Transcript Slide 1

Human Adaptation of Land Management
Mark Stafford Smith, CSIRO Sustainable Ecosystems
(+ Mark Howden, Rohan Nelson)
Vegetation Dynamics and Climate Change, 15th August 2007
Meeting on Ngunnawal country
Outline
• “Are changes in land management practice likely or able to be
changed in ways that will affect changes in vegetation distribution?”
• Yes…!
• but…
• Deconstructing…
• ‘land management practice’
• Drivers of ‘change’
• Can people adapt?
• Significance of ‘change’
• ‘vegetation distribution’
and
• Do we want to model these things?
CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 14-15 Aug 2007
“Significant” change: does it
matter to these feedbacks??
Basis
Contribution to global impacts
Global drivers
Regional
climate
Vegetation composition,
condition and function
Social/$ context
Policy context
Land use,
management
Ecosystem goods
& services
Plenty of examples of change:
– do they matter?
– can we direct them?
– is it useful to model them?
– will it help adaptation?
Longer-term feedbacks – economic, markets, regulatory, perceptual, behavioural
CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 14-15 Aug 2007
Types of change
• Management
• Land use, land cover, land condition, etc
• ‘Land use’  overall vegetation structure: major, long-term
• ‘Land management’  vegetation condition: capability of this vegetation
structure to deliver desired EGSs – can be major but usually insidious,
can be long-term or rapid
• Types of drivers
• Economic (markets, costs, incentives)
• Regulatory
• direct – land conservation, clearing, etc,
• indirect – water trading, wool board, FTAs, procurement, etc
• Behavioural (societal change + awareness, options & skills)
• Ability to respond appropriately = adaptive capacity
• Different for different styles of decisions under different drivers
CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 14-15 Aug 2007
Land use/management that could matter
• Examples abound
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Legislation to stop land clearing in Australia
Woody thickening in response to grazing/fire management
US’s Conservation Reserve Program (14.6m ha enrolled, $1.7bn)
Implication of EU CAP
Forest clearance in Asia and South America (~1/5th fossil fuel flux)
Salinisation in the MDB/WA wheatbelt, effects on water and albedo
Dust fertilisation of oceans off China, Sahara
etc
• Characterised in Australia by:
• Emergent effects of lots of small decisions in response to market
forces, diffusion of innovations, changing preferences, etc, OR,
• Impacts of major centralised ‘policies’ or low probability events
• Predictability dependent on target scale and type
CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 14-15 Aug 2007
Land use/management that could matter
• Examples abound
•
•
•
•
•
•
•
•
Legislation to stop land clearing in Australia
Woody thickening in response to grazing/fire management
US’s Conservation Reserve Program (14.6m ha enrolled, $1.7bn)
Implication of EU CAP
Forest clearance in Asia and South America (~1/5th fossil fuel flux)
Salinisation in the MDB/WA wheatbelt, effects on water and albedo
Dust fertilisation of oceans off China, Sahara
etc
• Characterised in Australia by:
• Emergent effects of lots of small decisions in response to market
forces, diffusion of innovations, changing preferences, etc, OR,
• Impacts of major centralised ‘policies’ or low probability events
• Predictability dependent on target scale and type
(Foley et al, 2005 Science 309)
CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 14-15 Aug 2007
James et al, 1999:
J.Arid Environments
Etter et al. 2006, J.Envir.Mgmt 79: 74-87
CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 14-15 Aug 2007
Adaptive capacity
• At multiple scales
• In individual farmers, conservation managers, traditional owners
• In regional communities, land care groups, land councils, NGOs,
local government
• In state and national government, industry bodies (eg. NFF),
transborder institutions (eg. MDBC), research capability and focus
• Internationally
• Not correlated well with impacts…
CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 14-15 Aug 2007
Adaptive capacity
• At multiple scales
• In individual farmers, conservation managers, traditional owners
• In regional communities, land care groups, land councils, NGOs,
local government
• In state and national government, industry bodies (eg. NFF),
transborder institutions (eg. MDBC), research capability and focus
• Internationally
• Not correlated well with impacts…
• Major focus now needed on adaptive capacity, adaptive
management, adaptive governance
• These represent a shift to a different paradigm or scenario which
itself would result in different futures for predicting other things
CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 14-15 Aug 2007
Classifying where to model adaptation
• Too easy to get overloaded with options…
CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 14-15 Aug 2007
Classifying where to model adaptation
• What types of decisions are we quite good at?
• Short run, rapid feedback/attribution, multiple players
experimenting, especially reversible impacts
• …and bad?
• Long run, slow (discounted) or hard to detect feedback/ attribution,
central monolithic decisions, irreversible impacts
• Continuum, but susceptibility to predictive modelling?
• Short-run – potential, with quasi-statistical/process models
• Long-run – no, use futuring and scenarios instead
• NB form of model to use for the ‘short-run’ (even feasibility)
may depend on the scenario
• e.g. economic driver for land use change may work well in a free
market future; may fail in a regionalised, conservation-oriented
scenario
CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 14-15 Aug 2007
Classifying where to model adaptation
• What types of decisions are we quite good at?
• Short run, rapid feedback/attribution, multiple players
experimenting, especially reversible impacts
• …and bad?
• Long run, slow (discounted) or hard to detect feedback/ attribution,
central monolithic decisions, irreversible impacts
• Continuum, but susceptibility to predictive modelling?
• ‘good’ – potential, with quasi-statistical/process models
• ‘bad’ – no, use futuring and scenarios instead
• NB form of model to use for the ‘good’ (even feasibility) may
depend on the scenario
• e.g. economic driver for land use change may work well in a free
market future; may fail in a regionalised, conservation-oriented
scenario
CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 14-15 Aug 2007
Classifying ctd
• What would you include in a vegetation model?
• ‘Significant’ vegetation change caused by management
• YES (big enough challenge)
• Endogenous feedbacks from veg change to human management
that create further ‘significant’ vegetation change
• ONLY IF short-run, multi-actor type of feedback, eg. through economics
• Even then – is there a credible context of adaptive capacity?
• NOT long-run, monolithic, policy-driven responses – use scenarios
• Caveats
• Time, space and institutional scale-dependent
• Predictable driver globally may be unpredictable locally
• eg. global aging – predictable types of labour shortages globally, but
uncertain regional implications given possible migration, etc
CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 14-15 Aug 2007
Examples
• Fire
• At broad level of human influence – at regional scales:
suppress >> big hot, or not >> ‘natural regime” = scenario?
• Land use change
• Rainforests, marginal lands – at regional+ scales:
driven by markets, so predictable in some scenarios
• Conservation instruments – driven by central policies: >>??
• Tree planting, biofuels due to C pricing?
• NB serious emergent implications for land use and food security
• Changes in crops, cultivars, timber species, etc
• Strong economic/market drivers – at regional scales:
predictable in some scenarios (efficiency gain responses probably
predictable in all, though wildcards eg. GM etc)
CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 14-15 Aug 2007
Conclusions
• Does human adaptation matter for vegetation change?
• Yes, at certain times and scales
• Should management effects be included in DGVMs?
• Yes, at scales and for processes where they matter
• Should causal agency be modelled?
• Major increase in complexity and potential uncertainty, so only where
this is worthwhile
• ie. What’s the purpose of the model? Is the effect significant?
• Even then, some types of decisions amenable at some scales, others
are not:
• Long-run, singular (unpredictable) decisions better handled in scenarios
• Emergent properties of many small, short-run decisions may be modelled
well under some scenarios, possibly different driver according to scenario
• Does human adaptation matter for humans?!
• Yes – but a focus on resilience and adaptive capacity crucial for this
CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 14-15 Aug 2007
Priorities
• Clarify what management/land use effects need to be included in
DGVMs
• Current land use change and management that significantly affects
feedbacks
• Assess significance at key scales and purposes
• Determine whether causal agency is usefully incorporated
• Focus on major endogenous feedbacks with significant impact on
primary purposes of DGVM
• Climate change itself having 1st order effect on economic/social/policy
system which drives major changes in land use/management?
• Filter these by pathways through ‘amenable’ decision types, else use
scenarios
• Key developmental pathways maybe worth considering also
• For adaptation, put major investment in other areas
• Targeted at adaptive capacity and resilience (esp. hearing Graham!)
• Underinvested at present
CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 14-15 Aug 2007
Thank you
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