Human Adaptation of Land Management

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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

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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?

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Basis
Vegetation composition,condition and function
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?
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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

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Land use/management that could matter

• Examples abound
• Legislation to stop land clearing in Australia
• Woody thickening in response to grazing/fire
  management
• USs 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

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Land use/management that could matter

• Examples abound
• Legislation to stop land clearing in Australia
• Woody thickening in response to grazing/fire
  management
• USs 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

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(No Transcript)
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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

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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

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Classifying where to model adaptation

• Too easy to get overloaded with options

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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/proc
  ess 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

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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

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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

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Examples

• Fire
• At broad level of human influence at regional
  scales suppress gtgt big hot, or not gtgt 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 gtgt??
• 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
  scalespredictable in some scenarios (efficiency
  gain responses probably predictable in all,
  though wildcards eg. GM etc)

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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. Whats 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

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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

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Thank you