Trees for Development...Ingrid Haeckel - Trees for Development: Reforestation in Veracruz - CRP 386 Fall 2009 8 towards total reforestation estimates, accounting for more than 50%

Trees for Development:

Merging Environmental Restoration and Poverty Alleviation

Objectives via Reforestation in Veracruz, Mexico

Ingrid Haeckel

Department of Geography and the Environment

University of Texas at Austin

December 10, 2009

CRP 386: Intro to Geographic Information Systems

Ingrid Haeckel - Trees for Development: Reforestation in Veracruz - CRP 386 Fall 2009

1

EXECUTIVE SUMMARY

Reforestation is gaining attention throughout the world, and particularly the tropics, as a

climate change mitigation strategy, as well as an opportunity to restore degraded areas

and ecosystem services that have been lost or diminished. The economic incentives

provided by emerging carbon markets have led many to speculate that reforestation could

provide synergistic environmental and development benefits in developing countries.

In Mexico, reforestation has emerged as a major environmental policy goal, highlighted

by the launch in 2007 of a new forestry program, Pro-Árbol. Pro-Árbol merges goals of

environmental restoration with poverty alleviation and aspires to create economic

development by fostering a sustainable forestry sector. Within Mexico, Veracruz has

been identified as one of the states with most success in meeting this goal. This project

examines whether the objectives to reduce poverty and marginalization in critical

environmental areas were targeted through reforestation funding during 2007-2009.

Suitable areas based on marginalization, indigenous population, soil degradation, and

hydrological service and biodiversity conservation areas are then ranked to prioritize

municipalities for future reforestation funding.

The 2007-2009 Pro-Árbol reforestation results indicate subsidies are being applied in

marginalized areas; however, environmental objectives need more attention. Women

were underrepresented among accepted project applicants and it is unclear to what extent

indigenous communities have participated. This analysis indicates that high priority areas

for reforestation, including Perote, Misantla, Orizaba, and Huayacocotla UMAFORs have

been well served by Pro-Arbol, but future outreach and funding for reforestation should

target the Totonacapan, Veracruz, Los Tuxtlas, and Uxpanapa UMAFORs. Altogether,

more monitoring and reporting is needed to assess the success of reforestation projects to

date and to more clearly address how they are contributing to rural development.


2

INTRODUCTION

Reforestation and forest restoration are major environmental goals throughout the

tropics following decades of widespread deforestation. Recently, however, international

negotiations and policies to mitigate climate change have begun to generate powerful

political and financial incentives to reverse the trend of forest loss. Growing trees store

and sequester large volumes of carbon and contribute to rainfall patterns through high

evapotranspiration, serving an important role in global climate (Bonan 2008). In addition

to carbon sequestration, however, reforestation can restore environmentally degraded

areas by reducing soil erosion, meanwhile improving water quality and infiltration, as

well as potentially increasing water capture. The use of native tree species in reforestation

projects will furthermore enhance the value of restored areas for biodiversity (Chazdon

2008). The potential to restore a variety of ecosystem services where they have been lost

has led to many recent efforts at making reforestation attractive to the rural communities

where restoration is most needed and can have the greatest potential benefit to society

(Lamb, Erskine, and Parrotta 2005).

Carbon forestry projects of the Kyoto Protocol’s Clean Development Mechanism

(CDM) have emphasized the synergistic benefits between reforestation and livelihoods;

however the reality of benefits to the rural poor has been questioned critically by many

scholars (Smith and Scherr 2003). Unless reforestation projects can overcome the

significant implementation challenges facing rural poor households and communities that

they are intended to benefit, such policies will likely result in the continued expansion of

large-scale commercial plantations with little environmental or social benefit. The high

start-up costs of reforestation or plantation establishment, as well as distant benefits and

high risk of the investment are major hurdles to the adoption of forest restoration by

smallholder farmers in the tropics (Coomes et al. 2008). However, with government

subsidies and appropriate information and technical support, reforestation could be

successful as an element of a diversified rural development strategy (Lamb, Erskine, and

Parrotta 2005).

Mexico is one country that has embraced the issue of reforestation together with other

forest conservation and management strategies, establishing a national forestry

commission (CONAFOR) in 2001 and developing a comprehensive new forestry

program, Pro-Árbol, in 2007. Since then, Mexico has invested approximately US $1.5

billion in Pro-Árbol, funding forestry projects on 6.8 million hectares of land and

lowering the national deforestation rate. Policymakers in Mexico have embraced the

potential of forest restoration projects to alleviate poverty and lead to development based

on sustainable forest resource management. However, promoting trees for development is

a very new approach to Mexican forestry policy, which until the early 1990s was largely

based on the promotion of commercial tree plantations, often with exotic species

(Carabias, Arriaga, and Cervantes Gutiérrez 2007). Today, approximately 98% of trees

planted in Pro-Árbol reforestation projects are native species, and there is great demand

to increase the diversity of native species utilized (Valtierra Pacheco et al. 2008).

Although the use of native species is likely to increase the benefit of reforested areas to

biodiversity and possibly hydrological services based on studies elsewhere


3

Figure 1.1


4

(e.g., Kanowski, Catterall, and Wardell-Johnson 2005; Farwig, Sajita, and Böhning-

Gaese 2008; Bruijnzeel 2004), the actual economic incentives and benefits to reforesting

agricultural land for the rural poor in comparison with alternative land uses have not been

clearly articulated or examined in Mexico.

Within Mexico, Veracruz has been identified as one of the states with most success in

reforestation under Pro-Árbol and with high potential for establishing commercial tree

plantations, estimated at approximately a million and a half hectares (CONAFOR 2009;

Velázquez Álvarez 2007). Veracruz is located on the Gulf Coast of Mexico, flanked to

the west by the Sierra Madre Oriental (Figure 1.1) and neighbored by the states of

Tamaulipas, Hidalgo, Puebla, Oaxaca, and Tabasco. While the majority of the state’s

territory lies in the coastal plain, elevations climb sharply up to 5,200 m above sea level,

the height of the Pico de Orizaba, the highest mountain in Mexico (Figure 1.2). The

diverse topography results in high diversity of ecosystems and biodiversity and

endemism. Only 8.8% of Veracruz’ natural vegetation remains, with 72.1% of the state’s

territory in agricultural and urban land use (Figures 1.3) (Velázquez Álvarez 2007). The

state remains fifth in Mexico in terms of deforestation, losing approximately 30,000

hectares annually despite recent restoration efforts (Figure 1.4) (Velázquez Álvarez

2007). Approximately a third of Mexico’s surface freshwater flows through the major

watersheds of Veracruz, which underscores the significance of maintaining the

hydrological functions provided by forest cover.

Although Veracruz is known for its strong agricultural and industrial sectors, many

municipalities continue to have high indices of socio-economic marginalization, with 37

of the state’s 212 municipalities ranked as highly marginalized in 2005 (CONAPO 2005).

Depressed markets for agricultural products and high unemployment have led to a

significant increase in transnational migration from Veracruz in the last decade (R.

Torres, pers. comm.). It is not known what role the agricultural economy or migration has

played in the recent decisions of land owners to reforest former agricultural lands.

Pro-Árbol

Pro-Árbol was founded in 2007 to unite and expand upon several previous forestry

programs, including the Programa de Desarrollo de Plantaciones Forestales

(PRODEPLAN) and the Programa de Desarrollo Forestal (PRODEFOR) under a single

framework. The program provides subsidies for the conservation, restoration, and

management of forest resources, as well as for planning to increase the competitiveness

and productivity in the commercial forestry sector. Pro-Árbol is an umbrella for the

support of diverse forest-related activities in Mexico, but reforestation under the category

of conservation and restoration is the flagship activity and one of the largest destinations

for subsidy funding. Land owners who apply for funding to reforest land must

demonstrate legal ownership, meet certain rules regarding the application of subsidies,

and own a minimum of five hectares of land or join other land owners in a group to reach

the minimum required area. Importantly, subsidies are only given for properties in areas

that would naturally be forested or that can develop forests (SEMARNAT, 2009). In

addition to providing a subsidy to cover most of the expense for labor to plant the trees,

CONAFOR provides the saplings free of cost. Nearly all trees planted are native;

however, Pro-Árbol has been strongly attacked for counting Agave and Opuntia species


5

Figure 1.2


6

Figure 1.3


7

Figure 1.4


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towards total reforestation estimates, accounting for more than 50% of the plants

reforested in 2007 (Valtierra Pacheco et al. 2008).

Applications for reforestation funding are prioritized according to several social and

technical parameters. Applicants from highly marginalized municipalities or

municipalities with over 50% indigenous population are given preference, as well as

women or members of community forestry organizations. Applicants who are in the

process of obtaining or have already completed certification in a forest management

program or related certification are also given preference, as well as those with land

under technical silvicultural supervision.

In addition to social and technical considerations, Pro-Árbol specifies areas eligible

for Mexico’s Payment for Ecosystem Services (PES) program, established in 2003 and

also administered by CONAFOR. In particular, areas eligible for payments are based on

the provisioning of hydrological services and biodiversity conservation. Multiple criteria

were utilized to determine these specific areas. Hydrological eligible areas are determined

based on forest cover, critical zones for aquifer recharge or in zones with problems of

surface water scarcity, water quality, and hydrological hazards. In particular, areas within

priority mountain regions or natural protected areas are given preference. Biodiversity

eligible areas are determined based on species distribution assessments, wetland

protected areas, priority mountain regions, and conservation priority areas determined by

the Comisión Nacional para el Uso y Conocimiento de la Biodiversidad (SEMARNAT,

2009).

Given the significant social and financial investment in reforestation under Pro-Árbol

since its inception in 2007, it is important to evaluate the program’s advances. Veracruz

will serve as a case study for examining the distribution of reforestation efforts during

2007-2009 to evaluate how the objectives of restoration and development were targeted

in funded projects. Geographic Information Systems (GIS) is employed as a tool to

weight Pro-Árbol’s unusually far-ranging objectives and rank the priority of certain

regions for future funding.


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PROBLEM STATEMENT & RESEARCH QUESTIONS

Given Pro-Árbol’s dual goals of environmental restoration and development,

reforestation subsidies should be targeted at municipalities with high levels of socio-

economic marginalization and high indigenous population, favor the participation of

women, as well as prioritize areas with known soil degradation or with high value to

hydrological services and biodiversity conservation. The distribution and density of

funded projects should reflect these diverse applicant selection criteria.

If reforestation funding thus far has not noticeably targeted these objectives, how

might future funding be more strategically allocated? Geographic Information Systems

(GIS) is a tool to store, manipulate, and analyze spatial data with applications for

planning. Suitability analysis is a method to identify and prioritize suitable areas based on

a set of criteria for a specific objective. Suitability analysis may be applied to identify and

rank areas in Veracruz based on socio-economic, cultural, and environmental criteria to

assist with the selection of future funded reforestation projects.

This study tests for the relationship between social and environmental variables and

total area reforested per municipality in Veracruz during the first three years of Pro-

Árbol. GIS is utilized to visualize initial reforestation results for Veracruz in terms of

area reforested and the gender composition of project participants. A methodology is then

created and tested for ranking eligible areas for reforestation in the state.

This particular study is limited by the coarse scale of municipal level data that is

available; however, the methodology could be adapted given more precise data on

individual applicants, such as socio-economic background and specific geographic

location. CONAFOR collects this information, but it is not publically available for

privacy reasons.

In summary, the main research questions are:

1) What if any relationship is there at the municipal level between the distribution

and density of reforestation projects and socioeconomic marginalization,

indigenous population, soil degradation, and hydrological and biodiversity

priority areas in Veracruz?

2) What areas are most suitable for future reforestation funding to further the goal of

environmental restoration and development?


10

METHODOLOGY

The project was carried out in the following five steps:

1. Data acquisition

2. Data organization

3. Suitability Analysis to rank municipalities for future Pro-Árbol funding

4. Linear regressions to test the realization of funding objectives by allocation to

projects during 2007-2009

5. Create maps

1. Data Acquisition

Two main types of data were utilized in this study: tabular data from the Mexican

census and CONAFOR in Microsoft Excel files, and spatial data in ESRI shapefile

format. My ability to test the research questions was limited by the level of detail in

public records regarding Pro-Árbol participants and the characteristics and location of

areas reforested, as well as by the availability of online spatial datasets for Mexico.

Approved reforestation projects funded by Pro-Árbol are published annually on

CONAFOR’s website; however, the information provided in reports differs from year to

year. At present time, reports are available for category C1.1 Reforestation projects for

the years 2007-2009. I copied all information on these projects into Excel spreadsheets,

including columns for applicant name, municipality, area to be reforested, and funding

approved for project. In addition to funded projects, additional reports are published on

applicants that were approved without funding due to budget shortfalls, as well as

applicants who were rejected for various reasons. These reports include the same

information as described above (without approved funding); however, for 2009 no data

was given for area in reports on applicants approved without funding or rejected.

In addition to reforestation data from CONAFOR, the municipal-level index of

marginalization was downloaded in an Excel file from the Comisión Nacional de

Población (CONAPO), which calculated the index using the Conteo de Población y

Vivienda 2005 by the Instituto de Información Geográfica e Informatica (INEGI), as well

as the Encuesta Nacional de Ocupación y Empleo del IV trimestre de 2005 and the

Conciliación Demográfica 2000-2005. Additional municipal-level census data on

population speaking an indigenous language was acquired from the Censo General de

Población y Vivienda 2000 from the INEGI website. I used this data as a proxy for

indigenous population.

Spatial data were obtained from several Mexican government websites. Nearly all

spatial data for Mexico are produced by INEGI, a selection of which is available for free

download online. However, INEGI’s shapefiles are projected using the ITRF 1992

geographic coordinate system, which is poorly supported by ArcGIS 9.3. As a result, I

only downloaded shapefiles from INEGI that were not available elsewhere. Instead, the

majority of the shapefiles utilized were downloaded from The Comisión Nacional para el

Conocimiento y Uso de la Biodiversidad (CONABIO) website. Finally, some shapefiles


11

were downloaded from CONAFOR’s E-Bosque website by request from the site

administrator.

The following data were utilized in analyses:

Pro-Árbol C1.1 Reforestation results for 2007, 2008, and 2009

CONAPO Index of Marginalization (2005)

INEGI Population Speaking Indigenous Languages (2000)

INEGI municipality shapefile

CONABIO state, localities, topography and 1993 land use and vegetation

shapefiles

CONAFOR hydrological and biodiversity eligible areas, 1993-2002 land use

change, 2002 Soil Degradation, and Veracruz UMAFORs

2. Data Organization

Reforestation data was copied into Excel into separate spreadsheets by year. The

gender of applicants was coded into male, female, or other, and uncertainties of gender

were clarified by a Mexican citizen. Other was used to represent ejidos, associations, and

governmental bodies that received funding from the program. Next, annual data on total

applicants, proportion by gender, and total area to be reforested was summarized by

municipality using the pivot table function in Excel. Finally, a new spreadsheet was

created to combine totals from the three years of data. The totals spreadsheet was copied

into a new Excel book to create a master spreadsheet for statistical and spatial analyses

organized with municipalities as rows.

The raw index of marginalization and the classification column (from very low to

very high) for Veracruz municipalities were copied from the CONAPO file into a new

Excel table with rows labeled by municipality. The marginalization index produced by

CONAPO weights the following variables:

Percent population over age 15 that is illiterate

Percent population over age 15 that did not complete primary school

Percent inhabitants in homes without plumbing or sewer

Percent inhabitants in homes without electricity

Percent inhabitants in homes without running water

Percent homes with dirt floor

Percent population in localities with less than 5000 inhabitants

Percent population earning less than two minimum wages

I opted not to use the index of human development from CONAPO in the study, which is

calculated using some of the variables above as well as GDP, because it is not used by

CONAFOR and because GDP is an inadequate measure of development in rural Mexico.

Following the same method, data on indigenous language for Veracruz municipalities

was copied from the census file into a new Excel table. The percent of inhabitants

speaking an indigenous language was calculated in a new column by dividing the number

of indigenous language speakers by total population for each municipality. The two


12

marginalization columns and percent indigenous language column were copied into the

master spreadsheet.

Spatial analysis was conducted in ArcMap 9.3 (ESRI). In ArcCatalog, all shapefiles

were defined and projected into the custom WGS84 Lambert Conformal Conic projection

commonly used in Mexico. Except for Mexican states, all shapefiles were clipped to the

shape of Veracruz and renamed with the prefix Ver- or Veracruz. In the attribute table for

the municipality shapfile (“Veracruz_municipios”) I created a new column (“Aream2”)

and used the Calculate Geometry function to measure the area of each municipal polygon

in square meters. A second new column was created (“Areaha”) and the Field Calculator

was used to divide Aream2 by 10,000 to calculate area in hectares for each municipality.

The edited table was downloaded as a .dbf file and opened in Excel to copy the Areaha

column into the master spreadsheet.

In ArcGIS, I selected by attribute in the Soil Degradation shapefile

(“Ver_Soildegradation2001_02”) attribute table for strong or extreme degradation

(degree 3 or 4) among the five potential types of soil degradation per polygon. In other

words, any polygon with the presence of one or more types of strong or extreme

degradation was selected. A layer was created from the selection and exported as a new

file (“Degradedsoils”) and the file was clipped to the municipality shapefile to create

“Degradedsoils_Clip.” I calculated the geometry of new shapes in a new column and

exported the file as a .dbf. In Excel, I used the pivot table function to sum the area of

degraded land per municipality and calculated this area as a percent of municipal land.

The column of percent degraded land was added to the master spreadsheet. This method

was repeated with the 1993 land use map to select areas classified as forest or jungle

(bosque or selva) and calculate a column for percent forested area per municipality.

The hydrological and biodiversity eligible area shapefiles were already clipped to

municipalities and included columns for the percent of municipal area in the respective

eligible areas. Therefore, the attribute tables for both files were simply exported as .dbf

files and the percent area column for each was copied into the master spreadsheet.

The completed master Excel spreadsheet included all columns mentioned above and

was added to a new map with the Veracruz_municipios shapefile. The spreadsheet was

joined to the shapefile using the municipality field and exported as a new shapefile called

“ProArbolSuitabilityRanking.”

3. Suitability Analysis to rank municipalities for future Pro-Árbol funding

With the exception of certain coastal and wetland ecosystems and a small region of

matorral (shrubland), most of the state of Veracruz was historically forested and is

suitable for reforestation. No municipality is entirely unsuitable for reforestation; thus, all

were included in the suitability analysis. The five variables I included in the ranked

suitability analysis for future Pro-Árbol reforestation funding were: Marginalization

Index, Percent Indigenous Population, Percent Area with Soil Degradation, and Percent

Area in Hydrological or Biodiversity Eligible Areas, respectively.


13

For all variables except marginalization, a higher numeric value corresponded to

higher suitability for reforestation according to Pro-Arból guidelines. Marginalization

was the opposite, with low (negative) index values corresponding to high

marginalization. In ArcMap, I created five data frames and added

ProArbolSuitabilityRanking to each. I displayed each variable using natural breaks with

five classes. I noted the value range for the natural break classes in each of my five maps

and then labeled the classes from 1 to 5, with 5 being most suitable for reforestation.

Next, I added six new columns to the attribute table of ProArbolSuitabilityRanking to

code the suitability class for each variable and sum the total. I then coded the class values

variable by variable into the new columns and used the Field Calculator to add the five

values in the sixth column, the total suitability ranking. Finally, I classified the suitability

ranking into three quantiles representing low, medium, and high priority for future

reforestation funding.

4. Linear Regressions to test the realization of funding objectives by allocation to

projects during 2007-2009

Linear regressions with single and multiple independent variables were utilized for

annual reforestation data to test for statistical relationships between the area reforested

per municipality in Veracruz and the five variables chosen for the suitability analysis. All

analyses were conducted in SPSS 16.0. Respective columns were copied from the master

spreadsheet into SPSS and formatted. Linear regressions with multiple independent

variables were run using all five variables for 2007, 2008, and 2009 reforested area data

and VIF measured to detect any effects of multicollinearity among variables, of which

there were none. In addition, linear regressions were run for single independent variables

for each of the three years. Beta values for variables were compared among years to

qualitatively evaluate the presence of shifts in funding allocation over time.

5. Create Maps, Charts, and Tables

In addition to the Series I reference and contextual maps for Veracruz presented in the

introduction, Series II includes maps presenting the results of Pro-Árbol C1.1

Reforestation during 2007-2009 in Veracruz according to total hectares reforested,

percent area reforested, total accepted applicants, percent female accepted applicants, and

rejected applicants. Data for Series II maps were classified manually; however, were

based on natural breaks. Series III includes five maps for the suitability analysis variables

with two frames each: one displaying original data or customary classification, and one

displaying the suitability ranking. A sixth map presents the final suitability ranking

classified into low, medium, and high priority for future reforestation funding.

Limitations

There are a number of limitations to my analysis, most importantly being the coarse scale

of publically available data on reforestation activities. There is a lack of detail on


14

individual participants, as well as a lack of spatial data for actual sites reforested in the

published reports. This precludes a more meaningful environmental analysis of the

suitability of reforested sites, as well as a more informational demographic analysis of

project participants. This analysis does not attempt to answer whether reforestation is a

viable approach to economic development or poverty alleviation in Veracruz, nor does it

present any preliminary estimates of impact in this area. It’s still too early to tell how

reforestation projects will impact individuals and communities and whether there will in

fact be synergies between reforestation and development. However, this is an important

area for future qualitative research.


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RESULTS

List of Tables

Series II Pro-Árbol C1.1 Reforestation Results, 2007-2009

Table 2.1: Summary of Pro-Árbol C1.1 Reforestation Applicants and Funded

Projects 2007-2009

Table 2.2: Linear regression results for models with a single independent variable

Table 2.3: Linear regression results for models with all five independent variables

List of Figures

Series I Context Maps – Included in Introduction


Figure 2.1: National Forestry Commission Forestry Management Units (UMAFORs) in

Veracruz

Figure 2.2: Total Hectares Reforested, Veracruz Municipalities 2007-2009

Figure 2.3: Percent Area Reforested, Veracruz Municipalities 2007-2009

Figure 2.4: Funded Pro-Árbol Applicants, Veracruz Municipalities 2007-2009

Figure 2.5: Rejected Pro-Árbol Applicants, Veracruz Municipalities 2007-2009

Figure 2.6: Percent Female Participation, Pro-Árbol Reforestation, Veracruz 2007-2009

Series III Suitability Ranking of Municipalities for Future Reforestation Funding

Figure 3.1: Marginalization in Veracruz

Figure 3.2: Indigenous Language in Veracruz

Figure 3.3: Hydrologic Service Prioritization in Veracruz

Figure 3.4: Biodiversity Service Prioritization in Veracruz

Figure 3.5: Soil Degradation in Veracruz

Figure 3.6: Suitability Ranking of Veracruz Municipalities for Pro-Árbol Funding


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Table 2.1: Summary of Pro-Árbol C1.1 Reforestation Applicants and Funded Projects

2007-2009

Result 2007 2008 2009 Total

Hectares reforested 33,043 24,978 15,614 73,635

Total accepted applicants 1944 2514 1343 5,801

Total accepted applicants

without funding 175 732 457 1364

Total rejected applicants 8 0 33 41

Percent individual female

accepted applicants* 16.3 17.5 14.9

Mean property size of

individual applicants*(ha) 15.4 9.6 11.4

Total subsidy (pesos) 31,707,239 27,611,221 18,060,491 77,357,711

*Excluding applications representing ejidos, associations, or government bodies

Table 2.2: Linear regression results for models with a single independent variable

Area Reforested 2007-2009

2007 2008 2009

Variable beta p-value beta p-value beta p-value

Marginalization 0.121 ns 0.144 ns 0.184 0.019

Percent Indigenous Pop -0.038 ns -0.082 ns -0.012 ns

Hydrological Priority 0.119 ns -0.006 ns -0.005 ns

Biodiversity Priority 0.113 ns 0.127 ns 0.154 0.033

Soil Degradation Priority -0.008 ns 0.057 ns 0.071 ns

R square 0.044

0.036

0.046

F 1.9

1.525

1.969

Model p-value 0.096

0.183

0.085

Sample size 212 212 212

ns = not significant.


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Table 2.3: Linear regression results for models with all five independent variables

Area Reforested 2007-2009

Marginalization 2007 2008 2009

beta value 0.153 0.102 0.136

R square 0.023 0.010 0.018

F 5.041 2.194 3.966

Model p-value 0.026 0.140 0.048

Percent Indigenous Pop 2007 2008 2009

beta value -0.044 -0.079 -0.008

R square 0.002 0.006 0

F 0.415 1.327 0.014

Model p-value 0.52 0.251 0.906

Hydrological Priority 2007 2008 2009

beta value 0.135 0.003 0.004

R square 0.018 0 0

F 3.879 0.002 0.003

Model p-value 0.05 0.963 0.956

Biodiversity Priority 2007 2008 2009

beta value 0.064 0.113 0.132

R square 0.004 0.013 0.018

F 0.864 2.729 3.752

Model p-value 0.354 0.1 0.054

Soil Degradation Priority 2007 2008 2009

beta value -0.081 0.002 0.001

R square 0.007 0 0

F 1.378 0.001 0

Model p-value 0.242 0.976 0.985

ns = not significant.


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Figure 2.1


19

Figure 2.2


20

Figure 2.3


21

Figure 2.4


22

Figure 2.5


23

Figure 2.6

Figure 3.1


25

Figure 3.2


26

Figure 3.3


27

Figure 3.4


28

Figure 3.5


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Figure 3.6


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ANALYSIS OF FINDINGS

Analysis is divided according to the two series of maps corresponding to respective

research questions.


Question: What if any relationship is there at the municipal level between the distribution

and density of reforestation projects and socioeconomic marginalization, indigenous

population, soil degradation, and hydrological and biodiversity priority areas in

Veracruz?

CONAFOR organizes forestry activities under forestry management units, or

Unidades de Manejo Forestal (UMAFORs), which are administrative regions sharing

similar environmental or cultural characteristics. Figure 2.1 displays the UMAFOR

regions for Veracruz and is useful for referring to the geographic distribution results. Table 1.1 summarizes descriptive results for C1.1 reforestation subsidized by Pro-

Árbol during 2007-2009. A total of 73,635 hectares were reforested during this period,

presented in Figure 2.2. Reforestation efforts were distributed throughout the state, with

only fourteen of the 212 municipalities lacking participants during the 2007-2009 period.

Figure 2.3 displays the reforestation in terms of percent area reforested by municipality.

This map normalizes the results and indicates a concentration of reforestation in the

central regions of Misantla, Perote, and Orizaba, as well as in Huayacocotla in the north.

A total of 5,801 applicants were approved for Pro-Árbol reforestation subsidies

(Table 1.1, Figure 2.4). The number of individuals that received support was actually

higher, since a portion of the applicants represented ejidos, or communal landholding

groups, as well as silvicultural associations and government bodies (i.e., municipal

governments, state parks, public universities). Applications on behalf of such

organizations were excluded from descriptive statistics on female participation and

property size to minimize biased results.

During the period reviewed, 1,364 applicants were accepted without funding for C1.1

reforestation projects because of budgetary limitations. Many applicants were or will be

granted priority for funding in subsequent years. Even if funding was not available,

applicants were still eligible for free saplings to plant.

Only 41 applicants were rejected for funding based on errors related to application

rules and program regulations. Of these, 10 were women. The distribution of rejected

applicants is presented in Figure 2.5. The low number of rejected applicants calls into

question the process of prioritization or selection of applicants based on program

objectives. If nearly all applicants are accepted, can subsidies be said to be targeted?

Greater clarity is needed in CONAFOR’s publicity materials regarding the prioritization

of applicants for reforestation subsidies. On the other hand, it may simply signal that

individuals who reach the final stage of application submission have already been

selected out from a larger initial pool of interested but less qualified citizens.


31

The average percent of female applicants by program year was between 14.9 and 16.3

percent (Table 1.1). Actual female participation may have been higher because they may

have participated in the organization applications mentioned above. Figure 2.6 displays

the distribution of female applicants granted funding. Although female participation was

high in some municipalities, 65 municipalities with participation had no accepted female

applicants Female participation appears to have been particularly lower in the

mountainous zones of the state. Female participation is limited by gendered divisions in

property rights, since the applicant to Pro-Árbol must demonstrate legal ownership in his

or her name.

Mean property size for individual applicants ranged between 9.6 to 15.4 hectares,

which corresponds to the size of many small to medium-scale landholders in Veracruz.

Typical property size varies from region to region in Veracruz, however, complicating

the analysis of property size n relation to marginalization. The smallest properties granted

subsidies were 3 ha.

A total of $77,357,711 pesos were awarded in subsidies to reforestation participants

in Veracruz during 2007-2009. Subsidies were equivalent to approximately US$850 per

hectare reforested. This is a large investment and reinforces the need for monitoring of

reforestation projects to assess the efficiency of the investment.

No significant relationships were found in the linear regressions testing independent

funding objective variables and area reforested (Table 1.3). However, for regressions

testing single independent variables there was a significant relationship between the

marginalization index and reforestation in 2007 and 2009 (Table 1.2). There was also a

significant relationship between hydrological priority areas and reforestation in 2007. For

all other years of data and variables relationships were not significant. There was no

multicollinearity among the variables tested and for the few variables that did have

significant results, R2

values were very low, indicating low explanation of the variance in

area reforested. There were no distinguishable trends over time in the beta values for

individual variables that would indicate conscious shifts in funding targets to meet

objectives over the years of the study.

The descriptive results and linear regression results cast some doubt on whether Pro-

Árbol program objectives are consciously being addressed through selective awarding of

reforestation subsidies. However, there are some indications that funding is reaching

marginalized municipalities regardless of a specific prioritization process, fulfilling at

least one of Pro-Árbol’s major goals. Nevertheless, the lack of significant relationship

between reforestation and environmental variables of concern to CONAFOR indicates

that the environmental objectives of Pro-Árbol should be more explicitly targeted in the

future.


32

Series III Suitability Ranking of Municipalities for Future Reforestation Funding

Question: What areas are most suitable for future reforestation funding to further the goal

of environmental restoration and development?

Figure 3.1 shows that with the exception of urban areas such as Coatzacoalcos,

Veracruz, Orizaba, Xalapa, and Poza Rica, much of the state of Veracruz is categorized

by indices of marginalization considered medium to high for Mexico by CONAPO. The

highest concentrations of marginalization are found in the montane regions of

Huayacocotla, Totonacapan, Orizaba, and Los Tuxtlas. The distribution pattern of

populations retaining indigenous languages is more evenly distributed throughout the

state, as seen in Figure 3.2. Identifiable concentrations of indigenous language are found

in the Otontepec and Totonacapan regions.

Figures 3.3 and 3.4 depict hydrological service and biodiversity conservation eligible

areas for payment for ecosystem services. Hydrological services are not surprisingly

concentrated in montane zones, as well as coastal zones prone to flood damage from

hurricanes and other extreme weather. Additionally, the Uxpanapa region, including the

Coatzacoalcos River watershed is highlighted as important areas to protect. In contrast to

the hydrologic eligible areas, biodiversity eligible areas are concentrated in the lower

elevations of the central montane zone and near large coastal wetland and inland marsh

ecosystems, such as the Laguna de Alvarado and lakes of Los Tuxtlas. These areas are

important for migratory bird species. Finally, soil degradation, presented in Figure 3.5, is

more sparsely and evenly distributed across ecoregions and elevation zones in Veracruz.

A slightly higher trend of degraded areas is noticeable along the coast.

The final ranked suitability map combining individual rankings for the five social and

environmental variables is presented in Figure 3.6. When compared with Figure 2.2, it

appears that the high priority areas of Perote, Misantla, Orizaba, and Huayacocotla have

been well served by Pro-Arbol, but that other priority areas in the northern and southern

areas of the state have not. In particular, Totonacapan, Veracruz, Los Tuxtlas, and

Uxpanapa UMAFOR regions should increase outreach to potential participants and

perhaps identify potential barriers to participation among residents in these areas.


33

CONCLUSIONS

To conclude, the 2007-2009 Pro-Árbol reforestation results indicate subsidies are

being applied in marginalized areas; however, environmental objectives need more

attention. It is additionally unclear to what extent indigenous communities have

participated. Women’s participation should be further encouraged and explored in greater

depth – are women involved in planting and maintaining trees, even if not named in Pro-

Árbol applications? Understanding how women participate or are affected by

reforestation activities is critical to the overall objectives of equitable development.

Based on this analysis, future outreach and funding for reforestation should target the

Totonacapan, Veracruz, Los Tuxtlas, and Uxpanapa UMAFOR regions. An analysis

conducted at the locality or property-level would provide greater detail regarding the

success of Pro-Árbol funding thus far, as well as help to orient future funding to those in

most need and in locations of greatest ecological restoration importance. This could

certainly be done within CONAFOR.

More importantly, CONAFOR should publish the results of monitoring for areas

already reforested over the long term to evaluate the success rate of projects and the long-

term benefits to participants. This data is of utmost importance to assessing the real value

of reforestation to both environmental restoration and economic development. More

information is needed to understand what land uses are being abandoned to reforestation,

why, and with what perceived benefits among property owners. Finally, physical

monitoring of the impacts of reforestation on the production and flow of ecosystem

services will provide empirical evidence for the environmental benefit of reforestation

with native species, of which little research exists.


34

REFERENCES

Bonan, G. B. 2008. Forests and climate change: Forcings, feedbacks, and the climate

benefits of forests. Science 320 (5882):1444-1449.

Bruijnzeel, L. A. 2004. Hydrological functions of tropical forests: not seeing the soil for

the trees? Agriculture, Ecosystems & Environment 104 (1):185-228.

Carabias, J., V. Arriaga, and V. Cervantes Gutiérrez. 2007. Las Políticas Públicas de la

Restauración Ambiental en México: Limitantes, Avances, Rezagos y Retos.

Boletín de la Sociedad Botánica de México 80 (Supplement):85-100.

Chazdon, R. L. 2008. Beyond deforestation: Restoring forests and ecosystem services on

degraded lands. Science 320 (5882):1458-1460.

CONAFOR. 2009. Boletin B038-2009: Veracruz, ejemplo de restauración y recuperación

de los recursos forestales Zapopan, Jalisco: CONAFOR.

Coomes, O. T., F. Grimard, C. Potvin, and P. Sima. 2008. The fate of the tropical forest:

Carbon or cattle? Ecological Economics 65 (2):207-212.

Farwig, N., N. Sajita, and K. Böhning-Gaese. 2008. Conservation value of forest

plantations for bird communities in western Kenya. Forest Ecology and

Management 255 (11):3885-3892.

Kanowski, J., C. P. Catterall, and G. W. Wardell-Johnson. 2005. Consequences of

broadscale timber plantations for biodiversity in cleared rainforest landscapes of

tropical and subtropical Australia. Forest Ecology and Management 208 (1-

3):359-372.

Lamb, D., P. D. Erskine, and J. A. Parrotta. 2005. Restoration of degraded tropical forest

landscapes. Science 310 (5754):1628-1632.

Secretaria de Medio Ambiente y Recursos Naturales (SEMARNAT). 2009. Reglas de

Operación del Programa ProArbol 2009. D.F., México: Diario Oficial de la

Federación.

Smith, J., and S. J. Scherr. 2003. Capturing the Value of Forest Carbon for Local

Livelihoods. World Development 31 (12):2143-2160.

Valtierra Pacheco, E., O. S. Magana Torres, M. Vanegas López, M. d. P. Lozano

Contreras, C. M. Hernández González, and L. H. Fierro Pérez. 2008. Evaluación

Externa de los apoyos de Reforestación 2007. D.F., Mexico: Colegio de

Postgraduados.

Velázquez Álvarez, J. 2009. Las selvas veracruzanas, riqueza natural en riesgo de

extinción. Milenio El Portal

2007 [cited December 10 2009]. Available from

http://www.biodiversityreporting.org/article.sub?docId=25449&c=Mexico&cRef

=Mexico&year=2007&date=May 2007.

http://www.biodiversityreporting.org/article.sub?docId=25449&c=Mexico&cRef=Mexico&year=2007&date=May

http://www.biodiversityreporting.org/article.sub?docId=25449&c=Mexico&cRef=Mexico&year=2007&date=May


35

DATA SOURCES

Censo General de Población y Vivienda. (2000). D.F., México: Instituto Nacional de

Estadística e Geografía (INEGI).

Degredación de Suelos COLPOS [computer files]. 2002. D.F., México: Comisión

Nacional Forestal (CONAFOR). Available by request:

http://www.cnf.gob.mx:81/emapas/Admin/Maps.aspx

Índices de Marginación 2005 [Excel file]. (2005). D.F., México: Consejo Nacional de

Población (CONAPO). Available: http://www.conapo.gob.mx/index.php?option=com_content&;view=article&id=78&Item

id=194

Marco Geoestadístico Nacional: Áreas Geoestadísticas Municipales [computer file].

(2005). D.F., México: Instituto Nacional de Estadística e Geografía (INEGI). Available:

http://mapserver.inegi.org.mx/data/mgm/?s=geo&;c=1298

Mapoteca digital: Entidades federativas, Curvas nivel, Uso de Suelo y Vegetación,

Localidades, and Carreteras [computer files]. (1996-2005). D.F., México: Comisión

Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO). Available:

www.conabio.gob.mx

Pro-Árbol Resultados [Excel and PDF files]. (2007-2009). D.F., México: Comisión

Nacional Forestal (CONAFOR). Available:

http://www.conafor.gob.mx/index.php?option=com_content&;task=view&id=26&Itemid

=455

Pro-Árbol Áreas Elegibles (Veracruz): Biodiversidad, Hydrológicos [computer files].

2008. D.F., México: Comisión Nacional Forestal (CONAFOR). Available:

http://www.conafor.gob.mx/index.php?option=com_content&;task=view&id=166&Itemi

d=401

Projection

Data were projected to the custom Lambert Conformal Conic (LCC) projection

commonly used in Mexico. Shapefiles originating in ITRF 1992 were discovered to

already be projected in LCC, although this was not found in the Metadata. Problems with

ArcMap’s lack of ITRF 1992 geographic coordinate system transformations were

avoided by projecting these files into WGS84 and then back into LCC.

The custom projection parameters are:

North_America_Lambert_Conformal_Conic

False_Easting: 2500000; False_Northing: 0

Central_Meridian: -102

Standard_Parallel_1: 17.5; Standard_Parallel_2: 29.5

Latitude_Of_Origin: 12.0

Linear unit: meter

http://www.cnf.gob.mx:81/emapas/Admin/Maps.aspx

http://www.conapo.gob.mx/index.php?option=com_content&;view=article&id=78&Itemid=194

http://www.conapo.gob.mx/index.php?option=com_content&;view=article&id=78&Itemid=194

http://mapserver.inegi.org.mx/data/mgm/?s=geo&;c=1298

http://www.conafor.gob.mx/index.php?option=com_content&;task=view&id=26&Itemid=455




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