February 2, 2026 • 18 mins
Are autonomous tractors and farm automation actually cost-effective? In this episode of the Purdue Commercial AgCast, Chad Fiechter and Josh Strine discuss new research on the economics of large-scale autonomous farm machinery and what it means for machinery investment and labor strategy on commercial corn and soybean farms. The results show that under today’s performance and cost levels, most farms aren’t yet in the economic “ballpark” for autonomy — helping producers understand when it could make sense and when it likely doesn’t.
The conversation covers machinery efficiency, hardware and software costs, labor wages, and equipment operating hours, along with how these factors affect profitability in whole-farm systems. It also explores where autonomous equipment might work first — including labor-constrained farms, expansion situations, and specific field operations — and how future improvements in technology could shift the outlook.
📄 Research article: https://doi.org/10.1016/j.atech.2025.101599 🌐 Article summary: https://purdue.ag/3Ze0oir 🌐 Purdue Center for Commercial Agriculture: http://purdue.edu/commercialag
📝 Transcript: https://purdue.ag/3ObvyET 🎧 Subscribe to audio: https://purdue.ag/agcast
#AutonomousFarming #FarmManagement #AgTechnology #Purdue #AgriculturalEconomics #CommercialAgriculture
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(00:00):
Welcome to the Commercial AgCast.
(00:01):
I guess I'm your host by default.
Chad Fiechter, assistant professor inthe Department of Agricultural Economics.
I'm also the research director of theCenter for Commercial Agriculture.
I've invited, and I would call you aco-host, not a guest, because I think
you have a very impactful role to play.
And this is Josh Stine.
We are going to talk aboutlarge scale autonomous machines.
We wanted to understand the economics.
(00:22):
There's a world where anautonomous machine could be more
efficient than human operation.
Most of the multinational farmmachinery companies announced that
they would make the corn and soybeanproduction cycle autonomous by 2030.
Josh, do you wanna say whoyou are and why you're here?
(00:44):
Hello, I'm Josh I'm currently a thirdyear PhD candidate here at Purdue.
Excited to talk about someresearch that I've done with Chad.
Perfect.
The reason that Josh is on thispodcast is Josh and I, along with,
James Lowenberg-DeBoer, who ourlistener may know, wrote a paper
recently on, the economics of largescale autonomous farm machinery.
(01:05):
We're looking at the largefarm equipment that you see
someone sitting in that tractor.
But there's a lot of conversationgoing on that going forward
that may not be the case.
So we're thinking about these large,tractors that currently run or usually
used, but being operated autonomous.
So just having no one in those cabs.
And we specify large scale.
(01:25):
'cause a lot of research is out therethat focuses on autonomy, but a lot more
discussion about smaller robots thatyou see that usually don't even have
the size to fit a person in the cab.
Yeah, perfect.
So, and, and the reason, Jess is akey part of this collaboration is
that Jess has done a ton of work onthe economics of autonomous machines.
(01:46):
Primarily those small scale swarmrobots, like, drone spraying
swarm robots, weeding robots.
He actually operates, at the university,an entire project where they raise
cereal grains, I think it's wheatand canola with autonomous machines.
But on a very small scale, I think theirlargest tractor might be 40 horsepower.
Okay.
(02:06):
So, just to set the stage, the reasonwe are interested is I think most of the
multinational farm machinery companiesannounced that they would make the corn
and soybean rotation cycle, the fullproduction cycle autonomous by 2030.
So we wanted to understand the economics.
So we use something that is aPurdue legacy to do this work.
(02:27):
Yeah, so we started with the PCLPmodel, which I believe stands for
the Purdue Crop Linear Program.
It was used as an extension tool,so it was created for this way for
farmers to come in and, kind of plugin their own farm and parameters
and see what kind of decisions theycan make to optimize production.
(02:48):
And really optimization is the keyword there with what we're looking at.
So we took this tool and wecreated our own case study farm
that we thought maybe represent,an Indiana farmer, a Midwest farm.
So really we went in and we optimized thiscase study farm, but we did it under a
bunch of different scenarios to comparewhat we conventionally see versus some
(03:10):
of these autonomy potential outcomes.
I wanna go back 'cause I just, Iwas at an extension event yesterday
where someone told me about runningthis model very, very long ago.
Where you would used to take these cardsthat you would program and you would
take 'em and run 'em in a mainframeof a computer and it would take a
significant amount of time running on amainframe for each individual solution.
(03:32):
What you did on your personallaptop is you ran solutions
for how many optimized farms?
So since we were unsure aboutautonomy in a range of different
ways, I wanted to run all of thevariations of possible outcomes.
And in order to do that,we ran 72,000 simulations.
(03:54):
It didn't take days necessarily torun one simulation, but to run through
each of those possible farms, I thinkin total it took about three or four
days of time running on my computer.
Yeah.
That's amazing.
Okay, so let's get into the dimensionsof large scale autonomous machines
that we defined as important tounderstand the economic performance.
(04:15):
The variation and uncertaintyaround autonomy is what led
to these 72,000 simulations.
And so when we're thinking about autonomy,how it interacts with our profitability
to farm, we came down with like five orsix key different levels of variation.
One of those is theautonomy intervention time.
So we don't know how much time it'sgonna require for an individual to
(04:36):
either put eyes on the equipmentor look at the technology that they
may have in the office to keep trackof that equipment that is running.
Additionally we have the efficiency.
So compared to a human sitting inthat cab, how fast is it able to
manage the operations in the field?
We have the hardware costs, sothis is, thought of as an upfront
cost of the technology that theyphysically put into the equipment.
(04:58):
And then also related to theautonomy as maybe a subscription fee.
So.
When we first did this, we thoughtabout a per acre fee that may be
required, each time it's used.
So if you think of a annual productionplan that may have a passive tillage, a
seeding pass, and then maybe spraying orharvest, each time you make one of those
passes would have an additional fee.
(05:20):
That was an assumption that we made.
And then outside of the autonomy,there's some other things that kind of
interplay with the profitability andrelative profitability related to labor.
So when we think about thisautonomous, it may be more profitable
if labor becomes more expensive.
So we have to look at thatchange in labor rates and then
also just labor availability.
As we have less labor available, thenyou can't do as much yourself, so
(05:43):
then you're gonna need that additionalautonomy equipment, or it may be
better suited in that circumstance.
All of those parameters waswith conversations with farmers,
people who have interactedwith these machines so far.
And we came down to there's thecurrent performance of what we
observe, at least demonstrated today,and we had to make some guesses.
(06:05):
Right.
Yeah.
So, based on what we thought,we, we worked with the
autonomous intervention time.
We went with 10%.
So that sets out to be six minutesof every hour that the machine runs.
For the efficiency.
We went with 80% of a human.
I don't remember what ourautonomy hardware cost was.
I think it might have been 40,000,but it's secured there in the paper
(06:25):
Well hang on.
The 40,000 is the cameras, the valves.
It's to make a machine autonomous.
Yes, that is to theupfront technology cost.
We did 40,000 for the unit.
Yeah.
Each power unit.
Each power unit.
Right?
Each power unit.
Our subscription fee.
We did it first based on a per acre,but if you look at the paper, the
number's a little weird because wehad to transition that to hector.
(06:47):
I think $3 per acre.
Which corresponds, I believe,to a $7 and 41 cents per hector.
Yeah, that would, that would be right.
We worked with $30 per hour of hired laborfor that rate, just because that compares
the most with our autonomy outcomes.
And then one thing I did forget tomention earlier, which may be a key
(07:08):
player here, is the machinery hours.
So in this autonomy, we may allowthose machines to run longer per
day because you don't necessarilyneed a person sitting in the cab.
And so in the, current.
Expectation, we thought maybe 18hours of tractor, time per day.
Okay.
So now we're gonna talkthrough our results.
(07:29):
'cause it's fairly quick.
And one of the reasons I wanted tohave this conversation with you today,
Josh, is because we've been quotedin a couple of popular farm outlets.
And we are dissatisfied.
We need to be more directin how we communicate this.
So I'm gonna jump to our first set ofresults where what we're comparing is
a farm of similar size operated, wherethe machines are conventionally operated
(07:51):
by humans, and one where all of themachines are operated autonomously.
So the same tractor in one scenariois driven by a human, and that
same tractor in the autonomousscenario is driven by machine.
We know this model isn't a hundredpercent right, so we obviously aren't
gonna sit here and say how muchprofit any of these farms will make.
We can both come up withreasons why it's wrong.
Yes.
(08:12):
What we really wanna focus onhere is the relative profitability
of these different scenarios.
And so we compared everythingto a baseline, which was just a
conventional farm, a fixed farm size,all of it is owner or hired labor.
And then comparing that against someautonomy scenarios, whether labor
is hired or labor is not hired.
(08:32):
And what we just described ismaybe the realistic short term
or a more ideal long term.
And just for clarity, that ideal is likewhen autonomous machines operate the same
as a human, you can run 'em just as longas you could if there was somebody in it.
We have minimal, costsfor subscription fees.
Right?
That's kind of how we'rethinking about ideal.
(08:53):
Exactly.
A hundred percent efficiency, minimumoversight, longer time running per day.
All of the parameters were set tothe maximum in our range basically.
So looking at the results as we compareall of those to our baseline, when labor
is available, we don't see in either theideal or the current realistic, situation
(09:13):
that the autonomy is more profitable.
We see looking at the numbers
right around three bucks an acre.
In the ideal,
If autonomy reaches perfection.
Yes, and maybe our more realisticshort term case, we see a
deficit of $63 per hector.
Divide that by 2.5 andyou'll get your per acre.
Okay.
So I wanna go to the part that both of ushave been a little bit frustrated with.
(09:36):
We, we have not done a good jobcommunicating, so, so I'm gonna give us
each a chance to communicate correctly.
What would take away that $7 and70 cents per hectare decrease.
What would the labor rate haveto be for you to be better off?
If we increased how much we have to paypeople for labor where you would still
be better off with autonomous machines?
Yes.
In the ideal case, it's 45.
(09:56):
So if autonomy reaches what we consider,the ideal scenario and the labor
rates were $45 an hour, which there'sprobably some farms that, that it with
all of the indirect cost of havingan employee could be getting close,
Yeah.
like we could make the argument.
Right.
$45 an hour.
But now if we go to the, the mostlikely scenario, what we observe,
(10:17):
large scale, autonomous machines doingtoday, what's the labor rate that
we would need to have to break even?
Yeah, when we look out there,right now, we would expect labor
rates to have to be around $140for autonomy to break even with our
Now you are also an expert in ag jobs.
Is, is that fair?
(10:38):
Let's say I dabbled.
Okay.
You dabbled in ag jobs, how many of thoseag jobs were $140 an hour labor rates?
Not very many.
I don't know if any of 'em wereuh, farm machinery operators.
So I think this is the part where we'vestruggled to communicate very directly is
that we're not in the ballpark, currently.
The current economics aroundthis are not in the ballpark.
(10:59):
But there is potential that wehave yet to capture the actual
efficiency that could be gained.
So there's a world, like engineerstell us, there's a world where an
autonomous machine could be moreefficient than human operation, which
would vastly change our numbers.
Yes, a hundred percent.
Right, but that's probably not athree to five year sort of thing.
That's probably a ways out.
(11:21):
No, it's not there yet.
An interesting thing that wedidn't include in this model.
So we, we've talked about.
Well, we just explained our resultsof, let's assume labor is unlimited.
We did look at the alternative, Iguess the opposite end of the spectrum
where there's no labor available.
And in that case, when we actually startrunning into this labor constraint,
very quickly, autonomy makes sense.
(11:42):
Because if you don't have autonomy,the owner's labor in themselves, is
not enough to farm the whole farm.
So it just makes sense toget that technology to be
able to operate all the land.
After having some of these conversationssince we published this paper, it got
some questions about, well, what if we hada partial labor constraint, and so that
would be something interesting to explore.
(12:03):
We didn't necessarily do it herebecause we've already said we
had 72,000 simulations and we
Need to call it somewhere.
Yeah.
Uh, so every multiple of that laborconstraint we add just multiplies
our number of simulations.
So now that we have an ideal ina current scenario, we can look
at that labor constraint and seehow it may affect profitability.
(12:24):
Another big one is looking atusing autonomy for specific,
production practices.
So maybe there are, things out there,part of the production plan that would
be more ideally used for autonomy.
Maybe you use it fortillage, but nothing else.
Maybe you use it during harvest whenyou already have another piece of
machinery in the field, so you don'tnecessarily need to oversee it.
(12:45):
You're still there.
So exploring that specialized useof autonomy towards a specific
production practice may be of interest.
Jess was one of the first topoint out, maybe we don't really
understand why we adopt technology.
And he pointed out the case of milkingrobots and it being life improving.
Can you give us your thoughts on that?
Is it gonna make people's livesbetter to the tune of sort
(13:09):
of whatever it would cost?
Yeah.
I'm gonna say that's gonna dependprobably on personal opinions.
If we think about some of the storiesI've read, you can now have the machines
running in the field, and be at homewith your family, watching a kid play
basketball or going, going out to a sport.
So it certainly provides an opportunityto be spending your time elsewhere,
(13:29):
maybe doing something enjoyable.
Or maybe it's doing other work aroundthe farm, whether it's paperwork or
if it's doing something back at theshop to get ahead of the next step.
So there's certainly opportunities thatthe second you don't have a person in
the cab, you're doing something else.
Whether that's worth the cost ofthis technology is probably gonna
vary by each person's preference, butthere, there are opportunities there.
(13:52):
Alright, so what do you sayto those people who say, Josh,
you just hate technology?
You just don't wanna see it succeed.
I would say that I don't hate technology.
I use technology every dayin my life, in my work.
That is like the least way tocharacterize Josh as he sits here
with his iPad, his, his MacBook.
(14:12):
There's a litany oftechnology that surrounds you.
This is outta curiosity, right?
Like this whole project didn'tstart from trying to shed
negative light on the possibility.
It was just to try to understandwhere are we at currently?
And what do you think is someof the first places that you
think there will be a chance forautonomous machines to be involved
in sort of Midwestern agriculture?
(14:34):
Based on if there is labor constraint,obviously that is number one.
Meaning it does not exist.
You cannot hire anyone.
Yes.
So that's very quickly the numberone place that it could come in.
Just for one other caveat, youcan't hire them for $145 an hour.
You literally can't find someonewho that will work for 145.
Like just to be incredibly precise.
(14:56):
Yeah.
So the labor is notavailable at that steep rate.
When I was a producer, I also saidsometimes that I couldn't hire anyone.
And it maybe was more reflective of howmuch I was willing to pay that person.
Yeah, that's fair.
So obviously the constraint labor.
The second opportunity, may be farmexpansion or larger sized farms.
So although we're assuming thiskind of fixed subscription fee on
(15:19):
every acre farmed, by expanding thenumber of hours that this tractor
may be running in the field a day.
You can still get more productiondone, within that window of possibility
in terms of planting and harvesting.
So that may be an opportunity that withthe same set of equipment we see it can
farm more acres, than it can conventional.
(15:40):
Just based on running more hours a day.
Yeah.
And I also wonder, a couple of thedimensions we didn't explore is individual
practices, maybe tillage, like I thinkthat's the one that I've heard the most.
Tillage may be one of the easiestplaces to automate, for the
simplicity of the operation.
Then thinking about the idea of maybeyou have paired, right, you have one,
a human operating one and a paired,solution, running in tandem too.
(16:04):
And that's somethingwe didn't really cover.
Is there anything else that youare interested in taking this
thing a little bit further?
I think those are theplaces I would start.
Looking at that labor constraintand finding where that
binding percent of labor is.
So when we didn't include aconstraint of labor, I think the
most we ever had to "higher in amonth" was less than 800 hours.
(16:25):
I don't remember what it was exactly,but when looking at the other
research, that 800 hours was usuallywhat they set the constraint at.
So we're already below what other researchhas said is maybe constraining labor.
So finding that actual spot that itbinds, looking at some profitability,
comparing these individualpractices, or even building the
(16:46):
choice of autonomy into the model.
So the way we did it here, was weassumed that the farmers either
all autonomy or all conventional.
And building in that model a choicefor autonomy, like maybe we can then,
instead of self diagnosing when theymay choose it, we can let this optimizer
choose where autonomy would make sense.
(17:07):
So I think this, as far as weknow, is the first to really look
at these large scale autonomy.
So there's a lot of room to growand a lot more things to figure out.
I think it brought up in both ofour minds as we were working on this
project, some indirect or some relatedquestions around how is autonomy
gonna impact Midwestern row crops?
(17:27):
The first one for me was, arewe gonna grow different things?
Right?
So we shifted to maximizing oneperson's time so drastically,
that, you know, we've got row cropsgrown here over and over again.
And will it be that autonomy would allowus to grow more labor intensive crops?
That was one of the ideas.
If we are trading machinery,because of operator and the
(17:53):
operator no longer is a constraint.
Does that change used machinery values?
That was, that was one of 'em.
Yeah, so it is an interesting conversationand, if you have interest or want
more information about this paper.
Thankfully because of Purdue's agreementwith the journal that we published
it in, it is available open source.
If you have interest, I would say wewould both be interested in having deeper
(18:15):
conversations if you have perspectivesor things that you're thinking
about, related to sort of how is thepotential of autonomy gonna impact us.
Yeah.
Anytime open to have those conversationsand really look forward to, being able
to continue to explore this realm.
Hopefully you've enjoyed this.
We will put a link tothe paper in the notes.
Again, invite questions.
(18:36):
And hopefully you'll let us know ifyou got something you wanna talk about.
Welcome to the Commercial AgCast.
(00:01):
I guess I'm your host by default.
Chad Fiechter, assistant professor inthe Department of Agricultural Economics.
I'm also the research director of theCenter for Commercial Agriculture.
I've invited, and I would call you aco-host, not a guest, because I think
you have a very impactful role to play.
And this is Josh Stine.
We are going to talk aboutlarge scale autonomous machines.
We wanted to understand the economics.
(00:22):
There's a world where anautonomous machine could be more
efficient than human operation.
Most of the multinational farmmachinery companies announced that
they would make the corn and soybeanproduction cycle autonomous by 2030.
Josh, do you wanna say whoyou are and why you're here?
(00:44):
Hello, I'm Josh I'm currently a thirdyear PhD candidate here at Purdue.
Excited to talk about someresearch that I've done with Chad.
Perfect.
The reason that Josh is on thispodcast is Josh and I, along with,
James Lowenberg-DeBoer, who ourlistener may know, wrote a paper
recently on, the economics of largescale autonomous farm machinery.
(01:05):
We're looking at the largefarm equipment that you see
someone sitting in that tractor.
But there's a lot of conversationgoing on that going forward
that may not be the case.
So we're thinking about these large,tractors that currently run or usually
used, but being operated autonomous.
So just having no one in those cabs.
And we specify large scale.
(01:25):
'cause a lot of research is out therethat focuses on autonomy, but a lot more
discussion about smaller robots thatyou see that usually don't even have
the size to fit a person in the cab.
Yeah, perfect.
So, and, and the reason, Jess is akey part of this collaboration is
that Jess has done a ton of work onthe economics of autonomous machines.
(01:46):
Primarily those small scale swarmrobots, like, drone spraying
swarm robots, weeding robots.
He actually operates, at the university,an entire project where they raise
cereal grains, I think it's wheatand canola with autonomous machines.
But on a very small scale, I think theirlargest tractor might be 40 horsepower.
Okay.
(02:06):
So, just to set the stage, the reasonwe are interested is I think most of the
multinational farm machinery companiesannounced that they would make the corn
and soybean rotation cycle, the fullproduction cycle autonomous by 2030.
So we wanted to understand the economics.
So we use something that is aPurdue legacy to do this work.
(02:27):
Yeah, so we started with the PCLPmodel, which I believe stands for
the Purdue Crop Linear Program.
It was used as an extension tool,so it was created for this way for
farmers to come in and, kind of plugin their own farm and parameters
and see what kind of decisions theycan make to optimize production.
(02:48):
And really optimization is the keyword there with what we're looking at.
So we took this tool and wecreated our own case study farm
that we thought maybe represent,an Indiana farmer, a Midwest farm.
So really we went in and we optimized thiscase study farm, but we did it under a
bunch of different scenarios to comparewhat we conventionally see versus some
(03:10):
of these autonomy potential outcomes.
I wanna go back 'cause I just, Iwas at an extension event yesterday
where someone told me about runningthis model very, very long ago.
Where you would used to take these cardsthat you would program and you would
take 'em and run 'em in a mainframeof a computer and it would take a
significant amount of time running on amainframe for each individual solution.
(03:32):
What you did on your personallaptop is you ran solutions
for how many optimized farms?
So since we were unsure aboutautonomy in a range of different
ways, I wanted to run all of thevariations of possible outcomes.
And in order to do that,we ran 72,000 simulations.
(03:54):
It didn't take days necessarily torun one simulation, but to run through
each of those possible farms, I thinkin total it took about three or four
days of time running on my computer.
Yeah.
That's amazing.
Okay, so let's get into the dimensionsof large scale autonomous machines
that we defined as important tounderstand the economic performance.
(04:15):
The variation and uncertaintyaround autonomy is what led
to these 72,000 simulations.
And so when we're thinking about autonomy,how it interacts with our profitability
to farm, we came down with like five orsix key different levels of variation.
One of those is theautonomy intervention time.
So we don't know how much time it'sgonna require for an individual to
(04:36):
either put eyes on the equipmentor look at the technology that they
may have in the office to keep trackof that equipment that is running.
Additionally we have the efficiency.
So compared to a human sitting inthat cab, how fast is it able to
manage the operations in the field?
We have the hardware costs, sothis is, thought of as an upfront
cost of the technology that theyphysically put into the equipment.
(04:58):
And then also related to theautonomy as maybe a subscription fee.
So.
When we first did this, we thoughtabout a per acre fee that may be
required, each time it's used.
So if you think of a annual productionplan that may have a passive tillage, a
seeding pass, and then maybe spraying orharvest, each time you make one of those
passes would have an additional fee.
(05:20):
That was an assumption that we made.
And then outside of the autonomy,there's some other things that kind of
interplay with the profitability andrelative profitability related to labor.
So when we think about thisautonomous, it may be more profitable
if labor becomes more expensive.
So we have to look at thatchange in labor rates and then
also just labor availability.
As we have less labor available, thenyou can't do as much yourself, so
(05:43):
then you're gonna need that additionalautonomy equipment, or it may be
better suited in that circumstance.
All of those parameters waswith conversations with farmers,
people who have interactedwith these machines so far.
And we came down to there's thecurrent performance of what we
observe, at least demonstrated today,and we had to make some guesses.
(06:05):
Right.
Yeah.
So, based on what we thought,we, we worked with the
autonomous intervention time.
We went with 10%.
So that sets out to be six minutesof every hour that the machine runs.
For the efficiency.
We went with 80% of a human.
I don't remember what ourautonomy hardware cost was.
I think it might have been 40,000,but it's secured there in the paper
(06:25):
Well hang on.
The 40,000 is the cameras, the valves.
It's to make a machine autonomous.
Yes, that is to theupfront technology cost.
We did 40,000 for the unit.
Yeah.
Each power unit.
Each power unit.
Right?
Each power unit.
Our subscription fee.
We did it first based on a per acre,but if you look at the paper, the
number's a little weird because wehad to transition that to hector.
(06:47):
I think $3 per acre.
Which corresponds, I believe,to a $7 and 41 cents per hector.
Yeah, that would, that would be right.
We worked with $30 per hour of hired laborfor that rate, just because that compares
the most with our autonomy outcomes.
And then one thing I did forget tomention earlier, which may be a key
(07:08):
player here, is the machinery hours.
So in this autonomy, we may allowthose machines to run longer per
day because you don't necessarilyneed a person sitting in the cab.
And so in the, current.
Expectation, we thought maybe 18hours of tractor, time per day.
Okay.
So now we're gonna talkthrough our results.
(07:29):
'cause it's fairly quick.
And one of the reasons I wanted tohave this conversation with you today,
Josh, is because we've been quotedin a couple of popular farm outlets.
And we are dissatisfied.
We need to be more directin how we communicate this.
So I'm gonna jump to our first set ofresults where what we're comparing is
a farm of similar size operated, wherethe machines are conventionally operated
(07:51):
by humans, and one where all of themachines are operated autonomously.
So the same tractor in one scenariois driven by a human, and that
same tractor in the autonomousscenario is driven by machine.
We know this model isn't a hundredpercent right, so we obviously aren't
gonna sit here and say how muchprofit any of these farms will make.
We can both come up withreasons why it's wrong.
Yes.
(08:12):
What we really wanna focus onhere is the relative profitability
of these different scenarios.
And so we compared everythingto a baseline, which was just a
conventional farm, a fixed farm size,all of it is owner or hired labor.
And then comparing that against someautonomy scenarios, whether labor
is hired or labor is not hired.
(08:32):
And what we just described ismaybe the realistic short term
or a more ideal long term.
And just for clarity, that ideal is likewhen autonomous machines operate the same
as a human, you can run 'em just as longas you could if there was somebody in it.
We have minimal, costsfor subscription fees.
Right?
That's kind of how we'rethinking about ideal.
(08:53):
Exactly.
A hundred percent efficiency, minimumoversight, longer time running per day.
All of the parameters were set tothe maximum in our range basically.
So looking at the results as we compareall of those to our baseline, when labor
is available, we don't see in either theideal or the current realistic, situation
(09:13):
that the autonomy is more profitable.
We see looking at the numbers
right around three bucks an acre.
In the ideal,
If autonomy reaches perfection.
Yes, and maybe our more realisticshort term case, we see a
deficit of $63 per hector.
Divide that by 2.5 andyou'll get your per acre.
Okay.
So I wanna go to the part that both of ushave been a little bit frustrated with.
(09:36):
We, we have not done a good jobcommunicating, so, so I'm gonna give us
each a chance to communicate correctly.
What would take away that $7 and70 cents per hectare decrease.
What would the labor rate haveto be for you to be better off?
If we increased how much we have to paypeople for labor where you would still
be better off with autonomous machines?
Yes.
In the ideal case, it's 45.
(09:56):
So if autonomy reaches what we consider,the ideal scenario and the labor
rates were $45 an hour, which there'sprobably some farms that, that it with
all of the indirect cost of havingan employee could be getting close,
Yeah.
like we could make the argument.
Right.
$45 an hour.
But now if we go to the, the mostlikely scenario, what we observe,
(10:17):
large scale, autonomous machines doingtoday, what's the labor rate that
we would need to have to break even?
Yeah, when we look out there,right now, we would expect labor
rates to have to be around $140for autonomy to break even with our
Now you are also an expert in ag jobs.
Is, is that fair?
(10:38):
Let's say I dabbled.
Okay.
You dabbled in ag jobs, how many of thoseag jobs were $140 an hour labor rates?
Not very many.
I don't know if any of 'em wereuh, farm machinery operators.
So I think this is the part where we'vestruggled to communicate very directly is
that we're not in the ballpark, currently.
The current economics aroundthis are not in the ballpark.
(10:59):
But there is potential that wehave yet to capture the actual
efficiency that could be gained.
So there's a world, like engineerstell us, there's a world where an
autonomous machine could be moreefficient than human operation, which
would vastly change our numbers.
Yes, a hundred percent.
Right, but that's probably not athree to five year sort of thing.
That's probably a ways out.
(11:21):
No, it's not there yet.
An interesting thing that wedidn't include in this model.
So we, we've talked about.
Well, we just explained our resultsof, let's assume labor is unlimited.
We did look at the alternative, Iguess the opposite end of the spectrum
where there's no labor available.
And in that case, when we actually startrunning into this labor constraint,
very quickly, autonomy makes sense.
(11:42):
Because if you don't have autonomy,the owner's labor in themselves, is
not enough to farm the whole farm.
So it just makes sense toget that technology to be
able to operate all the land.
After having some of these conversationssince we published this paper, it got
some questions about, well, what if we hada partial labor constraint, and so that
would be something interesting to explore.
(12:03):
We didn't necessarily do it herebecause we've already said we
had 72,000 simulations and we
Need to call it somewhere.
Yeah.
Uh, so every multiple of that laborconstraint we add just multiplies
our number of simulations.
So now that we have an ideal ina current scenario, we can look
at that labor constraint and seehow it may affect profitability.
(12:24):
Another big one is looking atusing autonomy for specific,
production practices.
So maybe there are, things out there,part of the production plan that would
be more ideally used for autonomy.
Maybe you use it fortillage, but nothing else.
Maybe you use it during harvest whenyou already have another piece of
machinery in the field, so you don'tnecessarily need to oversee it.
(12:45):
You're still there.
So exploring that specialized useof autonomy towards a specific
production practice may be of interest.
Jess was one of the first topoint out, maybe we don't really
understand why we adopt technology.
And he pointed out the case of milkingrobots and it being life improving.
Can you give us your thoughts on that?
Is it gonna make people's livesbetter to the tune of sort
(13:09):
of whatever it would cost?
Yeah.
I'm gonna say that's gonna dependprobably on personal opinions.
If we think about some of the storiesI've read, you can now have the machines
running in the field, and be at homewith your family, watching a kid play
basketball or going, going out to a sport.
So it certainly provides an opportunityto be spending your time elsewhere,
(13:29):
maybe doing something enjoyable.
Or maybe it's doing other work aroundthe farm, whether it's paperwork or
if it's doing something back at theshop to get ahead of the next step.
So there's certainly opportunities thatthe second you don't have a person in
the cab, you're doing something else.
Whether that's worth the cost ofthis technology is probably gonna
vary by each person's preference, butthere, there are opportunities there.
(13:52):
Alright, so what do you sayto those people who say, Josh,
you just hate technology?
You just don't wanna see it succeed.
I would say that I don't hate technology.
I use technology every dayin my life, in my work.
That is like the least way tocharacterize Josh as he sits here
with his iPad, his, his MacBook.
(14:12):
There's a litany oftechnology that surrounds you.
This is outta curiosity, right?
Like this whole project didn'tstart from trying to shed
negative light on the possibility.
It was just to try to understandwhere are we at currently?
And what do you think is someof the first places that you
think there will be a chance forautonomous machines to be involved
in sort of Midwestern agriculture?
(14:34):
Based on if there is labor constraint,obviously that is number one.
Meaning it does not exist.
You cannot hire anyone.
Yes.
So that's very quickly the numberone place that it could come in.
Just for one other caveat, youcan't hire them for $145 an hour.
You literally can't find someonewho that will work for 145.
Like just to be incredibly precise.
(14:56):
Yeah.
So the labor is notavailable at that steep rate.
When I was a producer, I also saidsometimes that I couldn't hire anyone.
And it maybe was more reflective of howmuch I was willing to pay that person.
Yeah, that's fair.
So obviously the constraint labor.
The second opportunity, may be farmexpansion or larger sized farms.
So although we're assuming thiskind of fixed subscription fee on
(15:19):
every acre farmed, by expanding thenumber of hours that this tractor
may be running in the field a day.
You can still get more productiondone, within that window of possibility
in terms of planting and harvesting.
So that may be an opportunity that withthe same set of equipment we see it can
farm more acres, than it can conventional.
(15:40):
Just based on running more hours a day.
Yeah.
And I also wonder, a couple of thedimensions we didn't explore is individual
practices, maybe tillage, like I thinkthat's the one that I've heard the most.
Tillage may be one of the easiestplaces to automate, for the
simplicity of the operation.
Then thinking about the idea of maybeyou have paired, right, you have one,
a human operating one and a paired,solution, running in tandem too.
(16:04):
And that's somethingwe didn't really cover.
Is there anything else that youare interested in taking this
thing a little bit further?
I think those are theplaces I would start.
Looking at that labor constraintand finding where that
binding percent of labor is.
So when we didn't include aconstraint of labor, I think the
most we ever had to "higher in amonth" was less than 800 hours.
(16:25):
I don't remember what it was exactly,but when looking at the other
research, that 800 hours was usuallywhat they set the constraint at.
So we're already below what other researchhas said is maybe constraining labor.
So finding that actual spot that itbinds, looking at some profitability,
comparing these individualpractices, or even building the
(16:46):
choice of autonomy into the model.
So the way we did it here, was weassumed that the farmers either
all autonomy or all conventional.
And building in that model a choicefor autonomy, like maybe we can then,
instead of self diagnosing when theymay choose it, we can let this optimizer
choose where autonomy would make sense.
(17:07):
So I think this, as far as weknow, is the first to really look
at these large scale autonomy.
So there's a lot of room to growand a lot more things to figure out.
I think it brought up in both ofour minds as we were working on this
project, some indirect or some relatedquestions around how is autonomy
gonna impact Midwestern row crops?
(17:27):
The first one for me was, arewe gonna grow different things?
Right?
So we shifted to maximizing oneperson's time so drastically,
that, you know, we've got row cropsgrown here over and over again.
And will it be that autonomy would allowus to grow more labor intensive crops?
That was one of the ideas.
If we are trading machinery,because of operator and the
(17:53):
operator no longer is a constraint.
Does that change used machinery values?
That was, that was one of 'em.
Yeah, so it is an interesting conversationand, if you have interest or want
more information about this paper.
Thankfully because of Purdue's agreementwith the journal that we published
it in, it is available open source.
If you have interest, I would say wewould both be interested in having deeper
(18:15):
conversations if you have perspectivesor things that you're thinking
about, related to sort of how is thepotential of autonomy gonna impact us.
Yeah.
Anytime open to have those conversationsand really look forward to, being able
to continue to explore this realm.
Hopefully you've enjoyed this.
We will put a link tothe paper in the notes.
Again, invite questions.
(18:36):
And hopefully you'll let us know ifyou got something you wanna talk about.
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