human adaptation of land management mark stafford smith, csiro sustainable ecosystems (+ mark...

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

CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 14-15 Aug 2007

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?


Basis

Vegetation composition,condition and function

Land use,management

Regionalclimate

Global drivers

Social/$ context

Policy context

Longer-term feedbacks – economic, markets, regulatory, perceptual, behavioural

Contribution to global impacts

“Significant” change: does it matter to these feedbacks??

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?


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


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


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)


James et al, 1999: J.Arid Environments

Etter et al. 2006, J.Envir.Mgmt 79: 74-87


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…


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


Classifying where to model adaptation

• Too easy to get overloaded with options…



• 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



• 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


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


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)


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


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

Contact UsPhone: 1300 363 400 or +61 3 9545 2176

Email: [email protected] Web: www.csiro.au

Thank you

human adaptation of land management mark stafford smith, csiro sustainable ecosystems (+ mark...

Documents

climate change workshop

land condition

land clearing

land councils

land usemanagement

examples of change

land cover

impacts slide