Creeping across the pasture land. Got dirt on
the boots and a working hand. From the milk house
home to the auction call. These are the folks
standing strong and tall. Yeah, this is... Welcome
to the Bullvine Podcast. Victor Cabrera came
to the U .S. from Peru as a student who waited
months just to get a single research paper in
the mail. Sixteen years later, at the University
of Wisconsin -Madison, he's built a system called
Dairy Brain that predicts mastitis days before
any clinical signs appear. Over 90 % accuracy,
on real farms, with data your operation already
generates. So why is a $40 billion industry still
running five software systems that can't talk
to each other? That's what this conversation
is about. Now, coming not from a North American
background into our production systems, can you
explain maybe how that brought a different perspective
and how that's maybe influenced your career?
Oh, for sure. It makes a huge difference because
my feeling, and I still kind of see this related
on my students who are international in the big
majority, come or we come with a lot of... passion
and expectatives. And so we give it all, basically.
I think that's a big difference. We are coming
and we see here a huge opportunity and we try
to take the best advantage of that. And we are
ready to learn as much as we can and take advantage
of the technology and try to, I mean, there are
things like, for example, all these professors
who We only knew because of the publications
or the books that we were studying in our countries.
They were here in the offices, and we could visit
with them. They were very approachable. One thing
we feel very different, at least that was my
experience in Peru, for example, if I knew of
a famous professor or researcher. I didn't have
access to that person very easily. At the time
I was studying as well, if I needed a new paper,
I needed to request it. It took several weeks
or months until I get it. The fact of coming
here and have this all handy makes a huge difference.
And we give it for granted nowadays, right? And
even now with all this AI and the internet, we
have access to everything. But I think that's
something, Still, there is a huge advantage here
in the developed world compared with the developing
world, right? And so I think we feel that and
we appreciate that. I think that's the main point.
We appreciate that and we take the best advantage
of that. Yeah, I was noticing, and I go through
some of the research papers and stuff you've
done, and one of the things I notice is, You're
not always advocating for more technology, but
you're talking about how it all works together
and how it's all fragmented. Was that one of
the first things that really stood out to you
in your research, or how does that come through?
No, I think it is somehow related. As you know,
we have developed in my lab since beginning my
career here. a large number of decision support
tools that actually combine the latest scientific
knowledge with the economics and with the decision
making on farms, trying to do something that
is very usable and easy to interpret tools for
dairy producers or dairy decision makers, right?
We have worked heavily on that, but as we were
working on those, one of the limitations we encounter
over and over even to now is the fact that this
in order to work well in the production systems
they need to be basically customized to the characteristics
of each one of these systems and therefore they
need data and the data in some cases could be
more general uh if you have or want more specific
decision making uh the more data you feed to
these tools the better would be the decision
making right so uh our constraint was and it
still is the fact that we need to extract the
data and many times the extraction is not only
from one system we need several systems within
the farm we need to combine the data maybe in
our spreadsheet and then we feed the data to
the tools to work better right and then we figure
out we say i mean this data is already available
on the farms but it's so fragmented that it's
difficult to put it together right and nowadays
we have so many systems that could help us on
that right and so we can write scripts that combines
the management software with the fit software
together with the genetic software and with the
data that comes directly from the dhi companies
and so that was actually the division and the
start of this daily brain project we are running
that is try to integrate these data sources uh
close to real time as the data being generated
they should be uh integrated and that in our
opinion will bring much and better insights to
the decision -maker that are not available nowadays.
Now, there is a lot of, and we get it especially
online, we get a lot of things when Peters talk
about intuition and what they've all felt and
what they've learned growing up versus the data.
And they kind of do, I know the saying is kind
of the eye of the master fattened cow, meaning
that it's the farmer. needs to be kind of it's
a good farmer who does it and your kind of research
from my findings and when we've leveraged a lot
of your research you essentially kind of say
that well no it's a combo of both but especially
a lot of using the data have you had a lot of
times or is there an instance where you found
when you were talking with producers who maybe
didn't believe in data when you've had to convince
them and it's worked well and how do you talk
to them about that i i think the best way uh
In my experience, working with producers and
decision makers in general is showing specific
examples, right? And hopefully previous farmers,
how they made decisions, how they changed what
they were doing because they were able to insert
data and make more data -driven decision making.
And we do that, I mean, in the areas of reproduction
or feed efficiency or maybe on the, I mean, one
specific example that I think it's difficult
to grasp for anyone is the replacement problem
on dairy herds, for example, right? We tend to
think normally. on the instant reward. If I'm
going to call this cow, it's going to be a huge
expense on the farm because even though I'm going
to receive some salvage value by selling this
animal, I will need to replace this animal, which
is much more expensive than what I will receive
as a salvage value. So it is a net negative,
a huge net negative. However, if we look in the
long term, if we project, with the best modeling
that we can do nowadays. And actually, the farmer
or the decision maker doesn't need to do any
modeling. Just to trust what the models will
do, it will show that many times it's worthwhile
to do that transaction of replacement because
in the long term, that means a better net return
overall on the herd. You have a space on the
farm, and you need to use that space, that lot.
with the best animal you can in the long term
not today right because and and that in order
to to convey that idea it's it's difficult because
it's difficult to convince a farmer that has
a cow that is producing 50 pounds of milk uh
to replace her right uh because uh i mean i'm
gonna be uh and It's not only that I'm going
to be losing that production, I'm going to be
spending time on doing this. But if we look as
a business proposition in the long term, maybe
it is worth it. But the only way to convince
that is with data. And when we do the simulation,
they may still think, this is a simulation, this
is a forecast, I don't know if that's going to
happen. We can demonstrate that, and that's what
we're trying to do now, actually going backwards,
looking at the data and see what has happened
and what could have happened, right? Or we have
the opposite as well. In this situation, the
decision was made, and this is what we have seen
years afterward, right? One of my favorite quotes,
and I don't know if it's verbatim, but that you
have is kind of, Data is a liability until it
becomes a decision. And I really like that because
it's something we kind of try to do a lot here.
It's one thing to have the data, but how do you
make decisions and how do you work with that?
In a world where everyone worships data, but
it's only as good as how it helps you make decisions,
how do you find that that does help decisions
and how do you find that people should use the
data to make those decisions? Sure, I completely
agree with the quote that you mentioned. I mean,
data by itself has no value, right? The data
needs to become information first, and after
information needs to be translated into actionable
decisions in order to have a value. And I think
one important point we made with these tools
and the daily brain and other things we are working,
another thing big we are working is this ruminant
farm system model. It's the idea of actually
to convert the data into specific actions on
the farm that will have a very concrete outcome
with normally, with an economic benefit. I mean,
I think also thinking in the future, these benefits
should also translate to other things like environmental
stewardship or welfare of the animals. I mean,
that's coming, and we need to be conscious about
those as well. But in general, I'm talking mostly
because I'm related with the North American dairy
industry. I have been lucky enough to see other
parts of the world. In North America, specifically
U .S., the bottom line is king, right? I mean,
what it moves the industry is the net return
at the end. And based on that, actually, we can
promote those things that are economical. They
have strong economic benefits, but at the same
time, will likely... will relate to other benefits
as well. Now, you've mentioned a couple times
about the dairy brain, and I'm really fascinated
by this. Could you maybe just give us some background
on how it came about and what it does and why
producers should be wanting to learn more about
it? Sure. I mean, in a nutshell, the dairy brain,
luckily, the name has had quite a visibility
because it means what it says. It is providing
brain to the dairy based on data -driven decisions,
right? The bottom line of the dairy brain is
basically help producers to make much better
informed decisions on their farms. That's the
bottom line. Now, to reach that point, which
has a strong value proposition we do need to
move backwards to the data and in order to get
the data in the right position we need to integrate
the data from different sources and we know the
industry has data very fragmented and maybe there
are other industries as well, but particularly
in the dairy industry, we have silos of data
that they don't talk to each other. It is not
uncommon to reach a farm that will have several
computers, each one dedicated to a different
software, for example, right? Which is, if you
think out of the industry, it's kind of surprising
because in order to to have a list of animals
for a purpose you need to print or extract data
from one software from another and manually or
or yeah manually most of the time you need to
connect the data to do the decisions and it is
uh in these times it is actually inefficient
and prone to errors and actually it it precludes
to do more sophisticated advanced decision making
now we can use all these analytics that are artificial
intelligence and machine learning and I mean
it's not the matter of just using them but in
order to use these sophisticated analytics we
need the data right and in order to provide that
additional value to the information we need to
have all the data integrated so basically a big
part of the daily brain consists on extracting
the data efficiently of these systems and put
it together in a system that could be usable
for decision making. Now, at the same time, because
I'm going to be completely open as well, this
part of the integration of the data, we never
expected it would be so much time and effort
demanding. And that's actually what has been
the bottleneck of the system. That's the part
that is taking more time and effort to do it.
But then after that, we need the analytics. And
actually, even before the data was available,
we wanted to demonstrate that the analytics are
important. and provide value to the system. So
we have published a number of papers showing
that, demonstrating that once we have the data
available, this is all what we can do with this
data. This is what it's going to provide, this
integrated data. Not only this integrated data,
but this pipeline, constant flow of integrated
data. It was interesting. I think about a month
ago, maybe a little more than a month ago, I
saw out of Europe that they were establishing,
I'll call it guidelines or protocols for how
data should be shared on the farm. And so that
you can find a unified data set, which given
what you've been doing, if you could ever be
able to have a unified way to transfer data would
make life a lot easier. I hope we're getting
there here in North America quicker, but I know
we're not there yet. Actually, yeah, we're not
there yet, but it will be coming hopefully soon.
One of my questions for you, and that's one thing
to talk about this on a level, Maybe if you could
give me an example. Let's say if we had a cow
that came up with early mastitis, how would that
work in the dairy brain, or how would the system
handle that if a cow in one system was showing
it, or how does that system work that way, say,
for a cow with early mastitis signs? Actually,
we would go even before that. We have a system,
for example. We have work with data. of the integrated
data in the dairy brain ecosystem that will give
us a chance of a specific animal would have higher
probabilities of having mastitis during the first
lactation, for example, right? We can use genetic
information on the animal. We can find information
of previous events during the raising of the
animal. And that gives us a chance compared to
the mates if this animal would have higher probability
of having mastitis. On top of that, while she
is starting the production life, as we know the
mastitis will not likely happen before the production,
when she starts the production life, whether
it is the first lactation or the second or third
or later lactation, we have additional information
that is very constantly flowing to the system,
which is the milking, basically, right? In addition
to previous events. I mean, the fresh period,
the transition period is very important, and
we put that together as well into the system.
But one important thing we have found, for example,
is the very specific information we would have,
the data we have of the milking. In every milking
we have, the speed of the flow of the milk we
can have even the conductivity of the milk when
it's been uh milk and and if you put on that
together other information like the i mentioned
before the genetics previous events and we can
have additional characteristics of the earth
that will also impact the animal we can have
days before the probability of an animal getting
sick. We can have a very good prediction. I'm
talking about more than 90 % of accuracy that
will give us a flag, say, this animal thinks
it's going to get sick next week. So if we receive
that, we can take it, right? We can do some things
on the farm. At least the first thing would be
try to find the animal. and try to monitor that
animal more closely to see what's happening.
There may be some alternatives to try to prevent
that through nutrition interventions, or we can
do prevention medicine as well. That could be
done. So I think we should think even in that
direction in which... The same as in humans.
I think that's something that's kind of in the
next generation we should try to prevent before
it happens. We should not try to cure what has
already happened. If it has happened, it will
also give additional information of what we could
do to try to figure out what would be the best
outcomes we could have based on the data once
again. I'm having a little feedback. I don't
know if that's me or... Let me see. I can try
to turn mine down a bit. Maybe it's maybe me.
Okay. Any better? Yes, it seems like. Okay. Now,
I did mention before, and I've had experience
in the past, you're talking about many systems
having to work together. But you're also talking
about many competitors because, you know, you
have Lely or DeLaval and Gia and all the different
milking systems. And then you've got like Dairy
Comp and all these different systems. And you're
wanting to feed it all into your data hub. How
did those conversations start and how did you
get them to start working with this or how has
that been going? Yeah, that's a very, very good
question and a very good. I do think in a certain
way we need to provide value to every player
into the ecosystem, starting from the farmer
to any one of the companies involved, right?
I do think there is commercial economic interest
of companies, which is understandable that they
would like to dominate the system. We have seen
this in other industries. are Apple user or you
are a Windows user, kind of like that. I mean,
in a certain way, it's happening. There are companies
that have more entry into the market level of
the farms, and it would not be rare already now
that they will choose a new technology based
on what they already have there to see if there
is compatibility among those systems, right?
Staying on that, I mean, in our experience within
the DERIC project, one thing that has been key
is the fact that we have the buyout of the producer.
The producer is the consumer of the technology,
and they have the say to share the data, right?
And so they have the capability to say to the
company, whether it is DERICOMP or whether it
is GIA or Baumatic, I want to share my data with
these researchers, in this case, to work on this
project. And so there it comes another layer
of complexity yet, because the companies, in
our opinion, in our experience, they will comply
with that request. They will try to do their
best in general. However, in our experience,
once again, they can make it very easy to consume
the data, or they can make it extremely difficult,
actually, right? So, I mean, sometimes for us,
the ideal situation would be they would provide
APIs, which is these application programming
interfaces. We use nowadays every day in our
smartphones to do a bank transaction. We are
sharing data. And even though that's very sensitive
data, it can be securely transferred. Our health
data, we can actually specifically say, I want
to share my data from this clinic with this pharmacy,
and it is securely done without no problems throughout
API. So what I'm trying to, the point I'm making
is, It is doable. Technically, it's possible
and it is happening as we speak. Now, if you
go to the daily industry and you look for APIs,
there are not many. I would start saying there
are not many companies that have, I mean, I would
say the big majority, they will not provide API.
The ones who are... having some APIs development
are in the development stage, in our opinion.
I was thinking as well, entering into this, I'm
not a programmer or computer scientist. I was
thinking it is binary. Yes, API or not, it's
not like that. It's a spectrum, right? So I would
say even those companies that are developing
APIs in the data industry are not at the level
of usability that we would have. wanted to have.
So we have created internally our own APIs with
the data we extract, right? But it's a system
that's still very fragile because whether we
receive a CSV, in some cases we receive spreadsheets,
we have extracted data even from PDF documents,
for example, and you know that's very fragile
because if in the spreadsheet they change a cell
location, it will screw the whole thing and we
need to do an investigative work to find out
where the problem was and try to recover and
redo the system, right? So those are challenges
that we are still living and I think we need
to overcome those. And I think one way to do
that is What I mentioned at the beginning, I
think if everyone feels confident that there
is going to be value in the system for each one
of them, it would be a much easier proposition
to everyone, right? Yeah, you mentioned at the
beginning another important thing. I'm part also
of groups that are working on standards like
ICAR or IDF in the dairy industry. working on,
for example, in this case, the one I'm more involved
in is about standardization of data from health
monitoring systems like sensors. And, I mean,
it's very interesting. And ICAR is now a big
institution and has the standard there. They
have a GitHub. But that's one thing. And the
other thing is which companies are actually using
them, right? That's a different story. are willing
to take the time to understand those and implement
on their own systems, right? And if we go even
further, the other thing, I mean, we have talked
about a lot of companies and we agree on that,
but also there should be another layer of data
education, I would say, at the farm level as
well. I do think farmers could play a role. into
the the system mostly in the cases that they
have the opportunity and the capacity to enter
their own data in some way or shape right like
for example mastitis that we discussed earlier
they can name different names to the same disease
they don't have a standard even inside the farm
everyone that records a mastitis event will mark
differently in the system and even the same person
across time will have different names, that makes
a nightmare in order to process the data later
on, right? And you've been doing it for several
years now. Can you tell me what the experience
on -farm has been with it and how it's been going?
Yes, I think we had a very good reception. I
think farmers understand what the idea and the
concept is. and also the potential value of it,
right? So we haven't been able yet to scale that
up, but the farms we have worked with, actually,
we have been able to show that value out there.
And that has been, I think, the most exciting
part of the dairy brain because there has been,
and still there is a lot of work which is under
the hood, right? You don't see. basically results
on all these scripts just to get the data ready
for. Once that gets out there into a system that
is very, I mean, an online system in which the
farmer can see real time what's going on the
farm, we can calculate a simple thing like income
or feed costs. If you go to a farm and you ask
for feed efficiency or income or feed cost, they
will say, yeah, I know this is my average, but
do you know group by group, even cow by cow,
if that's possible? And every day, that's a different
story, right? They will say, oh, I can calculate.
I can download here, download there, and do this
and do that. But that's very different than habit.
ready and see the evolution of that day to day.
So, for example, in the farm, we were able to
pinpoint even the small change in the silage,
how that's impacting feed efficiency and income
immediately. So you can even prevent or you can
take action to counteract what's going on at
those points in time, right? Because you are
seeing almost at the time. things are happening
on the farm yeah it was interesting i was uh
looking and it was farm fit data so st's product
um where they had the boluses and the real -time
data and you can see long before you ever would
see it in the bulk tank when a cow maybe went
off feed or a cow did something like that and
you have that data right then but the challenge
and this is where i think the dairy brain comes
in and other ones like that is the challenge
is it's in one spot unless someone's monitoring
it 24 -7, but they're also having to monitor
all these different spots all at once. It's very
hard to stay on top of it all. And that's where
I think this integration is where really the
future is, because what is the limitation or
what has been the technical challenge to stopping
you guys from being able to scale and work on
a large form? I mean, I think there are challenges
on the technical part, for sure. which are uh
doable they are uh we can overcome those i think
the the technology is improving so fast that
they should be i mean i'm thinking on for example
even from connectivity on farms uh internet access
that's changing fast and they are having more
and more uh access to that. That helps a lot.
From the updates on the systems on the farms
as well, the computer systems, we have had challenges
with using different Windows systems in different
computers on the same farm, for example, and
losing connectivity when they are doing updates
on the system. But those things are... Well,
that's – and then I think we have different versions
of the software of the company in different forms
as well. But those, I think, are – we can overcome
without no much problem. We have the scripts,
and we expect hopefully the upcoming year to
release as an open source, actually. That's kind
of – we have decided that – I mean, this is the
other thing. We live based on grants because
we are an academic institution without commercial
purposes, right? So that's one distinction. It
has its own benefits, but it has its drawbacks,
right? I mean, the companies have another type
of interest, but they have another power as well
to do this kind of thing, right? So, and basically
we have come to, in our grant process and that
kind of stops what we are working on. However,
I think... there is a benefit of doing this from
that standpoint of view rather than from the
commercial side. And I think there are initiatives
in the commercial side, in the industry side
that are worthwhile as well to look at, right?
But we understand those will have a different
interest and a different way to share whatever
they are learning, for example. But on top of
that, the other thing I think we need to keep
in mind in all this process is uh what's called
data governance in general i mean more on the
on the soft side not the technological part but
data ownership uh confidentiality of the data
uh i never uh thought we would spend so much
time and time and effort thinking and working
on that and actually we gather a special group
to discuss this this kind of things how is the
best way to think about data ownership on the
farms together with the companies the confidentiality
of the data how we protect the data of the farmers
and how we process the data into this system
so that that's something that we need to keep
in mind as we move forward as well i don't think
this is fully solved in other domains but It
is workable. I mean, we are working every day
on that, right? I mean, but that's something
I think we need more express guidelines in the
data industry. You know, it's been very interesting
because we've written about it a few times. It's
who owns data, and it's in many different ways.
You know, if you take the obvious genomics, the
genomics comes from the data that farmers provided,
and yet... now they pay to get their information
back so that's always been a hot topic but we
also have it in robots even transitioning the
same robot system from one farmer to the next
generation and the actual fees to transition
who owns the data and who has access to the data
from the same farm just from one generation to
the next there's been headaches so this whole
who owns data is a very challenging subject but
it's a subject we're going to have to get pretty
clear on because Pretty much everything is focused
off of using the data these days and who owns
it and who has access to it and how do we use
it. It's going to be a very interesting conversation
as we go forward, as you know probably better
than I do. Another interesting subject I saw
that I've come across on your research was more
of the part about how higher producing cows actually
have a smaller carbon footprint per unit of milk
or for milk production. And I know a lot of people
always think that bigger is or higher is a bad
thing. But why would you say that? How would
you explain to people the kind of sustainability
paradox that higher producing cows actually are
better for the environment and why? Well, yeah,
I think that's a very good point you're raising
there, because what we should be interested in
is what we call the. the the emissions intensity
right which is related to the the value product
we have from the production in this case milk
right so i i think it makes all the sense in
the world to measure the emissions per kilo of
milk produce right not the total production because
we do need that milk to be consumed by humans
right so it is a nutritional product uh we have
a huge demand so what we want to have is the
least emissions for each unit of that product
right now we should recognize and we should actually
mention this i think it's important for this
kind of discussion that a higher producing cow
will consume more feed and will produce overall
more amount of greenhouse gases, right? Anthetic
methane and also probably the manure methane
will be higher, total. But if we measure that
per unit of milk produced, it will be less, right?
So there is a marginal gain by producing more
per cow. And thus, anything we do, whether it
is at the farm level, at the herd level, or at
the specific cow level, to improve feed efficiency
will be a huge benefit to the environment. That's
for sure. So we can do, I mean, at the cow level,
we can, nowadays, we have the opportunity to
select cows for... feed efficiency residual feed
intake in canada there is the methane um methane
uh trait uh in the us soon likely and actually
here in in the department uh there are people
working on on this and unlikely there would be
a trait so you can select animals that are more
efficient in the production with respect to emissions
but uh Even without having that, we can do a
lot of stuff to reduce the emissions per unit
of production, which is being more accurate in
the feeding of the animals, using nutritional
grouping that for sure decreases the emissions
per unit of production. We are going to have
more feed efficiency overall on the herd, and
we're going to reduce our emissions per unit
of milk being produced, right? So, for sure.
Everything we do to improve the productivity
of the animals will have a direct impact on reducing
emissions. And this relates directly to methane
or greenhouse gases, but that's also true for
other emissions like nitrogen or phosphorus to
the environment. It's interesting because from
the research I've seen and some of it's from
yours as well is that typically the most efficient
or sustainable producing cow is usually your
most economically profitable cow as well. They
kind of go hand in hand. And I link that back
to your dairy brain because when we have all
our data systems together, we probably have a
clear understanding of just who those cows are
in the herd. that really are are truly both profitable
and providing the most sustainable options but
right now i don't know if we truly on a mass
scale have a data insight to see it for most
farmers but i do think it's very interesting
where how those two correlate yes and we will
eventually we will get there yes yes yes hopefully
so yes yes Another one I was looking at, and
it was another piece of your research I saw that
I've actually looked quite a bit is your beef
on dairy research. And in that, I saw the strategy,
the simulation you guys ran with many different
options and how you said that technically beef
on dairy can be a mathematical trap. It can be
right into an issue. Can you describe what that
trap is or how that works or what you were finding
showed? I think what that would refer to is the
fact that, I mean, there has been this new economic
alternative for dairy producers, which I think
is great. Because they can, in addition to produce
milk and salvage value on the animals that are
discarded, they can produce also off springs.
crossbred animals that have a very high value
in the market. I mean, without going to the reasons
why is that, I mean, the main reason is because
we have a high value for those crossbreds, right?
So there is an opportunity and looking at the
markets in the future, the opportunity seems
to be here to stay at least for the foreseeable
future. Okay, so that's good. But I do think
at some point there has been It has been overdone.
There has been, okay, we look at the opportunity
and we were having a better reproduction performance
and we used too much deep semen and we entered
into the problem that I think now we are coming
out, which was having not enough replacements,
right? Now, overall, as an industry, we produce
too many crossbreds. and we didn't produce enough
replacements to maintain our herds on the farms,
right? And then that has become to the issue
that now if you want to buy a replacement, it's
extremely expensive, and you don't find it in
the market indeed, right? Now, our tool, I think
in that sense, is very informative, and it is
very good that it can give you an opportunity,
not only... to see what's the proposition that's
going to give you most net return, but also the
proposition that will still keep producing enough
replacements on your herd, which I think is important.
And I think normally that's the rationale of
the producer. You want to produce within your
farm enough replacements. I mean, there may be
farmers that are willing to buy replacements
from outside, but that's... An uncertainty and
that's a risk only some are willing to take.
Right. So and the other thing I would mention
in that and that specific tool and that line
of work is the fact that normally the use of
beef semen, angus specifically, for example,
will go hand in hand with the use of sex semen
as well. So we can use more sex semen to our
top animals to gain replacements out of them.
And so we have enough. And at the same time,
that will give us a space to use more beef semen
on the rest of the animals to make value out
of them. So I think that's very important. So
we did that analysis. One thing I think just
since we're talking about to mention is if you
do that one -time analysis today, your herd demographics
are going to change nine months from now when
you're having the calvings from that breeding,
right? And then the recommendation is you need
to do this constantly until you reach a certain
level of stability on the herd to make sure you're
going to have those replacements. that you need
we do have additional work for example working
with these roofers that we call that long term
that we can do exactly the same analysis but
it's going to give us what's your best proposition
today to in the long term to still have a stability
on enough replacements and the best economic
proposition you know i think it's interesting
because when you look at the beef on dairy or
the replacement heifer issue we have right now,
you know, we're dealing with decisions that were
made back in 2023. So it's going to take a little
while. And I know that I looked at yesterday's
USDA data, and they show that, you know, they
expect 3 million heifers this year, but then
you factor in the mortality rates and all different
issues that have cut that down. We're probably
looking at about two and a half million. And
just to keep current production, we need 2 .8
million. So our current shortage is going to
continue for at least another year or two, because
I think it's only now people are starting to
realize that decision. But with biology and the
biological cycles and raising and all those times,
you know, it's two to three years before the
market. correct itself. We're still going through
that. You need to forecast. Andrew, I would need
to end a hard stop at the hour because I have
another meeting coming up. I'll just have one
more question then we'll finish up with. You
have built a lot of tools online and you offer
them free or some with a subscription to it.
What would you tell producers about what tools
are there? How can they use them? Why they should
use them? Stuff along that line. Yeah, I invite
everyone to visit the website and you are going
to have a very good feeling of these tools. I
mean, we have tried to do this very easy to use
and very practical for application on the farms
and all the tools. Almost all the tools have
come by questions from producers or decision
makers. So we have that into account very heavily
and very seriously in order to make tools that
we think are very useful to the farm. And as
you mentioned, they have been for free for a
long time. We needed to go to a subscription
mode that's going to start actually in 2026.
But still, anyone who wants to use and has the
need, we can actually entertain those considerations.
So I invite you to go to the website and contact
me if you want to use the tools. I would be glad
to entertain that. That was Dr. Victor Cabrera,
University of Wisconsin -Madison. If you want
to explore the free decision tools his lab has
built, replacement optimizers, beef on dairy
simulators, feed management calculators, head
to dairymgt .info. We'll drop the link in the
show notes. The full feature article that goes
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Search Dairy Brain and you'll find it. If this
episode made you think about how your own data
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