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Modeling the impacts of land-use change on vascular plant diversity for continental Africa. Rüdiger Schaldach, Jan Göpel, Jennifer koch Center for Environmental Systems Research University Kassel, Germany. Scope. African continent Strong population growth - PowerPoint PPT Presentation
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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.
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