沙漠研究 22-1, 219 -222 (2012 )"ICAL 1 / DT X"

Journal of Arid Land Studies－Refereed Paper－

Allometric Equations and Biomass Amount of Representative
Tunisian Arid Land Shrubs for Estimating Baseline
Hideki SUGANUMA*1), Kiyokazu KAWADA2), Abderrazak SMAOUI3), Kohei SUZUKI4),
Hiroko ISODA2), Toshinori KOJIMA1) and Yukuo ABE2)
Abstract: As one of the methods to sequester green house gases (GHG), arid land afforestation had been proposed, and some trial
experiments have been carried out in Western Australia. The authors attempted to apply this type of afforestation method to Tunisianarid and semi-arid area, and then conventional land-use types and vegetations were investigated. To estimate carbon credit fromsequestered carbon amount by planted trees, “Actual net GHG removals by sinks”, “Baseline net GHG removals by sinks” and“Leakage” in five carbon pools must be estimated with objective method. As a first step to estimate “Baseline net GHG removals bysinks”, allometric equations and biomass amount of representative perennial vegetation in Tunisian arid and semi-arid land wereestimated in this study. In future analysis, by repeated tree census and using reported literature data, we will be able to obtain accuratebaseline data.
Key Words: Allometric equation, Eucalyptus camaldulensis, Gafsa, Sbeitla, Vegetation survey

1. Introduction
One of countermeasures against global warming,
afforestation in arid and semi-arid land was proposed andtested (Yamada et al., 2003; Kojima and Egashira, 2011).
Using arid and semi-arid land as afforestation area rarely
competes with crop production and other land use, thus
large-scale area can be used with relatively low difficulties.
At the same time, afforestation in arid and semi-arid land
contributes to restoration of degraded land, and reducing runoff
event and erosion (Fraedrich et al., 1999; Grünzweig et al,2003).
As Maestre and Cortina (2004) indicated,mono-specific plantation had poor improvement effect onnatural environment, however, some modification ofafforestation methods will possibly avoid such negative impact.
And as Kojima and Egashira (2011) indicated, there is highpossibility to acquire huge amount of carbon emission credit
from large-scale afforestation in arid and semi-arid land. But
afforestation inside Australian continent alone did not haveenough contribution to avoid global warming, many othercandidates of arid land afforestation should be necessary.
One of such candidate was considered as arid and semi-arid
land of Mediterranean countries, and then Tunisia wasconsidered to become one of the candidate countries.
To calculate carbon emission credit, all the afforestation
area must be evaluated according to the method determined byUnited Nations Framework Conventions on Climate Change(UNFCCC, 2006).
UNFCCC (2006) determined that
“Actual net GHG removals by sinks”, “Baseline net GHG
removals by sinks” and “Leakage” in five carbon pools mustbe estimated with objective method. As a part of theevaluation method, baseline which is the estimation data of
CO2 change of original land use type, e.g. natural vegetation,must be estimated. To estimate baseline CO2 change innatural vegetation, allometric equations of dominant species ofeach natural vegetation should be necessary. Therefore, inthis study, we will report the results of allometric equation
creation and basic biomass data of Tunisian arid and semi-arid
land vegetation.

2. Materials and Methods
2.1. Research area
The research area in this study was set around the Gafsa(8˚48’E, 34˚25’N) and Sbeitla (9˚08’E, 34˚14’N) of Tunisia,
which belong to arid and semi-arid area. From the climatic
data of NOAA (2001-2011)A), mean annual temperature was20.3˚C and mean annual rainfall was 174 mm in Gafsa, andfrom that of NOAA (2009)A), mean annual temperature was
19.0˚C and annual rainfall was 304 mm in Kasserine (next toSbeitla). From basic visual interpretation of LANDSAT 5
TM images and google earth with ground truth, 11representative area of natural vegetation and 6 representative
area of forest and afforestation area were chosen as researcharea. In each research area, 15×15 m quadrate was created in* Corresponding Author: h.suganuma.yf19.frx99@gmail.com
3-3-1, Kichijoji-kitamachi, Musashino, Tokyo, 180-8633, Japan
1) Department of Materials and Life Science, Faculty of Science and Technologies, Seikei University

3) Center of Biotechnology, Borj-Cedria

2) ARENA, University of Tsukuba4) Graduate School of Life and Environmental Sciencerelatively homogeneous vegetation area.

Table 1. Allometric equations of Tunisian perennial species.
Species

2.2. Vegetation research
In each quadrate, plant species, tree or plant height, andmajor and minor axis of tree crown or plant width with ellipse
proximity were measured. For tall tree species (over 2 m andmore), Diameter at Breast Height (1.3 m): DBH was alsomeasured. Since the purpose of this study is estimation ofbaseline data, annual plants and plants under 5 cm height wereexcluded from this research.
After vegetation research inside each quadrate, aerial partof dominant species and species with relatively high frequency
of appearance were measured and cut down, and thenoven-dried in 100˚C over 24 hours and weighted.
2.3. Biomass estimation.
From oven-dried biomass data and plant size measurement
data, allometric equations were calculated with power function(Y = aXb), and this regression equation’s significance wasjudged by SPSS. Y is gram unit. Independent variable X
was set as Cpa [m2] or Cpa×H [m3]. Cpa was calculated fromπ, major and minor axis of plant width. When SPSS detected
outlier (over 2×S.D. in logarithmic transformation area), such
sample data were excluded from regression analysis, andallometric equations were recomputed. When SPSS detectedvariables of power function were not significant, regression
equation was changed from power function to linear function(Y = aX + b), and then allometric equations were recomputed.
As a result, one or more significant allometric equations
were calculated in each plant species, and then Root Mean
Square Error (RMSE) values were calculated to choose mostaccurate allometric equation. The smallest RMSE value
indicates most accurate regression equation.
Createdallometric equations were used for biomass calculation inside
the vegetation research quadrate.
To estimate biomass of plant species whose allometricequations were not created, pseudo allometric equation wascreated. This pseudo allometric equation was calculated fromall the cut down samples except Retama raetam. Thisequation has not so accurate equation but most of the Tunisianarid and semi-arid perennial plants were considered to becovered by this equation.
In addition, allometric equations of tall tree species andsome perennial plants were searched from some otherscientific articles, since cutting down of tall trees had large
difficulties and plants inside national reserve could not be cut
down. Pseudo allometric equation and found allometricequations from articles were also used for biomass calculation
inside the vegetation research quadrate.
From created and found allometric equations andIndependent
Value

Artemisia campestris

Cpa × H

Artemisia herba-alba

Cpa

Allometric equation
0.736

2RRMSE

0.994

3.49

Y = 1337 X

0.994

42.46

Y = 1020X

Arthrophytum schmittianumCpa × H

Y = 2261 X

0.971

97.43

Arthrophytum schmittianum sB

Cpa × HY = 2173 X

0.985

92.25

Arthrophytum schmittianum sH

Cpa × HY = 3236 X

0.993

27.01

Astragalus armatus

Cpa × HY = 2920 X

0.984

31.42

Gymnocarpos decanderCpa

Y = 3895 X1.450

0.983

62.59

Limoniastrum guyonianum

Cpa × H

0.833

0.957

158.5

Limonium pruinosum

Cpa × HY = 146.5 ln X + 721.7

0.961

147.1

Moricandia arvensis

Cpa

Y = 683.2 X

0.990

13.31

Retama raetam

Cpa × HY = 1224 X

0.996

87.78

Salsola sp.

Cpa

Y = 8163 X2

0.997

20.02

Salsola tetrandraCpa

Y = 1247 X

1.178

0.997

30.64

Stipa tenacissima

Cpa

Y = 1089 X

1.440

0.974

24.98

Suaeda mollis

Cpa × HY = 3773 X

0.997

29.47

Thymelaea microphylla

Cpa × HY = 777.5 X0.690

0.977

5.96

Other species

Cpa

1.163

0.937

121.6

Y = 2138 X

Y = 1269 X

vegetation measurement results, biomass amount (Mg ha-1) ofeach site and vegetation cover was calculated. These data
contribute to estimate CO2 emission of afforestation site
creation and baseline CO2 sequestration. In addition, frombiomass estimation data of forest and afforestation area,
sequestered carbon amount by afforestation was roughly
estimated.

3. Results and Discussion
3.1. Allometric equations
As shown on Table 1, allometric equations of Artemisia
campestris, Artemisia herba-alba, Arthrophytum schmittianum,
Astragalus armatus, Gymnocarpos decander, Limoniastrum
guyonianum, Limonium pruinosum, Moricandia arvensis,
Retama raetam, Salsola sp., Salsola tetrandra, Stipa tenacissima,Suaeda mollis and Thymelaea microphylla were created andchosen. Other species means before mentioned pseudo
allometric equation. Independent variable X is Cpa [m2] or
Cpa×H [m3], and dependent variable Y is biomass [g]. Some
allometric equations were created as power function, but theothers were created as linear function. Most of the equations
were created with high estimation accuracy (quite high R2values and low RMSE values). Only allometric equation ofLimonium pruinosum was created with logarithmic regression
equation since sample distribution on scatter plots did not fit topower or linear function. In addition, Helianthemum hirtum,Anabasis articulata and Aristida plumosa were perennial plant
species but their plant height was nearly equal, thus biomass ofthese three species were calculated from proportional
expressions according to Cpa [m2].
Allometric equations found in scientific articles ofTable 2. Research summary of representative Tunisian perennial
vegetation.
(Mg ha )Vegetation
cover

Arthrophytum schmittianum

34˚13'05.0"

8˚19'42.1"

1.22

0.12

Moricandia arvensis

34˚19'15.1"

8˚30'14.1"

0.86

0.12

Anabasis articulataGymnocarpos decander
Helianthemum hirtum
Salsola villosa
Salsola vermiculata
Thymelaea microphylla
Aristida plumosa
Arthrophytum schmittianum

34˚26'33.4"

8˚42'31.5"

0.29

0.01

34˚24'19.5"

8˚31'16.6"

1.64

0.12

34˚26'00.1"

8˚27'25.6"

0.98

0.05

34˚31'20.2"

8˚45'44.7"

2.93

0.24

34˚40'08.1"

8˚34'13.8"

0.60

0.11

Stipa tenacissima
Retama raetam
Artemisia campestris
Stipa tenacissima
Artemisia herba-alba
Salsola tetrandra

34˚52'07.4"

8˚31'37.6"

1.79

0.35

35˚12'30.4"

9˚07'32.2"

3.38

0.37

35˚09'26.3''

8˚41'51.5''

4.22

0.40

34˚21'53.0"

8˚40'27.2"

2.31

0.25

Site B
Site C
Site D

Site F
Site G
Site H
Site I
Site J
Site O
Site R

Biomass

longitude

Dominant Species

Site E

Site coordinate
latitudeSite No.

-1

Rosmarinus officinalis, Pinus halepensis and Eucalyptus
camaldulensis were as follows.
Rosmarinus officinalis (Uso et al., 1997)
Biomass = 366.9 + 381.7 exp(2×Cpa×H)(1)
where Biomass is gram unit and denotes total plant biomass.
Tall trees of Pinus halepensis (López-Serrano et al., 2005)
Biomass = 0.128DBH2.29(2)where DBH is centimeter unit, and Biomass is kg unit anddenotes total tree biomass.
Low trees of Pinus halepensis (Massada et al., 2006)
ln (Biomass) = -1.35 + 1.48 ln (Crown width) + 1.67 ln (H)(3)
where Biomass is kg unit and denotes total tree biomass,Crown width is meter unit and denotes mean tree crown widthand H is meter unit and denotes tree height.
Eucalyptus camaldulensis (Suganuma et al., 2006)
Biomass1 = 332.0×(DBH2×H)(4)
2 0.8119
Biomass2 = 189.3×(DBH )(5)
where Biomass1 is kg unit and denotes stem plus branchbiomass, and Biomass2 is kg unit and denotes leaf biomass,and DBH is meter unit and H is meter unit.
Appropriate allometric equations of Quercus ilex andJuniperus phoenicea were not found in scientific articles, andboth tree species has not high tree height and rarely appeared,
so equation (3) was temporally used for biomass estimation ofthese two species in this study.

3.2. Biomass estimation results of perennial vegetation.
As shown on Table 2, biomass amount of 11 Tunisian
representative perennial vegetation was estimated. Some of
the research area belongs to slight saline area and dominant
plant species had salinity tolerance. Such saline area should
be excluded from afforestation candidate, but representative
vegetation conditions were acquired. From Table 2, biomass
amount of all the research sites was revealed under 5 Mg ha-1.
Since perennial plants in arid and semi-arid area grew quite
slowly, baseline CO2 capture from atmosphere as plantbiomass was considered not to exceed 1 Mg-CO2 ha-1 year-1.
As Stipa tenacissima was cut down with regular interval, i.e.
18 months (Barbara et al., 2005), Stipa species vegetation suchas Site I has possibly high growth rate. Thus, Stipa
community was considered to have relatively high baseline
data. But the biomass data of Site O (conserved area near
national reserve) and that of Site I (regularly cut down for pulpproduction) had not so large difference, and therefore, Stipa
community was considered to grow fast but reach constantbiomass soon. These 11 vegetation data will become thebasic data for future satellite image analysis of biomass
distribution and baseline distribution estimation.
3.3. Biomass estimation results of woody vegetation.
As shown on Table 3, biomass amount of 4 Tunisian forest
in semi-arid area and 2 afforestation sites in arid and semi-arid
area was estimated. 4 sites (Site K, L, M and N) locatedinside the national reserve of Mt. Chambi, thus these forests ofPinus halepensis were well conserved. Since the highest
biomass of P. halepensis forest was over 100 Mg ha-1, Tunisiansemi-arid area was considered to have very high carbon
mitigation potential. However such huge potential, growthrate of P. halepensis forest was very low value under semi-arid
climate condition as reported in several articles (Grünzweig etal, 2003; Montes et al., 2004; Massada et al., 2006). As
Smiris et al. (2000) reported that P. halepensis grew fast and its
biomass amount finally became over 1000 Mg ha-1 underhumid and temperate condition, afforestation in semi-arid
degraded area near Sbeitla must be adopted some water-harvesting technologies or some equivalent techniques toTable 3. Research summary of representative Tunisian forest and afforestation sites.
Site coordinate
latitudelongitude-1

Biomass (Mg ha )upperlower

Canopycoverage

Vegetation
cover

Site No.

Dominant Species

Site K

Pinus halepensis

35˚10'25.6''

8˚40'21.6''

15.8

6.70

0.29

0.84

Site L

Pinus halepensis

35˚10'59.3''

8˚40'1.0''

86.5

7.13

0.68

0.97

Site M

Pinus halepensis

35˚12'14.4''

8˚39'58.9''

130.9

8.34

0.80

1.00

Site N

Pinus halepensis

35˚11'12.5''

8˚39'59.9''42.9

5.62

0.62

1.00

Site P

Eucalyptus camaldulensis

35˚11'47.0"

8˚52'33.2"

102.5

0.13

0.79

0.80

Site Q

Eucalyptus camaldulensis

34˚37'13.0"

8˚35'35.6"

70.2

n.a.

0.72

0.72

encourage biomass growth of P. halepensis.
On the contrary, afforestation area of Eucalyptus
camaldulensis in Tunisian arid and semi-raid area had
relatively high growth rate (over 2 Mg ha-1 year-1). In
addition, over 70 Mg ha-1 biomass will be accumulated inafforestation area, i.e. there were high carbon mitigation
capacity. However, since E. camaldulensis is alien species,
application of this tree species to afforestation should becomecarefully. But considering the situation that all the forests
around Gafsa (arid area) were observed only in oasis area,afforestation using E. camaldulensis should be introduced incertain extent for carbon mitigation and restoration of degraded
land.
From these results, comparing with accumulated biomass
amount in forest and afforestation sites to that of naturalvegetation or assumed baseline data, “baseline net GHG
removals by sinks” should be negligible data and initial carbon
loss of clear-cut of natural vegetation should be small amountin this Tunisian arid and semi-arid area.

4. Conclusion
From this research, allometric equations of representative
perennial plant species, biomass amount of representative
natural vegetation and woody vegetation were estimated.
These obtained data will contribute to estimate “Actual net
GHG removals by sinks”, “Baseline net GHG removals bysinks”. In future studies, more accurate estimation values andmany data sets should be acquired for statistical analysis anddetailed discussion. Since observation data of forest andafforestation sites revealed that huge amount of carbon as tree
biomass was sequestrated, much carbon credit was considered
to gain in future afforestation activities.
Acknowledgement
This work was conducted under the support of the Mitsui& Co., Ltd. Environment Fund, Grant-in-Aid for Scientific
Research (19880006) of Japan Society for the Promotion ofScience. Prof. Irie from ARENA, University of Tsukuba
greatly supported to obtain necessary data of Tunisia.
NoteA) NOAA NNDC climate data online (http://www7.ncdc.
noaa.gov/CDO/country)

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