Bogor, 21-22 October 2015
232 unmeasured ratio. This unmeasured ratio may be their parents species were not homozygot,
ratio 1:2:1 will be obtain if the species parents were homozygot Gardner et al., 1991; Brown, 1998.
Table 3: The Acacia hybrid percentage of seedling identified base on morphologycal marker developed by Rufelds 1988 simplified by Gan and Sim 1991.
Seed A.
mangium Acacia hybrid
A. auriculiformis
Group 1 30.0
70.0 Group 2
2.9 97.1
Group 3 6.0
92.5 1.5
The high hybrid seedling obtained from the first generation of Acacia hybrid expected to be useful in order to get the hybrid vigour. This study still be observed to record their growth iin
the nursery and then plant it as a hybrid vigour test. By hybrid vigour test hopely the second hybrid vigour will be obtain.
4. CONCLUSION Second generation seeds of Acacia hybrid were caterorized into 3 Groups due to their
morphological charcter i.e. looks like A. mangium seed, looks like A. auriculiformis seed and between A. mangium and A. auriculiformis seed. There were 6, 3, and 9 variations of seedling leaf
development patterns for seed group 1, 2 and 3 respectively. Percentage putative hybrid seedlings were 97.1, 92.5 and 70 for Group 2, 3 and Group 1 respectively.
ACKNOWLEDGMENT We would like thank you to Sinarmas Forestry for collaborating research on Acacia hybrid
development especially PT. Arara Abadi Riau, Pekanbaru and PT. Wirakarya Sakti Jambi. REFERENCES
Brown, T.A. 1998. Genetics: A molecular approach. What Mendel Discovered pp. 297-299.
India : Chapmann and Hall. Gan, E., Sim, B.L. 1991. Nursery identification of hybrid seedlings in open plots. In L.T.
Carron K.M. Aken eds., Breeding Technologies for Tropical Acacias. ACIAR Canberra. pp. 76-85.
Garner, E.J., Simmons, M.J., Snustad., D.P. 1991. Principles of Genetics. Mendelian Genetics pp. 24-29. New York: John Wiley Sons. Inc.
Kato, K., Yamaguchi, S., Chigira, Ogawa, Y., Isoda, K. 2012. Tube pollination using stored pollen for creating Acacia auriculiformis hybrid. Journal of Tropical Forest Science. 242,
209-216. Kato, K., Yamaguchi, S., Chigira, O., Hanaoka, S. 2014. Comparative study of reciprocal
crossing for establishment of Acacia hybrids. Journal of Tropical Forest Science, 264, 469- 483.
Kha, L.D.2001. Studies on the use of natural hybrids between Acacia mangium and Acacia auriculiformis in Vietnam. Hanoi: Agriculture Publising House.
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233 Kha, L.D., Harwood, C.E., Kien, N.D .2012. Growth and wood basic density of Acacia
hybrid clones at three location in Vietnam. New Forest, 43,13-29. DOI 10.1007s 1056- 011-926-y.
Khalid, I., Wahap, R., Sulaiman, O., Mohamed, A., Tabet, T., Alamjuri, R. H. 2010. Enhancing colour appearances of 15 cultivated 15 year old Acacia Hybrids through heat
treatment process. International Journal of Biology, 22,199-209. Nikles, D.G., Hardwood, C.E., Robson, K.J., Pomroy, P.C., Keenan, R.1998.
Management and use of ex situ genetic resources of some tropical Acacias species in Queensland. In J.W. Turnbull, H.R. Cropton, K. Pinyopusarerk Eds., Developments in
Acacias planting. ACIAR Canberra. pp. 184-196. Rokeya, U.K., Hossain, M.A., Ali, M.R., Paul, S.P. 2010. Physical and mechanical
properties of Hybrid Acacia Acacia auriculiformis x A. mangium. Journal of Bangladesh Academy of Sciences, 322,181-187.
Sukganah, A., Choong, C.Y., Russel, J., Neale, D., Wickneswari, R. 2013. Necleotide sequence analysis of two lignin genes in Acacia Auriculiformis x A. mangium hybrid for
enhancement of wood pulp quality. The Genetics an Genomes, 9, 1369-1381. Sunarti, S., Nai’em, M., Hardiyanto, E.B., Indrioko, S. 2013. Breeding strategy of Acacia
hybrid Acacia mangium x Acacia auriculiformis to increase forest plantation productivity in Indonesia. Journal of Tropical Forest Management, XIX2, 128-137.
Sunarti, S., Adyantoro, V.D. 2014. Variasi morfologi dan kualitas benih Akasia hibrida alamai generasi kedua F-2. In. A.Y.P.B.C. Widyatmoko, A. Nirsatmanto, L.
Baskorowati, B. Leksono Eds., Benih unggul untuk hutan tanaman, restorasi ekosistem, dan antisipasi perubahan iklim: Prosiding seminas nasional, Yogyakarta, 19-20 November 2014 pp.
347-353.
Wright, J.W. 1976. Introduction to Forest Genetic pp. 344-349. San Diego. California : Academic Press.
Yahya, R., Sugiyama, J., Gril, J.2010. Some anatomical features of Acacia hybrid, A. mangium and A. auriculiformisgrown in Indonesia with regard to pulp yield and strength
paper. Journal of Tropical Forest Science, 333, 343-351.
Bogor, 21-22 October 2015
234
PAPER B14 - Pantropical vs locally developed allometric equations: which will be the better option to estimate
aboveground biomass of Papua tropical forest?
Sandhi Imam Maulana
1
, Yohannes Wibisono
1
, and Singgih Utomo
2 1
Forestry Research Institute at Manokwari-Forestry Research and Development Agency Jl. Inamberi, Susweni PO BOX 159, Manokwari 98313-Papua Barat Tel. 0986 213437
2
Forest Management Vocational School-Gadjah Mada University Corresponding Email: frost_stickyahoo.com
ABSTRACT This study aims primarily to compare locally developed equation in Papua Island to
pantropical allometrics by Chave et al. 2005 and an improved pantropical allometric model by Chave et al. 2014. Measurements on biomass in this study were conducted directly based
on weighing and destructive. Final result has highlighted that the most appropriate local model to estimate total aboveground biomass in Papua tropical forest is
LogTAGB = -0.267 + 2.23 LogDBH +0.649 LogWD VIF=1.6; R
2
= 95; R
2
-adj= 95.1; F-stat= 775.04. Additionally, this model is also a better option when compared to both Chave et al. 2005 and
Chave et al. 2014’ improved pantropical equations in estimating TAGB in Papua Island with only 6.47 average deviation and 5.37 points of relative bias.
Keywords: allometric, biomass, Papua
1. INTRODUCTION Along with the progress of REDD+ in Indonesia and the high possibility of benefits that
might be achieved from forest carbon stocks conservation, a verifiable and precise estimation of carbon stocks in the country forestry sector is strongly needed. In regard to this matter,
forest carbon stocks estimation could rely on certain approaches depend on their scales, starting from the application of GIS for national level to field weighing for local level,
however, all these approaches still rely on trees’ biomass measurement Clark Kellner, 2012; Gibbs et al., 2007. At this point, to think of the high possibility of environmental
deterioration as a result from direct biomass measurements, together with the cost of such approach that tend to be very high, the alternative that has been generally used is an allometric
equation. This equation is basically a statistical model to estimate trees’ biomass using their biometrical characteristics, like height or diameter, which are non-destructive and simpler to
measure Eggleston et al., 2006; Maulana, 2014; Ngomanda et al., 2013. Up to now, the pantropical allometric equations that may have been commonly used across the globe,
including in Indonesia, are Chave
et al. 2005’ equations Lewis et al., 2009; Ngomanda et al., 2013. Nevertheless, in relation with regional differences in diameter, height and wood density
allometrics as evidenced by Maulana 2014, the lack of data measured from eastern part of Indonesia may question the degree of deviation and bias produced from the use of Chave et
al. 2005 pantropical allometrics in such area. Therefore, in regard to this issue, this study aims to compare locally developed equation in Papua Island to pantropical allometrics by
Chave et al. 2005 and an improved pantropical allometric model by Chave et al. 2014. Considering this aim, firstly, this study will produce local allometric equation for mixed species
across Papua Island as an update to previously published equation in Maulana 2014 using new data that are including four additional genus. It will then put forward an evaluation
Bogor, 21-22 October 2015
235 against both Chave et al. 2005 and Chave et al. 2014 equations using actual direct
measurements biomass data. 2. EXPERIMENTAL METHOD
2.1 Study site As depicted in Tabel 1, this study was conducted at six different regencies across Papua
Island. The table also illustrates number of trees felled in this study, which were 83, with dbh diameter at breast height1.3 m interval from 5 to 48.5 cm, consisting of eight different
genera. Tabel 1: Study area and number of tree felled per genera
Site Location
Genera Number
of tree felled
Map
1 Sorong
Anthocephalus 8
2 Mamberamo
Duabanga 8
3 Fak-fak
Intsia 13
4 Bintuni
Myristica 9
Palaquium 13
Syzygium 9
5 Keerom
Pometia 15
6 Raja Ampat
Vatica 8
Total tree felled 83
2.2 Biomass measurement Measurements on biomass in this study were conducted directly based on weighing and
destructive. The dry biomass of a pool of tree material was measured using an aliquot approach, which is a piece of sample with a known mass as a fraction of that the whole pool
of material. Based on this approach, dry biomass of a pool of material equal to fresh biomass of the pool material then divided by fresh biomass of its aliquot times the dry biomass of the
aliquot. This approach makes logic if only the ratio of dry over fresh biomass is homogeneous for the whole pool. Therefore, as suggested by Ketterings et al. 2001, each tree felled was
divided into five pools, namely
leaves, twigs diameter 3.2 cm, small branches diameter 3.2
–6.4 cm, large branches diameter 6.4 cm and stem. Each tree was felled so that its crown fell on the most open ground possible in its area, which could limit the destruction of
its foliage to the lowest possible loss. Once a tree felled, the volume of each section was calculated using S
malian’s formula as cited by de Gier 2003, so that total volume is the sum of the volume of each section. Meanwhile, branches and stems with maximum diameters of 15
cm were measured directly in the field using hang-up balance of 50 kg capacity with an accuracy of 1. Moreover, the smaller samples were weighed using a 1000 gr table scale with
an accuracy of 0.5. Three replications were taken for the samples from the partitioned trees and put into sealed plastic bags, and then brought to the laboratory to measure their moisture
content. From that point, an analytical balance with maximum capacity of 500 gr and an accuracy of 0.001 gr was utilized to weigh those samples. Dry weights were obtained by drying
the samples at 105
o
C temperature until the constant value was obtained Stewart et al., 1992;
1 2
3 4
5 6
Bogor, 21-22 October 2015
236 Ketterings et al., 2001.
In order to measure the wood density at the laboratory, samples were taken from the lower and upper parts of the main trunk sections with 2 meters interval for each section. To include
the inner and outer parts of the trunks with their barks, the samples were taken as a pie shape or cylinder Nelson et al., 1999. Water replacement method was used in measuring the wood
density. The samples were saturated at first to prevent size contraction during volume measurement. This was conducted through 48 hours rehy
dration. Each sample’s volume was obtained from the displaced water volume when submerged. Finally, the wood density was
equal to the oven dry weight divided by saturated volume. The dry weight of the stumps, stems, and branches with the diameter of 15 cm was calculated by multiplying the fresh
volume of each section by wood density. For the other partitioned trees, the dry weight was calculated through fresh weight multiplied by dry weight divide by fresh weight ratio of the
corresponding samples. The total dry weight of a tree is the sum of the dry weight of the stump, stem, branches, twigs, and leaves Stewart et al., 1992.
2.3 Allometric equations Based on findings in Maulana 2014, locally developed allometrics for mix species in this
study were established using two predictors, which are Diameter at Breast Height DBH and Wood Density WD. Hence, the equations to estimate Total Aboveground Biomass TAGB
were established according to basic models as follows:
LogTAGB= c + LogDBH + LogWD 1
TAGB = c + DBH + WD 2
TAGB = c + WD + DBH
2
3 Subsequently, in order to fulfill assumptions in regression establishment, two tests were
conducted, namely Variance Influential Factors VIF for multi-collinearity test, which aimed atequations with more than one predictor, and normal distribution of residual test. Allometric
model comparison and selection was analyzed using the value of standard error of the coefficient, F statistic, R-sq, R-sq adj based on Minitab 14.0 software. The chosen model
would be the one with the highest value for each criterion. Afterwards, using actual biomass measurement data in Papua Island, the chosen model in this
study was evaluated against Chave et al. 2005 models, which are Equation 4 and Equation 5,
as well as Chave et al. 2014’ pantropical allometrics as depicted in Equation 6. Meanwhile, ss suggested in Basuki et al. 2009; Ngomanda et al. 2013 and Tedeschi 2006, criteria for this
evaluation were including average deviation Equation 7 and relative bias Equation 8.
TAGB = WDexp[c+ LnDBH+ LnDBH
2
+dLnDBH
3
] 4
TAGB = WDexp[c+ LnDBH+ LnDBH
2
+dLnDBH
3
] 5
TAGB = 0.0673 x DBH
2
x WD x H
0.976
6 7
; 8
Where: B
i
= the actual aboveground biomass for tree-i b
i
= its estimation based on the model n = the number of observations
3. RESULT AND DISCUSSION
