Download How to study the combined domains of environment, economics

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 101
All 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
•
1.
2.
3.
4.
5.
Overall theme of these five sessions: Linking Social
and Ecological Systems: How do we study the whole
social-ecological system?
Theory, rationale, necessity
Human focus: governance and tenure
Human focus: Livelihoods
Landscape
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 socialecological system
• Land cover is influenced by land use, which is
one of the most obvious and important humanenvironment 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 Methods
1. Linking areas on the landscape to owners or
users and to long-term environmental records
2. Recording geographic locations of all data for
exploratory data analysis
3. Designating many small but sufficiently large
land areas as units of study
4. 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
Zones of Influence
Rindfuss, R. et al. Ch. 2 in Walsh and Crews-Meyer. 2004. Linking People, Place, and Policy: A
GIScience Approach
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 fragments
2. Papyrus swamps
3. Agricultural fields
a. Crop fields
b. Fallow fields
4. Pasture & grassland
5. Tea plantations
Complex mosaics of land use/cover
types
Kibale Park & Bigodi Superpixels
High Forest
Tea Plantation
Recent Clearing
Pasture - Fallow
Riparian Forest – Forest Fragment
Papyrus 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 25year 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 streamflow
measurements – 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
mm Equivalent Depth
500
400
300
P
PET
200
AET
D
100
0
1
2
3
4
5
6
7
8
9 10 11 12
Month of the Year
Sisaket Tambons
CCA Axis 1 and 2 Scores
1
0
-1
0
1
-1
-2
CCA Axis 2
-2
2
3
CCA Axis 1
Sample
Sites in
Sisaket
Sampled Tambons
Comparison Between All Sisaket Tambons and Sampled Tambons
Economic Variables
All Tambons
Sampled Tambons
Mean Std. Dev. Mean
Std. Dev.
POP
5428
2710
6511
3134
HH
981
434
1217
510
HH_AGR
916
400
1102
404
SURF3
11
13
12
17
FOREST
12
5
16
6
TWELL
361
219
429
272
DWELL
169
132
193
123
SWELL
195
173
240
246
PIPE2
43
91
39
95
DRINK_W
551
347
738
432
AGR_W_1
0
1
0
1
VILL
10
4
12
3
SURF1
9
4
10
3
SING1
418
391
426
412
SING3
373
382
326
342
SING5
12
47
29
87
SING7
0
1
0
0
SING9
0
0
0
0
SING11
1
4
0
0
SING13
3
5
3
3
SING15
1
4
0
0
SING17
24
40
64
112
RICEY
285
91
283
45
FERTIL2
1983
988
2256
580
ORCH2
0
1
0
0
VEGF2
0
0
0
0
0
0
0
0
FLOWER2
RICEN1
908
386
1072
323
RICEN2
4
21
27
66
RICEN3
3
20
2
5
RICEN4
3
31
0
0
RICENEW
FERTIL1
FERTIL31
FERTIL32
FERTIL33
CHEM
ORCH1
VEGF1
FLOWER1
RUBBER1
DRYAGR1
DRYAGRS
DRYAGRU
DRYAGRR
PUBF1
PUBF2
SOILQ11
SOILQ12
SOILQ2
SOILQ31
SOILQ32
SOILQ33
SOILQ34
SOILQ35
SOILQ36
SOILQ37
SOILQ38
SOILQ4
SOILQ5
PROPTY5
PROPTY7
All Tambons
Sampled Tambons
Mean Std. Dev. Mean
Std. Dev.
715
388
1031
285
890
378
1062
305
0
0
0
1
4
4
4
3
6
4
8
3
643
384
812
321
3
14
5
15
65
191
159
322
0
2
0
1
3
16
6
20
3
3
4
4
1
1
1
1
3
3
3
4
0
0
0
0
1
2
1
1
258
758
297
590
6
4
8
5
0
1
1
1
1
2
1
2
1
2
1
4
0
1
0
1
1
2
2
2
0
1
1
1
5
4
6
3
1
2
1
3
1
1
1
1
0
1
0
0
654
358
904
303
159
269
285
388
19599
11114
23118
11436
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.
Thailand
Laos
Siem Reap
Battambang
Kampong Thom: wealthy –
commerce, agriculture.
Phnom Penh
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
Major Cities
0 25 50
100
Kilometers
150
200
­
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)