's one of the key things is a lot of the tools
today we almost don't even
have to look at the data anymore,
we can just dump the data into a program run it through a system
never really see it. That's when I think we need to take
this extra step of the post-processing.
Samantha: What different challenges exist for the different crops that we use when it comes to
yield mapping and measuring that yield? Joe:
Well that's a great question. A couple of different
challenges that come to mind one, is in corn
and unfortunately for a lot of folks in the central
Midwest the wind damage they're gonna
experience for the people that didn't have to go ahead and
and remove that crop down corn is a huge challenge.
In corn you know, in the corn crop.
You have to slow down you typically have a lot of losses there if you're in that.
That's one of those
examples where you if you haven't calibrated it that low of a flow you expect you got to go
back and do another one at a really really low flow so you get a good average around that.
And of course, that's nobody wants that situation in
beans one of the biggest challenges, we've seen
is they dry down during the day a lot of times so people you're really frustrated. I've seen a
lot of yield maps where you start out harvesting and
then they come back later in the day the same
area and the yield values are way off and a lot of times that's moisture related. Again moisture
content affecting you know the in the way the grain impacts the plate
different things like that
in a mass flow sensor.
That's always been a big challenge, not sure we've ever come up with a great
solution for that. But those are two things
that come to mind pretty quickly. Jackson: Sure and you kind
of brought up and alluded to these different
types of yield monitors that are out there with the mass
flow sensor that you were talking about
you know, so you have your scales, your mass flow sensors,
your optical sensors, and then I guess over in Europe, they kind of have the nuclear, which we
don't have here in in the US,
but what are the what are the pros and cons of each one of these
different yield monitors and are there
any special considerations for those different styles of your
yield monitors out there? Joe: Yeah not having
any experience unfortunately with the nuclear- type
yield monitors but with mass flow and optical, you know you can get good data from both of them.
We've kind of covered the mass flow sensor
impact plate style. We kind of talked about calibration
for that. The
only other consideration and we've talked about test weight and how that can affect
that sensor performance.
The only thing I would caution people is if you are using an optical
sensor, you probably already know
the test weight is critical in the calibration process for that.
Because we're measuring essentially volume flow on the machine now and then we're calibrating
that with a weight a scale weight. So we need to know what the density of that grain was to go
back you know into the into the yield monitor. So test weight is in incredibly critical when
it comes to the optical the optical flow sensors. And you know most of the times I've
or at least I've heard when you buy those, you know your free gift with that is a little
scale where you can take your test weight out in the field. You know the little bucket
you know recalling that to my knowledge you're not supposed to just take the little bucket and
scoop up a lot of grain, you know there's a you want to kind of fill that very slowly, very
evenly, and things like that little things can make a difference. But that'd be the biggest
difference, you know we've actually seen data from both checked it versus scales, you can
on average you can have very good data from both both systems.
Samantha: So you talked about a little bit earlier how important it is to clean your data if you're
going to be using it for a later purpose, so when you think about using that yield data,
what are the best opportunities for getting value out of that?
Joe: Yeah, I think just understanding where industry is going and service providers you know.
More and more that data is going into future prescriptive efforts,
so if it's nutrient removal,
people are using it for that if it's future nitrogen
application, you know we're using it
for that. Just the understanding of you know, and if you have
three to five years understanding of
historically how that crop yield has varied across the field,
can be a really important part of that.
That's incredibly powerful. You know people as we move forward just having that baseline
with which to compare to if you can quantify yield variability across your field,
you can put a price on that and now you know what range. Of you know in other words economic
benefit or cost you have to work in work within if you're gonna
you know have a solution to what crop.
So figuring out what problem it is you're trying to solve
if what do I put into that problem, am I going to have a potential return for it,
and and so to that point I think the
on-farm research to me is one of the most powerful tools
we have moving forward. And as we've discussed you know the precision ag technologies, the GIS
analysis
that allows us to do those comparisons every year, we could be out the field doing
small plots here and there to test out different management strategies,
and we have to have as good
of yield data as we possibly can to make sure we have a lot of confidence in the results we get
from those, but that really tells us
you know, hey I made a change this year but you know it didn't
didn't pay off, I'm not going to try that again, or maybe it did
and I can move forward with that
in the future. Samantha: So
when you talk about also like farmers using their own data can you speak on
at all like what companies are doing with yield data if they
get access to your personal data?
Joe: Well unfortunately a lot of that would be speculation. But you know there there's
yeah, we I'd have to speculate too much with
what some of the the folks are doing out there, but
we know that there's power in for instance aggregated data.
You know some of the companies
that are they're remotely logging data are able to use that to learn about their hybrids,
their crop protection techniques, all these different things, so
it's all part of this big this future of big data and agriculture.
And can companies, you know if
they're collecting data from a
wide geographic region you know they know the rate of seed, the
type of seed as much more information they can collect it what happened during the year they
can use that to improve their products
or maybe the recommendations. That's what you hope to see.
So yeah it's hard to say exactly what they're doing, not being privy to some of those
discussions but again there's a lot of there is a lot of value in that aggregated data. And I'll
probably get in trouble for saying this, but I but I think
my opinion is precision ag data on farms is
much more valuable than what we're leading it on to be.
You just think about what it takes the investment to do a
research study in some of these fields,
if we could if we can turn the farmers' fields into those
which is what we're seeing out there.
In other words we can log all the information what went in when, where every input,
we can monitor the output, we can monitor weather data, pretty well remotely now.
Imagine the value of that replacing all these research studies with with precision ag
technology. That's a lot of value, so personally I think you know that's something we need to
need to think about and talk more openly
about in the future. Samantha: Especially when growers really
value that full field data versus plot data. You know people are really skeptical so there's a
plot data so that's good to hear too. Jackson: Yeah it's absolutely critical
to be a production scale.
You know to convince somebody that something works and and you've kind of already been alluding
to this the big data future of agriculture and how yield data can pertain to that, but
what are some other things that we may see coming you know within
yield data in the future as far as kind of yield data sharing technologies? You know machine
tracking technologies within the field,
machine to machine communication. And you know I guess even in
other crops like cotton you know what maybe some of the
yield technologies that are coming down
the pipeline? I know there seems like there's been some innovation there in recent years. So
Joe: Yeah you know, they have essentially flow sensors now
for cotton harvesters so they're able to do
some site-specific management with in cotton as well now so I think you know we're going
to continue to see a push for improving the quality of the yield data and you know we're
going to start to see efforts at at sub header with estimates of yield. And we've already
you know we've seen some research you know you can you can go out and look at patents that have
been you know applied and awarded to companies that
there's information there that hasn't been
really commercially made available yet. You know that gets typically filed pretty early
you know how do we how we break down a you know a 12-16 row header and and maybe use imagery or
some other machine vision type technology to to break that down, and say well we could actually
look a little bit you know more a higher resolution at. What's going on in that
machine we know the total flow, let's try and scale that a little bit across the header.
I know you know I have a lot of friends over in you know the agronomics and plant
sciences and I know they would love to see
more resolution and some of that data you know not just
you know longitudinally as we move through the field but laterally as well. And so I think
we're going to see some push but it's been great you know the the data quality piece has been
really challenging as we look at the future, which is going to have
artificial intelligence in it,
machine learning techniques you know
you cannot say enough about data quality and a lot of the
literature that I've read would tell you that most of the projects that fail
it's because of data related you know in quality issues.
So as we look more for those techniques
to take off you know data quality has got to be number one,
garbage in garbage out that's the saying.
Samantha: So that was a lot of awesome information. So
to tie it all together what would be the biggest
piece of advice or message you want to leave the listeners with? Joe: Well
the one thing I would do I
would say is you know don't be afraid to reach out if you need assistance with anything at all.
You know especially
the group you know the digital ag group and and at Nebraska Extension you know
we're working for the University
we're always willing to help out if if it's an issue with
you know calibration or I've got a data issue. You know, we learn when with the folks
that are out there that we work with and so um you know we've always prided ourselves
on being as unbiased a source as we possibly can and I think
we'd all do a great job of that. And
so that's the main thing is just you know don't be afraid to reach out if you have
questions if you're interested in something you know if you're listening to this that
you're interested in something. So don't be afraid to reach out to any member of the team and
and see you know if there's something we can help with yeah that'd be my one piece of advice is
you know don't hesitate to reach
out to folks for assistance. Samantha: And we'll put Dr.
Luck's email and contact in the show notes for any of you guys who want to reach out.
Jackson: Thank you again to Dr. Joe Luck for joining us on the FarmBits podcast.
Samantha: As his students we talk to him often
about yield data quality for On-Farm Research but we still
managed to learn a lot of new information from that interview. Personally, I really enjoyed
when he talked about how those yield maps are being used for things like prescription maps,
or multi-year analysis of field patterns.
Jackson: For sure, and following on your favorite part Sam,
I thought his discussion of the interaction between
harvesting practices in the field and how
those relate to the importance of post-processing your yield
data for future use is really important
information for people to consider,
especially during this harvest time that we're in right now.
So we hope that you join us next week as we do our
first Farmer Focus episode. Samantha: We are heading
out to the field to hear from a few farmers and learn about
their technology use during harvest.
Thank you for taking the time to join us
today on the FarmBits podcast. Jackson: We would like to thank
Nebraska Extension for their support of this podcast
and their commitment to providing high
quality informational material to members of the agricultural community in Nebraska and beyond.
Samantha: If you enjoyed this episode and
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Samantha: See you next week on another episode of FarmBits.