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Human Appropriation of NPP (HANPP) An accounting framwork for analysing land use processes in the Earth system Karlheinz Erb Institute of Social Ecology,

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Presentation on theme: "Human Appropriation of NPP (HANPP) An accounting framwork for analysing land use processes in the Earth system Karlheinz Erb Institute of Social Ecology,"— Presentation transcript:

1 Human Appropriation of NPP (HANPP) An accounting framwork for analysing land use processes in the Earth system Karlheinz Erb Institute of Social Ecology, Vienna in collaboration with: H. Haberl, V. Gaube, S. Gingrich, C. Plutzar, F. Krausmann, W. Lucht, A. Bondeau, et al. GEOSS support for IPCC assessments Geneva, Feb. 3, 2011

2 Overview Background: the integrated land system & the current mainstream state-of-the-art in LULC science The framework „Human Appropriation of Net Primary Production“: conceptual background & method Results: Global HANPP 2000 Examples: global production-consumption link, global bioenergy potentials Conclusions: data requirements, gaps, challenges and opportunites

3 State-of-the-art of LU science
From current mainstram land-use research... + ...towards an integrated understanding of land use Classification systems creating nominal-scale data Focus on land cover (biophysical structures, ecological systems) Focus on forest / non-forest dynamics Strategy: increasing spatial resolution Focus on society-nature interactions Broad range of land uses Continuous (rational) scales Explicitly addressing a wide range of spatial scales

4 Bridging disciplinary boundaries: the integrated land system
Outputs - Benefits Ecosystems Society Inputs - Investments Steffen et al. Science 1998 Matthews et al. 2000 management

5 HANPP – the ‚human appropriation of net primary production‘
Potential NPP Actual NPP NPP remaining after harvest Outputs - Benefits Change induced through land use natural ecosystem managed Ecosystems Society dNPPLC HANPP Inputs - Investments NPPh

6 Data integration NPP0: LPJ-DGVM NPPact NPPh Non-used areas Irrigation
Degradation NPPact NPPh Erb et al., J of Land Use Science, 2007

7 Result: Global HANPP 2000 NPPLC%: Productivity changes due to land
coversions << 10% >> Land use activities Biomass consumption HANPP%: Aggregated effect of land use and harvest << 24% >> Source: Haberl et al PNAS 2007 Krausmann et al., 2008

8 Summary of results HANPP 2000
Global HANPP amounts to 24% of NPP0 (aboveground 30%) Agriculture is the most important driver: Cropping and grazing contribute 3/4 of global HANPP. Feeding of livestock consumes 2/3 of the total amount of biomass used by humanity Considerable regional variation of HANPP, mainly depending on Consumption level (per capita HANPP in industrialized countries is about twice that of developing countries) Population density Technology: yields

9 HANPP data integration: ‚old‘ and ‚new‘ challenges
Ecosystems Socioeconomic Systems Area Land cover e.g. Modis GLC2000 Globcover Land use e.g. Census statistics: agriculture, forestry, grazing, settlements e.g. national economic data (SNA) CONSISTENCY ! CONSISTENCY ! CONSISTENCY Flows Ecosystem flows DGVMs: GPP, NPP, Respiration, water, nutrients Inputs - Outputs (Census) Statistics: agriculture, forestry, grazing Socioeconomic models

10 The HANPP framework: Data integration
Consistency extents and flows: yields [=flow per area and year] Prioritizing: correspondence of (national) land use census statistics and the (national) spatial extent more important than the accuracy of spatial information. But: how to deal with flawed census data? Comprehensiveness all ‘relevant’ land use types, inclusive “non-land-use” areas: 100% of each gridcell

11 Applications

12 Example I Linking ecosystem impacts and socio-economic drivers
HANPP eHANPP consumption Source: Erb et al, EE 2009a, Erb et al., 2009b

13 Example I Linking ecosystem impacts and socio-economic drivers
Source: Erb et al,EE 2009 Difference of „production“ and „consumption“ of „embodied HANPP“

14 Example I: Conclusions
A considerable flow: international “transfer” = 1.7 PgC/yr in 2000 [global deforestation: ~1.5 PgC/yr], increasing Large, densely populated countries, which do not yet participate, will soon do so (e.g. China, India) Drivers AND consequences of land use are global. No simple causal chains between drivers and associated impacts Sustainability challenge: High degree of international interdependence (vulnerability, resilience) high risk of shifting the environmental burdens to distant locations and withdrawing it from environmental legislation markets will not minimize burdens, as many ecosystems services have no price  need for global monitoring and management of biomass demand & supply

15 Example II Global bioenergy potentials

16 A scoping study: Explore the scale and option space on basis of HANPP analyses
Systematic combination of existing (e.g. FAO) assumptions and 2 – 4 modulations on developments until 2050 of: diets (4) livestock efficiency (2) agricultural yields (4) cropland expansion (2)  64 combinations (scenarios)

17 Results: Feasibility Analysis: 43 of 64 scenarios “feasible”
Not feasible Probably feasible Feasible Highly feasible For „feasible“ scenarios: bioenergy potential on „free“ cropland on high-quality grazing land crop residues Source: Erb et al., 2009c

18 Results Energy crop area [km²] (2.1 – (6.3) – 10.9 mio. km²)
Primary energy supply Energy crop area [km²] (2.1 – (6.3) – 10.9 mio. km²) Histogramm: feasible scenarios Energy crop yield [gC/m²/yr] Source: Erb et al., forthcoming Haberl et al., 2010, COSUST Haberl et al., 2011, Biomass & Bioenergy

19 Example II: Conclusions
Feeding a growing world population is – in principle - possible with ecologically sound agricultural production. Dietary levels will be most important. Energy crop potentials – ‚conventional wisdom‘ needs to be reconsidered: Sustainability constraints are decisive: Conservation / biodiversity Subsistence agriculture, food security, etc. GHG balance Climate change impacts are poorly understood but could be strong Bioenergy and globalization: Largest bioenergy potentials in Subsaharan Africa and Latin America: Caution – problem shifting! ‚Cascade utilization‘ – focus on recycling, re-use and efficiency improvement of biomass flow-chains

20 Conclusions: HANPP studies illustrate
Link land use – land cover is complex: no easy look-up table. Spatial seggregation between appropriation and consumption: Issues of scale, governance: drivers as well as consequences of land use are global. Important for the construction of causal chains Future biomass demand-supply: Options/potentials for sustainable biomass utilization are limited – requires integrated perspectives

21 Data challenges... Land-use assessments require land-cover and additional (‚socio-economic‘) information Many socio-economic drivers, mechanisms, processes of LU (change) and their impacts are not (yet) well documented. Basic research (still) required. List of EHV not ready yet. Links to MaB (UNESCO), LTER-LTSER The spatial and temporal scales of natural and socioeconomic processes are different Increasing spatial resolution is only a partial solution: the gain in detail allows to better describe LC, but contextual information is required to assess LU; social systems are not organized in grids Move beyond the S-o-A in LU-LC data:  consistency and comprehensiveness  abandon “hybrid”, ambiguous legends  complement “dominance” classes or “discrete” classification schemes with continuous parameters. Gradients are equally important, for LC and LU move beyond “agriculture”, “deforestation”, and “urban” land use land management is key

22 ... and opportunities Data gaps/deficits are ubiquitous:
missing socio-economic data flawed, incomplete census data ...and RS can contribute forestry (used vs. unused forests, forest degradation) grazing (intensity, spatial pattern of grazing, biomass harvest through grazing; effects of grazing) cropland fallow (where, frequency) rural infrastructure soil/vegetation degradation (where? how much land? how intensive?) ()NPP, ()Biomass stocks  yield the mutual benefits of combining RS data and “ground data”

23 Thank you for your attention!
The End Thank you for your attention! Further information/maps/data: ERC Start Grant LUISE


25 Explore the scale and option space: a NPP perspective
Solid consistent empirical data-bases for 2000 Land use: Consistency between pixels (5 min, 10x10 km) and statistical data at country level (cropland and woodlands according to FAO, FRA und TBFRA). Erb et al J. Land Use Sci. 2, National biomass balances : Production and consumption of biomass: Feed balances, processing losses, trade, incl. trends Krausmann et al Ecol. Econ. 65, HANPP: Spatially explicit integration of NPP flows (LPJ-DGVM) and anthropogenic biomass flows (5 min, 10x10 km). Haberl et al., Proc. Natl. Acad. Sci. 104, NPPact Harvest

26 Grazing livestock grazing is the largest fraction of the global biomass harvest (32%), a major driver of the human transformation of terrestrial ecosystems Statistics comprise only market feed – no information on grazed biomass available. “Grazing Gap” must be modelled as difference between demand & market feed supply very loose relation of land use and land cover (occurs in almost all ecosystems (hampers application of remote sensing techniques) Census statistics are of limited practicability, inconsistent, heterogenous definitions (e.g. artificial grasslands vs. natural grasslands) Grazing Gap Source: Krausmann et al. Ecological Economics 2008

27 Data Gap: grazing land ‚Result‘ Remaining area = Grazing land

28 Estimates on global grazing lands

29 Grazing land Russ Fed. India Egypt Finnland Norway China Mexico Brazil
Saudi Arabia Western Sahara Yemen

30 consequences of land use: Biodiversity Species richness is well correlated with NPPt – indirect support for HANPP/biodiversity hypothesis  = 0.708 Case study 1: Correlation between NPPt and autotroph species richness (5 taxa) on 38 plots sized 600x600 m, East Austria Haberl et al., 2004, Agric., Ecosyst. & Envir. 102, p213ff Case study 2: Correlation between NPPt and breeding bird richness in Austria, 328 randomly chosen 1x1 km squares. Haberl et al., Agric., Ecosyst. & Envir. 110, p119ff Case study 3: Correlation between NPPt and vertebrate richness in the Americas, 10,000 randomly chosen 5min gridcells Haberl et al., forthcoming



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