Rüdiger Schaldach, Jan Göpel, Jennifer koch

Center for Environmental Systems ResearchUniversity Kassel, Germany

Modeling the impacts of land-use change on vascular plant diversity for continental Africa

Scope

African continent• Strong population growth• Increasing agricultural production• Potential threat to ecosystems and

biodiversity

Identification of potential conflicts and trade-offs between agricultural development and protection of biodiversity!

Study design

• Adjustment of the spatial land-use model LandSHIFT to the African continent.

• Analysis of agricultural area potentials and their overlap with biodiversity distribution.

• Simulation of potential effects of agricultural development on biodiversity.

• Trade-offs between intensification and expansion of cropland area.

• Test of simple “conservation strategy” to avoid the use of areas with high biodiversity.

Land-use and land-cover change

(Foly et al., 2005)

(Geist and Lambin, 2002)

Drivers of land-use change

Land system

Humansub-system

Environmentsub-system

Dec

isio

n m

akin

g Ecosystem Services

Population

Economy

Society

Politics &planning

Culture

Technology

Hydrology

Vegetation

Soil

Topography

Atmosphere

Biogeochemistry

Biodiversity

Land use &Management

Environmentalchange

Based on GLP (2005)

The Land System perspective

The LandSHIFT model

(Schaldach et al., 2011)

Clim

ate

Cha

nge

Macro level(countries)

t t+1

Model drivers, e.g.- Population- Crop production

Spatial model integration

Micro level(Raster 5’)

Ecosystem processes

Land-use change

Model drivers on macro level

Input data on micro level

Suitability evaluation (t)Crop production (t)Yield increases (t)

m

jj

n

iii cpwsuit

11

Spatial allocation (t)„Multi-Objective Land Allocation“ heuristic

Spatial crop distributionLand-use pattern (t)

Crop yields (t)(LPJmL)

- Terrain slope- Infrastructure- Conservation area

Feedback on suitability and allocation (t+1)

Land-use activity „Crop cultivation“

10

Suitability evaluationMulticriteria Analysis (MCA):

m

jkjj

ikiiik cgpfwsuit

1=,

n

1=,=

i iw 1 =

Factor weights

Evaluationfunctions

1,0ii pf

Evaluationfactors

Crop yieldsTerrain slope…

Constraints

Constrainingfactors

LU-transitionsConservation areas…

1,0jj cg

Suitability factors

Constraints

11

Model calibration

1

jj m

kk

w

(Diakoulaki et al., 1995)

Table 3.5: suitability factor weights for the land use activity AGRO and the identified regions of Africa

Suitability factor weight

Central

Africa

weight

Eastern

Africa

weight

Northern

Africa

weight

Southern

Africa

weight

Western

Africa

Slope 0.203 0.099 0.070 0.109 0.151

Soil constraint 0.149 0.264 0.237 0.173 0.185

Population density 0.256 0.283 0.318 0.335 0.291

Available

infrastructure

0.152 0.136 0.134 0.174 0.187

Crop yield 0.239 0.218 0.241 0.209 0.187

“Objective factor weights”

Model performance: Southern Africa

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1

Remote Operating Characteristics (ROC)

AUC = 0,635

Suitability

Freq

uenc

y

Non cropland

Cropland

Indicator: Vascular plant diversity

Biomass Intactness Index (BII)

BII

i Taxa under consideration (= 1 vascular plants) j Ecosystem types (Diversity zones)k Land-use activityR Intrinsic species richness of i within ecosystem type j at the reference time (undisturbed)A Areal extent of land-use activity k within ecosystem type jI Species abundance relative to reference due to land-use activity k in ecosystem type j

• Impact factors derived from Alkemade et al. (2009)Undisturbed = 1; Intensive cropland = 0.1; Subsistence cropland = 0.3; Rangeland = 0.7; Urban land = 0.05

• Intrinsic species richness (R) derived from map of vascular plant diversity

The average population of vascular plants at a particular point in time relative to the population at a reference time (see Scholes & Biggs, 2005).

Area potentials for agriculture

AGRO RFRainfed agriculture

GRAZERangeland

Land use ≠ METRO or AGRO Land use ≠ METRO or GRAZE

Suitability cropswith

yield > 100 kg/ha Suitability rangelandwith

NPP > 100 kg/ha

Medium suitability RF

Rainfed potential Rangeland potential

GIS Analyse Pflanzendiversität

Suitability maps

Suitability category Land Use activity

AGRO RF

mio ha

AGRO RF

%

AGRO IR

mio ha

AGRO IR

%

GRAZE

mio ha

GRAZE

%

High suitability 32.59 1.39 43.46 1.85 0.12 0.01

Moderate suitability 1042.01 44.37 1163.15 49.52 1279.81 54.49

Marginal suitability 823.28 35.05 1138.00 48.45 562.01 23.93

No suitability 767.11 32.66 4.02 0.17 0 0

Overlap with diversity zonesD

iver

sity

zon

e

Area share of diversity zone

0% 20% 40% 60% 80% 100%

-

1

2

3

4

5

6

7

8

9

no suitability

marginal suitability

moderate suitability

high suitability

AGRO RF

GRAZE

Scenario analysis AfricaPlausible descriptions of how the future may unfold… scenarios until 2050 from the UNEP Global Environmental Outlook 4

Markets FirstFaith in markets and their advances for economy but also for social and environmental improvements. Population: 800 Mio - 1900 MioGDP/cap: 702 $ - 3300 $Food availability: 2460 kcal/day - 3476 kcal/dayClimate: dT = 2.2 K; CO2 = 563 ppmv

Sustainability FirstEmphasis on environmental and social concerns. Population: 800 Mio - 1400 MioGDP/cap: 702 $ - 4300 $Food availability: 2460 kcal/day - 4108 kcal/dayClimate: dT = 1.7 K; CO2 = 478 ppmv

Land-use change experiments

Scenario (GEO4)Sustainability FirstME 1

BIODIVConstraint

Scenario (GEO4)Sustainability FirstME 2

Yield increasesScenario (GEO4)Sustainability FirstME 3

Yield increasesBIODIVConstraint

Scenario (GEO4)Sustainability FirstME 4

Land-use map 1993

Cropland: 1.662.444 km²Rangeland: 7.104.683 km²

Simulation results 2025

ME 1: Suitability First ME 2: Suitability First + BIODIV

New cropland

New Rangeland Cropland: 3.071.274 km²Rangeland: 7.229.874 km²

Cropland: 3.546.883 km²Rangeland: 6.824.568 km²

Simulation results 2025

ME 3: Suitability First + YI ME 4: Suitability First + YI + BIODIV

New cropland

New RangelandCropland: 2.153.464 km²Rangeland: 7.541.907 km²

Cropland: 2.499.897 km²Rangeland: 7.384.696 km²

Cropland shares of diversity zones

1

2

3

4

5

6

7

8

9

0% 5% 10% 15% 20% 25% 30% 35%

2000biotech 2025tech 2025bio 2025susf 2025

Results - summary

  Base E1 E2 E3 E4Cropland [km²] 1662444 3071275 3546884 2138707 2499897Rangeland [km²] 7104683 7229874 6824568 7541907 7384696BII [%] 0,877 0,81 0,811 0,837 0,846

Summary and outlook

Summary• Spatially explicit LU-model LandSHIFT adapted to Africa.

• The study reveals potential conflicts between agricultural development and species diversity as well as between rangeland and crop cultivation (land-use activities).

• Simulation results show that intensification of agricultural management can significantly contribute to preserve biodiversity.

• The selected conservation strategy has positive effects that are not fully portrayed by BII.

Outlook• Regional analysis of BII will give more diverse overview.

• Further simulation runs needed to identify indirect land-use changes and to learn more about competition between activities.