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J. Bio. Env. Sci.
2016
164 | Janiola and Marin
RESEARCH PAPER OPEN ACCESS
Carbon Sequestration Potential of Fruit Tree Plantations in
Southern Philippines
Mark Daryl C. Janiola, Rico A. Marin
Department of Forest Resources Management-College of Forestry and Environmental Science,
Central Mindanao University, Musuan, Bukidnon, Philippines
Article published on May 27, 2016
Key words: Carbon sequestration, Fruit plantation, Mangifera indica, Nephelium lappaceum, Sandoricum
koetjape
Abstract
Global warming is recently considered a major concern worldwide due to massive emissions of greenhouse gases
to the atmosphere. Trees are seen as one of the mitigating measures of this problem due to its role in carbon
sequestration. This study is aimed to assess the carbon sequestration potentials of 15-year-old Mango (Mangifera
indica Linn.), 12-year-old Rambutan (Nephelium lappaceum L.) and the 32-year-old Santol (Sandoricum
koetjape Merr.) in Bukidnon. Potential carbon sequestered was determined in various carbon pools (trees,
understorey, litters and soil) of the three different fruit crop plantations. Field measurements and laboratory
analysis were used to measure biomass density and carbon stocks of the samples. Results revealed that among the
three plantations, the 32-year-old santol plantation had the highest value of total carbon stored with 203.62
ton/ha. This was followed by the 15-year-old mango plantation with 122.34 ton/ha. The 12-year-old rambutan
plantation had only 112.18 ton/ha carbon storage. In terms of carbon pools, the soil had the highest carbon stocks
in all plantation at 113.21 ton/ha, 96.76 ton/ha, 67.56 ton/ha for santol, rambutan and mango, respectively. The
carbon stocks for the trees were next highest with 86.02 ton/ha (Santol), 52.46 ton/ha (mango) and 13.13 ton/ha
(rambutan). The least among the carbon pools is the understory with 0.5 ton/ha, 0.7 ton/ha and 0.36 ton/ha for
rambutan, mango and santol plantations, respectively. Findings of this study suggest that fruit tree crops are
potential carbon sink and must be promoted as a land-use practice to help mitigate climate change.
*Corresponding Author: Rico A. Marin ricomarin@yahoo.com
Journal of Biodiversity and Environmental Sciences (JBES)
ISSN: 2220-6663 (Print) 2222-3045 (Online)
Vol. 8, No. 5, p. 164-174, 2016
http://www.innspub.net
J. Bio. Env. Sci.
2016
165 | Janiola and Marin
Introduction
Forests are crucial to the well-being of humanity.
Furthermore, it provides foundation for life on earth,
through ecological function and furnished a wide
range of essential goods and services (Carandang,
2005). Today, world’s forests are under pressure due
to the modernization of life and increasing human
population. According to Carandang (2005),
conversion and degradation of forests are forms of
forest destruction.
Climate change and global warming are the
associated effect due to the destruction of world’s
forest. Another form of forest destruction, which is
also a contributor to the increase of atmospheric
carbon is land use change of forest. Increasing
agricultural productivity is a primodial concern in
many developing countries like the Philippines and it
is a driver of change for land use purposes in the
forest (Sace, 2002).
The deteriorating global environment and destruction
of forest around the world had generated concern
among nations, governments, and international
organization (AFPSOS, 2009). Carbon has been
associated with evolving discussion of climate change
and global warming (Bowyer et al., 2012). On the
positive note, however, tropical forest had the largest
potential to mitigate climate change and global
warming through conservation of existing carbon
pools (Lasco and Pulhin, 2009).
Today, a lot of researches have been conducted
regarding carbon stock assessment. Most of these
researches were conducted within natural forest and
agroforestry farms. According to van Noordwijk
(2002), a more refined Carbon accounting system is
clearly needed to clarify changes in the terrestrial
carbon storage and to understand the present carbon
situation in various land cover types to include
grassland, agricultural land and fruit tree crop
plantation. The interest of this study is to determine
the carbon stock of fruit crop plantation, which is
believed to have very limited information on carbon
stock at present.
Materials and methods
Location of the study
A study was conducted in the fruit tree plantation
project of Bukidnon, Philippines. The three fruit
plantations were the 15-year-old Mango (Mangifera
indica Linn.) Plantation, 12-year-old Rambutan
(Nephelium lappaceum L.) Plantation and 32-year-
old Santol (Sandoricum koetjape Merr.) Plantation.
Fig. 1. Location map of fruit crop plantation production.
J. Bio. Env. Sci.
2016
166 | Janiola and Marin
The area was located at the center of the province of
Bukidnon which belonged to the third climatic type of
the Philippines, having no very pronounced season
usually from November to April and the rest of the
year was wet. The elevation of the area ranged from
200-260 meters above sea level (Fig. 1).
Sampling design
The study made use of the Randomized Complete
Block Design (RCBD) replicated two times. The
treatments of the study includes:
A= 12-year-old Rambutan Plantation
B= 15-year-old Mango Plantation
C= 32-year-old Santol Plantation
Establishment of sampling plot
The established nested sampling plots were based
from the method used by Hairiah, et.al (2010). Two 5
m × 40 m plots were being established in each fruit
plantation. Nested plots of 1 m × 1 m and 0.5 m × 0.5
m were established within the 5 m x 40 m for soil, for
litter and understory sampling, respectively. With the
used of GPS receiver, geoposition of each plot was
recorded.
Carbon stock calculation
Aboveground
Live tree biomass
Data collection to estimate carbon density was
conducted using the methods described by Hairiah et
al. (2010). This method had been applied in many
carbon related studies in the Philippines.
Two 200 m2 (5 m × 40 m) quadrats were established
in each fruit plantation. The two quadrats represent
replication per site. The plot was established by
running a 40 m centerline through the area. The trees
with Diameter at Breast Height (DBH) of 5 cm to 30
cm were measured as samples within 2.5 m of each
side of the 40 m centerline. A sample plot of 20 m ×
100 m was established per site to measure the
diameter and height of tree greater than 30 cm DBH.
For carbon computation of fruit trees, the equation by
Unruh et al. (1993) as cited by (Abucejo, 2012) was
used was used.
Cft (kg) = (Yftb) (0.45)
Where:Cft = Carbon yield of fruit trees
Yftb = Fruit tree biomass = ( exp[-2.4090 +
0.9522*In(D2HS)] )
D = DBH (cm)
H = Tree Height (m)
S = Wood density equivalent to 0.57
0.45 = Carbon Content of fruit trees
Understorey Biomass
Destructive sampling technique was employed within
the 5 m × 40 m quadrants. Four 1 m × 1 m sampling
plots were nested randomly for understorey sample
collection. For litters, a 0.5 m × 0.5 m was nested
uniformly in the lower left of 1 m × 1 m sampling plot.
For understorey, all vegetation less than 5 cm dbh
were harvested within the 1 m × 1 m quadrants. Total
fresh sample was weighed in the field and after which
a sub-sample of about 300 g was taken for oven
drying and carbon content analysis.
For litters, all undecomposed plant materials and
crop residues within 0.5 m × 0.5 m were collected.
Total fresh weight was then recorded in the field. A
sub-sample of about 300 g was taken for oven-drying
and carbon content analysis (Hairiah et al., 2010).
The carbon content analysis was done at the Soil and
Plant Analysis Laboratory (SPAL). Combustion
method or dry ashing was done in order to determine
the carbon content of plant and litter samples. The
method used volatile solids (largely carbon and
nitrogen), then burned at laboratory furnace at 500-
600 °C leaves off and leaving only the ash. By
weighing the ash and applying percentage conversion
of ash and volatile solids that burned off, the carbon
content was determined.
Understorey and litter samples were calculated using
the equation by Hairiah et al. (2010).
J. Bio. Env. Sci.
2016
167 | Janiola and Marin
WT= (TFW (kg)×SDW (g))/(SFW ×A)
Legend:
WT = Total Dry Weight (kg)
TFW = Total Fresh Weight (kg m2)
SDW = Subsample Dry Weight
SFW = Subsample Fresh Weight
A = Sample Area
C Stored=Total dry Weight × C Content
Below ground
Roots
Since the method for root biomass determination was
not yet standardized, an allometric equation was used
to determine root biomass and carbon (Lunsayan,
2008).
Root biomass was calculated through the use of
allometric equation from (Cairns et al., 1997).
Root Biomass = exp [ - 1.0587 + 0.8836 * In (AGB) ]
Where: exp = raise to the power of
In = natural logarithm of
AGB = Aboveground biomass
C Stored = Root biomass density x C content.
Where: A default value of 45% was used to determine
the carbon stored in root biomass, which was an
average carbon content of wood samples collected
from secondary forests from several locations in the
Philippines (Lasco & Pulhin, 2000) as cited in Labata
et al., (2012).
Soil
For soil sampling, two methods were applied, the
destructive soil sampling and undisturbed soil
sampling. By using the same nested sampling plot,
the soil samples were collected. For the undisturbed
soil sampling, samples were gathered in the 1 m × 1 m
sampling plot and destructive soil samples were
gathered at 0.5 m x 0.5 m, of which samples were
derived from 0-30 cm depth soil layer and about a
kilogram of soil sediments were taken for organic
analysis using Corg (Walkey and Black) Method
(Hairiah et al., 2010) at the SPAL in Central
Mindanao University. The soil samples for bulk
density determination was collected in undisturbed
spot of 1 m ×1 m sampling plot and a 5.4 cm × 10 cm
soil core cylinder was used in collecting the samples
by driving the soil core cylinder into 0-10 cm depth
soil layer (Hairiah et al., 2010).
Soil Carbon was calculated through an equation:
Carbon density ( Mg ha-1) = weight of soil × %SOC
Where: Weight of soil (Mg) = bulk density × volume
of 1 hectare
Bulk density (g/cc) = Oven-dried weight of soil /
Volume of canister
Volume of canister = π r2 h
Volume of one ha = 100m ×100m × 0.30m
Total C stored = C stored (t/ha) × area (ha).
Data analysis
The test of significant difference among treatments
was determined using the Analysis of Variance
(ANOVA). Duncan Multiple Range Test (DMRT), on
the other hand, was used in comparing treatment
means. The Statistical Package for the Social Sciences
(SPSS) version 16 was used in the data analyses.
Results and discussion
The inventory of fruit trees in sampling plots were
summarized in Table 1. The 12 year old rambutan
plantation had 27.5 mean sampled trees with mean
height and diameter of 7.33 m and 23.44 cm,
respectively. For the 15-year-old mango plantation, it
had 34.5 mean sampled trees with 12.96 m mean
height and 32.41 cm mean diameter. On the other
hand, the 32-year-old santol plantation has 39 mean
sampled trees with average height and diameter of
16.91 m and 37.03 cm, respectively. Data showed that
among the three plantations, santol being the oldest
(32 years) had the greatest diameter and height while
the rambutan plantation (12 years) had the least
being the youngest of the three plantations.
Soil condition
Table 2 shows the soil properties of the three
J. Bio. Env. Sci.
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168 | Janiola and Marin
plantations. The result for the soil condition among
the three plantations signifies that all three
plantations were at good condition.
As observed, the santol had the highest value among
the three plantations for soil pH, OM, OC, N and K.
Findings revealed that the soil condition of the 32-
year-old santol plantation is better compared to the
sites for mango and rambutan plantations. According
to Imoro et al. (2012), the soil pH largely controls
plant nutrient availability and microbial reaction in
the soil, especially the soil organic matter. Further, he
said that organic matter content is often related to
soil fertility. Accordingly, organic matter act as
reservoir of plant nutrients especially the three
important macronutrients (NPK) and micronutrients
(Okunwo et al., 2012). Furthermore, the presence of
this nutrients influence plant growth and affect
vegetation structure.
Table 1. Inventory of fruit trees for the three sites.
Measurements
Plantation
Rambutan
Mango
Santol
Mean No. of Trees
27.5
34.5
39
Mean Average Height (m)
7.33
12.96
16.91
Mean Average Diameter (cm)
23.44
32.41
37.03
Biomass and carbon production
Table 3 shows significant difference in biomass
production among the three fruit plantations. Results
show that Santol plantation (32-yr old) had the
highest amount of biomass production for trees
amounting to 166.71 ton/ha. For mango plantation
(15-yr old), tree biomass is 100.71 ton/ha. However,
biomass production of mango and santol plantations
does not differ significantly. This can be attributed to
the fact that both plantations had average diameter
greater than 30 cm and average height greater than
10 m. On the other hand, rambutan plantation had
the lowest amount of biomass produced with 24.70
ton/ha. This is due to its size having a diameter range
of 21.67 cm to 25.20 cm and average height of below
10 m. However, based on the size of trees, rambutan
can be classified under the medium size fruit tree
(Morton, 1987).
Table 2. Soil analysis of the three fruit tree plantation.
Plantation
pH
%OM
%OC
Total N (%)
Extr. P ppm
Exch. K ppm
Rambutan
4.97
4.24
2.47
0.12
4.06
45.00
Mango
4.93
3.06
1.78
0.10
1.25
40.50
Santol
5.69
5.26
3.06
0.17
1.97
255.00
Tree biomass is directly proportional to its diameter
at breast height (DBH) and total height.
In fact, Brown (2002) as cited by Gibbs (2007)
reported that DBH is 95% of the total biomass. In this
study, santol plantation presents the greatest biomass
production, which can be due to its huge average
diameter and height. On the other hand, Mango and
rambutan plantations had lesser biomass due to its
smaller diameter and height.
Significant difference was shown among the three
fruit plantations for aboveground carbon stock.
Santol fruit trees had mean carbon density of 75.02
ton/ha which was observed to be the highest among
the three plantations. This was followed by mango
plantation with a total carbon stock of 45.29.
Rambutan plantation, on the other hand, had carbon
stock of only 11.12 ton/ha and is the least among the
three plantations. Cubillas (2009) stated that carbon
storage was directly proportional to biomass density.
J. Bio. Env. Sci.
2016
169 | Janiola and Marin
Thus, like biomass, the same ranking of carbon
storage per site had resulted. Rambutan plantation
having smaller sizes and volume also resulted to have
the least biomass and carbon content. Pedregosa
(2009) had also parallel findings on the 40-year-old
rubber plantation being the greatest in biomass
compared to the 25-year-old and 5-year-old rubber
plantation. Accordingly, older trees undergone
photosynthetic activity with much longer time
compared to young trees and consequently are
absorbing and storing more carbon (Lunsayan,
2008). This may explain why the 32-year-old santol
plantation had the greatest carbon stock among the
three fruit trees measured in this study.
Table 3. Biomass and carbon production of different pool area.
Fruit
Plantation
Fruit Trees (ton/ha)
Understory (ton/ha)
Litter (ton/ha)
Roots (ton/ha)
SOIL (ton/ha)
Biomass
Carbon
Biomass
Carbon
Biomass
Carbon
Biomass
Carbon
Carbon
Rambutan
24.70 b
11.12 b
1.06 a
0.50 a
3.93 a
1.79 ab
4.46 b
2.01 b
96.76 a
Mango
100.71 a
45.29 a
1.51 a
0.70 a
3.25 a
1.62 b
15.95 a
7.17 a
67.56 a
Santol
166.71 a
75.02 a
0.78 a
0.36 a
6.93 a
3.43 a
25.74 a
11.60 a
113.21a
CV (%)
16.32
16.30
40.76
44.56
25.12
19.13
16.59
16.71
12.70
Mean of the same letters are not significantly different at 5% level of significance using Duncan Multiple Range
Test (DMRT).
The understorey biomass shows no significant
difference among the three plantations. Mango
plantation had the highest biomass density of 1.51
ton/ha, while santol plantation had the least with
0.78 ton/ha.
The difference of the understory biomass for each
plantation site was observed to be influenced by the
understory vegetation present in this study. Both 15-
year-old mango plantation and 12-year-old rambutan
plantations are dominated by carabao grass
(Paspalum conjugatum).
The only difference among the two plantations was
that mango plantation had taller understory
vegetation than to the rambutan plantation. For
santol plantation, the understory vegetation was
prone to weeding and disturbance due to the presence
of road network. Pedregosa (2009) stated that factors
like openness of canopy and presence of road network
may affect the growth of the understory vegetation.
Santol plantation had also a closer canopy due to its
large tree sizes. Close canopy makes understory
receive less intense light than in plants with open
canopy. Ostrom (2005) mentioned that attribute of
light environment had significant impact on plant
growth and vigor. Further, he stated that crown with
densely packed leaves may transmit less light than
one that consist elongated leaves with sparse crowns.
The mango and rambutan, on the other hand, had
more open canopy, thus, more understory are
observed in these plantations due to more light
reaching the ground. In an open canopy, the
understory is able to photosynthesize adequately
using such light from the sun.
The age of stand, the spacing and sizes of canopy
gaps, species and the multi-layering of foliage within
the stand all influence understory (Pett and Franklin,
2000). Furthermore, they stated that the amount of
light reaching the understory varied greatly and
overall understory conditions were influenced by
canopy structure as indicated by the higher
correlation between the herb-shrub layer and the
canopy-light environment.
For understorey carbon, the mean carbon density
among the fruit plantations was found to be
insignificant. The 15-year-old mango plantation had a
mean carbon of 0.70 ton/ha, while the 32-year- old
plantation had the least with 0.36 ton/ha.
J. Bio. Env. Sci.
2016
170 | Janiola and Marin
Table 4. Percentage of different aboveground carbon pool.
Plantation
Fruit Tree
Understorey
Litter
Total AGC
Rambutan
11.12 (83%)
0.50 (4%)
1.79 (13%)
13.4
Mango
45.29 (95%)
0.70 (1%)
1.62 (4%)
47.61
Santol
75.02 (95%)
0.36 (1%)
3.43 (4%)
78.81
The findings for understory carbon among the three
plantations show that the age of plantation does not
affect the amount of carbon being stored. Despite
being the oldest among the 3 plantations, santol
turned out to have the lowest amount of understory
carbon. This is due to the closeness of the canopy of
the santol plantation. Light cannot easily penetrate
the understory layer, thus, the growth of the
understory in santol plantation is low. Bartels and
Chen (2013) supported that overstory broad leaf
composition had direct positive effect on shrub layer
and herb layer. According to Cubillas (2009), the
growth of understory vegetation in natural forest is
dependent to sunlight, thus, the thicker the forest
canopy, the lesser the light penetration for the
understory vegetation especially herbaceous plants,
making them out-numbered. In rambutan and mango
plantations, the canopy is quite open where light
easily penetrates the understorey layer, thus, plants
grow and thrive vigorously.
Table 5. Percentage of different belowground carbon pool.
Plantation
Root
Soil
Total BGC
Rambutan
2.01 (2%)
96.76 (98%)
98.77
Mango
7.17 (10%)
67.56 (90%)
74.73
Santol
11.60 (9%)
113.21 (91%)
124.81
For litters, no significant difference in biomass
density was observed among the three fruit
plantations. The litter biomass density of santol
plantation had the highest value among the three
plantations amounting to 6.93 ton/ha. Rambutan
plantation had 3.93 ton/ha while mango plantation
had the least with 3.25 ton/ha.
Branches, leaves and fruit crop residues that fell on
the forest ground (litter) had a corresponding
biomass density, thus, the more litter harvested, the
greater biomass density it produced (Lunsayan,
2008). As observed, santol leaf litters were broader in
size and had longer petiole (18 cm long) compared to
rambutan and mango plantation. Rambutan leaves
are alternately pinnate compound 7-30 cm long which
is attached to a 1-2 cm petiole while mango had
evergreen alternate leaves with petioles 2.5-3.0 cm
long. Full grown leaves may be 10-32 cm long and 2-
5.4 cm wide (Morton,1987). Furthermore, santol
plantation also produces more litters since among the
three plantations, santol is older and had wider
canopy.
In terms of carbon from litters, santol plantation had
the highest value with 3.43 ton/ha followed by
rambutan plantation with 1.79 ton/ha. The mango
plantation had the lowest carbon stock at 1.62 ton/ha.
This result is supported by the fact that the older
santol fruit trees had denser canopy and greater
coverage compared to mango and rambutan
plantation. Thus, the santol plantation will most likely
shed greater amount of dry leaves than rambutan and
mango. This can be due also to the dry leaves and
petioles of the 32-year-old santol trees which are
bigger in size and would thereby give greater volume
of litters.
On the root biomass of fruit trees, the three
plantations showed significant difference. Santol
plantation showed the greatest value with 25.74
ton/ha. This was followed by mango plantation with
15.95 to/ha and rambutan plantation had the least
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171 | Janiola and Marin
with only 4.46 ton/ha. The result may explain that the
root biomass is positively related to aboveground
biomass. The equation by Cairns et al. (1997) uses the
aboveground biomass of fruit trees to determine its
root biomass. According to Law (2002) as cited by
Patricio and Tulod (2010), the mass of leaves and
stem is proportionally scaled to that of its roots in a
mathematically predictable way. This can be the
reason why santol plantation had highest mean in
terms of root biomass because among the three it has
the largest diameter, while rambutan had the least
diameter making it to have least root biomass density.
Findings revealed that below ground root carbon
among the three plantations shows significant
difference. The 32-year-old santol plantation had the
highest value for root carbon with 11.60 ton/ha. The
15-year-old mango plantation was second with 7.17
ton/ha, while the rambutan plantation had the least
at 2.01 ton/ha.
Table 6. Percentage table of different carbon pool compartment.
Plantation
Fruit Tree
Understorey
Litter
Root
Soil
Total C
Rambutan
11.12 (10%)
0.50
(1%)
1.79 (1%)
2.01 (2%)
96.76
(86%)
112.18
Mango
45.29 (37%)
0.70
(1%)
1.62 (1%)
7.17 (6%)
67.56
(55%)
122.34
Santol
75.02 (37%)
0.36
(1%)
3.43 (2%)
11.60 (5%)
113.21
(55%)
203.62
The result in root carbon density reflects only the
trend result for aboveground biomass and carbon
density of which the 32-year-old santol plantation
had the highest followed by the 15-year-old mango
and 12-year-old rambutan plantations.
This is because of the fact that aboveground biomass
was used in the equation by Cairns et al. (1997) to
determine the root biomass and root carbon. As
discussed by Cubillas (2009), carbon storage was
directly proportional to biomass density, thus the
same ranking of carbon storage per site had resulted.
The soil mean carbon density among the three
plantations shows no significant difference. Santol
plantation had a total soil mean carbon of 113.21
ton/ha, rambutan plantation had 96.76 ton/ha and
mango plantation had 67.56 ton/ha. The insignificant
difference of the three plantations can be due to the
uniformity of soil OM of the said sites. According to
Henry (2010), most of the soil carbon is found in the
0-30 cm depth soil layer. Moutinho (2005) also
described that 30% of soil carbon stock can be found
in the 0-5 cm soil layer.
Aboveground total carbon
The total aboveground carbon of the three fruit
plantations showed significant difference at 0.05 level
(Table 4). Santol plantation had a total mean carbon
of 78.81 ton/ha while mango plantation had 47.61
ton/ha. The least was the rambutan plantation with
13.4 ton/ha.
Findings showed that the santol plantation dominates
the aboveground carbon. This can be due to its
diameter and height which is greater compared to the
other two fruit plantations. Expectedly, the result of
the carbon storage is dependent on the biomass
production because carbon sequestration is a function
of biomass production (Lunsayan, 2008). Further,
since trees had the highest biomass density,
consequently, it stores the highest amount of carbon
among other aboveground biomass compartment
(understorey and litter). Labata (2012) reported that
85-94% of the aboveground biomass can be stored in
trees. Further, litter was only 2-6% and herbaceous
vegetation accounts only to 1-13%. In this study trees
showed 83-95% total aboveground carbon,
understorey with 1-4% and litter with 4-13%.
Below ground total carbon
No significant difference was noted for belowground
carbon among the three plantations. However, santol
J. Bio. Env. Sci.
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172 | Janiola and Marin
plantation had the highest belowground mean carbon
of 124.54 ton/ha. Rambutan plantation had 98.77
ton/ha, while mango plantation had 74.74 ton/ha.
The no significant difference of belowground can be
due to the very high soil carbon content in the three
sites. This is because most of the belowground total
carbon is found in soil and constitute about 90-98%.
Roots constitute only 2-10% carbon of total
belowground (Table 5).
Total carbon
The overall carbon storage shows significant
difference among the three plantations (Fig 2). The
32-year-old santol had the greatest amount of carbon
stored among the three plantations with 203.62
ton/ha. Its difference from mango and rambutan is
significant (Table 6). The 15-year-old mango
plantation had a total carbon stock of 122.34 ton/ha,
while rambutan plantation had the least value with
112.17 ton/ha. However, its difference with mango
plantation is not significant.
Mean of the same letters are not significantly different at 5% level of significanceusing Duncan Multiple Range
Test (DMRT).
Fig. 2. Graphical presentation of total carbon stock.
The result for the total carbon stored among the three
fruit plantations only shows that the santol plantation
dominates in terms of total carbon storage. This can
be due to its age and size of santol trees. The greater
the size of the vegetation, the most likely to contain
more C stocks. According to Sabukti et al. (2010), the
existence of trees with diameter more than 30 cm in a
certain land use system makes a large contribution to
the total carbon stocks. As observed in all the carbon
pool, the aboveground components especially the
fruit trees shows the greatest amount of biomass
present as well as the carbon.
Age, size, species and type of forest may influence
amount of carbon storage. In the study of Pedregosa
(2009) the 40 year old rubber plantation had 292.36
Mgha-1 carbon stocks then followed by 25 year old
and 5 year old rubber plantation with 238.39 Mgha-1
and 2.56 Mgha-1 carbon stocks, respectively. In the
study of Lasco et al. (2000), forest had carbon stocks
of 392.96 ton/ha being the highest, followed by
yemane, mangium and mahogany plantation with
294.16 ton/ha, 275.42 ton/ha and 192.02 ton/ha
carbon density, respectively. While in the study of
Lunsayan (2008) the 16-year-old carribean pine
plantation had 258.19 ton/ha carbon stocks then
followed by 14-year-old and 5 year old carribean pine
plantation with 212.15 ton/ha and 155.52 ton/ha,
respectively.
The aboveground carbon pool shows high amount of
carbon amounting to 10-36% of the total carbon
stored, tree gives off significant part to the
aboveground carbon. Litter amounts only to 1-3% and
J. Bio. Env. Sci.
2016
173 | Janiola and Marin
understorey only amounts to 1-2% of the total carbon
stored. However, among the carbon pool, soil had the
highest component amounting to 55-86% of the total
carbon. Soil can sequester more carbon because it is
where decomposition takes place from all the litter
debris and leaves of the tree, dead herbaceous plants
and continuous growth and death of roots (Bajuyo,
2012). The soil carbon is about two thirds of the
terrestrial biosphere carbon pool.
Conclusion
Fruit crop plantations are indeed potential as carbon
sink and are helpful in mitigating climate change. The
potentials of fruit trees to sequester carbon can be
comparable to that of forest trees and the fact that
these crops are also providing food and income to the
farmers. The promising contribution of fruit tree
plantations in solving food shortages and climate
change problems should encourage the Local
Government Units, Government and non-government
organizations in promoting and expanding these
land-use practices for food security and for global
warming mitigation purposes.
References
Abucejo J. 2012. Analysis of the Economic and
Environmental Services of Two Agroforestry Projects
in Caraga Region, Philippines, Unpublished MS
Thesis, Central Mindanao University, Philippines, 35.
APFSOS. 2009. Asia-Pacific Sector Outlook Study II.
Philippine Forestry Outlook Study. Forest
Management Bureau. Food and Agriculture
Organizations of the United Nations Regional Office
for Asia and the Pacific. Bangkok, 6.
Bajuyo I. 2012. Carbon Sequestration Potential of
Mangrove Plantation in Taytay, El Salvador, Misamis
Oriental, Unpublished Undergraduate Thesis, Central
Mindanao University, Philippines, 29.
Bartels SF, Chen H. 2013. Interactions Between
Overstorey and Understorey Vegetation Along An
Overstorey Compositional Gradient. Journal of
Vegetation Science 24(3), 546-548.
Bowyer J, Bratkovich S, Frank M, Howe J,
Stai S, Fernholz K. 2012. Carbon 101:
Understanding Carbon Cycle and the Forest Carbon
Debate. Dovetail Partners Inc,, 2.
Brown S. 1997. Estimating biomass and biomass
change of tropical forest: A primer, FAO Forestry
paper 134, 58.
Brown S. 2002. Measuring carbon in forests:
current status and future challenges Environmental
Pollution 116(3), 363-372.
Cairns M, Brown S, Helmer E, Baumgardner
G. 1997. Root Biomass Allocation In The World’s
Upland Forests. Oecologia 111(1), 1-11.
Carandang A. 2005. Forest Resources Assessment,
National Forest Assessment: Forest Policy Analysis
Philippines. Forest Resources Assessment
Programme, Forestry Department. Food and
Agriculture Organization of the United Nations
Working Paper 101/E, 1-35.
Cubillas M. 2009. Carbon Stock Assessment of
Selected Forest Patches in Musuan, Maramag,
Bukidnon. Unpublished Undergraduate Thesis,
Central Mindanao University, Philippines, 29-30.
Gibbs HK, Brown S, Niles JO, Foley JA. 2007.
Monitoring and Estimating Tropical Forest Carbon
Stocks: Making REDD a Reality. Environmental
Research Letters 2(4), 1-13.
Hairiah K, Dewis S, Agus F, Ekadinata A,
Rahayu S, Van Noordwijk M. 2010. Measuring
Carbon Stock Across Land Use Systems: A Manual.
Bogor Indonesia. World Agroforestry Centre. 55-102.
Henry M. 2010. C Stocks and Dynamics in Sub
Saharan Africa. Dissertation Submitted to Paris
Institute of Technology for Life, Food and
J. Bio. Env. Sci.
2016
174 | Janiola and Marin
Environmental Sciences ( AgroParis Tech ) and
Doctoral School on Integrated Systems in Biology,
Agricultural Sciences, Geosciences, Water Resources,
Environment ( SIBAGHE), 26.
Imoro ZA, Dery DA, Kwado KA. 2012.
Assessment of Soil Quality improvement under Teak
and Albizia. Journal of Science and Environmental
Management 3(4), 92.
Labata MM, Aranico EC, Tabaranza AC,
Patricio JHP, Amparado RFJr. 2012. Carbon
Stock Assesment of three Selected Agroforestry
System in Bukidnon, Philippines. Advances in
Environmental Sciences International Journal of the
Bioflux Society 4(1), 5-11.
Lasco R, Lales J, Arnuevo Ma.T, Guillermo I,
de Jesus A, Medrano R, Bajar O, Mendoza C.
2000. Carbon Dioxide ( CO2 ) Storage and
Sequestration in the Leyte Geothermal Reservation,
Philippines, Proceeding World Geothermal Congress,
639-643.
Lasco RD, Pulhin FB, Cruz RVO, Pulhin JM,
Roy SSN. 2005. Carbon Budget of Terrestrial
Ecosystem in The Pantabangan-Carranglan
Watershed. AIACC Working Papers 10, 1-23.
Lunsayan D. 2008. Carbon Sequstration Potential
of Carribean Pine ( Pinus carribea) Plantation in
Malaybalay City, Unpublished Undergraduate Thesis,
Central Mindanao University, Philippines, 30-40.
Morton J. 1987. Fruits of Warm Climate. Julia F.
Morton, Miami, FLL. [Online] Available at 199-201,
221-239, 262-265.
http://www.hort.purdue.edu/newcrop/morton/mang
o_ars.html
Moutinho P, Schwartzman S. 2005. Tropical
Deforestation and Climate Change. © Amazon
Institute for environmental Research. Washington
DC, USA 71. 1-132.
Okonwu K, Mensah SI. 2012. Effects of NPK (15:
15: 15) fertilizer on some growth indices of pumpkin.
Asian Journal Agric. Res. 6, 137-143.
Ostrom BJ. 2005. Effects of Forest Structure on The
Understory Light Environment and Growth Potential
of Oak Seedlings In A Close Canopy Riparian Forest.
Published Thesis. Auburn University, Alabama, 8-10.
Patricio, JHP, Tulod AM. 2010. Carbon
sequestration potential of Benguet pine (Pinus
kesiya) plantations in Bukidnon, Philippines. Journal
of Nature Studies 9(1), 99-104.
Pedregosa D. 2009. Carbon Stock Assessment of
Rubber Tree (Havea brasiliensis) Plantation in
Musuan, Maramag, Bukidnon, Unpublished
Undergraduate Thesis, Central Mindanao University,
Philippines, 29-34.
Pelt R, Franklin J. 2000. Influence of Canopy
Structure on the Understory Environment in Tall, Old
Growth, Conifer Forest. NRC Canada. Canadian
Journal for Researches 30, 1236-1243.
Sabukti R, Lusiana B, Noordwijk Mvan. 2010.
Aboveground Carbon Stock Assessment for Various
Land Use System in Nunukan, East Kalimantan, 24-
29.
Sace C. 2002. Greenhouse Economics. Munoz,
Nueva Ecija, Philippines, 1.
Unruh JD, Houghton RA, Lefebvre PA. 1993.
Carbon Storage in Agroforestry: An Estimate for Sub-
Saharan Africa. Climate Research 3, 39-52.
Van Noordwijk, M, Subekti R, Hairiah K,
Wulan Y, Farida A, Verbist B. 2002. Carbon
Stock Assessment for a Forest-to-Coffee Conversion
Landscape in Sumber-Jaya ( Lampung, Indonesia ):
From Allometric Equation to Land Use Change
Analysis 45, 75-86.
