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  

Land  is  limited  on  the  blue  planet…    

NASA  PPM    2  @KA_Nicholas  

3  

Yann

 Arthu

s-­‐Be

rtrand

 Gu

yra  

Vincen

t  Laforet  

@KA_Nicholas  

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

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  

FOTO: ELLIOT ELLIOT/JOHNÉR

Generationsmål SWEDEN’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

Assessing  Tradeoffs    

7  

Provisioning    

Regula/ng  

Cultural  

@KA_Nicholas  

Tradeoffs  for  Sustainable  Land  Use  

8  

Aesthetics

@KA_Nicholas  

Measuring:  Selec1ng  indicators    

9  

Shannon  index  

Red  listed    bird  abundance  

%  applied  N  retained  

Kg  CO2-­‐e/m2    

Tons  1mber  

Tons  cereal  

Aesthetics

@KA_Nicholas  

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  

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 notpossible during one (yearly) time step as a conse-

quence of the time it takes to grow a forest.

Conversions that are excluded by the conversionmatrix overrule the maximization of total probability.

Instead, the land use type with the highest total

probability for which the conversion is allowed willbe selected. In addition it is possible to specify that

certain conversions are only possible within delin-

eated areas, such as outside nature reserves. In thiscase a reference to a map indicating these zones is

made in the conversion matrix. The dynamics of the

land use types governed by local processes (‘bottom-up processes’ in Fig. 1) are also specified in the

conversion matrix. Instead of restricting a specific

conversion it is also possible to enforce a conversion

between land use types. When a specific conversionis expected within a specific number of years the

conversion will be enforced as soon as the number of

years is exceeded. Figure 3 illustrates this for theconversion of shrubland to forest which takes place

after a number of years depending on the growth

conditions at the location. Such locally determinedconversions are the result of specific management

practices or vegetation dynamics. Due to the spatial

variation in local conditions, these time periods arerepresented in a map (Fig. 3).

Locally determined conversions will, to some

extent, interfere with the allocation of the other landuse types that are driven by the regional demands due

to changes in conversion elasticity upon locally

determined conversions, i.e., the conversion to agri-culture is less difficult for recently abandoned

agricultural land than for shrubland. The resulting

conversion trajectories will cause intricate interac-tions between the spatial and temporal dynamics of

the simulation.

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 conditions

Conversion Elasticity (lu)

Competitive advantage (lu)

Location and land use type specific conditions

Location suitability (I,lu)

Neighborhood suitability (I,lu)

Update land use history information

Land use history

NO

NO

YES

YES

Itera

tivel

y ad

apt

com

petit

ive

adva

ntag

e of

la

nd u

se ty

pes

Tim

e st

eps

Agriculture

Abandonedfarmland

Shrubland

Forest

Agr

icul

ture

Aba

ndon

edfa

rmla

nd

Shru

blan

d

Fore

st

Conversion matrix

Fig. 2 Flow-chart of the allocation procedure of the Dyna-CLUE model

1170 Landscape Ecol (2009) 24:1167–1181

123

Dyna-­‐CLUE   Land  Use  Scenarios  

Land

 Use  

 Mod

eling  

LPJ-­‐Guess   Regression  Models  Ecosystem    

Service    

Mod

eling  

Visualize   Assess  tradeoffs   Policy  analysis  

Analysis  

Methods  

@KA_Nicholas  

Fig. 1 Study area and the two different farming systems. Pictures a, b show high-intensity farms and c, d low-intensity farms. e Shows theaverage land cover composition within 250 and 1000 m, respectively, from each farmhouse in the different farming systems

S104 AMBIO 2015, 44(Suppl. 1):S102–S112

123! The Author(s) 2015. This article is published with open access at Springerlink.com

www.kva.se/en

Malinga  et  al.,  2015,  Ambio  

High    Intensity  

Low  Intensity  

Contras1ng  farming  intensity  

@KA_Nicholas   12  

Current      

Double  Cereal    Produc1on  

   

Intensifica1on    

Produc/on      Crop  Area        N  input  (tons)          (ha)        (kg)  

Land  Use  Scenarios  Linked  to  Policy      

@KA_Nicholas  

Current  land  use  in  Sweden  

Cropland  

Map:  Åke  Nilsson,  MarkInfo,  Swedish  survey  of  Forest  Soils  

Forest  

@KA_Nicholas   14  

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  Frac1o

n  crop

land

 per  grid

 cell  

@KA_Nicholas   15  

Nitrogen  loss  under  land  use  scenarios      Current            2x  cereals                          Intensifica1on  

kton  N/year  @KA_Nicholas  

16  

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  

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  

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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:  M

arcel  Kerkhof,  Flickr  

#StandUpForScience-­‐  rally  today  at  12:00,  Jessie  Square  

@KA_Nicholas