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Rüdiger Schaldach, Jan Göpel, Jennifer koch Center for Environmental Systems Research University Kassel, Germany Modeling the impacts of land-use change.

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Presentation on theme: "Rüdiger Schaldach, Jan Göpel, Jennifer koch Center for Environmental Systems Research University Kassel, Germany Modeling the impacts of land-use change."— Presentation transcript:

1 Rüdiger Schaldach, Jan Göpel, Jennifer koch Center for Environmental Systems Research University Kassel, Germany Modeling the impacts of land-use change on vascular plant diversity for continental Africa

2 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!

3 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.

4 Land-use and land-cover change (Foly et al., 2005)

5 (Geist and Lambin, 2002) Drivers of land-use change

6 Based on GLP (2005) The Land System perspective

7 The LandSHIFT model (Schaldach et al., 2011) Climate Change

8 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

9 Model drivers on macro level Input data on micro level Suitability evaluation (t) Crop production (t) Yield increases (t) Crop production (t) Yield increases (t) Spatial allocation (t) „Multi-Objective Land Allocation“ heuristic Spatial crop distribution Land-use pattern (t) Crop yields (t) (LPJmL) Crop yields (t) (LPJmL) - Terrain slope - Infrastructure - Conservation area Feedback on suitability and allocation (t+1) Land-use activity „Crop cultivation“

10 10 Suitability evaluation Multicriteria Analysis (MCA): Factor weights Evaluation functions Evaluation factors Crop yields Terrain slope … Constraints Constraining factors LU-transitions Conservation areas … Suitability factors Constraints

11 11 Model calibration (Diakoulaki et al., 1995) “Objective factor weights”

12 Model performance: Southern Africa Remote Operating Characteristics (ROC) AUC = 0,635 Suitability Frequency Non cropland Cropland

13 Indicator: Vascular plant diversity

14 Biomass Intactness Index (BII) iTaxa under consideration (= 1 vascular plants) jEcosystem types (Diversity zones) kLand-use activity RIntrinsic species richness of i within ecosystem type j at the reference time (undisturbed) AAreal extent of land-use activity k within ecosystem type j ISpecies 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).

15 Area potentials for agriculture AGRO RF Rainfed agriculture GRAZE Rangeland Land use ≠ METRO or AGROLand use ≠ METRO or GRAZE Suitability crops with yield > 100 kg/ha Suitability rangeland with NPP > 100 kg/ha Medium suitability RF Rainfed potentialRangeland potential GIS Analyse Pflanzendiversität

16 Suitability maps

17 Overlap with diversity zones Diversity zone Area share of diversity zone AGRO RF GRAZE

18 Scenario analysis Africa Plausible descriptions of how the future may unfold… scenarios until 2050 from the UNEP Global Environmental Outlook 4 Markets First Faith in markets and their advances for economy but also for social and environmental improvements. Population: 800 Mio Mio GDP/cap: 702 $ $ Food availability: 2460 kcal/day kcal/day Climate: dT = 2.2 K; CO 2 = 563 ppmv Sustainability First Emphasis on environmental and social concerns. Population: 800 Mio Mio GDP/cap: 702 $ $ Food availability: 2460 kcal/day kcal/day Climate: dT = 1.7 K; CO 2 = 478 ppmv

19 Land-use change experiments Scenario (GEO4) Sustainability First ME 1 BIODIV Constraint Scenario (GEO4) Sustainability First ME 2 Yield increases Scenario (GEO4) Sustainability First ME 3 Yield increases BIODIV Constraint Scenario (GEO4) Sustainability First ME 4

20 Land-use map 1993 Cropland: km² Rangeland: km²

21 Simulation results 2025 ME 1: Suitability FirstME 2: Suitability First + BIODIV New cropland New Rangeland Cropland: km² Rangeland: km² Cropland: km² Rangeland: km²

22 Simulation results 2025 ME 3: Suitability First + YIME 4: Suitability First + YI + BIODIV New cropland New Rangeland Cropland: km² Rangeland: km² Cropland: km² Rangeland: km²

23 Cropland shares of diversity zones

24 Results - summary BaseE1E2E3E4 Cropland [km²] Rangeland [km²] BII [%]0,8770,810,8110,8370,846

25 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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