July 23, 2026 • 43 mins
Farm equipment markets are constantly changing, but what can auction data tell us about where agriculture is headed?
In this episode of the Purdue Commercial AgCast, hosts Chad Fiechter and Todd Kuethe are joined by Andy Campbell, Director of Insights at Tractor Zoom, to discuss how data is reshaping equipment markets and the decisions farmers make every day.
The conversation explores what drives equipment values, how auction trends reflect broader economic conditions, the growing role of artificial intelligence in agriculture, and why the next generation of farm machinery may look very different from today's fleets.
Whether you're evaluating your next equipment purchase, watching machinery values, or simply interested in where agricultural technology is headed, this episode offers practical insights grounded in real-world data.
In this episode:
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How TractorZoom collects and analyzes equipment market data
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What influences used equipment values
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What today's equipment market says about the farm economy
-
How AI is changing agriculture and equipment businesses
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Why autonomous machinery could transform farming over the next 20 years
Subscribe to the Purdue Commercial AgCast for research-based conversations that help commercial farmers and agribusiness professionals make better management decisions.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
I do think that there is a lot ofchallenges ahead of us in terms
of a US row crop economy, thatstill need to be figured out,
and largely on the demand side.
I don't know how much longer we can kindof out-bushel, some of the challenges that
are in front of us, and I start to seesome things, within the equipment world,
of can we provide the same kind of valuein a smaller, piece of equipment and maybe
(00:23):
even an older piece of equipment that ismore efficient, with the use of its time.
But I think, to really be competitivelong term, you know, I think we have
to definitely figure out demand.
Hey everybody, welcome backto the Commercial AgCast.
This is Chad Fiechter, assistantprofessor in the Department of
Agricultural Economics at Purdue,and I'm joined by my co-host
(00:44):
Todd Kuethe.
I'm also a professor here in thedepartment and a member of the
Center for Commercial Agriculture.
And, really excitedabout this, conversation.
Chad, you brought our guest in today.
I did.
Do you wanna tease a little bit, andthen we'll have him introduce himself?
Sure.
So I think that- I was introduced tothis guest by our former colleague,
Jim Mintert, who I think you and himconnected at a conference somewhere.
(01:08):
And then, Jim said, "Hey, Chad, you shouldtalk to Andy Campbell from TractorZoom."
Yes, Andy Campbell, directorof insights with TractorZoom.
But when I first met Jim, I believethis was at the NADA conference in '23
maybe, '24 possibly, but that's the NorthAmerican Equipment Dealers Association.
They run a big, association all acrossthe country for farm equipment dealers.
(01:30):
And he was up on stage, and I've followedthe Purdue, barometer for a long time.
And so he's on stage talking about theresults, and he made a mention, he's
like, "And this would be so cool ifI could trend the barometer and have
it correlate with equipment values."He's like, "I don't know if that's
possible." And that, that's how he ended.
He's like, "I don't knowif this is even possible."
(01:50):
And in the back of the room, I'mlike, "Oh," like, "this is definitely
possible." And so I went up andtalked to him afterwards, and,
and yeah, the rest is history.
'Okay, so
What does TractorZoom do- Uh-huh… that will make that possible?
So yeah, TractorZoom, you can kinda thinkof it as like where data meets diesel.
And we're the Kelley Blue Book, at leastwe started as a Kelley Blue Book of farm
(02:11):
equipment, all digital, and we have ahuge marketplace site where people can
go and find all the equipment they need.
We don't advertise, though.
We don't charge for it.
It's more or less a data aggregation.
And so we have about $85 billionworth of equipment valuation data
from dealers, from auctions, andwe pull that stuff in in real time.
And so we know how much stuff is worth,when it's worth, how much supply is
(02:34):
on the market, and you can kind ofimagine if you know all that information
about all the different pieces offarm equipment, and we're getting into
construction too, in real time, thenyou can start to really understand
what's driving the industry and, whateven projecting forward might happen
knowing that the supply's out there.
Okay.
And you have an interestingbackground as well.
Mm.
That- in, in a couple differentdimensions that we'll probably hit
(02:56):
on in the future, but talk about that
Yeah.
So it's certainly not a straight line.
And I was actually just gave a, anintro to a bunch of the interns that
we have on, staff here at TractorZoom the other day, and they're like,
how did you get from there to here?
Like, that doesn't make sense."Which is exactly what my wife says.
But I started on a farm, and so Istill have connections with the farm.
That part makes sense.
(03:16):
I grew up in northern Iowa.
I helped own and operatea row crop farm up there.
But my dad kicked me out and said,"There's no future in farming.
You gotta go get a different degree."So I went to the University of Iowa, got
a degree in chemical engineering, andthen did work for the food companies.
But I really liked the business aspect,so I came back and got my MBA, and
this is where it gets a little crazy.
that in a very roundabout way,I actually had ties with the
(03:39):
Hawkeye women's basketball program.
And, I was a grad assistant for LisaBluder and helped coach all them.
I was there beforeCaitlin Clark was there.
I'm sure
you'll get that question the rest of your
life.
I
know, yeah.
Oh, 100%.
Yeah.
She's like, "Do you know Caitlin Clark?"And it's like, I preceded Caitlin Clark.
But anyways, that actually led to myfirst job out of grad school, where
I was a college basketball coach anda professor at a private college,
(04:03):
and I would just… Doors opened up.
They happened to be both opportunitiesat the same time, so I'm like, "Oh,
I'll try it out." don't usuallysay no to a neat opportunity.
and so I coached and taught for anumber of years, and I know we'll
get back to kind of the teachingaspect and the academic aspect
of it, later in our conversation.
But one of my former students thereI stayed in touch with, and after I'd
left academia, started up a consultingfirm, went back into the industry a
(04:24):
little bit, and they were having troublewith their marketing, so I went in
to give them a little bit of, advice.
One thing led to another, and, I startedhelping them out for five hours a week
and then this data company, becamea little bit larger, and 10 hours a
week, 20, 40 hours a week, and, prettymuch after a while it just made sense.
Like, tie my boat to this bigger ship and,and we've been going gangbusters since.
(04:47):
Man, that's crazy.
so the origins here is that Andy, you,allowed us the opportunity to use tractor
zoom data to at least get a start And soearlier this year, that paper came out.
it was a really cool opportunity for me.
you gave us access to I think itwas 300 to 450 horsepower tractors.
Mm-hmm.
and it was quite a few years.
(05:07):
Did it go back into the early 2000s?
It
did, yes.
Mm-hmm.
and-
Yeah, so 20-plus years of data.
Okay.
and so anyways, maybe I canhit some high points, right?
Mm-hmm.
so what we did is we created this model,where we are estimating sort of like
the characteristics of the tractor, howdoes it relate to the auction prices?
And, ju- like just quick off the top,it was, you know, i- as we get into
those hour buckets, the sort of usagecategories of tractors, which we defined
(05:32):
fairly coarsely, but it was- Mm-hmm
the penalty or the price, decreasethat you would see for an additional
hour essentially kind of worked inhalf from maybe like 100… in the data
we're using, we said it was $120 forless than 500 hours, and then $60 for
between, I think it was 500 and 1,000.
Right.
it was reassuring in the sense thatthat's what I see when I run the
(05:53):
data on… I mean, it's a similar,shape of curve that I see on spares,
that I see on combines, tractors,any kind of houred, type of machine.
That's typically what I see,is that almost exponentially
decreasing rate of depreciation.
So you ran a Mm-hmm … ahedonic pricing model.
Which is basically the idea is you'rebuying a bundle of characteristics.
(06:14):
So you can decompose that price into thevalue of each of those characteristics.
So the classic example is you buya house, you're buying a set of
bedrooms, set of bathrooms, s- kitchen,attached garage, what have you, right?
And each of those things you're buyingin that big price, and you're basically
decomposing through regression.
Right into those pieces, right?
(06:34):
That's what you're doing.
So my question to Andy, like,was that sort of way we think of
things as an economist, was thatsurprising to you, expected to you?
so yeah, throwing in usage inthere, and then also age, any of
these other quantifiable things.
And then the non-quantifiables,essentially like, Chad, I think
you pulled out, like, okay, weneed to have brands as, potential,
(06:56):
inputs and control variables, yes.
so that was one, and thenalso the types of auction too.
I knew that that was important fromlooking at the data in the past.
I've done simple sorting and I couldsee that there was a difference.
but to be able to prove thatit was a difference, in this
model, I thought was interesting.
But yeah, not brand new.
Retirement auctions havethe highest premium.
(07:16):
And so, like basically, if youtake light tractors, I'm trying
to think about if I wrote it down.
I don't see it.
Mm-hmm.
But there's a significant premiumfor a tractor sold in a retirement
auction than in a dealer liquidationor a regular consignment sale.
Okay, now I have to ask.
Mm-hmm.
The people who are coming to bid, theyknow that it's a retirement auction?
(07:37):
Yeah, almost all re- auctions that go,you'll know if it's a retirement auction.
A retirement, estate, dealer,typically liquidation, you'll
know that it's one of those.
Okay.
So the thing that was interesting tome, so I, it, why it, it was, I was
having a conversation with a familymember and they were talking about a
retirement sale, and they were talkingabout how you go to retirement sales
(07:58):
to support that person who's retiring.
and it's like a community eventwhere, you, whether you bid or don't
bid, you need to go to support them.
So it's almost like, as an analogyhere, like the 4H livestock auctions.
Yes.
Where like no one sells pigs atthat price the rest of the year,
but we're, like, supporting thiskid and their dreams and the…
(08:21):
Was that surprising to you, Andy?
It wasn't surprising 'cause I'veseen it in the data in the past.
Okay.
Yeah,
sure.
So I knew that it would be up there.
The one thing that I don't knowis if it is that, you know, the
4H, I paid twice as much for thisheifer than I should type of thing.
or if it is truly because items thatmake it to retirement, and if a farmer
makes it all the way to retirement,they're probably a good operator.
(08:43):
And they know who it is, theyknow the machine ownership.
There's value in that.
Even when a dealer goes to resell amachine, if the dealer can attach the
ownership of who the previous owner was,that tends to increase the sale value.
So there's an aspect of thatgoing on, too, and I just
don't know to what degree- Yeah
… my thought was, does it signal that that group of machinery is better,
(09:06):
or is it some indication of how mu-how much value information is, right?
Yeah.
A couple pieces within that though.
I mean, you have estate sales as well.
You have liquidation sales.
So different situations why peopleessentially have left the game,
and retirement is still, is stillcrushes estate and the liquidation.
And another thing that I've done, Ihaven't studied this in retirement,
(09:26):
but retirement sales obviously havehappened every year that we've had, you
know, all these auction data points.
But there's different pe- reasons whypeople retired in '19 versus '21, '22.
Like in '19, they had gone through fouror five years of just hard farming times.
They're like, "I'm out. I don't see, youknow, a future in this." Whereas if people
retired in '22, '23, they're at the topof the game and they're just like that…
(09:51):
And so if you looked at different pointsin the, the ag economy, you might be able
to suss out, like, why do people retire?
And did, you know, were theother bidders charitable?
did they feel a need to be charitable iftimes are great versus in, you know, in
'19 you might've felt like you needed tobe charitable because times are not great.
Yeah.
Well- Yeah, that's an interesting
point.
Can we pivot a little bit?
(10:11):
Do you mind if I do a pivot?
that'd be fine.
I'll allow it.
We hear all the time at university,like, "Well, you guys should partner
with, industry more. Industry's now,like they're so data-centric. They
have all this great data," like…
and like I continually meet people who arecollecting data that either we wouldn't
be able to do or don't know how to do it,or they're doing it so efficiently, right?
So like if we wanted to build adata set to look at this research
(10:34):
question, it might take us a yearto find a grad student, train
them, get the data to work, right?
Like everything is so slow.
So I'm curious, Andy, what your thoughtsare from the industry perspective of
being involved with research at theuniversities, and maybe shed a little
light onto why isn't it happeningmore or should it or should it not?
Kind of curious.
Hmm.
(10:55):
Yeah.
it's good question.
I, I've got a little bit differentbackground 'cause I've been
in academia and so I know howslow things can move, at times.
but also when I came into this positionat TractorZoom, you know, we were
small fish, big pond, type of thing,and we knew that our strategy for
marketing had to be different than just,throw up Google Ads and all the rest.
And so we came at it at a perspectivethat we wanted to be the better educator,
(11:19):
that we wanted to share what we hadand try to just educate the industry,
learn constantly, 'cause we knew wedidn't know it all and we still don't.
But we always wanted to be thebetter educator and provide thought
leadership as our leading edge.
and so this ended up fittingreally well with that.
So it kind of fit within our philosophy.
It fit within my understanding ofwhat I was familiar with, in the past.
(11:39):
And we still had pretty bigdiscussions here at TractorZoom.
And I met with our CEO, our CFOand we talked about, data-sharing
agreements and looked over theform, the NDA and all that.
And so it did go back and forthquite a bit, but I think maybe
my perspective helped a littlebit, give them some reassurance.
but one of the things that I've come torealize, too, and I think this should be
(12:00):
taken into consideration with other peoplein the industry, is that, you know, with
AI and everything else now, that data isbecoming a little bit more of a commodity.
recency of data is key, so you'realways wanting to make the most
updated decisions, so it's that influxof data that's really important.
And then it's the usability of data.
so I think there's a lot of peoplethat are sitting on a data lake just
(12:20):
thinking it's their moat, when inactuality it's what do they do with that
data and how do they educate and howdo they provide value with that data.
that's their true value proposition.
and so as long as you can kind of protectyour data, what becomes more important
is what do you do with it and how doyou educate people with it and how do
you advance… from our perspective,how do you advance the industry.
That's becomes the real differentiator.
(12:41):
I know it's definitely worked for us.
and you sort of hint at somethingI think that's really key which is
like you are giving us something ofvalue- where you've put real hours
and man hours and in some cases yearsof expertise and whatever, right?
And then to like sort of just give thatto somebody, that's very trusting, right?
It's sort of like, like thereare people who probably would
loan us their cars before theywould loan us their data, right?
(13:03):
Yeah.
So I think there's, that'spart of it too, right?
Is like how do you, in someways you have to kind of build a
relationship or get to know- Right
I think.
my perception is that you werepatient, and that may be partially
because- you were in education, right?
So, there might be some sympathytowards us in that place.
But I do think this is a challenge we'rerunning into where the availability
of data that we can use, especiallybecause we probably can't pay, for
(13:27):
access to the data like, someonewho is really gonna benefit from it
of the way they're gonna, designtheir strategy on marketing their
used equipment or something like that.
and so I think that, us trying to figureout how do we do that and how could we
possibly create situations that would bevaluable for you to be the data provider
and for us to be bringing something to is,is really, it's an interesting problem.
(13:48):
I think, part of that, too, is that,I don't think academia, for better or
worse, is probably everywhere you'regonna wanna look for the answers to,
questions you need by 5:00 today.
but maybe if there's something that'ssort of like a toothache that's
been kinda gnawing at you and youkinda suspect something for a while,
Yeah, 'cause what we're good at-
Academics and dentists- are very well,like that's- that's a really nice thing.
(14:09):
Well, what we're good at is,doing something precisely-
that is difficult to get at, right?
And that might take time, but, sometimesyou just need to know, do I need to mail
this thing to this other place or not?
And, that's not necessarilywhere, what we do as research.
I think a lot of times industry,their research is like, I need
to solve this problem, today."
(14:31):
Right.
But that's kinda if… Andy, is part ofyour role there that education piece?
is it to be providing sort of thoseinsights, quicker than we could,
right, quicker than we could do?
Yeah.
that is.
I mean, that's, part of the reason why Ihave the title the director of insights.
Honestly, 'cause they couldn't finda hole to put me in, type of thing.
So this is what they-Dentist … they call it.
(14:51):
You're right.
Yeah.
but yeah, that is part of my roleis to, you know, half of it's
to bring information in and tounderstand things better, and then
half of it is to share it out.
And I think we're lucky here that wekind of have a perspective that things
are changing so fast that we don't knoweverything, and so we have a kind of
innate curiosity to want to learn more.
but we also have a challenge that mostcompanies have with a restriction on time.
(15:15):
We just don't have all the resources,and time being the biggest one.
And so I think that's where it reallyworked well for us, to work with you, is
we knew that to be very thorough in thisfashion, it would take a lot of, time
and resources that we didn't necessarilyhave, and not even the expertise to
go into the depth that you all did.
And so by partnering Ithelped a couple things.
(15:37):
Yeah, we didn't necessarily need theanswer by 5:00 today, and we had a
curiosity about it, and we neededsome objectivity and scientific
rigor that went along with this.
And so from that standpoint,it worked out really well.
And maybe it's because you guys kindof approached us on this, that we
didn't need the answer right away.
And so I could be patient because it wasa wait and bait, type situation where
(15:58):
it just, I could set it to the side andI could continue to work on my deal.
and it worked out.
I mean, did have to inherentlyhave the patience, but,
Yeah
… worked out.
Well, I think that's the thing, that I'mstruck by coming into academics later
in life, is that what we have is we dohave the time to go slow, when that was
(16:18):
not something that I ever had wheneverI was operating a business, right?
It was, it was, "I need to answerthis question now," or, "This
is the most pressing concern,"where we do have that time.
And so trying to figure out whereare those opportunities, right?
Maybe toothache sort of things wherewe can provide sort of that, we can
slow it down, we can be real precise-Mm … but it can still be valuable.
(16:39):
I think that's a uniquething to be shooting for.
Yeah, it's a little tricky too, right?
'Cause I think the other thing from,my perspective the reason I like to
interact with, industry or folks, outof the field, is that I don't always
necessarily know what those questions are.
Yeah.
Right?
So sometimes you have to say-Mm-hmm … like, "Hey, what are you
stuck on that you can't quite solve oryou can't quite figure out?" Because if
(17:01):
it's just whatever you bring up first,that's one you can probably already
get a good enough handle on, right?
and I think that's the other sortof where collaboration across
universities with, with industry.
that helps too, a lot of, like, "Well,here's what I'm stuck on," right?
Another question I have is, sothinking about people who would come
into TractorZoom and thinking aboutthese skills that are about being
(17:24):
precise or doing a data analysis, withthe of AI and the ability to sort
of like you can be a couple peoplenow, like how do you think about that
as the future workforce developmentfor people like you in these places?
Oh, just had conversationswith our, data scientist this
morning, about that exactly.
Of, you know, we have tradeevaluations that come in through
(17:46):
our system, for dealers, and we'retalking thousands collectively
across all of our dealers per day.
and they're so inundated with allthis information that it just takes
them so long to get to a particularanswer, and it's worth the time and
the rigor for them to get to an answeron a half a million dollar combine.
because if they're off by a littlebit, that ends up being quite a bit.
but if they're off by a little biton a mower, you know, a lawn and
(18:09):
turf type of piece of machinery, it'snot worth their time to go through.
And so we were talking about thiscontinuum of accuracy, that's needed,
within the data, and so there's definitelya trade-off on accuracy versus time.
And at some point, and it's getting to be,you know, the emphasis is more important
for time right now on many things,that they want time back for a lot of
(18:32):
these pieces, not necessarily accuracy.
Because manpower is reallyshort, time is money, And people,
consumers, expect an answer faster.
We're just trained to be ableto buy things on the spot.
And so what we're seeing in theindustry is not that accuracy's not
important, it's just we wanna befaster and also accurate, on things.
If that makes sense.
(18:53):
Sure.
you can kinda do thenitty-gritty at scale.
You can do a little clean up.
Right.
If we can do the nitty-grittyat scale, and the biggest
thing is providing confidence.
If whatever value- you provide tome, can you provide confidence to
me that how you got to this answer?
And, you know, that assurance of, "I trustyour data already, but how did you use
(19:14):
your data to provide this answer to me?"
That is what we're seeing.
Kind of the biggest question inthe market is the confidence in the
process of getting to the answer thatpeople are really after right now.
I'm curious your perspective on,you're watching data all the time.
Mm-hmm.
like, what is your perspective on wherewe're at as a farm economy right now?
(19:37):
Not necessarily sort oflike the big- Mm-hmm … ag
economy, but the farm economy.
And pr- I'm assuming most of whatyou would say would be reflective
of, like, the row crop economy.
Mm-hmm.
So United States row crop economy, I thinkthere's more headwinds than tailwinds.
And the, you know, when I watch the data,there's a lot of noise, that gets in
(19:57):
there that you can pay attention to thethings day to day, even week to week.
I think the larger headwinds of largerglobal competition, more challenging
international trade deals in the marketsthat are seemingly less and less there.
I do think that there is a lot ofchallenges ahead of us in terms
of a US row crop economy, thatstill need to be figured out,
(20:18):
and largely on the demand side.
I don't know how much longer we can kindof out-bushel, some of the challenges that
are in front of us, and I start to seesome things, within the equipment world,
of can we provide the same kind of valuein a smaller, piece of equipment and maybe
even an older piece of equipment that ismore efficient, with the use of its time?
But I think, to really be competitivelong term, you know, I think we have
(20:40):
to definitely figure out demand, youknow, biofuels, wherever that might
come from, new trade agreements.
But, and I think on the cost sideof things, it's been… it's looking
differently at our cost structure,not just continuing to do the same
things that we've always done.
do you think there's ever, I was having aconversation last week about, horsepower.
And, we, in the paper we talk aboutdifferences in brands, and there is
(21:01):
clearly premiums, related to differentbrands and it'd probably be best if we
didn't say them in a public podcast.
That's for sale.
No,
I'm just kidding.
That's, yeah, that's for sale.
give us a call if you wannasponsor the answer to that.
the question I have is will we everget to the point, you said getting
sort of more efficient, right?
Mm-hmm.
Where, that horsepower and the abilityto deliver work will become kind of
(21:24):
a commodity agnostic of the brand,or is that brand premium sort of
always going to exist as you, as youthink about the machinery industry?
I think it's gonna become less,and I've got a couple reasons
why I think it will become less.
for one, when I've studied differentbrands, what happens is the
paint fades, is what I call it.
Eventually you get this depreciationdown to where things depreciate at
(21:48):
high hours at about the same rate.
So the piece of equipment,whether it's red, green, yellow,
or purple, it depreciates aboutat the same rate per hour.
and the other thing, though, that Ilearned this past week, I was at a
large dealer executive conference, isthe growing prevalence of, they're not
called portals, but they're essentially,adapters that can plug one computer
(22:09):
system, so call it, like a John Deereoperation center, into a red tractor.
And so you have these converters thatallow people to brand switch a little
bit easier between brands of machinery.
Right.
And so as I think as machinery gets moreexpensive, that brand switching will
become a little bit easier with tech.
Well, and that's not, justfound in the ag sector, right?
The idea that, a technology companyis like, "We're gonna make it
(22:32):
seamless to just use nothing but ourtechnology for everything," right?
And then takes a little while, andthen someone says I got a solution.
You can plug this thing intothis other thing over here." And
then they're like, "Okay, cool.
That works for a whiletill we get the new one.
Now we're gonna make it-"where you can't do that.
it's like there's like this race,right, between, adaptability
and, Can I ask another question?
I have a- Yeah … I have one sort of-
Allowed.
We've talked a lot about, research.
(22:54):
but the other thing that wedo as professors, we teach.
And you've mentioned a couple timesabout sort of like the need to find
talent or people to work in your space.
So if we told you we can giveyou, like, I don't know, however
many students you want next, twoyears from now or a year from now.
Yep.
and you say, "Well, I will hire them allfor you if they can do these things."
What is it that, what are those thingsyou want us to give them or help
(23:17):
them with for you to be interested?
Or, and you meaning sort of like you-Mm-hmm … and your colleagues, right?
Right.
No, it, you know, very technicallyright now, if you have a Salesforce
developer, I will take them.
Okay … specifically to aplatform that we're running
on, that for us is a premium.
But also beyond that, you know, thetechnical aptitude of understanding data
(23:40):
and being able to manage projects, whichkind of agnostic, to any type of system,
even to any industry, is really important.
We have some people in here that,our data scientist came from the Air
Force and the grocery sector, buthas learned, within this process.
And, so I think as long as youhave the attitude over aptitude,
primarily, but then the ability to,understand data, data governance,
(24:07):
and, the importance of how data canbe used to make informed decisions.
So through the scientificresearch process.
Yeah.
Which again, gets back to fairly basic.
If they have an understanding of theindustry on the ag side or even the
construction side, that's an immediatebonus because then all of a sudden
they on-ramp a little bit faster.
Or if they have a deep understandingon a software platform that we run.
(24:28):
So whether it's coding on Python,SQL, or like I said, Salesforce,
obviously that's a huge plus, andthen they can learn the ag side.
So having an industry depth of knowledgeis helpful, but by far and large, the
people that have done the best here andthat, are still sticking around, that
are making big, differences, are justinnately curious, on what they do and,
(24:52):
humble in the way that they wanna learn,like just going in and soaking it up.
So we had a-
similar conversation with, anotherperson that works in the tech startup,
space about a year ago, and the thingthat I'm always curious to ask about,
which is like, which percentage ofthe people you work with and have
interacted with are sort of like farmpeople that like get into data- Mm-hmm
versus like sort of data peoplethat are like, "Well, I'll work in
(25:14):
farm," if that makes sense, right?
Mm-hmm.
Where do you start from?
is it a pretty even split or what, what,is there one they see more than the other?
No.
Way more on the… And I'm sittingright in the middle of Iowa.
Yeah.
so you'd think you'd be pretty heavy onthe farm side, and, there's myself and
two others here that are farm and dataaware, some more heavy in one than the
(25:36):
other, and pretty much everybody elseis either- not involved in either, or
just heavy on the data computer side,and then, I'm teaching them how to farm.
Okay.
Yeah, we met some guys thatworked for a robot company.
Yeah.
That they were like, "No,we're just really into data,"
and like there's ag robots.
It's like we're interested…We'll robot anything, right?
(25:57):
Yeah.
it's always interesting to me.
You can move faster ifyou understand both.
That's the one thing I will say, 'causethen you don't have to always ask
somebody some of the simple questions.
You just know what the next logical stepis 'cause you know the job to be done.
and that's a lot of what I do here, isjust kind of explain to people how the
row crop industry works, how farmersare gonna buy, or how dealers would
sell to farmers and what's going on.
But if you innately know that, thensometimes you can skip some of those
(26:21):
questions and those steps and justdevelop and move a little faster.
Okay.
So then you mentioned Iowa a coupletimes, which- Mm-hmm … also
brings up another question.
It's where
Caitlin Clark's from.
Yeah.
Yeah.
She's a legend.
Yeah.
That's like, this is gonna be like,
this is gonna, we're gonna replaythis back someday that you didn't
know who Caitlin Clark was.
Okay.
Wait, whoa, whoa, whoa.
This is… You're gonna look like the-
Hang on.
You're gonna look like the- I'mbeing misconstrued … you're
(26:42):
gonna look like the people whodidn't know what the internet was.
Hang on.
I know who Caitlin Clark is'cause she plays in Indiana now.
I also know that she played at Iowa.
I did not know that shewas originally from Iowa.
Okay.
that's the dimension- thatI was made aware of today.
Okay.
Good call.
Now, granted- That, that's- … amI, am I still under a rock?
Yes, I'll admit it.
Okay.
Yeah.
Okay.
But, but I didn't do the egregiousthing that you're painting me as doing.
Okay.
You're right.
Okay.
You didn't know who she was.
(27:04):
Yeah.
Yes.
Okay.
Go ahead.
Iowa, and we are in Indiana.
Mm. So the other thing that I think,like particularly working in the ag
space, there's usually this sortalike Purdue works with people in
Indiana, and if you're in Iowa, yougo to Iowa State or work with Iowa
State or hire people from Iowa State.
And like even, like we are, we're90 miles from Champaign, Illinois.
(27:27):
Yeah.
Right?
Like, like there's, they'renot, they're not worlds apart.
But, do you think that…
W- where do you see that now and wheredo you see that kind of in the future?
About the kind of the separation- For-… the geographic regional separation.
Yeah.
A- and, and as we think aboutsort of like businesses and
universities and hiring and-
Mm
… like what, what do you th- what, what are your thoughts there?
(27:49):
Yeah.
I see that, I don't wanna say degradingor like eroding, but, I see that changing
a lot faster as we go forward here.
maybe different for the undergradside of things, just because you have
in-state tuition and, and everything.
But one thing that really makes me thinkabout this is I had a conversation with
a CEO at a very large Canadian dealer,this past week, and he just made the
(28:11):
quick mention that, "Hey, you really haveto understand how Canadian agriculture
works to sell up here in Canada."
And he gave me a great educationoverview of how the canola market is
working, and the lentils and the pulsesand how their trade partnership works.
And it's just fascinating.
and he's alreadyenlightening me on all this.
And it just made me realize it, becauseequipment, it does have challenges
(28:31):
crossing the border, but data doesn't.
Yeah … and the world is shrinking, Ithink, so fast in the terms of how we're
using this mass amount of data to makethe types of decisions, that if, you know,
we're gonna kinda succeed as a overallfarm economy, that your regionality
matters for some things, soil and, and allthat, but, I think we have to expand out
(28:53):
beyond just our small microcosm becauseHonestly, row crops are a commodity, and
so you have to, I think, leverage, theseother partnerships, you know, further out
in order to collectively advance forward.
Well, I also think there is something,especially talking about the undergrads,
like, there's a lot of great, like,non-farm jobs in the ag sector, right?
(29:14):
But you generally have tomove to wherever those are.
And they're probably notin your home county, right?
So, like, and most people- that livein a rural area and like it, like
their rural area, but it doesn'tnecessarily mean I wanna go to…
If I like my small county in Indiana,doesn't necessarily mean I wanna do
something similar in Oklahoma, right?
Mm-hmm.
Or, but, like, but for a lot of jobsnow, too, in the ag space, like it's
(29:39):
not necessarily in a really rural area.
They, like, there, there's not a lotof difference to me, I'll start a
fight here between the two of you, Idon't see a lot of difference between,
like, Fort Wayne and Des Moines.
But- But yeah, but like you talkto people- … in either space and
they're like, "Oh, wait a second."
Yeah.
It's okay.
I'll let you have that opinion.
Yeah.
I mean, I guess one ofthem has Johnny Appleseed,
(30:01):
right?
I, okay.
I'll, I'll give you that.
Okay, so I think this issomething that I, I think you're
alluding to something that is
Really interesting.
You just said it too, Andy.
Like this idea that the awareness ofthe global or at least non-regional
economic factors pressing down, right?
(30:22):
It was, I think it was easier as akid to perceive that I was sort of
on an island, versus now it feelslike no, no, no, we- we're aware.
Like the- Yeah … choices that arehappening globally are affecting us, and
that is clearly affecting the ag economy.
I think that's a really cool thing.
But that's come over time.
What I wanna know, Andy, is you've beendoing this job now for a long time.
(30:46):
what's something that's kindablown your mind that you didn't
expect when you were coming in?
Or what's some of the recent ideasthat you've had, just sort of as
you're interacting with people around,like the Canadian thing is sweet.
Mm-hmm.
Yeah.
I think the dealerships that I interactwith, it could be small, like super
rural North Dakota or wherever else,and you start to talk to them five or
(31:09):
six years ago, and you had to be carefulon how you mentioned the word data and
technology, because it was very mucha, "Hey, are you gonna take my job?"
Like, "I know how to do this betterthan you," and, and I, you know.
Almost, just a pure reluctance, on that.
And now you have the same conversation,sometimes with the same people, but
definitely the same dealership and thesame regionality, and, now they lead
(31:31):
off the conversation with, "Hey, I justbuilt this agent, that is now looking
at these, you know, these data sources,and I'm extracting this from here.
How do I get your data in?
Can I do it through a, you know, afile drop or an API?" And this, you
know, same just pre-pandemic we'rehaving these conversations with people,
they're like, "Don't touch my data.
Don't do any kind of file sharing.
I, I…" And it has completely changedin a relatively short period of time.
(31:55):
We run an innovation councilwith our dealers all across the
United States, and the, almost theimpatience of them, and I'm glad.
It's because they're impatient.
They're like, "How comethis isn't built yet?
Can we do this?" Mm. Like, "CouldI theoretically take these ideas?"
it has blown my mind on howdata-forward they have become,
and I don't know what has shifted.
(32:16):
But it's fascinated and it's challenging,too, because, like I said, we can't
hire fast enough to do all the workthat we wanna do in front of us.
So that's probably beenone of the coolest things.
But then getting, Todd, even to yourpoint earlier, where you're like, you
know, can you do all these things insmall town areas or do you have to
move into these semi more metro areas?
Yeah.
And again, I would've saidfive years ago, yeah, you had
(32:37):
to move into more metro areas.
But now, like I said, I've got dealershipsthat are in small-ish communities,
you know, 5,000 people, but thepeople that work there live out in the
country on their own farm, and, they'reworking with us on AI technologies.
and they're, they're vibe coding.
So, you know, there's not a lot of thosejobs out there, but there are some.
so it's, a lot's changed.
(32:59):
It's funny, 'cause I was thinking, like,when we were kids, like the standard bad
guy scene in a movie is, like, there'dbe a barn somewhere, and they'd open it
up, and it'd be, like, full of computers.
Yeah.
And now that's standard.
Yeah, exactly.
Yep.
it's not like a-Yeah … they're not spies.
they're just farmer dudes.
Yeah,
yeah.
Mm-hmm.
I love it Oh, yeah.
Yeah, if I could show you guys theservers behind this wall right here.
Oh.
There's literally, it'sjust packed with servers.
(33:19):
Man, that's awesome.
Okay.
So, we should berespectful of Andy's time.
Yes.
do we need to pivot to how we wrapthis up with the lightning round?
We do.
Or do you have-
no, no, no.
I just wanna give Andy…Andy also has a podcast-
Oh
… that like I wanna make sure- Yeah, Andy- … that we make people aware
we have seven people- thatlisten to our podcast.
So for the seven people- … wherecould they find your podcast?
What's it called?
Hopefully they're not all riding in thesame car- 'cause viewership will plummet.
(33:42):
Yes.
Well, they could actually makeit plummet a little bit more
and then turn on our podcast.
So we've been running Beyondthe Hood podcast now for
almost two and a half years.
and the whole idea came about when Italked earlier about providing thought
leadership and the better educator wins,is we wanted to give our main audience,
our farm equipment dealers across country,a chance to look out beyond the hood.
(34:04):
Stop looking just under the hood,but how do you focus on your
business and look beyond the hood?
And so yeah, we'll bring in people thatI, have a chance to meet and think that
are interesting and have some perspectiveto share that allows dealers, usually
when they have windshield time drivingbetween farms, to think like, how
can I see this industry differently?
So it's been fun.
I wonder how many downloads theyget because somebody accidentally
(34:28):
thinks they're downloading anurban- Oh … planning podcast.
Yeah.
And so not- Sure … something thatwe thought of ahead of time, but-
I love it … we'll take a few ofthose, you know, random downloads.
Yeah.
You never know.
Man, they'll be like, "You know what? Iwas gonna listen to this thing I thought
was gonna be about like, communitycenters- Yeah … in poor neighborhoods,
but now I'm really into tractors."
Should we do the lightning round?
Yeah.
We need to do the
lightning round.
Okay.
Yeah.
Andy, we like to end, ourlistener, we recognize, has
(34:51):
been bored for some of this.
when we were talking, notwhen you were talking.
Oh,
of course.
Yeah.
And we're trying to bring them backwith a little, little punch at the end.
So we ask a lightning round.
So the idea is that you have to answerthese like quickly, short answer.
Okay.
So the first one, which is, if rightnow TractorZoom could take something
that you have and now double it, onething, what are you gonna double?
(35:13):
our data and engineering staff
I think you should do the time machine.
Okay.
So you can do it.
Do you wanna do the time machine?
I always ask these.
You should ask it.
Okay.
But I'm always more interestedin their personal use of the
time machine than the business.
So
we'll start- I have to specify that it'sa business issue time machine, 'cause
otherwise I don't want people recre-like, doing recreational time machine.
(35:34):
Okay.
We gotta take it seriously.
All right, so it's abusiness time machine.
You can go to any time, future,past, But it's for business purposes.
When are you going?
Definitely gonna go to the future.
And I think I'm gonna skip ahead
20 years
20 years
I, I think 20 years into the future,it's not gonna be within the next 10.
(35:54):
Tooling takes too long, but within 20years, I think our, farm equipment will
look significantly different, way smaller.
I think you're gonna see swarmsof farm equipment all over the
place in the United States.
Oh.
I like that.
I
like it.
You're also the first personwho's went into the future.
Yeah,
everybody else is going into
the
past.
I got data.
I know what happened in the past.
Oh, that's an interesting concept.
(36:15):
you time travel already, right?
I wanna violate a little bit ofthe sanctity of the lightning
round to just ask a follow-up.
just give us- a little pictureof what you're stepping off this
time machine- there in Iowa.
Like, what do you, what are you seeing?
If you're right,
if I'm right.
20 years into the future, so getting,yeah, essentially close to the turn
(36:37):
of the century, I think we're seeinga tipping of the world population.
Yields are way higher though, and soessentially all the farm fields are
a little bit smaller, concentrated.
I don't think we have acres onthe fringes as much anymore.
And I think you look out into the fieldand you see something about the size
of a Geo Metro, going along and…
But they'll connect to each other.
And so you might have, across a largefarm, six of these all connected together,
(37:02):
pulling modular, you know, a little bitof planter-type of systems, sprayers- Hmm
… certainly see-and-spray type of systems.
But then in the next field overit, which is a smaller farmer, you
don't have a smaller machine, youjust have two of those machines.
And so from an equipment flow standpoint,you don't have to go from an X9 combine
and try to figure out how a smallfarmer wants to buy a used X9, you
(37:23):
just find a smaller farmer that wantsto buy two swarms of what you have.
And so I think that flows so much better.
And then the technology will be better.
The AI, I mean, nobody's gonnabe sitting in that thing.
there'll just be a common operator.
I mean, I could be farming itfrom two hours away with little…
Probably won't even have atouchpad by that point in time.
It'll be embedded.
I kinda like that.
I'm just, I'm, like, I'm s- I, like,I just wanna live in this space for a
(37:45):
little bit 'cause I'm like, what arewe doing with all the unused land?
I'm walking around.
in my mind- Mm-hmm … I'm,like, going for a walk.
Shared common space, that
would be- Probably, it'sdefinitely not my current dog.
The question is do I havea new dog in the future?
Probably in-
I don't know.
I could go either way
… it's definitely not your current dog.
Yeah, definitely not my current dog.
No.
Sadly.
It doesn't work like that.
Sadly.
Biology.
S- oh, I don't know, though.
Technology, you never- It's true.
I, I don't know.
Okay.
Mm. Man,
(38:05):
That was really good.
Okay, so the last time that you hadtrouble sleeping 'cause you were
thinking about something related towork, what were you thinking about?
It would've been, like,four, four nights ago.
I was in Chicago presenting atthis, ag executive summit, and I
had, put together this presentationa couple weeks in advance.
They needed the slides early.
And we go out for drinks the nightbefore, I meet everybody that I'm
(38:28):
gonna present to, and I have beenwriting analyses, for these papers,
and I was essentially gonna rehash thatand share that information with them.
And everybody at this executive summitcame up and was, kinda clapped me
on the back, saying, "Hey, I readthat piece that you did in, this
publication." Everybody had read it.
And I'm like, "Well, crap.
what I'm gonna present to you tomorrowis not new." And, so I went back, and
(38:50):
they also were saying, like, "What'sit gonna be like in six months?
You know, 12 months?
What's it gonna be like nextyear?" Which I did not prepare.
So I stayed up till the nextday- essentially crunching new
numbers, and, had to weave thatinto the preexisting presentation.
So that, is very fresh in my mem- memory.
So we'll have to, play this for ourstudents, that'll be like, "This
(39:12):
is when it's good that you, like,stay up late working last minute."
Yeah, right.
You're Right?
Mm-hmm Like, there's the good situation.
It's not where you've intentionally-Yeah … fallen into this.
Right.
It's not true procrastination.
Although, I will tell you that probablymy undergrad years have prepared me well-
Yeah, exactly … for, Yeah, yeah Exactly.
Right.
Yeah … for that situation.
Me too.
Me too.
But man, I sympathize with you becauseI've had that too, where you submit
your slides early, and then allof a sudden you're like, "Oh, no."
(39:35):
Mm-hmm.
"They know all this stuff already."
I had one, my first, academic conferenceI went to out of graduate school, right?
So now I'm like, "I'm fully an economist.
I belong here." And I spent, like,three-quarters of the time just
talking about my dataset, and thenI realized later that day that,
like, everyone in that room had beenworking with that dataset for years.
(39:58):
I had just learned about it.
So, like, they knew, they have forgottenmore than I already knew at that point.
Mm-hmm.
Chad, you got one more to bring us home?
I want, I wanna ask a quick question.
Are we farming the same crops with swarms,or are we farming different things?
That is a great question.
In 20 years?
(40:19):
Yeah.
I think we're farming some ofthe same, mostly different.
Yeah, I don't see… If you justlook at, like, the past 10 years on
how much more competitive Brazil hasgotten towards us, and I know even here
recently they've had some hardshipsand they're not, you know, impervious
to some of these global macro factors.
but I don't see how we can continueto compete on the global scale on
wheat, and even soybeans and corn.
(40:41):
They're commodities.
It's named that for a reason.
And so I think we have to differentiate.
And, you know, with genetics and AI,there's some regulation stuff, but I
see a lot more opportunity for specialtycrops if the labor is automated.
especially the weed control, if thatcan be, organic and fit some certain
needs, and harvested more frequently.
(41:02):
so I think half of it might stillbe commodities, but I really
think you're gonna see a lot morespecialty, high value, closer
to the consumer type of crops.
20 years is not that long away,and we're gonna be retiring then.
I feel like right now we shouldstart the Indiana Banana Company-
Ooh … where we're gonna growbananas in Indiana in the future.
can I tell you somethingthat happened this weekend?
I had a buddy that taught me that youopen a banana by breaking it in half.
(41:25):
Have you ever done this?
I've tried it.
It just, It turns to mush.
I can't do it well.
I open it from the non-stem end.
I've always, my wholelife, opened the stem end.
And then I watched a video thatwas like, no, monkeys open them
like at the- by pinching this otherend, and that's how I do it now.
Okay.
So is breaking a banana inhalf a feat of strength?
Was I just with someonewho was really good at it?
Did you break a banana?
No, I didn't, I didn't want a
(41:46):
banana.
I break apples in half.
If you had an apple right here, I'd
show
you.
You break an apple in half?
Now
it's getting impressive.
Like that, yeah.
Like, that's like when I go hiking,I like to just split that apple,
eat it all, seeds and everything.
But banana, I'm… Well, itmust've been a real green banana.
I don't remember.
'Cause like if it's got a bit of brown in that, you're just making
banana bread in your hands.
(42:06):
It looked awesome though.
Special.
Okay.
I'm gonna try it.
Indiana Banana Company.
Here we go.
So.
Andy, thanks so much for takingsome time to chat with us.
And if you want, we're gonna just… I'llhave to verify with Chad, but we'll give
you 10% of Indiana Banana Company- justfor being here today, if you'll take 10%.
100%.
10%.
Oh, 10%.
Yeah.
I suppose.
10%. Yes, definitely.
Yeah.
I'll take it.
All right.
(42:26):
Thank you so much.
Thanks for having me.
This has been fun.
All right.
See ya, Andy.
All right, we hope you liked thatconversation with Andy Campbell.
He was gracious enough to spenda little extra time with us.
We actually went beyond the time thatwe had allotted for the conversation.
but again, wanna remind people,if you have data and wanna do cool
stuff with us, send us an email.
(42:47):
Let's have a conversation about it.
I, I love it.
I got nothing to add otherthan please like and subscribe,
I don't know the terms.
I'm a little bit behind the times.
But subscribe, listen,download, tell your friends.
Is there potentially another term?
I don't know.
Do people still like?
I don't know if they like.
Oh.
I don't know.
Subscribe and download.
Yeah, just, just come back.
Come back in a month.
(43:07):
We'll keep learning aboutthe ag economy together.
How about that?
I do think that there is a lot ofchallenges ahead of us in terms
of a US row crop economy, thatstill need to be figured out,
and largely on the demand side.
I don't know how much longer we can kindof out-bushel, some of the challenges that
are in front of us, and I start to seesome things, within the equipment world,
of can we provide the same kind of valuein a smaller, piece of equipment and maybe
(00:23):
even an older piece of equipment that ismore efficient, with the use of its time.
But I think, to really be competitivelong term, you know, I think we have
to definitely figure out demand.
Hey everybody, welcome backto the Commercial AgCast.
This is Chad Fiechter, assistantprofessor in the Department of
Agricultural Economics at Purdue,and I'm joined by my co-host
(00:44):
Todd Kuethe.
I'm also a professor here in thedepartment and a member of the
Center for Commercial Agriculture.
And, really excitedabout this, conversation.
Chad, you brought our guest in today.
I did.
Do you wanna tease a little bit, andthen we'll have him introduce himself?
Sure.
So I think that- I was introduced tothis guest by our former colleague,
Jim Mintert, who I think you and himconnected at a conference somewhere.
(01:08):
And then, Jim said, "Hey, Chad, you shouldtalk to Andy Campbell from TractorZoom."
Yes, Andy Campbell, directorof insights with TractorZoom.
But when I first met Jim, I believethis was at the NADA conference in '23
maybe, '24 possibly, but that's the NorthAmerican Equipment Dealers Association.
They run a big, association all acrossthe country for farm equipment dealers.
(01:30):
And he was up on stage, and I've followedthe Purdue, barometer for a long time.
And so he's on stage talking about theresults, and he made a mention, he's
like, "And this would be so cool ifI could trend the barometer and have
it correlate with equipment values."He's like, "I don't know if that's
possible." And that, that's how he ended.
He's like, "I don't knowif this is even possible."
(01:50):
And in the back of the room, I'mlike, "Oh," like, "this is definitely
possible." And so I went up andtalked to him afterwards, and,
and yeah, the rest is history.
'Okay, so
What does TractorZoom do- Uh-huh… that will make that possible?
So yeah, TractorZoom, you can kinda thinkof it as like where data meets diesel.
And we're the Kelley Blue Book, at leastwe started as a Kelley Blue Book of farm
(02:11):
equipment, all digital, and we have ahuge marketplace site where people can
go and find all the equipment they need.
We don't advertise, though.
We don't charge for it.
It's more or less a data aggregation.
And so we have about $85 billionworth of equipment valuation data
from dealers, from auctions, andwe pull that stuff in in real time.
And so we know how much stuff is worth,when it's worth, how much supply is
(02:34):
on the market, and you can kind ofimagine if you know all that information
about all the different pieces offarm equipment, and we're getting into
construction too, in real time, thenyou can start to really understand
what's driving the industry and, whateven projecting forward might happen
knowing that the supply's out there.
Okay.
And you have an interestingbackground as well.
Mm.
That- in, in a couple differentdimensions that we'll probably hit
(02:56):
on in the future, but talk about that
Yeah.
So it's certainly not a straight line.
And I was actually just gave a, anintro to a bunch of the interns that
we have on, staff here at TractorZoom the other day, and they're like,
how did you get from there to here?
Like, that doesn't make sense."Which is exactly what my wife says.
But I started on a farm, and so Istill have connections with the farm.
That part makes sense.
(03:16):
I grew up in northern Iowa.
I helped own and operatea row crop farm up there.
But my dad kicked me out and said,"There's no future in farming.
You gotta go get a different degree."So I went to the University of Iowa, got
a degree in chemical engineering, andthen did work for the food companies.
But I really liked the business aspect,so I came back and got my MBA, and
this is where it gets a little crazy.
that in a very roundabout way,I actually had ties with the
(03:39):
Hawkeye women's basketball program.
And, I was a grad assistant for LisaBluder and helped coach all them.
I was there beforeCaitlin Clark was there.
I'm sure
you'll get that question the rest of your
life.
I
know, yeah.
Oh, 100%.
Yeah.
She's like, "Do you know Caitlin Clark?"And it's like, I preceded Caitlin Clark.
But anyways, that actually led to myfirst job out of grad school, where
I was a college basketball coach anda professor at a private college,
(04:03):
and I would just… Doors opened up.
They happened to be both opportunitiesat the same time, so I'm like, "Oh,
I'll try it out." don't usuallysay no to a neat opportunity.
and so I coached and taught for anumber of years, and I know we'll
get back to kind of the teachingaspect and the academic aspect
of it, later in our conversation.
But one of my former students thereI stayed in touch with, and after I'd
left academia, started up a consultingfirm, went back into the industry a
(04:24):
little bit, and they were having troublewith their marketing, so I went in
to give them a little bit of, advice.
One thing led to another, and, I startedhelping them out for five hours a week
and then this data company, becamea little bit larger, and 10 hours a
week, 20, 40 hours a week, and, prettymuch after a while it just made sense.
Like, tie my boat to this bigger ship and,and we've been going gangbusters since.
(04:47):
Man, that's crazy.
so the origins here is that Andy, you,allowed us the opportunity to use tractor
zoom data to at least get a start And soearlier this year, that paper came out.
it was a really cool opportunity for me.
you gave us access to I think itwas 300 to 450 horsepower tractors.
Mm-hmm.
and it was quite a few years.
(05:07):
Did it go back into the early 2000s?
It
did, yes.
Mm-hmm.
and-
Yeah, so 20-plus years of data.
Okay.
and so anyways, maybe I canhit some high points, right?
Mm-hmm.
so what we did is we created this model,where we are estimating sort of like
the characteristics of the tractor, howdoes it relate to the auction prices?
And, ju- like just quick off the top,it was, you know, i- as we get into
those hour buckets, the sort of usagecategories of tractors, which we defined
(05:32):
fairly coarsely, but it was- Mm-hmm
the penalty or the price, decreasethat you would see for an additional
hour essentially kind of worked inhalf from maybe like 100… in the data
we're using, we said it was $120 forless than 500 hours, and then $60 for
between, I think it was 500 and 1,000.
Right.
it was reassuring in the sense thatthat's what I see when I run the
(05:53):
data on… I mean, it's a similar,shape of curve that I see on spares,
that I see on combines, tractors,any kind of houred, type of machine.
That's typically what I see,is that almost exponentially
decreasing rate of depreciation.
So you ran a Mm-hmm … ahedonic pricing model.
Which is basically the idea is you'rebuying a bundle of characteristics.
(06:14):
So you can decompose that price into thevalue of each of those characteristics.
So the classic example is you buya house, you're buying a set of
bedrooms, set of bathrooms, s- kitchen,attached garage, what have you, right?
And each of those things you're buyingin that big price, and you're basically
decomposing through regression.
Right into those pieces, right?
(06:34):
That's what you're doing.
So my question to Andy, like,was that sort of way we think of
things as an economist, was thatsurprising to you, expected to you?
so yeah, throwing in usage inthere, and then also age, any of
these other quantifiable things.
And then the non-quantifiables,essentially like, Chad, I think
you pulled out, like, okay, weneed to have brands as, potential,
(06:56):
inputs and control variables, yes.
so that was one, and thenalso the types of auction too.
I knew that that was important fromlooking at the data in the past.
I've done simple sorting and I couldsee that there was a difference.
but to be able to prove thatit was a difference, in this
model, I thought was interesting.
But yeah, not brand new.
Retirement auctions havethe highest premium.
(07:16):
And so, like basically, if youtake light tractors, I'm trying
to think about if I wrote it down.
I don't see it.
Mm-hmm.
But there's a significant premiumfor a tractor sold in a retirement
auction than in a dealer liquidationor a regular consignment sale.
Okay, now I have to ask.
Mm-hmm.
The people who are coming to bid, theyknow that it's a retirement auction?
(07:37):
Yeah, almost all re- auctions that go,you'll know if it's a retirement auction.
A retirement, estate, dealer,typically liquidation, you'll
know that it's one of those.
Okay.
So the thing that was interesting tome, so I, it, why it, it was, I was
having a conversation with a familymember and they were talking about a
retirement sale, and they were talkingabout how you go to retirement sales
(07:58):
to support that person who's retiring.
and it's like a community eventwhere, you, whether you bid or don't
bid, you need to go to support them.
So it's almost like, as an analogyhere, like the 4H livestock auctions.
Yes.
Where like no one sells pigs atthat price the rest of the year,
but we're, like, supporting thiskid and their dreams and the…
(08:21):
Was that surprising to you, Andy?
It wasn't surprising 'cause I'veseen it in the data in the past.
Okay.
Yeah,
sure.
So I knew that it would be up there.
The one thing that I don't knowis if it is that, you know, the
4H, I paid twice as much for thisheifer than I should type of thing.
or if it is truly because items thatmake it to retirement, and if a farmer
makes it all the way to retirement,they're probably a good operator.
(08:43):
And they know who it is, theyknow the machine ownership.
There's value in that.
Even when a dealer goes to resell amachine, if the dealer can attach the
ownership of who the previous owner was,that tends to increase the sale value.
So there's an aspect of thatgoing on, too, and I just
don't know to what degree- Yeah
… my thought was, does it signal that that group of machinery is better,
(09:06):
or is it some indication of how mu-how much value information is, right?
Yeah.
A couple pieces within that though.
I mean, you have estate sales as well.
You have liquidation sales.
So different situations why peopleessentially have left the game,
and retirement is still, is stillcrushes estate and the liquidation.
And another thing that I've done, Ihaven't studied this in retirement,
(09:26):
but retirement sales obviously havehappened every year that we've had, you
know, all these auction data points.
But there's different pe- reasons whypeople retired in '19 versus '21, '22.
Like in '19, they had gone through fouror five years of just hard farming times.
They're like, "I'm out. I don't see, youknow, a future in this." Whereas if people
retired in '22, '23, they're at the topof the game and they're just like that…
(09:51):
And so if you looked at different pointsin the, the ag economy, you might be able
to suss out, like, why do people retire?
And did, you know, were theother bidders charitable?
did they feel a need to be charitable iftimes are great versus in, you know, in
'19 you might've felt like you needed tobe charitable because times are not great.
Yeah.
Well- Yeah, that's an interesting
point.
Can we pivot a little bit?
(10:11):
Do you mind if I do a pivot?
that'd be fine.
I'll allow it.
We hear all the time at university,like, "Well, you guys should partner
with, industry more. Industry's now,like they're so data-centric. They
have all this great data," like…
and like I continually meet people who arecollecting data that either we wouldn't
be able to do or don't know how to do it,or they're doing it so efficiently, right?
So like if we wanted to build adata set to look at this research
(10:34):
question, it might take us a yearto find a grad student, train
them, get the data to work, right?
Like everything is so slow.
So I'm curious, Andy, what your thoughtsare from the industry perspective of
being involved with research at theuniversities, and maybe shed a little
light onto why isn't it happeningmore or should it or should it not?
Kind of curious.
Hmm.
(10:55):
Yeah.
it's good question.
I, I've got a little bit differentbackground 'cause I've been
in academia and so I know howslow things can move, at times.
but also when I came into this positionat TractorZoom, you know, we were
small fish, big pond, type of thing,and we knew that our strategy for
marketing had to be different than just,throw up Google Ads and all the rest.
And so we came at it at a perspectivethat we wanted to be the better educator,
(11:19):
that we wanted to share what we hadand try to just educate the industry,
learn constantly, 'cause we knew wedidn't know it all and we still don't.
But we always wanted to be thebetter educator and provide thought
leadership as our leading edge.
and so this ended up fittingreally well with that.
So it kind of fit within our philosophy.
It fit within my understanding ofwhat I was familiar with, in the past.
(11:39):
And we still had pretty bigdiscussions here at TractorZoom.
And I met with our CEO, our CFOand we talked about, data-sharing
agreements and looked over theform, the NDA and all that.
And so it did go back and forthquite a bit, but I think maybe
my perspective helped a littlebit, give them some reassurance.
but one of the things that I've come torealize, too, and I think this should be
(12:00):
taken into consideration with other peoplein the industry, is that, you know, with
AI and everything else now, that data isbecoming a little bit more of a commodity.
recency of data is key, so you'realways wanting to make the most
updated decisions, so it's that influxof data that's really important.
And then it's the usability of data.
so I think there's a lot of peoplethat are sitting on a data lake just
(12:20):
thinking it's their moat, when inactuality it's what do they do with that
data and how do they educate and howdo they provide value with that data.
that's their true value proposition.
and so as long as you can kind of protectyour data, what becomes more important
is what do you do with it and how doyou educate people with it and how do
you advance… from our perspective,how do you advance the industry.
That's becomes the real differentiator.
(12:41):
I know it's definitely worked for us.
and you sort of hint at somethingI think that's really key which is
like you are giving us something ofvalue- where you've put real hours
and man hours and in some cases yearsof expertise and whatever, right?
And then to like sort of just give thatto somebody, that's very trusting, right?
It's sort of like, like thereare people who probably would
loan us their cars before theywould loan us their data, right?
(13:03):
Yeah.
So I think there's, that'spart of it too, right?
Is like how do you, in someways you have to kind of build a
relationship or get to know- Right
I think.
my perception is that you werepatient, and that may be partially
because- you were in education, right?
So, there might be some sympathytowards us in that place.
But I do think this is a challenge we'rerunning into where the availability
of data that we can use, especiallybecause we probably can't pay, for
(13:27):
access to the data like, someonewho is really gonna benefit from it
of the way they're gonna, designtheir strategy on marketing their
used equipment or something like that.
and so I think that, us trying to figureout how do we do that and how could we
possibly create situations that would bevaluable for you to be the data provider
and for us to be bringing something to is,is really, it's an interesting problem.
(13:48):
I think, part of that, too, is that,I don't think academia, for better or
worse, is probably everywhere you'regonna wanna look for the answers to,
questions you need by 5:00 today.
but maybe if there's something that'ssort of like a toothache that's
been kinda gnawing at you and youkinda suspect something for a while,
Yeah, 'cause what we're good at-
Academics and dentists- are very well,like that's- that's a really nice thing.
(14:09):
Well, what we're good at is,doing something precisely-
that is difficult to get at, right?
And that might take time, but, sometimesyou just need to know, do I need to mail
this thing to this other place or not?
And, that's not necessarilywhere, what we do as research.
I think a lot of times industry,their research is like, I need
to solve this problem, today."
(14:31):
Right.
But that's kinda if… Andy, is part ofyour role there that education piece?
is it to be providing sort of thoseinsights, quicker than we could,
right, quicker than we could do?
Yeah.
that is.
I mean, that's, part of the reason why Ihave the title the director of insights.
Honestly, 'cause they couldn't finda hole to put me in, type of thing.
So this is what they-Dentist … they call it.
(14:51):
You're right.
Yeah.
but yeah, that is part of my roleis to, you know, half of it's
to bring information in and tounderstand things better, and then
half of it is to share it out.
And I think we're lucky here that wekind of have a perspective that things
are changing so fast that we don't knoweverything, and so we have a kind of
innate curiosity to want to learn more.
but we also have a challenge that mostcompanies have with a restriction on time.
(15:15):
We just don't have all the resources,and time being the biggest one.
And so I think that's where it reallyworked well for us, to work with you, is
we knew that to be very thorough in thisfashion, it would take a lot of, time
and resources that we didn't necessarilyhave, and not even the expertise to
go into the depth that you all did.
And so by partnering Ithelped a couple things.
(15:37):
Yeah, we didn't necessarily need theanswer by 5:00 today, and we had a
curiosity about it, and we neededsome objectivity and scientific
rigor that went along with this.
And so from that standpoint,it worked out really well.
And maybe it's because you guys kindof approached us on this, that we
didn't need the answer right away.
And so I could be patient because it wasa wait and bait, type situation where
(15:58):
it just, I could set it to the side andI could continue to work on my deal.
and it worked out.
I mean, did have to inherentlyhave the patience, but,
Yeah
… worked out.
Well, I think that's the thing, that I'mstruck by coming into academics later
in life, is that what we have is we dohave the time to go slow, when that was
(16:18):
not something that I ever had wheneverI was operating a business, right?
It was, it was, "I need to answerthis question now," or, "This
is the most pressing concern,"where we do have that time.
And so trying to figure out whereare those opportunities, right?
Maybe toothache sort of things wherewe can provide sort of that, we can
slow it down, we can be real precise-Mm … but it can still be valuable.
(16:39):
I think that's a uniquething to be shooting for.
Yeah, it's a little tricky too, right?
'Cause I think the other thing from,my perspective the reason I like to
interact with, industry or folks, outof the field, is that I don't always
necessarily know what those questions are.
Yeah.
Right?
So sometimes you have to say-Mm-hmm … like, "Hey, what are you
stuck on that you can't quite solve oryou can't quite figure out?" Because if
(17:01):
it's just whatever you bring up first,that's one you can probably already
get a good enough handle on, right?
and I think that's the other sortof where collaboration across
universities with, with industry.
that helps too, a lot of, like, "Well,here's what I'm stuck on," right?
Another question I have is, sothinking about people who would come
into TractorZoom and thinking aboutthese skills that are about being
(17:24):
precise or doing a data analysis, withthe of AI and the ability to sort
of like you can be a couple peoplenow, like how do you think about that
as the future workforce developmentfor people like you in these places?
Oh, just had conversationswith our, data scientist this
morning, about that exactly.
Of, you know, we have tradeevaluations that come in through
(17:46):
our system, for dealers, and we'retalking thousands collectively
across all of our dealers per day.
and they're so inundated with allthis information that it just takes
them so long to get to a particularanswer, and it's worth the time and
the rigor for them to get to an answeron a half a million dollar combine.
because if they're off by a littlebit, that ends up being quite a bit.
but if they're off by a little biton a mower, you know, a lawn and
(18:09):
turf type of piece of machinery, it'snot worth their time to go through.
And so we were talking about thiscontinuum of accuracy, that's needed,
within the data, and so there's definitelya trade-off on accuracy versus time.
And at some point, and it's getting to be,you know, the emphasis is more important
for time right now on many things,that they want time back for a lot of
(18:32):
these pieces, not necessarily accuracy.
Because manpower is reallyshort, time is money, And people,
consumers, expect an answer faster.
We're just trained to be ableto buy things on the spot.
And so what we're seeing in theindustry is not that accuracy's not
important, it's just we wanna befaster and also accurate, on things.
If that makes sense.
(18:53):
Sure.
you can kinda do thenitty-gritty at scale.
You can do a little clean up.
Right.
If we can do the nitty-grittyat scale, and the biggest
thing is providing confidence.
If whatever value- you provide tome, can you provide confidence to
me that how you got to this answer?
And, you know, that assurance of, "I trustyour data already, but how did you use
(19:14):
your data to provide this answer to me?"
That is what we're seeing.
Kind of the biggest question inthe market is the confidence in the
process of getting to the answer thatpeople are really after right now.
I'm curious your perspective on,you're watching data all the time.
Mm-hmm.
like, what is your perspective on wherewe're at as a farm economy right now?
(19:37):
Not necessarily sort oflike the big- Mm-hmm … ag
economy, but the farm economy.
And pr- I'm assuming most of whatyou would say would be reflective
of, like, the row crop economy.
Mm-hmm.
So United States row crop economy, I thinkthere's more headwinds than tailwinds.
And the, you know, when I watch the data,there's a lot of noise, that gets in
(19:57):
there that you can pay attention to thethings day to day, even week to week.
I think the larger headwinds of largerglobal competition, more challenging
international trade deals in the marketsthat are seemingly less and less there.
I do think that there is a lot ofchallenges ahead of us in terms
of a US row crop economy, thatstill need to be figured out,
(20:18):
and largely on the demand side.
I don't know how much longer we can kindof out-bushel, some of the challenges that
are in front of us, and I start to seesome things, within the equipment world,
of can we provide the same kind of valuein a smaller, piece of equipment and maybe
even an older piece of equipment that ismore efficient, with the use of its time?
But I think, to really be competitivelong term, you know, I think we have
(20:40):
to definitely figure out demand, youknow, biofuels, wherever that might
come from, new trade agreements.
But, and I think on the cost sideof things, it's been… it's looking
differently at our cost structure,not just continuing to do the same
things that we've always done.
do you think there's ever, I was having aconversation last week about, horsepower.
And, we, in the paper we talk aboutdifferences in brands, and there is
(21:01):
clearly premiums, related to differentbrands and it'd probably be best if we
didn't say them in a public podcast.
That's for sale.
No,
I'm just kidding.
That's, yeah, that's for sale.
give us a call if you wannasponsor the answer to that.
the question I have is will we everget to the point, you said getting
sort of more efficient, right?
Mm-hmm.
Where, that horsepower and the abilityto deliver work will become kind of
(21:24):
a commodity agnostic of the brand,or is that brand premium sort of
always going to exist as you, as youthink about the machinery industry?
I think it's gonna become less,and I've got a couple reasons
why I think it will become less.
for one, when I've studied differentbrands, what happens is the
paint fades, is what I call it.
Eventually you get this depreciationdown to where things depreciate at
(21:48):
high hours at about the same rate.
So the piece of equipment,whether it's red, green, yellow,
or purple, it depreciates aboutat the same rate per hour.
and the other thing, though, that Ilearned this past week, I was at a
large dealer executive conference, isthe growing prevalence of, they're not
called portals, but they're essentially,adapters that can plug one computer
(22:09):
system, so call it, like a John Deereoperation center, into a red tractor.
And so you have these converters thatallow people to brand switch a little
bit easier between brands of machinery.
Right.
And so as I think as machinery gets moreexpensive, that brand switching will
become a little bit easier with tech.
Well, and that's not, justfound in the ag sector, right?
The idea that, a technology companyis like, "We're gonna make it
(22:32):
seamless to just use nothing but ourtechnology for everything," right?
And then takes a little while, andthen someone says I got a solution.
You can plug this thing intothis other thing over here." And
then they're like, "Okay, cool.
That works for a whiletill we get the new one.
Now we're gonna make it-"where you can't do that.
it's like there's like this race,right, between, adaptability
and, Can I ask another question?
I have a- Yeah … I have one sort of-
Allowed.
We've talked a lot about, research.
(22:54):
but the other thing that wedo as professors, we teach.
And you've mentioned a couple timesabout sort of like the need to find
talent or people to work in your space.
So if we told you we can giveyou, like, I don't know, however
many students you want next, twoyears from now or a year from now.
Yep.
and you say, "Well, I will hire them allfor you if they can do these things."
What is it that, what are those thingsyou want us to give them or help
(23:17):
them with for you to be interested?
Or, and you meaning sort of like you-Mm-hmm … and your colleagues, right?
Right.
No, it, you know, very technicallyright now, if you have a Salesforce
developer, I will take them.
Okay … specifically to aplatform that we're running
on, that for us is a premium.
But also beyond that, you know, thetechnical aptitude of understanding data
(23:40):
and being able to manage projects, whichkind of agnostic, to any type of system,
even to any industry, is really important.
We have some people in here that,our data scientist came from the Air
Force and the grocery sector, buthas learned, within this process.
And, so I think as long as youhave the attitude over aptitude,
primarily, but then the ability to,understand data, data governance,
(24:07):
and, the importance of how data canbe used to make informed decisions.
So through the scientificresearch process.
Yeah.
Which again, gets back to fairly basic.
If they have an understanding of theindustry on the ag side or even the
construction side, that's an immediatebonus because then all of a sudden
they on-ramp a little bit faster.
Or if they have a deep understandingon a software platform that we run.
(24:28):
So whether it's coding on Python,SQL, or like I said, Salesforce,
obviously that's a huge plus, andthen they can learn the ag side.
So having an industry depth of knowledgeis helpful, but by far and large, the
people that have done the best here andthat, are still sticking around, that
are making big, differences, are justinnately curious, on what they do and,
(24:52):
humble in the way that they wanna learn,like just going in and soaking it up.
So we had a-
similar conversation with, anotherperson that works in the tech startup,
space about a year ago, and the thingthat I'm always curious to ask about,
which is like, which percentage ofthe people you work with and have
interacted with are sort of like farmpeople that like get into data- Mm-hmm
versus like sort of data peoplethat are like, "Well, I'll work in
(25:14):
farm," if that makes sense, right?
Mm-hmm.
Where do you start from?
is it a pretty even split or what, what,is there one they see more than the other?
No.
Way more on the… And I'm sittingright in the middle of Iowa.
Yeah.
so you'd think you'd be pretty heavy onthe farm side, and, there's myself and
two others here that are farm and dataaware, some more heavy in one than the
(25:36):
other, and pretty much everybody elseis either- not involved in either, or
just heavy on the data computer side,and then, I'm teaching them how to farm.
Okay.
Yeah, we met some guys thatworked for a robot company.
Yeah.
That they were like, "No,we're just really into data,"
and like there's ag robots.
It's like we're interested…We'll robot anything, right?
(25:57):
Yeah.
it's always interesting to me.
You can move faster ifyou understand both.
That's the one thing I will say, 'causethen you don't have to always ask
somebody some of the simple questions.
You just know what the next logical stepis 'cause you know the job to be done.
and that's a lot of what I do here, isjust kind of explain to people how the
row crop industry works, how farmersare gonna buy, or how dealers would
sell to farmers and what's going on.
But if you innately know that, thensometimes you can skip some of those
(26:21):
questions and those steps and justdevelop and move a little faster.
Okay.
So then you mentioned Iowa a coupletimes, which- Mm-hmm … also
brings up another question.
It's where
Caitlin Clark's from.
Yeah.
Yeah.
She's a legend.
Yeah.
That's like, this is gonna be like,
this is gonna, we're gonna replaythis back someday that you didn't
know who Caitlin Clark was.
Okay.
Wait, whoa, whoa, whoa.
This is… You're gonna look like the-
Hang on.
You're gonna look like the- I'mbeing misconstrued … you're
(26:42):
gonna look like the people whodidn't know what the internet was.
Hang on.
I know who Caitlin Clark is'cause she plays in Indiana now.
I also know that she played at Iowa.
I did not know that shewas originally from Iowa.
Okay.
that's the dimension- thatI was made aware of today.
Okay.
Good call.
Now, granted- That, that's- … amI, am I still under a rock?
Yes, I'll admit it.
Okay.
Yeah.
Okay.
But, but I didn't do the egregiousthing that you're painting me as doing.
Okay.
You're right.
Okay.
You didn't know who she was.
(27:04):
Yeah.
Yes.
Okay.
Go ahead.
Iowa, and we are in Indiana.
Mm. So the other thing that I think,like particularly working in the ag
space, there's usually this sortalike Purdue works with people in
Indiana, and if you're in Iowa, yougo to Iowa State or work with Iowa
State or hire people from Iowa State.
And like even, like we are, we're90 miles from Champaign, Illinois.
(27:27):
Yeah.
Right?
Like, like there's, they'renot, they're not worlds apart.
But, do you think that…
W- where do you see that now and wheredo you see that kind of in the future?
About the kind of the separation- For-… the geographic regional separation.
Yeah.
A- and, and as we think aboutsort of like businesses and
universities and hiring and-
Mm
… like what, what do you th- what, what are your thoughts there?
(27:49):
Yeah.
I see that, I don't wanna say degradingor like eroding, but, I see that changing
a lot faster as we go forward here.
maybe different for the undergradside of things, just because you have
in-state tuition and, and everything.
But one thing that really makes me thinkabout this is I had a conversation with
a CEO at a very large Canadian dealer,this past week, and he just made the
(28:11):
quick mention that, "Hey, you really haveto understand how Canadian agriculture
works to sell up here in Canada."
And he gave me a great educationoverview of how the canola market is
working, and the lentils and the pulsesand how their trade partnership works.
And it's just fascinating.
and he's alreadyenlightening me on all this.
And it just made me realize it, becauseequipment, it does have challenges
(28:31):
crossing the border, but data doesn't.
Yeah … and the world is shrinking, Ithink, so fast in the terms of how we're
using this mass amount of data to makethe types of decisions, that if, you know,
we're gonna kinda succeed as a overallfarm economy, that your regionality
matters for some things, soil and, and allthat, but, I think we have to expand out
(28:53):
beyond just our small microcosm becauseHonestly, row crops are a commodity, and
so you have to, I think, leverage, theseother partnerships, you know, further out
in order to collectively advance forward.
Well, I also think there is something,especially talking about the undergrads,
like, there's a lot of great, like,non-farm jobs in the ag sector, right?
(29:14):
But you generally have tomove to wherever those are.
And they're probably notin your home county, right?
So, like, and most people- that livein a rural area and like it, like
their rural area, but it doesn'tnecessarily mean I wanna go to…
If I like my small county in Indiana,doesn't necessarily mean I wanna do
something similar in Oklahoma, right?
Mm-hmm.
Or, but, like, but for a lot of jobsnow, too, in the ag space, like it's
(29:39):
not necessarily in a really rural area.
They, like, there, there's not a lotof difference to me, I'll start a
fight here between the two of you, Idon't see a lot of difference between,
like, Fort Wayne and Des Moines.
But- But yeah, but like you talkto people- … in either space and
they're like, "Oh, wait a second."
Yeah.
It's okay.
I'll let you have that opinion.
Yeah.
I mean, I guess one ofthem has Johnny Appleseed,
(30:01):
right?
I, okay.
I'll, I'll give you that.
Okay, so I think this issomething that I, I think you're
alluding to something that is
Really interesting.
You just said it too, Andy.
Like this idea that the awareness ofthe global or at least non-regional
economic factors pressing down, right?
(30:22):
It was, I think it was easier as akid to perceive that I was sort of
on an island, versus now it feelslike no, no, no, we- we're aware.
Like the- Yeah … choices that arehappening globally are affecting us, and
that is clearly affecting the ag economy.
I think that's a really cool thing.
But that's come over time.
What I wanna know, Andy, is you've beendoing this job now for a long time.
(30:46):
what's something that's kindablown your mind that you didn't
expect when you were coming in?
Or what's some of the recent ideasthat you've had, just sort of as
you're interacting with people around,like the Canadian thing is sweet.
Mm-hmm.
Yeah.
I think the dealerships that I interactwith, it could be small, like super
rural North Dakota or wherever else,and you start to talk to them five or
(31:09):
six years ago, and you had to be carefulon how you mentioned the word data and
technology, because it was very mucha, "Hey, are you gonna take my job?"
Like, "I know how to do this betterthan you," and, and I, you know.
Almost, just a pure reluctance, on that.
And now you have the same conversation,sometimes with the same people, but
definitely the same dealership and thesame regionality, and, now they lead
(31:31):
off the conversation with, "Hey, I justbuilt this agent, that is now looking
at these, you know, these data sources,and I'm extracting this from here.
How do I get your data in?
Can I do it through a, you know, afile drop or an API?" And this, you
know, same just pre-pandemic we'rehaving these conversations with people,
they're like, "Don't touch my data.
Don't do any kind of file sharing.
I, I…" And it has completely changedin a relatively short period of time.
(31:55):
We run an innovation councilwith our dealers all across the
United States, and the, almost theimpatience of them, and I'm glad.
It's because they're impatient.
They're like, "How comethis isn't built yet?
Can we do this?" Mm. Like, "CouldI theoretically take these ideas?"
it has blown my mind on howdata-forward they have become,
and I don't know what has shifted.
(32:16):
But it's fascinated and it's challenging,too, because, like I said, we can't
hire fast enough to do all the workthat we wanna do in front of us.
So that's probably beenone of the coolest things.
But then getting, Todd, even to yourpoint earlier, where you're like, you
know, can you do all these things insmall town areas or do you have to
move into these semi more metro areas?
Yeah.
And again, I would've saidfive years ago, yeah, you had
(32:37):
to move into more metro areas.
But now, like I said, I've got dealershipsthat are in small-ish communities,
you know, 5,000 people, but thepeople that work there live out in the
country on their own farm, and, they'reworking with us on AI technologies.
and they're, they're vibe coding.
So, you know, there's not a lot of thosejobs out there, but there are some.
so it's, a lot's changed.
(32:59):
It's funny, 'cause I was thinking, like,when we were kids, like the standard bad
guy scene in a movie is, like, there'dbe a barn somewhere, and they'd open it
up, and it'd be, like, full of computers.
Yeah.
And now that's standard.
Yeah, exactly.
Yep.
it's not like a-Yeah … they're not spies.
they're just farmer dudes.
Yeah,
yeah.
Mm-hmm.
I love it Oh, yeah.
Yeah, if I could show you guys theservers behind this wall right here.
Oh.
There's literally, it'sjust packed with servers.
(33:19):
Man, that's awesome.
Okay.
So, we should berespectful of Andy's time.
Yes.
do we need to pivot to how we wrapthis up with the lightning round?
We do.
Or do you have-
no, no, no.
I just wanna give Andy…Andy also has a podcast-
Oh
… that like I wanna make sure- Yeah, Andy- … that we make people aware
we have seven people- thatlisten to our podcast.
So for the seven people- … wherecould they find your podcast?
What's it called?
Hopefully they're not all riding in thesame car- 'cause viewership will plummet.
(33:42):
Yes.
Well, they could actually makeit plummet a little bit more
and then turn on our podcast.
So we've been running Beyondthe Hood podcast now for
almost two and a half years.
and the whole idea came about when Italked earlier about providing thought
leadership and the better educator wins,is we wanted to give our main audience,
our farm equipment dealers across country,a chance to look out beyond the hood.
(34:04):
Stop looking just under the hood,but how do you focus on your
business and look beyond the hood?
And so yeah, we'll bring in people thatI, have a chance to meet and think that
are interesting and have some perspectiveto share that allows dealers, usually
when they have windshield time drivingbetween farms, to think like, how
can I see this industry differently?
So it's been fun.
I wonder how many downloads theyget because somebody accidentally
(34:28):
thinks they're downloading anurban- Oh … planning podcast.
Yeah.
And so not- Sure … something thatwe thought of ahead of time, but-
I love it … we'll take a few ofthose, you know, random downloads.
Yeah.
You never know.
Man, they'll be like, "You know what? Iwas gonna listen to this thing I thought
was gonna be about like, communitycenters- Yeah … in poor neighborhoods,
but now I'm really into tractors."
Should we do the lightning round?
Yeah.
We need to do the
lightning round.
Okay.
Yeah.
Andy, we like to end, ourlistener, we recognize, has
(34:51):
been bored for some of this.
when we were talking, notwhen you were talking.
Oh,
of course.
Yeah.
And we're trying to bring them backwith a little, little punch at the end.
So we ask a lightning round.
So the idea is that you have to answerthese like quickly, short answer.
Okay.
So the first one, which is, if rightnow TractorZoom could take something
that you have and now double it, onething, what are you gonna double?
(35:13):
our data and engineering staff
I think you should do the time machine.
Okay.
So you can do it.
Do you wanna do the time machine?
I always ask these.
You should ask it.
Okay.
But I'm always more interestedin their personal use of the
time machine than the business.
So
we'll start- I have to specify that it'sa business issue time machine, 'cause
otherwise I don't want people recre-like, doing recreational time machine.
(35:34):
Okay.
We gotta take it seriously.
All right, so it's abusiness time machine.
You can go to any time, future,past, But it's for business purposes.
When are you going?
Definitely gonna go to the future.
And I think I'm gonna skip ahead
20 years
20 years
I, I think 20 years into the future,it's not gonna be within the next 10.
(35:54):
Tooling takes too long, but within 20years, I think our, farm equipment will
look significantly different, way smaller.
I think you're gonna see swarmsof farm equipment all over the
place in the United States.
Oh.
I like that.
I
like it.
You're also the first personwho's went into the future.
Yeah,
everybody else is going into
the
past.
I got data.
I know what happened in the past.
Oh, that's an interesting concept.
(36:15):
you time travel already, right?
I wanna violate a little bit ofthe sanctity of the lightning
round to just ask a follow-up.
just give us- a little pictureof what you're stepping off this
time machine- there in Iowa.
Like, what do you, what are you seeing?
If you're right,
if I'm right.
20 years into the future, so getting,yeah, essentially close to the turn
(36:37):
of the century, I think we're seeinga tipping of the world population.
Yields are way higher though, and soessentially all the farm fields are
a little bit smaller, concentrated.
I don't think we have acres onthe fringes as much anymore.
And I think you look out into the fieldand you see something about the size
of a Geo Metro, going along and…
But they'll connect to each other.
And so you might have, across a largefarm, six of these all connected together,
(37:02):
pulling modular, you know, a little bitof planter-type of systems, sprayers- Hmm
… certainly see-and-spray type of systems.
But then in the next field overit, which is a smaller farmer, you
don't have a smaller machine, youjust have two of those machines.
And so from an equipment flow standpoint,you don't have to go from an X9 combine
and try to figure out how a smallfarmer wants to buy a used X9, you
(37:23):
just find a smaller farmer that wantsto buy two swarms of what you have.
And so I think that flows so much better.
And then the technology will be better.
The AI, I mean, nobody's gonnabe sitting in that thing.
there'll just be a common operator.
I mean, I could be farming itfrom two hours away with little…
Probably won't even have atouchpad by that point in time.
It'll be embedded.
I kinda like that.
I'm just, I'm, like, I'm s- I, like,I just wanna live in this space for a
(37:45):
little bit 'cause I'm like, what arewe doing with all the unused land?
I'm walking around.
in my mind- Mm-hmm … I'm,like, going for a walk.
Shared common space, that
would be- Probably, it'sdefinitely not my current dog.
The question is do I havea new dog in the future?
Probably in-
I don't know.
I could go either way
… it's definitely not your current dog.
Yeah, definitely not my current dog.
No.
Sadly.
It doesn't work like that.
Sadly.
Biology.
S- oh, I don't know, though.
Technology, you never- It's true.
I, I don't know.
Okay.
Mm. Man,
(38:05):
That was really good.
Okay, so the last time that you hadtrouble sleeping 'cause you were
thinking about something related towork, what were you thinking about?
It would've been, like,four, four nights ago.
I was in Chicago presenting atthis, ag executive summit, and I
had, put together this presentationa couple weeks in advance.
They needed the slides early.
And we go out for drinks the nightbefore, I meet everybody that I'm
(38:28):
gonna present to, and I have beenwriting analyses, for these papers,
and I was essentially gonna rehash thatand share that information with them.
And everybody at this executive summitcame up and was, kinda clapped me
on the back, saying, "Hey, I readthat piece that you did in, this
publication." Everybody had read it.
And I'm like, "Well, crap.
what I'm gonna present to you tomorrowis not new." And, so I went back, and
(38:50):
they also were saying, like, "What'sit gonna be like in six months?
You know, 12 months?
What's it gonna be like nextyear?" Which I did not prepare.
So I stayed up till the nextday- essentially crunching new
numbers, and, had to weave thatinto the preexisting presentation.
So that, is very fresh in my mem- memory.
So we'll have to, play this for ourstudents, that'll be like, "This
(39:12):
is when it's good that you, like,stay up late working last minute."
Yeah, right.
You're Right?
Mm-hmm Like, there's the good situation.
It's not where you've intentionally-Yeah … fallen into this.
Right.
It's not true procrastination.
Although, I will tell you that probablymy undergrad years have prepared me well-
Yeah, exactly … for, Yeah, yeah Exactly.
Right.
Yeah … for that situation.
Me too.
Me too.
But man, I sympathize with you becauseI've had that too, where you submit
your slides early, and then allof a sudden you're like, "Oh, no."
(39:35):
Mm-hmm.
"They know all this stuff already."
I had one, my first, academic conferenceI went to out of graduate school, right?
So now I'm like, "I'm fully an economist.
I belong here." And I spent, like,three-quarters of the time just
talking about my dataset, and thenI realized later that day that,
like, everyone in that room had beenworking with that dataset for years.
(39:58):
I had just learned about it.
So, like, they knew, they have forgottenmore than I already knew at that point.
Mm-hmm.
Chad, you got one more to bring us home?
I want, I wanna ask a quick question.
Are we farming the same crops with swarms,or are we farming different things?
That is a great question.
In 20 years?
(40:19):
Yeah.
I think we're farming some ofthe same, mostly different.
Yeah, I don't see… If you justlook at, like, the past 10 years on
how much more competitive Brazil hasgotten towards us, and I know even here
recently they've had some hardshipsand they're not, you know, impervious
to some of these global macro factors.
but I don't see how we can continueto compete on the global scale on
wheat, and even soybeans and corn.
(40:41):
They're commodities.
It's named that for a reason.
And so I think we have to differentiate.
And, you know, with genetics and AI,there's some regulation stuff, but I
see a lot more opportunity for specialtycrops if the labor is automated.
especially the weed control, if thatcan be, organic and fit some certain
needs, and harvested more frequently.
(41:02):
so I think half of it might stillbe commodities, but I really
think you're gonna see a lot morespecialty, high value, closer
to the consumer type of crops.
20 years is not that long away,and we're gonna be retiring then.
I feel like right now we shouldstart the Indiana Banana Company-
Ooh … where we're gonna growbananas in Indiana in the future.
can I tell you somethingthat happened this weekend?
I had a buddy that taught me that youopen a banana by breaking it in half.
(41:25):
Have you ever done this?
I've tried it.
It just, It turns to mush.
I can't do it well.
I open it from the non-stem end.
I've always, my wholelife, opened the stem end.
And then I watched a video thatwas like, no, monkeys open them
like at the- by pinching this otherend, and that's how I do it now.
Okay.
So is breaking a banana inhalf a feat of strength?
Was I just with someonewho was really good at it?
Did you break a banana?
No, I didn't, I didn't want a
(41:46):
banana.
I break apples in half.
If you had an apple right here, I'd
show
you.
You break an apple in half?
Now
it's getting impressive.
Like that, yeah.
Like, that's like when I go hiking,I like to just split that apple,
eat it all, seeds and everything.
But banana, I'm… Well, itmust've been a real green banana.
I don't remember.
'Cause like if it's got a bit of brown in that, you're just making
banana bread in your hands.
(42:06):
It looked awesome though.
Special.
Okay.
I'm gonna try it.
Indiana Banana Company.
Here we go.
So.
Andy, thanks so much for takingsome time to chat with us.
And if you want, we're gonna just… I'llhave to verify with Chad, but we'll give
you 10% of Indiana Banana Company- justfor being here today, if you'll take 10%.
100%.
10%.
Oh, 10%.
Yeah.
I suppose.
10%. Yes, definitely.
Yeah.
I'll take it.
All right.
(42:26):
Thank you so much.
Thanks for having me.
This has been fun.
All right.
See ya, Andy.
All right, we hope you liked thatconversation with Andy Campbell.
He was gracious enough to spenda little extra time with us.
We actually went beyond the time thatwe had allotted for the conversation.
but again, wanna remind people,if you have data and wanna do cool
stuff with us, send us an email.
(42:47):
Let's have a conversation about it.
I, I love it.
I got nothing to add otherthan please like and subscribe,
I don't know the terms.
I'm a little bit behind the times.
But subscribe, listen,download, tell your friends.
Is there potentially another term?
I don't know.
Do people still like?
I don't know if they like.
Oh.
I don't know.
Subscribe and download.
Yeah, just, just come back.
Come back in a month.
(43:07):
We'll keep learning aboutthe ag economy together.
How about that?
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