(from Reyes et al. 1992)

3.1.3 BIOMASS EXPANSION FACTOR (BEF)

Broadleaf forests: Biomass expansion factor is defined as: the ratio of total aboveground oven-dry biomass density of trees with a minimum dbh of 10 cm or more to the oven-dry biomass density of the inventoried volume. Such ratios have been calculated from inventory sources for many broadleaf forest types (young secondary to mature) growing in moist to seasonally dry climates throughout the tropics. Sufficient data were included in these inventory sources to independently calculate aboveground biomass density and biomass of the inventoried volume (Brown et al. 1989). The reported inventoried volume in the studies was based on the definition given above. Analysis of these data show that BEFs are significantly related to the corresponding biomass of the inventoried volume according to the following equations (Brown and Lugo 1992):

(Eq. 3.1.4) BEF = Exp{3.213 - 0.506*Ln(BV)} for BV < 190 t/ha 1.74 for BV>=190t/ha (sample size = 56, adjusted r2 = 0.76) where: BV = biomass of inventoried volume in t/ha, calculated as the product of VOB/ha (m3/ha) and wood density (t/m3)

Conifer forests: No model for calculating biomass expansion factors for native conifer forests is available at present because of the general lack of sufficient data for the type of analysis performed for the broadleaf forests. However, one would expect that BEFs for tropical pine forests would vary less than for broadleaf forests because of the generally similar branching pattern exhibited by different species of pine trees. Biomass expansion factors have been calculated based on a limited data base of 12 stands of Pinus oocarpa growing in Guatemala (Peters 1977) and the methodology given in Brown et al. (1989). The inventoried volume in this case was defined as volume over bark/ha from the stump to the tip of the tree; i.e. main stem based on total height. Volumes of these stands ranged from 64 to 331 m³/ha. The BEFs based on biomass of the main stem ranged from 1.05 to 1.58, with a mean of 1.3 (standard error of 0.06). No significant relationship between BEF and main stem biomass was obtained. Until additional data become available, a BEF of 1.3 can be used, with caution, for biomass estimation of pine forests.

3.1.4 EXAMPLES OF CALCULATIONS OF BIOMASS DENSITY

To demonstrate the application of this methodology, aboveground biomass density is calculated for the following examples:

Example 1.

Broadleaf forest with a VOB = 300 m³/ha and weighted average wood density; WD = 0.65 t/m³

Step 1 Calculate biomass of VOB: = 300 m³/ha x 0.65 t/m³ = 194 t/ha

Step 2 Calculate the BEF (Eq. 3.1.4): BV > 190 t/ha, therefore BEF = 1.74

Step 3 Calculate aboveground biomass density (Eq. 3.1.1): = 1.74 x 300 x 0.65

= 338 t/ha

Example 2.

Broadleaf forest with a VOB = 150 m³/ha and weighted average wood density, WD = 0.55 t/m³

Step 1 Calculate biomass of VOB: = 150 m³/ha x 0.55 t/m³ = 82.5 t/ha

Step 2 Calculate the BEF (Eq. 3.1.4): BV < 190 t/ha, therefore BEF = 2.66

Step 3 Calculate aboveground biomass density (Eq. 3.1.1): = 2.66 x 150 x 0.55

= 220 t/ha

As can be seen from these two examples, although there is a two-fold difference in VOB/ha, there is only a 1.5-fold difference in aboveground biomass density.

3.1.5 ADJUSTMENTS TO APPROACH USING VOLUME EXPANSION FACTORS (VEF)

Forest inventories often report volumes to different standards, e.g., to minimum diameters greater than 10 cm. These inventories maybe the only ones available, and thus it is important that a means to unify the volume data to some kind of standard be developed so that these inventories can be used to estimate biomass density.

In an attempt to unify data on inventoried volume measured to a minimum diameter greater than 10 cm, volume expansion factors (VEF) were developed (Brown 1990). After 10 cm, a common minimum diameter for inventoried volumes ranges between 25-30 cm. Data from inventories that reported volumes to minimum diameters in this range were combined into one data set to obtain sufficient number of studies for analysis. The VEF is defined here as the ratio of inventoried volume for all trees with a minimum diameter of 10 cm and above (VOB₁₀) to inventoried volume for all trees with a minimum diameter of 25-30 cm and above (VOB₃₀). The uncertainty in extrapolating inventoried volume based on a minimum diameter of larger than 30 cm to inventoried volume to a minimum diameter of 10 cm is likely to be large and is not suggested. Estimates of the VEFs were based on a few inventories from tropical Asia and America which provided sufficient detail for this analysis (see Brown 1990). Volume expansion factors based on these inventories ranged from about 1.1 to 2.5, and they were related to the VOB₃₀ as follows:

(Eq. 3.1.5), VEF = Exp{1.300 - 0.209*Ln(VOB30)} for VOB30 < 250 m3/ha = 1.13 for VOB30 > 250 m3/ha (sample size = 66, adjusted r2 = 0.65)

To demonstrate the use of this correction factor to estimating biomass density, consider the following example:

Broadleaf forest with a VOB₃₀ = 100 m³/ha and weighted average wood density; WD = 0.60 t/m³

Step 1 Calculate the VEF from Eq. 3.1.5: = 1.40

Step 2 Calculate VOB₁₀: = 100 m³/ha x 1.40 = 140 m³/ha

Step 3 Calculate biomass of VOB₁₀: = 140 m³/ha x 0.60 t/m³ = 84 t/ha

Step 4 Calculate the BEF from Eq. 3.1.4: BV < 190 t/ha, therefore BEF = 2.64

Step 5 Calculate aboveground biomass density (Eq. 3.1.1): = 2.64 x 140 x 0.60

= 222 t/ha

3.1.6 USE OF INVENTORIES OF OPEN FORESTS AND WOODLANDS

No general approach for estimating aboveground biomass density of open forests and woodlands based on inventoried volume has been developed because of the general lack of suitable data. The method described above for closed forests is not generally applicable because trees have different branching patterns (often multi-stemmed) and inventoried volume of open forests and woodlands is usually measured to different standards than for closed forests. For example, inventories done in open forests and woodlands generally report inventoried volume per ha to minimum diameters less than 10 cm, and also often include branch volume. Earlier work suggested that total aboveground biomass density of open forests could be up to three times the inventoried volume (Brown and Lugo 1984), however further field testing would be needed to confirm this. It is recommended that the approach described in section 3.2 (next) for estimating aboveground biomass density be used for open forests and woodlands.

(introduction...)

Another estimate of biomass density is derived from the application of biomass regression equations to stand tables. The method basically involves estimating the biomass per average tree of each diameter (diameter at breast height, dbh) class of the stand table, multiplying by the number of trees in the class, and summing across all classes. A key issue is the choice of the average diameter to represent the dbh class. For small dbh classes (10 cm or less), the mid-point of the class has been used (e.g., Brown et al. 1989). The quadratic-mean-diameter of a dbh class would be a better choice, particularly for wider diameter classes. If basal area for each dbh class is known, the quadratic-mean-diameter (QSD) of trees in the class, or the dbh of a tree of average basal area in the class, should be used instead. To calculate the QSD, first divide the basal area of the diameter class by the number of trees in the class to find the basal area of the average tree. Then the dbh = 2 x {square root (basal area/3.142)}. For example, the dbh of a tree of basal area of 707 cm² = 2 x {square root (707/3.142)} = 30 cm.

3.2.1 BIOMASS REGRESSION EQUATIONS

The biomass regression equations for broadleaf forests were developed from a data base that includes trees of many species harvested from forests from all three tropical regions (a total of 371 trees with a dbh ranging from 5 to 148 cm from ten different sources; see Appendix 2; equation 3.2.2 in the table below was developed by Martinez-Yrizar et al. (1992)). The biomass regression equations can provide estimates of biomass per tree. The data base was stratified into three main climatic zones, regardless of species: dry or where rainfall is considerably less than potential evapotranspiration (e.g. <1500 mm rain/year and a dry season of several months), moist or where rainfall approximately balances potential evapotranspiration (e.g. 1500-4000 mm rain/year and a short dry season to no dry season), and wet or where rainfall is in excess of potential evapotranspiration (e.g. >4000 mm rain/year and no dry season). These rainfall regimes are just guides, and generally apply to lowland conditions only. As elevation increases, as in mountainous areas, temperature decreases as does potential evapotranspiration and the climate zone becomes wetter at a given rainfall. For instance, an annual rainfall of 1200 mm in the lowlands would be the dry zone, but at about 2500 m it would be the wet zone. Therefore, judgement should be used in selecting the appropriate equation.

Figure 1 - Relationship between oven-dry biomass of tropical trees and dbh for (a) biomass regression equations by all climatic zones and trees with dbh between 5 to 40 cm, and (b) equations for moist and wet zones for trees in the full range of dbh. The equations are given in Section 3.2.1.

(a) All zones

(b) Moist and wet zones

Biomass regression equations for estimating biomass of tropical trees. Y= biomass per tree in kg, D = dbh in cm, and BA = basal area in cm²

Equation Number	Climatic zone	Equation	Range in dbh (cm)	Number of trees	Adjusted r2
3.2.1	DRY a	Y = exp{-1.996+2.32*ln(D)}	5-40	28	0.89
3.2.2		Y =10^{-0.535+log10(BA)}	3-30	191	0.94
3.2.3	MOIST b	Y = 42.69-12.800(D)+1.242(D2)	5-148	170	0,84
3.2.4		Y = exp{-2.134+2.530*ln(D)}			0.97
3.2.5	WET c	Y = 21.297-6.953(D)+0.740(D2)	4-112	169	0.92

None of the regression equations should be used for estimating the biomass of trees whose diameter greatly exceeds the range of the original data.

^a Eq. 3.2.1 revised from Brown et al. (1989) for dry forest in India, and Eq. 3.2.2 from Martinez-Yrizar et al. 1992 for dry forest in Mexico (original equation based on BA). For dry zones with rainfall less than 900 mm/year use equation 3.2.2 and for dry zones with rainfall > 900 mm/year use equation 3.2.1. “exp” means “e to the power of”.

^b Both equations are based on the same data base; A. J. R. Gillespie, pers. comm. based on a revision of equation in Brown et al. (1989).

^c From Brown and Iverson (1992)

Analysis of the data bases implied that the trees within the dry and wet zones could be grouped together within a zone (Brown et al. 1989). Within the moist zone, the analysis indicated that different data bases were not statistically homogeneous and theoretically could not be grouped. For practical purposes, however, the moist zone was considered to be the population of interest and the different data bases were considered to be subsamples from this population. Thus a combined regression for the pooled data sets was developed (Brown et al. 1989).

Biomass regression equations for several species of pines combined into one data base was also developed. A simple method for estimating the biomass of palms was also developed (Frangi and Lugo 1985).

Broadleaf forests: Details of the: (1) evaluation of several linear, nonlinear, and transformed nonlinear regression equations, (2) the testing of the behavior of the equations, and (3) selection of the final equations are given in Brown et al. (1989). A listing of the original data are given in Appendix 2.

The behaviour of all these regression equations as a function of dbh is illustrated in Figure 1. Application of all five regression equations for smaller diameter classes shows that for a given dbh biomass is highest for trees in the moist zone (Fig. 1a; Eq. 3.2.3 and 3.2.4), followed by trees in the wet zone (Eq. 3.2.5), and trees in the dry zone (Eq. 3.2.1 and 3.2.2).

The regression equation for dry zone trees given by Eq. 3.2.1 gives higher biomass per tree for a given diameter than the regression developed for the Mexican dry zone (Eq. 3.2.2; see Fig. la). As tree diameters increase, the difference becomes larger so that by diameter 40 cm (the upper limit for the data bases) the biomass per tree based on Eq. 3.2.1 is about 1.7 times higher than that based on Eq. 3.2.2. The main reason for this trend is that the trees used for developing Eq. 3.2.1 grow in a dry deciduous forest zone of India that receives about 1200 mm/year of rainfall in contrast to the dry deciduous forests in Mexico where rainfall averaged about 700 mm/year. The result of this difference in rainfall regime is that the Mexican trees are shorter than those in India, and thus biomass for a given diameter is less. For example, height for the Mexican trees commonly ranged between 4 to 9 m, with an average height of about 7 m (Martinez-Yrizar et al. 1994), whereas those in India had heights up to about 15m (Bandhu 1973).

The tropical dry forest zone describes areas where rainfall is less than 1500 mm/year or so. For a dry zone where rainfall is similar to that for the dry deciduous zone of Mexico (about 700 to 900 mm/year or less), Eq. 3.2.2 could be used. For dry zone ‘forests at the wetter end of the zone, i.e., rainfall greater than 900 mm/year, Eq. 3.2.1 should be used. However, because of the high variability of tree biomass with rainfall in the dry zone, it is recommended that local biomass regression equations be developed, or at least a few trees harvested to test how well the two equations presented here fit the local conditions.

The moist zone equations (Eq. 3.2.3 and 3.2.4) give essentially the same biomass estimates for trees with dbhs up to about 80 cm (Fig. 1b). After this diameter limit, estimates of the biomass per tree diverge markedly. However, the estimates from Eq. 3.2.4 are closer to the original data (cf. Appendix 2) and the r² of the regression equation is higher than for Eq. 3.2.3 (0.97 versus 0.84). It is not recommended that any of the regression equations be used for estimating the biomass of trees whose dbh greatly exceeds the range of the original data. However, if trees with dbh greater than 160 cm or so are encountered in an inventory, it is recommended that Eq. 3.2.3 be used for these trees as the function behaves better in these larger classes. Equation 3.2.4 is an exponential function and biomass per tree increases rapidly at large diameters. In the ideal situation where many trees with dbhs larger than 150 cm are encountered, some new field measurement of their biomass should be made (see section 4.).

It is important that the biomass of trees with large dbh be estimated as accurately as possible because their contribution to the biomass of a forest stand is much more than their number suggests. For example, in mature moist tropical forests, the biomass in trees of dbh greater than 70 cm can account for as much as 40% of the stand’s biomass density, although the number of these trees corresponds to less than 5% of all trees (Brown and Lugo 1992, Brown 1996).

The regression equation for trees in the wet zone (Eq. 3.2.5) matches the original data well and behaves well at larger diameters. As with the moist equation however, caution should be taken in using the equation much beyond the original data.

Palm trees: In many tropical moist and wet forests, palms are sometimes common. Estimating their biomass is difficult as few studies have been made on this topic. Furthermore, many different species exist with different forms, different proportions of their mass in leaves, and different stem densities. To estimate the biomass of palms, height measurements as well as diameter measurements will be needed. A simple way to estimate their biomass is to compute the volume of the stem as a cylinder (basal area x stem height) and then multiply this by an estimate of the density. Wood density of palms varies considerably by species and within the stem of the same species, and it can range from about 0.25 to almost 1.0 t/m³ (Rich 1987). The biomass of the leaves also has to be added, which in total may range from 10 to 65% of the stem biomass (Frangi and Lugo 1985, Rich 1986). An alternative approach is to use a regression equation developed for the palm Prestoea montana, a common species in the moist forests of Puerto Rico. Two regression equations were developed, based on either total height or stem height as follows (from Frangi and Lugo 1985):

(Eq. 3.1.6) Y (biomass, kg) = 10.0 + 6.4 * total height (m); n=25, r2=0.96

(Eq. 3.1.7) Y (biomass, kg) = 4.5 + 7.7 * stem height (m); n=25, r2=0.90

An example is given here to demonstrate the variation in the estimates from the three different methods. For a palm of 15 cm diameter, 15 m total height, and 12 m stem height, the biomass estimates are:

Method 1	Based on volume: stem volume = 0.21 m3, wood density = 0.25 t/m3; stem mass = 53.0 kg; assume leaves are 65% of stem, total biomass = 87 kg
Method 2	Based on Eq. 3.2.6: total biomass = 86 kg
Method 3	Based on Eq. 3.2.7: total biomass = 97 kg

The three methods give similar values. Unless the forest is composed mostly of palms, any of these methods would be suitable for estimating biomass of palms scattered throughout moist or wet forests. However, the variation in wood density by species must be taken into consideration; higher wood density estimates need to be used for denser species. In the case of forest stands where palms are dominant, local biomass regression equations would need to be developed, or at least measurements of wood density of dominant palm species would need to be measured (see section 4.).

Conifer forests: Few data on the biomass of conifer trees for tropical zones exist. To develop a preliminary biomass regression equation, data on the biomass of harvested pine trees from eight literature sources, including pine forests from the southeastern USA, India, and Puerto Rico were compiled. Several species of pine included in these sources were combined into one data base and analyzed as was done for the broadleaf forests. The resulting equation is:

(Eq. 3.1.8) Y(kg) = exp{-1.170+2.119*ln(D)} D = dbh, cm; range in dbh = 2-52 cm; number of trees =63; adjusted r2=0.98

For most situations where an estimate of the biomass density of pine forests is needed, Eq. 3.2.8 can be used. However, if time and resources are available, a local biomass regression equation should be developed.

Below is an example of how to use the biomass regression equations with stand tables. The stand table example is for a moist forest in Ghana. Biomass density of this forest was estimated using the moist equation, Eq. 3.2.3. Maximum diameters are at about the upper limit for this equation (about 150 cm).

Use if the biomass regression equation, an example

Diameter class (cm)
5-20	20-40	40-60	60-90	90-120	120-150	>150
1. Number trees/ha
794	161	25.2	12.3	3.3	1.05	0.23
2. Mid-point of class, cm a
12.5	30	50	75	105	135	155 b
3. Biomass of tree at mid-point of class using Eq. 3.2.3; kg
70.5	646	2353	6563	15375	29038	41 187
4. Biomass of all trees, t/ha = (product of rows 1 and 3)/1000°
56	104	59.3	80.7	50.8	30.5	9.5
Total aboveground biomass = sum of row 4 = 391 t/ha

^a As no additional information was available the mid-point of the diameter class was assumed to represent the class; as the classes are wide this could overestimate the biomass density estimate.

^b Assumed to be diameter of largest class; choice of this upper limit when no additional data are present is problematic (see section 3.2.4).

^c To convert kg to t

Although the approach presented here has emphasized the use of regression equations with stand tables, the regression equations can also be used with individual tree measurements from stands. Using individual tree measurements overcomes the problem of choosing the diameter of the class.

3.2.3 PROBLEMS WITH REGRESSION APPROACH

Several problems exist with this method, namely: (1) the small number of large diameter trees used in the regression equations (e.g., for the moist equation, the largest dbh was 148 cm, with only five trees >100 cm diameter), (2) the open-ended nature of the large diameter classes of the stand tables, (3) wide and often uneven-width diameter classes, (4) selection of the appropriate average diameter to represent a diameter class, and (5) missing smaller diameter classes (i.e., incomplete stand tables to minimum diameter of 10 cm). To overcome the potential problem of the lack of large trees (problem 1), equations were selected that were expected to behave reasonably up to 150 cm or so or upon extrapolation somewhat beyond this limit (Brown et al. 1989). Rarely are stand tables encountered that contain trees much larger than the maximum dbh used in the regression.

The problem with open-ended large diameter classes is knowing what diameter to assign to that class. Sometimes additional information is included that educated estimates can be made, but this is often not the case. Clearly, further improvements in reporting the distribution of the largest diameter trees in stand tables would improve the reliability of the biomass density estimates as it is often these large trees that account for significant proportions of the total biomass density (Brown and Lugo 1992, Brown 1995). In the above example, the approximately 1.3 trees greater than 120 cm constitute about 70% of the biomass represented by the 794 trees in the smallest class.

Many inventories often report stand tables with wide and/or uneven-width classes. The most unbiased biomass density estimate is obtained when diameter classes are small, about 10 cm wide or smaller, and are even-width for the whole stand table. This problem is illustrated by the following example for a moist forest where in Example A the classes are 10 cm wide and in Example B two classes are combined to make them 20 cm wide.

Example A

Diameter class (cm)
10-19	20-29	30-39	40-49	50-59	60-69	70-79	80-89	90-99	100-109	110-119	>120
Number of stems/ha
183	80	35.1	11.8	4.7	2.3	1.5	0.9	0.5	0.4	0.2	0.5
Biomass/tree (kg) at mid-point of class(Eq. 3.2.3)
112	407	954	1 802	2995	4570	6563	9008	11936	15375	19354	23900
Biomass of trees (product of rows 1 and 2), t/ha
20.5	32.6	33.5	21.3	14.1	10.5	9.8	8.1	6.0	6.2	3.9	12.0
Total biomass density =178 t/ha

Example B

Diameter class (cm)
10-29	30-49	50-69	70-89	90-109	>110
1. Number of stems/ha
263	46.9	7.0	2.4	0.9	0.7
2. Biomass/tree (kg) at mid-point of class(Eq. 3.2.3)
232	1 338	3732	7727	13590	21 555
3. Biomass of trees (product of rows 1 and 2), t/ha
60.9	62.7	26.1	18.5	12.2	15.1
Total biomass density =196 t/ha

The biomass density in Example B, based on the 20 cm wide classes, is about 10% higher than that in Example A, based on the 10 cm wide class. In general, wider classes will overestimate the biomass density. However, regular estimation of biomass density as part of inventory analysis or accessibility to the field data should not encounter these problems because original inventory data generally includes details down to individual trees. Estimating the biomass of individual trees in inventory plots directly would overcome problems (2) to (4) given above. Foresters have wide experience in these type of calculations as they are basically no different from estimating volumes from volume equations.

To overcome the problem of incomplete stand tables, an approach has been developed for estimating the number of trees in smaller diameter classes based on number of trees in larger classes (Gillespie et al. 1992). It is recommended that the method described here be used for estimating the number of trees in one to two small classes only to complete a stand table to a minimum diameter of 10 cm. It is also emphasized that this method should only be used when no other data for biomass estimation are available.

The method is based on the concept that uneven-aged forest stands have a characteristic exponential or “inverse J-shaped” diameter distribution. These distribution have a large number of trees in the small classes and gradually decreasing numbers in medium to large classes. Pull details of the theory behind the approach and of the different methods tested are given in Gillespie et al. (1992). The best method was the one that estimated the number of trees in the missing smallest class as the ratio of the number of trees in dbh class 1 (the smallest reported class) to the number in dbh class 2 (the next smallest class) times the number in dbh class 1. This method is demonstrated in the following example:

1 Assume that: the minimum diameter class is 20-30 cm and we wish to estimate the number of trees in the 10-20 cm class.

2 The number of trees in the 20-30 cm class equals 80, and the number in the 30-40 cm class equals 35.

3 The estimated number of trees in the 10-20 cm class is the number in the 20-30 cm class x (number in 20-30/number in 30-40); this equals 80 x (80/35) =183.

To use this approach, diameter classes must be of uniform width, preferably no wider than 10-15 cm, and should not be used for estimating numbers of trees in more than two “missing’ classes.

3.3 BIOMASS ESTIMATES OF INDIVIDUAL

The regression equations reported above can be applied to inventories of individual trees planted in lines, as living fence posts, for dune stabilization, for fuelwood, etc. Biomass estimates for individual trees are particularly useful in drier regions where the trees are grown for all the aforementioned products and services. However, as discussed above, the regression equations for dry zone trees are based on a small data base. Furthermore, trees grown in lines or in more open conditions generally display different branching patterns and are likely to have more biomass for a given diameter than a similar diameter tree grown in a stand. Although the above regression equations could be used where no other data exist for rough approximations, new regression equations need to be developed for trees growing in open conditions.

3.4 BIOMASS ESTIMATES FOR PLANTATIONS

Estimating the biomass density of plantations can be done using techniques similar to those for native forests as described above. Inventoried volume can be converted to aboveground biomass density using Eq. 3.1.1 outlined in Section 3.1. However, the equation for BEFs (Eq. 3.1.4) would not necessarily work in the case of plantations of broadleaf species because tree form is likely to be different in managed forests and definitions of inventoried volumes are also likely to be different. It is recommended that BEFs be locally derived. The biomass regression equations for broadleaf species (Eq. 3.2.1-3.2.5) could also be used for plantations, but once again caution should be taken with their use. Direct biomass measurements of representative plantation trees should be made to check the validity of the regression equations, or even better local biomass regression equations should be developed. See Section 4 for further details on methods.

For plantations of conifer species, the average BEF of 1.3 given for pine forests in Section 3.1.3 could be used if measured volume was based on the total stem. In the case where diameters of individual plantation trees or diameter distributions are given, Eq. 3.2.8 could be used, taking the same precautions as for broadleaf species. Kadeba (1989) developed biomass regression equations for plantations of Pinus caribaea trees growing in the savanna zone of Nigeria with annual rainfall ranging from 1250 to 1800 mm/year. However, the data base spanned a small diameter range, about 15-25 cm, and each equation was based on 12 trees only. For situations that mimic the conditions of Kadeba’s study, the reader is referred to his work.

In situations where lack of resources prevent the development of local biomass regression equations for plantations, use of any or the above approaches would give a reasonably good estimate of the aboveground biomass density.

3.5 BIOMASS OF OTHER FOREST COMPONENTS

This primer does not include methods or approaches for making biomass density estimates for (1) understorey (including e.g., bamboo or rattan), (2) belowground woody biomass such as fine and coarse roots, (3) forest floor fine litter (e.g., dead leaves, twigs, fruits, etc.), nor (4) lying and standing dead wood. Most efforts on biomass estimation to date have generally focused on the aboveground tree component because it accounts for the greatest fraction of total biomass density and the methods are straightforward and generally do not pose too many logistical problems. Commonly reported ranges of biomass density estimates for these other components are given below, although they must be used with caution as the data base on which they are built is limited.

Summary of estimates of biomass density of other forest components, expressed as a percent of aboveground biomass in trees

Component	Percent of aboveground! biomass of mature forest!
Understorey	£ 3
Belowground (roots)	4-230
Fine litter (dead plant material)	£ 5
Dead wood	5-40

(sources of these data are given in the text)

The amount of biomass in understorey shrubs, vines, and herbaceous plants can be variable but is generally about 3 percent or less of the aboveground biomass of more mature forests (Jordan and Uhl 1978, Tanner 1980, Hegarty 1989, Lugo 1992). However, in secondary forests or disturbed forest, this fraction could be higher (e.g., up to 30%; Brown and Lugo 1990, Lugo 1992) depending on age of the secondary forest and openness of canopy. Palms are common in many tropical moist forests and they are also often ignored in forest inventories. Their contribution to total biomass density can be very variable, from nearly 100 percent to less than a few percent (see section 3.2.1 above for more details on estimating the biomass of this component).

The biomass of roots varies considerably among tropical forests depending mainly upon climate and soil characteristics (Brown and Lugo 1982, Sanford and Cuevas 1996). Root biomass is often expressed in relation to aboveground biomass, such as a root-to-shoot ratio (R/S ratio). A recent review of the literature gives the R/S ratios (from Sanford and Cuevas 1996) shown in the table on the next page.

These estimates of R/S are based on only a few studies (about 30) and not all of them are consistent with respect to depth of sampling and nor whether coarse roots were included. It seems clear from this discussion that more studies of root biomass and their relationship with other factors such as aboveground biomass, climate and soil, are needed.

The amount of dead plant material in a forest, or detritus, is composed of fine litter on the forest floor, (leaves, fruits, flowers, twigs, bark fragments, branches less than 10 cm diameter, etc.), standing dead trees and snags, and lying dead wood greater than 10 cm diameter. The biomass density of fine litter ranges from about 2 to 16 t/ha (average of 6 t/ha or less than 5% of aboveground biomass), with higher values generally in moist environments although no clear trend is apparent in the data base (Brown and Lugo 1982). The amount of fine litter on the forest floor represents the balance between inputs from litterfall and outputs from decomposition, both of which vary widely across the tropics.

The amount of dead wood in tropical forests is poorly quantified but extremely variable. It is potentially a large pool of organic matter, perhaps accounting for an amount equivalent to less than 10 percent to more than 40 percent of the aboveground biomass of a forest depending upon forest age and climatic regime (Saldarriaga et al. 1986, Uhl et al. 1988, Uhl and Kauffman 1990; Delaney et al. 1997). Lack of data on this significant forest component obviously can lead to underestimates of the total amount of biomass in a forest.

It is clear from the above discussion that ignoring these other forest components can seriously underestimate the total biomass of a forest by an amount equivalent to about 50 percent or more of aboveground biomass. Although beyond the scope of this primer, it is apparent that logistically and economically feasible methods and approaches must be developed to estimate this significant quantity of biomass and its range of uncertainty, especially for improving estimates of terrestrial sources and sinks of carbon and biogeochemical cycles of other elements.

Forest type	Range of R/S	Average R/S
Moist forest growing on spodosols	0.7 - 2.3	1.5
Lowland moist forest	0.04 - 0.33	0.12
Montane moist forests	0.11 - 0.33	0.22
Deciduous forests (e.g., tropical dry and seasonal forests)	0.23 - 0.85	0.47

4.1 IMPROVEMENTS IN FOREST INVENTORIES

The data requirements for estimating aboveground biomass density are basically no different from those for estimating volumes from forest inventories. Careful measurement and reporting of all tree diameters and volume in inventory plots as part of a statistically sound sampling design are the basic primary data needs, data that foresters have been collecting for decades. To improve the data for biomass estimation, all inventories should be conducted according to some agreed-upon standard. The effort and resources involved in conducting forest inventories is difficult to match by other field data collection programs, thus it is important that as new inventories are planned the need for new types of data be considered, such as change in biomass for use in making greenhouse gas emission inventories. By including a few extra measurements or analyses at marginal costs, so much more information useful for biomass estimation can be obtained. This section will focus on what additional measurements need to be taken to improve the utility of forest inventories for biomass estimation. As with all inventories, the forested area under study will need to be stratified into distinct strata, and the corresponding areas estimated. The product of biomass per ha for a given strata and the corresponding area will result in an estimate of the total biomass for the region.

· As a minimum, all trees of all species, whether presently commercial or not, to a minimum diameter of 10 cm or lower should be measured. Palm trees should also be inventoried where they occur. In drier forest formations or secondary forests, a larger proportion of the biomass will be in smaller diameter trees. Therefore, all trees to smaller diameters should be measured, e.g., to at least 5 cm minimum diameter.

· Stand tables should be reported with diameter classes of even width and no larger than 10 cm wide. All field measurements need to be archived so that follow-up studies or questions could be addressed.

· Stand tables must not group large diameter trees into one diameter class. Because of the importance of large trees to biomass density, serious errors could be introduced when assumptions about the number of large trees/ha are made.

· Estimation of volume should be standard for all inventories. However, standards are likely to vary among closed forests, open woodlands, and secondary forests, and the definition of measured volume must be clearly stated in inventory reports.

· For open forests/woodlands, young secondary forests, plantations, and individual trees plantings, it is preferable to use stand tables using small diameter classes or individual tree measurements.

· All inventory plots need to be located exactly on a map; this would allow for follow-up studies to measure biomass change for example.

· Information about inventoried areas and plots should be recorded, such as average height of stand, climatic zone, presence of natural disturbances (e.g., wildfires, tropical storms), average soil type (e.g., standard soil classification system and soil texture), elevation, and general topography (lowland, hilly, mountainous, steep slopes, etc.).

· Information on the degree of human disturbance, either past or present, in the inventory area is needed because it can affect aboveground biomass estimates and aid in explaining the estimates. Evidence of human disturbance includes presence of tree stumps, harvesting activity, logging roads or trails (old or new), other roads or tracks, cleared or young secondary forest patches (e.g., resulting from slash-and-burn agriculture), charcoal pits, and identification of tree species favored by humans.

· Other information such as distance to human settlements and fragmentation of forest area are also useful for assessing likely human impacts. A useful index for assessing fragmentation of forest is the ratio of the length of the perimeter of the forest to the area of the forest (perimeter-area ratio). This can be measured from aerial photos (often used in forest inventories) or remote sensing imagery if available.

4.2 FIELD MEASUREMENTS FOR DEVELOPING BIOMASS REGRESSION EQUATIONS

The aboveground oven-dry-weight of trees can be measured directly by felling them, oven-drying all components and then weighing them. However, it is not realistic to do this for all inventories. Instead, a practical solution is to develop regression equations based on data from felled trees where this is possible. Such functions should use some easily measurable dimension such as diameter (and sometimes height) as presented in Section 3.2.1. As discussed above, the equations presented in this primer are based on a relatively limited data base, especially for dry forest and conifer forest formations, and improvements in biomass estimation can be made with additional tree data.

During many forest inventories, a number of trees are felled to generate local volume equations. These same felled trees can be used for developing local biomass equations using the methods given below. In other situations, trees need to be selected for felling. The selection of these trees in multi-species forests poses a challenging sampling design, and it is recommended that the assistance of a forest biometrician be sought. However, as a guide, the selected trees must come from the population of interest, represent the major species in the forest, and represent all size classes. It is particularly important that trees in the larger diameter classes be well represented even though estimating their biomass is very time consuming because of their large size. If resources are limited, it is recommended that a couple of trees representing small, medium, and large diameters be selected and their biomass measured as described below. These could then be compared to the estimates derived from the appropriate regression equation, and if they are within acceptable limits no further sampling is needed. If, however, they are not within acceptable limits further sampling and biomass measurements would be needed to develop local biomass equations.

1 As for volume equations, dbh, or diameter above the buttress, must be measured. In the case of multi-stemmed trees, common in dry or open forests and woodlands, the diameter at 0.3 m above the ground is often used instead of dbh, and height measurements are also recommended (for further details see Stewart et al. 1992).

2 After the trees are felled as close to the ground as possible, they should be divided into their components, including main stem, branches of different size classes, leaves and twigs, and fruits.

3 Small branches (<10 cm basal diameter), leaves and twigs, and fruits should be weighed fresh in the field. Several sub-samples (at least five) of each component must be collected and their fresh weight determined. Then they must be oven dried to constant weight at 105°C. Weighing of dried sub-samples should be done as soon as possible after removing them from the oven because they soon absorb moisture and gain weight. For each sub-sample, a ratio of oven-dry-to-fresh weight can be calculated and an average ratio calculated. Multiplication of the total fresh weight of each component by the corresponding oven-dry-to-fresh-weight ratio will result in an estimate of the dry weight of the component.

4 For the larger branches and main stem (>10 cm diameter), it is generally not practical to weigh these fresh in the field. Instead, they should be cut into sections and the volume of each section calculated. The oven-dry-weight of these sections is determined as the product of volume and density (oven-dry-weight per unit of green volume). To estimate density, a disk of wood from each section should be removed. The volume of the disk can be calculated as the cross-sectional area of the disk times the thickness (measured at four points, 90° to each other) or by the water-displacement method. The water-displacement method is based on the principle that an immersed object displaces its own volume of water. The disk is carefully immersed in a container of water (pushing it down with a sharp pointed object), and the increase in water level when the disk is fully immersed is used to measure the increase in volume. To improve accuracy the container of water can be made absolutely full and as the disk is immersed all the water displaced by the disk is collected in a previously weighed container. The weight of the water displaced is weighed in grams and equals the volume of the disk in cm³, because 1 g of water has a volume of 1 cm³. Thus if the weight of the water displaced is 20 g, then the volume of the disk is 20 cm³. After volume measurement the disk is oven dried to constant weight at 105°C; this weight divided by its volume gives density. The weight of the stem and branches is then calculated, making sure all the measurements are in the same units (volume in cm³ and density in g/cm³).

5 The sum of weights of all the components results in the total oven-dried weight of the tree, generally expressed in kg.

Once the biomass of all selected trees has been determined, a regression equation similar to those given in the table in Section 3.2.1 can be developed using an available statistical package. Once again, it is recommended that the assistance of a forest biometrician or statistician be sought at this stage. For trees from the dry zone it is recommended that the oven-dry weight of the tree be correlated to the height (H) as well as diameter (D). Such an equation could take the form:

mass of tree = a + b * HD2, where a and b are regression coefficients

To develop local regression equations for palm trees, height is a better measure of biomass than diameter (see Section 3.2.1). At least three individuals from several height classes should be felled. For each palm tree, the height of the main stem should first be measured. Then the stem should be cut into three to four approximately equal length sections, depending upon the height, and their volume estimated. A disk from each section should be removed and its density (oven dry weight per green volume) measured as described above in step 4. The weight of the stem is then the sum of the product of the volume of each section and its density. The weight of the leaves then has to be determined. This can be determined by first counting the total number of leaves. Then about three to four leaves should be selected and the average of their oven-dry weight (at 105°C) determined. The total oven-dry weight of all leaves is calculated as the number of leaves multiplied by the dry weight per leaf. The oven-dry weight of the inflorescence (flower or fruit) should also be measured and added to obtain the total weight. A simple linear regression equation of total oven-dry biomass of the palm versus its height should be developed as given above in Section 3.2.1.

(introduction...)

The following tables for the three main tropical regions include examples of biomass estimates for different forest types estimated from forest inventories and the methods described above. All biomass estimates are based on trees to a minimum dbh of 10 cm and represent average values over the inventory area, which in some countries is the total forest area (indicated by the word “National” in parenthesis in the following tables).

5.1 TROPICAL AFRICAN COUNTRIES

Country	Forest type	General climate	Aboveground biomass (t/ha)
Benin	Closed forest	Dry	175
	Tree savanna	Dry	96
Burkina Faso (National)	Degraded tree savanna	Dry - long dry season	20
Cameroon	Primary	Moist	310
Gambia (National)	Gallery forest	Moist - dry season	140
	Closed woodland	Dry	97
	Open woodland	Dry	50
	Tree savanna	Dry	28
Ghana	Closed forest	Moist-short dry season	395
Guinea (National)	Mixed: closed, open, secondary	Moist	135
Mozambique	Dense forest	Moist - short dry season	120
	Dense forest	Moist - dry season	130
	Dense forest	Dry - long dry season	70

Inventory sources in order by country are: Marsch 1976, FAO 1983, Centre Technique Forestier Tropical 1969, Forster 1983, Alder 1982, ATLANTA Consult, n.d., FAO 1981.

5.2 TROPICAL AMERICAN COUNTRIES

Country	Forest type	General climate	Aboveground biomass (t/ha);
Bolivia	Closed forest	Moist	230
Brazil	Closed forest	Moist	315
Ecuador	Closed forest	Moist	182
French Guyana	Closed forest	Moist	309
	Riparian forest	Moist	275
	Savanna forest	Moist	205
Guatemala (Peten area)	Closed forest	Moist	242
Guyana (1)	Closed forest	Moist	254
	Logged forest	Moist	190
	Wallaba forest	Dry	145
Guyana (2)	Mixed forest	Moist	275
	Low mixed forest	Moist	192
	Liana forest	Moist	125
	Wallaba forest	Moist	148
	Wallaba forest on white sands	Moist	405
Nicaragua (1)	Orifino forest	Moist	240
	Lowland mixed	Moist	235
Nicaragua (2)	Mature forest	Moist	240
	Secondary	Moist	183
Panama	High density-mixed	Moist	239-366
	Low density-mixed	Moist	169-245
	Campnosperma forest high density	Moist	860
	Campnosperma forest low density	Moist	470
	High density-mixed	Wet	194-214
	Low density-mixed	Wet	120-125
	High density-mixed	Pre- & Lower montane rain	186-252
	Low density-mixed	Pre- & Lower montane rain	118-143
Peru	Primary	Moist	210
	Lightly logged	Moist	192
	Heavily logged	Moist	125
	Late secondary	Moist	140
	Young secondary	Moist	20
	Flooded secondary	Moist	195
	Low forest	Moist	155
Surinam	Upland forest	Moist	255
	Small crown-upland	Moist	136
	Savanna forest	Moist	195
	Riparian forest	Moist	217
	Liana forest	Moist	120
	Wallaba forest	Moist	250
Venezuela	Semi-deciduous	Dry	78

Inventory sources In order by country are: Sachtler 1979, FAO 1978, FAO 1971 a, Centre Technique Forestier Tropical 1975, FAO 1970a, Rees, 1963, FAO 1970b, FAO 1969, FAO/UNDP 1972, FAO 1972, Jankovic 1969, de Milde and Inglis 1974a,b, FAO 1971b

5.3 TROPICAL ASIAN COUNTRIES

Country	Forest type	General climate	Aboveground biomass (t/ha)
Bangladesh (1)	Closed - large crowns	Moist	210
	Closed - small crowns	Moist	150
	Disturbed closed	Moist	190
	Disturbed open	Moist	85
Bangladesh (2)	Closed - large crowns	Moist	206
	Closed - small crowns	Moist	162
Cambodia (1)	Dense	Moist	295
	Semi-dense	Moist	370
	Secondary	Moist	190
	Open	Moist	160
	Open	Dry	70
Cambodia (2)	Well to poorly stocked evergreen	Moist	100-155
	Deciduous	Moist	120
India	High to low volume closed	Dry	44-81
	Forest fallow	Dry	16
Malaysia-Peninsular (National)	Superior to moderate hill	Moist	245-310
	Poor hill	Moist
	Upper hill	Moist	275
	Disturbed hill	Moist	200
	Logged hill	Moist	180
	Forest fallow	Moist	140
	Freshwater swamp	Moist	220
	Disturbed freshwater swamp	Moist	285
	Logged freshwater swamp	Moist	185
Malaysia - Sarawak	Mixed dipterocarps-dense stocking, flat to undulating	Moist	325-385
	terrain
	Mixed dipterocarps-dense stocking, mountainous	Moist	330-405
	Mixed dipterocarps-medium stocking, flat to mountainous	Moist	280-330
Myanmar	Evergreen	?	60-200
	Mixed deciduous	?	45-135
	Indaing forest	?	10-65
Philippines	Old-growth dipterocarp	Moist	370-520
	Logged dipterocarp	Moist	300-370
Sri Lanka	Evergreen-high yield	Moist	435-530
	Evergreen-medium yield	Moist	365-470
	Evergreen-low yield	Moist	190-400
	Evergreen-logged	Moist	255
	Secondary	Moist	280

Inventory sources in order by country are: de Milde et al. 1985, Drigo et al. 1988, Rollet 1962, FAO 1971c, Government of India 1972, Government of Malaysia 1987, FAO 1973, FAO 1984-1985, Philippine-German Forest Resources Inventory Project, 1986-1988, FAO/UNDP 1969, Royal Forest Department of Thailand 1980.

(introduction...)

A new approach for estimating biomass density will be presented in this section that is based on a modelling method with GIS (geographic information systems) technology. It uses various existing digital data bases and maps of reliable inventories, population density, climate, vegetation, ecofloristic zones, soils, and topography. This method was developed as a means to extrapolate reliable inventory data that is generally limited in area coverage to biomass density estimates at larger scales, such as continents.

6.1 GENERAL APPROACH

Forest biomass density has been modelled in a multi-stage approach using GIS software packages and a variety of spatial and statistical data bases. For estimating forest biomass density using a GIS, the present distribution of forest biomass density was assumed to be based on the potential amount that the landscape can support under prevailing environmental conditions, and the cumulative impact of human activities on forests that reduce its biomass density. Many spatial data layers have been developed from existing data bases or were prepared by specialists (e.g., for the FRA 1990 Project; FAO 1993). These data layers were entered into a GIS (in this case ARC/INFO-GRID) and processed according to specifications of the model. The following GIS data layers, or digital maps, and statistical data bases were used as inputs to the modelling effort (further details are given in Brown et al. 1993, Iverson et al. 1994):

· Climatic index map based on FAO meteorological station data
· Precipitation map
· FAO soils map, recoded into depth, textural, and slope classes
· Topography map
· FRA 1990 Ecofloristic Zone maps
· FRA 1990 vegetation maps
· National and subnational boundary map
· Subnational population data (1970, 1980, and 1990) from FAO
· Biomass density estimates from reliable forest inventory data using the above methods

The first step in this analysis was to estimate a potential biomass density (PBD) for forests. This was accomplished by first developing an index of potential biomass density based on climatic, edaphic, and topographic factors. The potential biomass density map was masked with a forest map, produced by reclassifying all the forest classes of the FRA 1990 vegetation maps into one forest class. The potential biomass density index (PBI) was calculated according to a simple model, based on overlaying the following GIS data layers:

PBI = climatic index + precipitation + soil (texture, depth, slope) + topography

Each of these factors was spatially represented by a numerical scale whose values were ranked according to how the particular factor affected forest biomass (details of the scaling are given in Iverson et al. 1994). The digital maps were overlain according to the above model and the results calibrated and validated using existing forest inventories for mature forests, literature sources for small scale ecological studies, and the FRA 1990 ecofloristic zone (EFZ) map (Figure 2).

Figure 2 - Map of estimates of potential biomass density classes for forests of tropical African countries. The units are in Mg/ha which is the same as t/ha (1 Mg = 106 g = 1 ton).

The final step was to add the influence of all human activities that result in a reduction of biomass of forests. This step was accomplished by using the biomass estimates from the reliable forest inventory data. The first step was to calculate degradation ratios, defined here as biomass density estimates from the inventories (representing all forests of a subnational unit) divided by the potential biomass density for all forests in the same subnational unit. Several models were tried, but the best results (highest r²) were obtained from linear models of degradation ratios versus the natural logarithm of population density of the subnational unit, stratified into main ecoregions or forest types (e.g. closed forests and woodlands). A similar relationship was found for forest cover and population density stratified by ecological and geographic zones (FAO 1993). The significant relationships between degradation ratio or forest cover and population density are not meant to imply that the relationships are causal; many other socio-economic factors are involved. However, the empirical relationships are useful because data on population density are generally readily available, but reliable data on other socio-economic factors are often not so available.

Figure 3 - Relationship between degradation ratios (biomass density estimates from the inventories divided by the potential biomass density for all forests in the same subnational unit) and the natural logarithm (Ln) of the population density (number of persons/km²) for (a) closed forests and (b) open forests and woodlands. The second row of numbers on the x-axis of (a) are the actual population densities corresponding to the natural logarithm values.

(a) Closed forest

(b) Woodland

The data and regression equations used for the map of Africa are shown in Figure 3. The data base for these regression equations is a combination of data from inventories in tropical Asia and Africa. The estimates of degradation ratios for a given population density for these two regions overlap well perhaps implying a similar pattern of forest use. These regression equations were used with the potential biomass density map and population data to produce a map of degradation ratios by subnational regions for each tropical African country as shown in Figure 4. The degradation ratio map was then used with the potential biomass density map to produce a map of actual forest biomass density. As with all regression equations, the ones shown in Figure 3 should not be extrapolated too much beyond the data. Degradation of forest biomass density is not expected to continue according to the trends shown in Figure 3 at high population densities. For example, in the more industrialized countries, deforestation and degradation have been reversed irrespective of population development as other socioeconomic factors exert more influence.

6.2 EXAMPLES OF RESULTS

To date, this method has been completely applied to the tropical Asian and Africa regions only. An example of the map of actual biomass density for tropical Africa is shown in Figure 5. Verification of this approach with inventory data not used in the model suggests that it is sound. However, the results are presently limited by the quality of the input data such as the small number and coverage of reliable forest inventories, FAO soils map, and the current vegetation maps. Improvements in reliability of estimates will be brought about with improvements in these input data.

The results of the GIS modelling can be by region, subregion, or country. They will usually consist of area-weighted biomass density estimates (t/ha) summarized by various vegetation and ecofloristic zones (see below). Because of the low resolution in the input data bases, reporting below the national scale will result in decreasing reliability of results. It is therefore justified only in large countries such as India or Zaire.

Estimates of potential and actual biomass (density and total), as determined by modelling in a GIS, for most of the tropical countries in tropical Asia and Africa are shown in Table 6.1. Many of these values can be compared to the biomass density estimates given in Section 5 as a means of validating the modelling results.

Figure 4 - Map of degradation ratios, grouped into five classes, for subnational units of tropical African countries which contain forests.

Figure 5 - Map of estimates of actual biomass density classes for forests of tropical African countries. The units are in Mg/ha which is the same as t/ha (1 Mg = 106 g = 1 ton).

As of about 1980, the actual biomass densities of forests in tropical Asia were about 50% of their potential densities. Forests of Brunei, Cambodia, and Sarawak/Sabah have the highest actual biomass densities, >300 t/ha, representing about 58-72% of their potential amount. At the other end of the scale are the forests of Bangladesh and India that have actual biomass densities of<170 t/ha, or about 37% of their potential. Forests in these two countries appear to have been subject to high human pressure that has reduced the forest stock to very low values on average.

Of the 37 African countries with tropical forests, about half contained forests that appear to contain less than 60% of their potential biomass density. On average, forests in Congo, Equatorial Guinea, Gabon, and Liberia have the highest actual biomass densities at > 300 t/ha which represents about 65-92% of their potential biomass density. All these countries are dominated by lowland moist forests. Countries dominated by very dry climates such as Botswana, Somalia, Zimbabwe, and countries of the Sahel have the lowest actual biomass densities, <50 t/ha.

Table 6.1

Area-weighted average potential (without human impacts) and actual (with human impacts as of about 1980) biomass density estimates (t/ha) and degradation ratios (DR = inventory biomass density divided by potential biomass density) for tropical Asian and African countries.

Country/Region		Potential (t/ha)	Actual (t/ha)	DR
ASIA
Bangladesh		463	170	0.37
Brunei		577	382	0.66
Cambodia		419	301	0.72
India		348	129	0.37
Indonesia		533	262	0.49
Laos		342	272	0.80
Myanmar		388	231	0.60
Malaysia
	Peninsular	518	210	0.41
	Sarawak/Sabah	571	331	0.58
Philippines		511	223	0.44
Sri Lanka		413	200	0.48
Thailand		356	185	0.52
Vietnam		372	262	0.70
Average (area weighted)		437	224	0.5?
AFRICA
Angola		100	73	0.73
Benin		112	58	0.52
Botswana		15	13	0.91
Burkina Faso		65	34	0.53
Burundi		119	43	0.36
Cameroon		307	217	0.71
CAR		243	200	0.68
Chad		63	43	0.68
Congo		374	344	0.92
Cote d’Ivoire		276	165	0.60
Equatorial Guinea		442	318	0.72
Ethiopia		101	52	0.51
Gabon		375	339	0.90
Gambia		64	29	0.45
Ghana		182	83	0.45
Guinea		259	140	0.54
Guinea Bissau		153	85	0.55
Kenya		58	33	0.57
Liberia		466	305	0.65
Madagascar		322	196	0.61
Malawi		108	47	0.44
Mali		75	45	0.60
Mozambique		96	57	0.60
Niger		16	9	0.53
Nigeria		128	49	0.38
Rwanda		103	34	0.33
Senegal		50	32	0,62
Sierra Leone		411	199	0.48
Somalia		20	13	0.63
Sudan		95	64	0.67
Tanzania		83	45	0.55
Togo		155	72	0.46
Uganda		237	102	0.43
Zaire		297	206	0.69
Zambia		67	47	0.70
Zimbabwe		26	14	0.51

The biomass data were also summarized by ecoregions (reclassified ecofloristic zone map) as shown in Table 6.2. The general trends in biomass density by ecoregion for tropical Asia and Africa are consistent with expected patterns of biomass distribution: decreasing biomass density with decreasing moisture and increasing elevation. Most of the total biomass in continental Asia is in the lowland seasonal forests (52%) and only a small fraction is in the lowland dry forests (2%). However, in insular Asia, 89% of the total biomass is in the lowland moist ecoregion with only a trace in the seasonal zone. Forests in the continental dry zone and the insular seasonal zone appear to be the most degraded with actual biomass densities that are about 30-40% of their potential.

In general, forests in a given ecoregion of Africa have degradation ratios closer to 1.00, i.e., forest are less degraded than those of Asia (Table 6.2), presumably because of the lower population densities in the forested regions of Africa at present. The degradation ratios indicate that degradation increases with increasing aridity in both lowland and montane ecoregions of Africa, and that montane zones are more seriously degraded than lowland zones. The more advanced degradation of the montane zones of Africa probably reflect the more favorable climate that is generally preferred for human habitation and agriculture.

Table 6.2

Area-weighted average potential (without human impacts) and actual (with human impacts as of about 1980) biomass density (t/ha), and degradation ratios (DR = inventory biomass density divided by potential biomass density) for forests of tropical Asia and Africa by ecoregion.

Ecoregion	Potential	Actual (t/ha)	DR
CONTINENTAL TROPICAL ASIA:
Lowland moist	449	225	0.50
Lowland seasonal	350	187	0.53
Lowland dry	244	76	0.31
Montane moist	353	222	0.63
Montane seasonal	306	155	0.51
INSULAR TROPICAL ASIA
Lowland moist	543	273	0.50
Lowland seasonal	452	174	0.38
Montane moist	504	254	0.50
TROPICAL AFRICA
Lowland moist	412	299	0.73
Lowland seasonal	211	141	0.67
Lowland dry	92	60	0.65
Lowland very dry	33	20	0.61
Montane moist	197	105	0.53
Montane seasonal	78	37	0.47

(introduction...)

A goal of any forest resource monitoring program must be to address change in biomass as well as its state. Estimating national emissions of CO₂ on a regular basis or monitoring CO₂ mitigation projects also require information on change in biomass, both losses or gains. Because no real attempts have been made in the past to estimate the state of biomass in most tropical countries, estimating its change becomes a very difficult task. To substantially reduce errors in estimates of the state of biomass and its change, a new approach is needed that will incorporate in its design methodology for monitoring biomass density and its change more directly.

One way to estimate changes in biomass density and total biomass is to combine data on forest area change, by vegetation type, with biomass density values for the same vegetation types. Vegetation-cover transition matrices produced from the interpretation of remote sensing imagery, such as were produced by the FRA 1990 Project (FAO 1995) can provide data on forest-area change. This approach will only provide an “educated estimate” of biomass change because although the area change will have a high degree of statistical reliability at the regional and global levels, there will have been little to no improvement in the forest biomass density values.

7.1 BIOMASS CHANGE WITH FIELD STUDIES

A more comprehensive and reliable approach for estimating biomass change is to combine new field studies with analysis of high resolution remote sensing imagery. The remote sensing efforts would be used to delineate forests into various distinct biomass strata. Then, using a statistical design, permanent plots in these forest strata could be established, for example, by using the methods described in Lund (1992). For estimating biomass density directly, stand tables are sufficient with use of the generic biomass regression equations given in Section 3.2, or locally derived ones where resources allow (see Section 4.). At least two measurements on permanent plots are needed to estimate biomass change. These measurements should preferably be a minimum of 5 year apart, particularly in forests vulnerable to change. However, results from a one-time survey combined with change in area data could provide a more reliable pan-tropical estimate of biomass change than any one available at present.

The established permanent plots must be remeasured regularly and be integrated into a framework of continuous forest monitoring. This approach will provide the most accurate and precise estimates of total biomass and biomass density change for policy makers.

7.2 BIOMASS CHANGE WITH REMOTE SENSING/FIELD STUDIES AND GIS MODELLING

A key component of any resource monitoring program is to try and understand the processes affecting change. A better understanding of the processes involved will lead to improved planning and control of forest biomass degradation and ultimately economic savings. Furthermore, understanding the mechanisms involved in forest biomass change will enable predictions about future trends in forest resources to be made which will be directly applicable to the local demands, timber trade, and global issues of concern such as global change and biodiversity.

The basic approach would be to develop a model that relates loss in biomass density, e.g., the degradation ratios, to readily measurable indices of human activity and bio-physical parameters. A similar model could be developed for forest area loss, so that a combination of the two models would give total biomass change. The availability of modem technology of remote sensing and GIS makes this task feasible. The models should include the spatial component of the landscape and human activities.

The approach would be to use the GIS data layers such as those identified above in Section 6.1 and new ones as they become available. New data layers will likely result from remote sensing and field studies. These new GIS data layers could be combined in models to obtain various indices that most likely influence or are influenced by human use of the land such as: (1) bio-physical and climatic factors, (2) fragmentation indices (e.g., perimeter/area ratios of forest area), (3) transportation networks (e.g., roads, rivers, railways), (4) population density and it rate of growth, and (5) socio-economic and political factors (e.g., land tenure, market availability, GNP/capita). How bio-physical and climatic factors regulate potential forest biomass is reasonably well understood. What is not well understood is what measure of human activity should be used in the models. To date, percent forest cover and degradation of forest biomass density have been shown to be highly correlated with population density at subnational levels when stratified into ecological zones. But, as stated above, the high correlation does not imply causal relationships. We know that deforestation and degradation cannot continue indefinitely; eventually a limit is reached, beyond which forest cover and biomass density levels. Moreover, other socioeconomic factors will eventually become active and reverse the trends as has been the case in industrialized countries.

To bring about these new directions in estimating state and change in biomass will require a concerted effort and expenditure of resources. A first step is to increase training in GIS analysis, biomass estimation, field-inventory operations, and remote sensing analysis, in all countries. This could be accomplished by developing networks between forestry personnel in tropical countries and institutions performing these kinds of activities. A sharing of data bases among countries is needed also so that the issues of forest cover and biomass change can continue to be monitored with improved understanding of the mechanisms and processes that influence the changes.

8. REFERENCES

Alder, D., 1982. The development of forest energy resources Ghana. Forest inventory report for Subri River, Bonsa River, Neung and Pra Suhien forest reserves. GHA/74/013, Field document, Project Report No. 2, FAO, Takoradi, Ghana.

ATLANTA Consult, n.d. Inventaire forestier de la Guinee forestiere. Rapport de Synthese, Hamburg, Germany.

Bandhu, D., 1973. Chakia project tropical deciduous forest ecosystems. Pages 39-61 m L. Kern (ed.), Modeling forest ecosystems. EDFB-IBP-737, Oak Ridge National Laboratory, Tennessee, USA.

Brown, S., 1990. Volume expansion factors for tropical forests. Unpublished Paper, Prepared for the Forest resource Assessment-1990 Project (available from author).

Brown, S., 1996. Tropical forests and the global carbon cycle: estimating state and change in biomass density. Pages 135-144 in M. Apps and D. Price (eds.), The role of forest ecosystems and forest management in the global carbon cycle. NATO Series. Springer Verlag. NY.

Brown, S. and G. Gaston, 1995. Use of forest inventories and geographic information systems to estimate biomass density of tropical forests: application to tropical Africa. Environmental Monitoring and Assessment, in press.

Brown, S., A. J. R. Gillespie, and A. E. Lugo, 1989. Biomass estimation methods for tropical forests with applications to forest inventory data. Forest Science 35:881-902.

Brown, S., A. J. R. Gillespie, and A. E. Lugo, 1991. Biomass of tropical forests of South and Southeast Asia. Canadian Journal of Forest Research 21:111-117.

Brown, S. and L. R. Iverson, 1992. Biomass estimates for tropical forests. World Resources Review 4:366-384.

Brown, S., L. R. Iverson, A. Prasad, and D. Liu, 1993. Geographic distribution of carbon in biomass and soils of tropical Asian forests. Geocarto International 8(4):45-59.

Brown, S. L., Iverson, and A. E. Lugo, 1994. Land use and biomass changes of forests in Peninsular Malaysia during 1972-82: use of GIS analysis. Chapter 4 in V. H. Dale (ed.), Effects of land use change on atmospheric CO₂ concentrations: Southeast Asia as a case study. Springer Verlag, NY.

Brown, S. and A. E. Lugo, 1982. The storage and production of organic matter in tropical forests and their role in the global carbon cycle. Biotropica 14: 161-187.

Brown, S. and A. E. Lugo, 1984. Biomass of tropical forests: A new estimate based on forest volumes. Science 223:1290-1293.

Brown, S. and A. E. Lugo, 1990. Tropical secondary forests. Journal of Tropical Ecology 6:1-32.

Brown, S. and A. E. Lugo, 1992. Above ground biomass estimates for tropical moist forests of the Brazilian Amazon. Interciencia 17:8-18.

Brown, S., J. Sathaye, M. Cannell, and P. Kauppi, 1996. Mitigation of carbon emissions to the atmosphere by forest management. Commonwealth Forestry Re view 75:80-91.

Callister, D. J., 1992, Illegal tropical timber trade: Asia-Pacific. TRAFFIC International, Cambridge, UK. 83 pp.

Centre Technique Forestier Tropical, 1975. Inventaire papetier en Guyane Francaise. Fascicule I, II, and III. Republique Francaise de la Guyane, Office National des Forets.

Centre Technique Forestier Tropical, 1969. Republic Federal du Cameroon. Inventaire de 100.000 hectares de foret dense dans la region d’Edea. Fascicule I and II, Nogent-sur-Marne, France.

Delaney, M., S. Brown, A.E. Lugo, A. Torres-Lezama and N. Bello-Qunitero, 1997. The quantity and turnover of dead wood in permanent forest plots in six life zones of Venezuela, Biotropica, in press.

de Milde, R. and C. J. Inglis, 1974a. Forestry development in Surinam. Inventory of the Kabalebo area. Project Working Document No. 9, FO:/SF/SUR/71/506, FAO, Rome, Italy.

de Milde, R. and C. J. Inglis, 1974b. Forestry development in Surinam. Inventory of the Fallawatra area. Project Working Document No. 7, FO:/SF/SUR/71/506, FAO, Rome, Italy.

de Milde, R., Md. Shaheduzzaman, and R. Drigo, 1985. The high forest in the Chittagong District. Volumes 1 and 2, FAO/UNDP Project BGD/79/017, Field Document No. 11, Rome, Italy.

Drigo, R., Md. Shaheduzzaman, and J. A. Chowdhury, 1988. Inventory of forest resources of southern Sylhet Forest Division. Assistance to the Forestry Section-Phase ii. FAO/UNDP Project BGD/85/085, Field Document No. 3, Rome, Italy.

FAO, 1969. Estudio de los recursos agricolas y forestales del norteste, Nicaragua. Informe Final, Tomo iii. El desarrollo Forestal, Appendecies al Tomo iii. FAO/SF: 49/NIC-2, Rome, Italy.

FAO/UNDP, 1969. Pre-investment study on forest industries development. Ceylon, Final Report, Vol. II. Forest resources and management. FAO/SF: 60/CEY-5, Rome, Italy.

FAO, 1970a. Estudio de preinversion sobre desarrollo forestal. Guatemala. Informe Final Volumen ii, FAO/SF: 81/GUA 6, Rome, Italy.

FAO, 1970b. Forest industries development survey: Guyana. Inventory of the Ebini-Itaki area. Technical Report 9, FO: SF/GUY 9, Rome, Italy.

FAO, 1971a. Estudio de preinversion para del desarrollo forestal del Noroccidente, Ecuador. Informe Final, Tomo ii, Rome, Italy.

FAO, 1971b. Estudio de preinversion para del desarrollo forestal de la Guyana Venezolana, Venezuela. Informe Final, Tomo iv, Los recursos forestales y sus posibilidades para uso industrial. FAO/SF: 82/VEN 5, Rome, Italy.

FAO, 1971c. Forest survey of the lowlands west of the Cardamomes Mountains, Cambodia. Final Report. FAO/SF: 9 I/CAM 6, Rome, Italy.

FAO/UNDP, 1972. Investigacion sobre el fomento de la produccion de los bosques del noreste de Nicaragua. Inventario forestal de bosques latifoliados. FO: SF/NIC 9, Informe Tecnico 2, Rome, Italy.

FAO, 1972. Inventariacion y demonstraciones forestales. Panama. Reconocimiento general de los bosques y inventario detallado de Azuero. Vol 1. Methodo y realizacion de inventarios; Vol III. Resultados y comentaries. FO: SF/PAN 6, Informe Tecnico No. 12, Rome, Italy.

FAO, 1973. The timber species of the mixed dipterocarp forests of Sarawak and their distribution. A study prepared jointly by the Forest Department of Sarawak and the Forest Industries Development Project of the Food and Agriculture Organization of the United Nations. FO: DP/MAL/72/009, Working Paper 21, Kuala Lumpur.

FAO, 1978. Metodologia e procedimentos operacionais para o inventario de pre-investimento na floresta nacional do Tapajos. Projecto de desenvolvimento e pesquisa florestal. PNUP/FAO/IBDF/BRA/76/027, Ministerio da Agricultura, Brasilia.

FAO, 1981. Evaluacion de los recursos forestales de la Republic Popolar de Mozambique. Rome, Italy

FAO, 1983. Developpement des ressources forestieres et renforcement du service forestier. Haute-Volta. Inventaire Forestier National. FO: DP/UPV/78/004, Rapport Technique 3, Rome, Italy.

FAO - Forest Department of Burma, 1984-1985. National forest survey and inventory of Burma. FO:BUR/79/011 Working Papers Nos. 5, 7-12, Forest Department of Burma, Rangoon.

FAO, 1993. Forest resources assessment 1990 tropical countries. FAO Forestry Paper 112, Rome Italy.

FAO, 1995. Forest resources assessment 1990 global synthesis. FAO Forestry Paper 124, Rome Italy.

Forster, H., 1983. Evaluation of the national forest inventory of The Gambia. Gambian-German Forestry Project, PN 79.2145.5, Report No. 10, DFS GTZ.

Frangi, J. L. and A. E. Lugo, 1985. Ecosystem dynamics of a subtropical floodplain forest. Ecological Monographs 55:351-369.

Gillespie, A. J. R, S. Brown, and A. E. Lugo, 1992. Tropical forest biomass estimation from truncated stand tables. Forest Ecology and Management 48:69-88.

Government of India, Ministry of Agriculture, 1972. Preinvestment survey of forest resources in East Godavari (A.P.): inventory results. technical Report 3(2), New Delhi.

Government of Malaysia, 1987. Inventori hutan nasional II semenanjung Malaysia 1981-1982, Unit Pengurusan Hutan, Ibu Pejabat Perhutan, Semanenanjung Malaysia, Kuala Lumpur.

Hegarty, E. E., 1989. The climbers - lianas and vines. pages 339-354 in H. Lieth and M. J. A. Werger (eds.), Tropical rainforest ecosystems. Ecosystems of the World 14B. Elsevier, The Netherlands.

Iverson, L. R., S. Brown, A. Prasad, H. Mitasova, A. J. R. Gillespie, and A. E. Lugo, 1994. Use of GIS for estimating potential and actual forest biomass for continental South and Southeast Asia. Chapter 3 m V. H. Dale (ed.). Effects of land use change on atmospheric CO² concentrations: Southeast Asia as a case study. Springer Verlag, NY.

Jankovic, C. S., 1969. The Huallaga project. Forestry inventory of the project area. Parts 1 and IV. Forest types, description, and inventory tables. UNDP 119, FAO, Rome, Italy

Jordan, C. F. and C. Uhl, 1978. Biomass of a “tierra firme” forest of the Amazon basin. Oecologia Plantarum 13:387-400

Kadeba, O., 1989. Biomass equations for evenaged stands of Caribbean pine (Pinus caribaea) planted as an exotic in Nigeria. Journal of Tropical Forest Science 1:346-355.

Lugo, A. E., 1992. Comparison of tropical tree plantations with secondary forests of similar age. Ecological Monographs 62:1-41.

Lund, H. G., 1992: A primer on permanent plots for monitoring natural resources. Pages 1-10 in H. G. Lund, R. Paivinen. S. Thammincha (eds), IUFRO S 4.02.05 Proceedings, Remote sensing and permanent sample plot techniques for World forest monitoring, January 13-17, 1992, Pattaya, Thailand. Asorn Siam, Bangkok, Thailand.

Marsch, H. E., 1976. Evaluation des ressources forestieres en Republique Populaire du Benin. Inventaire d’amenagement de la foret de Lama. FO: DP/BEN 73/014, Rapport Technique 1, Cotonou.

Martinez-Yrizar, A. J. Sarukhan, A. Perez-Jimenez, E. Rincon, J. M. Maass, A. Solis-Magallanes, and L. Cervantes, 1992. Above-ground phytomass of a tropical deciduous forest on the coast of Jalisco, Mexico. Journal of Tropical Ecology 8:87-96.

Olson, J. S., J. A. Watts, and L. J. Allison, 1983. Carbon in live vegetation of major world ecosystems. DOE/NBB-0037, National Technical Information Service, US Department of Commerce, Springfield, VA 22161, USA. 152 pp.

Peters, R., 1977. Fortalecimiento al sector forestal Guatemala inventarios y estudios dendrometricos en bosque de coniferas. FO:DP/GUA/72/006, Informe Tecnico 2, FAO, Rome, Italy.

Philippine-German Forest Resources Inventory project, 1986-1988. National forest inventory.

Rees, T. I., 1963. Report to the government of British Guyana on forest inventory. Extended programme of technical assistance. FAO No. 1962, Rome, Italy.

Reichle, D. E. (Editor), 1981. Dynamic properties of forest ecosystems. International Biological Programme 23. Cambridge University Press, UK.

Reyes, G., S. Brown, J. Chapman, and A. E. Lugo, 1992. Wood densities of tropical tree species. USDA Forest Service, General Technical Report SO-88, Southern Forest Experiment Station, New Orleans, Louisiana, USA.

Rich, P. M. 1986. Mechanical architecture of arborescent rain forest palms. Principes 30:117-131.

Rich, P. M. 1987. Mechanical structure of the stem of arborescent palms. Botanical Gazette 148:42-50.

Rollet, B., 1962. Inventaire forestier de l’Est Mekong. Rapport au Governement du Cambodge. rapport No. 1500, FAO, Rome, Italy.

Royal Forest Department of Thailand, 1980. Report on forest inventory of pilot project area for development of reforestation in Northeast Thailand. Bangkok.

Sachtler, M., 1979. Proyecto forestal FAO/CDF inventaricion y manejo de recursos forestales. Trabajas de proyecto en el estudio de prefactibilidad del bosque “Los Chimanes”. FO: BOL/74/031, Documento de Trabajo No. 3, FAO, Rome, Italy.

Saldarriaga, J. G., D. C. West, and M. L. Thorp, 1986. Forest succession in the Upper Rio Negro of Colombia and Venezuela. Environmental Sciences Division Publication No. 2694 (ORNL/TM9712), Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.

Sanford, R. L. and E. Cuevas, 1996. Root growth and rhizosphere interactions in tropical forests. In S. S. Mulkey, R. L. Chazdon, and A. P. Smith (eds.). Tropical forest plant physiology. Chapman Hall, New York, in press.

Stewart, J. L., A. J. Dunsdon, J. J. Hellin, and C. E. Hughes. 1992. Wood biomass estimation of Central American dry zone species. Tropical Forestry Papers No. 26, Oxford Forestry Institute, University of Oxford, UK.

Tanner, E. V., 1980. Studies on the biomass and productivity in a series of montane rain forests in Jamaica. Journal of Ecology 68:573-588.

Uhl, C., R. Buschbacher, and E. A. S. Serrao, 1988. Abandoned pastures in eastern Amazonia. I. Pattern of plant succession. Journal of Ecology 76:663-681.

Uhl, C. and J. B. Kauffman, 1990. Deforestation, fire susceptibility, and potential tree responses to fire in eastern Amazon. Ecology 71:437-449.

Appendix 1 - List of wood densities for tree species from tropical America, Africa, and Asia.

WOOD DENSITIES (G/CM³ OR T/M³) OF TREE SPECIES FOR TROPICAL REGIONS OF THREE CONTINENTS

Species	Wood density
Tropical Asia
Acacia arabica	0.70 *
Acacia catechu	0.88
Acacia confusa	0.75
Acacia leucophloea	0.76
Acacia richii	0.69
Adina cordifolia	0.58, 0.59 +
Aegle marmelo	0.75
Agathis dammara	0.41
Agathis spp.	0.44
Agathis vitiensis	0.45
Aglaia diffusa	0.70
Aglaia iloilo	0.53
Aglaia llanosiana	0.89
Alangium longiflorum	0.65
Alangium meyeri	0.63
Albizzia amara	0.70 *
Albizzia falcataria	0.25
Albizzia lebbek	0.55, 0.66 +
Albizzia odoratissima	0.76
Albizzia procera	0.52 *, 0.59 +
Aleurites moluccana	0.25
Aleurites trisperma	0.43
Alnus japonica	0.43
Alphitonia philippinensis	0.40
Alphitonia zizyphoides	0.50
Alphonsea arborea	0.69
Alseodaphne longipes	0.49
Alstonia macrophylla	0.62
Alstonia scholaris	0.36
Alstonia spp.	0.37
Amoora aherniana	0.58
Amoora macrocarpa	0.55
Amoora spp.	0.60
Anisophyllea zeylanica	0.46 *
Anisoptera aurea	0.53
Anisoptera spp.	0.54
Anisoptera thurifera	0.54
Anogeissus latifolia	0.78, 0.79 +
Anthocephalus chinensis	0.36, 0.33 +
Antidesma pleuricum	0.59
Aphanamixis cumingiana	0.58
Aphanamixis perrottetiana	0.52
Araucaria bidwillii	0.43
Artocarpus blancoi	0.43
Artocarpus heterophylla	0.60
Artocarpus lakoocha	0.53 *
Artocarpus ovata	0.47
Artocarpus spp.	0.58
Azadirachta indica	0.69
Azadirachta spp.	0.52
Balanocarpus spp.	0.76
Barringtonia edulis	0.48
Bauhinia spp.	0.67
Beilschmiedia tawa	0.58
Berrya cordifolia	0.78 *
Bischofia javanica	0.54, 0.58, 0.62 +
Bleasdalea vitiensis	0.43
Bombax ceiba	0.33
Bombycidendron vidalianum	0.53
Boswellia serrata	0.50
Bridelia retusa	0.50
Bridelia squamosa	0.50
Buchanania lanzan	0.45
Buchanania latifolia	0.45
Bursera serrata	0.59
Butea monosperma	0.48
Calophyllum blancoi	0.51
Calophyllum inophyllum	0.57
Calophyllum neo-ebudicum	0.50
Calophyllum obliquinervium	0.58
Calophyllum spp.	0.53
Calophyllum vitiense	0.50
Calycarpa arborea	0.53
Cananga odorata	0.29
Canarium asperum var. asperum	0.50, 0.60 +
Canarium hirsutum forma scabrum	0.40
Canarium luzonicum	0.51
Canarium spp.	0.44
Canarium vanikoroense	0.54
Canarium vitiense	0.54
Canarium vrieseanum forma stenophyllum	0.56
Canthium monstrosum	0.42
Carallia calycina	0.66 *
Cassia fistula	0.71
Cassia javanica	0.69
Cassia spectabilis	0.48
Castanopsis philippensis	0.51
Casuarina equisetifolia	0.83
Casuarina nodiflora	0.85
Cedrela odorata	0.38
Cedrela spp.	0.42
Cedrela toona	0.43
Ceiba pentandra	0.23
Celtis luzonica	0.49
Chisocheton cumingianus	0.52
Chisocheton pentandrus	0.52
Chloroxylon swietenia	0.76, 0.79, 0.80 +
Chukrassia tabularis	0.57
Cinnamomum mercadoi	0.65
Cinnamomum spp.	0.43
Citrus grandis	0.59
Cleidion speciflorum	0.50
Cleistanthus collinus	0.88
Cleistocalyx operculatus	0.66
Cleistocalyx spp.	0.76
Cochlospermum gossypium + religiosum	0.27
Cocos nucifera	0.50
Colona serratifolia	0.33
Combretodendron quadrialatum	0.57
Cordia spp.	0.53
Cotylelobium spp.	0.69
Crataeva religiosa	0.53 *
Cratoxylon arborescens	0.40
Cryptocarya spp.	0.59
Cubilia cubili	0.49
Cullenia excelsa	0.53
Cynometra insularis	0.76, 0.91 +
Cynometra ramiflora	0.70
Cynometra spp.	0.80
Dacrycarpus imbricatus	0.45, 0.47 +
Dacrydium elatum	0.48
Dacrydium nausoriensis	0.52
Dacrydium nidulum	0.52
Dacrydium spp.	0.46
Dacryodes spp.	0.61
Dalbergia latifolia	0.75
Dalbergia paniculata	0.64
Decussocarpus philippinensis	0.50
Decussocarpus vitiensis	0.37
Degeneria vitiensis	0.35
Dehaasia triandra	0.64
Dialium spp.	0.80
Dillenia luzoniensis	0.69
Dillenia megalantha	0.69
Dillenia pentagyna	0.53
Dillenia philippinensis	0.61
Dillenia spp.	0.59
Diospyros embryopteris	0.63 *
Diospyros inclusa	0.68
Diospyros melanoxylon	0.68
Diospyros mindanaensis	0.69
Diospyros nitida	0.71
Diospyros philippensis	0.81
Diospyros pilosanthera	0.80
Diospyros poncei	0.81
Diospyros pyrrhocarpa	0.60
Diospyros spp.	0.70
Diplodiscus paniculatus	0.63
Dipterocarpus caudatus	0.61
Dipterocarpus eurynchus	0.56
Dipterocarpus gracilis	0.61
Dipterocarpus grandiflorus	0.62
Dipterocarpus kerrii	0.56
Dipterocarpus kunstlerii	0.57
Dipterocarpus spp.	0.61
Dipterocarpus warburgii	0.52
Dracontomelon dao	0.52
Dracontomelon edule	0.46
Dracontomelon spp.	0.50
Dryobalanops spp.	0.61
Drypetes bordenii	0.75
Durio spp.	0.53
Durio zibethinus	0.44, 0.53 +
Dyera costulata	0.36
Dysoxylum altissimum	0.42
Dysoxylum decandrum	0.51
Dysoxylum euphlebium	0.63
Dysoxylum quercifolium	0.49
Dysoxylum richii	0.49
Elaeocarpus serratus	0.40 *
Emblica officinalis	0.80
Endiandra laxiflora	0.54
Endospermum macrophyllum	0.40
Endospermum peltatum	0.31
Endospermum spp.	0.38
Enterolobium cyclocarpum	0.35
Epicharis cumingiana	0.73
Erythrina fusca	0.25
Erythrina suberosa	0.32
Erythrina subumbrans	0.24
Erythrophloeum densiflorum	0.65
Eucalyptus citriodora	0.64
Eucalyptus deglupta	0.34
Eugenia spp.	0.65
Fagraea gracilipes	0.84
Fagraea spp.	0.73
Ficus benjamina	0.65
Ficus botryocarpa	0.43
Ficus minahassae	0.42
Ficus spp.	0.39
Ficus variegata	0.28
Ganua obovatifolia	0.59
Garcinia myrtifolia	0.65
Garcinia spp.	0.75
Gardenia latifolia	0.64
Gardenia turgida	0.64
Garuga pinnata	0.51
Gluta spp.	0.63
Gmelina arborea	0.41, 0.45 +
Gmelina vitiensis	0.54
Gonocaryum calleryanum	0.64
Gonystylus bancanus	0.52
Gonystylus macrophyllus	0.52
Gonystylus punctatus	0.57
Grewia multiflora	0.46
Grewia tiliaefolia	0.68
Hardwickia binata	0.73
Harpullia arborea	0.62
Heritiera ornithocephala	0.68
Heritiera spp.	0.56
Heritiera sylvatica	0.77
Hevea brasiliensis	0.53
Hibiscus tiliaceus	0.57
Homalanthus populneus	0.38
Homalium spp.	0.76
Hopea acuminata	0.62
Hopea foxworthyi	0.64
Hopea plagata	0.88
Hopea spp.	0.64
Intsia bijuga	0.61, 0.68,0.74
Intsia palembanica	0.68
Kayea garciae	0.53
Kingiodendron alternifolium	0.48
Kleinhovia hospita	0.36
Knema spp.	0.53
Koompassia excelsa	0.63
Koompassia malaccensis	0.72
Koordersiodendron pinnatum	0.65, 0.69 +
Kydia calycina	0.72
Lagerstroemia parviflora	0.62
Lagerstroemia piriformis	0.50
Lagerstroemia speciosa	0.53
Lagerstroemia spp.	0.55
Lannea coromandelica	0.54
Lannea grandis	0.50
Leucaena leucocephala	0.64
Litchi chinensis var. philippinensis	0.88
Lithocarpus celebica	0.68
Lithocarpus llanosii	0.63
Lithocarpus soleriana	0.63
Litsea garciae	0.34
Litsea leytensis	0.35
Litsea perrottetii	0.45
Litsea spp.	0.40
Lophopetalum spp.	0.46
Macaranga bicolor	0.29
Macaranga denticulata	0.53
Madhuca fulva	0.53
Madhuca longifolia var. latifolia	0.74
Madhuca oblongifolia	0.53
Mallotus multiglandulosus	0.42
Mallotus philippensis	0.64
Mangifera altissima	0.55
Mangifera indica	0.52, 0.59 +
Mangifera merrillii	0.52
Mangifera spp.	0.52
Maniltoa grandiflora	0.76
Maniltoa minor	0.76
Mastixia philippinensis	0.47
Melanorrhea spp.	0.63
Melia dubia	0.40
Melicope triphylla	0.37
Meliosma macrophylla	0.27
Melochia umbellata	0.25
Mesua ferrea	0.83, 0.85 +
Metrosideros collina	0.70, 0.76 +
Michelia platyphylla	0.51
Michelia spp.	0.43
Microcos stylocarpa	0.40
Micromelum compressum	0.64
Milliusa velutina	0.63
Mimusops elengi	0.72 *
Mitragyna parviflora	0.56
Myristica castaneifolia	0.49
Myristica chartacea	0.49
Myristica gillespieana	0.49
Myristica spp.	0.53
Neesia spp.	0.53
Neonauclea bernardoi	0.62
Neotrewia cumingii	0.55
Ochna foxworthyi	0.86
Ochroma pyramidale	0.30
Octomeles sumatrana	0.27, 0.32 +
Oroxylon indicum	0.32
Ougenia dalbergiodes	0.70
Palaquium fidjiense	0.48
Palaquium hornei	0.70
Palaquium lanceolatum	0.55
Palaquium luzoniense	0.45
Palaquium philippense	0.41
Palaquium spp.	0.55
Palaquium tenuipetiolatum	0.50
Palaquium vitilevuense	0.48
Pangium edule	0.50
Parashorea malaanonan	0.51
Parashorea spp.	0.44
Parashorea stellata	0.59
Paratrophis glabra	0.77
Parinari corymbosa	0.76
Parinari insularum	0.65
Parinari spp.	0.68
Parkia roxburghii	0.34
Payena spp.	0.55
Peltophorum pterocarpum	0.62
Pentace spp.	0.56
Phaeanthus ebracteolatus	0.56
Phyllocladus hypophyllus	0.53
Pinus caribaea	0.48
Pinus insularis	0.47, 0.48 +
Pinus merkusii	0.54
Pisonia umbellifera	0.21
Pittosporum pentandrum	0.51
Planchonella vitiensis	0.77
Planchonia spectabilis	0.58
Planchonia spp.	0.59
Podocarpus neriifolius	0.52
Podocarpus spp.	0.43
Polyalthia flava	0.51
Polyscias nodosa	0.38
Pometia pinnata forma pinnata	0.58
Pometia spp.	0.54
Pouteria villamilii	0.47
Premna tomentosa	0.96
Pterocarpus indicus	0.52
Pterocarpus marsupium	0.67
Pterocymbium macrorater	0.47
Pterocymbium tinctorium	0.28
Pygeum vulgare	0.57
Quercus spp.	0.70
Radermachera pinnata	0.51
Salmalia malabarica	0.32, 0.33 +
Samanea saman	0.45, 0.46 4
Sandoricum koetjape	0.44
Sandoricum vidalii	0.43
Sapindus saponaria	0.58
Sapium luzonicum	0.40
Schleichera oleosa	0.96
Schrebera swietenoides	0.82
Semicarpus anacardium	0.64
Serialbizia acle	0.57
Serianthes melanesica	0.48
Sesbania grandiflora	0.40
Shorea agsaboensis	0.35
Shorea almon	0.42
Shorea assamica forma philippinensis	0.41
Shorea astylosa	0.73
Shorea ciliata	0.75
Shorea contorta	0.44
Shorea gisok	0.76
Shorea guiso	0.68
Shorea hopeifolia	0.44
Shorea malibato	0.78
Shorea negrosensis	0.44
Shorea palosapis	0.39
Shorea plagata	0.70
Shorea polita	0.47
Shorea polysperma	0.47
Shorea robusta	0.72
Shorea spp. balau group	0.70
Shorea spp. dark red meranti	0.55
Shorea spp. light red meranti	0.40
Shorea spp. white meranti	0.48
Shorea spp. yellow meranti	0.46
Shorea virescens	0.42
Sloanea javanica	0.53
Soymida febrifuga	0.97
Spathodea campanulata	0.25
Stemonurus luzoniensis	0.37
Sterculia ceramica	0.27
Sterculia foetida	0.47 *
Sterculia urens	0.67
Sterculia vitiensis	0.31
Stereospermum suaveolens	0.62
Strombosia philippinensis	0.71
Strychnos potatorum	0.88
Swietenia macrophylla	0.49, 0.53 +
Swintonia foxworthyi	0.62
Swintonia spp.	0.61
Sycopsis dunni	0.63
Syzygium cumini	0.70
Syzygium luzoniense	0.63
Syzygium nitidum	0.74
Syzygium simile	0.56
Syzygium spp.	0.69, 0.76 +
Tamarindus indica	0.75
Tectona grandis	0 50, 0.55 +
Teijsmanniodendron ahernianum	0.90
Terminalia arjuna	0.68
Terminalia belerica	0.72
Terminalia catappa	0.52
Terminalia chebula	0.96
Terminalia citrina	0.71
Terminalia copelandii	046
Terminalia foetidissima	055
Terminalia microcarpa	0.53
Terminalia nitens	0.58
Terminalia pterocarpa	048
Terminalia tomentosa	0.73, 0.76, 0.77 +
Ternstroemia megacarpa	0.53
Tetrameles nudiflora	0.30
Tetramerista glabra	0.61
Thespesia populnea	0.52
Toona calantas	0.29
Trema orientalis	0.31
Trichospermum richii	0.32
Tristania decorticata	0.91
Tristania micrantha	0.89
Tristania spp.	0.80
Turpinia ovalifolia	0.36
Vateria indica	0.47 *
Vatica mangachapoi	0.65
Vatica obscura	1.04 *
Vatica pachyphylla	0.78
Vatica spp.	0.69
Vitex parviflora	0.70
Vitex peduncularis	0.96
Vitex spp.	0.65
Vitex turczaninowii	0.49
Wallaceodendron celebicum	0.55, 0.57 +
Weinmannia luzoniensis	0.49
Wrightia tinctorea	0.75
Xanthophyllum excelsum	0.63
Xanthostemon verdugonianus	1.04
Xylia xylocarpa	0.73, 0.81 +
Zanthoxylum rhetsa	0.33
Zizyphus spp.	0.76
Zizyphus talanai	0.53
Zizyphus xylopyra	0.85
Tropical America
Albizzia caribaea	0.64
Albizzia spp	0.52
Alcornea latifolia	0 49
Alcornea spp	0.34
Alexa grandiflora	0.60
Alexa imperatricis	0.41,0.51 +
Alnus ferruginea	0.38
Alnus jorullensis	0.38
Anacardium excelsum	0.41
Anacardium spruceanum	0.42
Anadenanthera macrocarpa	0.86
Anadenanthera rigida	0.63
Andira inermis	0.63, 0.64 +
Andira retusa	0.67
Aniba perutilis	0.50
Aniba riparia/duckei	0.62
Aniba spp.	0.38, 0.60 +
Antiaris africana	0.38
Apeiba aspera	0.23
Apeiba echinata	0.36
Apeiba spp.	0.20, 0.24 +
Apeiba tibourbon	0.12
Artocarpus comunis	0.70
Aspidosperma album	0.68
Aspidosperma cruentum	0.71
Aspidosperma dugandii	0.77
Aspidosperma marchravianum	0.68
Aspidosperma megalocarpum	0.71, 0.81 +
Aspidosperma spp. (araracanga group)	0.75
Aspidosperma spp. (peroba group)	0.62, 0.65 +
Astronium graveolens	0.75, 0.80, 0.84, 0.89 +
Astronium lecointei	0.73
Bagassa guianensis	0.68, 0.69 +
Banara guianensis	0.61
Basiloxylon exelsum	0.58
Beilschmiedia pendula	0.54
Beilschmiedia sp.	0.61
Berthollettia excelsa	0.59, 0.63 +
Bixa arborea	0.32
Bombacopsis quinatum	0.38, 0.45, 0.51 +
Bombacopsis sepium	0.39
Borojoa patinoi	0.52
Bowdichia nitida	0.77
Bowdichia spp.	0.74
Brosimum acutifolium	0.55
Brosimum parinarioides	0.57
Brosimum potabile	0.53
Brosimum rubescens	0.73
Brosimum sp.	0.64, 0.84 +
Brosimum spp. (alicastrum group)	0.64, 0.66 +
Brosimum spp. (utile group)	0.43
Brosimum utile	0.41, 0.46 +
Brysenia adenophylla	0.54
Buchenavia capitata	0.61, 0.63 +
Buchenavia huberi	0.59, 0.79 +
Bucida buceras	0.93
Bulnesia arborea	1.00
Bursera simaruba	0.29, 0.34 +
Byrsonima aerugo	0.62
Byrsonima coriacea	0.64
Byrsonima coriacea var. spicata	0.61
Byrsonima spp.	0.61, 0.64,0.75 +
Cabralea cangerana	0.55
Caesalpinia spp.	1.05
Calophyllum brasiliense	0.51, 0.54, 0.55 +
Calophyllum mariae	0.46
Calophyllum sp.	0.65
Calycophyllum candidisimum	0.67
Campnosperma panamensis	0.33, 0.50 +
Carapa guianensis	0.56
Carapa sp.	0.47
Caryocar nr. barbinerve	0.62
Caryocar spp.	0.69, 0.72 +
Caryocar villosum	0.72
Casearia arborea	0.53
Casearia guianensis	0.70
Casearia praecox	0.69 *
Casearia sp.	0.62
Cassia moschata	0.71
Cassia multijuga	0.57
Casuarina equisetifolia	0.81
Catostemma commune	0.51
Catostemma spp.	0.55
Cecropia peltata	0.29, 0.30, 0.36 +
Cecropia spp.	0.36
Cedrela angustifolia	0.36
Cedrela huberi	0.38
Cedrela odorata	0.43, 0.44, 0.45 +
Cedrela spp.	0.40, 0.46 +
Cedrelinga catenaeformis	0.41, 0.53 +
Ceiba pentandra	0.23, 0.24, 0.25, 0.29 +
Centrolobium paraense var. orinocensis	0.69
Centrolobium spp.	0.65
Cespedesia macrophylla	0.63
Chaetocarpus schomburgkianus	0.80
Chlorophora tinctoria	0.71, 0.75 +
Clarisia racemosa	0.53, 0.57 +
Clathrotropis brunnea	0.82
Clathrotropis spp.	0.89
Clusia rosea	0.67
Cochlospermum orinocensis	0.26
Copaifera duckei/reticulata	0.62
Copaifera officinalis	0.59
Copaifera spp.	0.46, 0.55 +
Cordia alliodora	0.42, 0.47, 0.50, 0.57 +
Cordia apurensis	0.66
Cordia bicolor	0.43, 0.49 +
Cordia borinquensis	0.70
Cordia collococca	0.47
Cordia exaltata	0.41
Cordia fallax	0.36
Cordia goeldiana	0.50
Cordia sagotii	0.50
Cordia spp. (gerascanthus group)	0.74
Cordia spp. (alliodora group)	0.48
Cordia sulcata	0.60
Couepia sp.	0.70
Couma macrocarpa	0.50, 0.53 +
Couratari pulchra	0.50, 0.54 +
Couratari spp.	0.50
Couratari stellata	0.65, 0.78 +
Croton xanthochloros	0.48
Cupressus lusitanica	0.43, 0.44 +
Cyrilla racemiflora	0.53
Dacryodes colombiana	0.51
Dacryodes excelsa	0.52, 0.53 +
Dalbergia nigra	0.68
Dalbergia retusa	0.89
Dalbergia stevensonii	0.82
Declinanona calycina	0.47
Dialium guianensis	0.87
Dialyanthera spp.	0.36, 0.48 +
Dicorynia guianensis	0.60, 0.65 +
Dicorynia paraensis	0.60
Didymopanax morototoni	0.36, 0.40, 0.45 +
Didymopanax pittieri	0.43
Didymopanax sp.	0.74
Dimorphandra mora	0.99 *
Diplotropis purpurea	0.76, 0.77, 0.78 +
Dipterix odorata	0.81, 0.86, 0.89 +
Drypetes variabilis	0.69
Dussia lehmannii	0.59
Ecclinusa guianensis	0.63
Endlicheria cocvirey	0.39
Enterolobium cyclocarpum	0.34, 0.45 +
Enterolobium schomburgkii	0.82
Eperua spp.	0.78
Eriotheca longipedicellatum	0.45
Eriotheca sp.	0.40
Erisma uncinatum	0.42, 0.48 +
Erythrina sp.	0.23
Eschweilera amara	0.85
Eschweilera corrugata	0.66
Eschweilera grata	0.88
Eschweilera hologyne	0.76
Eschweilera odora	0.81, 0.85 +
Eschweilera sagotiana	0.82
Eschweilera spp.	0.71, 0.79, 0.95 +
Eschweilera subglandulosa	0.87, 0.89 +
Eschweilera tenax	0.62
Eschweilera trinitensis	0.77
Eucalyptus robusta	0.51
Eugenia compta	0.68
Eugenia pseudosidium	0.62
Eugenia stahlii	0.73
Euxylophora paraensis	0.68, 0.70 +
Fagara aff. F. martinicense	0.41
Fagara sp.	0.57
Fagara spp.	0.69
Ficus citrifolia	0.40
Ficus sp.	0.32
Genipa americana	0.57, 0.58, 0.66 +
Genipa spp.	0.75
Goupia glabra	0.67, 0.72 +
Guarea chalde	0.52
Guarea spp.	0.52
Guarea trichiloides	0.51, 0.52 +
Guatteria spp.	0.36
Guazuma ulmifolia	0.52, 0.50 +
Guettarda scabra	0.65
Guillielma gasipae	0.95, 1.25 +
Gustavia sp.	0.56
Helicostylis tomentosa	0.68, 0.72 +
Hernandia sonora	0.29
Hevea brasiliense	0.49
Himatanthus articulata	0.40, 0.54 +
Hirtella davisii	0.74
Humiria balsamifera	0.66, 0.67 +
Humiriastrum melanocarpum	0.60
Humiriastrum procera	0.70
Hura crepitans	0.36, 0.37, 0.38 +
Hyeronima alchorneoides	0.60, 0.64 +
Hyeronima laxiflora	0.59
Hymenaea courbaril	0.54, 0.76, 0.77 +
Hymenaea davisii	0.67
Hymenolobium excelsum	0.63
Hymenolobium sp.	0.64
Inga alba	0.53
Inga capitata	0.64
Inga coruscans	0.72
Inga floribunda	0.56
Inga ingoides	0.50
Inga laurina	0.62
Inga marginata	0.72
Inga sp.	0.49, 0.52, 0.58, 0.64 +
Inga splendens	0.55
Inga vera	0.59
Iryanthera grandis	0.63
Iryanthera hostmanii	0.50
Iryanthera spp.	0.46
Jacaranda copaia	0.35
Jacaranda hesperia	0.35
Jacaranda sp.	0.55
Joannesia heveoides	0.39
Lachmellea speciosa	0.73
Laetia procera	0.68
Lecythis davisii	0.82
Lecythis ollaria	0.72
Lecythis paraensis	0.88
Lecythis sp.	0.83
Lecythis spp.	0.77
Licania aff. micrantha	0.86
Licania alba	0.91
Licania apetala	0.64, 0.78 +
Licania densiflora	0.80
Licania hypoleuca	0.90
Licania macrophylla	0.76
Licania parviflora	0.76
Licania sp.	0.61, 0.79 +
Licania spp.	0.78
Licania cayennensis	0.99
Licania spp.	0.82
Lindackeria sp.	0.41
Linociera domingensis	0.81
Lonchocarpus sericens	0.78
Lonchocarpus spp.	0.69
Lonchocarpus straminens	0.75
Loxopterygium sagotii	0.56
Lucuma spp.	0.79
Luehea cymulosa	0.55
Luehea spp.	0.50
Lueheopsis duckeana	0.64
Mabea piriri	0.59
Machaerium spp.	0.70
Macoubea guianensis	0.40 *
Magnolia sororum	0.50
Magnolia splendens	0.59
Magnolia spp.	0.52
Maguira sclerophylla	0.57
Mammea americana	0.62
Mangifera indica	0.55
Manilkara bidentata	0.82, 0.84, 0.85 +
Manilkara sp.	0.89
Marila sp.	0.63
Marmaroxylon racemosum	0.78 *
Matayba domingensis	0.70
Matisia hirta	0.61
Maytenus ficiformis	0.67
Maytenus spp.	0.71
Mezilaurus itauba	0.68
Mezilaurus lindaviana	0.68
Michropholis garciniaefolia	0.64
Michropholis spp.	0.61
Minquartia guianensis	0.76, 0.79 +
Mora excelsa	0.80
Mora gonggrijpi	0.80
Mora magistosperma	0.88
Mora sp.	0.77
Mouriria guianensis	0.80
Mouriria huberi	0.75
Mouriria pseudo-germinata	0.65
Mouriria sideroxylon	0.88
Myrcia paivae	0.73
Myrcia splendens	0.80
Myrciaria floribunda	0.73
Myristica spp.	0.46
Myroxylon balsamum	0.74, 0.76, 0.78 +
Nectandra antillana	0.42
Nectandra concinna	0.54, 0.56 +
Nectandra coriacea	0.51
Nectandra rigida	0.59
Nectandra rodioei	0.91
Nectandra rubra	0.55
Nectandra sp.	0.43, 0.48, 0.72 +
Nectandra spp.	0.52
Ocotea glandulosa	0.46
Ocotea leucoxylon	0.45
Ocotea moschata	0.61
Ocotea rodioei	0.85, 0.86 +
Ocotea rubra	0.54, 0.55, 0.56 +
Ocotea spathulata	0.62
Ocotea spp.	0.51
Onychopetalum amazonicum	0.64
Ormosia krugii	0.50
Ormosia lignivalvis	0.58
Ormosia spp.	0.59
Ouratea sp.	0.66
Pachira acuatica	0.43
Paratecoma peroba	0.60
Parinari campestris	0.69
Parinari excelsa	0.64
Parinari rodolfi	0.72
Parinari spp.	0.68
Parkia belutina	0.42
Parkia multijuga	0.38
Parkia oppositifolia	0.24
Parkia pendula	0.51
Parkia spp.	0.39
Peltogyne porphyrocardia	0.92
Peltogyne spp.	0.79
Pentaclethra macroloba	0.65, 0.68 +
Pera glabrata	0.65
Pera schomburgkiana	0.59
Persea spp.	0.40, 0.47, 0.52 +
Petitia domingensis	0.66
Pinus caribaea	0.51
Pinus oocarpa	0.55
Pinus patula	0.45
Piptadenia communis	0.68
Piptadenia macrocarpa	0.83 *
Piptadenia pittieri	0.62, 0.76 +
Piptadenia psilostachya	0.67
Piptadenia rigida	0.73
Piptadenia sp.	0.58
Piptadenia suaveolens	0.72
Piranhea longepedunculata	0.90
Piratinera guianensis	0.96
Pithecellobium guachapele (syn. Pseudosamea)	0.56
Pithecellobium saman	0.48
Platonia insignis	0.70 *
Platymiscium pinnatum	0.80, 0.81 +
Platymiscium polystachium	0.73
Platymiscium spp.	0.71, 0.84 +
Podocarpus oleifolius	0.46
Podocarpus rospigliossi	0.40
Podocarpus spp.	0.46
Pourouma aff. apiculata	0.45
Pourouma aspera	0.28
Pourouma aff. guianensis	0.33
Pourouma aff. melinonii	0.32
Pouteria carabobensis	0.68
Pouteria egregia	0.89
Pouteria eugeniifolia	1.08
Pouteria gonggrijpii	0.84
Pouteria melinonii	0.63 *
Pouteria multiflora	0.74
Pouteria pomifera	0.76
Pouteria sp.	0.73
Pouteria spp.	0.64, 0.67 +
Prioria copaifera	0.40, 0.41 +
Protium crenatum	0.54
Protium decandrum	0.56
Protium heptaphyllum	0.40, 0.55 +
Protium neglectum	0.58, 0.64 +
Protium sp.	0.73
Protium spp.	0.53, 0.64 +
Protium tenuifolium	0.60
Pseudolmedia laevigata	0.64
Pterocarpus officinalis	0.32, 0.50 +
Pterocarpus rohrii	0.41
Pterocarpus sp.	0.46, 0.50 +
Pterocarpus spp.	0.44
Pterocarpus vernalis	0.59
Pterogyne nitens	0.66
Pterygota excelsa	0.58
Qualea albiflora	0.50
Qualea cf. lancifolia	0.58
Qualea dinizii	0.58
Qualea spp.	0.55
Quararibaea guianensis	0.54
Quercus alata	0.71
Quercus costaricensis	0.61
Quercus eugeniaefolia	0.67
Quercus spp.	0.70
Raputia sp.	0.55
Rheedia spp.	0.72
Rollinia exsucca	0.32
Rollinia sp.	0.34, 0.36 +
Rollinia spp.	0.36
Saccoglottis cydonioides	0.72
Sapium biglandulosum	0.45
Sapium cf. jenmanni	0.41
Sapium laurocerasus	0.38
Sapium sp.	0.38, 0.48 +
Sapium spp.	0.47, 0.72 +
Schinopsis spp.	1.00
Sclerolobium aff. chrysophyllum	0.62
Sclerolobium guianensis	0.56
Sclerolobium paniculatum	0.34
Sclerolobium spp.	0.47
Sickingia spp.	0.52
Simaba multiflora	0.51
Simarouba amara	0.32, 0.34, 0.38 +
Sloanea berteriana	0.80
Sloanea grandiflora	0.80
Sloanea guianensis	0.79
Spondias lutea	0.38
Spondias mombin	0.39, 0.40, 0.41 +
Sterculia apetala	0.33, 0.36 +
Sterculia pilosa/speciosa	0.53
Sterculia pruriens	0.46
Sterculia spp.	0.55
Stryphnodendron polystachum	0.52
Stylogyne spp.	0.69
Swartzia spp.	0.95
Swietenia macrophylla	0.42, 0.45, 0.46, 0.54 +
Symphonia globulifera	0.58
Tabebuia guayacan	0.82
Tabebuia heterophylla	0.58
Tabebuia heterotricha	0.82
Tabebuia pentaphylla	0.51
Tabebuia rosea	0.54
Tabebuia serratifolia	0.92, 0.95, 0.99 +
Tabebuia spectabilis	1.07
Tabebuia spp. (lapacho group)	0.91
Tabebuia spp. (roble)	0.52
Tabebuia spp. (white cedar)	0.57
Tabebuia stenocalyx	0.55, 0.57 +
Tachigalia myrmecophylla	0.56
Talisia sp.	0.84
Tapirira guianensis	0.47 *
Terminalia amazonia	0.66
Terminalia catappa	0.59
Terminalia guianensis	0.63
Terminalia lucida	0.65
Terminalia sp.	0.50, 0.51, 0.58 +
Tetragastris altisima	0.61
Tetragastris balsamifera	0.63, 0.67 +
Tetragastris panamensis	0.71
Tetragastris spp.	0.71
Toluifera balsamum	0.74
Torrubia cuspidata	0.47
Torrubia sp.	0.52
Toulicia pulvinata	0.63
Tovomita guianensis	0.60
Trattinickia burserifolia	0.44
Trattinickia rhoifolia	0.37
Trattinickia sp.	0.38
Trichilia propingua	0.58
Trichosperma mexicanum	0.41
Triplaris sp.	0.64
Triplaris spp.	0.56
Triplaris surinamensis	0.51
Trophis sp.	0.54
Vatairea lundellii	0.64
Vatairea spp.	0.60
Virola sebifera	0.48
Virola spp.	0.40, 0.44, 0.48 +
Virola surinamensis	0.37, 0.42 +
Vismia spp.	0.41
Vitex divaricata	0.62
Vitex gaumeri	0.56
Vitex orinocensis	0.53
Vitex spp.	0.52, 0.56, 0.57 +
Vitex stahelii	0.60
Vochysia ferruginea	0.42, 0.47 +
Vochysia guianensis	0.45
Vochysia hondurensis	0.33
Vochysia lehmannii	0.48
Vochysia maxima	0.46
Vochysia spp.	0.40, 0.47, 0.79 +
Vochysia tetraphylla	0.48
Vochysia tomentosa	0.36
Vouacapoua americana	0.79
Warszewicsia coccinea	0.56
Xanthoxylum martinicensis	0.46
Xanthoxylum spp.	0.44
Xylopia columbiana	0.51
Xylopia emarginata	0.59
Xylopia frutescens	0.64 *
Tropical Africa
Afzelia bipindensis	0.66 *
Afzelia pachyloba	0.63 *
Afzelia spp.	0.67
Aidia ochroleuca	0.78 *
Albizia ferruginea	0.47 *
Albizia glaberrima	0.52 *
Albizia gummifera	0.51 *
Albizia spp.	0.52
Albizia zygia	0.46 *
Allanblackia floribunda	0.63 *
Allophyllus africanus f. acuminatus	0.45
Alstonia congensis	0.33
Amphimas ferrugineus	0.63 *
Amphimas pterocarpoides	0.63 *
Anisophyllea obtusifolia	0.63 *
Annonidium mannii	0.29 *
Anopyxis klaineana	0.74 *
Anthocleista keniensis	0.50 *
Anthonotha macrophylla	0.78 *
Anthostemma aubryanum	0.32 *
Antiaris africana	0.37
Antiaris spp.	0.38
Antrocaryon klaineanum	0.50 *
Aucoumea klaineana	0.37
Autranella congolensis	0.78
Baillonella toxisperma	0.71
Balanites aegyptiaca	0.63 *
Baphia kirkii	0.93 *
Beilschmiedia corbisieri	0.63 *
Beilschmiedia diversiflora	0.63 *
Beilschmiedia kweo	0.56 *
Beilschmiedia louisii	0.70 *
Beilschmiedia membranifolia	0.50 *
Beilschmiedia nitida	0.50 *
Berlinia bracteosa	0.60 *
Berlinia confusa	0.56 *
Berlinia spp.	0.58
Blighia welwitschii	0.74 *
Bombax buonopozense	0.32 *
Bombax chevalieri	0.41 *
Bombax rhodognaphalon	0.36 *
Bombax spp.	0.40
Brachystegia cynometroides	0.56 *
Brachystegia laurentii	0.45 *
Brachystegia mildbraedii	0.50 *
Brachystegia spp.	0.52
Bridelia grandis	0.50 *
Bridelia micrantha	0.47 *
Calpocalyx heitzii	0.66 *
Calpocalyx klainei	0.63 *
Canarium schweinfurthii	0.40 *
Canthium rubrocostratum	0.63 *
Carapa procera	0.59
Casearia battiscombei	0.50
Cassipourea euryoides	0.70 *
Cassipourea malosana	0.59 *
Ceiba pentandra	0.26
Celtis brieyi	0.50 *
Celtis mildbraedii	0.56 *
Celtis spp.	0.59
Celtis zenkeri	0.59 *
Chlorophora excelsa	0.55
Chrysophyllum albidum	0.56 *
Cleistanthus mildbraedii	0.87 *
Cleistopholis patens	0.36 *
Coelocaryon preussii	0.56 *
Cola cordifolia	0.50 *
Cola gigantea	0.46 *
Cola gigantea var. glabrescens	0.46 *
Cola natalensis	0.70 *
Cola sp.	0.70 *
Combretodendron macrocarpum	0.70
Conopharyngia holstii	0.50 *
Copaifera mildbraedii	0.63 *
Copaifera religiosa	0.50 *
Cordia africana	0.40 *
Cordia millenii	0.34
Cordia platythyrsa	0.36 *
Corynanthe gabonensis	0.56 *
Corynanthe pachyceras	0.63 *
Coula edulis	0.78 *
Croton macrostachyus	0.50 *
Croton megalocarpus	0.57
Cryptosepalum staudtii	0.70 *
Ctenolophon englerianus	078 *
Cylicodiscus gabonensis	0.80
Cynometra alexandri	0.74
Dacryodes buettneri	0.53 *
Dacryodes edulis	0.50 *
Dacryodes igaganga	0.53 *
Dacryodes klaineana	0.70 *
Dacryodes le-testui	0.50 *
Dacryodes normandii	0.50 *
Dacryodes spp.	0.61
Daniellia klainei	0.45 *
Daniellia ogea	0.40 *
Daniellia soyauxii	0.45 *
Desbordesia pierreana	0.87 *
Detarium senegalensis	0.63 *
Dialium bipindense	0.83 *
Dialium dinklagei	0.72
Dialium excelsum	0.78 *
Didelotia africana	0.78 *
Didelotia brevipaniculata	0.53
Didelotia letouzeyi	0.50 *
Diospyros kamerunensis	0.78 *
Diospyros spp.	0.82
Discoglypremna caloneura	0.32 *
Distemonanthus benthamianus	0.58
Drypetes gossweilleri	0.63 *
Drypetes sp.	0.63 *
Ehretia acuminata	0.51 *
Enantia chlorantha	0.42 *
Endodesmia calophylloides	0.66 *
Entandrophragma angolensis	0.45
Entandrophragma candollei	0.59
Entandrophragma cylindricum	0.55
Entandrophragma utile	0.53
Eribroma oblongum	0.60 *
Eriocoelum microspermum	0.50 *
Erismadelphus exsul	0.56 *
Erythrina vogelii	0.25 *
Erythrophleum ivorense	0.72
Erythroxylum mannii	0.50
Fagara heitzii	0.41 *
Fagara macrophylla	0.69
Ficus iteophylla	0.40 *
Ficus mucuso	0.39 *
Funtumia africana	0.40 *
Fumtumia latifolia	0.45 *
Gambeya africana	0.63
Gambeya lacourtiana	0.63 *
Gambeya madagascariensis	0.56 *
Gambeya spp.	0.56 *
Garcinia gerardii	0.66 *
Garcinia mannii	0.78 *
Garcinia punctata	0.78 *
Gilbertiodendron dewevrei	0.65 *
Gilbertiodendron grandiflorum	0.66 *
Gilbertiodendron mayombense	0.63 *
Gilletiodendron mildbraedii	0.87 *
Gossweilerodendron balsamiferum	0.40
Guarea cedrata	0.48
Guarea laurentii	0.56 *
Guarea thompsonii	0.55 *
Guibourtia arnoldiana	0.64
Guibourtia demeusei	0.70 *
Guibourtia ehie	0.67
Guibourtia pellegriniana	0.74 *
Guibourtia spp.	0.72
Guibourtia tessmannii	0.74 *
Hannoa klaineana	0.28 *
Harungana madagascariensis	0.45 *
Hexalobus crispiflorus	0.48 *
Holoptelea grandis	0.59 *
Homalium letestui	0.66 *
Homalium spp.	0.70
Hylodendron gabonense	0.78 *
Hymenostegia afzelii	0.78 *
Hymenostegia pellegrini	0.78 *
Irvingia gabonensis	0.71
Irvingia grandifolia	0.78 *
Julbernardia globiflora	0.78
Khaya grandifoliola	0.60
Khaya ivorensis	0.44
Khaya senegalensis	0.60
Klainedoxa gabonensis	0.87
Lannea welwitschii	0.45 *
Lecomtedoxa klaineana	0.78 *
Letestua durissima	0.87 *
Lophira alata	0.87 *
Lovoa trichilioides	0.45 *
Macaranga conglomerata	0.40 *
Macaranga kilimandscharica	0.40 *
Maesopsis eminii	0.41
Malacantha sp. aff. alnifolia	0.45 *
Mammea africana	0.62
Manilkara cuneifolia	0.81 *
Manilkara lacera	0.78 *
Markhamia hildebrandtii	0.50 *
Markhamia platycalyx	0.45 *
Memecylon capitellatum	0.77 *
Microberlinia bisulcata	0.63 *
Microberlinia brazzavillensis	0.70
Microcos coriaceus	0.42 *
Milletia laurentii	0.70 *
Milletia spp.	0.72
Mitragyna ciliata	0.45
Mitragyna stipulosa	0.47
Monopetalanthus coriaceus	0.45 *
Monopetalanthus durandii	0.50 *
Monopetalanthus heitzii	0.39
Monopetalanthus letestui	0.50 *
Monopetalanthus pellegrinii	0.47 *
Musanga cecropioides	0.23
Nauclea diderrichii	0.63
Neopoutonia macrocalyx	0.32 *
Nesogordonia fouassieri	0.70 *
Nesogordonia papaverifera	0.65
Newtonia buchananii	0.48 *
Newtonia glandulifera	0.74 *
Ochtocosmus africanus	0.78 *
Odyendea gabonensis	0.32 *
Odyendea spp.	0.32
Oldfieldia africana	0.78 *
Ongokea gore	0.72
Oxystigma oxyphyllum	0.53
Pachyelasma tessmannii	0.70 *
Pachypodanthium confine	0.58 *
Pachypodanthium staudtii	0.58 *
Paraberlinia bifoliolata	0.56 *
Parinari excelsa	0.69
Parinari glabra	0.87 *
Parinari goetzeniana	0.78 *
Parkia bicolor	0.36 *
Pausinystalia brachythyrsa	0.56 *
Pausinystalia cf. talbotii	0.56 *
Pentaclethra eetveldeana	0.63 *
Pentaclethra macrophylla	0.78 *
Pentadesma butyracea	0.78 *
Phyllanthus discoideus	0.76 *
Pierreodendron africanum	0.70 *
Piptadenia gabunensis	0.70 *
Piptadeniastrum africanum	0.56
Plagiostyles africana	0.70 *
Poga oleosa	0.36
Polyalthia suaveolens	0.66 *
Premna angolensis	0.63 *
Pteleopsis hylodendron	0.63 *
Pterocarpus angolensis	0.59
Pterocarpus soyauxii	0.61
Pterygota bequaertii	0.56 *
Pterygota spp.	0.52
Pycnanthus angolensis	0.40
Randia cladantha	0.78 *
Rauwolfia macrophylla	0.47 *
Ricinodendron heudelotii	0.20
Saccoglottis gabonensis	0.74 *
Santiria trimera	0.53 *
Sapium ellipticum	0.50 *
Schrebera arborea	0.63 *
Sclorodophloeus zenkeri	0.68 *
Scottellia chevalieri	0.50 *
Scottellia coriacea	0.56
Scyphocephalium ochocoa	0.48
Scytopetalum tieghemii	0.56 *
Sindoropsis letestui	0.56 *
Staudtia stipitata	0.75
Stemonocoleus micranthus	0.56 *
Sterculia oblonga	0.61
Sterculia rhinopetala	0.64
Strephonema pseudocola	0.56 *
Strombosia glaucescens	0.80
Strombosia grandifolia	0.74 *
Strombosiopsis tetrandra	0.63 *
Swartzia fistuloides	0.82
Symphonia globulifera	0.58 *
Syzygium cordatum	0.59 *
Tarrietia densiflora	0.63
Tarrietia utilis	0.54 *
Terminalia superba	0.45
Tessmania africana	0.85 *
Testulea gabonensis	0.60
Tetraberlinia bifoliolata	0.54 *
Tetraberlinia tubmaniana	0.60 *
Tetrapleura tetraptera	0.50 *
Tieghemella africana	0.55
Tieghemella heckelii	0.55 *
Trema guineensis	0.40 *
Trema sp.	0.40 *
Trichilia heudelotii	0.50 *
Trichilia prieureana	0.63 *
Trichoscypha arborea	0.59 *
Triplochiton scleroxylon	0.32
Uapaca spp.	0.60
Vepris undulata	0.70 *
Vitex doniana	0.40
Xylopia aethiopica	0.50 *
Xylopia chrysophylla	0.70 *
Xylopia hypolambra	0.63 *
Xylopia quintasii	0.70 *
Xylopia staudtii	0.36 *

+ The wood densities specified pertain to more than one bibliographic source
* Wood density is derived from the regression equation in section 3.1.2

Appendix 2 - Original biomass data used to develop the biomass regressions equations for broadleaf forests.

BIOMASS DATA FOR REGRESSION EQUATION FOR THE DRY ZONE (EQ. 3.2.1)

Sources:

1. Mann, H. S. and S.K. Saxena, 1982. Improvement of energy resources in Indian arid zone. Pages 283-292 in P. K. Khoska (ed.). Improvement of forest biomass, symposium proceedings, Indian Society of Tree Science, Solan, India.

2. Bandhu, D., 1973. Chakia project tropical deciduous forest ecosystems. Pages 39-61 in L. Kerr (ed.), Modeling forest ecosystems. EDFB-IBP-737, Oak Ridge National Laboratory, Tennessee, USA.

dbh (cm)	Biomass (Kg)	Source
3.7	5.0	1
8.5	14.8	1
20.3	166.5	1
24.0	241.0	1
31.0	417.0	1
41.0	510.9	1
23.2	343.1	2
23.5	446.7	2
23.8	243.3	2
24.2	367.5	2
31.8	454.4	2
15.2	52.0	2
17.1	27.1	2
18.4	69.9	2
23.2	202.6	2
29.6	414.0	2
30.2	394.8	2
34.7	550.8	2
10.8	32.6	2
18.7	92.3	2
21.6	191.4	2
39.2	407.7	2
29.9	225.3	2
30.2	378.9	2
17.1	81.8	2
34.0	709.7	2
19.4	113.9	2
14.6	51.2	2
12.4	64.1	2

BIOMASS DATA FOR REGRESSION EQUATIONS FOR MOIST ZONE (EQ. 3.2.3 AND 3.2.4)

Sources:

1. C. Jordan and C. Uhl, 1986, provided unpublished tree data.

2. Russell, C., 1983. Nutrient cycling and productivity of native and plantation forests at Jari Florestal, Para, Brazil. PhD dissertation. University of Georgia, Athens, USA.

3. Yamakura, T., et al., 1986. Tree size in a mature dipterocarp forest stand in Sebula, East Kalimantan, Indonesia. Southeast Asian Studies 23:451-478.

4. Hozumi, K., et al., 1969. Production ecology of rain forests in Cambodia. I. Plant biomass. Nature and Life in Southeast Asia 6:1-51.

5. R. A. Houghton, 1994, provided unpublished tree data.

dbh (cm)	Biomass (Kg)	Source:
6.4	17.0	1
6.6	22.2	1
6.8	31.7	1
7.6	18.7	1
8.0	30.8	1
8.2	30.4	1
8.3	38.1	1
8.6	43.3	1
9.2	42.4	1
9.3	44.9	1
17.8	143.7	1
19.1	213.8	1
20.1	165.1	1
21.3	332.2	1
23.7	520.0	1
10.5	62.0	1
12.4	94.8	1
13.0	143.6	1
13.2	57.3	1
24.3	227.8	1
26.7	315.7	1
27.1	645.9	1
30.2	458.4	1
31.2	788.7	1
34.4	923.9	1
35.0	933.5	1
35.7	614.2	1
36.6	852.3	1
38.8	909.8	1
42.0	1 933.4	1
47.8	1 903.5	1
48.5	2 258.5	1
55.4	3 932.8	1
58.3	1 885.5	1
58.6	3819.3	1
67.5	2 695.1	1
14.6	64.0	1
12.7	39.3	1
19.3	89.4	1
9.4	19.1	1
12.1	16.5	1
10.0	47.0	2
11.6	73.0	2
11.8	92.3	2
12.1	57.4	2
12.9	148.5	2
13.1	70.9	2
14.2	144.0	2
15.0	197.2	2
15.1	148.9	2
16.0	169.9	2
34.6	1 347.0	2
36.8	1 247.7	2
38.0	1 807.4	2
13.5	53.5	2
15.0	136.3	2
130.5	42 843.0	3
127.0	24 952.6	3
64.1	7 580.4	3
64.3	4 749.1	3
58.6	2 236.9	3
46.5	2 308.9	3
53.4	4 002.5	3
48.1	2 367.3	3
29.5	993.5	3
25.3	559.6	3
22.1	317.5	3
20.8	194.5	3
22.9	239.7	3
22.6	443.4	3
26.3	518.9	3
25.0	485.5	3
20.2	277.5	3
25.6	627.4	3
36.0	1 108.2	3
25.4	481.5	3
30.1	841.3	3
12.8	82.5	3
13.5	148.2	3
15.3	77.3	3
10.5	38.8	3
11.9	109.6	3
13.7	120.4	3
19.0	189.8	3
11.4	22.4	3
19.0	169.4	3
9.8	50.8	3
12.2	45.7	3
10.4	35.2	3
16.3	170.4	3
12.2	75.3	3
14.8	433.2	3
15.0	115.4	3
11.6	67.4	3
12.2	82.5	3
5.5	6.3	4
6.5	12.7	4
6.0	10.8	4
5.9	10.6	4
9.4	26.8	4
5.8	11.3	4
5.7	8.9	4
6.3	10.5	4
7.2	17.8	4
9.2	29.7	4
6.4	12.8	4
5.7	12.1	4
5.0	5.2	4
8.2	19.4	4
9.9	41.8	4
7.0	17.6	4
6.6	17.2	4
5.0	8.7	4
6.7	15.4	4
10.1	23.2	4
11.8	67.5	4
8.7	20.9	4
5.2	5.9	4
7.4	6.7	4
5.0	7.4	4
5.2	10.1	4
5.5	8.5	4
5.9	12.8	4
5.0	8.5	4
8.7	25.9	4
6.8	16.4	4
7.5	20.4	4
5.3	7.8	4
6.5	19.4	4
5.7	8.8	4
5.7	9.1	4
9.2	26.2	4
10.8	36.6	4
10.5	49.9	4
5.0	5.3	4
10.4	32.4	4
5.2	5.3	4
7.6	19.2	4
7.0	19.3	4
7.6	24.1	4
14.6	88.3	4
12.0	70.1	4
6.1	12.8	4
5.3	12.0	4
14.1	81.2	4
12.1	27.9	4
11.8	69.1	4
5.3	8.9	4
19.0	190.0	4
16.6	116.8	4
27.4	399.2	4
37.0	691.5	4
24.0	420.0	4
27.0	559.2	4
25.5	375.6	4
21.1	247.5	4
41.3	1 336.8	4
9.3	36.6	4
8.1	35.6	4
5.2	7.7	4
7.2	15.7	4
5.6	9.4	4
133.2	20 599.4	4
5.9	7.8	4
5.5	16.1	4
5.9	4.2	4
5.9	4.1	4
148.0	39 900.0	5
128.0	23 200.0	5

BIOMASS DATA FOR REGRESSION EQUATIONS FOR THE WET ZONE (EQ. 3.2.5)

Sources:

1. Ovington, J. D. and J. S. Olson, 1970. Biomass and chemical content of El Verde lower montane rain forest plants. Ch. H-2 in H. T. Odum and R. F. Pigeon (eds.), A tropical rain forest. Division of Technical Information TID 24270, National Technical Information Service, Springfield, Va.

2. Edwards, P. J. and P. J. Grubb, 1977. Studies of mineral cycling in a montane rainforest in New Guinea. Journal of Ecology 65:943-969.

3. A. Joyce, 1989, provided unpublished tree data for Costa Rica.

dbh (cm)	Biomass (Kg)	Source
11.7	53.5	1
5.7	7.9	1
7.1	13.3	1
5.1	3.2	1
6.1	9.4	1
19.8	192.2	1
7.6	14.5	1
17.5	155.4	1
5.6	4.1	1
5.6	7.2	1
5.3	4.9	1
8.6	11.4	1
19.3	122.9	1
31.2	384.2	1
35.6	489.9	1
45.7	523.2	1
5.6	6.4	1
5.8	4.8	1
11.2	15.4	1
18.5	140.3	1
10.9	24.6	1
10.3	26.3	1
10.9	21.5	1
8.1	9.4	1
12.9	29.4	1
20.8	121.9	1
4.5	3.4	1
4.1	2.5	1
4.3	0.4	1
5.3	2.1	1
10.9	27.3	1
7.4	11.6	1
6.0	4.4	1
4.3	3.2	1
9.9	25.4	2
11.8	63.5	2
12.7	45.1	2
13.1	60.7	2
13.7	85.3	2
14.3	62.1	2
14.6	42.9	2
15.3	59.9	2
15.9	49.5	2
16.2	141.2	2
16.6	113.8	2
17.5	248.3	2
17.5	77.4	2
19.1	151.4	2
19.4	161.9	2
20.1	121.4	2
20.1	222.8	2
20.4	209.1	2
20.7	166.7	2
21.6	292.5	2
22.0	271.3	2
27.1	268.9	2
27.7	374.9	2
31.8	598.0	2
34.1	872.3	2
36.9	1 226.0	2
40.1	791.0	2
48.4	1 047.3	2
54.1	1 457.0	2
54.1	1 843.7	2
70.0	1 706.5	2
78.0	3 903.0	2
18.1	230.3	2
28.6	432.3	2
43.9	1 292.9	2
110.0	6061.6	2
9.9	15.8	2
10.8	15.4	2
14.3	32.6	2
33.1	1 158.0	2
64.9	1 692.7	2
18.0	153.4	3
21.9	187.9	3
21.9	136.5	3
75.5	4 503.6	3
47.6	1 731.8	3
54.5	2137.3	3
26.4	420.7	3
116.0	13922.1	3
36.8	733.3	3
79.0	5 726.4	3
40.5	836.3	3
32.3	716.1	3
51.4	2441.7	3
65.8	3 288.0	3
51.2	1 671.9	3
76.0	5 066.2	3
81.6	5 229.4	3
97.5	5 453.6	3
99.5	8 962.3	3
55.2	1 941.0	3
19.5	231.2	3
20.4	182.4	3
29.7	438.2	3
17.6	131.6	3
47.2	1 516.6	3
70.5	2 279.1	3
66.4	3576.1	3
44.5	1 037.8	3
50.0	1 545.4	3
44.5	1 230.7	3
93.9	5615.2	3
112.4	5 803.9	3
97.3	4 386.6	3
71.5	2 130.6	3
115.0	7915.6	3
40.4	1 126.5	3
70.1	2 727.5	3
54.2	1 937.1	3
70.8	3101.1	3
47.3	1 470.9	3
70.4	3371.5	3
18.4	104.5	3
51.4	1 434.1	3
66.6	2121.1	3
21.7	150.1	3
87.5	4 976.3	3
74.3	3 204.9	3
38.5	916.9	3
26.5	223.5	3
24.9	199.6	3
63.7	3172.9	3
73.1	3 093.4	3
72.0	3 392.9	3
93.2	6 976.8	3
80.0	3 976.5	3
38.8	648.9	3
36.3	593.5	3
47.0	974.0	3
55.2	1 873.1	3
88.0	4884.1	3
52.9	1 277.6	3
17.6	112.4	3
28.2	455.1	3
3.7	2.0	3
3.6	3.5	3
7.5	21.9	3
19.1	175.1	3
24.3	350.4	3
17.3	121.0	3
9.6	42.2	3
8.2	27.0	3
8.5	17.9	3
12.5	66.3	3
31.6	464.3	3
4.4	3.8	3
27.5	399.0	3
32.2	695.5	3
32.2	299.8	3
32.8	460.9	3
32.2	577.6	3
16.2	99.1	3
44.5	1 374.5	3
35.9	705.7	3
14.9	62.6	3
45.3	1 528.5	3
58.2	1 915.4	3
41.0	730.5	3
50.1	1 913.7	3
21.6	340.1	3
12.2	44.2	3
19.1	129.1	3
16.2	85.8	3
27.7	406.6	3
21.2	210.6	3
21.6	197.6	3
35.3	740.1	3
32.0	444.1	3
34.9	905.5	3
44.2	1 310.4	3
50.4	2 172.6	3
20.4	233.1	3

FAO TECHNICAL PAPERS

FAO FORESTRY PAPERS

1	Forest utilization contracts on public land, 1977 (E F S)
2	Planning forest roads and harvesting systems, 1977 (E F S)
3	World list of forestry schools, 1977 (E/F/S)
3 Rev. 1	World list of forestry schools, 1981 (E/F/S)
3 Rev. 2	World list of forestry schools, 1986 (E/F/S)
4/1	World pulp and paper demand, supply and trade 1 - Vol. 1, 1977 (E F S)
4/2	World pulp and paper demand, supply and trade - Vol. 2, 1977 (E F S)
5	The marketing of tropical wood in South America, 1976 (E S)
6	National parks planning, 1976 (E F S**)
7	Forestry for local community development, 1978 (Ar E F S)
8	Establishment techniques for forest plantations, 1978 (Ar C E* F S)
9	Wood chips - production, handling, transport, 1976 (C E S)
10/1	Assessment of logging costs from forest inventories in the tropics - 1. Principles and methodology, 1978 (E F S)
10/2	Assessment of logging costs from forest inventories in the tropics - 2. Data collection and calculations, 1978 (E F S)
11	Savanna afforestation in Africa, 1977 (E F)
12	China: forestry support for agriculture, 1978 (E)
13	Forest products prices 1960-1977, 1979 (E/F/S)
14	Mountain forest roads and harvesting, 1979 (E)
14 Rev. 1	Logging and transport in steep terrain, 1985 (E)
15	AGRIS forestry - world catalogue of information and documentation services, 1979 (E/F/S)
16	China: integrated wood processing industries, 1979 (E F S)
17	Economic analysis of forestry projects, 1979 (E F S)
17 Sup. 1	Economic analysis of forestry projects: case studies, 1979 (E S)
17 Sup. 2	Economic analysis of forestry projects: readings, 1980 (C E)
18	Forest products prices 1960-1978, 1980 (E/F/S)
19/1	Pulping and paper-making properties of fast-growing plantation wood species - Vol. 1, 1980 (E)
19/2	Pulping and paper-making properties of fast-growing plantation wood species - Vol. 2, 1980 (E)
20	Forest tree improvement, 1985 (C E F S)
20/2	A guide to forest seed handling, 1985 (E S)
21	Impact on soils of fast-growing species in lowland humid tropics, 1980 (E F S)
22/1	Forest volume estimation and yield prediction - Vol. 1. Volume estimation, 1980 (C E F S)
22/2	Forest volume estimation and yield prediction - Vol. 2. Yield prediction, 1980 (C E F S)
23	Forest products prices 1961-1980, 1981 (E/F/S)
24	Cable logging systems, 1981 (C E)
25	Public forestry administrations in Latin America, 1981 (E)
26	Forestry and rural development, 1981 (E F S)
27	Manual of forest inventory, 1981 (E F)
28	Small and medium sawmills in developing countries, 1981 (E S)
29	World forest products, demand and supply 1990 and 2000, 1982 (E F S)
30	Tropical forest resources, 1982 (E F S)
31	Appropriate technology in forestry, 1982 (E)
32	Classification and definitions of forest products, 1982 (Ar/E/F/S)
33	Logging of mountain forests, 1982 (E F S)
34	Fruit-bearing forest trees, 1982 (E F S)
35	Forestry in China, 1982 (C E)
36	Basic technology in forest operations, 1982 (E F S)
37	Conservation and development of tropical forest resources, 1982 (E F S)
38	Forest products prices 1962-1981, 1982 (E/F/S)
39	Frame saw manual, 1982 (E)
40	Circular saw manual, 1983 (E)
41	Simple technologies for charcoal making, 1983 (E F S)
42	Fuelwood supplies in the developing countries, 1983 (Ar E F S)
43	Forest revenue systems in developing countries, 1983 (E F S)
44/1	Food and fruit-bearing forest species - 1. Examples from eastern Africa, 1983 (E F S)
44/2	Food and fruit-bearing forest species - 2. Examples from southeastern Asia, 1984 (E F S)
44/3	Food and fruit-bearing forest species - 3. Examples from Latin America, 1986 (E S)
45	Establishing pulp and paper mills, 1983 (E)
46	Forest products prices 1963-1982, 1983 (E/F/S)
47	Technical forestry education - design and implementation, 1984 (E F S)
48	Land evaluation for forestry, 1984 (C E F S)
49	Wood extraction with oxen and agricultural tractors, 1986 (E F S)
50	Changes in shifting cultivation in Africa, 1984 (E F)
50/1	Changes in shifting cultivation in Africa - seven case-studies, 1985 (E)
51/1	Studies on the volume and yield of tropical forest stands - 1. Dry forest formations, 1989 (E F)
52/1	Cost estimating in sawmilling industries: guidelines, 1984 (E)
52/2	Field manual on cost estimation in sawmilling industries, 1985 (E)
53	Intensive multiple-use forest management in Kerala, 1984 (E F S)
54	Planificación del desarrollo forestal, 1984 (S)
55	Intensive multiple-use forest management in the tropics, 1985 (E F S)
56	Breeding poplars for disease resistance, 1985 (E)
57	Coconut wood - Processing and use, 1985 (E S)
58	Sawdoctoring manual, 1985 (E S)
59	The ecological effects of eucalyptus, 1985 (C E F S)
60	Monitoring and evaluation of participatory forestry projects, 1985 (E F S)
61	Forest products prices 1965-1984, 1985 (E/F/S)
62	World list of institutions engaged in forestry and forest products research, 1985 (E/F/S)
63	Industrial charcoal making, 1985 (E)
64	Tree growing by rural people, 1985 (Ar E F S)
65	Forest legislation in selected African countries, 1986 (E F)
66	Forestry extension organization, 1986 (C E S)
67	Some medicinal forest plants of Africa and Latin America, 1986 (E)
68	Appropriate forest industries, 1986 (E)
69	Management of forest industries, 1986 (E)
70	Wildland fire management terminology, 1986 (E/F/S)
71	World compendium of forestry and forest products research institutions, 1986 (E/F/S)
72	Wood gas as engine fuel, 1986 (E S)
73	Forest products: world outlook projections 1985-2000, 1986 (E/F/S)
74	Guidelines for forestry information processing, 1986 (E)
75	Monitoring and evaluation of social forestry in India - an operational guide, 1986 (E)
76	Wood preservation manual, 1986 (E)
77	Databook on endangered tree and shrub species and provenances, 1986 (E)
78	Appropriate wood harvesting in plantation forests, 1987 (E)
79	Small-scale forest-based processing enterprises, 1987 (E F S)
80	Forestry extension methods, 1987 (E)
81	Guidelines for forest policy formulation, 1987 (C E)
82	Forest products prices 1967-1986, 1988 (E/F/S)
83	Trade in forest products: a study of the barriers faced by the developing countries, 1988 (E)
84	Forest products: World outlook projections - Product and country tables 1987-2000, 1988 (E/F/S)
85	Forestry extension curricula, 1988 (E/F/S)
86	Forestry policies in Europe, 1988 (E)
87	Small-scale harvesting operations of wood and non-wood forest products involving rural people, 1988 (E F S)
88	Management of tropical moist forests in Africa, 1989 (E F P).
89	Review of forest management systems of tropical Asia, 1989 (E)
90	Forestry and food security, 1989 (Ar E S)
91	Design manual on basic wood harvesting technology, 1989 (E F S)
	(Published only as FAO Training Series, No. 18)
92	Forestry policies in Europe - An analysis, 1989 (E)
93	Energy conservation in the mechanical forest industries, 1990 (E S)
94	Manual on sawmill operational maintenance, 1990 (E)
95	Forest products prices 1969-1988, 1990 (E/F/S)
96	Planning and managing forestry research: guidelines for managers, 1990 (E)
97	Non-wood forest products: the way ahead, 1991 (E S)
98	Timber plantations in the humid tropics of Africa, 1993 (E F)
99	Cost control in forest harvesting and road construction, 1992 (E)
100	Introduction to ergonomics in forestry in developing countries, 1992 (E F I)
101	Management and conservation of closed forests in tropical America, 1993 (E F P S)
102	Research management in forestry, 1992 (E F S)
103	Mixed and pure forest plantations in the tropics and subtropics, 1992 (E F S)
104	Forest products prices 1971-1990, 1992 (E/F/S)
105	Compendium of pulp and paper training and research institutions, 1992 (E)
106	Economic assessment of forestry project impacts, 1992 (E/F)
107	Conservation of genetic resources in tropical forest management - Principles and concepts, 1993 (E/F/S)
108	A decade of wood energy activities within the Nairobi Programme of Action, 1993 (E)
109	Directory of forestry research organizations, 1993 (E)
110	Proceedings of the Meeting of Experts on Forestry Research, 1993 (E/F/S)
111	Forestry policies in the Near East region - Analysis and synthesis, 1993 (E)
112	Forest resources assessment 1990 - Tropical countries, 1993 (E)
113	Ex situ storage of seeds, pollen and in vitro cultures of perennial woody plant species, 1993 (E)
114	Assessing forestry project impacts: issues and strategies, 1993 (E F S)
115	Forestry policies of selected countries in Asia and the Pacific, 1993 (E)
116	Les panneaux a base de bois, 1993 (F)
117	Mangrove forest management guidelines, 1994 (E)
118	Biotechnology in forest tree improvement, 1994 (E)
119	Number not assigned
120	Decline and dieback of trees and forests - A global overview, 1994 (E)
121	Ecology and rural education - Manual for rural teachers, 1995 (E S)
122	Readings in sustainable forest management, 1994 (E S)
123	Forestry education - New trends and prospects, 1994 (E F S)
124	Forest resources assessment 1990 - Global synthesis, 1995 (E F S)
125	Forest products prices 1973-1992, 1995 (E F S)
126	Climate change, forests and forest management - An overview, 1995 (E)
127	Valuing forests: context, issues and guidelines, 1995 (E)
128	Forest resources assessment 1990 - Tropical forest plantation resources, 1995 (E)
129	Environmental impact assessment and environmental auditing in the pulp and paper
	industry, 1996 (E)
130	Forest resources assessment 1990 - Survey of tropical forest cover and study of change processes, 1996 (E)
131	Ecología y enseñanza rural - Nociones ambientales básicas para profesores rurales y extensionistas, 1996 (S)
132	Forestry policies of selected countries in Africa, 1996 (E/F)
133	Forest codes of practice - Contributing to environmentally sound forest operations, 1996 (E)
134	Estimating biomass and biomass change of tropical forests - A primer, 1997 (E)

Availability: January 1997

Ar	-	Arabic
C	-	Chinese
E	-	English
I	-	Italian
F	-	French
P	-	Portuguese
S	-	Spanish
Multil	-	Multilingual
*		Out of print
**		In preparation

The FAO Technical Papers are available through the authorized FAO Sales Agents or directly from Sales and Marketing Group, FAO, Viale delle Terme di Caracalla, 00100 Rome, Italy.

This publication is a practical guide for those who designed forest inventories in which biomass estimation is a component. It describes primary data and measurement requirement for assessment of biomass per hectare. As supporting material it provides wood density data for a large number of tropical species. The primer also presents methods that are available for estimating biomass per hectare of tropical forest using mainly existing data. To this end equation and default values are given for a variety of cases. Finally, biomass per hectare estimates are presented estimates are presented for many tropical countries using the methodologies given.