Human Adaptation of Land Management Mark Stafford Smith, CSIRO Sustainable Ecosystems (+ Mark Howden, Rohan Nelson) Vegetation Dynamics and Climate Change, 15 th August 2007 Meeting on Ngunnawal country

CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 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?

CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS Aug 2007 Basis Vegetation composition, condition and function Land use, management Regional climate 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?

CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 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 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 (~1/5 th 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 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 (~1/5 th 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 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 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 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 Aug 2007 Classifying where to model adaptation Too easy to get overloaded with options…

CSIRO. Vegetation Dynamics and Climate Change Workshop, AAS 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 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 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 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 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 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 1 st 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 Us Phone: or Web: Thank you