The System Dynamics of Forest Cover in the Developing World:
Researcher vs. Community Perspectives
Abstract
Efforts to increase forest cover in the developing world will only succeed if the root causes of
deforestation are addressed. It is therefore important to know if the researchers designing
reforestation initiatives and the local people who implement them have similar views of how and
why forest cover changes over time. In this study, I compared causal loop diagrams of forest
cover dynamics on Negros Island, Philippines generated by researchers working for the World
Wildlife Fund with causal loop diagrams generated by community members in upland Negros.
The diagrams were significantly different, with very few variables in common, indicating the
presence of a gap between researcher perception of declining forest cover and local perceptions
of the same issue. A group model-building exercise involving both researchers and community
members could help to bridge this perception gap and generate a more holistic view of the causes
of declining forest cover.
Keywords: deforestation; development; group model-building; reforestation; Philippines
I. Background
Global efforts to promote reforestation in the developing world are gaining prominence, due to
increasing interest in carbon sequestration as a tool to combat climate change (Houghton, Unruh,
and Lefebvre 1993; Gibbs et al. 2007). The success of reforestation initiatives will require an
understanding of the most appropriate scale of governance at which reforestation initiatives
should be implemented—whether local, regional, national, international, or all of these.
Recently, local and community-based reforestation initiatives have become popular with funding
agencies and development organizations (Ye 2006; Walton et al. 2006). However, the literature
also suggests a wide array of macro-level causes of deforestation, such as global trade regimes,
poverty, population dynamics, and agricultural expansion (Colchester and Fay 2007; Geist and
Lambin 2002; Allen and Barnes 1985). Clearly, if these large-scale causes are not addressed,
local attempts at reforestation will be overwhelmed by a broader trend towards deforestation.
Deforestation and reforestation may therefore be seen as alternate states of the same variable,
forest cover (Roque et al. 2000). Some researchers have attempted to synthesize the different
scales at which reforestation and deforestation operate, but few have delved into the dynamic
interactions among scales and drivers (Kummer and Turner 1994; Lambin et al. 2001).
Obviously, the particular driving forces that affect forest cover are highly dependent on the
geographical location and scale of the study area (Geist and Lambin 2002).
In the Philippines, community-based reforestation projects have been attempted for several
decades, with mixed success (Balbarino and Alcober 1999; Walton et al. 2006; Shively 1999).
Meanwhile, deforestation in the Philippines—including inside of protected areas—remains a
concern (Sheeran 2006). The complex and dynamic behavior of forest coversuggests that a
modeling exercise may reveal important and unexpected insights for decisionmakers (Vennix
1996; Meadows 2008). Furthermore, the need for reforestation initiatives to take into account
both local context, scientific knowledge, and economic dynamics suggest that a group model-
building exercise involving both researchers and community representatives could be beneficial
(Vennix 1996; Van den Belt 2004).
Group model-building has been used in a wide variety of resource management contexts,
including water systems and wildlife management (Pahl-Wostl and Hare 2004; Beall and Zeoli
2008). Although it can be costly and time-consuming, the process of building a system dynamics
model with a group of participants allows for social learning among stakeholders, as they share
opinions, information, and perspectives on a given problem (Van den Belt 2004). Group model
building also can provide a space for stakeholders to reach consensus on difficult or contentious
issues. By bringing together participants with different types of knowledge, a group model-
building process can take advantage of the maximum potential range of qualitative and
quantitative information relevant to the problem being addressed (Vennix 1996). Finally,
comparative studies have demonstrated that problem solving teams who use group system
dynamics modeling generate more structured discussions and a more complete critical analysis
than groups using more traditional facilitation methods (Dwyer and Stave 2008). For all of these
reasons, group model-building may be an appropriate tool for designing reforestation initiatives
in the Philippines.
My goal for this study was to determine if an analysis of deforestation’s causes and options for
reforestation in the Philippines could benefit from a group system dynamics model building
approach. If the researcher-generated and community-generated local models of forest cover
decline are significantly different, insight into the problem of decreasing forest cover may be
gained by bringing researchers and local community members together to learn from one another
in a model-building exercise (Vennix 1996). Identifying and modeling the causes of forest cover
decrease is a critical first step in halting deforestation and promoting regrowth of deforested
areas.
The study objective was therefore to compare an ‘expert’, or researcher, view of the causes of
deforestation and potential for reforestation in the Philippines with a local or community view to
determine the potential for a participatory model building exercise to advance understanding of
the problem. Specific questions to be answered include: (1) Are there significant differences
between the researcher view and the community view of the causes of a decrease in forest cover?
(2) Do both researchers and community participants identify causes of forest cover decrease
operating at multiple spatial scales? (3) Do researchers and community members identify
different ‘leverage points’ where policies or incentives might be used to generate an increase in
forest cover?
IL. Methodology
In this study, I compare a researcher-generated causal loop diagram of forest cover dynamics on
an island in the central Philippines with a community-generated causal loop diagram of forest
cover dynamics, to determine if there are significant differences between these two groups’
views of the issue. For this study, I am taking the view that the variable of interest is ‘forest
cover’, which may decline (through deforestation) or increase (through reforestation) over time.
This is the view taken by the researchers who created the causal loop diagram I am using in the
analysis (Roque et al. 2000).
Negros island, located in the central Philippines, has historically been a region of high terrestrial
and coastal biodiversity (Alcala 2001). However, the island’s landscape, which was
predominantly tropical seasonal forest as recently as the 1950s, has been changed dramatically
through deforestation and intensive agriculture, with adverse consequences for the island’s
biodiversity, water quality and forest resources. By some estimates, 95% of the island’s original
forest cover has been removed (Lopez-Gonzaga 1994). The Philippine government has identified
reforestation of the country’s mountainous regions as a development priority (National
Economic and Development Authority 2004). Local non-governmental organizations working in
the uplands of Negros are also promoting reforestation projects for controlling erosion and
protecting water supply.
Three prominent socio-economic patterns characterize Negros Island: a distribution of
agricultural lands that disadvantages the poor; a high rural poverty rate; and a high population
density (Roque et al. 2000; Riedinger 1995; National Statistics Office of the Philippines 2009).
Negros served as a center of Philippine sugar production beginning in the mid-1800’s, during
which time land-grabbing by Spanish nobility was sanctioned by the colonial government for the
purpose of building the country’s sugar export industry. Many large-scale landowners on Negros
today are the descendents of these Spanish nobles, leaving the island’s poorer residents to
practice marginal subsistence farming in steep mountainous areas (Lopez-Gonzaga 1987).
Negros also has a higher incidence of families living in poverty at 22%, compared with the
national poverty incidence of 17% (National Statistics Office of the Philippines 2009).
The World Wildlife Fund published a study of Negros Island’s deforested condition and a causal
loop diagram of the deforestation problem in 2000(Roque et al. 2000). I used this diagram as a
benchmark to compare and contrast this researcher view with a community-based view of
deforestation. The WWF research team conducted sectoral studies of Philippine biodiversity,
demography, economy, and politics, and generated several causal loop diagrams specific to key
points of concern for the organization (Figure 1). Accompanying text detailed the reasoning
behind each diagram. The WWF’s goal was to identify the root causes of biodiversity loss in the
Philippines, and forest cover was used as a proxy for biodiversity, given limited data availability
on indigenous species.
Forest cover
* +
— > tree cutting <—__
Commercial .
logging +# Access factor —tp Conversion to
mags + agriculture
Kaingin
(slash/burn)
Community Fuelwood
loggii demand Demand for
logging f rs
agricultural
commodities \
Upland
migration Export market
Timber demand ? demand
Socio-economic
policies
ra Sugar demand
‘Sugar elite ay
share of ty
political power Trade policies
Figure 1: Causal loop diagram depicting forest cover decline on Negros Island,
generated by researchers from the World Wildlife Fund (Roque et al. 2000, p. 296).
Two main positive feedback loops are embedded in the model; these are discussed in
the text below.
To generate a causal loop diagram reflecting a local community’s view on the causes of
deforestation on Negros, I traveled to the municipality of Canla-on, near one of the island’s
remaining forest reserves and its highest peak. I attended a community workshop conducted by a
local NGO, the Negros Institute for Rural Development (NIRD). The goal of this workshop was
to identify risks facing the community, and to analyze the community’s vulnerability to and
ability to mitigate these risks. Forest cover was an integral component of community risk
vulnerability and was discussed extensively; multiple inter-related causes of forest cover change
were agreed upon by the workshop participants. I supplemented the workshop material with
follow-up interviews with NIRD staff, who have worked in the Canla-on region for over fifteen
years. The causal loop diagram constructed from the workshop and interviews is included below
(Figure 2). The participants in the workshop were not aware of the researcher-generated view of
forest cover loss, so the second causal loop diagram was generated completely independently.
Forest cover _
+
Tree planting
+
Tree cutting
+ 7
Agrarian reform
Disruption of water
availability
Poverty incidence . \
%
Farm productivity
Unemployment —
Figure 2: Causal loop diagram depicting forest cover dynamics on Negros Island,
generated through workshop discussions and interviews with community members in
Canla-on, Negros. The positive feedback loop in the model is labeled.
Commercial logging,
+
Access to materials 7
* Ethanol demand
Municipal government support
TH. — Results and Discussion
The researcher-generated and community-generated causal loop diagrams were substantially
different, containing almost none of the same variables. One of the prominent differences was
the number and nature of feedback loops in the two diagrams. The community-generated
diagram contained only one positive feedback loop, in which tree cutting by farmers leads to a
disruption of water availability (especially during drought), which in turn leads to lower farm
productivity, which spurs farmers to cut even more trees to put more land in production. The
positive feedback loops in the researcher-generated diagram pertained mainly to population and
to elite capture of resources on Negros Island. According to the researchers, population density
spurs immigration to Negros, as more people create more employment opportunities for those
from neighboring islands (as housekeepers, farmworkers, shop owners, etc.). Immigration, of
course, increases population density, in a classic ‘attractor’ type of situation. Another feedback
loop involves the sugar elites’ share of monetary wealth, which is increased through commercial
logging, which they control (according to the researchers). Sugarcane landowners’ wealth
increases population density, as the landowners hire more workers for the sugarcane fields.
Population density increases demand for fuelwood, which sugarcane laborers must purchase
from the commercial loggers/landowners, thereby increasing their wealth. All of these positive
feedback loops help to explain the extreme state of deforestation that Negros Island has suffered
historically.
Another difference between the researcher-generated and community-generated diagrams was
the community’s identification of local governments (municipality scale) as having the most
leverage to promote reforestation efforts. It is logical that the community would identify the
municipal government as having a large influence on forest cover, as this is the scale of
government the community deals with most directly in their daily lives and with which they are
most familiar. The researcher-generated diagram left out the municipal scale of government
altogether, possibly overlooking a critical leverage point for affecting forest cover.
The researchers’ diagram included only tree harvesting and deforestation, not reforestation,
which might lead to a positive trend in tree cover. This is likely because they were concerned
with the biodiversity effects of deforestation, so primary forest was seen as non-replaceable with
second-growth forest. However, there is some evidence from the field that agroforestry systems
consisting of a mix of planted trees such as cacao and old-growth forest can maintain high levels
of insect and soil biodiversity (though rare plants tend not to thrive in these environments)
(Steffan-Dewenter et al. 2007). It is therefore helpful to consider tree planting and agroforestry
as a strategy for biodiversity maintenance.
It was not surprising that the community-generated causal loop diagram tended to emphasize on-
farm or community decisions, while researchers identified macro-level drivers affecting forest
cover, such as Philippine agricultural trade policy and population dynamics. Community
participants identified poverty and unemployment in rural areas as key factors leading to tree
cutting and a decrease in forest cover, while researchers mentioned poverty and unemployment
only as effects of forest cover decline, not as causes of this trend. The explanation for the causal
relationship given by community members was that cash-strapped farmers may resort to cutting
and selling any trees that remain on their property, even if they see these trees as beneficial in
providing soil stabilization, water protection, and fruit or fiber. A second mechanism related to
laborers employed in paid agricultural work on neighboring farms. If facing unemployment, they
are likely to find a patch of land to cultivate with subsistence crops, even if the land is inside of
an ostensibly protected forest area.
The researchers’ causal loop diagram had more variables than the community-generated
diagram, perhaps because the researchers took a longer time to develop their diagram and
performed more background research on the issues. If the community were able to re-visit their
diagram over time, more variables might be added. One prominent variable mentioned in the
researcher diagram as a driver was rural population density, which was absent from the
community diagram. The Philippines has one of the highest population growth rates in Southeast
Asia, and this may contribute to demand for farmland and deforestation in rural areas
(UNESCAP 2006).
The community-generated diagram referenced the Philippines’ agrarian reform program, which
was intended to remove land from large plantations and put it in the hands of small-scale
farmers. Reviews of the program have called it anything from a complete failure to a mixed
success (Riedinger 1995). The community participants in this study agreed that farmers who are
recipients of land under this program benefit from it financially. However, they also identified a
critical unexpected consequence of agrarian reform; namely, that it can encourage deforestation.
As land passes from large-scale owners to the direct control of small farmers, many of these
farmers make the decision to maximize the productive capacity of the land by cutting down the
trees and planting rice, corn or vegetables instead. These individual decisions have negative
collective consequences for the water regime and soil erosion in the watershed.
Another difference between the community and researcher-generated diagrams had to do with
the drivers of commercial logging on Negros. The community identified ethanol production,
which has several private investors on the island, as driving tree harvest in the Canla-on area.
Presumably, the trees are being used to make cellulosic ethanol. According to the community
workshop participants, these trees are plantation-grown (not primary growth), but are not being
replaced or harvested in a sustainable manner. In contrast, the WWF researchers cited lumber
demand as the driver behind commercial logging on Negros. This probably has to do with the
ten-year time gap between the two studies. The WWF book came out in 2000, before ethanol
production had begun on Negros.
In summary, the researcher-generated diagram emphasized large scale drivers, historical trends,
and political decisions made at the national and international scale as the drivers of deforestation.
In the researcher model, individual farmers and rural residents are helplessly caught in a system
not of their own making, which almost compels them to engage in activities that reduce, rather
than increase, forest cover. In contrast, the community-generated diagram identified some trends
over which residents had no control, but placed a lot of emphasis on landowners’ individual
decisions, and the ability of the local, municipal government to influence these decisions through
regulations and incentives.
The degree of difference between the two diagrams was striking. The researcher diagram was
generated using an analysis of the historical context behind current deforestation patterns, so it is
logical that there would be some differences between this diagram and the community diagram,
which focused on current trends and dynamics. The patterns and causes of deforestation on
Negros have shifted over the past decades, and the WWF researchers did not intend all of their
variables to reflect the current situation. However, they used their causal loop diagram to draw
policy conclusions, so they clearly believe it to be relevant for current decisionmaking around
forest resources. Both researcher and community perspectives undoubtedly have merit, and could
generate significant learning through their interaction. In fact, the perspectives may be
complementary, as they emphasize actions occurring at different scales.
IV. Conclusions and Next Steps
The community-based assessment of deforestation’s root causes yielded substantially different
variables, feedbacks, and leverage points compared with the researcher-generated assessment.
This implies a fairly serious perception gap between researchers who design reforestation
initiatives and communities on the ground who implement them. However, both the researchers
and community participants agreed that deforestation is driven internally on Negros Island by
positive feedback loops; any balancing feedbacks are introduced by exogenous forces (for
example, agrarian reform). This indicates that promoting reforestation on Negros will almost
certainly require external intervention. A group model-building exercise, involving both
researchers and community members, could potentially enhance systemic understanding of the
deforestation issue and generate more robust reforestation initiatives.
It is instructive to note that the community identified local government units as the scale at which
the maximum leverage to promote reforestation could be applied, while researchers did not
mention this governance scale at all. This implies that local solutions that promote reforestation
might be overlooked by researchers in favor of initiatives that operate at a higher level of
governance. Community participants also identified an important feedback that has not been
considered yet in either the agrarian reform literature or the reforestation literature; namely, that
land redistribution could potentially encourage deforestation by giving farmers the right to cut
trees on redistributed land. The process of redistributing land to small-scale farmers might
therefore provide an opportunity to present these farmers with incentives to keep part of their
land in forest. A group model-building exercise that integrates the community-generated model
with the researcher-generated model could help both groups to learn from one another and to
understand the multi-scalar nature of the reforestation challenge more clearly.
Although this study generated some insights into mechanisms of reforestation in the Philippines,
a purely qualitative model in this case is probably not sufficient to generate robust conclusions
on policy directions. For example, in the community-generated model, agrarian reform has both
a positive feedback to forest cover (through decreasing poverty), and a negative feedback to
forest cover (by allowing farmers to cut trees on land they now own) (Figure 1). Without a
quantitative component to the model, it is impossible to know which of these feedback loops is
dominant. Parameterizing the model with quantitative data is therefore a critical next step in the
model-building exercise.
This study demonstrated that a group model-building exercise to integrate researcher and
community views on forest cover could yield important insights into points of leverage for
policymakers. Such an exercise could improve the success rate of reforestation initiatives by
designing these initiatives in a way that takes a systemic and dynamic view of forest cover in the
Philippines. Previous reforestation efforts in the Philippines have had mixed success (Fujisaka
1994), so incorporating a promising new tool like group system dynamics modeling could move
the policy discussion forward in a meaningful way.
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