- And we have our second speaker, Barath Raghavan,
and the title of his paper is
Computational Agroecology: Sustainable Food Ecosystem Design
and really, I think all these authors conspired
to write really interesting
but really difficult to pronounce
titles of papers for the session chair,
so I have to kind of practice for this.
So, Barath, please.
- [Barath] All right.
So, I'd like to tell you about computational agroecology,
which is what we see as a new field
of sociotechnical system design
to build new ecosystems and use computing
to actually guide those ecosystems,
the creation and the management of those ecosystems
to produce sustainable food.
And this is work with Bonnie, Sarah, Juliet, Bill, and Don
from UC Irvine, University of Illinois, and Westmont.
So I'm gonna start with something pretty obvious,
which is food is fundamental to our existence as humans,
and everybody here ate today, and yet most of our food
came from a farm that's like this.
And this is radically different
from the way that we've eaten as a human species
for most of our existence.
This field right here represents only one crop.
It represents a scale of farming that never really existed
until just the last eighty years.
And this system, industrial agriculture,
has a whole host of problems that are behind it.
It's dependent on resources that are fossil based
that produce ecosystem impacts that are felt
in a wide range of contexts around the world,
and simply put, it can't keep going.
It's also a very inefficient system
as Michael Pollan makes clear here,
that it takes 10 calories of fossil fuel energy now
to produce a calorie of modern supermarket food.
This is actually the opposite of what we want, right?
We're taking energy from the Sun
and yet somehow we're wasting that,
and we have to put in a bunch of fossil fuel energy
to actually produce the food energy
that we consume as humans.
Now this is something that can't go on forever
because we're talking about limited resources,
and so the question is, how do we transform this system?
Here's a depiction from NOAA
showing the farms of the Midwest
and the runoff from all of those farms
going into the Gulf of Mexico,
and this is creating an anoxic dead zone
in the Gulf of Mexico.
There are many other places in the world where this is true,
and it kills the life in the ocean and the rivers,
and this is all actually wasted resources
if you think about it.
These are excess fertilizers and excess chemicals
that are running off of these fields
that are being applied because they're cheap at the moment
due to various reasons, temporarily,
and so the question is, what do we do?
How do we move on from this?
Now there's a field of science called agroecology,
a scientific discipline that uses ecological theory
to study, design, manage, and evaluate agricultural systems
that are productive and also resource conserving.
Now this science has been developed
over the last 30, 40 years, but it has much deeper roots.
It goes back to many different cultures' systems
of sustainable agriculture that go back thousands of years.
Not every region of the world has had techniques
that are similar to agroecology,
but I can give you a few examples
of small scale agroecology in practice.
So here's a polyculture garden
at a community garden in Urbana, Illinois.
The participants of this garden
have created multistory polyculture,
polyculture being many plants being planted together
to produce many crops.
Here's a example at the Food Bank Garden
in Seattle, Washington,
perennial herbs attracting pollinators.
This isn't the primary purpose of the garden,
but these pollinators and other beneficial insects
are being attracted to the garden
and providing extra benefits to the main crops
that are being grown there.
And more complex perennial polycultures
such as this one in Pahoa, Hawaii,
where there's several hundred different fruit and nut
and vegetable crops being grown here,
over 1,000 different cultivars,
and they're all in a very complex arrangement,
all being harvested from the same piece of land.
Now there are many other complementary techniques
that are out there, organic farming, biodynamic,
companion planting, permaculture, and biointensive.
Many of these have been developed independently.
Agroecology you could think of
as drawing some of the best techniques from all of them,
there's overlap between all of these different techniques,
but as a scientific discipline,
drawing together the best of all of these.
But the main problem that they have and agroecology have
is one of scale.
Industrial agriculture has scaled the world over,
has produced perhaps the most food per unit land
with the least labor of any system
that's ever been developed.
And so it is clearly scaled.
These systems have not scaled yet.
Agroecology has not scaled yet, and this is really
what we're trying to tackle in this work.
And you can look at why these two systems
have scaled or not scaled.
You look at industrial agriculture,
a system based on monoculture, on annual crops,
where you plant it, you plant the seed,
you harvest at the end of the season,
and you plant the seed again the next season.
It's a uniform practice.
Anywhere you go, you wanna create a cornfield,
you cut everything down, you plant the corn,
you harvest the corn, and you repeat the process,
and you put in inputs to keep that process going.
An agroecological system depends
often on a complex polyculture,
many different plants interacting in complex ways,
not just across space horizontally,
but vertically as well, and over time,
because there are different successionary processes,
ecological processes, that take place.
You're talking about a mix of annuals and perennials.
Some will stay for a long time, some will not.
They will grow, they will shade each other,
they will different interactions under the soil.
These are site specific as a result.
You care about the topography of the land,
how these are gonna interact with the water flow,
with the Sun, with long term climate change,
and they're ecosystem based.
Rather than providing inputs of fossil fuel based
fertilizer, pesticide, and herbicide,
we're talking about actually having ecosystem services.
You wanna have the animals and insects support the plants.
You wanna have beneficial insects eating the pests.
You wanna have fungi and mycorrhizal interactions
underneath the soil.
All of these things go into designing
this complex ecosystem that is gonna create
a sustainable multicrop yield.
And so what we wanna build is computational agroecology,
a field, really, of systems
to enable agroecology to actually scale.
And these are different types of computation systems,
so I'm gonna briefly walk through them.
We talk more about them in the paper.
So, just to give you an example
to think about what these might look like,
this is a design from David Holmgren in Australia
who's a permaculture designer,
and one of the pioneers in that field.
Just to give you an idea of something like agroecology,
and this is a diagram of one of his sites.
And these are some of the considerations
that went into it, and these are the things
that an expert is considering when building a site,
but there aren't enough experts out there,
and the knowledge that they need to keep in their head
is overwhelming, so that there are very few people
who can actually do this.
If you just look at all of these things,
and you consider one thing, which is plant selection.
On that farm, for example, that I showed you in Hawaii,
you're talking about several hundred,
maybe a thousand different plants that are being chosen
out of tens of thousands of possibilities,
and you need to figure out how to lay them out,
how to build the water flow
so that you actually have water that's at the right level
for every single plant that's in the ecosystem.
Some may grow fast, some may be compost crops,
so you need to figure out
when you're gonna be cutting those down
and redistributing that organic matter back into the soil.
All of these interactions are very difficult to figure out,
even for an expert, and they often get it wrong.
For a beginner, it's nearly impossible.
And so the challenge for us is we have a expanded notion
of what a farm is and what a farmer is in our view.
What we'd like to do is expand this notion
out to the users of our systems,
are these new farmers on a wide array
of different types of lands,
everything from a sidewalk strip and a backyard
to a hundred acre or a thousand acre farm.
The whole spectrum can allow for
an agro and ecological design
that is an ecosystem that's fully functional
and that is producing food.
And so these are the four classes of systems
that we're thinking about,
data, design, implementation, and maintenance.
And so one of the big challenges here
is that even identifying
where this data is gonna come from is difficult.
All of those different properties
that somebody needs to consider,
that data is scattered out there.
It's not consistent.
It's not systematically collected.
So, just to give you an example,
somebody like myself who's very interested in fruit trees
knows nothing about fungi and mycorrhizal interactions.
Somebody else might be very interested
in how water flows across the land,
but may not have much knowledge
about insects and beneficial insect interactions.
So all of these pieces of knowledge exist out there
in practitioners and farmers, in the scientific literature,
and what we need to do is build systems to crowdsource that.
There are so many different types of knowledge, too,
even making them consistent in a way,
there's a meta level of crowdsourcing work
to figure out how to integrate it
so that we can build systems on top of it
to actually do design.
And that takes us to our next stage,
which is actually doing a design of an agroecosystem.
What we'd like is a user, somebody who wants to create
one of these agroecological farms on any scale, to say,
"Here's a site.
"I wanna build something here.
"I wanna actually grow some crops."
And they specify some degree of constraints
and also objectives.
They wanna grow these five crops
and anything else that happens to grow well here,
and these are the things that they can provide,
these are the limitations that they have.
And the system will then work with them
in an interactive, guided co-creation of a farm
on that site, an agroecological system.
And the system will present a design possibly,
or multiple designs, and the user will say,
"No, this isn't quite right.
"I would like to have this instead of that.
"I don't know how to get this plant, so we can't do that."
There are all of these different aspects
to the design process.
So, once the user has settled on a design,
we need tools to actually implement this on the land.
This is not as simple as, you buy a tractor
and you plow and then you plant seed.
We're talking about so many different crops,
they have to planted in certain ways,
you have to dig trenches in certain ways
to make sure the water flows right.
There are a lot of factors to it involved.
For a beginner this is very difficult.
And so we wanna have both systems that guide somebody
when they're actually out there on the land,
but maybe even robotics that help with this.
There's a lot of different directions you can go
when you're talking about implementing these designs.
And finally, once you've actually laid it out on the land,
you actually have to maintain it.
You have to get both your harvest from this agroecosystem,
and you also wanna maintain it.
You have to know that this tree over here needs to be pruned
and this crop over here needs to be replanted
in the following way.
And here too, there's opportunity for robotic help,
but also just guidance on how
to actually maintain this system over time,
and there's a lot of sensing that needs to be done,
but that's not primary.
The primary task is really working with the users
as they are on the land to figure out
how to maintain their systems.
So, these four components we hope we'll actually help
create more agroecosystems and more interest in agroecology,
and these are the directions that we're working in.
And with that, be happy to take any questions about this.
Thanks.
- [Red Haired Woman] Thank you.
(audience applause)
Do we have any questions for Barath?
Yes?
- [Voiceover] Hi, fascinating talk.
So, are there some kinds of basic primitives
that you can use?
I mean, there's a really large problem.
There's tons and tons of possible combinations.
Are there like, "Okay, these five crops work well together,
"and therefore we can build this out pretty easily
"as an overall system,"
because this obviously very ambitious.
- Yeah, so there's a lot of work in specific climate zones
of crops that work well together,
and a lot of the work that's going in agroecology
actually is trying to re-learn
a lot of the traditional knowledge that's been lost.
So one of the big places that agroecology is being developed
is in Central America, and they are trying to revive
a lot of the tropical agriculture systems
that are no longer being used, but were sustainable,
and yes, they had complex crop interactions
that worked well for them.
One of the big challenges, and this is something
that we're hoping we can actually mine,
is what worked well in one place
no longer works well in a lot of these places
because the climate zones have shifted,
and are shifting actually rapidly enough
that this is causing a problem,
and so extracting that traditional knowledge
and then applying it to another place
or projecting into the future and saying,
"This crop worked well here, but 50 years from now
"it's not gonna work," so we need to factor that in.
So there's that kind of thing.
One of the big challenges is also that complex polycultures,
once you're trying to do a lab experiment
that goes beyond three or four crops,
it gets so hard to track the results
that often in the literature they don't track the results.
So most research papers will focus on,
"These three crops we planted together,
"and we got a greater yield
"than if we planted them separately."
And then there'll be some other group
of three or four crops,
and so what we need to do is work on combining them,
and then we're actually gonna have to feed back
the results from these systems
back to the scientific literature, hopefully.
- [Voiceover] Yeah, I guess that's what a wonder about,
is you're framing this that the ecosystem
has to be very complex, but maybe ten crops at a time
is sufficient, and you don't necessarily
have to monitor at that scale to figure out
exactly every little interaction,
because you maybe already know
what the potential interactions could be,
and you could just.
- Yeah, exactly.
Actually, one of the things that we're hoping
is that the quality of the models
does not have to be great to get good designs,
because eventually, at its limit,
we're talking about modeling all of nature,
which is a little bit beyond what we think we can do,
so hopefully we have to model
just a small part of nature
to be able to get a relatively sustainable ecosystem.
- [Voiceover] Thank you.
- [Voiceover] Hey Barath, great talk.
Super interesting.
I was wondering about time scales,
especially in the case, I think, of polyculture,
sometimes it can take a very long time
to see what's working, and I was wondering
if you had any ideas about how that might be addressed?
It's not your typical scenario for A-B testing.
- Yeah, so that is a big problem.
And you're wondering particularly
how do you verify whether a design you've produced
is gonna work?
- [Voiceover] Is going to work, is working,
yeah, whatever it might.
- Yeah, so one of the challenges in this work is,
how do we even know whether our system
is working or not, in general?
So we came up with roughly five techniques
that we're gonna try to validate our systems.
The ultimate test is always, you have to build it
and actually see what happens.
One of the big ways is actually
what we're calling a agroecological Turing test,
where you say, "Here's a design that was produced.
"We don't know if a expert produced this
"or whether the computer produced this."
And then we're gonna give that to a panel of experts,
and they need to figure out
whether this was produced by the computer
or by some expert out there who knows what they're doing
when they're building these farms,
and what are the flaws in it?
And then we feed that back.
So that's one way to test whether we've gotten it right,
is really just use the human experts,
the very few that are out there.
Another is really that you have to build it out
and compare it to industrial agricultural,
because at the end of the day, people are interested in
how much food can I produce on this piece of land?
And we are thinking of doing side by side tests.
- [Voiceover] Cool, awesome.
Thank you.
- [Voiceover] Hi, super interesting topic.
I was wondering about the relationship
between computational agroecology
and traditional ways of knowing
about agroecological systems,
because you mentioned that a lot of the work in agroecology
is essentially reviving traditional systems of knowing
and traditional ways of knowing about how to manage a farm,
and how do you see that this complements,
because you say it's complicated to know
all the interactions, yet traditionally,
systems have been there to actually know these things,
and actually create these combinations.
So how do you see that this fits in with that
and complements traditional ways of knowing?
- That's a good question.
The way I see it, and I'm sure
there are agroecological experts
who would disagree with this,
but a lot of the traditional knowledge is site specific,
in the sense that, I wouldn't say a specific farm,
but a specific bioregion, and so the interactions of plants
that happen to work well in that bioregion
are what that traditional knowledge will contain,
and so that knowledge may have been lost,
and you can revive it
and then create sustainable systems there.
But, for example, here in California,
the agroecological knowledge, if we can revive it,
I think it's very unlikely,
and many other places in the world,
those systems really haven't existed for ages,
and in many parts of the world,
especially in Europe through the Middle East,
agricultural in the more monoculture sense
has been around for a very long time, to the extent that
there haven't been agroecological systems there
for thousands of years.
And so there are places where that traditional knowledge
and the site specific information
just doesn't exist anymore, so we have to recreate it.
And what worked a couple of thousand years ago
the last time that those systems existed
may not work anymore, the climate has changed,
the region has changed, the soil has changed.
So what we're hoping is that
this can actually be scalable across different regions,
that the same knowledge here in California
will map to a mirror climate that's in Chile,
in South Africa, in Western Australia.
All of these places have similar climates,
they have similar characteristics,
and so you get a more global system that way.
- [Voiceover] Thank you.
- [Red Haired Woman] Thank you very much.
(audience applause)