household demographic change and land use/land cover change in the brazilian amazon
Post on 14-Jul-2016
218 Views
Preview:
TRANSCRIPT
ORIGI NAL PAPER
Household demographic change and land use/landcover change in the Brazilian Amazon
Leah K. VanWey Æ Alvaro O. D’Antona Æ Eduardo S. Brondızio
Published online: 31 May 2007
� Springer Science+Business Media, LLC 2007
Abstract Demographic interest in population and environment has grown in re-
cent decades. One of the most prominent research areas in this tradition addresses
the impact of population on land use and land cover change. Building on this
tradition, we examine the effects of household demographic composition on land
use and land cover on small farms in two study areas in the Brazilian Amazon.
Fixed effects regression models of used area and forested area show few consistent
effects of changes in household demography on land use and land cover change.
Effects are inconsistent with the household life cycle model that currently dominates
the literature on household demographic effects in frontiers. Changes in the number
of children and women, particularly young women, have the most significant effects
on land use and land cover change. We conclude by arguing that households stra-
tegically access cash for investment in agriculture and that specific strategies are
determined by economic and institutional context.
Keywords Household demography � Brazil � Amazon � Land use/land cover
change
L. K. VanWey (&)
Anthropological Center for Training and Research on Global Environmental Change,
Department of Sociology, Indiana University, Bloomington, IN, USA
e-mail: lvanwey@indiana.edu
A. O. D’Antona
Anthropological Center for Training and Research on Global Environmental Change and NEPO,
University of Campinas, Campinas, Sao Paulo, Brazil
E. S. Brondızio
Anthropological Center for Training and Research on Global Environmental Change,
Department of Anthropology, Indiana University, Bloomington, IN, USA
123
Popul Environ (2007) 28:163–185
DOI 10.1007/s11111-007-0040-y
Debates have raged for centuries on the importance of human demography in
environmental change (Bongaarts, 1992; Boserup, 1981; Carr, 2004; Ehrlich &
Holdren, 1971; Malthus, 1989 [1803]; Rindfuss, Turner, Entwisle, & Walsh, 2004;
VanWey, Ostrom, Meretsky, 2005). This issue has seemed pressing at various
points in history because of positive (and often high) population growth rates and
various negative environmental trends (e.g., increasing air and water pollution,
desertification, global climate change, and local food shortages). However, the most
influential analyses of the relationship between population change and environ-
mental change have often focused on macro-level trends and correlations and not on
the actions or characteristics of individuals and households (Dietz & Rosa, 1997;
Ehrhardt-Martinez, Crenshaw, & Jenkins, 2002; Lambin et al., 2001; O’Neill,
MacKellar, & Lutz, 2001; Pebley, 1998; Perz & Skole, 2003). More recently, theory
development and empirical research has explored the population and environment
relationship at the household level, with the strongest work done on household land
use decision-making (Entwisle & Stern, 2005; Marquette, 1998; McCracken,
Siqueira, Moran, & Brondızio, 2002; McCracken et al., 1999; Walker & Homma,
1996; Walker, Perz, Caldas, & Silva, 2002).
This paper builds on such recent research on land use and land cover change,
specifically on theories of land use and land cover change on rural, agricultural
parcels of land associated with households (Brondızio et al., 2002; McCracken
et al., 1999; Pan et al., 2004; Perz, 2001; Perz & Walker, 2002; Pichon, 1996b;
Walker et al., 2002; Walker & Homma, 1996). We draw on this work to motivate
our consideration of household demographic effects on land use and land cover. We
specifically draw on the household life cycle model, which has been influential in
arguing that households are not homogenous in their reactions to outside forces
(prices, credit, etc.), that they follow patterns of land use change over time based on
changes in the demographic composition as the household ages.
Empirical tests of this theory have tended to examine the effects of such variables as
time since arrival in the property, age of the household head, or number of adult males
on land use and land cover. In contrast, we focus on one set of mechanisms proposed by
the theory—the changes in household composition over time. The theory argues that
the changing composition of the household, in terms of both age and gender of
members, should drive changes in land use and land cover. We draw on a rich set of
retrospective data, including detailed measures of changes in household composition
and changes in land use. Using a fixed effects model to control all time-invariant
characteristics of households and study areas, we test the effects of changing household
composition using data from two rural study areas around the cities of Altamira and
Santarem in the state of Para in Brazil (see Fig. 1). These areas provide a robust test of
the household life cycle approach, as they are both frontiers (where the theory has
primarily been applied) but have different histories and biophysical characteristics.
We find little support in our models for the predictions of the household life cycle
model. Changes in the number of adult or adolescent males in the household, who
provide the bulk of the agricultural labor, have no significant effects on land use or
land cover change in either study area. In contrast, increases in the number of children
or women in various ages groups (or the entry of these groups into the household)
significantly affect changes in pasture, perennials, and forest in the two study areas.
164 Popul Environ (2007) 28:163–185
123
The consistency of the lack of support across these two different regions provides
greater confidence in our conclusion that the household life cycle model needs
rethinking. Specifically, the assumption that households are unconnected to larger
labor and capital markets and rely only on household labor for farming does not hold.
Households strategically access cash from off-farm employment, primarily of
women, and from government assistance programs, and they invest in cash crops. The
specific form of household demographic effects then depends on the economic and
institutional context, on who has most access to employment and assistance programs.
Fig. 1 Location and road networks in study areas, Altamira and Santarem, Para, Brazil
Popul Environ (2007) 28:163–185 165
123
Household demography and land use/land cover in frontiers
Research considering the effects of household demography on land use or land
cover in frontier regions, as our study areas are, has focused on the ‘‘household life
cycle.’’ The focus on the developmental cycle of the household is based in
anthropology (Goody, 1958), and arguments about the mechanisms (particularly the
demographic composition of the household) are based on the Chayanovian peasant
economy model (Chayanov, 1966; Walker et al., 2002, 2004; Walker & Homma,
1996). Walker (2003) and Walker and Homma (1996) develop the theoretical
principles underlying the modeling of household demographic and other effects by
combining the Chayanovian approach with a household production model (and
recognizing the changing institutional context of the frontier). In its basic form, this
approach assumes that households have no access to capital or to hired labor, and
that households focus on production to meet consumption needs (rather than to
accumulate capital). When households enter a frontier, in which land is abundant
and labor and capital are scarce, their land use decisions are determined by
household demography. Household demography influences the decisions in three
ways.
First, it represents the consumption needs of the household. In Chayanov’s
original formulation, peasants exist outside of a monetary or exchange economy and
therefore produce primarily to meet consumption needs. This argues for a positive
effect of children and elderly dependents, and of household size on the extent of
land used. Second, household demography determines the amount of labor available
for farming which, in the absence of capital and labor-saving technology,
determines the amount of land that can be used. This argues for a positive effect
of the number of working age members of the household, particularly males in a
setting where men do the majority of the farm work, on the extent of land used.
Third, as the owners of land and their children age and as their children move to
other properties or to urban areas, the time horizon of the owners changes.
Households with many small children have a short time horizon, seeing only the
need to feed and care for the family for the next few years. As these children
become able to help with farm work, and available labor increases beyond the
minimum necessary to support the family, households begin to make investments in
perennial crops or pasture. These are activities that require many years of
investment before generating a return, but which provide a higher return (depending
on market conditions) and/or can be managed with less labor in the long run. While
some have argued for using the age of the household head as an additional covariate
in models to test the role of aging, the theoretical basis of the changes seen over
time in empirical analyses is the changing demographic composition of the
household.
This approach has been used by many researchers studying household land use
decision-making in the Brazilian and Ecuadorian Amazon (Brondızio et al., 2002;
Futemma & Brondızio, 2003; McCracken et al., 1999, 2002; Moran, Brondızio, &
McCracken, 2002; Pan et al., 2001, 2004; Perz, 2001; Perz & Walker, 2002; Pichon,
1996b; Walker et al., 2002). Walker, Perz and colleagues have used this approach in
their study area around Uruara in the state of Para in the Brazilian Amazon (located
166 Popul Environ (2007) 28:163–185
123
between our two study areas). Based on their own analyses and on an extensive
review of the literature, they find mixed evidence for household life cycle effects
using a variety of data and methods. Walker et al. (2002) review the extensive
relevant literature, which shows mixed effects of household demographic compo-
sition on land use. Measures of age of the household head, age of the head at arrival
in the property, family size, and the numbers of adult males, females and children
have generally non-significant effects on a variety of outcomes used by other
researchers, including areas or percent of property in annuals, perennials, pasture
and forest, area deforested, and a variety of measures of production.
Walker et al. (2002) also conduct an empirical analysis of the effects of
household demographic characteristics on farm system (combination of land uses)
using survey data for a sample of farm households. Results show that the
dependency ratio (ratio of consumers to producers) and male family workers each
significantly predict only one of six farming systems. These effects are consistent
with the theoretical approach, as the number of male family members has a positive
effect on the probability of growing ‘‘annuals with perennials’’ and the dependency
ratio has a negative effect on the probability of growing ‘‘perennials with annuals.’’
However, the evidence is not overwhelming for these demographic effects. The
number of men in the household and the dependency ratio generally have non-
significant effects on farm system. The age of the household head has no significant
effect in any of their models. Using the same approach and the same data, Perz
(2001) finds that the number of adults in the household (undifferentiated by gender)
has a positive effect on the area in perennials and pasture and on cattle production.
The number of children has no significant effect in these models.
McCracken, Brondızio and colleagues have also used a household life cycle
approach in their study area in Altamira, one of the areas included in our analyses in
this paper (Brondızio et al., 2002; McCracken et al., 1999, 2002). This research used
remotely sensed measures of forest cover, and also used the time since settlement
(opening of a property) as a proxy for household life cycle. The authors find
evidence of changing deforestation rates (based on remotely sensed data) over the
household life cycle (Brondızio et al., 2002; McCracken et al., 1999), with initially
low rates of deforestation after settlement, followed by a peak in deforestation
within 3–5 years after settlement and another peak 10–15 years after settlement
(Brondızio et al., 2002). However, they use the time since settlement as a proxy for
the life cycle stage of the household. They use a cohort-based approach, examining
each settlement cohort separately and finding the same over time pattern for each
but not exploring the mechanisms underlying that pattern. They also point out that
there is more variation within cohorts than across cohorts, reflecting differences in
household characteristics (including life cycle stage and household demography at
settlement).
Bilsborrow, Pichon and colleagues have examined household demographic or
household life cycle effects on land use among farm colonist households in the
Northern Ecuadorian Amazon. They initially used their field observations to argue
for a refinement of traditional models of rural livelihoods which assumed land
scarcity and labor abundance, the reverse of what is found in Amazonian frontiers
(Pichon, 1996b). In empirical analysis using survey data from their sample of farms
Popul Environ (2007) 28:163–185 167
123
in a Northern Ecuadorian Amazon settlement area, they find that women with
children under 12 are more likely to participate in farmwork (Thapa, Bilsborrow, &
Murphy, 1996). They also find that area in perennials and pasture increases as a
function of household size but that the area in food crops in not significantly related
to household size (Pichon, 1996a). They similarly find household life cycle effects
on forest clearing (Marquette, 1998). However, they find that no measures of
household size or composition significantly predict farm income, participation in
off-farm work or income from cattle (Murphy, 2001).
In more recent work, this team has both linked remotely sensed data to their
survey data and used generalized linear mixed models to estimate household and
community effects on land use / land cover (Pan et al., 2001, 2004; Pan &
Bilsborrow, 2005). This recent work uses the percent of area in a variety of land
covers or land uses (summing to 100% of the property) as dependent variables in
statistical models that simultaneously predict all of the outcomes (dealing with the
correlations between choices about, e.g., perennials and pasture). These models do
not show strong support for the household life cycle or household production
approaches, but they do show some of the expected effects of household labor
supply and children on area in annuals, perennials, pasture and forest (with slightly
different specifications of household demographic variables in each paper). For
example, models in Pan and Bilsborrow (2005) show a positive effect of males on
perennials and a negative effect of males on forest, while showing a negative effect
of children on perennials and a positive effect of children on annuals.
In many of these articles and other work by these research teams predicting
related outcomes, the time on the property has a significant effect on the farm
system or extent of various land uses. The authors largely interpret this as a
household life cycle effect. However, this assumes that only young families settle on
new properties. We and others argue that the effects of the time since arriving on the
property reflects a different sort of cycle. Barbieri, Bilsborrow, and Pan (2005)
argue for a property life cycle, in which land is cleared at different rates depending
on the duration of residence on a property. VanWey, Brondızio, D’Antona, and
Moran (2007) argue for a learning process, where new arrivals in frontiers must
clear large areas of land to experiment with different crops and inputs. Older
residents of new frontiers and newer residents of old frontiers (where agricultural
techniques and knowledge have diffused through the population) need not
experiment in this way and instead can specialize in crops appropriate for their land.
Overall, the empirical literature on the effects of household demography on land
use or land cover is mixed. While there are strong theoretical underpinnings for the
household life cycle model and for effects of household labor and dependents, the
empirical literature shows few significant effects in a large number of models with
varying specifications of independent variables. Further, the classic work in this area
has been challenged by the introduction of the property life cycle in place of the
household life cycle. The variable results could be due to variations across study
areas (as all are based on case studies) or across measurement of dependent
variables (particularly between remotely sensed and survey data), or to inconsistent
specification of independent variables. In particular, the focus of household life
cycle and household production models on household labor and dependents has led
168 Popul Environ (2007) 28:163–185
123
many past researchers to include only measures of the number of adults or adult
males, and number of children or dependency ratio, rather than decomposing the
household into all of its constituent age-gender groups. We endeavor to make a
more rigorous, though still limited, test of these household demographic effects by
specifying identical models, with household demography decomposed into all age-
gender groups and with dependent variables measured from both survey data and
remotely sensed data, for two study areas.
Study areas
Figure 1 shows the locations of our two study areas within the state of Para in Brazil
and the basic road and river networks in each study area. Altamira is a region of
rolling topography, including frequent steep slopes that are unsuitable for many
crops. It is characterized by relatively fertile soils and plenty of available water. The
main rural economic activities are cattle ranching and cocoa production, along with
a variety of subsistence and cash cropping. Figure 2 shows the time of first clearing
(based on classified satellite data) for this study area. In this image, only black areas
still have old growth forest by 2003. This image shows that Altamira exhibits the
traditional fishbone pattern of deforestation radiating out from planned roads. The
Transamazon Highway runs east–west in the center of the study area, with north–
south feeder roads (shown for the study area in Fig. 1) intersecting it at roughly
5 km intervals. Settlement in this region was planned by the Brazilian federal
government agrarian reform agency (Instituto Nacional de Colonizacao e Reforma
Agraria, INCRA), which apportions land to settlers towards the goal of decreasing
landlessness and inequality. INCRA laid out a grid of 100 hectare properties with
500 m of road frontage on side roads (or 400 on the highway) and gave these
properties to settlers beginning in the early 1970s (Moran, 1981).
Fig. 2 History of deforestation in Altamira study area, Para, Brazil
Popul Environ (2007) 28:163–185 169
123
Figure 2 shows how the clearing of old growth forest progressed from that point.
Very little clearing was evident before 1970 (not shown), while the rapid clearing by
colonists in the 1970s and 1980s is evident in the white (cleared by 1975) and
lightest gray (cleared by 1985). Previous research on this region has shown the
cycles of deforestation undertaken by colonists, both as a function of the time since
a lot was first settled (was ‘‘opened’’) and as a function of macroeconomic and
political forces (Brondızio et al., 2002; McCracken et al., 1999). As described
above, this research analyzed the rate of deforestation between successive satellite
images for properties grouped by ‘‘cohort,’’ the time of first clearing and settlement
on the property (based on 5 hectares of clearing). It was found that properties follow
a standard trajectory of high deforestation rates at three to five and then 10–15 years
after settlement. However, the magnitude of the deforestation rate (as opposed to the
pattern over time) was determined more by the particular economic and political
circumstances in a particular time period.
The area around Santarem is also shown in more detail in Figs. 1 and 3.
Santarem’s position at the confluence of the Amazon and Tapajos Rivers has made
it an important commercial center for centuries. It experienced earlier recent
waves of settlement, in the 1930s, 1950s, and 1970s–1980s. These waves of
settlement are evident in Fig. 3, showing the clearing of old growth forest by dates
after the advent of remotely sensed data. A large portion of the study area was
cleared before the early 1970s, and the vast majority of the area had been cleared
at some point before 2001.
The biophysical characteristics of the Santarem study area are also distinct from
those of the Altamira study area. The topography is flatter in Santarem with the
exception of a narrow band of steep slopes near the Amazon River. The soils are
Fig. 3 History of deforestation in Santarem study area, Para, Brazil
170 Popul Environ (2007) 28:163–185
123
less fertile in Santarem, with a relative paucity of the fertile terra roxa (alfisols)
that is more common in Altamira. Santarem also experiences more frequent water
shortages. Wells must be deep and water is hard to come by. As a result, Altamira
is more suited to the raising of cattle and permits more effective production of
perennial crops. This is not to say that production of perennials is not possible in
Santarem, or that farmers cannot raise cattle, but respondent reports indicate that
both are harder. This tendency away from cattle production and perennials is
exacerbated by the recent introduction of soybeans into the Santarem area, with
the multinational Cargill constructing a deep water port on the Amazon River in
the city accompanied by land consolidation and large scale mechanized soy
production.
The differences in topography and settlement history between Altamira and
Santarem have led to more secondary growth and a more complex landscape in
Santarem. Despite the majority of the area having been cleared by the 1990s, there
is a lot more secondary growth evident in satellite images of Santarem (not shown).
This reflects the lower prevalence of pasture and the larger proportion of the area in
some sort of secondary growth (from small scrubby growth to areas that are
indistinguishable from forest in satellite imagery). The higher level of complexity in
the landscape includes clearing following both road networks and river networks
and a preponderance of small, irregularly shaped properties. This reflects the longer
and unplanned settlement history of Santarem.
Data and measures
The samples for the social survey data collection are based on INCRA property
grids for each study area. In Altamira, the grid represents the settlement plan for the
region, while in Santarem the grid is for planned settlement in a small portion of the
region and is a regularization of existing land tenure in the remainder of the region.
In Altamira, we selected a stratified random sample of properties from the property
grid. Each property in the grid was assigned to a settlement cohort, based on the
year in which a cleared area of 5 hectares was visible in a satellite image. Within
each cohort, we then selected a random sample of properties, with the goal of an
equal representation of cohorts in the final sample, despite the larger number of
properties in the earlier cohorts in the population.
We visited each of these properties in either 1997 or 1998 and interviewed the
household of the property owner, conducting interviews usually with the male and
female heads of household about land use and production and about household
demography and economy, respectively. In some cases, we substituted an alternate
property for the originally sampled property when the sampled property was vacant
or the owners were impossible to locate to interview.1 The interview with the male
head of household included information on the various land uses on the property at
the time of acquisition and at the time of the survey, and included a land use history
1 Sampled properties were not replaced with alternates because of owners refusing to participate. In this
wave of data collection we had no farmers refuse to participate in our study.
Popul Environ (2007) 28:163–185 171
123
showing the changes in area in certain land uses over the ownership of the property.
The conversations about current and past land use were facilitated by sketch maps
that the interviewers drew with the farmers and by satellite images from four dates
showing the property, which the interviewers interpreted with the farmer. This
information allows us to estimate the area in various uses in any year that the
household owned the property. The interview with the female head of household
included information about the current and past composition of the household,
allowing us to construct measures of the household size and composition in any year
since the household arrived on the property. All of the properties in this sample
(subject to some restrictions described below) are included in our analyses for this
paper.
In Santarem, we also sampled from an existing property grid with the goal of an
equal representation of properties occupied at different times. Because of the longer
settlement history, we were not able to stratify the population of properties in the
study area by time of first clearing. Instead, we stratified by region within the study
area. Each region follows a major road—from West to East, the Cuiaba-Santarem
Highway (BR-163), smaller roads to Jabutı and Mojuı dos Campos, and the Curua-
Una Highway—which was opened in a different era, leading to settlement of the
region at a different time. Within each region, we overlaid a grid of 3 km by 3 km
cells and selected a sample of these cells to achieve a spatially clustered sample to
reduce transportation costs. Within each of these cells, we selected five target
properties and four alternates (or fewer if there were fewer than nine properties in
the cell).
We visited these target properties in the summer of 2003 and attempted to
interview both the household of the owner of the property and any other households
on the property. Because of the long settlement history and a more active land
market in Santarem, we encountered three situations that made the achievement of
interviews in all sampled properties difficult. First, many properties had been
divided and the area covered by the sampled property was occupied by multiple
properties or by parts of multiple properties. In this case, we attempted to interview
all the properties that were wholly or partially in the area covered by the sampled
property. Second, many other properties had been aggregated with others into large
farms, often managed by absentee owners or used for commercial farming with no
households resident onsite. In this case, we interviewed the owning household if that
household lived on some portion of the aggregated land and managed it themselves.
If the farm owner was absent and/or the farm was managed completely as a
commercial endeavor, we collected a limited set of data about the current owner and
land use transformations on the property from neighbors and workers. Third, we
encountered areas in which the property grid bore no resemblance to the actual
division of land and no informants could remember a time in which land had been
partitioned in that way. In this case, we again attempted to conduct interviews in all
properties wholly or partially in the area covered by the sampled property. For the
analysis below, we restrict the sample to the households of property owners on the
properties that are still owned and managed as family properties (as opposed to as
commercial farms).
172 Popul Environ (2007) 28:163–185
123
In both study areas, we further restrict our sample in three ways for the analyses
for this paper. First, we include only households who have owned their properties
for approximately 10 years prior to the survey. The exact dates of ownership differ
between study areas because of different availability of remotely sensed data. We
use images from 1988, 1991, and 1996 for Altamira and from 1991 and 2001 for
Santarem. We use survey data from 1986 through 1996 for Altamira and from 1989
through 2001 for Santarem, giving us 2 years of data for measuring household
characteristics prior to the first image. We limit the sample to households who have
owned their properties in all these years. Second, we limit the sample to properties
that have less than 10% cloud or cloud shadow in each of the remotely sensed
images. This limitation reduces the error in the land use measure based on the
satellite data, as the area in forest calculated from the satellite data is based only on
the observable part of the property (i.e., the part without clouds). Third, we limit the
sample to properties that are larger than 5 hectares (a size that precludes even
subsistence farming over the long term). This is only a consideration in Santarem as
all of the properties in Altamira are substantially larger than that.
We create five dependent variables (in each study area) from the survey and
satellite data for the analyses for this paper. First, we measure the area planted in
annuals in a given year. This value is measured directly in a land use history
collected from respondents. Second, we measure the area in pasture in a given year
by beginning with the area in pasture when the property was acquired, adding
pasture area added over time (reported in the land use history), and subtracting
pasture allowed to go fallow over time (reported in the land use history). Third, we
measure perennials using the same procedure as pasture, beginning with the area in
perennials when the property was acquired and adding or subtracting areas reported
in the land use history. Fourth, we create measures of the area on the property in
forest from the survey data. The area in forest is calculated as the area in forest
when the household acquired the property minus any areas that have been cleared
between arrival and a given year. While these variables covary to a certain extent, in
the sense that a property cannot be 100% forest and 100% crops, they do not exactly
covary nor do they sum to property size as many properties have substantial areas in
fallow and all properties have some areas used for houses and yards, roads, and/or
water.
Fifth, we use one measure of land cover based on remotely sensed data. This
measures the area in a property that is in forest. The satellite images are all Landsat
images, taken in July of 1988 and 1991, and June of 1996 over the Altamira study
area and in July of 1991 and 2001 over the Santarem study area. We used a
combination of supervised and unsupervised classification techniques to classify
each pixel in these images into a specific land cover (or into cloud or cloud shadow).
These classifications produced a continuous surface of land cover for each study
area for each time point. We then partitioned the portion of the landscape associated
with each surveyed property using the boundaries of the property in the property
grid (Evans, VanWey, & Moran, 2005). These boundaries were updated during
fieldwork based on the information provided by farmers and on GPS (global
positioning system) points taken at the corners of properties.
Popul Environ (2007) 28:163–185 173
123
The classification we completed includes the following categories: forest,
secondary succession 3 (SS3), secondary succession 2 (SS2), secondary succession
1 (SS1), bare, pasture, water, cloud, and cloud shadow for Altamira (Moran et al.,
2002; Tucker, Brondızio, & Moran, 1998);2 forest (two types), forest/SS3, SS2/SS3,
SS2, SS1/SS2, SS1, bare/SS1, bare, cloud, and cloud shadow for the first Santarem
image; and bare (divided into high and low reflectance), agriculture, pasture, SS1,
SS2, forest, and water for the second image from Santarem.3 Because these
categories are based on ecological considerations, including the amount of
vegetation and the relative amounts of herbaceous and woody vegetation, making
measures that are comparable to the survey-based measures is difficult. The most
comparable measure is the measure of forest, and we include that measure here. We
use only forested area as a satellite-based dependent variable primarily as a test for
the sensitivity of our survey-based results to alternative measures. The measure of
forest cover in the analyses below uses the area classified as forest or SS3 in
Altamira and the area classified as forest or forest/SS3 in Santarem. SS3 is not old
growth forest, but it is densely vegetated area that can be considered similar to old
growth forest (evidenced by our inability to distinguish SS3 from forest in our
Santarem classification). We create a measure of the area in the property that is
forested in each year for which satellite data are available.
Our independent variables include the composition of the household and dummy
variables to capture period effects. Because we are using fixed effects models (see
below for model description), all time invariant household or property character-
istics are controlled, including such things as the area of the property, the age of the
householder when he acquired the property, the location of the property, etc. Thus,
our models examine only the effects of time varying household characteristics, in
this case household composition. We measure the household demographic
composition in two ways in any given year. We first create count variables
showing the number of household members in the following categories: children
(ages 0–11), female adolescents (ages 12–18), male adolescents (ages 12–18),
female adults (ages 19–49), male adults (ages 19–49), older females (ages 50+) and
older males (ages 50+). These numbers are created by taking the demographic
composition of the household at the time of the survey, removing any current
members who were not yet in the household in the year (for example, children who
had yet to be born), adding any past members who had left by the time of the survey
(for example, children who were in the household but had left by the time of the
survey), and then computing the number of members in each sex-age group from the
sexes and dates of birth of all of the members. To reduce the effect of errors in the
recall of the exact timing of events and to reflect the possible lag time between
household demographic change and land use change, these counts are averaged over
2 SS3 is the most advanced secondary growth, while SS1 is the least. In 1991, the classification also
included a category for sugar cane, capturing a short-lived explosion of sugar cane cultivation during the
operation of a factory for converting sugar cane to alcohol. These areas were subsequently abandoned or
converted to other uses, primarily cocoa. We do not use this category in our analyses.3 The differences among the classifications reflect both differences in the ability of the research team to
distinguish categories and differences in the initial uses of the classified imagery.
174 Popul Environ (2007) 28:163–185
123
the three years leading up to the measurement of the dependent variable (e.g., 1986,
1987, and 1988 for observations in 1988).
We tested two other measurements of household composition in our models. We
created dummy variables for groups of values for each of the groups (e.g., zero
children, 1 child, two children, etc.), based on the counts averaged over the 3 years
leading up to the target year. For example, if the value of the count of children was
1.33, we assigned that household to be 1 on a dummy measuring whether there was
1 child in the household, while if the value was 1.67 we assigned the household to
be a 1 on a dummy measuring 2 or 3 children in the household (2 or 3 because so
few households had exactly 2 or exactly 3). We then created dummy variables
indicating whether or not there was a household member in each age-gender group.
We created these measures by assigning the household a value of 1 on the dummy if
the household had any members in the age-gender group in at least 2 of the 3 years
leading up to the target year. In comparisons between models, the models using the
count measures or the dummies indicating any member in the age-gender group
outperformed the models with the dummies for different numbers of members
within each age-gender group. Thus, the models shown in Tables 2 and 3 include
some models with the count measures and some with the dummies, depending on
which performed better according to model comparisons using the Bayesian
Information Criterion (Raftery, 1995) and the Akaike Information Criterion (Judge,
Griffiths, Hill, Lutkepohl, & Lee, 1985).
These measures allow us to distinguish between the effects of dependents
(children and older members) and prime working age household members, and
allow us to examine the gendered nature of demographic effects. The household life
cycle or household production approach argues for increases in production as a
function of dependents and as a function of members who are able to contribute
labor to production. Because of the nature of farming in these regions, the majority
of the labor is supplied by men, suggesting that the number of females, children and
older members should have a positive effect on production (given their role as
consumers), but that the number of working age males should have a stronger effect
(Siqueira, McCracken, Brondızio, & Moran, 2003).
The descriptive statistics on the dependent and independent variables for the two
analysis samples (for Altamira and for Santarem) are shown in Table 1. Differences
in the areas in various uses in the average household-year in the two study areas
reflect differences in property size, history, and major economic activities. In both
areas, households plant an average of around 3 hectares of annuals each year,
reflecting the amount needed for subsistence. In Altamira, however, households also
have an average of 34 hectares of pasture and 10 hectares of perennials in the
average household-year. This reflects the economic base of the region, with most
families producing for the market and the biophysical conditions being appropriate
for cattle raising and perennials (mostly cocoa). The average areas in forest based on
the two measurements (survey and satellite) show the recent occupation of Altamira
and the smaller average property size in Santarem (34.61 hectares versus an average
of 110.64 in Altamira). Properties in Altamira were settled sometime between the
early 1970’s and the mid 1990’s, meaning that many properties still have a large
area of relatively undisturbed forest. Properties in Santarem were settled over the
Popul Environ (2007) 28:163–185 175
123
course of the 20th century, with most settled prior to 1970, resulting in more clearing
by the study dates and subdivision creating small properties.
The measures of household demographic composition vary relatively little
between the two areas. Both reflect settled households past their childbearing years
at the time of the survey (because of the sample restriction requiring them to have
owned the property for more than 10 years). In the average household-year in both
study areas, households have approximately one and a half children, 1.3 adolescents
(mostly male), and less than one older member. The dummy variables tell a similar
story, showing that households in each area have children in approximately 60% of
the household-years. While households in Altamira have young women in a higher
proportion of the household-years, in both study areas households have young men
Table 1 Descriptive statistics for analysis samples (household-years), Altamira and Santarem, Para,
Brazil
Variable Altamira Santarem
Mean Std Dev Min Max Mean Std Dev Min Max
Annuals 2.69 4.22 0.0 50.0 3.11 4.44 0.0 50.0
Pasture 34.05 40.26 0.0 494.5 2.22 4.13 0.0 21.0
Perennials 9.76 14.30 0.0 97.0 0.83 1.29 0.0 8.0
Forest Area (Survey) 58.97 49.62 0.0 409.0 10.13 14.82 0.0 61.0
Forest Area (Satellite) 70.50 50.39 2.3 417.7 13.19 14.66 0.0 76.5
Household composition (count variables)
Kids (0–11) 1.52 1.65 0.0 7.3 1.39 1.63 0.0 6.3
Females (12–18) 0.59 0.77 0.0 4.0 0.49 0.71 0.0 2.7
Males (12–18) 0.78 0.93 0.0 4.3 0.78 0.95 0.0 4.0
Females (19–49) 1.21 1.13 0.0 8.0 0.90 0.75 0.0 3.0
Males (19–49) 1.57 1.31 0.0 7.7 1.17 1.06 0.0 5.7
Females (50+) 0.39 0.48 0.0 1.0 0.45 0.48 0.0 1.0
Males (50+) 0.54 0.49 0.0 2.0 0.59 0.48 0.0 1.7
Household composition (dummy variables)
Kids (0–11) 0.62 – 0.0 1.0 0.59 – 0.0 1.0
Females (12–18) 0.42 – 0.0 1.0 0.36 – 0.0 1.0
Males (12–18) 0.47 – 0.0 1.0 0.46 – 0.0 1.0
Females (19–49) 0.78 – 0.0 1.0 0.69 – 0.0 1.0
Males (19–49) 0.83 – 0.0 1.0 0.70 – 0.0 1.0
Females (50+) 0.39 – 0.0 1.0 0.45 – 0.0 1.0
Males (50+) 0.54 – 0.0 1.0 0.60 – 0.0 1.0
Year
1988 0.35 – 0.0 1.0 – – – –
1991 0.35 – 0.0 1.0 0.50 – 0.0 1.0
1996 0.30 – 0.0 1.0 – – – –
2001 – – – – 0.50 – 0.0 1.0
N 540 Household-Years 148 Household-Years
176 Popul Environ (2007) 28:163–185
123
in more years (46–47% of years) than they have young women (36% or 42% of
years). The proportions of years with older members almost exactly match the
average number of older members, because households who have older men or
women tend to have only one of each.
Households in Altamira tend to have more prime working age members, with 1.2
such women and 1.6 such men. Households in Santarem have less than one 19–
49 year old man and 1.2 such women in the average household-year. These
tendencies are again mirrored in the dummy variables. In Altamira, households have
prime working age women in 78% of years and prime working age men in 83% of
years. In Santarem, reflecting the older settlement there (and therefore slightly
greater representation of older households), households have prime working age
women in 69% of years and prime working age men in 70% of years.
Analytic strategy
To estimate the effects of changing household composition on changes in land use,
we employ fixed effects linear regression models. These models can be estimated
using a variety of technical procedures with the same results.4 Intuitively, we can
think of a fixed effects model as estimating the effects of changes in independent
variables on changes in dependent variables, holding constant the effects of any
time-invariant (observed or unobserved) characteristics of the cases. Intuitively we
can think of the following equation for the case of two observations of each case:
Yi2 � Yi1ð Þ ¼ b02 � b01ð Þ þ b1 Xi2 � Xi1ð Þ þ b2 Zi � Zið Þ þ e2 � e1ð Þ
where the left hand side is the change in the dependent variable for case i between
time 1 and time 2. This change is a function of the change in the intercept terms, the
effect of the change in the time-varying independent variables (X) and an error term.
The third term reduces to zero because the time-invariant characteristics of the cases
(Z) are the same at time 1 and time 2. In our models we estimate the effects of
changes in household composition (the X variables) on changes in land use (the Yvariables), holding constant such time-invariant characteristics as initial conditions
on the property, time of settlement, risk aversion, past experience with various
crops, and property size (the Z variables).
In order for these models to effectively estimate the effects of changing
household composition on changes in land use, two conditions must be met. We
must include multiple observations for each household and the household
composition must vary over time within households. In each of our analysis
samples, we include at least two observations for each household (see sample
4 For ease of analysis, we use the xtreg, fe command in Stata. We replicated the results using two other
methods: using OLS regressions with dummy variables for all households and using OLS regressions of
deviations in a given dependent variable from the household mean on deviations in the independent
variables from their household means. These methods all produced the same coefficient estimates for the
effects of household composition, though the OLS using deviations method produced smaller standard
errors because it did not account for the degrees of freedom lost because of the household fixed effects.
Popul Environ (2007) 28:163–185 177
123
selection description above). In tables available upon request from the authors, we
show that there is variation in the measures of household composition over time.
Results
Table 2 shows the fixed effects linear regression models for the Altamira sample.
Each model controls for time period, to account for secular changes in forest cover
and macro level period effects (e.g., credit programs and economic conditions).
Models of the area on the property in perennials and in forest (using satellite-based
measures) estimate the effects of change from no members to any members of each
age-gender group in the household. The models of area in annuals, pasture, and
forest (using survey-based measures) estimate the effects of changes in the number
of household members in each age-gender group.5 These models show few
significant effects of changes in household composition on land use change. Those
effects that are significant do not correspond to expectations based on household life
cycle or household production theories. Increases in the number of children (or
changes from no children to some children) increase the amount of pasture and
decrease the area in perennials, instead of increasing the area in annuals as predicted
by these theories.
Even more unexpected, the significant effects of adolescents, adults and older
household members are all effects of female household members. Adding female
adolescents (either through in-migration or children aging into adolescence)
significantly decreases forested area and has a marginally significant positive effect
on area in pasture. The household life cycle model argues that when children move
into adolescence, the household becomes able to plan for the future and therefore
will invest in land uses with a longer time until return (i.e., pasture and perennials).
However, the key adolescents for this increase in pasture and perennials should be
male adolescents, who provide much of the farm labor. Older women (50+) also
have significant effects on land use, negatively affecting both perennials and forest
(though only with the satellite-based measure of forest). Again following the
household life cycle and household production approaches, these older women
should affect only the household’s consumption needs and therefore primarily the
area in annuals. While older households are expected to be reducing their planted
area, it should be the transition of men rather than women out of prime working ages
that precipitates the change.
Table 3 shows the same models for Santarem, with the changes in the number of
household members in each age-gender group predicting the area in annuals and in
perennials, and the change from no member to some members in each age-gender
group predicting the area in perennials and in forest. This table points to even fewer
significant effects of household composition in this older settlement area, and what
effects are significant largely contradict the results from Altamira. Children have
5 In these models and models of other dependent variables, we tested for collinearity and found no
problems. The maximum variance inflation factor for any variable in any of the models was 2.94 in
Santarem and 1.96 in Altamira.
178 Popul Environ (2007) 28:163–185
123
opposite sign effects on pasture, perennials and survey-based measures of forest in
Santarem relative to Altamira; in Santarem increases in the number of children have
a marginally significant negative effect on pasture, a non-significant positive effect
on perennials, and a marginally significant positive effect on the survey-based
measure of forest.
The pattern that is consistent across the tables, other than the relative lack of
effects (only 4 of 35 household composition measures significant at .10 in Table 3,
what we would expect by chance), is the effect of women. While fewer effects are
significant in Santarem, increases in the number of prime working age women have
a significant negative effect on area in pasture, consistent in direction with the effect
estimated in the Altamira sample. The added presence of an older woman has a
marginally significant positive effect on forest area (measured from satellites), an
effect opposite that of the significant negative effect in Altamira.
Table 2 Fixed effects regression models of the effects of changes in household demographic
composition on land use and land cover change, Altamira, Para, Brazil
Annuals Pasture Perennials Forest (Survey) Forest (Satellite)
B se(B) B se(B) B se(B) B se(B) B se(B)
Changes in household composition (count variables)
Kids (0–11) 0.24 0.23 1.41+ 0.77 �0.79 0.70
Females (12–18) 0.14 0.29 1.84+ 0.98 �1.54+ 0.89
Males (12–18) �0.08 0.26 �0.99 0.88 0.59 0.80
Females (19–49) �0.37 0.36 �1.09 1.21 1.11 1.10
Males (19–49) 0.29 0.28 �0.66 0.94 0.50 0.86
Females (50+) 0.52 0.77 2.53 2.57 0.26 2.33
Males (50+) 1.16 0.79 2.75 2.63 �0.76 2.39
Changes in household composition (dummy variables)
Kids (0–11) �1.33* 0.47 �1.88 2.02
Females (12–18) 0.11 0.38 �4.52* 1.63
Males (12–18) 0.14 0.36 2.45 1.55
Females (19–49) �0.17 0.71 �4.76 3.03
Males (19–49) �0.39 0.62 1.76 2.66
Females (50+) �1.80* 0.74 �8.70* 3.18
Males (50+) �0.31 0.67 4.69 2.87
Year
1988 0.67 0.44 �12.99* 1.45 �0.82* 0.37 13.56* 1.32 4.23* 1.59
1991 �0.10 0.37 �8.33* 1.24 �0.61+ 0.33 8.34* 1.13 �1.39 1.41
1996 – – – – – – – – – –
Constant 1.28 1.30 39.00* 4.33 12.30* 1.22 51.11* 3.93 74.57* 5.20
N (obs) 540 540 540 540 540
N (groups) 191 191 191 191 191
Note. +p < .10, *p < .05
Popul Environ (2007) 28:163–185 179
123
These effects suggest few robust conclusions about the effects of household
demographic change on land use change, leading us to question our models. In
defense of the models, we note that the pattern of period effects is consistent with
aggregate patterns and macroeconomic effects. In Altamira and in Santarem, there is
a steady decline in the area in forest over time, with the decline per year greater in
Altamira due to the larger properties and more recent opening of the frontier. There
is a correspondingly steady and large increase in the area in pasture in Altamira but
not in Santarem, reflecting the maturing of the frontier in Altamira and the
inadequate available water (below the threshold for successful ranching) in most of
the Santarem study area. There are slight changes over time in the areas in annuals
and perennials, following the implementation of credit programs to promote cocoa
production (for which the landscape is suited in Altamira).
Table 3 Fixed effects regression models of the effects of changes in household demographic
composition on land use and land cover change, Santarem, Para, Brazil
Annuals Pasture Perennials Forest (Survey) Forest (Satellite)
B se(B) B se(B) B se(B) B se(B) B Se(B)
Changes in household composition (count variables)
Kids (0–11) 0.83+ 0.47 �0.39+ 0.23
Females (12–18) 2.00* 0.59 �0.31 0.29
Males (12–18) �0.81 0.49 0.03 0.24
Females (19–49) 1.30 0.96 �1.02* 0.46
Males (19–49) 0.41 0.54 0.00 0.26
Females (50+) �0.78 1.77 �0.87 0.86
Males (50+) 0.22 1.40 0.29 0.68
Changes in household composition (dummy variables)
Kids (0–11) 0.25 0.24 2.46+ 1.42 �0.28 2.21
Females (12–18) 0.05 0.19 �0.31 1.16 0.72 1.79
Males (12–18) �0.01 0.18 0.27 1.06 �1.10 1.65
Females (19–49) 0.03 0.31 1.17 1.87 3.66 2.90
Males (19–49) �0.11 0.25 0.09 1.47 �3.00 2.28
Females (50+) 0.36 0.34 1.33 2.05 5.34+ 3.18
Males (50+) �0.31 0.29 2.46 1.76 �2.42 2.72
Year
1991 �0.19 0.83 �0.54 0.40 �0.19 0.17 2.32* 0.99 7.39* 1.53
2001 – – – – – – – – – –
Constant 0.28 2.50 4.30* 1.21 0.85+ 0.49 4.55 2.92 8.51 4.52
N (obs) 148 148 148 148 148
N (groups) 74 74 74 74 74
Note. +p < .10, *p < .05
180 Popul Environ (2007) 28:163–185
123
Conclusions
The household life cycle model of land use and land cover change in familial
properties in rural agricultural frontiers has been influential in population and
environment research (e.g., Moran, Brondızio, & VanWey 2005; Walker, 2003;
Walker et al., 2002). However, the results of empirical studies reviewed by Walker
et al. (2002) and those presented in this paper call into question the mechanisms
proposed by the theory. Specifically, while a number of studies verify a consistent
pattern over time that is a function of time since arrival (or time since the property
was opened), there are few consistent effects of household demographic compo-
sition across study areas and studies. The results presented in this paper show that
changes in the very groups who should have the largest effects, adolescent and
working age males, have no significant effects on land use and land cover change.
Instead, what significant effects are present are of women and children.
This lack of support for the mechanisms proposed by the household life cycle
model indicates shortcomings in the model. In particular, the model’s assumptions
of household production using only household labor and for only household
consumption do not hold in our study areas. The importance of cash, both coming
into the household and used for investments in agriculture (either technology or
hired workers), shows that this assumption is incorrect. Off-farm employment for
household members and hired labor to complete household and farm tasks are both
available. In particular, women have access to off-farm employment in nearby urban
areas. In our study areas and in other similar frontier areas in the Ecuadorian
Amazon, women complete more education and have more access to urban
employment, resulting in more circular or rural-urban migration among women
(Barbieri & Carr, 2005; Marquette, 1998). This employment can generate cash for
the household, essential for hiring labor to expand agricultural areas or for investing
in agricultural technologies. Cash can also come to the household from government
assistance programs, particularly the bolsa escola and rural retirement programs.
The government provides the bolsa escola for each child who is enrolled in school,
to encourage families to keep their children in school for longer. The government
also provides rural retirement money for older persons who have worked in
agriculture. Thus, children, female adolescents (because they are in school while
males are not), and older people in the household are likely to be bringing money
into the household.
As expected if access to cash is essential for investments in agricultural
production, our results show that households with more children, female adolescents
and older members are able to increase their participation in the most successful
farm activities. Diversifying into pasture or perennials is essential for being
‘‘successful’’ in Altamira, increasing income and status. Increasing production of
annuals is a surer path to success in Santarem, given the biophysical characteristics
of the region favoring annuals over pasture and the disease problems that farmers
have had with black pepper (the main perennial crop). The models of pasture and
perennials in Altamira (Table 2) and of annuals in Santarem (Table 3) show part of
the pattern that would be expected if government entitlements were allowing
families to increase their participation in these production strategies. In Altamira,
Popul Environ (2007) 28:163–185 181
123
increasing the number (as opposed to the presence) of children and female
adolescents increases the area in pasture. In Santarem, increasing the number of
children and female adolescents increases the area in annuals. The expected results
are not present in the model of perennials in Altamira, possibly because the usual
way of increasing production in perennials is the use of sharecroppers or permanent
workers rather than hired temporary laborers.
These models contrast with interpretations of past research showing a cyclical
pattern of deforestation at the household-farm property level. The strongest
predictor in past research has been the time since the property was first settled,
which has been interpreted as a household life cycle effect. Barbieri et al. (2005)
interpret this as a property life cycle effect, showing the process of clearing that
happens over time on a previously completely forested property as it is converted to
agropastoral production. In other work, we argue that this cycle represents a process
of settlers learning the appropriate uses of the land in a new frontier (VanWey et al.,
2007). The results presented here cannot speak to the exact reasons for the cyclical
pattern found in previous research. However, they add to this growing body of work
by showing that mechanisms proposed by the household life cycle model do not
explain the pattern. Specifically, they call into question the Chayanovian assumption
of a closed household without connection to labor or capital markets.
The pattern of results and the differences between study areas are also subject to
social and environmental contextual effects that we can not estimate in our models.
We speculate that government programs provide access to cash for households. Yet,
the bolsa escola program began nationwide in the late 1990s and early 2000s. Thus,
our analyses in Altamira do not include a time with this program, while our analyses
in Santarem include the beginning of this program. However, older persons (both
men and women) are covered by rural retirement programs throughout the period
under study. Similarly, the importance of women and their earning power is not
constant over time. It is characteristic of a particular period in the development of
the rural-urban linked system in the Amazon. It is likely that the future will bring
more widespread urban opportunities and therefore more access to cash employ-
ment for men. The environmental differences between our study areas determine in
part how (or if) the cash is invested in agriculture. We argue that cash is invested in
pasture in Altamira and annuals in Santarem because of differences in the suitability
of the land (and related differences in the development of agricultural markets). The
effects of these differences are themselves likely to be contextually determined. For
example, the suitability of Santarem for mechanized row crops is leading to an
expansion of large-scale soy production which may displace all small farming from
the region. It will increase available off-farm employment for all household
members but decrease the area of non-consumption crops for rural residents.
This paper therefore argues for a more nuanced view of both contextual effects
and household strategies. Economic and institutional context determine the effects
of household demography on land use and land cover change. Rather than either in-
migration to the frontier or expansion of economic opportunities in regional cities
representing a steamroller of change that inevitably leads to deforestation by
smallholder farmers, household demography interacts with the development of labor
markets and government programs to determine land use on a given farm.
182 Popul Environ (2007) 28:163–185
123
Households act strategically to access cash (from employment or from government
assistance) and then to invest it in non-subsistence land uses. This suggests several
possible pathways to intensification of agriculture in frontiers in the future as a
result of possible economic and institutional forces. First, the expansion of
government transfer programs could lead to more (capital-)intensive agriculture
among smallholders by providing them with more cash to invest. Second, the
growth of urban areas and urban employment in the Amazon could similarly
provide scarce cash income to small farmers. Third, the expansion and regulari-
zation of credit programs and credit provision in frontiers could allow farmers to
make investments without having to engage in extensive off-farm employment to
access cash.
References
Barbieri, A. F., Bilsborrow, R. E., & Pan, W. K. (2005). Farm household lifecycles and land use in the
Ecuadorian Amazon. Population and Environment, 27(1), 1–27.
Barbieri, A. F., & Carr, D. L. (2005). Gender-specific out-migration, deforestation and urbanization in the
Ecuadorian Amazon. Global and Planetary Change, 47(2–4), 99–110.
Bongaarts, J. (1992). Population growth and global warming. Population and Development Review, 18(2),
299–319.
Boserup, E. (1981). Population and technological change: A study of long-term trends. Chicago:
University of Chicago Press.
Brondızio, E. S., McCracken, S. D., Moran, E. F., Siqueira, A. D., Nelson, D. R., & Rodriguez-Pedraza,
C. (2002). The colonist footprint: Toward a conceptual framework of land use and deforestation
trajectories among small farmers in the Amazonian frontier. In C. H. Wood & R. Porro (Eds.),
Deforestation and land use in the Amazon (pp. 133–161). Gainsville, FL: University Press of
Florida.
Carr, D. L. (2004). Proximate population factors and deforestation in tropical agricultural frontiers.
Population and Environment, 25(6), 585–612, 528.
Chayanov, A.V. (1966). The theory of peasant economy. Homewood, IL: Richard D. Irwin.
Dietz, T., & Rosa, E. A. (1997). Effects of population and affluence on CO2 emissions. Proceedings of theNational Academy of Sciences of the United States 94(1), 175–179.
Ehrhardt-Martinez, K., Crenshaw, E. M., & Jenkins, J. C. (2002). Deforestation and the environmental
Kuznets curve: A cross-national investigation of intervening mechanisms. Social Science Quarterly,83(1), 226–243.
Ehrlich, P. R., & Holdren, J. P. (1971). Impact of population growth. Science, 171, 1212–1217.
Entwisle, B., & Stern, P. C. (Eds.) (2005). Population, land use and environment: Research directions.
Washington, DC: National Academies Press.
Evans, T. P., VanWey, L. K., & Moran, E. F. (2005). Human-environment research, spatially explicit data
analysis, and GIS. In E. F. Moran & E. Ostrom (Eds.), Seeing the forest and the trees: Human-environment interactions in forest ecosystems (pp. 161–185). Cambridge, MA: MIT Press.
Futemma, C., & Brondızio, E. S. (2003). Land reform and land-use changes in the lower Amazon:
Implications for agricultural intensification. Human Ecology, 31(3), 369–402.
Goody, J. (1958). The developmental cycle in domestic groups. Cambridge, England: Published for the
Department of Archaeology and Anthropology at the University Press.
Judge, G. G., Griffiths, W. E., Hill, R. C., Lutkepohl, H., & Lee, T.-C. (1985). The theory and practice ofeconometrics. New York: John Wiley and Sons.
Lambin, E. F., Turner, B. L., Geist, H. J., Agbola, S. B., Angelsen, A., Bruce, J. W., Coomes, O. T.,
Dirzo, R., Fischer, G., Folke, C., George, P. S., Homewood, K., Imbernon, J., Leemans, R., Li, X.
B., Moran, E. F., Mortimore, M., Ramakrishnan, P. S., Richards, J. F., Skanes, H., Steffen, W.,
Stone, G. D., Svedin, U., Veldkamp, T. A., Vogel, C., & Xu, J. C. (2001). The causes of land-use
and land-cover change: moving beyond the myths. Global Environmental Change-Human andPolicy Dimensions, 11(4), 261–269.
Popul Environ (2007) 28:163–185 183
123
Malthus, T. R. (1989) [1803]. An essay on the principle of population. Cambridge: Cambridge University
Press.
Marquette, C. M. (1998). Land use patterns among small farmer settlers in the northeastern Ecuadorian
Amazon. Human Ecology: An Interdisciplinary Journal, 26(4), 573–598.
McCracken, S., Siqueira, A., Moran, E. F., & Brondızio, E. S. (2002). Land use patterns on an agricultural
frontier in Brazil; Insights and examples from a demographic perspective. In C. H.Wood & R. Porro
(Eds.), Deforestation and land use in the Amazon (pp. 162–192). Gainsville, FL: University Press of
Florida.
McCracken, S. D., Brondizio, E. S., Nelson, D., Moran, E. F., Siqueira, A. D., & Rodriguez-Pedraza, C.
(1999). Remote sensing and GIS at farm property level: Demography and deforestation in the
Brazillian Amazon. Photogrammetric Engineering & Remote Sensing, 65(11), 1311–1320.
Moran, E. F. (1981). Developing the Amazon. Bloomington, IN: Indiana University Press.
Moran, E. F., Brondızio, E. S., & McCracken, S. (2002). Trajectories of land use: Soils, succession, and
crop choice. In C.H. Wood & R. Porro (Eds.), Land use and deforestation in the Amazon (pp. 193–
217). Gainsville, FL: University of Florida Press.
Moran, E. F., Brondızio E. S., & VanWey L. K. (2005). Population and environment in Amazonia:
Landscape and household dynamics. In B. Entwisle & P. C. Stern (Eds.), Population, land use andthe environment. Washington, DC: National Academies Press.
Murphy, L. L. (2001). Colonist farm income, off-farm work, cattle, and differentiation in Ecuador’s
northern Amazon. Human Organization, 60(1), 67–79.
O’Neill, B. C., MacKellar, F. L., & Lutz, W. (2001). Population and climate change. Cambridge, UK:
Cambridge University Press.
Pan, W., Murphy, L., Sullivan, B., & Bilsborrow, R. E. (2001). Population and land use in ecuador’s
northern Amazon in 1999: Intensification and growth in the frontier. Presented at Population
Association of America Annual Meetings, Washington, DC.
Pan, W. K. Y., & Bilsborrow, R. E. (2005). The use of a multilevel statistical model to analyze factors
influencing land use: A study of the Ecuadorian Amazon. Global and Planetary Change, 47, 232–252.
Pan, W. K. Y., Walsh, S. J., Bilsborrow, R. E., Frizzelle, B. G., Erlien, C. M., & Baquero, F. (2004).
Farm-level models of spatial patterns of land use and land cover dynamics in the Ecuadorian
Amazon. Agriculture Ecosystems & Environment, 101(2–3), 117–134.
Pebley, A. R. (1998). Demography and the environment. Demography, 35(4), 377–389.
Perz, S. G. (2001). Household demographic factors as life cycle determinants of land use in the Amazon.
Population Research and Policy Review, 20(3), 159–186.
Perz, S. G., & Skole, D. L. (2003). Social determinants of secondary forests in the Brazilian Amazon.
Social Science Research 32(1), 25–60.
Perz, S. G., & Walker, R. (2002). Household life cycles and secondary forest cover among small farm
colonists in the Amazon. World Development, 30(6), 1009–1027.
Pichon, F. J. (1996a). Land-use strategies in the Amazon frontier: Farm-level evidence from Ecuador.
Human Organization, 55(4), 416–424.
Pichon F. J. (1996b). Settler agriculture and the dynamics of resource allocation in Frontier
Environments. Human Ecology, 24(3), 341–371.
Raftery, A. E. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111–
163.
Rindfuss, R. R., Turner II, B. L., Entwisle, B., & Walsh, S. J. (2004). Land cover/use and population. In
G. Gutman, T. Janetos, C. Justice, E. F. Moran, J. Mustard, R. R. Rindfuss, D. Skole, & B. L. Turner
II (Eds.), Land change science: Observing, monitoring, and understanding trajectories of change onthe earth’s surface. Boston: Kluwer Academic Publishers.
Siqueira, A. D., McCracken, S. D., Brondızio, E. S., & Moran, E. F. (2003). Women and work in a
Brazilian agricultural frontier. In G. Clark (Ed.), Gender at work in economic life (pp. 243–267).
New York: Altamira Press.
Thapa, K. K., Bilsborrow, R. E., & Murphy, L. (1996). Deforestation, land use, and women’s agricultural
activities in the Ecuadorian Amazon. World Development, 24(8), 1317–1332.
Tucker, J., Brondızio, E. S., & Moran, E. F. (1998). Rates of forest regrowth in Eastern Amazonia: a
comparison of Altamira and Bragantina regions, Para State, Brazil. Interciencia, 23(2), 64–73.
VanWey, L. K., Brondızio, E. S., D’Antona, A. O., & Moran, E. F. (2007). Households, frontier
development, and land use change in the Brazilian Amazon. Anthropological Center for Training
and Research on Global Environmental Change.
184 Popul Environ (2007) 28:163–185
123
VanWey, L. K., Ostrom, E., & Meretsky, V. (2005). Theories underlying the study of human-
environment interactions. In E. F. Moran & E. Ostrom (Eds.), Seeing the forest and the trees:Human-environment interactions in forest ecosystems (pp. 23–56). Cambridge, MA: MIT Press.
Walker, R. (2003). Mapping process to pattern in the landscape change of the Amazonian frontier. Annalsof the Association of American Geographers, 93(2), 376–398.
Walker, R., Drzyzga, S. A., Li, Y. L., Qi, J. G., Caldas, M., Arima, E., & Vergara, D. (2004). A
behavioral model of landscape change in the Amazon Basin: The colonist case. EcologicalApplications, 14(4), S299–S312.
Walker, R., Perz, S., Caldas, M., & Silva, L. G. T. (2002). Land use and land cover change in forest
frontiers: The role of household life cycles. International Regional Science Review, 25(2), 169–199.
Walker, R. T., & Homma, A. K. O. (1996). Land use and land cover dynamics in the Brazilian Amazon:
An overview. Ecological Economics, 18, 67–80.
Popul Environ (2007) 28:163–185 185
123
top related