Project update presented at American Geophysical Union 2016.

1. Defining,
Measuring,
&
Incen1vizing
Sustainable
Land
Use
to
Meet
Human
Needs
Kimberly
A.
Nicholas1,
Mark
V.
Brady,
Stefan
Olin,
Johan
Ekroos,
Jonathan
W.
Seaquist,
Veiko
Lehsten,
Henrik
G.
Smith,
Marianne
Hall
1Lund
University
Centre
for
Sustainability
Studies,
Lund,
Sweden
@KA_Nicholas
kimnicholas.com
13
December
2016
Photo:
Tim
Lindstedt,
Flickr

2. Land
is
limited
on
the
blue
planet…
NASA
PPM
2
@KA_Nicholas

3. 3
Yann
Arthus-­‐Bertrand
Guyra
Vincent
Laforet
@KA_Nicholas

4. 4
#GlobalGoals
hap://www.un.org/sustainabledevelopment/

5. Project
Research
Ques1ons
1. What
tradeoffs
does
future
land
use
change
in
Sweden
imply
for
key
ecosystem
services?
2. How
do
changes
in
ecosystem
service
delivery
from
change
in
land
use
affect
human
welfare?
3. How
can
maximum
human
welfare
from
land
use
be
incen1vized?
4. How
do
Swedish
land
use
decisions
affect
overall
ecosystem
service
delivery
globally?
5
Ecosystem
structure
Ecosystem
func1on
Ecosystem
service
Benefit
Value
Ajer
Haines-­‐Young
and
Potschin,
2014
@KA_Nicholas

6. FOTO: ELLIOT ELLIOT/JOHNÉR
GenerationsmålSWEDEN’S ENVIRONMENTAL OBJECTIVES
• Sustainable Forests
• Varied Agricultural Landscape
• Zero Eutrophication
• Reduced Climate Impact
• Clean Air
• Natural Acidification Only
• Flourishing Lakes & Streams
• Good-Quality Groundwater
• Balanced Marine Environment
• Rich Diversity of Plant &
Animal Life

7. Assessing
Tradeoffs
7
Provisioning
Regula/ng
Cultural
@KA_Nicholas

8. Tradeoffs
for
Sustainable
Land
Use
8
Aesthetics
@KA_Nicholas

9. Measuring:
Selec1ng
indicators
9
Shannon
index
Red
listed
bird
abundance
%
applied
N
retained
Kg
CO2-­‐e/m2
Tons
1mber
Tons
cereal
Aesthetics
@KA_Nicholas

10. Measuring
Sustainable
Land
Use
10
Shannon
index
Red
listed
bird
abundance
%
applied
N
retained
Kg
CO2-­‐e/m2
Tons
1mber
Tons
cereal
Aesthetics
LPJ-­‐Guess
Regression
model
Land
cover
analysis
@KA_Nicholas

11. Aesthetics
Climate
Projec1ons
whole is determined, as demands are not specified for
the individual land use types within this group.
The maximization of the total probability at each
individual location is checked against a set of
conversion rules as specified in a conversion matrix
(Figs. 2, 3). This conversion matrix indicates which
conversions are possible for each land use type, e.g.,
the conversion from agriculture to forest is not
possible during one (yearly) time step as a conse-
quence of the time it takes to grow a forest.
Conversions that are excluded by the conversion
matrix overrule the maximization of total probability.
Instead, the land use type with the highest total
conversion it is also possible to enforce a conver
between land use types. When a specific conver
is expected within a specific number of years
conversion will be enforced as soon as the numb
years is exceeded. Figure 3 illustrates this for
conversion of shrubland to forest which takes p
after a number of years depending on the gro
conditions at the location. Such locally determ
conversions are the result of specific managem
practices or vegetation dynamics. Due to the sp
variation in local conditions, these time periods
represented in a map (Fig. 3).
Locally determined conversions will, to s
Land Use (i,t)
Land Use (i,t+1)
Does the allocated area equal the
demanded area for all land use
types/groups
Is the conversion allowed?
Make all enforced conversions
Assign land use with highest total
probability to location (i)
Land Use type specific condition
Conversion Elasticity (lu)
Competitive advantage (lu)
Location and land use type spec
conditions
Location suitability (I,lu)
Neighborhood suitability (I,lu)
Update land use
history information
Land use
history
NO
NO
YES
YES
Iterativelyadapt
competitiveadvantageof
landusetypes
Timesteps
Agriculture
Abandoned
farmland
Shrubland
Forest
Agriculture
Abandoned
farmland
Shrubland
Forest
Conversion matrix
Fig. 2 Flow-chart of the allocation procedure of the Dyna-CLUE model
1170 Landscape Ecol (2009) 24:1167–
Dyna-­‐CLUE
Land
Use
Scenarios
Land
Use
Modeling
LPJ-­‐Guess
Regression
Models
Ecosystem
Service
Modeling
Visualize
Assess
tradeoffs
Policy
analysis
Analysis
Methods
@KA_Nicholas

12. S104 AMBIO 2015, 44(Suppl. 1):S102–S112
Malinga
et
al.,
2015,
Ambio
High
Intensity
Low
Intensity
Contras1ng
farming
intensity
@KA_Nicholas
12

13. Current
Double
Cereal
Produc1on
Intensifica1on
Produc/on
Crop
Area
N
input
(tons)
(ha)
(kg)
Land
Use
Scenarios
Linked
to
Policy
@KA_Nicholas

14. Current
land
use
in
Sweden
Cropland
Map:
Åke
Nilsson,
MarkInfo,
Swedish
survey
of
Forest
Soils
Forest
@KA_Nicholas
14

15. Cropland
changes
under
land
use
scenarios
Cropland
Area
(m
ha)
3.3
6.0
2.6
Cropland
%
of
total
area
8%
15%
6.5%
Current
2x
cereals
Intensifica1on
Frac1on
cropland
per
grid
cell
@KA_Nicholas
15

16. Nitrogen
loss
under
land
use
scenarios
Current
2x
cereals
Intensifica1on
kton
N/year
@KA_Nicholas
16

17. Results:
Change
in
Ecosystem
Services
Aesthetics Aesthetics Aesthetics
Current
2x
cereals
Intensifica1on
*Preliminary
• Either
doubling
or
intensifying
crop
produc1on
decreases
N
reten1on
by
ca.
40%
@KA_Nicholas
17
Today
2x
increase

18. Op1mal
trade-­‐off
between
conflic1ng
ecosystem
services
Marginal
Cost
of
biodiversity
loss
Marginal
Benefit
of
food
produc/on
Economic
Value
($)
Intensity
of
food
produc/on
(%
of
profit
maximizing
N
kg/ha)
0%
100%
(=
Today!)
Socially
op/mal
b
a
c
@KA_Nicholas
18

19. 19
Financial
support
from:
•
Lund
University
Pufendorf
Advanced
Study
Group
• Swedish
Research
Council
Project
Grant
2014-­‐5899,
“Agromes:
Mapping
the
environmental,
economic,
and
social
tradeoffs
of
European
farming
systems
across
scales.”
Thank
you!
Photo:
Marcel
Kerkhof,
Flickr
#StandUpForScience-­‐
rally
today
at
12:00,
Jessie
Square
@KA_Nicholas