So, good morning everyone.
We're going to start again now.
You've got the dubious honor of having me for the rest of the day.
Okay, don't look quite so sad about that.
It won't be as bad as you think.
To start with, because I'm not going to talk all day.
So, the idea is that this afternoon you are going to engage in an
exercise, which I'm going to take you through a little bit later on.
So, it's about you doing the work.
And I'm just going to float around and sort of give you a few ideas
about what you're doing.
To start with, though, I'm just going to give you a couple of talks to
give you some background information about some of the issues we're
going to be addressing as part of the exercise this afternoon.
I've been asked to give a sort of very broad overview about some of
the important land use change issues in the past, present and in the
future.
Just a few examples.
Now, I'm going to do this by, first of all, looking at very sort of
broad trends at the global scale.
And I'm gradually going to come down the scale levels through
continental Europe, mostly, and then down to particular case studies.
And all the way through, what I'm going to try and do is to introduce
you to the different notions of different drivers of change, so the
underlying causes of change, but also to think about the processes of
land use change themselves that are affected by those drivers.
So, just for all of the tree-huggers in the room, I thought I'd start
with forest areas.
I thought I'd get your attention, Sebastian, with that one.
So, this is some general data from the Millennium Ecosystem
Assessment, which looks at changes in forest area globally from pre
-conversion situations.
So, it's not sort of time-limited, it's just from when forest was
converted in the past.
And what you can see, there are different colours on this particular
map.
So, we have the areas which are in the pale green give the current
forest cover areas.
We have areas in red which show the net loss of forest, so
deforestation.
And we have areas in the dark green which show areas of reforestation,
so gains in the forested areas.
And what's quite interesting about this picture, and I'm going to show
you this sort of trade-off as we go through these various things,
people think about trees, they think about deforestation.
And, of course, you can see the classic Amazonian and equatorial
deforestation geography that everyone's familiar with.
But if you look at places like Europe and North America, you can see
that there's been fairly important reforestation over the last
decades.
So, a number of different reasons for this.
So, most of these slides, what you'll see at the bottom is that I've
listed a few drivers here, just to give you some general ideas about
what are the underlying causes of this.
So, things like population growth, obviously the economics of timber
production, the use of forest for local fuel, and competition with
other land uses, such as agriculture and urban, are some of the sort
of general-level drivers that are important in understanding these
processes.
So, let's just take that on.
So, I mentioned to you that there's a different geography of
deforestation and reforestation.
So, this is some data from the World Bank, which just divides the
world according to country groups based on income levels.
So, what you've got are the low, lower-middle, upper-middle, high
income classes.
And what you can see here is the medium rate of forest reduction or
deforestation, which, as you can see from this table here, is highest
in the low-income countries.
This gives a halving time of forest areas of 90 years.
If you look down at the high-income countries, then you have a
negative value, so actually you're getting reforestation and negative
deforestation.
So, that's the situation.
So, clearly, there are different processes of forest land use change
going on in different parts of the world.
And if you zoom into the European context, for example, we can see
that certainly since the 1960s, there's been a fairly substantial
increase in forested areas.
So, if you just look at the y-axis, we're probably at a guess around
about 12% to 14% increase in area over the last 50 to 60 years.
So, it's quite an important trend.
So, what are the differences here between the poorer countries of the
world and the more developed countries of the world?
Well, there's a number of reasons for this, which are sort of
basically listed on the slide here.
So, for example, in developed countries, there's no longer the use of
wood as a major fuel source.
So, we have wood substitutes, for example.
It's the end, as we'll see a little bit later, the end of the era of
agricultural expansion.
So, in fact, agricultural areas in Europe are contracting, so we can
have more forested area.
Developed countries tend to have different and diversified trade
patterns, manufactured goods rather than primary sector goods.
And finally, what's increasingly important in the European context is
that forests are seen for their amenity value as public services.
So, they tend increasingly to be protected because of that reason.
So, a number of different processes that are unfolding in different
parts of the world, which we need to understand to understand those
differences.
So, I mentioned competition with agriculture, and this graphic
demonstrates this quite nicely.
So, what we have here are the bars, which are in green, show the
change in the area of forested areas.
The areas that are in purple show the change in agricultural areas,
both for the globe as a whole, but also broken down into those major
world regions.
And what you can see is, again, the similar trends I was showing you
earlier.
If you look at, say, what should we pick out here?
If you look at Africa or Latin America, then you get decreases in
forested area.
That means the bars go to the left-hand side from the zero line in
this particular case.
But if you look at other countries, such as Europe, then you have
increases.
So, if you look at this particular bar, you have increases in forested
area.
That's the European Union.
China has increases in both forested area, but also increases in
agricultural area, which is quite interesting.
So, you can see some of that trade-off that's going on here.
Again, in the European context, we have decreasing agricultural areas,
but increasing forested areas, which is different in different parts
of the world.
So, clearly, there is a tension and a competition between these
different land uses.
So, let's move on to cultivated areas.
I think, as Almut was introducing earlier on, clearly, the area of
cultivation is extremely important across the globe.
It's one of the biggest influences that people and society have had on
the Earth's system through land-use change.
In the year 2000, about 24% of the terrestrial surface was under
cultivation.
The brown areas on this graph just demonstrate where those areas are.
So, it's an extremely important spatial extent.
Again, you can see the mid-latitude areas.
Cultivation is extremely important.
What's interesting, though, is we're observing certain trends in the
rural areas that relate to agriculture.
What this shows is two interesting curves.
The lower curve is the GDP, which is the share of the global GDP
attributed to the rural areas.
What you can see from the 1800s, or from about 1800, rural areas
accounted for, and this is largely agriculture, but also forestry,
largely accounted for about 80% of the global GDP.
That has fallen rapidly, where we're down to a situation where the
curve stops in the mid-1970s, but we're down to about 3%, 4%, or 5%.
So, there's an enormous change in the balance of the economics of the
use of land.
At the same time, what you can also see on the top curve is quite
interesting.
This is the rural population and how that share has changed through
time as well.
Back in 1800, almost 100% of the world's population were located in
rural areas, so cities were actually quite small.
But what you now see, for the first time relatively recently, the
share of the rural population has now fallen to below 50%.
What that means, of course, is that we are increasingly an urbanized
society.
Urban land use is one of the key issues that we need to think about.
Continuing my grand tour of the major global themes, if we think in
terms of urbanization, what we've seen, this is some data from the
National Geographic, if you look back in the 1950s, there were very
few what are termed mega-cities, so cities with more than 10 million
people, 10 million inhabitants.
But what you see in the year 2000 is the number of these cities has
increased substantially all over the world, so it's a globalized
phenomenon, and there are projections into the future about how that
will increase.
So, urbanization is extremely important.
The consequences of all of these types of global changes are, again,
the types of things that Almut was introducing earlier, which is that
the natural biomes, the natural world that we consider pre
-intervention from people, has changed dramatically over the world.
And these different types of biomes, you can see, are affected in very
many different ways because of land use change.
Okay, so let's just drill down a little bit.
I started off at the global level.
I'm just going to bring it down to the European continent now, mainly
because the exercise you'll do later is focused on Europe.
So, we'll just look at some of the processes here.
So, I talked a little bit about the decline in areas of agriculture in
Europe and North America.
This is often surprising to people.
They don't necessarily realize.
People assume that agriculture is constantly expanding everywhere.
In actual fact, it's not.
In North America and Europe and other parts of the world, you have
these fairly substantial decreases, and as you can see, it's almost a
linear trend.
And over the period from 1960 up to more or less the year 2000, again,
you have something in the order of about 12% to 15% decline in the
area of agriculture.
So, can we understand, this is from observational data, can we
understand what's going on and why this is the case?
Well, one of the crucial things is, let's say, broadly termed the
Green Revolution, is due to this.
So, this shows a graphic for over the same period of time, from 1960
up to about the year 2000, the change in the yields of wheat, one
particular crop.
Actually, most crops follow similar trends to this.
And what you can see, so, wheat yields on average, let's look at the
top curve first of all.
This is the former EU15, so the Western European countries, and this
is the Central European, Central and Eastern European countries, who
are now part of the EU27.
So, the data's a little bit old, but it's just to show a point.
If you look at this curve, again, you've got a fairly linear response
of increasing crop yields.
If you look at the axes here, you're going from two tonnes per hectare
up to nearly six tonnes per hectare.
So, we've increased crop yields by almost threefold over that period
of time.
That's an enormous increase in production.
So, that's the case for the EU15.
The other curve is quite interesting as well.
So, you may have noticed that it follows a different trajectory.
It's very, very similar in the East of Europe up until a certain point
of time.
So, I guess you can probably understand what's going on there.
Anyone want to hazard a guess?
Very good, thank you.
And actually, what happened, you can see pretty much it's at the end
of the 80s and the early 90s that you have this decline.
And this was really all to do with changes in the farming structure,
reallocation of land, lack of financial resources to invest in things
like fertiliser and other agrochemicals.
So, the yields actually decreased.
So, that was an example of a driver of geopolitical change having an
influence on crop yields, which ultimately will have an influence on
land use.
Now, why is this significant?
I'm showing you earlier the decreases in agricultural areas.
Well, if you think about this quite simply, if you increase the
production from a unit of land by three times, you need, on average,
if everything else stays the same, a third of the amount of land to
match the same demand for food.
Well, it's not quite as dramatic as that, but actually that's pretty
much what's going on.
Because we could produce more food per unit area, we actually need
less area to produce the same amount of food.
So, let's look at a few other drivers and processes of change.
This is an interesting example, a very typical one.
So, this is the area of maize cultivation.
Again, this is in Western Europe.
This is the data I just got to hand.
And it's quite an interesting curve you can see there.
So, going back from the 2000s back to the 1970s, you've got a fairly
steady, gradual change with year-to-year variability increasing.
But what you'll see is around about the mid-60s, this dramatic
increase in maize areas.
The reason for that is just very simply that this was the introduction
of new varieties of maize which could be produced under cooler
climates.
So, at that point, you had a northward expansion of maize production
across the European continent.
So, the driver there is biotechnology and development leading to
increases in the geographic distribution of this particular crop.
So, land use change driven by technology.
Here's another example.
So, drilling down again.
I don't know why my map here has become decapitated.
Anyway, I'm from Edinburgh.
There we are.
Scotland has disappeared, but we won't worry about that.
So, this shows data.
Again, this is observational data for the production of oilseed rape
in Great Britain.
It was probably before your time, but I certainly remember, and some
of the others in this room will remember, Joe doesn't remember here,
the time when suddenly these yellow flowers appeared in the landscape
when people started producing oilseed rape.
And that's because the change was very, very rapid.
So, in the 1970s, you just had a scattering.
This is the distribution of oilseed rape in the east of England.
Scotland's not there because there was none in Scotland at that time.
1985, a fairly massive expansion and further expansion through the
90s.
And it follows this, actually, as I'll come back to a little bit later
on to explain to you, this logistic sigmasoidal curve.
So, what happened?
It's all to do with international trade.
Due to changes in the U.S.
soil bean production, the European Union introduced a subsidy for the
production of oilseed rape.
Okay?
Simple as that.
You introduce a subsidy.
It's another driver.
It's a policy change, and you get a big change in land use at this
particular crop as a consequence.
It's not quite as straightforward as that, and I'll explain a little
bit later on.
Keep in mind here that what you've got is actually not an immediate
uptake of this new crop, even though it was probably profitable to do
so.
And I'll come back to explain some of the reasons for that a bit later
on.
I'll show those data later.
Right.
So, here's another example.
There's nobody here from Belgium, is there, except for Sebastian.
Is that right?
I'm going to show you some data from Belgium.
So, this is, you know, I'm just sort of drilling down.
We're just thinking, again, about processes and drivers of change.
So, now, this is a commune in the Belgian Ardennes, in the south of
Belgium.
And, as Sebastian will know, Belgium is a great place to do land use
change studies.
And you probably know why, Sebastian, because Belgium has,
unfortunately, been ruled by just about all of the major European
powers at some point in history.
And what a new invader does when they turn up, the first thing they do
is they map out the resources they've got available to them.
So, if you go back in time, there are brilliant land use maps that
have been created by the Austrians, the Spanish, the Dutch, the
French, etc.
Right.
Who all turned up.
So, what we could do, and this is what was done by a study I can
acknowledge here, was to reconstruct past land use from these data.
And what's interesting about this is that if you ask somebody who
knows the Ardennes, especially if you ask a Belgian person, they would
say, oh, yeah, the Ardennes, I know what that is.
It's coniferous forest and grassland and a little bit of cropping.
That's the typical Ardennes landscape, right?
That is the natural landscape.
I've even heard the Ardennes described as one of the last wildernesses
in Europe, which is completely barmy, as you can see from these maps,
right?
That's what people think of the Ardennes looking like, which it does
today.
But if you go back to the 18th century, that's a very different
picture.
There are no coniferous trees whatsoever.
You've got mixed land uses.
You've got heathland in grey.
You've got orchards for fruit.
You've got a whole mix.
You've got wet meadows.
You've got more surface water, et cetera.
And what you can see is through time, you have this sort of complex
mosaic of different land uses.
Through time, you get the evolution, a change from the deciduous to
the coniferous forestry, right up to the present or the year 2000 in
this case, showing what the Ardennes looks like today.
So it's a big, big change.
What's going on here, and it's actually quite typical, you'll see
something similar probably in these alpine-type landscapes, is that
one of the biggest drivers here was the introduction of the railways,
so improvements in transport.
Because if you improve transport, actually you can import food, which
can be grown somewhere else.
So you don't have to be so self-sufficient.
So back in the 18th century, these communities would have been really
quite isolated.
Because they were isolated, they had to have a diversity of land uses
to be self-sustaining in food production.
Once you introduce the railways, you don't need that, and you can also
use the railways to export timber as well.
So there's lots of processes going on here, which are largely driven
again by technology and innovation, as well as a whole range of other
factors.
Right, same in Belgium.
I want to switch back to an urban example again.
This is another interesting paradox that sometimes people
misunderstand about urban land use change.
So earlier on I was talking about the urbanisation through the
development of megacities, and that's an important process in many
parts of the world.
And what you can see is the same process has happened in most parts of
Europe as well, but historically.
So what this shows, these graphs, they show a population through time,
1830 up to 1990 more or less, and each of these curves represents a
commune, so an administrative region in Belgium, just south of
Brussels.
So this is in the French-speaking part of Belgium.
And what you can do, if we take one example here, if we go with, oh
let's go with this brown one here, so Chamonix.
So what you can see is you have a certain population in 1830, it's
fairly stable, and then you have depopulation.
So these are all rural communes, they're just south of Brussels.
So you have a depopulation, which is what we're seeing in the rest of
the world, which is quite typical, complete depopulation, and then
boom, suddenly something happens.
It's all changed.
So now the population is much greater than it was back in 1830.
And you can see that pattern repeated, the blue one here.
Villers-Laville does the same thing, depopulation and then just
starting.
And you can see that this rapid upswing happens at different points in
time for these different communes.
Anyone want to guess what's going on here?
Nobody wants to guess.
Not even Sebastian.
Okay, let's have a look.
I'll explain it to you.
So what you've got here, if you look at the red curve, this is the
commune called Rixensa, and you've got another one here, the black
one, Autelie-Louvain-la-Neuve.
Now these actually started this upward swing earlier.
It started earlier because these communes are closer to Brussels.
And what's going on here is a process, it says in the titles, there
was a bit of a clue for any of you who wanted to hazard a guess, is a
process known as peri-urbanisation.
So what's happening in a lot of European and North American cities is
that people are leaving the cities.
The cities are actually depopulating.
The population of Brussels is actually reducing within its urban core.
And people are moving out into rural environments for a whole range of
reasons because they like fresh air, they want the space, they want to
bring up kids, but also for technological reasons because transport is
so much better that people can commute from rural areas into a city
centre for jobs.
So that whole process of where people live has changed completely.
And what you'll see is that these communes, which pick up later on
through that peri-urban process, are actually further away from
Brussels.
So it takes more development in transport networks before people can
start commuting from far away.
So peri-urbanisation is all about urban development around cities in
areas which are characterised by having a rural character, but
actually they are partially urbanised, or increasingly so.
And that's what it looks at a sort of aggregate level.
So this is the green data.
The red blotches on this map show the distribution of urban areas.
And we can zoom in to a couple of places here.
So where we were looking at just now is this area just south of
Brussels.
There's Brussels.
And you get this sort of very scattered pattern of urbanisation.
But what's quite interesting about this, you can see there's a couple
of zooms there.
I've zoomed into Brussels.
So that was just south of Brussels.
So you have urban distribution all over the place.
But if you look at the Netherlands, look at the pattern.
It's really, really different.
It looks different.
And do you know the difference between the two?
Why is the Netherlands different from Belgium, given that, you know,
they're very, very close together?
Nobody's going to guess.
You'll have to start guessing a bit more, sooner or later.
Go on, Jonathan.
Thank you.
Yeah.
Spot on.
Yeah.
Exactly.
Absolutely correct.
So the Netherlands has amongst the strictest land-use planning policy
in Europe.
So it constrains urban development in rural areas.
So you get this very compact development pattern.
In Belgium, there was no land-use planning policy up until a couple of
decades ago at all.
So people could build houses wherever they wanted to build houses.
And in fact, it's quite interesting.
If you go to Belgium in the summer, some parts just south of Brussels
here, you think you're passing through forest.
If you go there in the winter, when the leaves have fallen off the
trees, you realise it's a town.
So it's completely intermingled.
People can build wherever they want to or could do in the past.
So that's the main difference.
So another important driver is policy intervention for land-use
planning policies in determining land-use change.
Right.
This is just for Tom, because Tom comes from this little red dot here.
So this is a region of the east of England called East Anglia.
It's about 200 kilometres across.
Something like that, I think, Tom, isn't it?
So you've got Norwich and Ipswich and Cambridge and Peterborough, the
four main towns.
So I just want to show you this, just to take this a little bit
further, in talking about some of the drivers and causes of land-use
change.
And it comes back to understanding people and the choices that people
make.
So you should never forget that land-use is a set of human-mediated
processes.
It's people who make decisions about land-use.
They have choices and they have preferences.
And urban land-use is a really nice example of this.
Right.
So what I'm going to show you here was a study that was done on
observational data, socioeconomic data, about the types of households
and how they're distributed across this region.
I'm going to toggle between these two slides, actually.
So what we're doing is, if you look in this map, these sort of
splodges here show individual administrative regions where there are
data available for those regions.
The four outside maps zooms into the four major cities that I
mentioned earlier.
And what we're trying to understand with this is where do people live?
What are the choices that people make about where they choose to live?
Because that ultimately affects the pressures on new urban
development.
We want to try and understand those processes.
So what we did here was a PCA cluster and a cluster analysis to create
this type of table.
So what we got are a range of household types here.
So you've got an isolated student.
You've got single people, couples, couples with dependent children,
single -parent families, couples with non-dependent children, and
retired people.
So that's a typology of household types, which we can interpret from
the data.
And what we're trying to understand is where do those people live?
Where's the predominant places where they live?
And that's what's marked on the map.
So if you see here, on the map, you'll see that there are these 12
clusters, which are identified.
What the figures obviously represent is if you're an isolated student,
there's a preponderance for you to live in the cluster 12 on the map,
which I'm going to show you in a moment, and some in cluster 10 as
well, and less so in 11, but very few in the others.
So that's all this is showing.
So it's where people want to live.
So students, where are students going to live?
I keep asking you questions.
They are fantastic.
You're going to go far.
Right, there we go.
So if you look on cluster 12, so it's this black color, and if you
zoom in, here we go, we can zoom into Cambridge.
There we are, nice black blotch in the middle there.
They've got quite a good university at Cambridge, nearly as good as
the University of Edinburgh.
And then if you zoom in here to Norwich, this is the University of
East Anglia.
Okay, so we've got a nice blotch there as well.
So that's good.
So things are looking good, actually, for this analysis.
So let's pick out a few more.
What about single people?
Where do single people live?
Any of you single people?
No one?
It's changed since my day.
City centers, great, fantastic.
Let's have a look.
So single people, what were they?
Hang on.
Single people, preponderance in number eight.
There we are.
That was pointed out.
So where do we go?
Yeah, this one here.
So cluster number eight, we'll have a look at that.
And cluster eight is pink.
Very good.
Who said that?
There we are.
So we have nice blotches of pink in city centers.
So actually, people are making choices about where they live based on
their life stage.
And we could go through this.
I'm not going to go through all of them.
I might just pick out one or two here.
So if we look at single-parent families, also tend to be in city
centers.
Couples, we've got seven and ten.
Shall we have a look at couples?
Seven and ten, so it's blue, dark blue and light blue.
Again, you sort of see it's a bit suburban around the edges of the
city, but also quite a bit of blue in the rural areas as well.
And actually, what you find is as you go through these lifecycle
stages, that people, when they're students and single and single
-parent families, tend to live in cities.
And as they become couples and they have children, then they
increasingly move into the peri-urban and rural interior.
Then what happens is we retire.
So let's have a little look.
So this is definitely for Tom.
So retired people are here, cluster number four.
The domination of that type is in here.
And also number six.
So we have a look on the map.
Who's going to tell me where old people live?
The coast.
Brilliant.
Have you got a house on the coast, Sebastian?
No, that's all right.
Yeah, very good.
Anyway, so what did I say?
What's the cluster?
Four.
Yeah, four and a bit of six.
So four is yellow, bright yellow.
Oh, my gosh, look at that.
There we are.
So people really do live in bungalows on the coast when they retire.
I didn't know it was true, but it's actually true.
And actually, if you look as well, you'll also see that they live in
the suburbs as well, probably in bungalows in the suburbs as well.
So that's what happens.
Anyway, so it's quite interesting to do these types of analyses
because, again, what we're understanding, one of the big drivers for
doing this study was that the demographics of the region is changing.
There are more old people in other parts of Europe.
And if you get more old people, and they all want to live in bungalows
at the coast, that puts enormous pressure on the coastal area.
So we need to try and understand how those demographics will change.
And this is what we're basically looking at.
We can understand, as people go through this sort of caricatured life
cycle and then die at the end, then we know where they want to go and
what are the pressures on the landscapes in those particular areas.
And actually, I'm not going to run the model next.
We don't have time.
But we also use this type of information to build models of urban land
use change based on these choice preferences.
Right.
Well, that's a bit about the past.
So now I'm going to talk a bit about the future.
So basically, when we try to understand future land use change, we're
trying to use all of the information and knowledge that we learned
from observations of the past.
It doesn't mean the future is going to be the same as the past, as my
stockbroker tells me every time I try and buy some shares, but it can
be a good guide to the future.
So I'll be talking about drivers, the underlying causes of land use
change.
So we need to think about drivers.
So these are the types of generic drivers I've been talking about.
Policy, other socioeconomics, demographics, technology, climate
change.
Increasingly, the future will be an important driver.
So we need to understand the drivers.
And the exercise you have this afternoon will be focusing on some of
those drivers and understanding them.
But we also need some form of model, right?
So we want to understand what is the present distribution of land use
and how is it going to change into the future.
So we have a model, we have the drivers, the drivers force the model,
and we look at how the outcomes change.
Because, of course, we don't have observations for the future, so we
have to build models based on our knowledge of how land use systems
operate and how individual land users make decisions about land use.
So that's the sort of generic way in which we think about
understanding future land use change.
And this is the type of outcome you can generate.
So this shows projections up to the year 2080 for the cropland area in
Europe, actually it's EU15, so Western Europe in this case, which is
based on four scenarios, which I'm sure you're familiar with, the old
IPCC -ESRES scenarios.
Are you all familiar with those?
Put your hand up if you're not familiar with those.
Great, that's good.
Brilliant, so I don't have to spend time explaining that.
So you know these are the four marker scenarios, A1, A2, B1, and B2.
Change in cropland, now what's interesting about this graphic, I don't
have to explain all the details, but you just look at the legend, and
it's a bit washed out, all of these values are negative.
So this is not an extrapolation of the past, this is based on a model
of cropland systems.
All those values are negative, so again we're seeing a continuation of
the trend we've observed in the past, which is declining agricultural
areas.
And some of these are substantial, so you look at these bright green
areas, in the A1, where you remove all subsidies to support farmers,
you get major declines, up to 80% of cropland areas in certain parts
of the world, which have less of a comparative advantage for the
production of crops.
Some scenarios, those reductions are a little less.
What's going on here?
Well, actually most of this is driven by, again, the assumptions that
were made in this analysis about the effects of technology on changing
crop yields into the future.
And that's actually quite a contentious issue.
It's clearly crucial.
So if we think about if the past trends over the last 40 or 50 years
continued in the future, we'd have another three or fourfold increase
in production.
Again, that's going to equate to a large reduction in the area of
cropland required to produce the same quantity of food.
The reason it's contentious is that people argue about whether we can
sustain that past increase in crop yields into the future.
Is there a biological limit to the biomass potential of crops?
And it's unknown.
So you can only make assumptions.
That's why, as we're going to look at later, we're going to explore
some scenarios of these types of things.
What I do know, when we did this study, we spent some time talking to
crop breeders.
And what's quite interesting is that when crop breeders create a new
variety of a crop, they have to go through a period of trialing, which
can take sometimes 10, 12 years.
And what they were saying is that past trend is definitely going to
continue for the next 10 or 12 years because we have crops that will
be able to do that.
So we've got quite high certainty for the next period of time.
We don't know into the distant future.
Anyway, let's have a little look at this.
So it's a bit confusing.
The map before gave relative change in cropland.
This is the same data for the four SRES scenarios, but this time it's
cropland area but given as an absolute percentage.
So that means that currently about 23% of the surface area of the EU15
is under cropland.
And these are the curves that I showed you up to 2080 on the map.
So you were looking at maps for these points here.
So that's the sort of spread we got.
And if we think about it, so talking about some of these processes, we
were looking previously, this is exactly the same curve I showed you
earlier of decreasing agricultural areas from the observational data,
and actually you could fit a pretty good linear trend through that.
Good fit.
So what we could do, we can think about, there's no connection between
these data and this model.
This is modeled independently, but we can test where our scenarios lie
with respect to what's happened in the past, just by simply doing an
extrapolation of that red line into the future using that simple
equation.
And it looks like that.
Look at that.
Isn't that fantastic?
So we've got a spread of scenarios which seem to mimic, even though
they're totally independent from the observational data, that seem to
mimic things that have happened in the past.
That doesn't prove anything.
It's just nice to look at it.
And this is why it gets a bit tricky.
So I'm glad Jonathan is in the room, actually, for the image model.
So this is a study that Reinhart, as you pointed out, is going to
reproduce as part of the LUT4C project, which was an intercomparison
exercise done in about 2006, 2007, across different land use models.
And there were a range of models.
So on these graphics, all of the model runs that were done for lots of
different scenarios were sort of mapped onto each other by looking at
comparisons between the scenarios.
So the global markets, for example, is like an A1 SRES scenario.
So you can think of them almost as SRES scenarios.
But there was a matching made to compare different studies that ran
different scenarios.
So what you have here, each of these curves represent one of seven
different land use models that we used in this exercise to generate
land use futures.
And again, just for consistency, I'm sticking with European cropland
areas in this case as well.
It's a mix of global models, global integrated assessment models, such
as image and others, for which the European window was cut out.
But there are also models that were developed specifically as land use
change models for Europe.
So the model I've been showing you is called A-Team.
So that's actually curve number six, if you wanted to know that.
And curve number seven, that's your boss, Reinhardt.
That's his model.
So that's there.
And let's have a little look at this data.
So let's go to the top level here.
So again, this shows relative change in cropland projected into the
future for an A1 type scenario.
So what you can see is...
Well, actually, it's a bit shocking, isn't it, what you can see?
Can you see the divergence that you get?
Those models are not even agreeing on the direction of change.
They're going in completely different directions.
So even though I talked to all those arguments about why we could
probably expect decreases in cropland areas, some models have
projected massive increases up to 60% of agricultural cropland areas
in Europe.
I don't know where they're going to find the land for that.
And what is it, number two?
Image.
Oh, it's the image model.
So there we go.
So image is up there.
But what you can find, actually...
You find similar trends around here.
Some of them match up a little bit better.
That scenario is a bit better.
This one is clearly bad.
I'm not surprised that this one's bad because this assumes a free
market laissez-faire economics world, which doesn't exist.
So we actually have no observational data to test models of that type
of world.
So it's not surprising that they're really different.
But what you can also observe here is that the two regional models,
curves 6 and 7, actually, even though these were done by two
independent modelling teams working on different models in different
parts of Europe, actually generally at least have the same trends and
actually also have very similar magnitudes of change.
You can find curves 6 and 7 are quite close all the way through.
Again, that doesn't prove anything other than actually global models
seem to give a different answer to land use change in the future than
regional models.
And you can take your pick as to which one you think you would like to
rely on.
I don't think it actually matters because a lot of what modelling is
about is the discussion and dialogue that comes around from exploring
these types of similarities and differences.
And sometimes the differences are more illuminating than the
similarities.
If every model was the same, there'd probably be something seriously
wrong.
Right.
But that's just to highlight to you that we need to be aware of the
fact that models do introduce this great diversity in terms of future
land use.
Right, so going back to the correct model...
No, sorry, going back to my model.
This is...
The consequences of all those declines in cropland is that you have
abandoned land or surplus land, right, in principle.
Whether there's such a thing as abandoned agricultural land is
debatable.
I'd happily debate that with you a little bit later on.
But we know that there's land which is no longer need for agricultural
food production.
So what can we do with it?
Quick zoom into Aberdeen on the east coast of Scotland, which is this
very grey area here, quite fittingly.
If anyone's ever been to Aberdeen, it's really grey.
And so what we're doing here is actually exploring the use of land for
non -food production, right.
So actually that frees up land.
If you've got land available that's not required for food because
you're producing more food on intensive areas, then you can start
producing bioenergy crops, right.
So different types of bioenergy crops.
And again, different patterns of bioenergy.
The irony here is that the scenario, the A1 scenario with the largest
decrease in cropland areas actually is the scenario that gives you
most opportunity for producing bioenergy, which is quite intriguing
because you've got more land that's available.
Anyway, so other uses of land that we need to think about.
The problem with things like bioenergy in many cases, certainly in the
European context, is that it's new, right.
It's a novel land use, and we're sometimes faced with this.
I mean, it's being produced increasingly, but we don't have a long
-term record to really understand what's going on.
Now, if this works, it'll be a small miracle.
Oh, yeah, small miracle.
There we go.
Right, I'm going to talk you over there.
So talk about bioenergy cropping now.
And this is to introduce to you some other notions of important
processes that we need to think about when looking at land use change.
So what you've got here is a model.
It's actually what's called an agent-based model, but I'm not going to
go into the details of this, which is looking at modelling the
distribution of two bioenergy crops.
So one is miscanthus, tiger grass.
It's very high grass used for biomass, and short rotation coppicing.
It's partly economically driven, so there's a whole set of
assumptions.
This is a particular scenario out to 2050.
And is that not looping?
I'll run it again.
So what you've got is the sort of yellowy areas show the percentage
area of cropland, OK?
So the bright yellows give the highest areas.
The red dots that you see is the sort of co-development of energy
production plants, because to produce the biomass you need local
energy plants where you can generate heat and electricity from the
crop itself.
And what you've got is this sort of expansion.
Now, if you remember from the earlier slides I was showing, I showed
you the oilseed rape trajectories through time.
Remember that?
Well, actually, if you notice some of those maps, if you looked at it,
they look very similar to this, other than previously oilseed rape was
on the eastern side of the country, but this is on the west.
What you're getting here is a process known as spatial diffusion.
And what's driving this is simply contact and exchange of knowledge
between individual land users.
So why would a farmer grow a new crop?
They have no experience of growing miscanthus.
Why would they grow it?
It's a high risk for them.
So they have to overcome that hurdle of producing this particular
crop.
It was the same with oilseed rape.
It was incredibly economic to produce oilseed rape.
It was actually called black gold, I think, at one stage, because it
was so profitable for farmers.
Yet if you saw from those maps I showed you earlier, it took several
years before the full potential distribution of oilseed rape was
realised.
And it's the same in our modelling simulations here.
We could even look at what's going on by comparing back to those data.
So what I've got here, the red curve shows exactly the same oilseed
rape data I showed you previously.
The black curve shows the model energy crops that I showed you
previously as well.
And this is simply rebased, because they're different periods of time,
but it's rebased, you know, start from zero and go from there.
And even though, again, these are completely separate data sets, what
you find is the model simulations follow this same trajectory.
We know it's a spatial diffusion process, because spatial diffusion
always leads to a logistics curve like this, a sigmoid soil curve, an
S -shaped curve, where you get people who find that it's too high a
risk to switch land use, then you get early adopters, and then
everyone else piles in and copies them, because actually they
physically see that their neighbour is growing a new crop, and they
talk to them about it down the pub or whatever.
Everyone comes in, and then you reach a sort of saturation level,
where you've used up all the potential land.
Now, the interesting thing about this is, well, first of all, the
model seems to be able to mimic observational data, even though it's a
different period of time, and it's a different crop, but it does seem
to be fairly structural, that for the uptake of a new novel crop by
farmers, it takes up to about 20 years before you get full uptake.
So there's a time lag, and that time lag is all to do with this need
for knowledge to be dispersed throughout the community of farmers, the
community of land users.
Now, that's really significant in terms of land use change, because,
well, in this particular case, the UK government is subsidising
farmers to grow bioenergy crops, and they're assuming, OK, if we give
them this big subsidy, then we'll get this amount of bioenergy.
Well, as you can see from this, you don't get that.
You don't get the amount you expected until 20 years into the future.
So it can be really critical, from a policy perspective, that you have
these time lags built into land use change.
Right, wine.
I have to finish with wine.
I know it's a bit early, it's only lunchtime, but this is in
anticipation of the...
We get one free glass later, Tom, do we?
Is that right?
OK.
I just want to introduce another little case example here, because I
think it's interesting.
So the wine sector is obviously very important in Europe and elsewhere
in the world.
It's also very interesting from a climate change perspective, because,
as you probably know, grapes and vines are very, very sensitive to the
climate.
That's why the climate is such an important determinant of where
grapes are grown and the quality of those different grapes.
And actually, you can observe already changes, because there's long
-term records from vineyards, changes in the local climatic conditions
in some of these areas, which have... I'm not going to go into all
these graphs, lots of things, but it has a direct effect on the
phenology of the grape production.
So harvest dates are becoming earlier, the grapes are ripening
earlier, flowering is earlier, blood break is different as well.
So things are just changing in the wine area.
So this is in Burgundy, these dates in Burgundy.
You can also see there's been an increase in productivity.
So the two main grapes in Burgundy are Pinot Noir for reds and
Chardonnays for the whites.
So there's been changes, right?
They've been quite significant.
But where it gets really crucial, and this is what we need to
understand, OK, so what?
That doesn't matter.
We can still produce grapes here, it's just a little bit different.
And of course, we can always adapt to this problem by changing grape
variety, can't we?
So Pinot Noir is a cool climate grape variety.
It likes cooler climates.
So why don't we just change to Syrah instead, which is a warmer
climate grape, right?
Easy.
Technological fix solved.
No, you can't do that.
Of course you can't.
If you're a Burgundian, so actually you've probably heard of the
concept of terroir.
Have you heard of terroir?
Well, you should have done.
This is terrible if you haven't.
OK, some of you have heard of it.
So terroir is the French concept of the combination of landscape,
soils, climate, the culture of the region, which all goes into
producing a specific wine from a regulated region, generally.
So the terroir of Burgundy is just perfect for producing Pinot Noir
wines.
And the thing is with Pinot Noir wines, some of the best ones will
sell for hundreds if not thousands of euros per bottle.
It's incredibly expensive wines come from that part of France.
So if you're selling your Pinot Noir Red for 2,000 euros a bottle, why
are you going to start producing Syrah?
And how much will you be able to sell your Burgundian Syrah wine for
when you've just told everyone that your terroir is Pinot Noir?
So there's a big issue here that land users are going to be facing
with, with issues like climate change, that the changing growing
conditions, it's not just simply that you can have a technological
fix, you change a variety or you change something.
There's a whole set of cultural and economic and value-based issues
that affect the decisions that land users will make, and it may
constrain what they're able to do.
So what this graph shows is some bizarre, ancient, don't worry about
the details, IPCC exaggerated future scenario to 2,500 or something,
no, 2,100.
And all it does, it just shows the current distribution of the sort of
vineyard climatic zones across France.
So the white splotches here show the actual distribution of vineyards.
So you've got here, which is basically a sort of Mediterranean area.
You've got an area here with sort of like Bordeaux and Burgundy, sort
of cooler climate.
And then you've got the northern part, the Loire, mostly in the top of
Burgundy, which is a very cool climate region.
So that's just climatically driven, climatic thresholds.
So for future scenario, you know, what happens is, well, actually what
was a cool climate Loire-type situation becomes a Mediterranean
situation.
That has got enormous implications for the people in Loire who grow
Pinot Noir and Gamay and Sauvignon Blanc or Chenin Blanc, things like
that.
Those grape varieties are no longer going to be grown in those areas.
So what happens?
I quite like this bit, though.
The arrow's going across the channel to Britain.
So it's like, whoops, where's it going?
And actually that's already happening.
There's a lot of increasing production of wine in the south of
England.
And it's actually really good.
Take it from me.
It really is good.
So these things are shifting.
And how are the individual land users going to deal with that?
Right.
I'm going to stop there.