Chapter 6
The previous chapter presented descriptive statistics of the household sample and
explored potential relations between farm and household characteristics and forest
clearing and land use among small farmers in the SLNP. This chapter will investigate the
separate effects of these factors on the outcomes of land use and deforestation by colonist
farmers through statistical modeling, specifically, multiple regression (MR) analysis.
Composite time-series Landsat TM satellite images (Chapter 1), descriptive
community (Chapter 4) and household data (Chapter 5) agree that, as elsewhere in the
tropics, small farmer agricultural expansion is the proximate cause of forest clearing in
the SLNP. Colonist farmers convert forest to a host of agricultural land uses, each with a
potentially distinct impact on forest conversion. Unlike frontier environments in other
tropical lowland environments in Latin America (particularly in the Amazon basin, but
also, to a lesser extent, in neighboring Belize and Mexico), in the SLNP colonist farmer
land use is relatively homogeneous. Virtually all the farmers grow maize and little else,
and a third of them have a few head of cattle. Thus, the descriptive data from Chapters
Four and Five indicate that the vast majority of forests cleared in the SLNP have been
converted directly to maize fields which ultimately become fallow land.
Variations across households in land use are much smaller in this sample
compared to many similar studies in other frontier regions in Latin America. With a
remarkably uniform land use of maize and few significant additional land uses as found
in other frontier regions, in the form of market crops and perennials with widely diverse
land demands (save cattle among a quarter of the sample), complex relations between
humans and the land are greatly reduced in the SLNP. Therefore, household variability
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in land cleared may be more strongly predicted by demographic, political-economic,
socio-economic, and ecological factors, and usefully modeled in a direct relation.
Since forest conversion is a function of farmer land use, modeling forest clearing
only does not take into account the various land uses for which a farmer has cleared his
land. Therefore, in addition to presenting a model on forest clearing and on the percent
of forest cleared on a farm holding, I will present MR models with the four major land
uses for farms in the sample: maize, fallow, forest, and pasture. As only a quarter of the
farms in the sample have cattle, the latter model will be a logistic regression (LR), which
will produce likelihood ratios of membership in the “pasture” group. This is an important
model since households with pasture have cleared, on average, twice as much land as
those without pasture (see Figure 6.1). Amount of land in pasture and whether or not a
farm has pasture are highly significant variables in the cleared land and percent of farm
cleared models but are excluded due to concerns of endogeneity.
The first section of this chapter describes the selection of predictor variables
based on theoretical considerations and on their impact on the predictive power of the
models. The following section explains hypotheses regarding the relations between the
predictors selected and land use outcomes. Model specifications for the six final models
are discussed next, including some of the diagnostics used to assess potential violations
of model assumptions. Lastly, the six models will be presented and their results
interpreted.
A. Selection of Variables
The models in this chapter were designed to capture patterns of farmer land use
and land clearing in the SLNP region while also considering these relations to theory.
The variables examined were selected from the literature presented in Chapter Two, from
surveys in the Ecuadorian Amazon (e.g., Bilsborrow and Pan, 2001) and the Mexican
Yucatán (e.g., Klepeis and Turner II, 2001), and were designed to fit the study area with
help from Norman Schwartz, Pro-Petén-Conservation International (Corzo-Márquez and
Obando, 2000), Jorge Grunberg of CARE (Macz and Grunberg, 1999), Edgar Calderón
Rudy Herrera and Edgar Calderón of The Nature Conservancy (The Nature Conservancy,
1997), and by members of the Guatemalan agro-forestry aid institute, Centro Maya.
195
Lastly, variables were derived from additions and modifications of earlier instruments
with the help of my Peténero interview team and from many long discussions with
community leaders in the SLNP. Based on the literature on tropical frontier deforestation
and knowledge of local conditions, the overarching hypothesis was that deforestation for
agricultural extensification is related to a host of conditions thought to be important in
frontier environments. Generally these conditions included factors such as family size
and composition, and availability of land, and of human, social, and labor capital.
The first step taken in the data analysis for this chapter was to analyze sets of
predictors by category using sequential multiple regression. Several hundred variables
from head of household surveys were initially considered for inclusion in the models.
Following considerations of theory and data quality, this number was reduced to 104
variables that were probed with descriptive analyses such as means, medians,
frequencies, and standard deviations. From this second set, approximately seventy-five
variables were selected for descriptive analysis in Chapter Five via cross-tabulations.
It was from this last iteration in the winnowing process that I ran many sequential
regressions to trim the models to their current form, again based on theoretical concerns
and model fit. In examining potential contributions of predictors to the models, several
measures were used, including R2 values, t-ratios, and the sign of the Beta coefficients as
related to prior expectations. First, as I am particularly interested in demographic effects,
given the proposed connection in this dissertation between migration and land use, twelve
demographic variables were tested against each other in each of the models. Consistently
insignificant independent variables were usually removed, unless theoretical importance
compelled me to retain them. Following the analysis of relations among demographic
variables, and between them and each of the six outcome variables, variables from other
categories were included in the model. Sixty household and farm characteristic variables,
five political-economic variables, and three ecological variables were each included
sequentially in the model, one set after another. I then combined all variables that were
significant at the 0.15 level in most of the models at the set level together in six large
“full” models. I further paired down the full models by omitting variables that were
insignificant at the 0.15 level in the majority of the models. Again, variables remained in
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several cases because of theoretical considerations even though they were insignificant
predictors of the outcome variables.
Once I had six completed models, a pattern emerged that merited further
attention. The models for Pasture and Cropland were behaving differently from the
other four models. I therefore redid the process described above for each of these two
models. I changed the independent variables to produce a better fit for the logistic model
Pasture. I justify doing so for three reasons. First, the Pasture model probes a
fundamentally different question (a question of membership) than the other land use
models (questions of extent). Second, cattle ownership is qualitatively a different kind
of land use than is maize farming. And third, only a quarter of the sample own cattle,
suggesting a selective or different group of farmers. The resulting model produced a
noticeably higher R2 value than when the same set of predictors from the other five
models was employed. However, because virtually all farmers have land in crops, and it
is a major component of overall cleared land, I ultimately decided to keep the same
independent variables for this model as for the other multivariate regression models with
one exception.
B. Definitions of Variables and Hypotheses
The hypothesized relations between the predictor variables and land use by settler
households are summarized in (Table 6.1.). These hypotheses come from the literature
on colonization and deforestation in the Latin American tropics in general (Chapter 2),
and in Petén, Guatemala specifically (Chapter 1). A plus or minus sign is assigned to
indicate the anticipated direction of the relation between the predictors and land use
outcomes. Each suite of factors is described in this section. Where predictor variables
were excluded in the pasture model, they are shaded in gray in Table 6.1.
The land use outcomes feature three mutually exclusive variables: cropland,
fallow land, and forest; an aggregate land use variable, cleared land; a composite
variable, percentage of land cleared; and a dichotomous variable, cattle ownership. The
vast majority of farms in the sample are comprised entirely or almost entirely of cropland
(virtually all in maize), fallow, and forest. Though farmers in the region use manzanas
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(0.7 hectares), all measurements are converted to the metric units of hectares for
comparison to other regions.
Since the aggregate variable, Cleared land, is a direct measure of a household’s
impact on the forest, summing up all of its land uses, it is included as an outcome
variable. The percentage of land cleared is included as a dependent variable as well.
The latter is a key variable to examine since it contains farm size in the denominator.
This is useful as it is expected that more land leads to more of each land use. But the
percent of each land use indicates trade-offs in the decision-making process. I will now
discuss hypotheses related to the independent variables that were ultimately selected for
the six models of land use and land cover in the SLNP.
1. Demographics Factors
Household size is expected to be positively related to agricultural extensification
principally for two reasons. First, more mouths to feed stimulates greater demand for
production. Second, more hands to work the farm means that production may be
increased through extensification without hiring labor outside the household. These
arguments hark back to Malthus (1873) and Chayanov (1986). More recently they were
corroborated in Latin American frontier environments (Pichón, F.J., 1997; Rosero-Bixby
and Palloni, 1998). Yet it may be expected that household size is negatively related to
the most extensive land use, pasture, since livestock activities should depend more on
farm size than on number of farm laborers (as discussed by authors featured in Chapter
Two, e.g., Mac Donald, 1981; Hecht, 1983; De Walt, 1985; Buschbacher, 1986; Shane,
1986; Joly, 1989; Loker, 1993; Hemming, 1994; Fujisaka et al., 1996; Humphries, 1998;
Walker et al., 2000).
2. Political-economic Factors
Contact with an NGO is dichotomous (0 = no contact, 1 = some contact). This
variable measures whether or not a farmer has had any contact with a conservation or
development organization (whether NGO or GO). I am uncertain as to the potential
relation between contact with an NGO and land use. To the extent that such contact is
with an environmental NGO or government agricultural organization promoting
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conservation and sustainable agriculture, it is expected to be associated with less forest
clearing, less land in fallow, crops, and pasture, and more land in forest (as found in
Brazil by Almeida, 1992). However, contact with agricultural extension agents and
credit lending agencies could be related to a farmer purchasing cattle or adopting
technologies that make land use more profitable, possibly leading to more clearing.
No land title is defined as the absence of any credible claim to legal ownership of
the farm. This includes not completing any of the steps necessary for acquiring a legal
title, such as having solicited a request for land ownership to INTA (The National
Institute for Agrarian Reform), having the farm measured by INTA (or a professional
surveyer), or having a provisional title. Only farms outside the core zone of the park or
farms that are part of cooperatives (the two together represent 31% of the sample) are
able to assert legal claims to their farms on an individual or collective basis.
Following the debate by several authors mentioned in Chapter Two (e.g.,
Thiesenhusen, 1991; Fearnside, 1993; Kaimowitz, 1995; Clark, 1996), I anticipate that
having legal claim to the farm is associated with less forest clearing. Particularly, it is
anticipated that relatively less land will be sown in crops. With a legal claim, a farmer
may invest in crop intensification while for squatters agricultural intensification makes
little sense if a farmer anticipates he may abandon the farm or be evicted from it in the
near future. However, land titling also enables a farmer to avail himself of credit which,
given farmer desires, is likely to be invested in purchasing cattle. Thus, were it not for
the influence land title may have on cattle adoption, I would anticipate the total amount
cleared and the percentage cleared to be positively associated with lack of secure land
title. In sum, I expect lack of secure land title to be associated with extensification in the
form of cropland and fallow land while the relation to cleared land and percent cleared
could be positive or negative.
3. Socio-economic Factors
Household socio-economic characteristics
Ethnicity is a dichotomous variable measuring membership or not in an
indigenous group (one quarter of the sample is indigenous, divided more or less evenly
between Q’eqchí Maya and other Maya groups). It is an open question as to how
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ethnicity might affect land use on the frontier. I suspect it will be insignificantly related
to most outcomes. As mentioned in Chapter Five, cross-tabular analyses indicated only a
modest relation between ethnicity and agricultural extensification. However, this topic
deserves further consideration given that most of Petén’s population, especially its small
farmers, is Maya. I therefore include the variable in the models. The findings will add to
the small literature that has examined the influence on forest clearing of indigenous
versus non-indigenous colonists (Atran, 1999; Corzo-Márquez and Obando, 2000; Fagan,
2000; Carr, forthcoming-a).
Educational achievement is dichotomous: it indicates whether or not the
household head has ever attended school. Since nearly half the heads of household have
never attended school, this measure was chosen over a continuous variable representing
years or levels of schooling attained. It is uncertain if educational achievement is related
to extensification, intensification, or both. The educational achievement of the head of
household could have a positive effect on agricultural extensification if education leads to
a higher motivation to increase living standards through increasing production. However,
production could be increased by intensification if education increases means an
increased knowledge of farm management techniques such as the ability to read
instructions on fertilizer and herbicide applications (e.g., Moran, 1984; Godoy et al.,
1998). An alternative is that education is related to a greater level of environmental
awareness, and may be expected to reduce agricultural extensification. However, few
SLNP farmers thought that the park’s conservation should inhibit their farming (Chapter
5).
In the Ecuadorian Amazon, Pichón (1997) found that educational achievement of
the household head was weakly negatively associated with percent of land in forest and
positively related to land in pasture. This was interpreted as more education being
associated with the ambition to own cattle. Given that the “culture of pasture” described
in Chapter Two appears to exist among SLNP colonists, probably a good measure for
farmer’s ambitions in general is whether or not they desire to have cattle or not).
Land in previous residence is a continuous variable expressing the amount of land
available to the household in its previous residence (most respondents had no land). This
category includes legally owned land or land on which the household had homesteader’s
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rights. Only 2% of the sample had legal title to their land and almost 70% had no land to
farm in origin areas.
Having access to land in previous residence suggests greater farming knowledge,
which may be associated with more forest clearing for market production. Further, given
the extreme poverty of the sample and the likely inability of farmers to purchase land in
settled communities, access to land prior to migrating may imply having lived in former
land abundant frontier regions where swidden farming was learned. It is therefore
expected to exert a positive effect on extensification.
Lastly, for those with access to land in origin areas, the household came not to
obtain land, as did most, but to expand landholdings. In this way, migration to the SLNP
may be viewed as a form of agricultural extensification. Presumably, those who enjoyed
land ownership prior to settlement in the SLNP area relinquished their land rights with
the intention to farm more land in the SLNP and to invest in livestock or crops primarily
for market sale.
A household head is considered to have engaged in Off-farm labor if he worked
off the farm for a wage any time during the previous 12 months. As described in Chapter
Five, this usually means working on nearby farms on a day by day basis as a hired field
hand. Presumably, if the household head engages in work off the farm, less time can be
devoted to working on one’s own farm. Therefore, off-farm labor is expected to have a
negative relation with agricultural extensification (this relation was found in the
Ecuadorian Amazon: Pichón, F.J., 1997; Bilsborrow and Pan, 2001).
The variable, Rents land, is dichotomous and refers to households that work a plot
in exchange for money, crops, labor, or for free (as is often the case with a landless friend
or relative). Households that rent land are expected to dedicate a much greater proportion
of their farm-holdings to crop production (e.g., Shriar, 1999). Renters typically have
small plots, often little more than a few manzanas for subsistence production. Because of
these small plots, little absolute forest clearing is expected among renters, but a much
greater proportion of land cleared and in crops is hypothesized. Because fallow land
would take away from precious land that could be dedicated to crops, in the case of very
small rented plots, it is expected that farmers will leave very little land in fallow. Further,
in some cases, as mentioned in Chapter 4, renters farm a plot for only a year or two, often
201
in exchange for clearing the forest for larger landholders who intend to sow pasture grass.
Usually, renters will have no fallow land since they will rent only enough land for actual
crop use.
Farm characteristics
Size of total holdings is expected to be a powerful predictor of land use. Simply,
the larger the farm, the more land that is cleared; but the larger the farm, the more land
may also be conserved and maintained in forest. Large farms are therefore expected to
have more land in all major land uses, i.e., forest, crops, and fallow, but a lower
percentage of their farm cleared for crops and fallow. Labor limitations constrain the
percentage of land that can be cleared more than does farm size once the farm is over a
certain size, perhaps twenty hectares. Larger farms are also anticipated to be associated
with cattle ownership since cattle are highly land-demanding (e.g., Mac Donald, 1981;
Hecht, 1983; De Walt, 1985; Buschbacher, 1986; Shane, 1986; Joly, 1989; Loker, 1993;
Hemming, 1994; Fujisaka et al., 1996; Humphries, 1998; Walker et al., 2000).
Conversely, small farms are expected to be associated with less land in each of
the land uses and to be associated with little cattle ownership and a greater percentage of
the farm cleared, particularly in crops (and a smaller percentage in forest and fallow). As
noted in Chapter Five, the latter is expected because subsistence requires a minimum
amount of land to be cropped regardless of size of total holdings.
Distance to road is measured in kilometers by footpath from each farmer’s
principal farm-holding to whichever is closest between the Naranjo and Bethel roads, the
only two roads offering access to the core zone of the park. Several studies in the Latin
American tropics have noted the inverse relation between deforestation and distance to
road at the regional level (e.g., Rudel and Horowitz, 1993; Fujisaka et al., 1996; Nelson
and Hellerstein, 1997; Pichón, F.J., 1997; Sader et al., 1997).
I expect farm distance to a road to be negatively associated with total and
percentages of forest cleared and land in crops and fallow when farm size is included in
the model. I expect distance to road to be negatively related to cattle ownership for two
reasons. Pasture is associated with legal claim to the farm which is more likely among
earlier colonists located closer to a road. Secondly, distance to a road should be
202
negatively associated with the value of the farm since transportation costs make
successful farming more difficult. Therefore farmers who are able to afford cattle are
more likely to have farms located in desirable locations closer to the road.
Further, as discussed in Chapters Two and Five, distance to the road is negatively
related to duration on the farm, and thus cleared land, particularly land in fallow (newer
farms have yet to complete their crop:fallow cycle). When duration is controlled for in
the models, again, I anticipate road proximity to be a proxy for market access, and to
therefore be positively related to all categories of cleared land. Nevertheless, a
reasonable alternative hypothesis is that the cost of transporting agricultural products to a
road may encourage more crops to be grown in compensation.
Duration on the farm is a categorical variable equal to “0”, households occupying
their farm for fewer years than the mean, and “1”, households occupying their farm for
longer than the mean number of years. A continuous variable for duration on the farm
was insignificant in the full models in which other variables apparently diminished its
significance (e.g., household size, farm size, and distance to road). This is likely at least
partially due to the low variability across the sample in farm duration. It appears that
duration may not have a strong affect in itself until a certain number of years on the farm
has been reached since it takes several years for crops to be planted and at least one
fallow rotation to be completed.
Duration on the farm is expected to be related to agricultural extensification and
cattle ownership. Evidence of land consolidation in the evolution of frontiers
corroborates this expectation (Almeida, 1992). Similar to trends reported by Murphy and
others (1997) in Ecuador and Almeida (1992) in Brazil, it appears from data presented in
Chapter Four and Five that earlier arrivals to the SLNP selected the best locations for
farms in terms of natural endowments such as flat land, and the availability of water, and
in terms of proximity to roads. These households may be more likely to take advantage
of their farm’s geography by expanding production through forest clearing. More
importantly, longer duration on the farm allows for more years of land clearing and the
desire for owning more cattle to be realized.
Additional agricultural fields is defined as any household with more than one
farm plot. Additional agricultural fields are expected to be associated with greater forest
203
clearing and with cattle ownership. I expect that the use of an additional field implies a
desire to expand crop production or to have cattle on one plot and crops on another.
Simply, if a farmer seeks to add a plot to his overall landholdings, most likely he is doing
so to use it for a productive purpose. Indeed, in the informal land “market” of the SLNP,
not using the land would invite squatters to claim it as theirs.
The cropping of the nitrogen-fixing legume, velvet bean, and the application of
herbicides are the two most common types of intensification in the region. Only 11% of
the sample use any other form of intensification, such as chemical fertilizers. Velvet
Bean and/or herbicides is a categorical variable with 0 assigned to households who use
neither intensification technique, 1 for households which employ one or the other, and 2
to households employing both.
As addressed in Chapter Two, intensification should allow greater production in
less area. Indeed, it may encourage a reduction in cropland due to increased labor
investment per unit space (e.g., Geertz, 1968; Turner II et al., 1977; Lundahl, 1981;
Bilsborrow, 1987; Brush and Turner II, 1987; Martine, 1990; Collier and Horsnell, 1995).
Thus, given that there is little leisure time among SLNP farmers (Chapter 4), labor
invested in intensification may compete directly with labor invested in extensification,
reducing pressure on farm forests.
The use of velvet bean allows the compression of fallow rotations, and the
application of herbicides facilitates cropping in successive years (see, e.g., Mausolff and
Ferber, 1995; Shriar, 1999). Therefore, these forms of intensification are expected to be
negatively related to land in fallow. However, it is possible that agricultural
intensification may ultimately promote extensification since producing more on less land
can free up land for cattle expansion (Zweifler, 1994).
Assets is a categorical variable defined as ownership of a radio, a horse, a car, or a
chainsaw. One point is assigned to each asset. Assets are expected to be positively
associated with forest clearing for crops and pasture (e.g., Almeida, 1992; Rudel and
Horowitz, 1993; Murphy et al., 1997; Mather et al., 1999). More assets reflect the ability
of a farmer to hire laborers to clear land and harvest crops, and to purchase cattle. It may
also reflect on previous ambitious, market-oriented behavior.
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4. Ecological Factors
Soil fertility is a dichotomous variable: 0 for those without fertile soil and 1 for
those with fertile soil. This measures the perception of farmers relative to the overall
quality of soil conditions on their farm and therefore conflates potential variability in soil
quality within farms and is not corroborated by physical measurement. Soil fertility is
expected to be positively associated with forest and cropland and negatively related to
cleared land, percentage of land cleared, land in fallow, and cattle ownership.
As discussed in Chapter Two, an overwhelming number of cases of rapid
deforestation reported in the Latin American tropics have involved pasture expansion
following soil degradation (Hecht, 1985; Moran et al., 1994; Pichón, F.J., 1997). Pasture
expansion often makes forest conversion irreversible and tends to spur further forest
clearing as soils are rapidly weathered (Mac Donald, 1981; Hecht, 1983; De Walt, 1985;
Shane, 1986; Eden et al., 1990; Loker, 1993; Walker et al., 2000).
Conversely, farmers with better soils may be encouraged to favor cropland over
pasture (and, therefore, over forest) since pasture grass will survive on impoverished soils
for years after the soil is too exhausted for successful crop production (e.g., Hecht, 1983).
Lastly, as in most farming regions, farmers appear to prefer flat land when
choosing where to clear land. Presumably then, farms with Flat land were selected
preferentially for agricultural expansion farms than farms of greater relief. It would then
follow that these farms would have more forest cleared and more land in crops and
fallow.
C. Model Design
The first six models are standard multiple regressions (MR). Multiple regression
is an extension of bivariate regression. The main difference is that multiple independent
variables (instead of one) are combined to predict the value of the dependent variable (for
more on multivariate methods, see, e.g., Tabachnick and Fidell, 1996). The results of
MR yield an equation that represents the best prediction for a dependent variable from a
set of continuous or dichotomous independent variables. The equation takes the general
form:
205
kk XBXBXBAY ...2211 +++=
where Y is the vector of dependent variables (DVs), A is the intercept, the Xs are the
independent variables (IVs), and the Bs are the coefficients assigned to each predictor
during the regression. The purpose of regression analysis is to demonstrate relations
among variables, but such relations do not indicate causality. Theoretical and empirical
knowledge external to the regression analyses is necessary for credible speculations as to
potential causal relations among variables in the model.
For the specific DVs analyzed here, the equation takes the form:
where each Y is the value of dependent variables for land in maize, fallow, forest, cleared
land, and percent of land cleared at the farm level, A is the intercept for each dependent
variable, the Xs are the independent variables for each DV and the Bs are the coefficients
assigned to each IV.
Each independent variable was evaluated relative to its contribution to the model
after all the other IVs are entered. Thus, each independent variable is assessed by what it
adds to the prediction of the DV, that is, different from, or “over and above,” the
predictability provided by all other IVs. It is therefore possible for a variable to appear
unimportant in the model when it is actually highly correlated with the dependent
variable.
Green (1991) offers an in-depth treatment of required sample size. For testing
multiple correlation, the simple rule of thumb for minimum sample size relative to the
number of independent variables in the model is that n (sample size) should be equal to or
greater than 50 + 8*m (where m = the number of IVs). For testing the relation of
individual predictors to the dependent variable in the model, n should be equal to or
greater than 104 + m. The sample size here of 241 exceeds 170 in the first case. In the
second case 241 exceeds 119. These rules assume a “medium size” relation between the
IVs and DV of alpha equal to 0.05 and Beta equal to .20, a rule that is met with all but a
,...1111__ kkoutcomeuseland XBXBXBA +++=y
206
handful of IV-DV relations. Still, even employing the more conservative estimate for
minimum sample size, requiring 15 cases per predictor, 241 still exceeds 225, or 15
multiplied by the maximum number of IVs (15) in the models.
Since one of the outcome variables proposed to be measured here, whether one
has pasture or not, is dichotomous, the last equation presented in this chapter is a logistic
regression model. Logistic regression (LR) is used to predict group membership from a
set of independent variables. Logistic regression is related to multiple regression analysis
with a dichotomous DV, but LR makes no assumptions of normality about the
distribution of the predictors. Independent variables do not have to be normally
distributed, linearly related, or of equal variance. Further, as in MR, the IVs may be
continuous, discrete or dichotomous (Tabachnick and Fidell, 1996).
The general equation for logistic regression takes the form:
ijj XBAY
Y Σ+=��
���
�
−1ln
such that the linear regression equation equals the (natural log of the) probability of
membership in one group divided by the probability of membership in the other group.
Coefficients are estimated with the aim of yielding the best linear combination of
independent variables to maximize the likelihood of obtaining the observed frequencies
(Tabachnick and Fidell, 1996).
For the dependent variable analyzed here, in order to predict the probability of
having pasture (or not), the equation takes the form:
where � is the estimated probability that the ith case is a member of the pasture category.
For both models, the independent variables are a combination of continuous,
discrete and dichotomous variables. No cases had missing values. A correlation matrix
was produced for all of the variables included in the models. There were two correlations
above 0.5 among the independent variables. Household density and size of total holdings
,1
ˆ332211
332211
XBXBXBA
XBXBXBA
ee
pastureY +++
+++
+=
207
were correlated at 0.55 and rents land and household density were correlated at 0.51.
ANOVA tests of interaction were run with these two variables and with their interaction
variables in each of the six models.1 No interaction affects or suppressor variables were
found among variables with correlations above 0.2.
Diagnostics were employed to examine regression assumptions. Four statistically
significant outliers were revealed to be influential points according to several measures.
When these four points were removed from the models, overall significance (R2)
decreased insignificantly. A review of the original surveys suggest that these outliers are
not a result of unreliable responses or data entry error. The data points therefore
remained in the models.
Some of the variables demonstrated skewness and kurtosis. However, in a large
sample, variables with significant skewness rarely deviate sufficiently from normality to
substantively change analysis results (Waternaux, 1976). Similarly, kurtosis from
violations of normality disappear with 200 or more cases (Waternaux, 1976). The
variable size of total holdings demonstrates heteroscedasticity, but was not transformed. I
chose not to transform this variable because of the complexity of interpreting transformed
heteroscedastic variables. Further, the variable was highly significant in each model and
failure of linearity of residuals does not invalidate a regression analysis so much as
weaken it (Tabachnick and Fidell, 1996).
D. Regression Results
The final models are shown in Tables 6.2. through 6.7. The final adjusted R2
values for the six models were 0.54 for Cleared land, 0.77 for Forest, 0.22 for Cropland,
0.37 for Fallow, and 0.50 for Percentage of land cleared. The Nagelkerke R2 for Pasture
was 0.60. These R2 values indicate a reasonably good to very good association between
the dependent variables and the predictor variables. I will summarize significant results
for each model (based on using the 0.05 level) before considering the relations between
predictors and the land use or agricultural extensification variables by predictor category.
1 Only one interaction was significant. The relationship between the interaction variable householddensity*size of total holdings and forest was significant at the .15 level. Household density wassubsequently removed from the models as it failed to significantly increase overall goodness of fit (exceptfor in the cropland model where both it and household size were significant predictors).
208
First, Cleared land (Table 6.2.) is positively related to household size, ethnicity,
having land in previous residence, size of farm holdings, having additional agricultural
fields, and use of velvet bean and/or herbicides. Cleared land is negatively associated
with lack of land title, having any formal education, off-farm labor, renting land, and
distance to road.
The standardized regression coefficient for size of total holdings is several times
larger than that of any other variable in the model. This is expected since more land
should mean more land in use. Distance to road was the second most important variable
and its sign was in the hypothesized direction, as expected, since distance from the road
decreases the incentive of production for market.
Velvet bean and/or herbicides and off-farm labor followed distance to road as the
most important variable. The sign is not in the expected direction however, suggesting
that agricultural intensification may ultimately be encouraging extensification, possibly to
free up land for cattle expansion. As expected, off-farm labor was related with less forest
clearing since time away from the farm to work elsewhere means less time can be
devoted to clearing one’s own land.
Rents land and additional agricultural fields were the next most important
variables. They were were significant at the 0.01 level and met theoretical expectations
In the first case, renters have small plots on which little total land is cleared. In the
second case, the use of an additional field (not adjacent to one’s primary farm) reflects
having cleared more land for agriculture.
The remaining five variables were significant at the 0.05 level. Lack of land title
was negatively related to forest clearing. This contradicts a large body of literature which
considers secure land title as an incentive to forest conservation. Maya were also found
to clear more forest than Ladinos. This supports some previous research in Petén, but
must be considered within the context of a relatively small sample of Maya settlers and
the fact that the sample is comprised of several different Maya groups. Approximately
half the Maya in the sample are Q’eqchí, and grouping them into one category with other
Maya groups is problematic.
Educational achievement and land in previous residence were also significant. It
appears that education is inversely related to extensification, supporting one of my
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competing hypotheses. In the case of land in previous residence, the positive relation
meets the expectation that these farmers are either wealthier or were accustomed to
extensive farming in remote locations in their previous residence.
Lastly, household size was significant at the 0.02 level. This positive relation
controlling for other significant variables supports the notion that more family members
stimulates demand for subsistence food production while also augmenting the means to
increase production.
As forestland is the exact opposite of cleared land, the inverse relation is observed
for all variables in the model compared to cleared land, except one (Table 6.3.). Size of
total holdings, as explained above, is a very strong predictor of forest, with several times
the effect size of any other variable in the model.
Cropland (Table 6.4.) is positively associated with size of farm holdings, having
additional agricultural fields, and the use of velvet bean and/or herbicides. Cropland is
negatively related to lack of land title. In this model, lack of land title had a considerably
stronger effect than did any other predictor, including size of total holdings. This
supports one of the competing hypotheses that having legal claim to the farm is
associated with relatively more land cleared for pasture than for crops. Size of total
holdings and additional agricultural fields were the next strongest variables in the model
and supported hypotheses of agricultural extensification
An unexpected relation emerged with the use of velvet bean and/or herbicides
positively related to cropped land. This was unexpected since intensification measures
allow for more crops to be produced, reducing the need to extensify agriculture. Lastly, a
marginally positive association was observed between cropland and Maya etnicity.
Fallow land (Table 6.5.) is positively associated with household size, lack of land
title, having land in previous residence, farm size, duration, occupying additional
agricultural fields and the use of velvet bean and/or herbicides. As reported for cleared
land, size of holdings is the most important variable in the model. Rents land and
distance to road were the second most important predictors. Rents land is negative, as
hypothesized, since renters change farm plots from season to season without letting land
go fallow. This relation was clearly expected and is reflected in the relatively strong
standardized Beta coefficient. Distance to road is also an important variable and, as
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predicted, is negatively related to fallow land since farmers further from the road may be
less market-oriented.
Lack of land title and the cropping of velvet bean or the use of herbicides are the
next most important variables and are in the expected direction, as reported in cleared
land. In the first case, the relation likely reflects that lack of title may favor crops over
cattle. Regarding the use of velvet bean and/or herbicides, the relation was hypothesized
to reduce fallow rotations. Perhaps this hypothesis was unmet because fallow land
reflects past land use while velvet bean and/or herbicides reflects current intensification
techniques. Indeed, the cropping of velvet bean could have been stimulated by the very
fact that farmers who have already cleared much of their land for crops over time, may
wish to conserve some forest as an insurance for future harvests and for bequeathing
farmland with good harvest potential to their children. Further, it is possible that farmers
are extensifying and intensifying simultaneously, as could be the case, for example, with
farmers with cattle or market-oriented farmers.
Additional agricultural fields, land in previous residence, duration on the farm,
off-farm labor, and household size were all in the expected direction with each relating to
extensification in the form of more fallow land. Lastly, as described above for cleared
land, education is negatively related to fallow land.
Having pasture (Table 6.6.) was positively associated with contact with an NGO
or GO, assets, and distance to road. Only the relation between assets and pasture met
expectations among the positive associations. Alternative hypotheses are presented
below. No land title and off-farm labor were negatively related to pasture, both meeting
expectations.
I have presented the results of the relations between independent variables and the
total amount of land devoted to each of the major land uses leading to forest clearing and
the binary outcome of pasture. Now I will briefly discuss the relation between predictors
and the relative proportion of land farmers devote to competing land uses. Since we
assume a priori that more land will be positively associated with all land uses, examining
percent of farm cleared allows for an interpretation of potential trade-offs between certain
land uses leading to forest conversion.
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The percent of farm cleared is positively associated with household size, having
land in previous residence, renting land, duration, having additional fields, and the use of
velvet bean and/or herbicides. All but the latter of these relations were anticipated.
Percent of farm cleared is negatively associated with having attended primary school,
off-farm labor, size of farm holdings, and distance to road. It is also marginally inversely
related to soil fertility. It was uncertain what the relation would be between education
and percent of land cleared, but the remaining negative relations were anticipated. Size
of farm holdings is nearly three times as important as any other variable in the model.
The direction of the sign is, as expected, negative, the opposite of the relation with total
land cleared. Distance to road is the next most important predictor, supporting the
hypothesis that greater distance to a road makes market access more difficult,
discouraging extensification.
Following farm size and distance to the road, household size and land renting are
the next most important variables. Household size is significant at the 0.01 level and
suggests that for consumption and demand reasons, larger families clear a larger
percentage of their land. Rents land is, as anticipated, positively related to percent
cleared since renters tend to have small plots on which only crops are grown.
The remaining relations between independent variables and percent of land
cleared were significant at the 0.05 level. As found in the total land cleared model,
having attended school is negatively associated with percentage of the farm cleared,
while duration, off-farm labor, having land in previous residence, additional farm plots,
and the use of velvet bean and/or herbicides was positively related to the percentage of
the farm cleared. Lastly, soil fertility was marginally related in a negative direction with
cleared land.
I have discussed the relations between the direction and strength of predictors in
each of the models. I will now describe the relations between independent predictors and
the six land use outcomes, discussing potential relations between predictors and
agricultural extensification leading to forest clearing.
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E. Discussion of Regression Results for Explanatory Variables
1. Demographic Factors
Household size had a significant positive association as hypothesized with most
measures of land extensification (but cropland), and a negative association with land in
forest. It is insignificantly related to having pastureland in crops. The relation is
significant (0.02 level) with cleared land and forest. However, with the key dependent
variable, percent of land cleared, it is the third strongest predictor in the model,
significant at the .01 level. The addition of one household member is associated with
nearly one-half a hectare more of land cleared and a 1% increase in percent of land
cleared. This offers support for the argument that more household members stimulate
greater demand for crops for household consumption and/or for sale to market.
2. Political-economic Factors
It was expected that contact with an NGO or other development organization
would be associated with less forest cleared, less land in fallow, crops, and pasture, and
more land in forest, assuming most such contacts were with conservation organizations.
However, this variable is significant only with pasture, and the relation is positive. This
would not be a sanguine result for the effects of conservation efforts in the region. I
speculate instead that this relation could be capturing the effect that a greater proportion
of farmers with cattle have visited credit-lending agencies (often to receive loans for
purchasing cattle). In addition, this contact may have involved contact with other non-
conservation organizations, such as agricultural development agencies.
No land title is significantly related to crops, fallow land, and pasture. Lacking
legal claim to the farm was associated with more than three fewer hectares of land
cleared, and more than two fewer hectares of land in crops.
Some legal claim to the farm, however tenuous, meant a much greater likelihood
of having pasture. A land title enables credit for purchasing cattle and is associated with
more established, wealthier households (as indicated in Table 5.15.). It was unexpected
that the relation would be negative with crops since squatter farmers were considered
more likely to have a greater proportion of land in crops relative to pasture. Apparently
this variable is capturing some of the effect of renters, none of whom have land titles.
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Renters have very little land in general to extensify holdings. However, there may be
some problems with measurement regarding this variable since any household with
evidence that they have merely initiated the steps towards legal land titling are included
in the “land title” category.
3. Socio-economic Factors
Household socio-economic characteristics
Ethnicity is positively associated with forest clearing. Being Maya leads to an
increase of nearly three hectares of forest cleared. Although its Beta weight is lower than
several variables in the model, ethnicity is positively related with cleared land and
negatively associated with forest at the 0.05 level. This offers tentative support for the
notion that indigenous farmers are more expansive farmers than their Ladino neighbors.
However, prior work with the same sample indicated only slight differences between
Q’eqchí and Ladino land use when duration on the farm is taken into account(Carr,
forthcoming-a). This implies that non-Q’eqchí Maya may be the most land extensive of
the three groups (Ladino, Q’eqchí, and other Maya) and suggests the importance of
examining differences in land use among indigenous groups rather than combining them
all into one category. Further, the relation between ethnicity and percent of farm cleared
was insignificant.
The educational achievement of the head of household is negatively related to
agricultural extensification (total land cleared and percent cleared) and positively
associated with forest cover. Households whose head had attended school at some point
has 7% less land cleared on the farm. This contradicts the argument that education
stimulates consumption aspirations, and the motivation and ability to increase production
(Pichón, 1997; Moran 1984; Godoy, Groff et al. 1998). It supports the notion that some
minimum level of education promotes agricultural intensification, perhaps by way of
sufficient literacy to increase the likelihood of adoption of intensification techniques.
While this association could be thought to reflect that those with more education are more
likely to engage in off-farm activities, this is already controlled for by the inclusion of
off-farm work in the model. Moreover, it is unlikely since 97% of the farmers work in
agriculture primarily.
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Having access to land to farm in the previous residence is associated with greater
levels of extensification in all of the land use variables (but cropland), corroborating the
importance of land use experience prior to migration (e.g., Almeida, 1992). Land in
previous residence is significant at the .15 level for pasture but did not noticeably
increase the goodness of fit of the entire model. Apparently, these households may not
have been as influenced as was previously expected by aspirations of clearing more land
to invest in market crops or in livestock (as found in, e.g., Almeida, 1992). That land in
previous residence is positively associated with fallow land may be suggestive of farmers
that came from previous frontier environments where land was sufficiently abundant for a
“bush fallow” crop rotation. Future research could include this as a dummy variable in
the model.
As hypothesized, off-farm labor is negatively associated with forest clearing,
percentage of land cleared, fallow, and pasture, and positively associated with forest.
Only two variables in the cleared land model had higher Beta values. Households with
heads participating in off-farm labor at some time during the year (usually a couple of
weeks total) reduced by 7% the percentage of land cleared on the farm. This supports the
theory that household heads that work off the farm have less time to invest labor in
agricultural production on their own farm (e.g., Murphy et al., 1997).
Renting land is negatively associated with cleared land and fallow, and is
positively associated with percentage of land cleared and forest, all as hypothesized.
Renters increase the percentage of cleared land on their farms by almost 10%, a large
effect. The variable is insignificant in relation to crops and pasture. In the case of a
small farmer, often an itinerant renter, fallow land is superfluous if the farmer intends to
work a plot for only a year or two. As with off-farm labor, the variable was quite
important as only two variables in the cleared land model had higher Beta values.
Farm characteristics
Size of total holdings is, as expected, far and away the strongest predictor of land
use. Size of total holdings had approximately three times the effect of any other variable
in the cleared land and percent cleared models, based on the Beta coefficients which
reflect the actual variance across each variable. Thus, in Table 6.7, on the results for
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percent of land cleared, it has a Beta of 0.58 compared to 0.18 for distance to a road and
0.13 for household size and off-farm labor. This has a simple explanation. The larger the
farm, the more land that may be cleared for crops and that may be maintained in fallow
and in forest, but the lower the percentage of the farm that is cleared. Thus, for each
additional hectare farmers cleared one quarter of a hectare, but 1% less of the total farm
holdings. Farm size is also associated with cattle ownership at the .15 level but is not as
significant as land title and was thus excluded from the Pasture model.
Distance to road is negatively associated with cleared land and with percentage of
land cleared. Farmers cleared nearly one half a hectare less (nearly 1% of the farm) per
kilometer of distance to the road. Distance to road had the second largest effect after size
of total holdings on percent of farm cleared. This is consistent with results found in other
frontier environments such as in Ecuador, Bolivia, and Brazil (as discussed in Chapter
Two). The association is also negative with land in fallow, which was expected. Farmers
located further from the road may be less market-oriented and have been on the farm less
time than farmers closer to the road. Hence, they have had less time to clear the forest on
their farm. However, contrary to expectations, Distance to road is positively associated
with pasture, suggesting perhaps that the cost of transporting agricultural products to a
road may encourage the adoption of livestock for farmers with remote farms. After all,
unlike maize, cattle can walk themselves to the road!
Additional agricultural fields was positively related to cleared land and
percentage of land cleared, crops, and fallow (all at the 0.05 level or better), and
negatively associated with forest cover, all as expected. Occupying additional fields to
farm increased cleared land by over four hectares (with most being put in crops) and
percent of farm cleared by almost 10%. This supports the theory that ownership of an
additional field implies a desire to expand crop production. I have described above how
the process of acquiring those additional fields should lead to these effects, which should
be controlled for to determine the other relations here which are the principal ones of
interest.
As expected, duration on the farm was positively associated with percentage of
land cleared with 6% more land cleared per additional year on the farm. Duration on the
farm was also positive significantly related to fallow, as expected, with more than two
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hectares of fallow added per year on average. It was insignificantly related to cropland
and total land cleared. It makes sense that it is more related to fallow than to cropland
(though weakly) since farmers may crop the same or similar amounts of land each year,
but in doing so over time, fallow land is accumulated. Relative to total land cleared, it
appears that other variables, notably distance to road, are reducing the significance of the
association.
The use of velvet bean and/or herbicides was positively associated with all
measures of agricultural extensification and was negatively related with forest cover.
These results are oppostite of expections. Intensification allows greater production in
less area, hence an insignificant relation with cropland. As mentioned previously,
agricultural intensification may ultimately promote extensification by allowing more land
to be sown in pasture while minimizing the contraction of grain production below a
minimally desired threshold for household consumption. The possibility that longer
duration has led to soil decline and thus increased the use of velvet bean cannot be
determined from the data.
4. Ecological Factors
Flat land and soil fertility were not significantly related to any outcome variables
at the 0.05 level, and therefore failed to meet the expectation that they would be
associated with agricultural extensification. However, there was a marginal negative
association (significant at the 0.10 level) between soil fertility and percent of land
cleared, meeting hypothesized expectations. Farmers with fertile land cleared 6% less of
their farm than did those with poor or mediocre soil. It is likely that there is little
variability in ecological conditions among the farms in the sample for either of these
factors to be highly significant. With abundant forestland remaining on most farms, soil
depletion is also not yet an issue since good yields can usually be obtained on new plots
of recently cleared land. Similarly, land abundance allows farmers to avoid cropping on
slopes that are so steep as to cause substantial erosion.
This chapter explored the separate effects of farm and household factors on the
outcomes of deforestation and land use by colonist farmers. Household size, being Maya,
217
size of total farm holdings, having land in previous residence, occupying additional
agricultural fields, and the use of intensifying techniques (but in an unexpected direction)
were all positively related to cleared land in a cross-sectional sample of farmers in the
SLNP. Household size, having land in previous residence, duration on the farm, having
additional farm fields, and the use of velvet bean and/or herbicides were positively
related to percent of the farm cleared. The R2 values for the models of cleared land and
percent of land cleared exceeded 0.50. However, when examining underlying causes of
forest clearing, it is evident that a 100% correlation exists between migration and forest
clearing. Thus an immediate prerequisite to forest clearing is the decision of farm
households to migrate to the SLNP from origin areas. Chapter seven therefore examines
out-migration to the SLNP through interviews with community leaders in municipios of
high-out-migration to the SLNP.
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Table 6.1.Expected Associations of Predictors to Land Use
PredictorsCleared
LandPercent Cleared Fallow Forest Crops Pasture
1. Demographics factorsHousehold size + + + - + ?2. Political-economic factorsContact with an NGO ? ? ? ? ? ?No land title ? ? + ? + -3. Socioeconomic FactorsHousehold socio-economic characteristicsEthnicity(a) ? ? ? ? ? ?Educational achievement(b) ? ? ? ? ? ?Desire for cattle(c) + + + - + NALand in previous residence(d) + + + - + +Off-farm labor(e) - - - + - -Rents land - + - - - -Assets(f) + + + - + +Farm and Farming CharacteristicsSize of total holdings + - + + + +Distance to road - - - + - -Duration on the Farm + + + - + +Additional agricultural fields + + + + + +Velvet Bean and/or herbicides(g) - - - + - ?4. Ecological factorsSoil fertility - - - + + -Flat land + + + - + +a) Defined as 0 = Ladino, 1 = indigenous.b) Defined 0 = never attended school, 1 = has attended school (in reference to the household head).c) Of those without cattle, defined as 0 = do not plan to have cattle in 2008, 1 = does plan to have cattle in 2008.d) Defined as 0 = no access to land in previous residence, 1 = some access to land in previous residence.e) Defined as 0 = no participation in off-farm labor during previous 12 months, 1 = some participation in previous 12 months.f) Assets are measured such that one point each is assigned to the following items: radio, automobile, chainsaw, and horse. (g) Uses v. bean/herbicides equals: 0=no usage, 1=crops velvet bean or applies herbicides, 2=crops velvet bean and applies herbicides.
219
Table 6.2. Regression results for:
Cleared Land Adjusted R Square: 0.54
Unstandardized
B Std. Error t Sig. St. Beta
(Constant) 7.65 3.13 2.448 0.02
Household size 0.45 0.19 2.338 0.02 0.11
Contact with NGO or GO -0.75 1.19 -0.625 0.53 -0.03
No land title -3.17 1.55 -2.048 0.04 -0.12
Ethnicity(a) 2.89 1.35 2.135 0.03 0.10
Educational achievement -2.72 1.20 -2.264 0.03 -0.11
Land in previous residence 0.17 0.08 2.231 0.03 0.10
Off-farm labor -4.50 1.68 -2.682 0.01 -0.15
Rents land -3.27 1.20 -2.724 0.01 -0.13
Size of total holdings 0.24 0.03 7.402 0.00 0.44
Distance to road -0.42 0.14 -3.078 0.00 -0.17
Duration on the farm 1.52 1.22 1.252 0.21 0.06
Additional agricultural fields 4.26 1.60 2.664 0.01 0.13
Velvet bean and/or herbicides(b) 2.89 0.88 3.274 0.00 0.15
Flat land -1.74 1.23 -1.416 0.16 -0.07
Soil fertility -1.86 1.22 -1.525 0.13 -0.07
(a) 0=Ladino, 1 = indigenous. n=241
(b) Crops velvet bean and/or applies herbicides
220
Table 6.3. Regression results for:
Forest land Adjusted R Square: 0.77
Unstandardized
B Std. Error t Sig. St. Beta
(Constant) -7.65 3.13 -2.448 0.02
Household size -0.45 0.19 -2.338 0.02 -0.08
Contact with NGO or GO 0.75 1.19 0.625 0.53 0.02
No land title 3.17 1.55 2.048 0.04 0.08
Ethnicity(a) -2.89 1.35 -2.136 0.03 -0.07
Educational achievement 2.72 1.20 2.264 0.03 0.08
Land in previous residence -0.17 0.08 -2.231 0.03 -0.07
Off-farm labor 3.27 1.20 2.724 0.01 0.09
Rents land 4.50 1.68 2.682 0.01 0.11
Size of total holdings 0.76 0.03 23.048 0.00 0.96
Distance to road 0.42 0.14 3.078 0.00 0.12
Duration on the farm -1.52 1.22 -1.252 0.21 -0.04
Additional agricultural fields -4.26 1.60 -2.664 0.01 -0.09
Velvet bean and/or herbicides(b) -2.89 0.88 -3.275 0.00 -0.11
Flat land 1.74 1.23 1.416 0.16 0.05
Soil fertility 1.87 1.22 1.526 0.13 0.05
(a) 0=Ladino, 1 = indigenous. n=241
(b) Crops velvet bean and/or applies herbicides
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Table 6.4. Regression results for:
Cropland Adjusted R Square: 0.22
Unstandardized
B Std. Error t Sig. St. Beta
(Constant) 5.92 1.54 3.832 0.00
Household size 0.09 0.10 0.946 0.35 0.06
Contact with NGO or GO -0.72 0.59 -1.219 0.22 -0.07
No land title -2.44 0.77 -3.188 0.00 -0.23
Ethnicity(a) 1.14 0.67 1.699 0.09 0.10
Educational achievement -0.96 0.59 -1.612 0.11 -0.10
Land in previous residence 0.04 0.04 0.919 0.36 0.06
Off-farm labor -0.54 0.59 -0.908 0.37 -0.06
Rents land -1.30 0.83 -1.563 0.12 -0.11
Size of total holdings 0.05 0.02 2.821 0.01 0.22
Distance to road 0.00 0.07 0.012 0.99 0.00
Duration on the farm -0.34 0.60 -0.573 0.57 -0.04
Additional agricultural fields 2.35 0.79 2.974 0.00 0.18
Velvet bean and/or herbicides(b) 0.93 0.44 2.136 0.03 0.13
Flat land -0.05 0.61 -0.088 0.93 -0.01
Soil fertility -0.15 0.60 -0.250 0.80 -0.02
(a) 0=Ladino, 1 = indigenous. n=241
(b) Crops velvet bean and/or applies herbicides
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Table 6.5. Regression results for:
Fallow land Adjusted R Square: 0.37
Unstandardized
B Std. Error t Sig. St. Beta
(Constant) -1.33 2.66 -0.500 0.62
Household size 0.37 0.16 2.257 0.03 0.12
Contact with NGO or GO 0.27 1.01 0.268 0.79 0.01
No land title 2.70 1.32 2.052 0.04 0.14
Ethnicity(a) 1.86 1.15 1.610 0.11 0.09
Educational achievement -1.90 1.02 -1.862 0.06 -0.10
Land in previous residence 0.16 0.07 2.408 0.02 0.13
Off-farm labor -2.06 1.02 -2.021 0.04 -0.11
Rents land -4.29 1.43 -3.001 0.00 -0.19
Size of total holdings 0.14 0.03 4.892 0.00 0.34
Distance to road -0.41 0.12 -3.581 0.00 -0.23
Duration on the farm 2.06 1.03 1.995 0.05 0.11
Additional agricultural fields 2.74 1.36 2.018 0.05 0.11
Velvet bean and/or herbicides(b) 1.92 0.75 2.558 0.01 0.14
Flat land -1.24 1.04 -1.191 0.24 -0.07
Soil fertility -1.33 1.04 -1.276 0.20 -0.07
(a) 0=Ladino, 1 = indigenous. n=241
(b) Crops velvet bean and/or applies herbicides
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Table 6.6. Regression results for:
Pasture Nagelkerke R Square: 0.60
B S.E. Sig.
Household size 0.09 0.07 0.22
No land title -4.095 0.62 0.00
Contact with NGO or GO 1.02 0.45 0.02
Assets(a) 0.82 0.27 0.00
Off-farm labor -1.22 0.49 0.01
Distance to road 0.14 0.06 0.01
(Constant) -1.58 0.67 0.02
(a) One point each is assigned to the following items: radio, automobile, cha
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Table 6.7. Regression results for:
Percent of Farm Cleared Adjusted R Square: 0.50
Unstandardized
B Std. Error t Sig. St. Beta
(Constant) 0.74 0.08 9.015 0.00
Household size 0.01 0.01 2.705 0.01 0.13
Contact with NGO or GO -0.04 0.03 -1.239 0.22 -0.06
No land title -0.02 0.04 -0.564 0.57 -0.03
Ethnicity(a) 0.04 0.04 1.181 0.24 0.06
Educational achievement -0.07 0.03 -2.109 0.04 -0.10
Land in previous residence 0.00 0.00 2.217 0.03 0.11
Off-farm labor -0.07 0.03 -2.370 0.02 -0.12
Rents land 0.10 0.04 2.170 0.03 0.13
Size of total holdings -0.01 0.00 -9.394 0.00 -0.58
Distance to road -0.01 0.00 -3.233 0.00 -0.18
Duration on the farm 0.06 0.03 2.008 0.05 0.10
Additional agricultural fields 0.09 0.04 2.150 0.03 0.11
Velvet bean and/or herbicides(b) 0.06 0.02 2.455 0.02 0.12
Flat land -0.01 0.03 -0.286 0.78 -0.01
Soil fertility -0.06 0.03 -1.738 0.08 -0.09
(a) 0=Ladino, 1 = indigenous. n=241
(b) Crops velvet bean and/or applies herbicides