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Social and Biophysical Dynamics of Reforesting Systems: Tensions between Macroscale Theories and Loc


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Title: Social and Biophysical Dynamics of Reforesting Systems: Tensions between Macroscale Theories and Loc

Social and Biophysical Dynamics of Reforesting
Systems Tensions between Macro-scale Theories
and Local-scale Findings
  • Tom P. Evans1
  • 1 Department of Geography and Center for the
    Study of Institutions, Population and
    Environmental Change
  • 2 Workshop in Political Theory and Policy
  • Indiana University
  • Bloomington, IN (USA)
  • Contact
  • May 23, 2007

Trajectories of Global Land Cover Change
  • Considerable amount of research focused on
    proximate causes of deforestation, especially
    tropical deforestation
  • Lambin et al. 2001, Global Env. Change

Reforestation in Context of Global Environmental
  • Less attention has been focused on reforestation
  • Reforestation of increasing relevance
  • Global climate change and carbon related policy
    programs (carbon trading)
  • Biodiversity loss
  • Impact of forest regrowth on hydrological
  • What is the potential for reforestation to offset
    losses of carbon due to deforestation locally and
  • What trajectories of land cover change are likely
    among countries currently experiencing net

Forest Transition Theory
Foley et al. 2005. Science
Pathways of Forest Transition
  • Transition to Forest Regrowth/Recovery
  • Rudel et al. 2005, Global Env. Change and
  • Economic Development Path
  • As economy develops, labor shifts from on-farm
    activities to off-farm wage labor opportunities
  • Loss of on-farm labor results in abandonment of
    more remote, less productive areas
  • Path reinforced when governments purchase
    unproductive areas
  • Much of the US National Forest System followed
    this pathway
  • Greece, Ireland, Portugal, USA(?)
  • Forest Scarcity Path
  • As timber products become more scarce, price for
    timber increasese, (some) landowners plant trees
    instead of crops (e.g. India Foster and
    Rosenzweig, 2003)
  • Path reinforced when governments initiate
    reforestation programs and subsidize tree
    planting efforts
  • India, China, Bangladesh, Vietnam

Pathways of FTT
  • Economic Development Path generally occurs at a
    higher deforestation trough point or level of
    net forest cover than the Forest Scarcity Path
  • US, European countries, generally 15-30
  • Less developed countries, generally lt 15

Rudel et al. 2005
Reforestation Transitions
  • Forest Transition Theory
  • Tied to process of economic development and
  • Therefore, generally applied to national scale
  • Applicability at regional or local scale is
    uncertain and one source of criticism
  • What switches system from deforestation to
  • What determines rate of reforestation?
  • What determines (probable) point of maximum

Forest Cover
System States and FTT
  • Forest transitions and system states
  • Five phases or system states can be imagined in
  • Natural system
  • Deforestation
  • Stable forest cover (trough)
  • Reforesting
  • Reforested ? ???
  • Is shift between states the product of large
    shocks or modest dynamic changes?
  • Black death 14th century Europe
  • Great Depression (1920s) - US
  • World War II (1940s) - US

Forest Cover
Complexity and Land Change Science (LCS)
  • Geography/Human Dimensions of Global Change
    perspectives on complexity (Manson 2001)
  • Algorithmic complexity
  • Difficulty in defining salient system components
  • Simplest possible system definition that can
    replicate behavior of the system (Chaitin 1992)
  • Deterministic complexity
  • Interaction between system components can lead to
    shifts in system state or equilibria
  • Aggregate complexity
  • emergent behavior approach interactions of
    agents/actors at one scale leads to particular
    outcomes (e.g. landscape configuration) at
    another scale

Complexity and Land Change Science (LCS)
  • Deterministic complexity
  • Resilience Alliance http//
  • Aggregate Complexity
  • Local level decision-makers (households),
    aggregate scale outcomes (regional scale land
    cover composition and pattern)
  • Interactions between households

Deforestation in Eastern United States
Deforestation in Eastern United States
  • Indiana (68) and Ohio (72) experienced the most
    dramatic loss of forest cover in the United
  • Large areas suitable for agricultural production
    where native ecosystem type is forest
    (pasture/grassland native state in Iowa, Dakotas,
    most of Illinois)

Reforestation in Eastern United States
Reforestation in Eastern United States
  • Most states have experienced net reforestation,
    especially Northeast US
  • Modest gains in Midwest US
  • Subset of states net deforestation
  • Florida, NJ urban growth
  • North Carolina, Tennessee?

(No Transcript)
  • Flat topography in north, rolling topography in
  • One major urban area
  • Indianapolis (850,000 residents)
  • Landscape dominated by corn/soybean rotation in
    north and mosaic of forest and agriculture in

Local Scale Dynamics
  • Private land owners critical to trajectory of
    forest cover change in Indiana
  • 87 of forest land in Indiana is on private land
  • Remaining forest lies within federal and state
    managed lands, the majority of which are actively
  • Selective harvesting, some clearcutting
  • Communities are relatively weak sources of
    institutions in this context
  • Some counties have planning/zoning but not all,
    and those counties that do have zoning do not
    necessarily have plans that reinforce healthy
    forest management
  • Stay tuned…

Trajectories of Forest Change in Indiana
Monroe County, Indiana (Midwest US)
  • Roughly 30x40km, modest but steady population
  • 100,000 in 1980 ? 120,000 in 2000
  • Net Forest cover increase
  • 43 in 1939, 60 in 1997
  • This trough point and net amount of reforestation
    analogous to that seen in Northeast USA

Net Reforestation in Monroe County
  • Aerial photography 1939-2003 provides the ability
    to develop spatially explicit data farther back
    in historical record

Macro Explanations of Reforestation?
  • Macro Explanations
  • Demographic change?
  • Plausible 1940-1960
  • 1970-2000???
  • Commodity prices?
  • Majority of reforestation occurred in early to
  • Great Depression
  • Land abandonment, particularly marginal,
    non-sustainable agricultural areas

Biofuels and Ethanol Production
  • 16 million hectares currently enrolled in CRP,
    much of which is due to expire
  • Price of corn
  • 1.86 in January 2006
  • gt 3.70/bushel now (with projections gt 4/bushel)
  • Huge implications for global food prices
  • Huge implications for current trend of
    reforestation in US

Local Scale Dynamics
  • Household level surveys 1998 and 2003, land cover
    data derived from Landsat satellite imagery 1984
    and 1997
  • HH survey results integrated with landcover data
    through parcel boundary data
  • Both reforestation and deforestation occurring,
    but more reforestation (8.4) than deforestation
    (4.9) for a net forest cover increase of 3.6
  • Older landowners more likely to have
  • Parcels with more steep topography exhibited more
  • Overall, statistical results explain a relatively
    low proportion of the variance for parcel level
    land cover change

Local Scale Dynamics
  • Large proportion of modeling literature focuses
    on relatively simple explanations for land cover
    change trajectories
  • Topography
  • Accessibility, distance to markets
  • These simplistic explanations under-emphasize the
    role of decision-making, and especially the
    diversity of decision-making strategies employed
    by land holders
  • Why would two landowners with similar land
    attributes (parcel size, topographic
    distribution, accessibility) make different land
    management decisions?

Spatial Clustering of Landcover Change
  • Concentration of parcels with steep slopes in NE
    and East part of county
  • Concentration of parcels with shallow slopes in
    SW part of county
  • NW and south-central areas exhibit heterogeneous
    mix of steep parcels and shallow parcels

Spatial Clustering of Landcover Change
  • Most locations exhibit heterogeneous mix of
    landcover change trajectories
  • Selected locations of clustered LCC
  • Deforestation
  • (large blue dots)
  • Reforestation
  • (large pink dots)

Agent-Based Modeling of Land Cover Change
  • Agent-based modeling focuses on individual actors
  • How do interactions between actors at local scale
    produce particular landscape outcomes at higher
  • How does heterogeneity (diversity) of actors
    affect land management outcomes?

Focus on Parcel/Household level analysis
  • Land-cover patterns at the landscape scale as
    emergent property of interactions at local scale
  • Ownership parcels as building blocks of the
    landscape that are related to both composition
    and configuration

Agent Based Modeling of Land Cover Change
  • Model of labor and resource allocation
  • Labor allocated to various land uses according to
    time series of price data
  • Forest, crops, pasture, off-farm labor
  • Homo-economicus or Economic Man…
  • Perfect decision maker
  • Perfect information, makes decisions that
    optimize utility
  • Model using landowners with diverse
    characteristics/preferences produces best fit to
    observed land cover change at parcel level
  • (Evans and Kelley 2006, IJGIS)

Land Change Science - Integrated Research Design
Spatially Explicit Natural Resource Based Experime
Empirical Data Analysis
Empirical Data Analysis
Theoretical Developments Policy Recommendations
Data Collection
Experimental research
  • Common in some fields (psychology, economics) and
    becoming accepted in other fields (political
    science, anthropology)
  • D. Kahneman, 2002 Nobel Prize
  • Ostrom and Nagendra 2007. Insights on linking
    forests, trees, and people from the air, on the
    ground and in the laboratory. Proceedings of the
    National Academy of Sciences.
  • Spatial experimental research
  • Test theories of land-use decision-making to
    support empirical data analysis from the field
  • Resource allocation experiment

Spatial Experiment Resource allocation
  • Basic structure
  • 15x15 cell landscape
  • 9 subjects/partitions, 25 cells each (5x5) 45
  • Subject decision Place cells in either use B or
    G resource
  • Prices of B and G change through experiment
  • 40 total decision-making rounds (prices
  • Cash payout, subjects told their payout is
    proportional to their success in the experiment
  • Total revenue received through all rounds

Experiment Interface
Spatial Decision-Making Experiments
  • Comparison of landscapes from experiments and
    landscapes from agent-based model demonstrate
    that experimental subjects often do not choose
    optimal resource allocation

Spatial Decision-Making Experiments
  • Landscapes emerging from Non-optimal subject
    decisions have different spatial characteristics
    than optimal landscapes produced from
    simulations of perfect decision-makers
  • Greater landscape diversity
  • More landscape edge
  • Directly supports findings from agent-based
  • Diverse agent types
  • More complex landscape patterns than predicted
    solely by land suitability

Landscape Edge of Simulated Agents and
Experimental Subjects
Landscape Edge of Simulated Agents and
Experimental Subjects
Matching Experiments to Simulations
Optimal allocation by round
Simulation run No positive spatial
externality Heterogeneous suitability
Experiment run No positive externality Heterogeneo
us suitability
Simulation run Positive spatial
externality Heterogeneous suitability
Experiment run Positive externality Heterogeneous
Complexity in Transition from Deforestation to
  • We do see support for shock explanation of
    system state change, transition from
    deforestation to reforestation
  • Continuing process of reforestation is more
  • Change in economic opportunities
  • As economy develops, more off-farm wage labor
  • Importance of heterogeneity, change in
    preferences, experiences and information among
  • Evidence from household surveys supported by
    insights gained from agent-based models and
    experimental research
  • Diversity of household level attributes key to
    the regional level pattern of land cover change,
    transition from deforestation to reforestation
  • In-migration
  • New agricultural practices introduced
  • Agricultural extension
  • Innovators adopt new agricultural methods,
    diversify land uses
  • Education
  • Environmental valuation
  • Risk, learning

Limitations of Forest Transition Theory
  • Reforestation has generally occurred in areas
    most marginal for agricultural production
  • Steep slopes, areas of low accessibility, poor
  • Physiographic diversity of reforested state is
    lower than pre-settlement forest cover
  • Lowland forests, valley bottoms
  • Implications for ecological diversity
  • Large amount of reforestation, especially in
    developing countries, is plantation forest (e.g.
  • Alleged benefits in terms of carbon, more
    questionable ecological benefits
  • Hydrological benefits
  • Loose definition of forest
  • Rubber plantations in Laos, palm oil categorized
    as forest in Southeast Asia
  • Again, questionable ecological benefit

Rubber Adoption in PDR Loas
  • Rural landscape dominated by smallholders
    practicing shifting cultivation
  • Diverse land suitability, areas suitable for
    lowland rice vs. areas for upland crops
  • Introduction of rubber from China (social
  • 7 year lag between planting trees and collecting
  • Major household decision to allocate land, must
    consider level of risk aversion and prediction of
    future rubber market prices

Rubber Adoption in PDR Loas
  • Agent based model
  • Households allocate labor to different land
    holdings based on HH labor availability
  • HH decide to maintain existing land uses or
    transition to new land uses
  • Spatial interaction and social network measure,
    lead to faster or slower adoption within
  • Are early adopters better off than late adopters?
  • Demo

  • Midwest US/Brazilian Amazon project
  • U.S. National Science Foundation (US research)
  • Elinor Ostrom, Shanon Donnelly, Hugh Kelley,
    Wenjie Sun, Jimmy Walker, Sean Sweeney, Jerry
    Busemeyer, Vicky Meretsky
  • Laos PDR project
  • Jeff Fox, John Vogler, Khamla Panvilay
    East-West Center
  • NASA, NSF funding
  • Center for the Study of Institutions, Population
    and Environmental Change (CIPEC) Workshop in
    Political Theory and Policy Analysis Indiana

  • Elmqvist and e. al. (2007). "Patterns of Loss and
    Regeneration of Tropical Dry Forest in
    Madagascar The Social Institutional Context."
    PLoS ONE 2(5).
  • Evans, T. P. and H. Kelley (2004). "Multi-scale
    analysis of a household level agent-based model
    of landcover change." Journal of Environmental
    Management 72(1/2) 57-72.
  • Evans, T. P. and H. Kelley (in press). "Assessing
    the transition from deforestation to forest
    regrowth with an agent-based model of land cover
    change for South-Central Indiana (USA)."
  • Evans, T. P., W. Sun, et al. (2006). "Spatially
    explicit experiments for the exploration of
    land-use decision-making dynamics." International
    Journal of Geographical Information Science
    20(9) 1013-1037.
  • Foley, J. A., R. DeFries, et al. (2005). "Global
    Consequences of Land Use." Science 309(5734)
  • Geist, H. J. and E. F. Lambin "Proximate Causes
    and Underlying Driving Forces of Tropical
    Deforestation." BioScience 52(2) 143-150.
  • Lambin, E. F., B. L. Turner, et al. (2001). "The
    causes of land-use and land-cover change moving
    beyond the myths." Global Environmental Change,
    Part A Human and Policy Dimensions 11(4)
  • Ostrom, E. and H. Nagendra (2006). "Insights on
    linking forests, trees, and people from the air,
    on the ground, and in the laboratory."
    Proceedings of the National Academy of Sciences
  • Perz, S. G. (2007). "Grand Theory and
    Context-Specificity in the Study of Forest
    Dynamics Forest Transition Theory and Other
    Directions." The Professional Geographer 59(1)
  • Perz, S. G. and D. L. Skole (2003). "Secondary
    Forest Expansion in the Brazilian Amazon and the
    Refinement of Forest Transition Theory." Society
    Natural Resources 16(4) 277-294.
  • Rudel, T. K., O. T. Coomes, et al. (2005).
    "Forest transitions towards a global
    understanding of land use change." Global
    Environmental Change 15(1) 23-31.

  • Thank you.…
  • Questions?