How to study the combined domains of environment, economics, and social
behavior in Thailand and Cambodia and Uganda and Tanzania
Binford, Michael W.1, Lin Cassidy1, John Felkner2, Robert M. Townsend2, Alan L.
Kolata3, Jane Southworth1.1Department of Geography, University of Florida, 2Department of Economics,
3Department of Anthropology, University of Chicago
Human Ecology 101• Ecology: The study of interactions between
organisms and their environment (That’s it!)
• Humans are organisms – not special in any way except perhaps cultural learning; new adaptations can be inherited culturally.
• Environment includes other humans (intraspecific), non-human biota (interspecific: vertebrates, invertebrates, microbes), non-living environment (water, nutrients, climate, mineral soils, atmosphere).
• Microbes play multiple roles: direct (pathogens) and indirect (biogeochemical cycles)
Human Ecology 101All social systems exist within physical and biological environments and interact with them, so studying separated social and ecological systems makes sense only if one wants a limited perspective. Nonetheless, they have been separated by disciplinary tunnel vision for generations.
Thinking is changing: integrating multiple disciplines for large questions, problem solving.
Authors of this paper: Geographers, economists, anthropologists, landscape ecologists
Global Question• Overall theme of these five sessions: Linking Social
and Ecological Systems: How do we study the whole social-ecological system?
1. Theory, rationale, necessity2. Human focus: governance and tenure3. Human focus: Livelihoods4. Landscape5. Infrastructure (including this methodological paper)
• THEORY-RICH but DATA POOR– Data are expensive. – We must be careful how they are collected
Specific Question
• Given that we have some theory (with appropriate models) that leads to falsifiable hypotheses with well-defined variables, how do we sample to measure things in the field so as to assure representative variation from both social and environmental domains?
Limits
We confine our perspective of human behavior in this case to economic and cultural behaviors.
“Environment” will be limited to land cover in space, soil moisture in space and time.
Some Fundamentals: Define the Problem
• Land cover/vegetation is a physical, visible manifestation of the activities of a social-ecological system
• Land cover is influenced by land use, which is one of the most obvious and important human-environment interactions.
• Specific social-ecological system question: How do environmental, economic, and cultural variations interact to drive landscape dynamics; of which land-cover and land-use changes are the visible dependent variables?
Decade+ of Work• LUCC Agenda: Linking land use to land cover to
explain the changes over time has been the focus of over a decade of research – Turner et al. 1994 Research Agenda.
IHDP/IGBP– NAS/NRC. 1998. People and Pixels– Walsh and Crews-Meyer. 2004. Linking
People, Place, and Policy: A GIScience Approach
• Hasn’t actually done much with environment or with human ecology – mostly social science.
The Usual Methods• Using remotely sensed data to describe land cover and
its change over time• Measurements of “biophysical” factors thought to be
important– DEM, Hydrography, vegetation, soils– Static information
• Interviews with land tenure holders to determine causes of particular changes– Households, firms, institutions– Social, economic information
• Statistical (incl. econometric) analyses seeking correlations to test hypotheses
• How do we do this?
Four Linking Methods1. Linking areas on the landscape to owners or
users and to long-term environmental records2. Recording geographic locations of all data for
exploratory data analysis3. Designating many small but sufficiently large
land areas as units of study4. A priori strategies to assure representative
sampling jointly across socio-ecological domains
5. Others?
1. Linking areas on the landscape to owners or users and to long-term environmental records.
McCracken S.D., B. Boucek, E.F. Moran. 2004. Deforestation trajectories in a frontier region of the Brazilian Amazon. Ch 10 in Walsh and Crews-Meyer. (eds). Linking People, Place, and Policy: A GIScience Approach.
Social Drivers of Land-cover Change
Nucleated Villages and Dispersed Lands
Crawford, T. 2004. Ch. 5 in Walsh and Crews-Meyer. 2004. Linking People, Place, and Policy: A GIScience Approach
Nucleated Villages and Dispersed Lands
Rindfuss, R. et al. Ch. 2 in Walsh and Crews-Meyer. 2004. Linking People, Place, and Policy: A GIScience Approach
Rindfuss, R. et al. Ch. 2 in Walsh and Crews-Meyer. 2004. Linking People, Place, and Policy: A GIScience Approach
Zones of Influence
Zones of Influence
Rindfuss, R. et al. Ch. 2 in Walsh and Crews-Meyer. 2004. Linking People, Place, and Policy: A GIScience Approach
Crawford, T. 2004. Ch. 5 in Walsh and Crews-Meyer. 2004. Linking People, Place, and Policy: A GIScience Approach
2. Recording geographic locations of all data for exploratory data analysis
• Common to all data collection: location• GIS data and software forms the
integrating storage, manipulation, retrieval, analytical tools, communication of results
• Even if information is not inherently spatial, utility is enhanced - future analyses
Field Work
3. Designating many small but sufficiently large land areas as units of study
• Replication• Controlling for various factors –
independent variables• Size of individual areas must capture
variation from several domains– Land tenure and other human activities– Biodiversity and land cover– Other factors
Kibale Landscapes
Major Land Use / Land cover Types in Kibale
1. Forest & forest fragments2. Papyrus swamps3. Agricultural fields
a. Crop fieldsb. Fallow fields
4. Pasture & grassland5. Tea plantations
Complex mosaics of land use/cover types
Kibale Park & Bigodi Superpixels
High ForestTea PlantationRecent Clearing
Pasture - FallowRiparian Forest – Forest FragmentPapyrus Swamp
Kibale and Tarangire to Scale
Tarangire Savanna Landscape
Tarangire Landscapes
4. a priori strategies to assure representative sampling jointly across the domains
• Sampling protocols sharply defined to address research questions.
• Statistical analyses defined• Representative of variables testable• Control to eliminate possible biases
General Study Area
The Original Problem• Income Growth in
Thailand over Past 25 Years has been Phenomenal
• But, There are Regional Disparities in Income Growth
• Economists ask “Why?”
• Conventional wisdom suggests an environmental cause for regional income disparities: the soils are less fertile and there precipitation is more variable in the poorer regions.
The Questions
• Is environmental variability an important underlying cause of income-growth disparity? Rural, agriculture and Risk
• How are environmental variables correlated with economic variables?
• Specifically, how does environmental variability create risk to agricultural production, and how do farmers and their villages cope with risk?
The Hypotheses
• Soil Fertility and Precipitation Amount and Timing are Positively Correlated with 25-year Income Growth and Other Economic Variables.
• “Good” and “bad” years environmentally, i.e. droughts or floods, will also be “good”and “bad” years economically.
Testing the Hypotheses
• Measure income growth, access to credit and insurance, use of financial institutions,
• Measure temporal and spatial variability of environmental factors, including soil fertility, weather patterns, fertilizer use and access to water of useful quality.
• Correlate economic and environmental variables to discover relationships.
Sampling in Thailand
• Four Changwats (States or Provinces) in Thailand selected to represent a broad spectrum of wealth and income growth– Lop Buri: in the center of
the “Rice-Bowl of Asia”– Chachoengsao: in the
growth corridor east of Bangkok
– Buri Ram: Lower wealth on the edge of the northeast
– Sisaket: poor province in the northeast
The Sampling• Logistic capability of sampling a total of 200 villages (50
per Changwat) with 15 households in each village sampled for economic factors and 10 for environmental factors.
• Villages organized in Tambons (Sub-Counties) for which we had economic and environmental data.
• So the question for this phase of the project is: How can we be sure to select villages in such a way that the individual and joint variations of environmental and economic factors will be sampled representatively?
Definitions of Environmental Variability
• Temporal variability is defined by monthly precipitation and streamflowmeasurements – water availability.
• Spatial variability is defined by the distribution of vegetation and soils types, or “Land Cover.”
• Spatially explicit soil moisture estimation (Felkner and Binford 2002)
Methods
• Monthly Precipitation used to create soil moisture budgets for several soils (50, 100, 150 and 200 mm available water capacity).
• Satellite Remote Sensing (Landsat Thematic Mapper) used to create maps of spectral land-cover classes
Precipitation• Annual total precipitation
is not significantly different across the study area, but the timing is.
• The Northeast has shorter rainy seasons.
Worst Year Soil Moisture Budget for Sisaket, Thailand
0
100
200
300
400
500
1 2 3 4 5 6 7 8 9 10 11 12
Month of the Year
mm
Equ
ival
ent D
epth
PPETAETD
Sisaket Tambons CCA Axis 1 and 2 Scores
CCA Axis 1-2 -1 0 1 2 3
CC
A A
xis
2
-2
-1
0
1
Sample Sites in Sisaket
Sampled Tambons
Comparison Between All Sisaket Tambons and Sampled TambonsEconomic Variables
All Tambons Sampled Tambons All Tambons Sampled TambonsMean Std. Dev. Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
POP 5428 2710 6511 3134 RICENEW 715 388 1031 285HH 981 434 1217 510 FERTIL1 890 378 1062 305HH_AGR 916 400 1102 404 FERTIL31 0 0 0 1SURF3 11 13 12 17 FERTIL32 4 4 4 3FOREST 12 5 16 6 FERTIL33 6 4 8 3TWELL 361 219 429 272 CHEM 643 384 812 321DWELL 169 132 193 123 ORCH1 3 14 5 15SWELL 195 173 240 246 VEGF1 65 191 159 322PIPE2 43 91 39 95 FLOWER1 0 2 0 1DRINK_W 551 347 738 432 RUBBER1 3 16 6 20AGR_W_1 0 1 0 1 DRYAGR1 3 3 4 4VILL 10 4 12 3 DRYAGRS 1 1 1 1SURF1 9 4 10 3 DRYAGRU 3 3 3 4SING1 418 391 426 412 DRYAGRR 0 0 0 0SING3 373 382 326 342 PUBF1 1 2 1 1SING5 12 47 29 87 PUBF2 258 758 297 590SING7 0 1 0 0 SOILQ11 6 4 8 5SING9 0 0 0 0 SOILQ12 0 1 1 1SING11 1 4 0 0 SOILQ2 1 2 1 2SING13 3 5 3 3 SOILQ31 1 2 1 4SING15 1 4 0 0 SOILQ32 0 1 0 1SING17 24 40 64 112 SOILQ33 1 2 2 2RICEY 285 91 283 45 SOILQ34 0 1 1 1FERTIL2 1983 988 2256 580 SOILQ35 5 4 6 3ORCH2 0 1 0 0 SOILQ36 1 2 1 3VEGF2 0 0 0 0 SOILQ37 1 1 1 1FLOWER2 0 0 0 0 SOILQ38 0 1 0 0RICEN1 908 386 1072 323 SOILQ4 654 358 904 303RICEN2 4 21 27 66 SOILQ5 159 269 285 388RICEN3 3 20 2 5 PROPTY5 19599 11114 23118 11436RICEN4 3 31 0 0 PROPTY7 16264 8764 19672 9955
NONE SIGNIFICANTLY DIFFERENT
Conclusions• Economic variables already used to select Changwats.• Only two discernable classes of “environment”: Forested
uplands and non-forested lowlands.• Therefore, only a simple stratified random sample of
Tambons is required to capture both economic and environmental variation.
• Satellite remote sensing allowed us to do a rapid, inexpensive, and synoptic assessment of “environment”without knowing a priori very much about the distribution of land cover in widespread areas of Thailand, and provided a defensible sampling design for capturing the individual and joint variation between environmental and economic variables.
Field and Soil Sampling – Linked
to Household/Village/
Tambon
General Soil Fertility Results
Sampling in Cambodia
Four Provinces in Cambodia also selected to represent a broad spectrum of wealth and income growth. All with access to Tonle Sap.
Battambang: wealthy for rich agriculture, commerce.
Kampong Thom: wealthy –commerce, agriculture.
Siem Reab: Somewhat poor, but location of Angkor Wat temple –tourism major industry.
Otdar Meanchey: Very poor, last redoubt of Pol Pot. Created in late ’90’s because of difficulty of administration due to remnants of civil war.
Vietnam
ThailandLaos
Phnom Penh
Siem ReapBattambang
0 50 100 150 20025
Kilometers
Major Cities
The Best Method?
• Depends on one’s research question, theory, models, variables.
• Scale of landscape, scale of human activities, scale of biophysical processes and patterns
• Invent your own, but the primary objective is to assure representative, unbiased sampling.
Thanks To:
• National Science Foundation, SBR-9515306, and NICHD (National Institute of Child Health and Human Development) 5-RO1-HD27638-06 (R.M. Townsend, PI)
• National Science Foundation, BCS-0433787 (Alan L. Kolata, Michael W. Binford, and Robert Townsend, PIs)
• National Oceanic and Atmospheric Administration, Office of Global Programs NA56GP0360 (M.W. Binford, PI)
