Rapid carbon stock appraisal
Kalahan, Nueva Vizcaya, Philippines
Grace B. Villamor, Nelson Pampolina, Reginald Forcadilla,
Nonoy Bugtong, Jerome Alano, Delbert Rice, Tina Omas,
Reymar Castillo, Dennis Pulan
Rapid carbon stock appraisal
Kalahan, Nueva Vizcaya, Philippines
Grace B. Villamor, Nelson Pampolina, Reginald Forcadilla, Nonoy Bugtong,
Jerome Alano, Delbert Rice, Tina Omas, Reymar Castillo, Dennis Pulan
Working paper 106
LIMITED CIRCULATION
Correct citation
Villamor GB, Pampolina N, Forcadilla R, Bugtong N, Alano J, Rice D, Omas T, Castillo R, Pulan D.
2010. Rapid Carbon Stock Appraisal: Kalahan, Nueva Vizcaya, Philippines. Working paper 106.
Bogor, Indonesia: World Agroforestry Centre (ICRAF) Southeast Asia Program. 87p
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Working Paper 106
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About the authors
Grace Villamor
Grace Villamor is currently a researcher at the Center for Development Research (ZEF) in
Bonn, Germany, and a research fellow under the REDD ALERT project of the World
Agroforestry Centre in Southeast Asia. Prior to that, she was involved in the Rewarding
Upland Poor for Environmental Services they provide (RUPES phase 1) program in the
Philippines where she was working together with the Kalahan Educational Foundation for
developing rewards schemes for carbon sequestration and biodiversity conservation.
Contact: grace.villamor@gmail.com
Nelson P. Pampolina
Nelson P. Pampolina is an Associate Professor and Coordinator for Research Extension and
Linkages in the College of Forestry and Natural Resources, University of the Philippines at
Los Baños.
Contact: nelmpampolina@yahoo.com
Reginald Forcadilla
Reginald Forcadilla is a forester from the University of the Philippines at Los Baños.
Contact: reggie_forcadilla@yahoo.com
Nonoy Bugtong
Nonoy Bugtong is an Agroforester with the Kalahan Educational Foundation.
Jerome Alano
Jerome Alano is a GIS specialist at the ASEAN Biodiversity Centre.
Contact: jerome.alano@gmail.com
Delbert Rice
Delbert Rice is the Director for Research at the Kalahan Educational Foundation.
Contact: kalahan2@gmail.com.
Tina Omas
Tina Omas is an Agroforester with the Kalahan Educational Foundation.
Reymar Castillo
Reymar Castillo is a Forester at the University of the Philippines at Los Baños.
Contact: foresterei_uplb@yahoo.com
Dennis Pulan
Dennis Pulan is a Dendrologist at the University of the Philippines at Los Baños.
-i-
Abstract
A research method called Rapid Carbon Stock Appraisal (RaCSA) was conducted in Kalahan
Forest Reserve (KFR), in Nueva Vizcaya Province, Northern Luzon, Philippines from August
2009 to January 2010. The aim of this activity was to support communities, such as the
Ikalahan people, to establish basic data needed in negotiating with carbon markets in a costeffective and time-efficient manner. The appraisal involved a combination of methods and
activities (for example, plot-level carbon measurement, spatial analysis of land-use cover,
focus group discussions, key informant interviews and a review of the literature).
There were several key results of the appraisal.
•
•
•
•
•
Land-use types and farming practices. The majority of Ikalahan are swidden farmers
practising traditional farming (for example, pang-omis, which involves integrating
tree seedlings of species such as Alnus in the swidden farms). Five major land-use
and land-cover types were identified and assessed, that is, agriculture, agroforest,
grassland, reforestation and secondary forests.
Plot-level carbon stocks. The estimated carbon stock of land-use systems in the KFR
ranged 0.61–77.86 Mg/ha for aboveground carbon; and 21.8–67.4 Mg/ha for
belowground. Total (above- and belowground) carbon stock was estimated to range
54.31–151.13 Mg/ha. These results are low compared to other carbon assessments
conducted in the country.
Land-use and land-cover changes. Land-use and land-cover changes within KFR
between 1981 and 2001 were assessed. A decrease in forest, pine and agriculture
occurred while there was an increase in old pine and reforestation (for example,
mahogany). Carbon values from monitoring plots in 1994 and 2003 were used to
extrapolate the land-cover types of the 1981 and 2001 maps, respectively. Based on
the results, total carbon stock was approximately 375.8 Gg in 1994 and 452.1 Gg in
2003, that is, a 21% increase in 12 years.
Carbon emissions. From the land-cover changes, we estimated that the KFR
sequestered carbon annually at an average of 0.5 Gg and that 1.4 Gg of carbon was
emitted each year over the period 1989 to 2001.
The Kalahan Educational Foundation is the major stakeholder in the KFR. It has
established its own rules and regulations related to natural resources development
and has supported traditional farming practices and management strategies (for
example, their ‘forest improvement technology’) to enhance the carbon stock within
the KFR. Currently, the Foundation is exploring the Clean Development Mechanism
market. Future options and their implications for the KFR are included in the paper.
Keywords
carbon stock assessment, farming practices, Ikalahan Ancestral Domain, land-use change
- ii -
Acknowledgements
The RaCSA implementation was conducted by the Kalahan Educational Foundation in
collaboration with the Forest Biological Sciences Department, College of Forestry and
Natural Resources, University of the Philippines at Los Baños, Laguna, and the World
Agroforestry Centre (ICRAF) Southeast Asia Program through the Trees in Multi-Use
Landscapes in Southeast Asia project (funded by the German Federal Ministry for Economic
Cooperation and Development (BMZ)) and the Rewards for, Use of, and Shared Investment
in Pro-poor Environmental Services phase 2 program.
- iii -
Contents
1.
Introduction ..................................................................................................................... 1
2.
Land Tenure and Ownership ......................................................................................... 3
2.1 Carbon Stocks Assessment .......................................................................................... 3
3.
Objectives of the study and expected outputs ............................................................... 5
3.1 Objectives: ................................................................................................................... 5
3.2 Expected Output: ......................................................................................................... 5
4.
Methodology..................................................................................................................... 7
4.1 Site Orientation and Reconnaissance Survey .............................................................. 7
4.2 Selection of Prospective Sites ..................................................................................... 7
4.3 Site Preparation and Establishment of Sampling Transect .......................................... 7
4.4 Sampling sites and major land uses ............................................................................. 8
4.5 Primary and Secondary Data Collection and Processing .......................................... 10
5.
Results and Discussion .................................................................................................. 13
5.1 Farming and Livelihood Conditions.......................................................................... 13
5.2 Land Use Characteristics and Practices..................................................................... 18
5.3 Plant Diversity and Composition .............................................................................. 21
5.4 Carbon Stocks ........................................................................................................... 23
5.5 Land Use Change Dynamics in KFR ........................................................................ 29
5.6 Carbon emissions by land use/cover change ............................................................. 35
5.7 Carbon Offset Options............................................................................................... 39
5.8 Scenario Building and Future options ....................................................................... 40
6.
Conclusion and recommendation ................................................................................. 43
6.1 Conclusion ................................................................................................................. 43
6.2 Recommendation ....................................................................................................... 43
References .............................................................................................................................. 45
-v-
List of Tables
Table 1. Major land-use types identified ................................................................................... 9 Table 2. Physical areas devoted to rice by production environment (in hectare), selected
barangays, Sta. Fe, Nueva Vizcaya, 2000 ................................................................. 13 Table 3. Livelihoods of the people (percentage) ..................................................................... 14 Table 4. Summary of livelihoods’ assessment in the KFR...................................................... 15 Table 5. Physical area devoted to fruits and vegetable production (in hectare), selected
barangays, Sta. Fe, Nueva Vizcaya, 2000 ................................................................. 15 Table 6. Mean cropping, fallow periods and cycle lengths employed by selected farmers
in the KFR................................................................................................................. 17 Table 7. Characteristics of the different land uses and practices of local communities in the
KEF mountain ecosystem ......................................................................................... 19 Table 8. Characteristics and activities of various key drivers of change in the Kalahan
landscape................................................................................................................... 20 Table 9. Percentage of trees with different diameter ranges from various land uses .............. 21 Table 10. Population density per plot in the canopy, intermediate and undergrowth layers
in different land uses ................................................................................................. 22 Table 11. Plot-level aboveground biomass carbon stocks ....................................................... 23 Table 12. Mean aboveground carbon stocks in land uses sampled in the KFR ...................... 24 Table 13. Plot-level belowground biomass carbon-stock ........................................................ 24 Table 14. Mean belowground carbon stocks in land uses sampled in the KFR ...................... 25 Table 15. Soil carbon and carbon stock................................................................................... 25 Table 16. Mean soil carbon-stock per land use ....................................................................... 26 Table 17. Plot-level mean carbon-stock of each land use ....................................................... 26 Table 18. Total carbon stock at plot-level in the KFR ............................................................ 26 Table 19. Land-cover classes in the KFR, 1989 ...................................................................... 29 Table 20. Land-cover classes in the KFR, 2001 ...................................................................... 30 Table 21. Land-cover changes between 1989 and 2001 (area in ha)....................................... 32 Table 22. Mean biomass in 1994 and 2003 and the blocks and plots sampled ....................... 33 Table 23. Carbon densities based on biomass-monitoring plots in the KFR........................... 33 Table 24. Plots with very high estimated carbon densities...................................................... 35 Table 25. Land-cover types and carbon densities used ........................................................... 35 - vi -
Table 26. Mean carbon emissions from land-use changes, 1994–2003 .................................. 37 Table 27. Mean carbon emissions per year, 1994–2003 ......................................................... 38 Table 28. Future options and their implications for the KFR .................................................. 40 List of Figures
Figure 1. Location of Kalahan Forest Reserve .......................................................................... 4 Figure 2. Sampling sites where five major land uses were observed ........................................ 8 Figure 3. Nested plot design for sampling various carbon stocks ............................................. 9 Figure 4. Percentage of species’ composition in three structural layers in various land uses . 23 Figure 5. Total (above- and belowground) carbon stocks and their relative composition in
the KFR (Upper panel: absolute values in Mg/ha. Lower panel: as percentage) ..... 27 Figure 6. Distribution of land-cover-derived carbon density in the KFR, based on a carbonstock estimate (2009) ............................................................................................... 28 Figure 7. Land-cover classes in the KFR, 1989 ...................................................................... 29 Figure 8. Land-cover map of the KFR, 1989 .......................................................................... 30 Figure 9. Land-cover classes in the KFR, 2001 ...................................................................... 30 Figure 10. Land-cover map of the KFR, 2001 ........................................................................ 31 Figure 11. Overall land-cover change within the KFR............................................................ 31 Figure 12. Distribution of land-cover-derived carbon density in the KFR in 1989 (upper
panel) and 2001 (lower panel) .............................................................................. 34 Figure 13. Target sites for CDM project (red dots) ................................................................. 39 - vii -
1. Introduction
The Ikalahans are the indigenous people of the province of Nueva Vizcaya, northeastern
Philippines. They belong to the Kalanguya-Ikalahan tribe and inhabit the Ikalahan Ancestral
Domain. They are largely swiddeners who plant sweet potato, ginger, gabi, cassava and
vegetables and build terraces to grow upland rice.
Encompassing a total of 38 000 ha, the Ikalahan Ancestral Domain, of which the Kalahan
Forest Reserve comprises 14 730 ha, lies in the Cordillera and Caraballo mountains and is
overlooked by Mt Akbob (1658 m) in the northwest and Mt Talabing (1717 m) in the
southwest (KEF 1993). Dividing the watershed between the two peaks and determining the
water flow lies a ridge known as Bantay Lakay. Elevation varies 600–1717 m above sea level,
with average annual rainfall recorded at over 4000 mm and temperatures ranging 8–24 ˚C
(RUPES website1). The majority of the forests are secondary and for the most part tree
species found in this entirely mountainous region are endemic dipterocarps. There are also
areas where the coverage is predominantly pine or oak on the western and apex zones of the
ridge respectively. The study covered approximately 10 000 ha, excluding the grasslands and
sanctuary regions.
In 1973, the Kalahan Educational Foundation (KEF) was established by the Ikalahan tribal
elders to protect their communities from possible eviction because the Government at that
time was unable to defend their rights. The Foundation’s mission is to promote the education
of the Ikalahan people and protect the environment of their ancestral domain. Among its aims
is to provide sustainable, forest-based livelihoods, improved watersheds and biodiversity
(KEF 1993). From its inception, KEF has been recognised as a community-based
organization. It legally represents the Ikalahans in their community-based forest management
agreement, in which they are the pioneers in the Philippines.
1
http://rupes.worldagroforestry.org/researchsite_kalahan/2 -1-
2. Land tenure and ownership
The Indigenous Peoples’ Rights Act of 1997 (RA 8371) strengthens the rights of the Ikalahan
to their ancestral land and led to the approval in 1999 of their ancestral domain claims that
cover 58 000 ha.
Other laws such as the Wildlife Resources Conservation and Protection Act of 2001 (RA
9147) and the National Integrated Protected Areas System of 1992 (RA 7586) are legal
mandates to establish and protect critical habitats and species.
Further, the Memorandum of Agreement No. 1 of 1973 is an agreement between the KEF and
the Bureau of Forest Development that recognizes the rights of the Ikalahans to manage their
ancestral land and ‘utilize the area to the exclusion of all other parties not already “subsisting”
within the area at the time of signing’. The agreement specifically allocated 14 730 ha of land
to be managed directly by the Ikalahan through the KEF for a period of 25 years, renewable
for another 25 years.
2.1 Carbon-stock appraisal
The KEF is currently developing a 900 ha Clean Development Mechanism (CDM) project
inside the ancestral domain. The results of a Rapid Carbon Stock Appraisal (RaCSA) were
intended to provide essential baseline information for the negotiation of carbon credits with
potential carbon buyers. The appraisal would also help provide experience and insight into
reducing the transaction cost of such projects.
RaCSA is part of a ‘negotiation support toolbox’ for rapid appraisal of landscapes developed
by the World Agroforestry Centre (ICRAF) Southeast Asia Program through the Trees in
Multi-Use Landscapes in Southeast Asia project. The project had several aims.
1) Bridge the gaps between local, public/policy and scientific modellers’ knowledge.
2) Increase recognition and respect for these multiple knowledge systems.
3) Provide quantification of trade-offs between economic and environmental impacts at
landscape scale.
4) Enable joint analysis of plausible scenarios based on available data and information.
-3-
Figure 1. Location of Kalahan Forest Reserve.
-4-
3. Objectives of the study and expected outputs
3.1 Objectives
1.
To identify the different land-use practices at the site and the key drivers of change in the
landscape.
2.
To estimate the carbon stocks of the main land uses at plot and landscape levels.
3.
To assess the opportunity to use or adjust policy frameworks to enhance or maintain the
carbon stocks in the area.
4.
To complete the modelling of land-use and carbon dynamics of the Kalahan using GIS
and/or remote sensing.
3.2 Expected outputs
1.
Carbon stock per land-cover and land-use assessed and calculated.
2.
Land-use practices that enhance or maintain carbon stocks identified and documented.
3.
Results from the carbon-stock appraisal used as the baseline for the CDM project (initial
stage of development of the project design document).
4.
Scenarios featuring different drivers of change in the landscape (using remote sensing)
presented and assessed.
-5-
4. Methodology
4.1 Site orientation and reconnaissance
The research team was oriented by community representatives regarding the purpose of the
carbon-stock study and the coverage of the project site (Figure 1). Available maps (for
example, topographic and vegetation) were useful in identifying the various land uses within
the 48 000 ha ancestral domain. A three-dimensional model of the area was instrumental in
gaining appreciation of the whole site and approximating logistics and costings prior to
fieldwork (Figure 2). Reconnaissance was conducted in September 2009 to finalise the
carbon-stock study sites.
4.2 Selection of sites
The major land uses in the study area were first identified using the vegetation maps and the
results of the reconnaissance with farmers and through secondary data. The sites were
selected by locating areas that had high conservation values in the context of the appraisal.
This step involved identifying areas with one or more features such as a high richness of
species; featured ‘flagship’ species; enjoyed a unique habitat; or were experiencing rapid
resource or habitat degradation. These features were considered against the various land uses
and local human populations. The secondary data available from the KEF were used as
baseline information. Participatory mapping was conducted involving the community and
other stakeholders, forming part of the capacity-building strategy of the project. A total of five
land uses from fifteen barangays (smallest government unit in the Philippines) within the
KEF were identified. All sites were classified as secondary forest, agroforest farm,
agricultural area, grassland or reforestation (Table 1). The corresponding land uses were
situated in two or more sites.
4.3 Site preparation and establishment of sampling transects
The sampling sites and transects were prepared by measuring and pegging 20 m x 100 m plots
in the various land uses (Figure 3). Two sampling transects were established for each land use
to estimate carbon stock above- and belowground. We used a metre tape to measure distance
and GPS Garmin to locate the coordinates. Each sampling transect was demarcated to obtain
the following.
•
Tree species, with diameter at breast height of 5.0 cm and above within the
whole transect.
•
Plants in the intermediate layer, with diameter below 5.0 cm and height of above
1 m sampled in a 3 m x 3 m sub-plot within the transect plot.
•
Undergrowth vegetation, with height below 1 m sampled within four smaller
sub-plots measuring 1 m x 1 m each.
-7-
•
Necromass or litter fall, collected from one plot in the intermediate layer and
four plots in the undergrowth, with each plot measuring 0.25 m x 0.25 m.
•
Soil, sampled using a trowel (5 cm diameter and 30 cm length), at depths of 0–
20 and 20–30 cm.
For each of the land-use samples, the team used a slightly modified protocol from the ASB
Lecture Note 4b (Hairiah et al. 2001).
4.4 Sampling sites and major land uses
Figure 2. Sampling sites where five major land uses were observed.
-8-
Table 1. Major land-use types identified
No. 1. Identified land uses Secondary forest 2. Agroforest •
Subsets Pine‐dominated Dipterocarp‐dominated Myrtaceous oak‐
dominated Tree‐crop/fruit‐crop 3. Agriculture •
•
Garden/vegetable Swidden/fallow 4. Grassland 5. Reforestation •
•
•
•
•
Abandoned Pasture Pure grassland Old rehabilitated Pine‐ and Alnus‐
dominated •
•
•
Figure 3. Nested plot design for sampling various carbon stocks.
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Barangay Sta. Rosa Baracbac Malico Plot Code S1T1 S4T1 S2T1 Sta. Rosa Baracbac Bacneng Bacneng Tactac Atbu Atbu Sta. Rosa Malico Bacneng Imugan S1T2 S4T2 S5T1 S5T2 S6T2 S7T1 S7T2 S2T4 S2T3 S5T1 S8T3 S8T1 4.5 Primary and secondary data collection and processing
4.5.1 Taxonomic characterisation
All vascular plants within the established transect were identified using local names and were
verified using morphological characteristics from the field and herbarium collection at the
KEF and the University of the Philippines at Los Baños museum. The identity of plants was
further verified from references. Unknown plants were kept for future verification and their
codes were used in the computation of parameters. Sterile samples of known and unknown
species were collected for herbarium purposes and were preserved at the KEF and the
university. The taxonomic list was prepared showing local, scientific and family names and
plant habitat.
4.5.2 Measurement of biometrics and biomass
The height and diameter of trees at breast height (DBH) in the canopy and intermediate layers
within the transect plot were estimated in metres and measured with a diameter tape,
respectively, for proper encoding in an MS Excel spreadsheet (Figure 4).
Plant density, or the number of individuals in each layer, and transect plots were counted
using the formula:
Plant Density (N)
=
Density of each plant species
Unit Area of Sampling Plot
The biomass of each plant in the canopy, intermediate and undergrowth layers, together with
leaf litter, was computed using the following:
a.
Allometric regression for aboveground biomass of all trees greater than 5.0 cm DBH
using the equation prepared by Ketterings et al. (2001):
y = 0.11 p D 2.62
where
y = aboveground tree biomass
p = average wood density equivalent to 0.9035 gram.cc-1
(Pulhin 2008)
D = tree DBH
b.
Estimated belowground biomass in trees and intermediate layers was equivalent to 15%
of the aboveground tree biomass as proposed by Delany (1999).
c.
Destructive harvesting of randomly sampled above- and belowground biomass of
undergrowth plants represented by mean values of 5–10 samples of either wildling
indigenous tree and agroforestry species, agricultural crops, grass, shrubs, vines, ferns or
palms.
d.
Actual samplings of litter fall to represent necromass from all structural layers.
- 10 -
e.
Soil samples were placed in labelled plastic bags, air dried and taken to the Soils
Laboratory of the Soil Science Department of the College of Agriculture, University of
the Philippines at Los Baños for analysis. The method used for the analysis was the
Walkey-Black method (PCARR 1981). The mean bulk density of the 2006 soil carbon
calculation in the KFR was used (Appendix 2). The dry weight of the soil and the
equivalent carbon stock was determined using the following formula:
Soil mass at specified depth (Mg)
= Bulk density at specified depth (Mg/m3) x
10 000 m2 x depth (m)
Soil carbon at specified depth (Mg) = Soil mass at specified depth (Mg) x % organic
carbon at specified depth/100
4.5.3 Carbon-stock estimations at plot and landscape levels
With the values of biomass computed from plants and litter fall obtained from five different
land uses, the amount of carbon stock at plot and landscape levels was estimated. This was
achieved by using the mean carbon value from plant tissues obtained by Dixon et al. (1993)
from similar sites and ecosystem, together with the 45% generic carbon value commonly used
in much of the literature as a carbon estimate for plant cells (Raven et al. 1999). On average,
the percentage of carbon in agricultural farm and grassland ecosystems was 40% while in
agroforest, reforestation and secondary forest it was 45%.
At the landscape level, the method used for estimation of carbon stock was extrapolation
based on a land-cover map. Two ‘snapshots’ over time for each of the landscapes’ carbon
stocks were made by re-attributing the land-cover map of the particular year with
corresponding plot-level carbon stock. The output was a carbon-stock estimation based on
aboveground biomass calculations from land cover in 1994 and 2003.
- 11 -
5. Results and discussion
5.1 Farming and livelihoods’ conditions2
5.1.1 Land access
The average size of landholding per household was 3 ha, one-third of which was cultivated
while the rest was forested. Water was the determining factor in whether or not to cultivate
the land, especially for rice production (tables 2 and 5). Community access was allowed in
production forests and prohibited in the watersheds and sanctuaries. Land tenure was based on
the ancestral domain claim, which was approved in 1999.
Table 2. Physical areas devoted to rice by production environment (in hectare), selected barangays,
Sta. Fe, Nueva Vizcaya, 2000
No. 1. 2. 3. 4. 5. 6. Barangay Irrigated Rainfed Bacneng 10 0 Baracbac 20 0 Imugan 15 0 Malico 0 0 Sta. Rosa 25 6 Unib 20 0 Total 90 6 Source: Department of Agriculture, Sta. Fe, Nueva Vizcaya Upland 7 10 2 10 2 3 34 Total 17 30 17 10 33 23 130 5.1.2 Livelihood options
The majority of the people in the study area were farmers (Table 3). They were indigenous
swiddeners with camote (sweet potato) and upland rice as their staple crops. Off-farm
activities consisted of forest-fruit processing and soft-broom production (from tiger grass).
Others were employed as professionals in the local government offices, Kalahan Academy
and the KEF.
A livelihoods’ assessment was conducted through the KEF’s involvement with the NonTimber Forest Products Exchange Program3. Table 3 shows that more than 50% of farmers in
Bacneng, Baracbac, Imugan and Unib were more engaged with off-farm activities compared
to the other barangays. Table 4 shows the barangays that are most concentrated on broom
making. Table 5 shows the areas devoted to fruit and vegetable production.
2
3
Most of the information provided in this section was taken from Villamor and Pindog (2008). A regional non-governmental organization. - 13 -
Table 3. Livelihoods of the people (percentage)
Barangays/Villages Major Occupation Imugan Malico Sta. Rosa Unib Bacneng Farmers 70 90 94 100 90 Professionals * 25 5 1 0 6 Business/ 5 5 5 0 4 Traders 100 100 100 100 100 * For example, teachers, government bureaucrats, soldiers, health workers and police Source: Stakeholder analysis conducted in 2009 - 14 -
Baracbac 96 2 2 Tactac 80 10 10 100 100 Table 4. Summary of livelihoods’ assessment in the KFR
Barangays Bacneng Baracbac Imugan Unib 40 50%: broom making Malico 67 15%: broom making Sta. Rosa 57 18%: broom making ~15 km ~15 km ~20 km Local traders Consolidators Supplier of tiger grass (as raw material) Broom making Farming Local traders Consolidators Supplier of tiger grass (as raw material) Broom making Farming Local traders Consolidators Supplier of tiger grass (as raw material) Broom making Farming Local traders Consolidators Brooms, baskets, quilts Brooms, baskets Brooms, baskets Brooms, baskets Number of households Crafts population 250 70%: broom making 115 90%: broom making Geographical accessibility (distance from town) Sources of income 5 km 3 km 149 29%: broom making; 23%: basket weaving 7 km Broom making Swidden Farming Broom making Swidden Farming Supplier of tiger grass (as raw material) Broom making Farming Local traders Solano* Baguio Brooms, baskets Local traders Solano Market (current) Craft products Brooms * Neighbouring town or city Source: Non‐timber forest product (NTFP) project 2009, unpublished Table 5. Physical area devoted to fruits and vegetable production (in hectare), selected barangays, Sta. Fe, Nueva Vizcaya, 2000
Total Area (ha) Vegetables Upland Bacneng 229.08 45.0 Baracbac 105.73 37.5 Imugan 51.57 13.75 Malico 37.51 17.75 Sta. Rosa 25.40 11.00 Unib 30.09 8.75 Total (‐) no data Source: Department of Agriculture, Sta. Fe, 2000 Permanent Crops Temporary Crops Lowland Root Crops Mango Citrus Coffee Guava 10.25 27.1 12.50 7.0 1.50 4.00 85.25 35.0 20.60 11.50 12.00 2.50 70.0 0.47 0.04 0.13 0.09 0.23 1.07 0.43 0.44 0.06 0.05 0.30 5.03 0.20 0.40 0.20 0.16 0.80 6.8 3.36 2.29 0.67 0.50 2.26 - 15 -
Other fruits 5.0 0.10 1.24 0.08 ‐ 0.89 Papaya Banana 0.14 ‐ ‐ ‐ 0.01 ‐ 0.54 1.17 0.31 0.12 0.09 0.36 5.1.3 Farming practices
The Ikalahan are known for their indigenous knowledge practice systems that are
environmentally sustainable. These include:
•
•
•
Day-og and gengen are composting techniques on level and sloping land
respectively.
Balkah is a contour line of deep-rooted plants, which trap eroded topsoil at the belt
line (Rice 2000).
Pang-omis is a method of expediting the fallow. It was invented by one of the
tribal elders after attending an ecology seminar. Farmers intercrop tree seedlings,
for example, Alnus nepalensis, in their swidden farms along with sweet potato.
A study of the farming systems and fallow management of households within the KFR
(Banaticla et al. 2008) indicated that families use a much smaller area of land (around
2.93 ha) than the limit imposed by the community (10 ha) for farming and other purposes.
The inherent physical limitations in the amount of land suitable for farming, declining
population densities (except in villages nearest to the urban centre) and current cropping and
fallow cycles (Table 6) also indicated the tendency towards sedentarization of agriculture.
Former swidden fields were under long fallow and these were further protected by direct
interventions of the community through regulation of forest clearing and other forest
protection and rehabilitation activities (Appendix 4).
- 16 -
Table 6. Mean cropping, fallow periods and cycle lengths employed by selected farmers in the KFR
Res‐
pondent No. Age Residence (barangay) Time span reported (years)* No. of swiddens opened through time No. of swiddens with more than one cropping cycle Mean cropping period (years) Mean fallow period (years) Mean crop:fallow ratio 0.46 Mean crop‐fallow cycle length (years) 22.13 7.00 15.13 (1–14) (1–29) 9.33 7.33 1.27 16.66 2. 62 Baracbac 1960–2008 (48) 3 0 (3–16) (0–22) 13.17 17.00 0.77 30.17 3. 70 Unib 1959–2008 (49) 5 2 (4–26) (1–45) 8.25 5.30 1.56 13.55 4. 75 Baracbac 1951–2008 (57) 3 3 (3–16) (1.5–14) 8.40 16.50 0.51 24.90 5. 48 Imugan 1978–2008 (30) 3 2 (1–13) (16–17) 8.50 8.50 1.00 17.00 6. 60 Malico 1984–2008 (24) 2 0 (4–16) (6–11) 13.25 14.33 0.92 27.58 7. 75 Unib 1950–2008 (58) 2 1 (4–39) (5–23) 3.50 16.50 0.21 20.00 8. 70 Malico 1986–2008 (22) 4 0 (2–5) (10–23) 8.5 11.00 0.77 19.50 9. 45 Unib 1985–2008 (23) 2 0 (3–14) (2–20) Mean 8.88 12.40 0.83 21.28 * An initial list of 20 respondents were chosen but was narrowed down to 9 because of the difficulty of obtaining complete histories from each respondent. All nine respondents, except one, were female, residents of the KFR from birth, had no formal education or reached only the primary level, married or widowed, with farming as primary occupation up to the time of interview Source: Banaticla et al. 2008
1. 59 Baracbac 1974–2008 (34) 9 1 - 17 -
5.2 Land-use characteristics and practices
The major land uses in the Kalahan mountain ecosystem were classified into five, based on
the dominant vegetation and community activities, as shown in Table 1 and described below.
5.2.1 Agriculture
The agricultural areas were represented in barangays Bacneng, Tactac and Atbu. The
agriculture at these sites was generally situated in an open condition located on relatively flatto-sloping terrain. Structurally, the vegetation was more undergrowth with few trees and an
intermediate layer on the perimeter of farms, represented by a mix of crops (camote, cassava,
beans, rice, corn, taro, okra, ginger) planted in patches, grown using a combination of
traditional swidden farming and non-traditional systems that used inputs to increase
production.
5.2.2 Agroforest
This land use in barangays Sta. Rosa, Baracbac and Unib was dominated by a mixture of
agricultural fruit crops (avocado, mango, guava, citrus, papaya) planted in-between forest
trees (for example, mahogany, Gmelina, narra) and was, hence, classified as agroforest. The
land use was basically situated on moderate slopes with a semi-open canopy created by fruit
and large trees, with little intermediate growth but abundant undergrowth layers. Minimal
practices were applied, such as brush-cutting to clear some land for favoured crops and no
tilling of the soil.
5.2.3 Grassland
The grassland at two sites in barangay Malico and another area in barangay Atbu were usually
abundantly stocked in open areas on moderate-to-steep terrain. The areas were dominated by
Imperata cylindrica, with several species of ferns, shrubs and a few patches of small trees.
The main land-use practice was pasturing, although other areas were already abandoned,
inviting fires.
5.2.4 Reforestation
This land use was established about 10–15 years ago in barangay Imugan using either Alnus
or Gmelina and in barangay Bacneng with Benguet pine combined with mahogany.
Reforestation sites were situated on moderate-to-steep slopes with a semi-open canopy with
little intermediate growth but abundant undergrowth layers. There was some intercropping of
coffee in reforested areas planted with Alnus and agricultural farming adjacent to the Gmelina
plots but pure planting of mixed trees in other areas.
- 18 -
5.2.5 Secondary forest
This land use was dominated by either dipterocarp pine or myrtaceous oak forest ecosystems.
Areas in barangay Baracbac, Sta. Rosa and Malico that featured this type of land use were
covered with large diameter trees ranging 20–70 cm DBH. The forests were located on
middle-to-higher elevated land with semi-closed canopy and fewer understorey layers. The
dipterocarp forest was dominated by palosapis (Anisoptera thurifera), white lauan (Shorea
contorta), bagtikan (Parashorea malaanonan) and guijo (Shorea guiso). Non-dipterocarp
species included Benguet pine (Pinus kesiya), Philippine oak (Lithocarpus ovalis), legume
(Pterocarpus indicus) and myrtaceae (Syzygium sp.). There were no practices recorded for
this land use.
Table 7. Characteristics of the different land uses and practices of local communities in the KEF
mountain ecosystem
Land use Agriculture Agroforest Grassland Reforestation Community (GPS reading) Bacneng N16°11'57.6''; E 120°56'19.6'' Tactac N16°08'42.1'' E 120°56'32.4'' Atbu N16°08'26.4' E 120°56'345.0'' Sta Rosa N 16°10'50.7'' E120°51'36.0'' Baracbac N 16°11'08.2'' E120°55'32.6'' Unib N 16°09'26.2'' E120°55'32.6'' Malico 1 N16°08'118.2' E 120°56'58.3'' Malico 2 N 16°10'10.9' E 120°51'24.4'' Atbu N 16°10'27.9'' E 120°52'09.7'' Bacneng N 16°08'56.7'' E 120°56'11.5'' Imugan1 N 16°09'18.6'' E 120°54'25.7'' Land‐use practices Agricultural farming using combined traditional swidden farming and non‐
traditional systems Physical features Dominant species Generally in an open condition located on relatively flat‐to‐
sloping terrain structurally showing more undergrowth and few trees and with an intermediate layer on the perimeter of farms Mixed agricultural crops (camote, cassava, beans, rice, corn, taro, okra, ginger) planted in patches Largely situated on moderate slopes with a semi‐open canopy with little intermediate but abundant undergrowth layers Fruit‐bearing (avocado, mango, guava, citrus, papaya) and tree (mahogany, Gmelina, narra) crops Intercropping with mostly fruit‐
bearing and tree crops Usually abundant in open areas along moderate‐to‐steep terrain. Structurally, undergrowth layer dominated with abundance of grasses with very few patches of small trees Mostly Imperata cylindrica and Themed triandra but with some species of ferns, shrubs and other grasses Commonly used as pasture though some areas were left abandoned making them prone to grassfire On steep‐to‐very steep slopes with slightly open canopy with dominant trees and intermediate and undergrowth layers Dominance of 10–
15 year‐old plantation of either Alnus, Benguet pine or Gmelina Intercropping of coffee in reforested areas planted with Alnus and agricultural farming adjacent to Gmelina areas - 19 -
Land use Secondary forest Community (GPS reading) Imugan2 N 16°09'08.0'' E 120°54'11.8'' Baracbac N 16°10'14.6'' E 120°51'55.4'' Sta Rosa N 16°10'37.4'' E 120°51'07.2'' Malico N 16°09'26.2' E 120°55'32.6'' Physical features Located on middle‐to‐
higher elevated areas with a semi‐closed canopy and fewer understorey layers Dominant species Dominance of dipterocarps (palosapis, white lauan, guijo) and non‐dipterocarp (pine, Philippine oak, legume, Syzygium) trees Land‐use practices but pure planting of mixed trees in other areas Absence of any land‐use practices within, except for tree planting in pine forest 5.2.6 Key drivers of change
The key players that could contribute to changes (either positive or negative) in the landscape
were households, the KEF organization, local political leaders and conservationists (Table 8).
‘Households’ includes all family members residing in the ancestral domain. ‘The KEF’ refers
to the foundation that manages the mountain ecosystem, together with key barangay leaders
that oversee the political existence of the community. ‘Conservationists’ includes bird
watchers, academics, researchers and ecotourists.
The changes that influence the landscape of the mountainous ecosystem were categorized as
socio-economic and political, biophysical and chemical, anthropogenic, and indirectly natural.
The implementation of laws related to the environment—such as those pertaining to clean air,
solid waste management, chemical application, protected area management, bio-invasion and
threatened species—falls under socioeconomic and political activities.
Table 8. Characteristics and activities of various key drivers of change in the Kalahan landscape
Stakeholders Households Composition Members of the family Function Provides basic family role KEF Board and members Manage mountain ecosystem Local political leaders Barangay captains and youth leaders Oversee the political needs of the community as legal owners of the ancestral domain Conservationists Bird watchers, ecotourists, researchers, academics Conduct conservation research - 20 -
Activities that drive change in landscape Intermarriage of local to foreigners Introduction of verified or unverified upland farming technologies Implementation of KEF policies regarding the overall use and management of natural resources in the area (Appendix 4) Making decisions with regards to political activities that affect or are related to land ownership, use of farm land and natural resources, entry of outsiders to the area, and implementation of environmental laws ( clean air, solid waste management, chemical application, protected area management, bio‐invasion, threatened species etc) Frequency of visits to the different areas by conservationists; activities that could be against bio‐prospecting, solid waste management and other environmental laws 5.3 Plant diversity and composition
The diversity and composition of plants—particularly those in the canopy, intermediate and
undergrowth layers that capture carbon physiologically during photosynthetic activities—
varied depending on location, plot and land use, as presented below and in Table 7 above.
Table 9 shows the percentage of trees with various diameters. Figure 4 shows the proportion
of species’ composition in three structural layers in various land uses. Table 10 presents the
percentage of population density of plants in the different structural layers.
Table 9. Percentage of trees with different diameter ranges from various land uses
Type of land use Agriculture < 5 cm 5–30 cm > 30 cm 14.81 81.48 3.70 Agroforest 20.16 74.31 5.53 Grassland 43.24 51.35 5.41 Reforestation 44.70 48.84 6.46 Secondary forest 16.49 72.68 10.82 5.3.1 Agriculture
In agricultural areas, stocks of carbon were pooled in common cultivated crops like upland
and hybrid rice (Oryza sativa), beans (Vigna sesquipedalis), corn (Zea mays), taro (Colocasia
esculentum), luya (Zingiber officinale), saging (Musa sapientum) and okra (Abelmoschus
esculentus). Although classified as agricultural, there were, however, trees with diameters
ranging 5–30 cm, representing about 81.5% of all trees, such as mango (Mangifera indica),
suha (Citrus maxima) and hamak. All other trees in this category that had less than 5 cm and
greater than 30 cm comprised 14.8 and 3.7%, respectively.
5.3.2 Agroforest
Carbon stocks in plants in agroforestry systems were represented by fruit (Citrus sp., Psidium
guajava, Mangifera indica) and tree crops (Ficus nota, Alnus nepalensis, Eriobotrya
japonica, Leucaena lueocephala, Pinus kesiya and Ficus septica). Among these, the most
dominant was Citrus sp. (29.51%), followed by Ficus nota (5.33%) and Alnus nepalensis
(4.92 %). The diameters of trees varied: 20.2% were at less than 5 cm DBH; 74.3% had DBH
of 5–30 cm; while only 5.5% were greater than 30 cm DBH.
5.3.3 Grassland
The grassland ecosystem was characterised as ‘purely grassland’ or ‘abandoned pastureland’.
The former was dominated by Paspalum conjugatum, Crassocephallum crepidioides and a
local grass named tab-an. The latter ecosystem had an abundance of Pennisetum
alopecuroides, Oleandra pistillaris and Imperata cylindrica. Sparsely interspersed through
- 21 -
the ecosystem were patches of trees (Ficus septica, Boehmeria densiflora, Ficus nota,
Saurauia latibractea, Persea americana and Mangifera indica). There were also species of
moss (Portulaca grandiflora), busikad (Cyperus kyllingia), kilob (Dicranopteris linearis),
cogon (Cyperus kyllingia), landrina (Borreria ocymoides), pal-ot (Miscanthus sinensis),
dilang baka (Elephantopus tomentosus), kawad-kawad (Polytrias amaura) and two unknown
local plants (buyot and galakgak). The percentages of trees with respect to DBH was 43.2%
(> 5 cm), 51.4% (5–30 cm) and 5.4% (> 50 cm).
5.3.4 Reforestation
In reforestation areas, the species used were Benguet pine (Pinus kesiya), citrus (Citrus sp.),
coffee (Coffea arabica), Alnus (Alnus nepalensis), narra (Pterocarpus indicus), guava
(Psidium guajava), mahogany (Swietenia macrophylla) and amuwag (Clethra sp.). The
dominant species for the whole land use were coffee (Coffea arabica), amuwag (Clethra sp.)
and Alnus (Alnus nepalensis), composing 21.45%, 13.30% and 11.18% of the total of
observed tree species, respectively.
5.3.5 Secondary forest
In secondary forest, the dominant species were Benguet pine (Pinus kesiya), is-is (Ficus
ulmofolia) and white lauan (Shorea contorta) with values of 15.54%, 13.47% and 12.44%,
respectively. Large trees in the sampled plots of secondary forest—exemplified by Pinus
kesiya, Shorea contorta and Anisoptera thurifera–had greater percentages of individuals with
DBH of small (44.7%) and medium (48.8%) than those with large DBH, that is, greater than
50 cm (6.4%).
Table 10. Population density per plot in the canopy, intermediate and undergrowth layers in different
land uses
Type of land use Trees Intermediate Undergrowth Agriculture 24 279 1296 Agroforest 244 299 864 Grassland 39 286 1593 Reforestation 564 112 1075 Secondary forest 193 80 366 Note: Plot size for canopy, intermediate and undergrowth layers were 2000 m2, 9 m2 and 1 m2, respectively. - 22 -
Undergrowth
1.50
17.45
17.34
Intermediate
2.03
14.91
21.25
Trees
32.21
30.20
6.40
12.52
61.39
57.28
Reforestation
Secondary forest
83.06
81.05
61.41
Agriculture
Agroforest
Grassland
Figure 4. Percentage of species’ composition in three structural layers in various land uses.
5.4 Carbon stocks
5.4.1 Aboveground
Aboveground carbon stock in land-use systems in the KFR were estimated to range 0.61–
77.86 Mg/ha (Table 11). The highest value recorded was in the reforestation area, with 32%
of trees contributing to total aboveground carbon stock (Figure 4).
Table 11. Plot-level aboveground biomass carbon stocks
Land use Agriculture Agroforest Grassland Reforestation Secondary forest Sample plot code Tree Intermediate Understorey Total Mg/ha Mg/ha Mg/ha Mg/ha S5T2 10.042 0.207 0.025 10.274 S6T2 0.000 0.577 0.037 0.614 S7T1 0.754 0.663 0.014 1.430 S1T2 1.682 0.310 0.002 1.994 S3T1 30.588 0.093 0.073 30.753 S4T2 19.025 0.547 0.028 19.599 S2T3 0.000 0.604 0.095 0.699 S2T4 9.807 0.575 0.031 10.412 S7T2 0.760 0.556 0.026 1.342 S5T1 77.479 0.324 0.055 77.857 S8T1 25.890 0.119 0.030 26.039 S8T3 62.293 0.149 0.037 62.479 S1T1 37.054 0.409 0.038 37.502 S2T1 4.541 0.028 0.041 4.611 S4T1 44.652 0.035 0.037 44.723 - 23 -
Table 12. Mean aboveground carbon stocks in land uses sampled in the KFR
Tree Intermediate Understorey Total Mg/ha Mg/ha Mg/ha Mg/ha Agriculture 3.599 0.482 0.025 4.106 Agroforest 17.098 17.098 0.034 34.230 Grassland 3.522 0.578 0.050 4.151 Reforestation 55.220 0.197 0.041 55.458 Secondary Forest 28.749 0.157 0.039 28.945 Land use The mean aboveground carbon stock for each land use ranges 4.11–55.46 Mg/ha (Table 12).
Land uses such as reforestation, agroforest and secondary forest have higher carbon content
where trees are a higher proportion compared to other plant forms (Figure 4).
The carbon-stock values generated are far smaller compared to the values of similar land
cover. Lasco and Pulhin (2003) recorded average carbon densities of 207.9 Mg/ha for
secondary forest, 45.4 Mg/ha for agroforest, 12.1 Mg/ha for grassland and 59.0 Mg/ha for tree
plantations. This observation could be attributed to the tree composition of the sampled plots.
For example, large trees in the sampled plots of secondary forest—exemplified by Pinus
kesiya, Shorea contorta and Anisoptera thurifera–had greater percentages of individuals with
DBH of small (44.7%) and medium (48.8%) than those with large DBH, that is, greater than
50 cm (6.4%).
5.4.2 Belowground
Delany (1999) proposed belowground biomass of trees and intermediate layers equivalent to
15% of the aboveground tree biomass. The carbon content is presented in Table 13, while the
mean land-use carbon stock is shown in Table 14.
Table 13. Plot-level belowground biomass carbon-stock
Land use Agriculture Agroforest Grassland Reforestation Secondary forest Sample plot code Stump & roots Mg/ha Intermediate Mg/ha Understorey Litter Mg/ha Total Mg/ha S5T2 S6T2 S7T1 S1T2 S3T1 S4T2 S2T3 S2T4 S7T2 S5T1 S8T1 S8T3 S1T1 S2T1 S4T1 3.766 0.000 0.283 5.606 10.196 6.342 0.000 3.677 0.285 25.826 8.630 20.764 12.351 1.514 14.884 0.078 0.216 0.248 1.035 0.031 0.182 0.201 0.216 0.209 0.108 0.040 0.050 0.136 0.009 0.012 0.035 0.035 0.025 0.028 0.040 0.027 0.049 0.025 0.030 0.017 0.012 0.016 0.021 0.032 0.018 3.879 0.251 0.557 6.668 10.266 6.551 0.250 3.918 0.523 25.951 8.682 20.830 12.509 1.555 14.913 - 24 -
Table 14. Mean belowground carbon stocks in land uses sampled in the KFR
Stump & roots Mg/ha Intermediate Mg/ha Understorey Mg/ha Total Mg/ha Agriculture 1.349 0.181 0.032 1.562 Agroforest 7.381 0.416 0.032 7.829 Grassland 1.321 0.208 0.034 1.564 Reforestation 18.407 0.066 0.015 18.488 Secondary Forest 9.583 0.052 0.024 9.659 Land use 5.4.3 Soil Carbon
The organic soil carbon of the various land uses is presented in Table 15. The estimated
belowground carbon stocks are between 21.8 and 67.4 Mg/ha. Reforestation has the highest
soil carbon stock in the area. In 2006, the soil carbon density values of grassland ranged from
35.36–47.22 Mg/ha (Pulhin et al. 2006). The current value (39.09 Mg/ha) of grassland falls in
the middle of that range.
Table 15. Soil carbon and carbon stock
Sample plot code Land uses S6T2 S5T2 Agriculture S7T1 S3T1 S4T2 Agroforest OM% OC% Carbon stock Mg/ha 4.74 2.76 49.87 4.53 2.63 47.52 3.15 1.83 33.07 4.54 2.64 47.70 4.00 2.33 42.10 S1T2 4.93 2.87 51.86 S2T4 2.59 1.51 27.29 4.52 2.63 47.52 4.05 2.35 42.46 4.82 2.8 50.60 S2T3 Grassland S7T2 S5T1 S8T3 Reforestation S8T1 S2T1 S1T1 S4T1 Secondary forest 6.39 3.71 67.40 5.80 3.37 60.90 3.56 2.07 48.79 2.08 1.21 21.86 3.37 1.96 35.42 The mean soil carbon of the KFR (Table 16) was lower compared to other studies conducted
in Leyte and Tanay, Rizal, which were 52.70 Mg/ha and 55 Mg/ha, respectively (Lasco et al.
1999).
- 25 -
Table 16. Mean soil carbon-stock per land use
Mean total Mg/ha Land use Agriculture 43.49 Agroforest 47.22 Grassland 39.09 Reforestation 59.63 Secondary Forest 35.36 Mean total 44.96 5.4.4 Total carbon stock
The estimated total (above- and belowground) carbon stock of different land-use systems in
the KFR ranged 54.31–151.13 Mg/ha (Table 17). The results were low compared to
assessments conducted in other areas of the country.
Table 17. Plot-level mean carbon-stock of each land use
Land use Tree Mg/ha Intermediate Mg/ha Understorey Mg/ha Litter Mg/ha Soil & litter Mg/ha Total Mg/ha Agriculture 3.60 0.48 0.03 5.15 45.05 54.31 Agroforest 17.10 0.32 0.03 6.06 55.05 78.56 Grassland 3.52 0.58 0.05 10.06 40.65 54.87 Reforestation 55.22 0.20 0.04 17.67 78.00 151.13 Secondary forest 28.75 0.16 0.04 20.59 45.02 94.55 Table 18. Total carbon stock at plot-level in the KFR
Below‐ ground Aboveground Land use Agriculture Agroforest Grassland Reforestation Sample plot code Total Mg/ha Tree Mg/ha Intermediate Mg/ha Understorey Mg/ha Litter Mg/ha Soil & litter Mg/ha S5T2 10.04 0.21 0.03 5.61 51.40 67.29 S6T2 0.00 0.58 0.04 3.01 50.12 53.74 S7T1 0.75 0.66 0.01 6.84 33.63 41.90 S1T2 1.68 0.31 0.00 0.55 58.53 61.07 S3T1 30.59 0.09 0.07 14.82 57.97 103.54 S4T2 19.03 0.55 0.03 2.82 48.65 71.07 S2T3 0.00 0.60 0.10 6.59 47.77 55.06 S2T4 9.81 0.57 0.03 19.23 31.21 60.85 S7T2 0.76 0.56 0.03 4.37 42.98 48.70 S5T1 77.48 0.32 0.05 23.39 76.55 177.80 S8T1 25.89 0.12 0.03 18.17 69.58 113.79 S8T3 62.29 0.15 0.04 11.45 87.87 161.80 - 26 -
Below‐ ground Aboveground Land use Sample plot code Secondary forest Total Mg/ha Tree Mg/ha Intermediate Mg/ha Understorey Mg/ha Litter Mg/ha Soil & litter Mg/ha S1T1 37.05 0.41 0.04 7.49 34.37 79.36 S2T1 4.54 0.03 0.04 30.15 50.34 85.11 S4T1 44.65 0.03 0.04 24.12 50.33 119.18 160.0
C stock, Mg/ha
140.0
120.0
100.0
80.0
intermediate
60.0
understorey
40.0
tree
20.0
litter
Soil &litter
0.0
120
100
C stock composition (%)
80
intermediate
60
understorey
40
tree
20
litter
soil&litter
0
Figure 5. Total (above- and belowground) carbon stocks and their relative composition in the KFR
(Upper panel: absolute values in Mg/ha. Lower panel: as percentage).
- 27 -
Carbon stocks from soil and litter contribute about 50–80 percent of the total carbon
(Figure 5). The reforestation area has the highest total carbon stock both from soil and tree
components.
5.4.5 Landscape carbon-stock estimation
The estimated mean carbon stocks (Table 17) of the major land-use types was plotted in the
land cover map of 20014 to view the distribution of carbon density (Figure 6).
Figure 6. Distribution of land-cover-derived carbon density in the KFR, based on a carbon-stock
estimate (2009).
4
At the time of writing, the latest satellite image of this area awaits processing - 28 -
5.5 Land-use change dynamics in the KFR
Landscape-level carbon-stocks were estimated from land-cover types. By integrating the
changes in vegetation cover with carbon-stock measurements at plot level, changes in carbon
stock in the landscape can be estimated. Land-cover maps of 1989 and 2001 that were
processed by Ekadinata and Nugroho (in preparation) were used for this estimation. There
were seven major land-cover classes identified.
1)
Forest: characterised by more or less dense and extensive natural tree cover.
2)
Secondary forest: re-grown woodland area.
3)
Mahogany: areas dominated by Swietenia mahogany with ages of 10–30 years.
4)
Pine:– areas dominated by Pinus kesiya (Benguet pine).
5) Agricultural land: areas with less trees and cultivated by sweet potato, ginger, potato,
banana and corn.
6) Rice fields: both irrigated and non-irrigated, cultivated with hybrid and native rice
varieties.
7) Fallow: areas that are left idle to regain soil productivity and planted with Alnus
nepalensis.
5.5.1 Land cover in 1989
About 39% (8500 ha) of the area was classified as agricultural land. Natural and secondary
forest covered 20% (4300 ha) and 3% (670 ha) of the area, respectively (Table 19). About
27% (5800 ha) of the study area was covered by pine forest. Figure 8 shows the land cover
map of 1989.
Table 19. Land-cover classes in the KFR,
1989
Classes Area (ha) % Forest Secondary forest 4162.6 19 670.9 3 Old pine 1513.3 7 Pine 4256.0 20 Mahogany 321.4 1 Agriculture 8473.9 39 Fallow 359.5 2 Rice field 976.4 4 Settlement 458.1 2 Grassland 28.1 0.1 Cloud 401.3 2 Shadow 172.7 1 21794.0 100.0 Total Figure 7. Land-cover classes in the KFR, 1989.
- 29 -
Figu
ure 8. Land-coover map of thhe KFR, 1989.
Sourcce: ICRAF
5.5..2 Land cov
ver in 2001
1
Aboout 15.6 % (33400 ha) of area
a was classsified as natu
ural forest, a 3.5% decreaase from 198
89.
Agriiculture areaa covered aroound 8150 haa, a decrease from 39% too 37% of thee total area, while
w
old pine
p increaseed 7% to 10%
%. Figure 100 shows the laand-cover map
m of 2001.
Tablle 20. Land-coover classes inn the KFR,
2001
Classes Area (ha)) % 3394.1 15.6 Secondary forest 373.6 1.7 Old pine 2125.1 9.8 Pinee 3978.8 18.3 Mahogany 529.9 2.4 Agrriculture 8154.8 37.4 Fallow 340.9 1.6 Ricee field 1516.4 7.0 Setttlement 514.4 2.4 Graassland 35.9 0.2 Cloud 601.7 2.8 Shaadow 228.4 1.0 21 794.0 100.0 Forest Total Figure 9. Land-cover cllasses in the K
KFR, 2001.
- 30 -
Legend 2001 Land Cover
Figure 10. Land-cover map of the KFR, 2001.
Source: ICRAF
5.5.3 Land-cover change matrix
Area (ha)
A land-cover change matrix is presented in Table 21. There was a considerable decrease of
mature forest, secondary forest, pine forest and agriculture areas. On the other hand, there was
an increase in old pine forest, mahogany plantation, rice field, grassland and settlement areas.
Figure 11. Overall land-cover change within the KFR.
- 31 -
Table 21. Land-cover changes between 1989 and 2001 (area in ha)
1989 2001
Land use Forest Forest Secondary forest Old pine 3145.23 Secondary Old pine forest Pine 20.7 Rice field 187.11 Settle‐
ment 1.62 Grass
land 9.27 Mahogany Agriculture Fallow 308.88 8.64 209.43 Cloud Shadow Total 271.71 4162.6 370.08 126.63 6.03 47.52 0.81 37.71 0.09 1.8 52.56 27.63 670.9 1134.9 28.8 257.4 5.94 61.02 0.9 1.17 10.53 12.6 1513.3 Pine 945.18 1897.2 87.57 784.44 74.61 279.45 17.37 8.91 107.64 53.64 4256.0 Mahogany 302.22 8.91 0.09 6.3 0.18 2.97 0.72 321.4 Agriculture 1362.42 90.18 6257.97 149.85 421.56 69.3 9.72 67.23 45.63 8473.9 Fallow 56.34 4.77 185.4 73.8 33.3 0.09 0.54 4.5 0.72 359.5 Rice field 166.77 317.97 5.13 432.45 9.18 2.07 14.76 28.08 976.4 Settlement 29.34 13.68 458.1 Grassland 10.8 7.38 6.75 1.71 0.18 0.18 0.63 0.45 28.1 Cloud 165.51 1.98 23.13 34.02 0.54 62.91 3.24 45.36 0.54 1.98 26.19 35.91 401.3 Shadow 83.34 1.53 21.87 15.75 1.17 15.48 10.44 0.09 13.68 9.36 172.7 Total Source: ICRAF 3394.1 373.6 2125.1 3978.8 529.9 8154.8 340.9 1516.4 514.4 35.9 601.7 228.4 - 32 -
415.08 5.5.4 Carbon monitoring plots
KEF’s agroforestry program monitored plant biomass in 106 plots within the KFR between
1994 and 2003 (Figure 11). Table 22 shows the biomass generated.
Table 22. Mean biomass in 1994 and 2003 and the blocks and plots sampled
Land use No. of blocks No. of plots 1994 Mean biomass (Mg/ha) 2003 Mean biomass (Mg/ha) Agriculture 13 30 32.73 47.55 Forest 7 20 20.76 28.65 Secondary forest 3 8 39.89 56.71 Old pine 13 19 28.00 40.71 Pine 16 23 30.35 41.48 Rice field 4 5 17.14 23.73 Mahogany* 1 1 30.79 53.50 Total 57 106 *Only one mahogany plot appeared after the plot’s coordinates were intersected on the 1989 and 2001 land‐
cover maps The carbon densities for 1994 and 2003 were obtained from these plots (Table 23). The total
carbon budget estimated from the land cover was obtained from the total area of each landcover type (excluding the areas under cloud and shadow). Figure 10 shows the land-cover
density maps that indicate increases of carbon stock over the period 1994–2003.
Table 23. Carbon densities based on biomass-monitoring plots in the KFR
1994 2003
Land use Carbon density (Mg/ha) Carbon density (Mg/ha) Agriculture Forest Secondary forest Old pine Pine Rice field Mahogany 14.73 9.34 17.95 13.66 14.81 6.86 13.86 21.40 12.89 25.52 19.87 19.91 9.49 21.07 It was estimated that the total carbon stock was approximately 375.8 Gg5 in 1994 and
452.1 Gg in 2003 or a 21% increase in 9 years. This may be due to the increase of old pine
and reforestation and the decrease of agricultural areas.
5
1 Gg (Gigagram) = 1000 Mg (Megagram) - 33 -
Carbon (Mg/ha) Carbon (Mg/ha)
Figure 12. Distribution of land-cover-derived carbon density in the KFR in 1989 (upper panel) and
2001 (lower panel).
It is also interesting to note that there were a few plots with much higher carbon densities than
the average (Appendix 3), as shown in Table 24. From a statistical point of view, these are
outliers that affect the average values. These plots were not used in the extrapolation. We
suggest they should be validated on the ground.
- 34 -
Table 24. Plots with very high estimated carbon densities
1994
No. Land use Biomass (Kg/ha) Biomass (Mg/ha) 2003
Carbon density (Mg/ha) Biomass (Kg/ha) Carbon density (Mg/ha) Biomass (Mg/ha) 1. Old pine 48336.30 193.35 87.01 59124.61 236.50 106.42 2. Pine 62915.89 251.66 113.25 72941.69 291.77 131.30 3. Forest 50376.52 201.51 90.68 62108.81 248.44 111.80 4. Forest 61598.65 246.39 110.88 72842.67 291.37 131.12 5. Forest 30341.68 121.37 54.62 38550.60 154.20 69.39 6. Agriculture 27768.31 111.07 49.98 39703.36 158.81 71.47 From these monitoring plots, one noticeable carbon value was observed in the agriculture
category. In 2003, monitoring plots in agriculture areas had an average of 21.4 Mg/ha, which
was more than that of forest and pines. This suggests that farmers planted more high-carbon
trees outside the forest or it could be due to the sedentarisation of agriculture, which was
noted by Banaticla et al. (2008) (see page 13).
5.6 Carbon emissions by land-use and land-cover change
Carbon emissions from land-use and land-cover changes between 1989 and 2001 were
calculated using the derived carbon densities from this study (with addition from another
study of land-cover types not sampled locally), as shown in Table 25.
Table 25. Land-cover types and carbon densities used
Land‐cover type from image classification Mean carbon densities (aboveground) Mg/ha Sources Agricultural land 17.61 KEF monitoring plots* Dipterocarp/mahogany 45.0 Recent data Fallow (swidden‐fallow) 19.7 Recent data Forest (mature) 28.9 Recent data Grassland 4.1 Recent data Pasture land 10.4 Recent data Pine 17.53 KEF monitoring plots* Old pine 16.76 KEF monitoring plots* Rice field 8.17 KEF monitoring plots* Secondary forest 21.74 KEF monitoring plots* Settlement 4.1 *Average of the 1994 and 2001 carbon densities (Appendix 3) - 35 -
ICRAF (Kalimantan data) Based on our calculations (Table 26), the KFR sequestered an average of 0.30 Mg/ha of
carbon less than what was emitted (average 0.82 Mg/ha) from its land-cover changes between
1989 and 2001. The carbon emission potential was 0.5 Mg/ha. Table 26 shows the estimated
yearly average carbon emissions. From this, it is estimated that per year the KFR is emitting
1.4 Gg of carbon while sequestering 0.5 Gg.
- 36 -
Table 26. Mean carbon emissions from land-use changes, 1994–2003
Secondary forest Forest Old Pine Pine Mahogany Agriculture Fallow Rice field Settlement Grass Total Forest Secondary Forest 0 0 0 0.177159 ‐0.00595 0.123002 0.009783 0.187419 0.001925 0.011016 0.504357 0 0 0 0.024403 ‐0.00645 0.009812 7.43E‐05 0.023411 7.27E‐05 0.001454 0.05278 Old Pine 0 0 0 0 ‐0.03732 ‐0.0052 ‐0.0008 0.024051 0.000523 0.00068 ‐0.01806 Pine 0 0 0.03209287 0 ‐0.1105 0.010798 ‐0.00753 0.119632 0.01068 0.005478 0.060653 Mahogany 0 0 0 0 0 0.011365 0.000104 0.010646 0 0.000338 0.022454 Agriculture 0 0 0 ‐0.01875 ‐0.11503 0 ‐0.01719 0.174666 0.041655 0.005843 0.071189 Fallow 0 0 0 0.005687 ‐0.00554 0.021267 0 0.017617 6.44E‐05 0.000387 0.039485 Rice field 0 0 0 ‐0.07139 0 ‐0.13175 ‐0.00271 0 0.001714 0.000387 ‐0.20375 Settlement 0 0 0 0 0 0 0 0 0 0 0 Grass 0 0 0 ‐0.00664 0 ‐0.00444 ‐0.00483 ‐0.00032 0 0 Total 0 0 0.03209287 0.110461 ‐0.28078 0.034866 ‐0.02311 0.557123 0.056634 0.025581 - 37 -
‐0.01623 Mg/ha 0.512875 emission Mg/ha 0.815232 emitted Mg/ha 0.302356 sequestered Table 27. Mean carbon emissions per year, 1994–2003
Forest Secondary forest Forest Secondary Forest 0 0 0 0.014763 ‐0.0005 0.01025 0.000815 0.015618 0 0 0 0.002034 ‐0.00054 0.000818 6.19E‐06 Old Pine 0 0 0 0 ‐0.00311 ‐0.00043 Pine 0 0 0.00267441 0 ‐0.00921 0.0009 Mahogany Old Pine Pine Mahogany Agriculture Fallow Rice field Settlement Grass Total 0.00016 0.000918 0.001951 6.06E‐06 ‐6.7E‐05 0.002004 ‐0.00063 0.009969 0.04203 0.000121 0.004398 4.36E‐05 5.66E‐05 ‐0.00151 0.00089 0.000457 0.005054 0 0 0 0 0 0.000947 8.71E‐06 0.000887 0 2.81E‐05 0.001871 Agriculture 0 0 0 ‐0.00156 ‐0.00959 0 ‐0.00143 0.014556 0.003471 0.000487 0.005932 Fallow 0 0 0 0.000474 ‐0.00046 0.001772 0 0.001468 5.37E‐06 3.22E‐05 0.00329 Rice field 0 0 0 ‐0.00595 0 ‐0.01098 ‐0.00023 0 0.000143 3.22E‐05 ‐0.01698 Settlement 0 0 0 0 0 0 0 0 0 0 0 Grass 0 0 0 ‐0.00055 0 ‐0.00037 ‐0.0004 ‐2.7E‐05 0 0 ‐0.00135 Total 0 0 0.00267441 0.009205 ‐0.0234 0.002905 ‐0.00193 0.046427 0.00472 0.002132 0.04274 0.067936 0.025196 Mg/ha emission Mg/ha emitted Mg/ha sequestered - 38 -
5.7 Carbon-offset6 options
1)
CDM Market: The KEF is negotiating a CDM project. Potential sites for this project are
abandoned agricultural and grassland areas. A list of participants is being prepared
together with their planting strategies for the proposed CDM sites (Figure 13).
Figure 13. Target sites for CDM project (red dots).
Plant species that local farmers preferred to plant (some already have planted) were tuai
(Biscofia javanica), Alnus (Alnus nepalensis) and rain tree (Albizia saman). Among the
proposed planting schemes were reforestation with mixed tree species. Others propose to
implement nurse tree to integrate climax species (for example, Benguet pine and
dipterocarps). However, a possible problem under this target market is meeting the CDM
requirements of forest definition, baseline, leakage and additionality7. Thus, the
voluntary carbon market is likely to be the best for the KFR owing to its increasing
carbon stock.
2)
Voluntary market: The data and information generated from this study will be used to
find voluntary carbon markets. However, the baseline should be well established. The
forest improvement technology developed by the KEF could potentially enhance the
carbon stock of the standing forests (Appendix 5) at the same time as maintaining the
6
A reduction in carbon dioxide emission by a third party purchased by a heavy carbon dioxide producer as part of
carbon emissions trading. 7
CDM projects must result in ‘reduction in emissions that are additional to any that would occur in the absence of
the certified project activity’. - 39 -
biodiversity within. The KEF is optimistic that this could be used as a management
strategy to tap ‘reducing emissions from deforestation and degradation’ (REDD)
markets.
5.8 Scenario building and future options
This section presents the results of Forest, Agroforest, Low-value Landscape Or Wasteland
(FALLOW) model application in the KFR that was conducted by Suyamto et al. (2011)8
under the Rewarding Upland Poor for the Environmental Services they provide (RUPES)
project (phase 1). The FALLOW model simulates landscape dynamics and the consequences
of the application of different drivers in various scenarios.
5.8.1 Baseline
Using population growth (at a rate of 1.78%) as the driver, the model predicted that within the
next three decades (2001–2030), the landscape would experience a decrease in forest area of
about 85 ha/yr and an increase of agricultural/grassland area of about 85 ha/yr. Depletion of
biodiversity, carbon stock and sediment-filtering capacity would occur at the rate of
0.4 species/yr, 53 Gg/yr and 117 Gg/yr, respectively. Secondary expenses of the people would
increase at a relatively low rate of about PHP 110 per capita per year.
5.8.2 Future options
Three options were identified based on existing livelihoods (1 and 2) and alternative landuses (3) within the KFR, with possible future implications.
Table 28. Future options and their implications for the KFR
Options Option 1: Improve non‐timber forest products’ (NTFP) productivity and markets (by increasing productivity and price 2x, 6x and 10x from the baseline) Option 2: Provide better off‐farm jobs (increase incomes from off‐farms jobs 2x, 6x and 10x from the baseline) 8
Implications By increasing NTFP productivity and price up to 10x from the baseline, agricultural land expansion can only be reduced at an average of about 233 ha or 8% per year •
By increasing income from off‐farm jobs 2x from the baseline, agricultural land expansion could decrease at an average of 289 ha or 10% per year •
By increasing income 6x, agricultural land expansion could decrease at an average of 551 ha or 17% per year and forests could increase at an average of 229 ha or 2% per year •
By increasing income 10x, agricultural land expansion could decrease at an average of 1005 ha or 31% per year and forests could increase at an average of 834 ha or 8% per year Detailed information on data inputs of the model and some assumptions can be found in this working paper. - 40 -
Options Option 3: Promote tree‐based systems (for example, cacao and coffee) through extension, subsidy and market improvements Implications Among the tree‐based systems scenarios, coffee could be adopted at the fastest rate, followed by cacao and mahogany. This assumes that economically, smallholder tree‐
based systems are more profitable than pasture and, biophysically, pasture can be converted into tree‐
based systems. These efforts would replace grasslands with more valuable systems Source: Suyanto et al. (2011) (draft working paper) Appendix 6 shows the additionality from each scenario on biodiversity (that is, species
numbers in four functional groups: pioneer, early succession, medium succession and late
succession), carbon stocks, watershed functions (that is, sediment-filtering capacity) and
people’s welfare (that is, non-food expenses per capita).
- 41 -
6. Conclusion and recommendations
6.1 Conclusion
The matrix below summarises the findings of the appraisal.
Value: Opportunity: •
•
•
Major land‐use and land‐cover types—
agriculture, agroforest, grassland, secondary forest and reforestation—were assessed and their carbon stocks were calculated KFR has its own farming practices that enhance carbon stocks in the area, such as pang‐omis, in which Alnus species are integrated into swidden farming •
KEF has long‐term biomass monitoring plots to support carbon‐offset trading and already has skills to monitor carbon stocks within KFR (to reduce transaction cost) KEF’s own farming practices and technology can be used as a strategy to explore voluntary markets Trust: Threat: •
KEF’s rules and regulations on natural resources control the cutting of trees inside KRF. It also initiates the active participation of each village in tree‐planting activities •
•
Encroachment of outsiders owing to intermarriages (concern over changing farming practices) Limited livelihoods’ options (certificate of ancestral domain title holders might seek to sell their land) 6.2 Recommendations
ƒ
For the voluntary carbon market, further research is required to assess the potential of the
KEF’s forest improvement technology for REDD.
ƒ
More ground-truthing activities are need to validate the landscape-level carbon
estimations.
ƒ
Process the recent satellite image of the area and use it for analysis of land-use and landcover changes and carbon dynamics.
- 43 -
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- 46 -
Appendix 1: List of plant species and its biomass
per land use
- 47 -
Appendix 1: Reforestation – list of plant species and biomass
No.
Local name
biomass
2
Kg/m
268.00
No.
32
Local name
Alnus
biomass
2
Kg/m
301.62
No.
63
Local name
Alnus
biomass
2
Kg/m
637.24
No.
Local name
94
Amuwag
biomass
2
Kg/m
1
Alagai
20.50
2
Alnus
6.04
33
Alnus
301.62
64
Alnus
648.34
95
Amuwag
20.50
3
Alnus
15.59
34
Alnus
312.19
65
Alnus
659.57
96
Amuwag
20.50
4
Alnus
20.50
35
Alnus
319.36
66
Alnus
665.22
97
Amuwag
24.00
5
Alnus
21.18
36
Alnus
330.30
67
Alnus
682.37
98
Amuwag
24.00
6
Alnus
27.85
37
Alnus
330.30
68
Alnus
778.40
99
Amuwag
27.85
7
Alnus
41.58
38
Alnus
349.05
69
Alnus
835.88
100
Amuwag
27.85
8
Alnus
114.46
39
Alnus
364.51
70
Alnus
951.40
101
Amuwag
27.85
9
Alnus
124.31
40
Alnus
368.44
71
Alnus
958.49
102
Amuwag
27.85
10
Alnus
242.83
41
Alnus
396.67
72
Alnus
6.04
103
Amuwag
27.85
11
Alnus
46.93
42
Alnus
396.67
73
Alnus
9.70
104
Amuwag
27.85
12
Alnus
58.84
43
Alnus
396.67
74
Alnus
20.50
105
Amuwag
27.85
13
Alnus
87.85
44
Alnus
396.67
75
Alnus
46.93
106
Amuwag
27.85
14
Alnus
87.85
45
Alnus
421.90
76
Amuwag
14.49
107
Amuwag
27.85
15
Alnus
87.85
46
Alnus
430.53
77
Amuwag
14.49
108
Amuwag
27.85
16
Alnus
168.85
47
Alnus
439.26
78
Amuwag
14.49
109
Amuwag
27.85
17
Alnus
168.85
48
Alnus
439.26
79
Amuwag
14.49
110
Amuwag
27.85
18
Alnus
194.33
49
Alnus
448.10
80
Amuwag
14.49
111
Amuwag
27.85
19
Alnus
194.33
50
Alnus
484.54
81
Amuwag
14.49
112
Amuwag
27.85
20
Alnus
205.15
51
Alnus
484.54
82
Amuwag
14.49
113
Amuwag
27.85
21
Alnus
207.91
52
Alnus
484.54
83
Amuwag
14.49
114
Amuwag
87.85
22
Alnus
222.06
53
Alnus
484.54
84
Amuwag
14.49
115
Amuwag
87.85
23
Alnus
222.06
54
Alnus
493.93
85
Amuwag
19.20
116
Antipolo
9.70
24
Alnus
222.06
55
Alnus
532.60
86
Amuwag
20.50
117
Avocado
72.45
25
Alnus
222.06
56
Alnus
532.60
87
Amuwag
20.50
118
Avocado
532.60
26
Alnus
252.09
57
Alnus
532.60
88
Amuwag
20.50
119
Avocado
753.66
27
Alnus
252.09
58
Alnus
542.54
89
Amuwag
20.50
120
Avocado
862.23
28
Alnus
258.38
59
Alnus
583.48
90
Amuwag
20.50
121
Avocado
75.38
29
Alnus
281.15
60
Alnus
609.99
91
Amuwag
20.50
122
Avocado
87.85
30
Alnus
284.50
61
Alnus
609.99
92
Amuwag
20.50
123
Avocado
284.50
31
Alnus
284.50
62
Alnus
631.73
93
Amuwag
20.50
124
Avocado
594.00
- 48 -
No.
Local name
biomass
2
Kg/m
No.
Local name
biomass
2
Kg/m
No.
Local name
biomass
2
Kg/m
No.
Local name
biomass
2
Kg/m
125
Ayuhip
11.02
156
Benguet Pine
1227.20
187
Coffee
8.11
218
Coffee
8.11
126
Balanti
12.93
157
Benguet Pine
1260.54
188
Coffee
8.11
219
Coffee
8.11
127
Bauang
6.04
158
Benguet Pine
1346.31
189
Coffee
8.11
220
Coffee
8.11
128
Bauang
6.04
159
Benguet Pine
1435.57
190
Coffee
8.11
221
Coffee
8.11
129
Benguet Pine
105.11
160
Benguet Pine
1881.99
191
Coffee
8.11
222
Coffee
8.11
130
Benguet Pine
145.53
161
Bilwa
9.70
192
Coffee
8.11
223
Daguay
9.70
131
Benguet Pine
171.30
162
Bilwa
13.44
193
Coffee
8.11
224
Danglin
14.49
132
Benguet Pine
194.33
163
Bilwa
46.93
194
Coffee
8.11
225
Ginnabang
17.34
133
Benguet Pine
227.88
164
Bilwa
76.88
195
Coffee
8.11
226
Gmelina
58.84
134
Benguet Pine
268.00
165
Bilwa
87.85
196
Coffee
8.11
227
Gmelina
14.49
135
Benguet Pine
330.30
166
Bilwa
199.70
197
Coffee
8.11
228
Gmelina
75.38
136
Benguet Pine
337.73
167
Bilwa
202.41
198
Coffee
8.11
229
Guava
7.03
137
Benguet Pine
337.73
168
Bini
6.04
199
Coffee
8.11
230
Guava
36.63
138
Benguet Pine
356.73
169
Bini
7.74
200
Coffee
8.11
231
Guava
8.11
139
Benguet Pine
439.26
170
Bini
14.49
201
Coffee
8.11
232
Guava
9.70
140
Benguet Pine
461.56
171
Buta buta
9.70
202
Coffee
8.11
233
Guava
27.85
141
Benguet Pine
461.56
172
Buta buta
36.63
203
Coffee
8.11
234
Guava
46.93
142
Benguet Pine
484.54
173
Buta buta
36.63
204
Coffee
8.11
235
Guava
105.11
143
Benguet Pine
484.54
174
Buta buta
114.46
205
Coffee
8.11
236
hauili
87.85
144
Benguet Pine
557.68
175
Canthum
9.70
206
Coffee
8.11
237
Hili-hili
145
Benguet Pine
604.63
176
Coffee
6.04
207
Coffee
8.11
238
Ihit
291.28
146
Benguet Pine
637.24
177
Coffee
6.04
208
Coffee
8.11
239
Ihit
20.50
147
Benguet Pine
723.43
178
Coffee
6.04
209
Coffee
8.11
240
Ihit
138.95
148
Benguet Pine
723.43
179
Coffee
8.11
210
Coffee
8.11
241
Ipil-ipil
149
Benguet Pine
753.66
180
Coffee
8.11
211
Coffee
8.11
242
Kahoy dalaga
65.43
150
Benguet Pine
753.66
181
Coffee
8.11
212
Coffee
8.11
243
Kahoy dalaga
951.40
151
Benguet Pine
784.67
182
Coffee
8.11
213
Coffee
8.11
244
Kahoy dalaga
36.63
152
Benguet Pine
916.47
183
Coffee
8.11
214
Coffee
8.11
245
Kulatingan
153
Benguet Pine
951.40
184
Coffee
8.11
215
Coffee
8.11
246
Lablaban
25.50
154
Benguet Pine
987.15
185
Coffee
8.11
216
Coffee
8.11
247
Lablabang
105.11
155
Benguet Pine
1146.24
186
Coffee
8.11
217
Coffee
8.11
248
Lablabang
430.53
- 49 -
14.49
24.00
6.04
Reforestation continues…
No.
Local name
biomass
2
Kg/m
No.
Local name
biomass
2
Kg/m
No.
Local name
biomass
2
Kg/m
249
Ladau
87.85
280
Mangga
6.04
311
Narra
670.91
250
Langka
27.85
281
Marang
81.47
312
Narra
882.33
251
Langka
69.59
282
Molave
20.50
313
Padpad
14.49
252
Langka
79.92
283
Molave
27.85
314
Padpad
79.92
253
Lapting
87.85
284
Mussaenda setosa
20.50
315
Padpad
284.50
508.22
285
Narra
6.04
316
Padpad
461.56
6.04
286
Narra
6.04
317
Palai
254
Liwliw/Hauili
255
Macaranga
256
Macaranga
6.04
287
Narra
6.04
318
Papaya
6.04
257
Macaranga
134.66
288
Narra
9.70
319
Papaya
12.43
258
Mahogany
14.49
289
Narra
16.74
320
Papaya
14.49
259
Mahogany
46.93
290
Narra
20.50
321
Papaya
20.50
260
Mahogany
1435.57
291
Narra
20.50
322
Papaya
20.50
11.48
261
Mahogany
6.04
292
Narra
27.85
323
Papaya
24.00
262
Manga
10.13
293
Narra
32.05
324
Pitikan
19.20
263
Manga
20.50
294
Narra
58.84
325
Piwi
20.50
264
Manga
24.00
295
Narra
61.42
326
Santol
36.63
265
Manga
34.75
296
Narra
78.39
327
Sapinit
14.49
266
Manga
62.74
297
Narra
96.24
328
Suha
6.69
267
Manga
87.85
298
Narra
134.66
329
Suha
258.38
268
Manga
281.15
299
Narra
168.85
330
Suha
723.43
269
Manga
291.28
300
Narra
168.85
331
Suha
816.44
270
Manga
330.30
301
Narra
213.50
332
Tibanglan
114.46
271
Manga
356.73
302
Narra
222.06
333
Tuwal
272
Manga
484.54
303
Narra
236.78
273
Manga
532.60
304
Narra
268.00
274
Manga
637.24
305
Narra
291.28
275
Manga
653.94
306
Narra
330.30
276
Manga
693.95
307
Narra
368.44
277
Manga
723.43
308
Narra
400.81
278
Manga
723.43
309
Narra
498.67
279
Manga
810.02
310
Narra
642.78
Total
- 50 -
58.84
73625.34
No.
Local name
biomass
2
Kg/m
Secondary Forest
No.
Local name
biomass
2
Kg/m
No.
Local name
biomass
2
Kg/m
No.
Local name
biomass
2
Kg/m
No.
Local name
biomass
2
Kg/m
1
Adawai
26.268954
32
Benguet Pine
110.6587
63
Hagahaka
32.937682
94
Palosapis
29.487379
2
Adawai
124.3082
33
Benguet Pine
154.60227
64
Halinghing
39.551651
95
Palosapis
35.68119
3
Alagasi
11.017251
34
Benguet Pine
168.84816
65
Hauili
7.3792394
96
Palosapis
52.674717
4
Alagasi
78.389935
35
Benguet Pine
194.33328
66
Hauili
29.487379
97
Palosapis
56.322386
5
Alagau
9.7046768
36
Benguet Pine
227.87747
67
Iilog
18.563665
98
Palosapis
75.384151
6
Alagau
24.003636
37
Benguet Pine
287.87751
68
Iilog
134.66232
99
Palosapis
145.53084
7
Alagau
38.560999
38
Benguet Pine
298.14853
69
Ilo-ilog
43.670247
100
Palosapis
145.53084
8
Alagau
105.10542
39
Benguet Pine
315.76216
70
Itangan
25.499865
101
Palosapis
168.84816
9
Alagau
168.84816
40
Benguet Pine
341.4751
71
Itangan
33.837194
102
Palosapis
168.84816
10
Alagau
1178.2167
41
Benguet Pine
372.39289
72
Kamiling
16.159476
103
Palosapis
202.41227
11
Amuwag
27.849561
42
Benguet Pine
409.16755
73
Kamiling
20.503207
104
Palosapis
337.72582
12
Amuwag
32.937682
43
Benguet Pine
753.664
74
Kolalabang
15.03348
105
Palosapis
396.67228
14.489144
106
Palosapis
637.24009
15.03348
107
Palosapis
951.4035
13
Amuwag
36.62586
44
Benguet Pine
875.6026
75
kubangbang liit
14
Amuwag
45.824393
45
Benguet Pine
979.93283
76
La huet
15
Amuwag
48.042718
46
Benguet Pine
1046.0322
77
Ladao
124.3082
108
Pangnan
13.957109
16
Antipolo
76.878003
47
Benguet Pine
1083.9073
78
Ladaw
20.503207
109
Pangnan
20.503207
17
Apitong
7.7398193
48
Benguet Pine
1194.4076
79
Litan
6.3601929
110
Pangnan
36.62586
18
Ayohip
17.335929
49
Benguet Pine
1320.2104
80
Loklohong
78.389935
111
Pangnan
58.838158
19
Ayohip
36.62586
50
Benguet Pine
1745.3628
81
Luglohong
18.563665
112
Pangnan
72.450359
20
Ayohip
210.6947
51
Benguet Pine
1776.3308
82
Molave
44.739342
113
Pangnan
87.845743
21
Balete
951.4035
52
Benguet Pine
2506.3025
83
Molave
252.09138
114
Pangnan
105.10542
22
Bangat
20.503207
53
Bini
64.07435
84
Pad pad
35.68119
115
Pangnan
194.33328
23
Bangat
52.674717
54
Bini
78.389935
85
Pad pad
61.42192
116
Pangnan
1463.0459
24
Benguet Pine
16.159476
55
Binukau
219.18247
86
Pad pad
73.908297
117
Pili nut
19.8435
25
Benguet Pine
27.052154
56
Bolalog
12.433748
87
Padpad
32.053038
118
Piwi(Is-is)
15.03348
26
Benguet Pine
28.253622
57
Buta buta
10.568326
88
Palosapis
7.0291869
119
Piwi(Is-is)
17.335929
27
Benguet Pine
28.661271
58
Buta buta
147.76709
89
Palosapis
7.0291869
120
Piwi(Is-is)
19.8435
28
Benguet Pine
34.751666
59
Dagwey
20.503207
90
Palosapis
8.1110429
121
Piwi(Is-is)
31.183169
29
Benguet Pine
39.054392
60
Guijo
58.838158
91
Palosapis
15.590223
122
Piwi(Is-is)
46.925489
30
Benguet Pine
46.925489
61
Guijo
76.878003
92
Palosapis
19.8435
123
Piwi(Is-is)
51.492081
31
Benguet Pine
84.620084
62
Guijo
284.50297
93
Palosapis
20.503207
124
Salingogon
7.3792394
- 51 -
Secondary Forest continues…
No.
Local name
biomass
2
Kg/m
No.
Local name
biomass
2
Kg/m
No.
125
Salingogon
11.477724
156
White Lauan
417.63058
126
Salingogon
637.24009
157
White Lauan
439.25684
127
Tabangawan
103.29329
158
White Lauan
693.95008
128
Tabangawan
124.3082
159
White Lauan
882.33399
129
Tibanglan
34.751666
160
White Lauan
1099.2901
130
Tiklad
27.849561
161
White Lauan
1194.4076
131
Tiklad
52.674717
162
White Lauan
1346.3061
Total
38331.918
132
Tiklad
64.07435
133
Tiklad
76.878003
134
Tiklag
36.62586
135
Uyok
28.661271
136
Uyok
57.571816
137
Uyok
261.56209
138
White Lauan
12.433748
139
White Lauan
14.489144
140
White Lauan
19.197019
141
White Lauan
36.62586
142
White Lauan
86.223631
143
White Lauan
87.845743
144
White Lauan
94.521435
145
White Lauan
105.10542
146
White Lauan
124.3082
147
White Lauan
124.3082
148
White Lauan
168.84816
149
White Lauan
168.84816
150
White Lauan
168.84816
151
White Lauan
258.38129
152
White Lauan
376.37403
153
White Lauan
392.56008
154
White Lauan
396.67228
155
White Lauan
396.67228
- 52 -
Local name
biomass
2
Kg/m
No.
Local name
biomass
2
Kg/m
Agroforest
No.
Local name
biomass
2
Kg/m
No.
Local name
biomass
2
Kg/m
No.
Local name
biomass
2
Kg/m
No.
Local name
biomass
2
Kg/m
8
Adawai
11.95
8
Benguet pine
6.04
34
Citrus
6.04
65
Citrus
6.04
24
Adawai
34.75
9
Benguet pine
27.85
35
Citrus
6.04
66
Citrus
6.04
58
Adawai
224.96
10
Benguet pine
27.85
36
Citrus
6.04
67
Citrus
6.04
4
Alagai
9.29
11
Benguet pine
27.85
37
Citrus
6.04
68
Citrus
6.04
1
Alnus
9.70
12
Benguet pine
27.85
38
Citrus
6.04
69
Citrus
6.04
2
Alnus
20.50
13
Benguet pine
4757.32
39
Citrus
6.04
70
Citrus
6.04
3
Alnus
9.70
29
Bini
42.62
40
Citrus
6.04
71
Citrus
6.04
47
Alnus
124.31
46
Bini
112.55
41
Citrus
6.04
72
Citrus
6.04
50
Alnus
145.53
1
Binunga
6.04
42
Citrus
6.04
73
Citrus
6.04
54
Alnus
181.32
5
Binunga
9.70
43
Citrus
6.04
74
Citrus
6.04
57
Alnus
222.06
15
Binunga
105.11
44
Citrus
6.04
75
Citrus
6.04
64
Alnus
426.20
14
Citrus
1528.40
45
Citrus
6.04
76
Citrus
6.04
65
Alnus
439.26
15
Citrus
6.04
46
Citrus
6.04
77
Citrus
6.04
67
Alnus
609.99
16
Citrus
6.04
47
Citrus
6.04
78
Citrus
6.04
69
Alnus
711.55
17
Citrus
6.04
48
Citrus
6.04
79
Citrus
6.04
28
American kapok
723.43
18
Citrus
6.04
49
Citrus
6.04
80
Citrus
6.04
17
Atsuete
130.46
19
Citrus
6.04
50
Citrus
6.04
81
Citrus
6.04
28
Avocado
41.58
20
Citrus
6.04
51
Citrus
6.04
82
Citrus
6.04
20
Avocado
216.33
21
Citrus
6.04
52
Citrus
6.04
83
Citrus
26.27
6.69
22
Citrus
6.04
53
Citrus
6.04
84
Citrus
97.97
138.95
2
Bakhi
25
Balanti
36.63
23
Citrus
6.04
54
Citrus
6.04
85
Citrus
59
Balanti
227.88
24
Citrus
6.04
55
Citrus
6.04
86
Daguey
14.49
1
Bawang
6.04
25
Citrus
6.04
56
Citrus
6.04
87
Danglin
9.70
5
Bawang
9.29
26
Citrus
6.04
57
Citrus
6.04
88
Guava
13.44
9
Bawang
13.44
27
Citrus
6.04
58
Citrus
6.04
89
Guava
58.84
42
Bawang
105.11
28
Citrus
6.04
59
Citrus
6.04
90
Guava
84.62
55
Bawang
194.33
29
Citrus
6.04
60
Citrus
6.04
91
Guba-gubai
27.85
4
Benguet pine
58.84
30
Citrus
6.04
61
Citrus
6.04
92
Hanga
105.11
5
Benguet pine
87.85
31
Citrus
6.04
62
Citrus
6.04
93
Ihit
305.12
6
Benguet pine
105.11
32
Citrus
6.04
63
Citrus
6.04
94
Ipil-ipil
11.95
7
Benguet pine
105.11
33
Citrus
6.04
64
Citrus
6.04
95
Ipil-ipil
65.43
- 53 -
Agroforest continues…
No.
96
Local name
biomass
2
Kg/m
No.
271.25
97
Ipil-ipil
994.39
128
Mahogany
36.63
98
Jacobina sp.
20.50
129
mahogany
105.11
99
Jual
268.00
130
Mahogany
583.48
Kahoy dalaga
101
Kamilin
Mahogany
biomass
2
Kg/m
Ipil-ipil
100
127
Local name
No.
9.70
158
Local name
biomass
2
Kg/m
Saging
2.52
159
Santol
1023.70
160
Suha
41.58
161
Suha
124.31
36.63
131
Mangga
20.50
162
Suha
284.50
8.89
132
Mangga
6.04
163
Suha
396.67
1435.57
102
Kangah
20.50
133
Mangga
9.70
164
Suha
103
La hwe/ La huit
45.82
134
Mangga
57.57
165
Syzygium sp. (Hangan)
104
Lablabang
17.34
135
Mangga
79.92
166
Talanak
15.59
105
Lablabang
52.67
136
Mangga
145.53
167
Talanak
27.05
106
Lablabang
52.67
137
Mangga
699.79
168
Tibig
107
Lablabang
168.85
138
Mangga
916.47
169
Tibig
9.29
108
Ladau
20.50
139
Nangka
230.82
170
Tibig
15.59
109
Ladau
202.41
140
Nangka
281.15
171
Tibig
252.09
110
Ladau
356.73
141
Narra
156.92
172
Tuai
1936.24
111
Ladau
753.66
142
Ngak ngak
24.74
173
Tubang
1023.70
112
Ladaw
334.00
143
Nganga
51.49
113
Langka
128.39
144
Nganga
86.22
114
Lapting
19.84
145
Nganga
105.11
115
Litan
36.63
146
Ngatngat
116
Lithocarphus
6.04
147
Niog
Total
21.86
1023.70
117
Liwliw
36.63
148
Oak (Lithocarphus)
36.63
118
Liwliw
57.57
149
Patat
46.93
119
Liwliw
72.45
150
Pitikan
24.74
120
Liwliw
159.27
151
Saging
2.52
121
Liwliw
168.85
152
Saging
2.52
122
Liwliw/Hauili
9.29
153
Saging
2.52
123
Lukban/Suha
17.34
154
Saging
2.52
124
Lukban/Suha
58.84
155
Saging
2.52
125
Lukban/Suha
670.91
156
Saging
2.52
126
Madlakat
46.93
157
Saging
2.52
- 54 -
105.11
6.04
29890.57
No.
Local name
biomass
2
Kg/m
Agriculture
No.
Local name
1
Saging
biomass
2
Kg/m
11.01
No.
32
Local name
ipil ipil
biomass
2
Kg/m
58.84
2
Saging
11.01
33
Dita
3
Saging
12.53
34
Guava
951.40
1.60
4
Saging
12.53
35
Hamak
3.38
5
Saging
9.60
36
Adaway
9.70
6
Saging
11.01
37
Hamak
9.70
7
Saging
9.60
38
Hamak
9.70
8
Saging
9.60
39
Tual
9.70
9
Saging
11.01
40
Kamiling
14.49
10
Saging
11.01
41
Lablabang
20.50
11
Saging
12.53
42
Liwliw
20.50
12
Saging
12.53
43
Balanti
27.85
13
Saging
9.60
44
Pitikan
46.93
14
Saging
11.01
45
Idu-iduh
58.84
15
Saging
11.01
46
16
Saging
12.53
17
Saging
12.53
18
Saging
17.71
19
Saging
9.60
20
Saging
12.53
21
Saging
15.88
22
Manga
396.67
23
Manga
484.54
24
Manga
532.60
25
Manga
583.48
26
Manga
637.24
27
Suha
222.06
28
Suha
356.73
29
Suha
484.54
30
papaya
31
citrus
Bawang
Total
145.53
5399.5106
58.84
7.74
- 55 -
Grassland
No.
Local name
biomass
2
Kg/m
No.
Local name
biomass
2
Kg/m
1
Ammowag
6.041014
32
Banana
8.2865541
2
Ammowag
6.041014
33
Banana
8.2865541
3
Ammowag
27.849561
34
Banana
9.5983291
4
Ammowag
46.925489
35
Banana
9.5983291
5
Benguet pine
72.450359
36
Banana
9.5983291
6
Kahoy dalaga
72.450359
37
Banana
11.012776
7
Ammowag
87.845743
38
Banana
11.012776
8
Benguet pine
145.53084
39
Banana
11.012776
9
Benguet pine
145.53084
40
Banana
12.530761
10
Benguet pine
194.33328
41
Banana
12.530761
11
Benguet pine
284.50297
42
Banana
12.530761
12
Benguet pine
356.72788
43
Banana
14.153104
13
Benguet pine
396.67228
44
Banana
14.153104
14
Benguet pine
1435.5749
45
Banana
14.153104
15
Benguet pine
1624.8221
46
Banana
15.880582
16
Bawang
6.041014
47
Banana
15.880582
17
Avocado
20.503207
48
Banana
15.880582
18
Avocado
20.503207
Total
5283.2753
19
Manga
20.503207
20
Manga
20.503207
21
Manga
20.503207
22
Banana
5.9672923
23
Banana
5.9672923
24
Banana
5.9672923
25
Banana
5.9672923
26
Banana
5.9672923
27
Banana
5.9672923
28
Banana
7.076534
29
Banana
7.076534
30
Banana
7.076534
31
Banana
8.2865541
- 56 -
Appendix 2: List of intermediate and undergrowth
- 57 -
A. Agriculture (S5T2)
Undergrowth
Species
Alatin
Bulak manok
Busikad
Intermediate
S5T2
No. of individuals
7
3x3
178
51
Species
Ayas-as
No. of individuals
7
Cogon
6
Dilang baka
4
Dilang butiki
1
Gonoy
4
Euphorbia hirta
5
Kamot kabag
7
Habugan
7
Kamoteng baging
Kamot pusa
4
Kulapi
30
Kamote
7
Makahiya
15
1
Kulapi
19
Panibat
3
Kulitis
8
Sapinit
3
Uoko
4
Leptochloa chinensis
20
Ligad-ligad
10
Makahiya
25
Mutha
5
Panibat
71
Paragis
24
Paragis like
4
Putokan putokan
9
Sampalok sampalokan
5
Tagulinaw
3
Tuhod manok
45
- 58 -
Agriculture (S6T2)
Intermediate
S6T2S5
3x3
Undergrowth
Species
Species
N
N
Bulak manok
25
Bulak manok
89
Cogon
15
Camote cordate
36
Camote lobed
22
Dilang baka
Kamoteng kahoy
1
10 /clump
Cogon
5
Makahiya
1
Crassucephaum
Panibat
3
Cupphea sp.
2
Uoko
2
Cyperus iria
1
Digitaria sp.
5
Gabi
4
Guava
1
Kaliskis dalag
3
Kudzu
1
Ligad ligad
6
Luya
Makahiya
Okra
Panibat
- 59 -
10
7
16
2
24
Sampalokan
1
Susoloyeli sp.
5
Tabang
3
Tagulinaw liitan
1
Takip kuhol
14
Upland rice
18
Agriculture (S7T1)
Intermediate
S7T1S3
3x3
Undergrowth
Species
N
Local names
N
Baka-baka
2
Baka baka
4
Bakhi
1
Bakhi
2
Coronitas
9
Bulak manok
66
Centrocema pubiscens
20
Golon/cogon
Hagonoi
68
4
Chistella dentata
Lokdo
13
Cogon
Runo
22
Cyperus iria
8
110
50
Suag kabayo
8
Dilang aso
5
Tambo
3
Galakpak
32
Uoko
8
Hakate
14
Higis manok
2
Kaitana
1
kandikandilaan
10
Kulapi
42
Panawal
73
Panibat
5
Pa-o
29
Paragis
2
Paspalum distichum
3
Pulat
8
Uoko
3
Walis-walisan
4
- 60 -
Agroforest (S1T2)
Intermediate
S1T2S2
3x3
Undergrowth
Species
Alam-am (fern)
Bakhi
Cogon
Dilang baka
N
Local name
10
2
150
6
N
Alam-am
9
Alinaw
1
Amuwag
9
Baka baka
12
Guava
1
Bakhi
2
Runo
6
Cogon
52
Galakgak
13
Panawal
Sida/ Kulat
25
1
Hakati
4
Kalawag
3
Kulapi
56
Palat
3
Panawal
26
Paol
1
Wild berry
2
- 61 -
Agroforest (S3T1)
Intermediate
S3T1S1
3x3
Undergrowth
Species
N
Local name
N
Acanthaceae
1
Arachis sp.
Alagau
6
Baluingia
31
2
Alam-am
9
Bogus
4
Avocado
1
Bulak manok
37
Ayusan
1
Busikad
10
Bagaluan
2
Carabao grass
35
Binunga
1
Christella dentata
6
Dama de noche
3
Compositae
3
Gnetum latifolium
1
Dilang aso
6
ground orchid
5
Dilang Baka
17
Kamiring
1
Fimbristylis
1
Katurog
6
Higis manok
2
Leei sp.
3
Hyptis
1
Marang
2
Kandilaan
2
Rattan
1
Kawad kawad
8
Rubus mollucanus
1
Kudzu
4
Salagong sibat
1
Kulitis
1
Spaglottis sp.
1
Ligad-ligad
2
Subiang
1
Lubi-lubi
2
Tiger grass
1
Mischanthus
1
Tuai
1
Mutha
5
Tulibos tilos
1
Pako
7
Wild Strawberry
2
Panawal
20
Zingiber sp.
1
Panibat
4
Rattan
2
Uoko
Zingiber
- 62 -
14
1
Agroforest (S4T2)
Intermediate
Local name
Achuete
Undergrowth
N
Local name
2
Bulak manok
Ayas-as
3
Busikad
Hauili
1
Carabao grass
Hyptis sp.
Kamote kahoy
13
Dilang butiki
N
14
1
130
6
2
Hithit
Kandikandilaan
3
Ipil-pil
Kullio kulliot
2
Kamra kamra
13
Kandikandilaan
14
57
Okra-okrahan
11
12
Synedrella nodiflora
2
Kulapi
Tambo
2
Landrina
Yautia
4
Lokdo
Makahiya
Panibat
Rice
3
3
4
12
4
139
Sampasampalukan
1
Sitsit
43
Uoko
10
- 63 -
Grassland (S2T3)
Intermediate
S2T3S2
3x3
Undergrowth
Species
N
Local name
N
Alam-am
9
Alam-am
7
Amorseko
60
Amorseko
1
Amuwag
9
Apgad
1
Bakhi
8
Bakhi
6
Buyot
1
Bigas bigasan
15
Cogon
25
Bulak manok
12
Dilang baka
17
Busikad
60
5
Buyot
35
20
Cogon
54
Giant bracken fern
Kulapi
Pakong alakdan
1
Cyperus iria
6
Panawal
2
Dilang baka
31
Paragis
14
Galakgak
28
Runo
17
Kamra kamra
9
Kawad kawad
19
Kilob
57
Kilob babae
7
Kollo kolliot
3
kulapi
19
Landrina
38
Leptocloa chinensis
2
Ligad-ligad
1
Lubi lubi
1
Lycopodium
7
Malatabako
1
Moss
112
Pal-ot
35
Pandan
7
Paspalidum flavidum
11
Paspalum distichum
11
Tabang
Takip kuhol
4
11
Themeda triandra
9
Wild strawberry
4
- 64 -
Grassland (S2T4)
Intermediate
S2T4S3
3x3
Undergrowth
Species
N
Localname
N
Bakhi
13
Ammowag
1
Cogon
8
Amorseko
37
Giant bracken fern
6
Apiit
Golon
5
Bagingay
28
Guava
2
Baka baka
22
Panawal
7
Bakhi
44
Baludgangan
22
Benguet pine
4
Bulak manok
17
Chrysopogon aciculatus
Cyperus iria
Elephantopus scaber
2
8
26
2
Galakgak
15
Golon
50
Kaibuan
91
Kaliskis ahas
62
Kamra-kamra
17
Kilob
41
Ligad-ligad
5
Lycopodium
21
Panawel
3
Panibat
3
Paspalidum distichum
34
Paspalum conjugatum
6
Takip kuhol
Themeda triandra
- 65 -
22
7
Grassland (S7T2)
Intermediate
Local names
Undergrowth
N
Local names
N
Baka baka
2
A-apid
5
Hagonoy
3
Anwad
44
Kandi-kandilaan
5
Bulak manok
55
Camote
16
Kulapi
10
Lantana
3
Christella dentata
21
Pulat
4
Cyperus iria
11
Tab-an
3
Dioscorea flabelleflora
1
Talahib
12
Gatas-gatas
2
Uoko
15
Gattodan
3
Hagonoy
1
Hakati
90
Higis manok
2
Kamra-kamra
3
Kulapi
17
Paspalum distichum
4
Patpati
10
Tab-an
50
Talong-talungan
1
Tambo
5
Uoko
39
Vernonia sp.
8
Wakal
6
- 66 -
Reforestation (S5T1)
Intermediate
S5T1S2
3x3
Undergrowth
Species
No. of individuals
Local name
No. of individuals
Avocado
1
Alikbangon
5
Binunga
1
Avocado
5
Dilang butiki
8
Baging
2
Ipil ipil
1
Bulak manok
Kakauate
1
Calopogium
Kollo kolliot
2
Carabao grass
Mahogany
9
Cyperus sp.
Papaya
1
Dayang
Sapinit
11
46
1
60
9
53
Dilang baka
1
Talingpunay
5
pako
1
Uoko
5
Euphorbia hirta
4
grass
1
Kulapi
12
Kullo kuliot
11
Mahogany
4
Makahiya
2
Malvaceae
1
Panibat
1
Paragis
1
Silver fern
1
Tuhod manok
8
Tutumpak
4
Uoko
- 67 -
14
Reforestation (S8T1)
Intermediate
S8T1S2
3x3
Undergrowth
Species
No. of individuals
Localname
No. of individuals
Amuwag
4
Akba grass
10
Buta buta
1
Bulak manok
21
Dilang baka
3
Carabao grass
Hagonoy
2
Mutha
Kahoy dalaga
1
Dilang baka
Kalulot
4
Kaliskis dulog
Bakhi
5
Kulapi
88
Wild strawberry
2
Kuliot
6
Lobi lobi
2
Makahiya
8
Myrtaceae
1
Padpad
2
Panawal
16
Sun flower
10
Tuhod manok
Uoko
- 68 -
106
9
43
200
7
13
Wedelia sp.
6
Wild strawberry
2
Reforestation (S8T3)
Intermediate
Undergrowth
S8T3S1
3x3
Species
Species
No. of individuals
No. of individuals
Balbas pusa
1
Asak
Hagonoy
1
Balbas pusa
15
8
Kape
2
Baludgangan
20
Kulliot
5
Christella dentata
27
Lubi lubi
6
Dilang baka
6
Panawal
22
Hagonoy
8
Pneumatopteris levis
5
Hyptis sp.
23
Uoko
2
Kape
12
Kulapi
66
Langkuas
1
Lokdo
7
Malvaceae (Gummamela)
5
Panawal
20
Paspalidum flavidum
22
Rubus sp.
2
Uoko
21
Uyot
3
Wild strawberry
6
Secondary Forest (S1T1)
Intermediate
Undergrowth
Local name
S1T1S3
Species
3x3
Alam-am
2
Cogon
12
Dilang baka/Baka baka
No. of individuals
2
Alam-am
3
Baka baka
2
30
Panawal
6
Guava
Tagulinau
1
Kaibuan
- 69 -
12
Ayusan
Kaibuan
No. of individuals
Cogon
33
1
95
kaliskis ahas
1
Kulapi
2
Pal-ot
2
Panawal
4
Pulat
1
Tagulinau
7
Tan-al
1
Secondary Forest (S2T1)
Intermediate
S2T1S5
Undergrowth
Species
No. of individuals
Local name
No. of individuals
Alam-am
1
Alam-am
9
Iilog
2
Baka Baka
8
Kahoy dalaga
1
Bakhi
2
Lemon tree
2
Blechnum
4
Syzidium sp.
2
Cogon
Wild Strawberry
2
Galakgak
63
1
Kilob
12
Panawal
50
Runo
9
Sabung-sabung
1
Wild Strawberry
2
Secondary Forest (S4T1)
Intermediate
S4T1S2
3x3
Undergrowth
Species
Local name
No. of individuals
Alambrillong gubat
1
Ayas-as
1
Binukaw
1
Baka baka
2
Guijo
4
Bayabas
1
Ligas
3
Cogon
18
Mayapis
2
Hauili
1
Mutha
1
Kandikandilaan
1
Palosapis
1
Kasupangil
1
Pangnan
2
Kubamba
1
White lauan
2
Kulapi
1
No. of individuals
- 70 -
Makahiya
10
Palosapis
1
Santol
1
Siver fern
1
Tutumbak
3
Uoko
1
Appendix 3: List of biomass monitoring plots
- 71 -
Appendix 3: KEF Monitoring plots per landuse
A.
Agriculture
1994
Block #
Biomass
(Kg/ha)
Plot #
2003
Biomass
(Mg/ha)
C (Mg/ha)
Biomass
(Kg/ha)
Biomass
(Mg/ha)
C (Mg/ha)
28
2
3078.40
12.31
5.54
1814.68
7.26
3.27
28
4
5497.46
21.99
9.90
9120.05
36.48
16.42
30
2
9388.91
37.56
16.90
13982.93
55.93
25.17
30
3
11129.47
44.52
20.03
15308.42
61.23
27.56
30
4
12172.38
48.69
21.91
17058.03
68.23
30.70
31
1
11429.49
45.72
20.57
14133.02
56.53
25.44
31
2
4460.36
17.84
8.03
5206.40
20.83
9.37
31
3
5710.47
22.84
10.28
9502.75
38.01
17.10
31
4
12639.93
50.56
22.75
18186.43
72.75
32.74
33
1
6069.49
24.28
10.93
7122.50
28.49
12.82
33
3
5456.07
21.82
9.82
8347.99
33.39
15.03
33
4
4662.13
18.65
8.39
7808.80
31.24
14.06
34
1
11013.75
44.05
19.82
15706.90
62.83
28.27
34
2
15035.80
60.14
27.06
20002.51
80.01
36.00
36
1
10294.75
41.18
18.53
15152.17
60.61
27.27
36
2
9899.49
39.60
17.82
15657.72
62.63
28.18
40
1
10965.71
43.86
19.74
14652.10
58.61
26.37
40
4
3209.10
12.84
5.78
5778.51
23.11
10.40
41
1
1699.00
6.80
3.06
3657.09
14.63
6.58
41
2
6133.84
24.54
11.04
8912.22
35.65
16.04
41
3
12957.73
51.83
23.32
19406.49
77.63
34.93
42
2
8291.43
33.17
14.92
15879.53
63.52
28.58
42
3
14015.91
56.06
25.23
20465.11
81.86
36.84
47
2
8960.75
35.84
16.13
12842.17
51.37
23.12
48
3
1228.06
4.91
2.21
3051.25
12.20
5.49
58
2
1922.93
7.69
3.46
3346.62
13.39
6.02
59
1
5926.25
23.71
10.67
6916.20
27.66
12.45
59
2
10033.32
40.13
18.06
13483.81
53.94
24.27
59
3
9458.03
37.83
17.02
13741.02
54.96
24.73
59
4
Average
7608.13
30.43
13.69
10303.55
41.21
18.55
8181.73
32.73
14.73
11887.32
47.55
21.40
- 72 -
C ‐ density (Mg/ha)
35.00
30.00
25.00
20.00
15.00
C (Mg/ha)
10.00
Linear (C (Mg/ha))
5.00
0.00
0
10
20
30
40
Plot number
Figure 1. Average C-densities in agriculture areas.
B. Rice field 1994
Block #
Biomass
(Kg/ha)
Plot #
Biomass
(Mg/ha)
2003
Biomass
(Kg/ha)
C (Mg/ha)
Biomass
(Mg/ha)
C (Mg/ha)
14
1
3345.261
13.38
6.02
5224.299
20.90
9.40
14
2
5576.314
22.31
10.04
7630.863
30.52
13.74
24
4
5747.104
22.99
10.34
7353.663
29.41
13.24
26
1
4970.109
19.88
8.95
6658.986
26.64
11.99
45
3
1789.096
7.16
3.22
2788.914
11.16
5.02
4285.58
17.14
7.71
5931.35
23.73
10.68
Average
14.00
C densities (Mg/ha)
12.00
10.00
8.00
6.00
C (Mg/ha)
4.00
Linear (C (Mg/ha))
2.00
0.00
0
2
4
6
Plot number
Figure 2. Average C-densities in rice fiels areas.
- 73 -
C. Forest
1994
Block #
Biomass
(Kg/ha)
Plot #
Biomass
(Mg/ha)
2003
C (Mg/ha)
Biomass
(Kg/ha)
Biomass
(Mg/ha)
C (Mg/ha)
1
2
11763.25
47.05
21.17386
14178.56
56.71
25.52142
1
3
16709.57
66.84
30.07723
20946.36
83.79
37.70344
1
4
2080.026
8.32
3.744048
2992.232
11.97
5.386017
4
1
674.2393
2.70
1.213631
1384.778
5.54
2.492601
4
2
1685.045
6.74
3.033081
2713.238
10.85
4.883829
4
4
1263.151
5.05
2.273673
2406.447
9.63
4.331604
20
1
3738.924
14.96
6.730064
5847.02
23.39
10.52464
20
2
3917.678
15.67
7.05182
6722.766
26.89
12.10098
24
1
4093.377
16.37
7.368079
5994.237
23.98
10.78963
24
2
4241.263
16.97
7.634274
6691.106
26.76
12.04399
24
3
5887.197
23.55
10.59695
6975.474
27.90
12.55585
52
1
4245.595
16.98
7.642072
5834.626
23.34
10.50233
52
2
4396.309
17.59
7.913356
5948.26
23.79
10.70687
52
3
10734.26
42.94
19.32166
15789.91
63.16
28.42184
56
1
552.0802
2.21
0.993744
1004.158
4.02
1.807484
56
2
6233.833
24.94
11.2209
8597.786
34.39
15.47601
3
6031.132
24.12
10.85604
7747.869
30.99
13.94616
5190.996
20.76
9.343793
7163.225
28.65
12.89381
56
Average
40
C density (Mg/ha)
35
30
25
20
C (Mg/ha)
15
Linear (C (Mg/ha))
10
5
0
0
5
10
15
20
Plot number
Figure 3. Average C-density in forest areas.
- 74 -
D. Old Pine
1994
Block #
Biomass
(Kg/ha)
Plot #
Biomass
(Mg/ha)
2003
C (Mg/ha)
Biomass
(Kg/ha)
Biomass
(Mg/ha)
C (Mg/ha)
15
1
3906.49
15.63
7.63
4580.40
18.32
8.94
28
3
2692.93
10.77
5.26
8116.41
32.47
15.84
29
3
8343.79
33.38
16.29
11253.64
45.01
21.97
34
3
8537.13
34.15
16.66
12404.76
49.62
24.21
34
4
7748.78
31.00
15.13
11602.79
46.41
22.65
35
1
6775.14
27.10
13.23
9062.74
36.25
17.69
35
2
12660.14
50.64
24.71
17332.27
69.33
33.83
37
1
14150.60
56.60
27.62
20214.78
80.86
39.46
39
3
10118.99
40.48
19.75
14759.74
59.04
28.81
39
4
10220.22
40.88
19.95
15065.34
60.26
29.41
40
2
9028.43
36.11
17.62
11047.04
44.19
21.56
40
3
3994.58
15.98
7.80
7953.81
31.82
15.53
48
2
1191.48
4.77
2.33
3068.35
12.27
5.99
55
1
594.87
2.38
1.16
1088.47
4.35
2.12
55
4
4594.43
18.38
8.97
6230.41
24.92
12.16
57
1
1882.36
7.53
3.67
3219.83
12.88
6.29
57
3
6257.61
25.03
12.21
10500.25
42.00
20.50
58
1
12141.11
48.56
23.70
15888.20
63.55
31.01
62
1
8139.27
32.56
15.89
9999.37
40.00
19.52
6998.86
28.00
13.66
10178.35
40.71
19.87
Average
40.00
C density (Mg/ha)
35.00
30.00
25.00
20.00
C (Mg/ha)
15.00
Linear (C (Mg/ha))
10.00
5.00
0.00
0
5
10
15
20
Plot no.
Figure 4. Average C-density in old pine areas.
- 75 -
E. Pine dominated
1994
Block #
Plot #
14
3
Biomass
(Kg/ha)
2003
Biomass
(Mg/ha)
C (Mg/ha)
4147.16
16.59
Biomass
(Kg/ha)
8.10
6311.48
Biomass
(Mg/ha)
C (Mg/ha)
25.25
12.32
15
2
3465.97
13.86
6.77
5520.39
22.08
10.78
15
3
2054.86
8.22
4.01
3367.21
13.47
6.57
15
4
1579.28
6.32
3.08
2775.82
11.10
5.42
26
2
4857.36
19.43
9.48
6519.10
26.08
12.73
26
3
4864.56
19.46
9.50
6519.10
26.08
12.73
28
1
2335.94
9.34
4.56
4430.88
17.72
8.65
29
1
8317.63
33.27
16.24
10976.87
43.91
21.43
29
2
10026.75
40.11
19.57
12742.78
50.97
24.87
30
1
10519.46
42.08
20.53
14651.07
58.60
28.60
35
3
16977.68
67.91
33.14
22044.44
88.18
43.03
35
4
9691.50
38.77
18.92
5755.52
23.02
11.23
39
1
17695.88
70.78
34.54
26378.33
105.51
51.49
39
2
16676.13
66.70
32.55
25499.69
102.00
49.78
45
2
5559.16
22.24
10.85
10183.62
40.73
19.88
47
1
11596.02
46.38
22.64
15792.35
63.17
30.83
48
1
953.22
3.81
1.86
748.76
3.00
1.46
55
3
2722.74
10.89
5.31
4663.60
18.65
9.10
57
2
3767.62
15.07
7.35
5844.26
23.38
11.41
58
3
1922.93
7.69
3.75
3346.62
13.39
6.53
60
1
11353.82
45.42
22.16
14813.59
59.25
28.92
60
2
15251.12
61.00
29.77
18125.65
72.50
35.38
62
2
8181.58
32.73
15.97
11474.69
45.90
22.40
7587.75
30.35
14.81
10368.95
41.48
20.24
Average
C densities (Mg/ha)
50.00
45.00
40.00
35.00
30.00
25.00
20.00
15.00
10.00
5.00
0.00
C (Mg/ha)
Linear (C (Mg/ha))
0
5
10
15
20
25
Plot No.
Figure 5. Average C-density in pine areas.
- 76 -
F. Secondary Forest
1994
Block #
Biomass
(Kg/ha)
Plot #
2003
Biomass
(Mg/ha)
Biomass
(Kg/ha)
C (Mg/ha)
Biomass
(Mg/ha)
C (Mg/ha)
11
1
13875
55.50
24.97
16498.87
66.00
29.70
11
2
18330.01
73.32
32.99
23784.55
95.14
42.81
11
3
7928.554
31.71
14.27
11721.84
46.89
21.10
11
4
8704.358
34.82
15.67
15829.79
63.32
28.49
16
1
6674.291
26.70
12.01
9590.779
38.36
17.26
16
2
9276.825
37.11
16.70
12599.29
50.40
22.68
16
3
5680.229
22.72
10.22
8351.617
33.41
15.03
9319.37
37.28
16.77
15044.32
60.18
27.08
9973.58
39.89
17.95244
14177.63
56.71
25.51974
20
Average
40.00
C density (Mg/ha)
35.00
30.00
25.00
20.00
C (Mg/ha)
15.00
Linear (C (Mg/ha))
10.00
5.00
0.00
0
2
4
6
8
10
Plot number
Figure 6. Average C-density in secondary forest.
- 77 -
Appendix 4: Rules and Regulations
- 78 -
Appendix Rules and regulations
Natural resources development program and
agro-forestry rules and regulations
I. SWIDDEN FARMING PERMIT
A. Any person who wants to prepare a new farm clearing (uma) must get a permit from the
Agro-forestry Office. A fee of five (5.00) pesos shall be collected for the permit.
B. Only residents of the Kalahan Reserve shall be granted a swidden farming permit.
C. Any person who wants to cultivate land outside his/her own claim must obtain a written
permission from the claimant. This practice shall be discouraged.
D. Whenever a newly cleared area is to be burned, the owner must maintain a fireline with a
width of 10 meters. This should be inspected first by a forest guard before the clearing is
burned. Violation of this regulation shall be penalized for causing forest fires.
E. Clearing in reserved areas, parks, watersheds, sanctuaries, research sites shall not be
allowed.
F. Forest guards neglecting their duties with regards to these policies shall be subjected to
administrative sanctions.
G. Penalties
1. Anybody clearing or extending clearings in restricted areas shall be fined PhP500 and
will be required to repair the damage or shoulder the equivalent cost of said repair.
2. Anybody clearing without a permit shall be fined 250 pesos. Clearing any area other
than the inspected site is considered clearing without a permit.
II. TREE CUTTING PERMIT
A. Any person who wants to cut any tree must first get a permit from the Agro-forestry
office.
B. The permit shall identify the tree to be cut and the time frame within which the tree
should be cut and removed from the forest.
C. A “minute” of the lumber needed shall be required from the applicants. This must be
approved by the Barangay Captain of the area where the tree is to be used.
D. Tree cutting permits shall only be issued upon approval of the Agro-Forestry office and
upon payment of the corresponding permit fee as to the following purposes:
E. Profit sharing from the permit fees to be collected shall be implemented based on a 4060% scheme between the barangay and the KEF respectively.
F. No tree shall be cut without the proper mark of the Forester responsible for the Forest
Improvement Technology (FIT) activities under the Natural Resources Development
Program. No permit shall be issued to cut any tree not so marked. This includes salvage
trees or sanitation cutting. The mark will indicate the direction to fell. The foresters shall
avoid issuing permits to be implemented during the rainy season when forest damage
may be severe.
G. Penalties
1. First offense: any person violating these regulations shall be fined 400 pesos fore
every tree cut. Any lumber, slab or other products obtained will be confiscated.
- 79 -
2. Second offense: Violators shall be fined 400 pesos and shall be denied a cutting
permit in the future. Any lumber, slab, or other products obtained shall be
confiscated.
III. CHAINSAW REGISTRATION AND OPERATIONS
A. All chainsaws operating within the Kalahan Reserve must be registered annually with the
Agro-forestry Office. A copy of the registration will be furnished to the CENRO. A
charge of 200 pesos registration will be paid by the owner/operator per year.
B. A maximum of 14 chainsaws shall only be allowed to operate within the Kalahan
Reserve. Replacements or new chainsaws shall not be allowed.
C. The entry or operations of unregistered chainsaws in the Kalahan Reserve is absolutely
forbidden.
D. A forest charge will be collected from the chainsaw owners/operators equivalent to 15%
of the lumber price generated purposely for forest improvement.
E. No lumber shall be brought outside the Kalahan Reserve. Accepting orders, selling, or
donating lumbers to any person, group, or institution outside the Reserve is prohibited.
F. PENALTIES: Any person found violating any of these regulations will be fined as
follows:
1. First offense: Any person who accepts lumber orders to donate or sell to persons
outside of the Kalahan Reserve will be fined 500 pesos.
2. Second offense: Permanent cancellation of chainsaw registration.
3. Any chainsaw owner or operator who fails to pay the proper forest charges within 90
days shall be suspended from the operation of his chainsaw until his obligation is
paid in full.
4. Operations of unregistered chainsaws shall be fined 500 pesos and an additional fine
of 400 pesos for every tree cut.
5. Failure to renew chainsaw registration in 2 months after the expiration of its
registration shall be a ground for cancellation of the permit to operate.
IV. FISHING
A. Residents of the Kalahan Reserve are free to do fishing by traditional means but
chemicals and electricity shall not be allowed under any circumstances. Non-residents are
strictly forbidden to fish within the Kalahan Reserve.
B. Penalties: Violators of this policy shall be fined as follows:
1. Use of illegal fishing methods will be fined 400 pesos per violator and all fishing
supplies and/or equipment will be confiscated.
2. Non-residents who fish within the Kalahan Reserve shall be charged with illegal
entry in addition to being punished for illegal fishing.
C. Use of “natural tuba” in halap may be allowed provided that the waterflow be returned
immediately after fishing.
V. FOREST FIRES
A. Limited prescribed burning in grazing lands may be allowed provided that the interested
party obtains a permit describing the specific area to be burned and the date and time of
- 80 -
burning. Only a forester shall be allowed to issue this permit. A charge of five pesos will
be paid.
Any fire which occurs which is not covered by a swidden permit or grazing land burning
permit shall be considered as a forest fire.
B. Penalties
1. Any person who causes a forest fire shall pay the proper remuneration for all persons
involved in putting out the fire.
2. The guilty party must pay or repair all damages to houses, fruit trees, forest trees, etc.
3. The guilty party must reforest the burned area.
4. The guilty party must pay a fine of 500 pesos.
VI. QUARRYING
A. Quarrying in the riverbeds shall be supervised by the Barangay concerned in cooperation
with the Agro-Forestry Office.
B. Clearing stone from the road shall not be considered quarrying.
VII. ILLEGAL ENTRY
A. Persons who are not bonafide residents of the Kalahan Reserve are not entitled to harvest
or utilize the natural resources within the Kalahan Reserve.
B. Penalties: Any person violating this regulation shall be fined a minimum of 500 pesos or
a maximum of 5,000 pesos and any and all harvested forest products shall be confiscated.
Said violation may also be reported to the DENR or PNP with a request that violators be
prosecuted according to law.
VIII.
SANCTUARIES AND WATERSHEDS
A. The KEF has designated two Watershed-Sanctuaries within the Kalahan Reserve. All
plant and animal resources found therein are under protection. Hunting, catching animals
and harvesting plants are prohibited. Gathering of limited samples for research purposes
may be permitted subject to permission from the KEF and Barangay authorities.
B. Barangays are encouraged to identify additional watersheds within their jurisdiction. FIT
may be practiced inside unless the watershed is also declared to be a sanctuary.
C. Penalties
1. Violations of this regulation shall be punished with a fine of at least 1,000 pesos but
not more than 10,000 pesos depending on the severity of the violation. Any and all
products or resources obtained by the violator shall be confiscated.
2. Attempts to violate this regulation shall be considered as consummated violations.
IX. HUNTING
A. Seasonal hunting is allowed outside the sanctuaries during the following periods:
Animals: July to August
Birds: November to December
- 81 -
The night bird catching “Akik” has not regulation provided to cover this issue. A larger
body should reconsider this to resolve issues.
B. Penalties
Any person found violating this regulation shall be fined 500 pesos plus confiscation of
harvest and hunting equipment.
X. LAND CLAIMS
A. Each bonafide resident family may claim a maximum of ten (10) hectares of private land
within the Kalahan Reserve. Each claimant must make and implement a land use plan of
which 25% shall be dedicated to environmental protection and register the same with the
Agro-forestry Office. Each claimant shall be issued a copy his/her claim.
B. Any claimant who does not begin implementation of his/her land use plan within a period
of five (5) years from its registration may have his/her claim reduced in size.
C. Sale, mortgage or transfer of possession of any land claim to other bonafide residents of
the Kalahan Reserve shall require the approval of the Board of Trustees (BOT) through
the NRDP Agro-forestry Office which shall maintain an up-to-date record of all such
claims.
D. Sale, mortgage or transfer of possession of any land claim to any person who is not a
bonafide resident of th4e Kalahan reserve shall not be allowed and the KEF will not
recognize such transactions.
E. All surveys, including relocation and subdivision, shall be done by the Agro-forestry
Office of the KEF. The Agro-forestry office shall charge the amount of 800 pesos for the
first day and 600 pesos for each succeeding day needed for the resurvey to cover costs of
labor in the field, equipment, transportation, materials and registration. Disputes over
boundaries must be discussed first among the concerned claimants and referred to the
Tribal Elders and Barangay officials. Failure of the accomplishment of the survey due to
unclarified boundary disputes shall be charged against the claimant requesting resurvey.
XI. MISCELLANEOUS POLICIES
A. Tree planting: All barangays covered by the Kalahan Reserve are encouraged to initiate
and actively participate to the regular tree planting activities in their respective
barangays.
B. Banned Species: Cutting and or gathering of the banned or endangered plant or animal
species inside the Kalahan Reserve is strictly prohibited.
C. Certification of lumber origin: A Certification of Lumber Origin may be issued by the
Agro-Forestry Office to individuals who wish to move lumber from a house within the
Reserve to some location outside of the Reserve provided that the lumber are originally
sourced from within the Kalahan Reserve with proper permits.
D. Ban of chemical pesticides: In Keeping with the KEF policy of environmental
cooperation in all undertakings that involve the natural resources, no chemical pesticides
be used within the Reserve. It was understood that use of these will have adverse effects
on the soil, biodiversity, and human health.
- 82 -
The effects of thrown pesticides in the river, the guilty party is obliged to pay the damage
on lives and properties.
E. Collection of fines and fees: All fines must be collected within three (3) months from the
date they were promulgated. Fines not paid within three months shall be charged an
interest of 3% per month. For the share of the barangays from all fines and fees, it shall
be given every 12th month of the year.
F. Disposition of fines: Fines shall be shared by KEF and the Barangay concerned. The 75%
shall go to the apprehending party and 25% shall be given to the other party. When an
individual apprehends the violator, he/she shall receive 50% of the fine and the KEF and
the concerned barangay shall be entitled to 25% each
G. Other actions: Violations may be referred to higher authorities for action if violators fail
to comply.
H. Lumber price: P6.00 per board foot
I. Orchid gathering moratorium: Moratorium on gathering orchids in all parts of the
Reserve shall be imposed on January 1, 2002. Training on orchid production shall also be
conducted.
J. Effectivity: February 1, 2001.
Approved this day of December 5, 2000 at Imugan, Sta. Fe, Nueva Vizcaya.
- 83 -
Appendix 5: Forest Improvement Technology (FIT)
- 84 -
Appendix 5: Forest Improvement Technology (FIT) Source: Rice (2000)
The goal of FIT is to improve the forest, rather than simply improve the short-term income of the forest
farmer. In the long run this will lead to more sustainable increases in income. Trees are cut continuously
in small amounts rather than all together every thirty years. In this way the forest ecosystem can be
maintained.
Each year the forest farmer makes a selection of trees to be cut. He checks the forest for crooked,
damaged or crowded trees that need to be removed to improve the forest. When these have been removed,
they are sawn into lumber. It may not be first-class wood but it can be used or sold. Simple equipment is
used and the sawdust, tops and branches are left to rot because they restore fertility to the forest soil and
help maintain biodiversity. The forest farmer does not separate the potential crop trees from the other
trees because he knows that all trees have a role to play in the forest.
In natural forests there is a continuous process of rejuvenation. Trees die or are felled by storms. In this
way the canopy is opened and, because the microclimate is not damaged, young seedlings get a chance to
develop. FIT follows this natural process. Mature trees that have stopped growing are removed to create
favourable conditions for forest rejuvenation. If this is done every year, the forest will continue to develop
and improve. The removal of individual trees does not hurt the forest or its environment and provides first
class lumber. If there are large open spaces, a forest pioneer species will be planted first. Agricultural
crops are not planted between the trees because they would bother the other plants that need to grow to
make a good forest. The population of one or two species of large or small plants can be increased by
enrichment planting. This can be very favourable as long as the forest is not turned into a plantation.
As the forest grows, biodiversity will continue to improve and many species of insects, small animals,
grasses and other plants will move in. This is good because all of these species help each other and the
improved biodiversity will encourage the forest to grow faster and become healthier. The forest farmer
will only cut a small amount of growth allowing the forest to improve each year.
The growth-rate presently expected in Philippine forests is about 4.5 cubic meters per hectare per year.
Under proper management, using FIT, the forest can produce as much as 15 - 20 cubic meters per hectare
per year. Such an analogue forest still retains the characteristics of a natural forest. It is not a plantation. It
still has high bio-diversity and is an effective watershed with a high percolation rate. It will also provide a
sanctuary for many kinds of wild orchids, animals, birds and insects.
If each forest farmer cares for 5 hectares of good forest, he may harvest up to 80 cubic meters of first
class lumber every year without damaging the forest. That would provide him with higher cash income
than many professionals and he would still have plenty of time to produce his own food on the farm.
Once the forest has developed, it can be sustained indefinitely.
- 85 -
Appendix 6: FALLOW Model results on biodiversity, carbon stocks and sediment filtering capacity - 86 -
Appendixx 6: Predictedd time-averageed relative addditionality on eecosystem serrvices and com
mmunity welffare in
Kalahan 2001-2030 peerdio under soome scenarios (in %).
- 87 -
WORKING PAPERS IN THIS SERIES
2005
1.
Agroforestry in the drylands of eastern Africa: a call to action
2.
Biodiversity conservation through agroforestry: managing tree species diversity within a
network of community-based, nongovernmental, governmental and research organizations in
western Kenya.
3.
Invasion of prosopis juliflora and local livelihoods: Case study from the Lake Baringo area of
Kenya
4.
Leadership for change in farmers organizations: Training report: Ridar Hotel, Kampala, 29th
March to 2nd April 2005.
5.
Domestication des espèces agroforestières au Sahel : situation actuelle et perspectives
6.
Relevé des données de biodiversité ligneuse: Manuel du projet biodiversité des parcs
agroforestiers au Sahel
7.
Improved land management in the Lake Victoria Basin: TransVic Project’s draft report.
8.
Livelihood capital, strategies and outcomes in the Taita hills of Kenya
9.
Les espèces ligneuses et leurs usages: Les préférences des paysans dans le Cercle de
Ségou, au Mali
10.
La biodiversité des espèces ligneuses: Diversité arborée et unités de gestion du terroir dans le
Cercle de Ségou, au Mali
2006
11.
Bird diversity and land use on the slopes of Mt. Kilimanjaro and the adjacent plains, Tanzania
12.
Water, women and local social organization in the Western Kenya Highlands
13.
Highlights of ongoing research of the World Agroforestry Centre in Indonesia
14.
Prospects of adoption of tree-based systems in a rural landscape and its likely impacts on
carbon stocks and farmers’ welfare: The FALLOW Model Application in Muara Sungkai,
Lampung, Sumatra, in a ‘Clean Development Mechanism’ context
15.
Equipping integrated natural resource managers for healthy Agroforestry landscapes.
17.
Agro-biodiversity and CGIAR tree and forest science: approaches and examples from
Sumatra.
18.
Improving land management in eastern and southern Africa: A review of policies.
19.
Farm and household economic study of Kecamatan Nanggung, Kabupaten Bogor, Indonesia:
A socio-economic base line study of Agroforestry innovations and livelihood enhancement.
20.
Lessons from eastern Africa’s unsustainable charcoal business.
21.
Evolution of RELMA’s approaches to land management: Lessons from two decades of
research and development in eastern and southern Africa
22.
Participatory watershed management: Lessons from RELMA’s work with farmers in eastern
Africa.
23.
Strengthening farmers’ organizations: The experience of RELMA and ULAMP.
24.
Promoting rainwater harvesting in eastern and southern Africa.
25.
The role of livestock in integrated land management.
26.
Status of carbon sequestration projects in Africa: Potential benefits and challenges to scaling
up. Social and Environmental Trade-Offs in Tree Species Selection: A Methodology for
Identifying Niche Incompatibilities in Agroforestry [Appears as AHI Working Paper no. 9]
28.
Managing tradeoffs in agroforestry: From conflict to collaboration in natural resource
management. [Appears as AHI Working Paper no. 10]
29.
Essai d'analyse de la prise en compte des systemes agroforestiers pa les legislations
forestieres au Sahel: Cas du Burkina Faso, du Mali, du Niger et du Senegal.
30.
Etat de la recherche agroforestière au Rwanda etude bibliographique, période 1987-2003
2007
31.
Science and technological innovations for improving soil fertility and management in Africa: A
report for NEPAD’s Science and Technology Forum.
32.
Compensation and rewards for environmental services.
33.
Latin American regional workshop report compensation.
34.
Asia regional workshop on compensation ecosystem services.
35.
Report of African regional workshop on compensation ecosystem services.
36.
Exploring the inter-linkages among and between compensation and rewards for ecosystem
services CRES and human well-being
37.
Criteria and indicators for environmental service compensation and reward mechanisms:
realistic, voluntary, conditional and pro-poor
38.
The conditions for effective mechanisms of compensation and rewards for environmental
services.
39.
Organization and governance for fostering Pro-Poor Compensation for Environmental
Services.
40.
How important are different types of compensation and reward mechanisms shaping poverty
and ecosystem services across Africa, Asia & Latin America over the Next two decades?
41.
Risk mitigation in contract farming: The case of poultry, cotton, woodfuel and cereals in East
Africa.
42.
The RELMA savings and credit experiences: Sowing the seed of sustainability
43.
Yatich J., Policy and institutional context for NRM in Kenya: Challenges and opportunities for
Landcare.
44.
Nina-Nina Adoung Nasional di So! Field test of rapid land tenure assessment (RATA) in the
Batang Toru Watershed, North Sumatera.
45.
Is Hutan Tanaman Rakyat a new paradigm in community based tree planting in Indonesia?
46.
Socio-Economic aspects of brackish water aquaculture (Tambak) production in Nanggroe
Aceh Darrusalam.
47.
Farmer livelihoods in the humid forest and moist savannah zones of Cameroon.
48.
Domestication, genre et vulnérabilité : Participation des femmes, des Jeunes et des catégories
les plus pauvres à la domestication des arbres agroforestiers au Cameroun.
49.
Land tenure and management in the districts around Mt Elgon: An assessment presented to
the Mt Elgon ecosystem conservation programme.
50.
The production and marketing of leaf meal from fodder shrubs in Tanga, Tanzania: A pro-poor
enterprise for improving livestock productivity.
51.
Buyers Perspective on Environmental Services (ES) and Commoditization as an approach to
liberate ES markets in the Philippines.
52.
Towards Towards community-driven conservation in southwest China: Reconciling state and
local perceptions.
53.
Biofuels in China: An Analysis of the Opportunities and Challenges of Jatropha curcas in
Southwest China.
54.
Jatropha curcas biodiesel production in Kenya: Economics and potential value chain
development for smallholder farmers
55.
Livelihoods and Forest Resources in Aceh and Nias for a Sustainable Forest Resource
Management and Economic Progress
56.
Agroforestry on the interface of Orangutan Conservation and Sustainable Livelihoods in
Batang Toru, North Sumatra.
57.
Assessing Hydrological Situation of Kapuas Hulu Basin, Kapuas Hulu Regency, West
Kalimantan.
58.
Assessing the Hydrological Situation of Talau Watershed, Belu Regency, East Nusa Tenggara.
59.
Kajian Kondisi Hidrologis DAS Talau, Kabupaten Belu, Nusa Tenggara Timur.
60.
Kajian Kondisi Hidrologis DAS Kapuas Hulu, Kabupaten Kapuas Hulu, Kalimantan Barat.
61.
Lessons learned from community capacity building activities to support agroforest as
sustainable economic alternatives in Batang Toru orang utan habitat conservation program
(Martini, Endri et al.)
62.
Mainstreaming Climate Change in the Philippines.
63.
A Conjoint Analysis of Farmer Preferences for Community Forestry Contracts in the Sumber
Jaya Watershed, Indonesia.
64.
The highlands: a shared water tower in a changing climate and changing Asia
65.
Eco-Certification: Can It Deliver Conservation and Development in the Tropics.
66.
Designing ecological and biodiversity sampling strategies. Towards mainstreaming climate
change in grassland management.
67.
Towards mainstreaming climate change in grassland management policies and practices on
the Tibetan Plateau
68.
An Assessment of the Potential for Carbon Finance in Rangelands
69.
ECA Trade-offs Among Ecosystem Services in the Lake Victoria Basin.
69.
The last remnants of mega biodiversity in West Java and Banten: an in-depth exploration of
RaTA (Rapid Land Tenure Assessment) in Mount Halimun-Salak National Park Indonesia
70.
Le business plan d’une petite entreprise rurale de production et de commercialisation des
plants des arbres locaux. Cas de quatre pépinières rurales au Cameroun.
71.
Les unités de transformation des produits forestiers non ligneux alimentaires au Cameroun.
Diagnostic technique et stratégie de développement Honoré Tabuna et Ingratia Kayitavu.
72.
Les exportateurs camerounais de safou (Dacryodes edulis) sur le marché sous régional et
international. Profil, fonctionnement et stratégies de développement.
73.
Impact of the Southeast Asian Network for Agroforestry Education (SEANAFE) on agroforestry
education capacity.
74.
Setting landscape conservation targets and promoting them through compatible land use in the
Philippines.
75.
Review of methods for researching multistrata systems.
76.
Study on economical viability of Jatropha curcas L. plantations in Northern Tanzania assessing
farmers’ prospects via cost-benefit analysis
77.
Cooperation in Agroforestry between Ministry of Forestry of Indonesia and International Center
for Research in Agroforestry
78.
"China's bioenergy future. an analysis through the Lens if Yunnan Province
79.
Land tenure and agricultural productivity in Africa: A comparative analysis of the economics
literature and recent policy strategies and reforms Boundary organizations, objects and agents:
linking knowledge with action in Agroforestry watersheds
81.
Reducing emissions from deforestation and forest degradation (REDD) in Indonesia: options
and challenges for fair and efficient payment distribution mechanisms
2009
82.
Mainstreaming climate change into agricultural education: challenges and perspectives
83.
Challenging conventional mindsets and disconnects in conservation: the emerging role of ecoagriculture in Kenya’s landscape mosaics
84.
Lesson learned RATA garut dan bengkunat: suatu upaya membedah kebijakan pelepasan
kawasan hutan dan redistribusi tanah bekas kawasan hutan
85.
The emergence of forest land redistribution in Indonesia
86.
Commercial opportunities for fruit in Malawi
87.
Status of fruit production processing and marketing in Malawi 88. Fraud in tree science
89.
Trees on farm: analysis of global extent and geographical patterns of agroforestry
90.
The springs of Nyando: water, social organization and livelihoods in Western Kenya
91.
Building capacity toward region-wide curriculum and teaching materials development in
agroforestry education in Southeast Asia
92.
Overview of biomass energy technology in rural Yunnan (Chinese – English abstract)
93.
A pro-growth pathway for reducing net GHG emissions in China
94.
Analysis of local livelihoods from past to present in the central Kalimantan Ex-Mega Rice
Project area
95.
Constraints and options to enhancing production of high quality feeds in dairy production in
Kenya, Uganda and Rwanda
2010
96.
Agroforestry education in the Philippines: status report from the Southeast Asian Network for
Agroforestry Education (SEANAFE)
97.
Economic viability of Jatropha curcas L. plantations in Northern Tanzania- assessing farmers’
prospects via cost-benefit analysis.
98.
Hot spot of emission and confusion: land tenure insecurity, contested policies and competing
claims in the central Kalimantan Ex-Mega Rice Project area
99.
Agroforestry competences and human resources needs in the Philippines 100. CES/COS/CIS
paradigms for compensation and rewards to enhance environmental Services
101. Case study approach to region-wide curriculum and teaching materials development in
agroforestry education in Southeast Asia
102. Stewardship agreement to reduce emissions from deforestation and degradation (REDD):
Lubuk Beringin’s Hutan Desa as the first village forest in Indonesia
103. Landscape dynamics over time and space from ecological perspective
104. A performance-based reward for environmental services: an action research case of
“RiverCare” in Way Besai sub-watersheds, Lampung, Indonesia
105. Smallholder voluntary carbon scheme: an experience from Nagari Paningahan, West Sumatra,
Indonesia