CES Technical Report 131,   August 2013

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Section 1

Section 2

Section 3

Section 4

Section 5

Section 6

Section 7

Section 8

Section 9

Section 10

Above Ground Standing Biomass of three micro watersheds in Himachal Pradesh

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Introduction

Biomass in all its form provides about 14% of the world’s energy (Hall and Challe, 1991). The dependency on forest resources is high in developing countries like India, which has an estimated 329 million hectares that can be used for biomass production under various classes like cropland, forests, plantation, etc. The projected biomass demand for India is around 516 Mt/yr (Bhattacharya et al. 2003).

Forests play an important role in global carbon cycling, since they are large pools of carbon as well as potential carbon sinks and sources to the atmosphere. Accurate estimation of forest biomass is required for greenhouse gas inventories and terrestrial carbon accounting. The needs for reporting carbon stocks and stock changes for the Kyoto Protocol have placed additional demands for accurate surveying methods that are verifiable, specific in time and space, and that cover large areas at acceptable cost (IPCC, 2003; Krankina et al. 2004; Patenaude et al. 2005; UNFCCC, 1997).

Forest biomass varies over climatic zone, altitude and region (Brown et al., 1989). Plant biomass is therefore a metric fundamental to understanding and managing forest ecosystems, whether to estimate primary production, nutrient pools, species dominance, responses to experimental manipulation, or fuel loads for fire. In recognition of its central importance, models of ecosystem processes often include plant biomass or biomass-related variables as inputs and outputs (Northup et al. 2005).

In the Himalaya, vegetation ranges from tropical monsoon forest to alpine meadow and scrub, across elevation gradients. The Himalayan forest biomass is important as a large population of hill folk are still dependent on forest biomass to meet their daily requirement (Singh and Singh, 1987). The trend in biomass in the Himalayan region shows an increase in biomass with increase in altitude for different strands up to an altitude of 2700 m and shows decrease hence on, as the vegetation above 3000 m is sparse and are mostly of alpine grassland types at above 3500 m (Singh et al. 1994)
Pinus roxburghii Sarg. (chir pine) forest are dominant along the low-to-mid montane belt of Central and Western Himalaya (Chaturvedi and Singh, 1987) due to high regeneration potential, growth rate, establishing in degraded habitats, pipe like boles and high volumes.

Disturbance has become a widespread feature in most of the forests all over the Himalaya (Singh and Singh, 1992), therefore, knowledge on ecological processes and biotic pressure can help in understanding the persistence of long-lived plant communities. A sustained regeneration and growth of all species in the presence of older plants is required for better growth of any plant community (Ramakrishnan et al., 1981). Humans have made considerable impacts in the Himalayan region, estimating such changes accurately would be of particular value to Himalayan people, whose subsistence agriculture depends on forest productivity to maintain livestock and soil fertility.

For an assessment of forest biomass, forest inventory is most commonly used and it differs depending on scope and purpose. Inventories are being designed to obtain information on other uses of the forest like recreation, grazing, wildlife and water conservation. It is designed to measure forest biomass rather than or in addition to traditional volume. Species specific equations that describe relationships between plant attributes and biomass are more accurate and flexible. Furthermore, it is preferable to use region or site-specific relationships where possible. Species size–biomass relationships could differ as plants alter allocation patterns in response to soils, climate and disturbance. Changes in structure and composition of vegetation are often accompanied by changes in biomass. (Brown and Luo 1992). Forest biomass varies over climatic zone, altitude and region (Brown et al., 1989, 1991)

In this report we have examined the variation in above ground standing biomass of three micro watersheds which has a forest widely used by the local people for fuel wood, fodder, and for grazing of cattle in the Himachal Pradesh region across varying altitude and vegetation types.

Methods

Study Area:

The department of Science and Technology (DST), Government of India, has taken a major initiative to create a bio-geo database and ecological modelling for western Himalayas. Initially three watersheds have been identified in Himachal Pradesh. The three micro watersheds having typical mountain village ecosystem were selected across three altitudinal zones (Figure 1). The forests in the three watersheds are managed as reserve forest by the state forest department, where cutting of trees is prohibited. However, lopping and collection of fallen wood for household purpose by the villagers are noted in all three watersheds. Spatial bounds of the respective watersheds were delineated from the digital elevation model (DEM) generated from the remote sensing data. The characteristics of the watersheds is summarised in Table 1.

Table 1. Geographical attributes of study area

Micro watersheds	District	Main watershed	Latitude (°N)	Longitude (°E)	Altitude(m amsl)	Area (sq.km)
Mandhala	Solan	Yamuna	30.87-30.97	76.82-76.92	400-1100	14.53
Moolbari	Shimla	Yamuna	31.07-31.17	77.05-77.15	1400-2000	10.50
MeGad	Lahaul and Spiti	Chandrabhaga	32.64-32.74	76.46-76.74	2900-4500	46.05

Figure 1: Boundary map of three watersheds
* - indicates the location of villages in the watershed

Climate

The climate is distinguished in three axes, in the Western Himalayas: (1) a vertical axis determined by the effect of altitude on temperature; (2) a transverse axis determined by topography along which rain shadow effects cause decreasing precipitation and increasingly extreme (continental) temperature fluctuations from SW to NE across the main ranges; (3) a longitudinal axis determined by a geographical trend of decreasing monsoon precipitation (June-September) and increasing winter snowfall (December-April) from SW to NW along all the ranges. The third axis is important in determining major ecological trends over the entire length of the Himalayan chain, but it is less important than the other two axes in determining the ecology of localities within the Western  Himalayas (Gaston et al., 1983).

Vegetation type

The three watersheds represent different vegetation types;

Mandhala watershed: This watershed falls in lower shiwalik range (400 - 1100m) and are characterised by dry evergreen tree species and scrub vegetation. The major tree species in this watershed is  Flacourtia montana, Acacia catechu, Grewia optiva, Toona ciliata, Albizia procera, Haldina cordifolia, Acacia sp., Lannea coramandelica, Mitragyma parviflora, along with Nyctanthus arbor-tristis, Carissa apaca, Dodonaea viscose and Woodfordia fruticosa. Most of the forests here have been deforested and hill ranges completely covered with Lantana camera weed. Also scattered trees of Holoptelia integrifolia, Dalbergia sisoo, Morus nigra, etc. occur along the field bunds and other open lands.

Moolbari watershed: The vegetation here is characteristically of middle temperate type of the Himalayan region of the mid altitude ranges (1400-2000m). The vegetation in this watershed consisted of mixed deciduous and sub-tropical pine forest in two different altitudinal ranges. Former till an altitude of 1500m and later beyond 1500m. Apart from Pinus other species seen are Pyrus pashia, Rubus ellipticus, Berberis sp, and in moist localities species of Quercus leucotrichophora and Q. glauca and Rhododendron arboretum. On the exposed hill slopes in pine forests Euphorbia royleana is encountered. Between 1800-2000 m, oak forests species such as Quercus leucotrichophora, Q. glauca dominate along with Rhododendron arboretum, Lyonia ovalifolia.

MeGad Watershed: This watershed falls in the rain shadow region of the Himalayas (2900-4500m), and receives less than 80cm rainfall annually, there is high snowfall during winter and temperature goes to as low as -20˚C in this season. This watershed comprises of temperate, alpine and sub-alpine vegetation.

• Temperate vegetation: It consists of woody trees at altitude of 2500-3200 such as Pinus wallichiana, Juniperus recurva, Picea smithiana, Abies pindrow, Cedrus deodara that form the natural forests. Along the streams and irrigated canals are planted trees of Salix and Popular sp.
• Alpine - Sub-alpine vegetation: Mostly stunted, scattered bushes of Juniperus communis, Berberis sp., etc along with herbaceous elements such as Ranunculus, Pedicularis, Potentilla, Polygonum, Geranium, Anemone, Corydalis, etc., are commonly encountered.

Quantification of Bioresources

Vegetation Sampling to estimate Above Ground Biomass (AGB)

Belt transect of 250 x 4 m was laid randomly throughout the water shed. In each transect, for each tree GBH (Girth at Breast Height in cm) and height (in m) is noted along with its identification. Coordinates were marked using GPS at the start and end points in each transect and at every 100m interval. Litter weight is measured in four 1m X 1m quadrat within each transect. Using densiometer, canopy cover is measured at start, end point and at 100 meter interval in each transect.

The above ground standing biomass is estimated transect wise through non-destructive sampling using standard regression models suitable for these agro-climatic zones (Brown et al., 1989; Schroeder et al., 1997) and are given in Table 2. The AGB is estimated for each transect in all three watersheds and represented as tonnes/hectare (t/ha).

Table 2. Regression models used to estimate AGB in the study region

 Results

AGB in Mandhala

Mandhala watershed falls in the lower Shiwaliks, receives low rainfall and hence are considered as dry regions. We used Model 1 and 2 from Table 2 for estimation of AGB. Table 3  provides transect-wise estimated AGB in Mandhala watershed.

Table 3. Transect-wise AGB in Mandhala watershed

Transect #	Basal Area (sq.cm)	Model 1 (t/ha)	Model 2 (t/ha)
1	5969.9	31.60	17.42
2	12398.1	69.80	36.17
3	8993.1	44.79	26.24
4	4060.3	17.91	11.85
5	5386.4	33.02	15.71
6	5697.4	21.66	16.62
7	685.0	2.71	2.00
8	4317.8	15.18	12.60
9	4862.5	21.70	14.19
10	2084.4	8.92	6.08
11	2615.4	12.58	7.63
Mean±Sd	5188.23±256.32	25.44±18.93	15.14±9.5
Range	685.0 - 12398.1	2.71 - 69.8	2.00 - 36.17

Estimated AGB varied from 2.71-69.8 to 2.00-36.17 t/ha according to respective models. The variations among transects were significantly different (Analysis of Variance, ANOVA, F = 3.97, P=0.04) between Model 1 and 2, though the overall estimate is not different between the models (Students t, t=1.61, P=0.12).  Figure 2 is the scatter plot of basal area against above ground biomass estimate based on Model 1 and Model 2. Model 2 mentioned in Table 4 is better suited for Mandhala water shed (n=11, R²=1).

Figure 3. Scatter plot for AGB estimation in Mandhala watershed.

Table 4. AGB estimation regression models based on basal area (BA in sq.cm).

AGB in Moolbari

This watershed falls in mid Himalayan ranges and receives higher rainfall (compared to Mandhala and MeGad). Regression models 3, 4 and 5 for wet region are used to estimate AGB. Table 5  provides transect-wise estimated AGB in Moolbari watershed.

Table 5. Transect-wise AGB in Moolbari watershed.

Variations among transects were not significant (Model 3 vs. 4, F=1.28, p=0.64; Model 3 vs. 5, F=1.7, p=0.32; and Model 4 vs. 5, F=1.32, p=0.59), indicating homogeneity among transects. Among the three models, mean AGB estimate between models were also similar, except for model 3 and 5 (Model 3 vs. 4, t=1.09, p=0.28; Model 4 vs. 5, t=1.73, p=0.094; and Model 3 vs. 4, t=2.76, p=0.009). Figure 3 is the scatter plot of basal area against above ground biomass estimate based on Models 3, 4 and 5. Table 6 lists the models with their R² values.

Figure 4. Scatter plot for AGB estimation in Moolbari watershed.

Table 6. AGB estimation regression models based on basal area (BA in sq.cm, n=16).

AGB was also estimated considering entire Moolbari watershed as temperate broadleaf forest and dividing further according to dominant forest type (Table 7).

Table 7. Species-wise estimate of AGB in Moolbari watershed.

Forest type	Model	AGB (t/ha)	Reference
Quercus forest	AGB = 1.8409BA 0.89262	221.79	Li and Luo (1996)
Pine and other conifers	AGB= 0.5168*(Volume)+33.2378	31.26	Fang et al., (1998)
Miscellaneous broad leaved	AGB=0.5+25000D 2.5 /D 2.5+ 246872	19.17	Schroeder et al., (1997)
Total		272.22

AGB in MeGad Watershed

For the marked reduction in precipitation, MeGad is considered as dry region for the estimation of AGB. Table 8 details the estimated AGB based on Model 1 and 2 listed in Table 2.

Table 8. Transect-wise AGB in MeGad watershed.

On comparing Model 1 vs. Model 2, mean estimate of AGB were significantly different (t=2.25, P=0.044). AGB estimates for each transect varied considerably, although not statistically significant (F=4.83, P=0.08). Figure 4 depicts basal area against AGB in MeGad watershed and regression models of the same are in Table 9.

Figure 5. Scatter plot for AGB estimation in MeGad watershed.

Table 9. AGB estimation regression models based on basal area (BA in sq.cm).

Both model 1 and 2 have significant r² values. Model 2 is better suited than model 1 for its simplicity in calculation. Considering different forest types, AGB estimated for MeGad are listed in Table 10. These estimates vary from 140.37 to 400.34t/ha for the region.

Table 10. Species-wise estimate of AGB in MeGad watershed.

Forest type	Model	AGB (t/ha)	Reference
Picea - Abies forest	AGB = 50.8634 + 0.5406(BA)	400.34	Li and Luo (1996)
Mixedconifers	AGB= 0.5168*(Volume)+33.2378	352.93	Fang et al., (1998)
Pines and other conifers	AGB= 0.5168*(Volume)+33.2378	336.96	Fang et al., (1998)
Conifers	AGB= (0.5+15000D 2.7) /(D 2.7+ 364946)	140.37	Schroeder et al., (1997)

Validation of Biomass results:

The results of biomass estimation for the three watersheds were validated with the biomass estimation results in published literature.

The results show that for most of the forest types in the study area, the biomass obtained is well within the range of biomass obtained in similar forests. However very low biomass is noted in Mandhala watershed, the forests of Mandhala region are highly degraded and invasive weeds like lantana and euphotorium have put constraint on the growth and regeneration of natural vegetation

Litter Weight:

The dry litter in the ground is measured in each belt transect at start point, end point and at every hundred metre interval. A plot of 1m X 1m is made and all the dry leaf litter falling within the plot is collected in a bag and weighed with a 500g spring balance.

Table 9: Values of Litter weight and canopy cover in transects

Litter weight in grams	Canopy cover in %	Litter wt	Canopy cover closed
90	32.4	110	36
80	52.16	370	76
10	0	70	74
120	53.2	170	82
45	11.6	130	92
130	91.68	120	56
20	0.16	80	54
40	25.12	90	35
170	81.28	125	4
370	75.04	0	8
120	4.32	350	72
110	33.44	0	2
80	29.28	250	76
150	81.28	0	4
260	92.72	270	96
240	91.68	0	5
310	41.76	0	4
70	72.96

Graph 1: Litter Weight and canopy cover

A graph was plotted between the litter weight and canopy cover. A linear trend line was plotted in the graph to see the relation; though not much a significant relation is seen, increase in canopy cover will cause the litter weight to increase.

Discussion

In the Himalaya, vegetation ranges from sub-tropical monsoon forest to alpine meadow and scrub, across elevation gradients. Plant species richness in the three watersheds varied along elevation and is represented in Figure 6. The overall trend indicates a gradual increase in species richness till mid altitudinal regions and then decrease with increase in elevation. Though the polynomial relationship is not significant (r²=0.32), but this is similar to general pattern of species richness along elevation in the Himalayas. Such pattern is evident in relative abundance of plant species.

Figure 6. Plant species richness along elevation gradient in the three watersheds.

Figure 7 illustrates the trend in relative abundance (no. of individuals/1000 sq.m) of plant species in each transect against elevation gradient. It is interesting to note that, low altitude (500-1000m) and very high altitude (2700-3500m) had lesser abundance compared to mid altitude (1300-1600m).

Figure 7. Relative abundance along elevation gradient in the three watersheds.

Species richness has not influenced the relative abundance in the three watersheds as evident in Figure 8 (r²=0.26). Hence, it can be inferred here that relative abundance of the individuals in a region influences the AGB than species richness. This apparent non-influence is evident in scatter plot of species richness against AGB (Figure 9, r²=0.005 ).

Figure 8. Scatter plot of relative abundance versus species richness in three watersheds

Figure 9. Scatter plot of species richness and above ground biomass in the study region

Influence of relative abundance on the AGB estimate in the study region is illustrated in Figure 9. The apparent lower AGB estimate at higher abundance are due to plantation trees of lower girth classes from Mandhala water shed.

Figure 10. Scatter plot of relative abundance and above ground biomass in the study region

The trend in biomass in the Himalayan region shows an increase in biomass with increase in altitude for different strands up to an altitude of 2700 m and shows decrease hence on, as the vegetation above 3000 m is sparse and are mostly of alpine grassland types at above 3500 m (Singh et al. 1994). This is evident in Figure 10 based on elevation gradient and estimated AGB in the study area.

Figure 11. Scatter plot of above ground biomass against elevation gradient

Pinus roxburghii Sarg. (chir pine) forest are dominant along the low-to-mid montane belt of Central and Western Himalaya (Chaturvedi and Singh, 1987) due to high regeneration potential, growth rate, establishing in degraded habitats, pipe like boles and high volumes.

Disturbance has become a widespread feature in most of the forests all over the Himalaya (Singh and Singh, 1992), therefore, knowledge on ecological processes and biotic pressure can help in understanding the persistence of long-lived plant communities. A sustained regeneration and growth of all species in the presence of older plants is required for better growth of any plant community (Ramakrishnan et al., 1981). Humans have made considerable impacts in the Himalayan region, estimating such changes accurately would be of particular value to Himalayan people, whose subsistence agriculture depends on forest productivity to maintain livestock and soil fertility.

The distribution and dynamics of biological resources must be understood to provide a rational basis for planning and management decisions, without which conservation of these resources in the natural habitats would be impossible (Keith and Sanders, 1990; Khoshoo, 1992).

Land use patterns needs to be considered before analyzing the area that will be potentially available for biomass production, it is essential to understand the projected trends in land use pattern for the future.

Watershed approach in the Himalayan region has become acceptable in undertaking land improvement measures (Sharma et al.1992), however, the development has not achieved the desired pace due to lack of information available on the watershed level and because of sectoral approaches adopted by various departments working in the Himalaya (Sundriyal et al. 1994b).
Therefore, in many areas re-construction of disturbed ecosystems should be taken up on a priority basis, both for biodiversity conservation and for maintaining landscape productivity (Behera et al., 2006)

Conclusion

The basal area, tree density and biomass per hectare is high for the watershed in the middle elevation, the mid domain effect observed in the Himalayan region is same here. The basal area and number of trees/ha for all three watershed is less than in comparison with undisturbed sites of the Himalayan region.

References:

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Annexure – I

Biomass is defined as the total amount of aboveground living organic matter in trees expressed as oven-dry tons per unit area. Biomass estimation of vegetation in general, forests in particular has received serious attention over the last few decades for the very reason that components of climate change are associated with change in the biomass of a region. From ecosystem perspectives, biomass estimation helps in ecosystem productivity, energy and nutrient flows, and for assessing the contribution of changes in forest lands to the global carbon cycle. The potential carbon emission that could be released to the atmosphere due to degradation of forest and forest resource, deforestation and conversion of forested area into other land-use can be determined by biomass estimation. Hence, the precise estimation of biomass becomes necessary for understanding the importance of forest both at global scale as well as regional scale for their impact in climate change.

Literature review

Methods involved in estimation of above ground biomass: There are two approaches for estimating aboveground biomass  (AGB) in trees, namely

• Destructive, 
• non-destructive.

The destructive method involves harvesting tree samples and estimating wood density and above ground biomass followed by deriving a probable relationship between tree girth, height with above ground biomass. Rai (1984) developed equations based on destructive sampling of four tropical rainforests of Western Ghats of Karnataka and compared the regression models against D2H and DBH (where D is diameter in cm, H is height in m and DBH is diameter at breast height in cm) , both in log and non-log models. Most of recent non-destructive methods involving allometric equations are based on earlier destructive sampling methods. Destructive sampling gives precise biomass estimation, but cannot be done in all situations and more over this requires considerable amount of trees to be cut. Hence biomass estimation through non-destructive method is adapted nowadays.

In non-destructive techniques, forest biomass is estimated mathematically, using functions which relate the diameter of a tree to its biomass- or parts of its biomass- (leaves, bark, bole wood etc). This approach is further divided into a direct approach using allometric equations, and an indirect approach using biomass expansion factors.

i) Allometry is the relation between the size of an organism and the size of any of its parts, and allometric equation is usually expressed in power-law form or in logarithmic form. Once an allometric equation has been developed, the biomass can be estimated in a forest stand using just the simple measurements of diameter.

The allometric equation's general form is usually written as,

y = bxa
or
ln y = lnb + aln x

where b is a constant, called allometric coefficient and a is the allometric exponent.

Ketterings et al. (2001) proposed allometric equation based on original data collected in field study in Indonesia. It is more flexible and reduces estimated errors due to change in site. Laurance et al. (1999) estimated above-ground dry biomass of living trees including palms in central Amazonia. The biomass attained from 231 to 492 tha^-1, with a mean of 356 ± 47 tha^-1.

ii) Biomass expansion factor and root-to-shoot ratio: To calculate living aboveground biomass in plantations, a biomass expansion factor is applied to the commercial volume of trees as well as wood density. The biomass expansion factor is the ratio of total aboveground oven-dry biomass of trees with a minimum DBH of 10 cm or more to the oven-dry biomass of the inventoried volume (Brown, 1997). Biomass expansion factors from inventories in tropical Asia, America, and Africa were reported to be 1.1 and 2.5 (Brown and Lugo 1990; Brown, 1997). Root-to-shoot ratio is expressed as the ratio of the weight of the root to the weight of the top. For instance, a high root-to-shoot ratio means that the tree has much more biomass belowground than aboveground. The equation for estimating total biomass as follows:

TB =[V •D• BEF]• (1+ R)

where:
TB = total biomass
V = merchantable volume (see Chapter 4 in the GPG/LULUCF)
D = basic wood density
BEF = biomass expansion factor for conversion of commercial volume to aboveground biomass
R = root-to-shoot ratio

Other methods

Gillespie et al., (1992) estimated AGB based on commercial inventory stand and stock tables through a variety of techniques. The most accurate method of estimating the number of stems in smaller (10-15 cm) diameter classes used the ratio of the numbers of stems in the two smallest diameter classes.

Mandal and Laake (2005) using LAI (Leaf area index) estimated AGB. LAI was extracted from the canopy photos, taken by hemispherical camera and analysed by the use of Gap Light Analyzer. They developed two linear models for above ground biomass and LAI. The LAI is related with the photosynthetically active surface (for photosynthesis etc.) which allows the tree to grow and accumulate the biomass. Now LAI is widely used as a reliable tool to develop the relationship with forest biomass (Kiniry et al., 1999). Lu et al., (2002) developed equations based on image data and vegetative inventory data to estimate biomass in moist tropical areas in Brazilian Amazon basin. These are suitable to estimate above ground biomass of dense vegetation areas.  Above ground biomass is difficult to quantify over large areas using traditional technique (direct estimation). Remote sensing has opened an effective way to estimate forest biomass and carbon (Rosenqvist et al., 2003) over large areas. Based on Lidar remote sensing technique, Lefsky (1999) developed equation of canopy structure and above ground biomass. The equation explained 84% of variance in above ground biomass (P<0.0001). These were validated in the temperate deciduous, temperate coniferous and boreal coniferous biomes.

Santos et al., (2003) proposed equation based on different polarizations of Polarimetric band Synthetic Aperture Radar data for tropical primary and secondary forests. Okuda et al., (2004) aerial photographs taken by satellite remote sensing and inventory data collected by destructive sampling in the same area and developed equations for in an old-growth primary forest and in a regenerating logged forest, both within the tropical rainforest of the Pasoh Forest Reserve in Peninsular Malaysia.

Above ground biomass estimate in different biomes

Tropical forest
Murphy and Lugo (1986) estimated plant biomass, based on destructive sampling of trees in dry Guanica forest of Puerto Rico. Total Above ground biomass for live plants was 44.9 tha⁻¹. Live trees (DBH ≥ 5 cm) had 16 tha⁻¹ aboveground live biomass and 68 tha⁻¹ dead biomass. Ground vegetation (DBH< 5 cm) had 28.7 tha⁻¹ above ground biomass. Live woody plant biomass was 65.6 % of the total biomass, epiphytes were 0.2 %, standing dead stems were 12.2% (82% for stems DBH<5 cm), and leaf and wood litter 22%. Total biomass of live foliage for trees and ground vegetation was 4.6 tha⁻¹. Root biomass was 45 tha⁻¹ or 50 percent of total live plant biomass. Approximately 70% of the root biomass was concentrated within 40 cm of the soil surface, with 57% in the upper 10 cm.

Rai and Proctor (1986) had studied four evergreen rainforest sites Agumbe, Bannadpare, Kagneri and South Bhadra at 575-800 m altitude in Karnataka, southern India. They found the total AGB to be 420-649 tha⁻¹ and the root fraction to be 13.9-20.2 tha⁻¹. Girth increment data over 35 years were available for one site and these were used with biomass estimates to calculate the approximate mean annual increase of above-ground and root (>5-cm girth) biomass in the four sites. These were 6.4-11.1 tha⁻¹ for aboveground material and 0.2-0.4 tha⁻¹ for roots. In one plot biomass was estimated by destructive sampling of small trees (<5 cm dbh) and herbs. The combined AGB of these fractions was 7.2 tha⁻¹.They developed six allometric equations based on destructive sampling of 465 trees.

Similar allometric equations were developed by Overman et al., (1994) based on destructive sampling of 54 trees in terra firme forest near Araracuara in Colombia, Ares and Fownes (2000) for tropical ash (Fraxinus uhdei) trees growing on organic uplands soils in the island of Hawaii, Cordero and Kanninen (2002) with 17 trees of Bombacopsis quinata in two different climatic zones of Costa Rica. Foliage, branch, and stem biomass were highly correlated with DBH (r > 0.68, n = 17).

Cummings et al., (2002) developed equations based on forest inventories and quantified the total above ground biomass (TAGB) and forest structure in 20 intact Amazonian tropical sites in Western Brazil. The TAGB of open forest ranged from 288 to 346 tha⁻¹, with a mean of 313 tha⁻¹; desce forest TAGB ranged from 298 to 533 tha⁻¹, with a mean of 377 tha⁻¹; and ecotone forests TAGB ranged from 298 to 422 tha⁻¹, with a mean of 350 tha⁻¹. Mean TAGB for all 20 sites was 341 tha⁻¹.

For mixed hard wood forest trees near Franklin, North Carolin, Elliott et al., (2002) developed allometric equations. These equations were used to estimate branch and stem biomass of Acer rubrum, Cornus florida, Liriodendron tulipifera, Quercus prinus, Quercus rubra, Robinia pseudoacacia trees and to estimate biomass for all oaks and all tree species. Tiepolo et al. (2002) proposed equation based on destructive sampling of fern tree (Cyathea spp.) at the Atlantic Forest biome in Paraná State, Brazil. They also estimated the average carbon stock using established equations for submontane forest was 135.9 tCha⁻¹, lowland forest was 106.8 tCha⁻¹, floodplain forest was 64.12 tCha⁻¹, advanced/medium forest was 106.1 tCha⁻¹, medium secondary forest was 101.96 tCha⁻¹, young secondary forest was 42.89 tCha⁻¹, pasture strata was 2.4 tCha⁻¹, and for shrub land was 7.4 tCha⁻¹.

For moist tropical forest, Chave et al., (2003) estimated AGB at Barro Colorado Island, Panama. Total mean above ground biomass was 281±20 tha⁻¹. Palms contributed 1.5 tha⁻¹ (i.e. about 0.5%). The mean AGB change over 15 years was + 0.21 tha⁻¹year⁻¹ with a 95% confidence of -0.68 to 0.63 tha⁻¹year⁻¹ and no spatial autocorrelation was found in AGB growth or mortality across subplots (r²< 0.001 in all cases). Branch falls and partial breakage of stems contributed 0.46 tha⁻¹year⁻¹ to the AGB loss. Trees (diameter < 10cm) contributed about 5% of AGB increment.

For dry deciduous forests of Madhya Pradesh, India, Kale et al., (2004) developed allometric equations based on non-destructive sampling of five species, viz. Ziziphus xylopyra, Tectona grandis, Lannea coromandelica, Bauhinia racemosa, and Miliusa tomentosa. These equations were validated to estimate species-specific bole biomass at the local and regional level. For developing equations they measured Circumference at breast height (cbh) and bole (cone) height of each selected tree. With the help of stem-borer, wood samples were taken from all the representative girth classes of each species. Length of each wood sample and its oven-dried weight were noted. Bole volume (V) and coefficient of dry weight (cd) were calculated and finally multiplied together to get the sample tree biomass in kilograms.

For pine forest shrub layer, Sah et al., (2004) developed equations based on destructive sampling 10 common hardwood species in lower Florida Keys. Total shrub biomass included plant parts of all sizes and shrub fine fuel included the biomass of leaves, reproductive parts, and stems <6.3 mm diameter. The equations were validated in estimating shrub biomass in pine forests elsewhere in the Caribbean basin, and in other dry tropical environments. Similarly, Aboal et al., (2005) developed allometric equations for five species (Erica arborea, Ilex canariensis, Laurus azorica, Myrica faya and Persea indica) in four types of laurel forest in the Garajonay National Park on Gomera Island in the Canary Islands. Biomass were estimated using total volume of trunk, primary branches (i.e. those inserting directly to the trunk), and secondary branches and used the previously published values for the density of dry wood.

Chave et al., (2005) developed equations using a dataset of 2,410 harvested trees (DBH ≥ 5 cm), in 27 sites across the tropical forests in three continents: America, Asia, and Oceania. Models were tested and selected best predictive models for dry, moist, wet and mangrove forests. These are valid in the range 5–156 cm for D, and 50–1,000,000 for ρD2H. (D is trunk diameter at 130cm above ground, H is total tree height and ρ is wood specific gravity) for broadleaf tree species. Murali et al., (2005) developed and tested statistically allometric equations based on data collected from published reports to estimate biomass of deciduous tropical forests.

Brandies et al., (2006) proposed equations based on destructive sampling of 30 trees (that were cleared for road construction) from Bucida buceras, Bursera simaruba, Eugenia monticola, Krugiodendron ferreum, Bourreria succulenta and Gymnanthes lucida at deciduous dry forest site near Ponce, Puerto Rico. Equations were established to estimate leaf, woody, and total AGB for Bucida buceras and mixed dry forest species and to estimate inside and outside bark total and merchantable stem volume using both diameter at breast height and total height, and diameter at breast height alone for B. buceras, Bursera simaruba and mixed dry forest species.

Temperate forest

Czapowskyj et al., (1985) presented equations based on destructive sampling of young black spruce (Picea mariana) in northern Maine. They used weighted and ordinary least squares model to construct the equations for small trees from 1 to 15 cm dbh and for trees less than 2 m in height, respectively. In complete oven dry tree biomass around 80% was above ground biomass and 20% was stump(less than 6 cm in height) and roots.  Fang et al., (1998) proposed equations based on inventory data and statistically tested with the data sets of 758 field measurement in China. Estimated total forest biomass was 9103×106 t with the contribution of forests, special product plantations and bamboo forests was 8592×106, 326×106, and 185×106 t, respectively. The area-weighted mean biomass density was 84 tha⁻¹.

Brown et al., (1999) estimated biomass density and carbon stocks, using inventory data of all eastern USA forests. For hardwood forests, total biomass density was 36 to 344 tha⁻¹ and mean of area-weighted was 159 t ha⁻¹ and for softwood forest, total biomass density was 2 to 346 tha⁻¹ and mean of area-weighted was 110 tha⁻¹. The total biomass pool was 20500 ×106 t and hardwood forests were 80% of total biomass. In Kloesterhede, Denmark, Ingerslev and Hallbäcken (1999) proposed equations based on destructive sampling of  59-year-old Norway spruce stand. They harvested five trees from each of four plots (Control, Ca MgPS, CaMgPS + NPK, and CaMgP + NPK).Biomass of stem wood, stem bark, living branches and older needles were closely correlated (r²=0.70) with the tree size. After 9 year investigation the biomass and stem volume growth were not affected (P≤0.05) by the different treatements, but above ground biomass were increased by NPK fertilization. The amount of P in the above ground biomass increased from 2.3 kg ha⁻¹ to more than 5 kg ha⁻¹ after treatments.

Ponette et al., (2001) proposed equations based on destructive sampling of Pseudotsuga menziesii (Mirbel) Franco stands in French forest. Total AGB increased from ~160 tha⁻¹ in the youngest stands to 360 tha⁻¹ in the 54-year old stands. Stem wood biomass was 70 to 80% of total aboveground biomass. Stem bark or needles contributed ≤10% and bark contributed 30 to 60% to total stem biomass. For Acer pensylvanicum L. and Castenea dentate (Marsh.) Borkh in the Appalachian mountains of western Virginia, USA, King (2003) developed equations based on destructive sampling. Similarly, Xiao and Ceulemans (2004) based on destructive sampling for 10-year-old Scots pine (Pinus sylvestris ). In central Japan, for temperate deciduous forest, Jia and Akiyama (2005) developed allometric equations, assessing seven components of carbon storage. The total carbon storage was 440.6 tCha⁻¹.

Lefsky et al., (2005) used equation to calculate total aboveground biomass and the LCD (Lidar change detection) approach was applied to estimate only NPPAw (aboveground wood production) for relatively young stands in western??????. They estimated: 6.7 tha⁻¹ year⁻¹ from the LCD approach, 6.8 tha⁻¹year⁻¹ from Biome-BGC, and 7.2 tha⁻¹year⁻¹ from the inventory approach. Biome-BGC is a daily time-step biogeochemistry model with physiologically based algorithms for photosynthesis, autotrophic respiration and heterotrophic respiration.

Zianis et al. (2005) presented equations for Abies spp, Eucalyptus spp., Picea spp and  Quercus spp. based on the  review of Europian tree species. They proposed 607 biomass equations and 30 stem volume equations. Hall et al., (2006) used BioSTRUCT (Biomass estimation from stand STRUCTture) model for AGB. This model was based on georeferenced field plots to generate empirical relationships between continuous estimates of forest structure attributes and remote sensing image data represented as spectral response variables. Average AGB estimates were within 4 tha⁻¹ and stand volume was within 4 m3/ha of field plot values, statistically similar to a validation sample data set for both AGB (P = 0.61) and stand volume (P = 0.65). Wang (2006) developed and tested statistically allometric equations based on destructive sampling of 10 species in northeastern Chinese temperate forest. These were validated with field samples and relative errors were estimated.

Above ground biomass estimate in Himalaya

In the central Himalaya region, Rana et al., (1989) developed equations based on destructive sampling of trees at 300-2200 m. The biomass was 199 and 787 tha⁻¹ and the net primary productivity was 12.8-27.9 tha⁻¹year⁻¹. The net production in trees ranged between 9.1 and 25.1 tha⁻¹ year⁻¹, respectively, in chir-pin/mixed-broadleaf forest and tilonj-dominated mixed-oak forest. Percentage net production in dominant trees in their respective forests was: 83.2-84.8% of Shorea robusta in sal forests; 31.1-94.7% in Pinus roxburghii, respectively, for chir-pine/mixed-broadleaf forest and chir-pine forest; 66.0% in Quercus leucotrichophora in mixed banj-oak/chir-pine forest; 70.4% in Quercus lanuginose in rianj-dominated mixed-oak forest; and 34.7% in Quercus floribunda in tilonj-dominated mixed-oak forest.

Tiwari (1992) developed and tested statistically allometric equations based on non harvest technique. Wood samples for bole were collected using stem borer and results obtained through models compared with the estimates obtained through harvest method. 

Zomer and Menke (1993) proposed equation based on the site index of Himalayan alder (Alnus nqbalensis) and large cardamom (Amomum subulatum) at the Kosi and Mechi zones of the Eastern Development Region of Nepal. Tree biomass production was 14 tha⁻¹yr⁻¹ and after thinning standing biomass was increased in average 11 tha⁻¹yr⁻¹. After 25 years plantation average quantified total standing biomass was 273 tha⁻¹.

Singh et al., (1994) estimated the biomass and Net primary productivity in Kumaun forests of Indian central Himalaya. Biomass was 500-600 tha⁻¹, from foothills to 2600m elevation, but for P.roxburghii at 1300 and 1750 m was ≈200 t ha⁻¹. Above 2600m biomass was 170 tha⁻¹ in birch forest with rhododendrons at 3100-3200 m elevation. At ≈3300 m elevation, the biomass was 40t ha⁻¹. In S. robusta forest at 300 m and in Q. floribunda forest at 2200 m elevation, forest biomass exceeded 700t ha⁻¹. 2400 t ha⁻¹ biomass occurred at 3400 m elevation in the Nepal central Himalaya. In Kumaun and Nepal, Q. semecarpifolia forest attained similar maximal biomass, up to 550-600 tha⁻¹ near 2600 m elevation. Total net primary productivities (NPP) were 15-20 tha⁻¹yr⁻¹ from the foothills to 2700 m elevation, and they declined above 2700 m. Q. floribunda forest and a C.deodara plantation forest had high productivity 25.1 and 28.2 tha⁻¹yr⁻¹ respectively at 2200 m.

Sundriyal and Sharma (1996) proposed equations using tree volume and specific wood density for Quercus lamellose, Castanopsis tribuloides, Symplocos theaefolia, Eurya acuminate, Alnus nepalensis, Other species group and total species in a temperate forest of the Mamlay watershed in Sikkim Himalaya. Tree density varied from 536 to 756 trees ha⁻¹ and basal area from 39.79 m²ha⁻¹ to 81.0 m²ha-1. Net primary productivity was 8.32 tha⁻¹y⁻¹ for wood biomass and 1.80 tha⁻¹y⁻¹for floor phytomass (excluding litter). 75% of the total biomass was contributed by dicotyledons and 25% by monocotyledons and ferns. For 1-4 yr old poplar (Populus deltoides) plantations in the Tarai belt of the central Himalayan mountains, Lodhiyal and Lodhiyal (1997) gave allometric equations. The total vegetation biomass was 12.0 tha⁻¹ for 1-yr-old and 113.0 tha⁻¹ for 4-yr-old. Luo et al., (2002) developed the QZNPP model for estimating the Net primery Productivity (NPP) on the Tibetan plateau based on the data collected from forest and grassland inventories and ecological research sites.  Garkoti (2007) generated equations based on sampling of Acer cappadocicum and Meliosma dilleniaefolia trees which were fallen due to heavy snowfall in the west central Himalayas. Total vegetation biomass was 308.3 t ha⁻¹ and the annual litter fall was 6.2 t ha⁻¹.

Above ground biomass estimate in plantations

Fang et al., (1998) proposed equations based on inventory data and statistically tested with the data sets of 758 field measurement in China. Estimated total forest biomass was 9103×106 t with the contribution of forests, special product plantations and bamboo forests was 8592×106, 326×106, and 185×106 t, respectively. The area-weighted mean biomass density was 84 tha⁻¹. For 19 lianas at eastern Brazilian Amazon fores, Gerwing and Farias (2000) developed equation based on harvesting of these species. Total stand above ground live biomass was estimated as 314 tha⁻¹ of which 43 tha⁻¹ (14%) was lianas. Liana leaf area index (LAI) varied from 1.3 m²m⁻² to 5.3 m²m⁻².

Goel and Behl (2005) developed linear regression equations between growth parameters and productivity for 8-yr-old Casuarina glauca at Banthra in Lucknow, Uttar Pradesh. These equations were based on plant height (h), diameter (d²) or both (d²h). Height alone had relatively poor functional correlation with yield (r² = 0.45). At the age of 8 years, stand productivity was 68.2 tha⁻¹(oven dry biomass) out of which relatively 80.3% of biomass was allocated to stem wood (54.8 tha⁻¹). Both branch wood (8.4 tha⁻¹) and leaves (5 tha-1) contributed marginally. Similarly, Uri et al. (2007) generated equations based on destructive sampling of 8-year-old stands of silver birch growing on different soil types. The density of the studied stands varied from 3060 to 36 200 trees per ha and their above-ground biomass varied from 6.0 to 22.9 tha⁻¹.

Salis et al., (2006) developed equations based on destructive sampling of five species: Protium heptaphyllum, Magonia pubescens, Diptychandra aurantiaca, Terminalia argentea and Licania minutiflora and sampling of a miscellaneous group of 11 different less abundant species in a region of woodland savanna on Rio Negro farm in the Pantanal of Nhecolandia, Brazil. Miehle et al., (2006) established equation based on photosynthesis and evapotranspiration-nitrification-denitrification and decomposition forest model for 4 and 6 year old Eucalyptus globules  in 28 permanent sample plots of southeastern Australia.

Above ground biomass estimate for other vegetation types

Sharifi et al., (1982) established equation based on non-destructive sampling of Prosopis glandulosa in the sonoran desert of California. Total above-ground biomass was 43-760 kg per plant and 1.9-8.5 kg m-2 canopy area. Stand biomass was 23,000 kg ha-1 to 3,500 kg ha-1. Net above-ground primary production was 2.2 kg m-2 canopy for shrub forms and 5.3 kg m-2 canopy for tree forms. Mean stand production was 3,650 kg ha-1. 51.5% of productivity was produced by new woody tissues of trunk and branches and 33.6% was produced by leaves.

Brown et al., (1997) used equations developed by Schroeder et al., (1997) to determine quantities and distribution of aboveground biomass density of US eastern hardwood trees (> 70 cm diameter). For trees, palms and lianas of primary and secondary forest in Colombia, In north Peru, for Prosopis pallida, Pardon and Navarro (2004) developed and tested allometric equations based on destructive sampling of 17 individuals. Singh et al. (1997) developed allometric equations based on destructive sampling of Petlnisetum pedicellatum at the Jayant coal mine in the Singrauli coal field. The relation was established between incident light and grass biomass under developing canopies of tree plantations on the mine spoil.

Návar et al. (2002) proposed equations based on destructive sampling of tree in the Tamaulipan thornscrub of northeastern Mexico. The total standing  biomass was 60.31 ± 12.24 tha–1, composed of leaf 2.51 ± 0.47 tha–1, branch 24.44 ± 4.88 tha–1, stem 9.80 ± 2.62 tha–1, and root 23.56 ± 4.25 tha–1.

Baker et al. (2004) used equations and updated inventory data to analyze the biomass change in 59 sites of old-growth Amazonian forest. They found that the above-ground dry biomass in trees that are more than 10 cm in diameter (AGB) has increased since plot establishment by 1.22 ± 0.43 tha-1yr-1 or 0.98 ± 0.38 tha-1yr-1, if individual plot values were weighted by the number of hectare years of monitoring.

Jepsen (2006) developed equations based on biometric data and quantitative data of fallow land trees in Sarawak, Malaysia.The biomass of fallows became saturated after 6 years at 47 t dry matter(DM)per hectare and biomass accumulated at the  rate of  up to 12.7 t carbon per hectare per year.

Sierra et al. (2007) developed allometric equations Tree diameter explained the variation in individual tree biomass for aboveground and belowground pools with the exception of palms, for which height was the explanatory variable.

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42. Ponette Quentin, Ranger Jacques, Ottorini Jean-Marc, Ulrich Erwin. 2001. Aboveground biomass and nutrient content of five Douglas-firs stands in France, Forest Ecology and Management, 142: 109-127.
43. Rai, S. N. 1984. Above ground biomass in tropical rainforests of Western Ghats. Indian For. 110:754–764.
44. Rai, S.N. and Proctor, J. 1986. Ecological studies on four forests in Karnataka, India. I. Environment, structure, floristics and Biomass, Journal of Ecology. 74:439–454.
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47. Sah J.P, Ross M.S, Koptur S. and Snyder J.R. 2004. Estimating aboveground biomass of broadleaved woody plants in the understory of Florida Keys pine forests, Forest Ecology and management, 203:319-329.
48. Salis Suzana M., Assis Marco A. , Mattos Patricia P. , PiaoAntonio C.S. 2006. Estimating the aboveground biomass and wood volume of savannawoodlands in Brazil’s Pantanal wetlands based on allometric correlations, Forest Ecology and Management. 228:61–68.
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BIOMASS MODELS

TABLE I: HIMALAYAN FOREST

EQUATIONS	REFERENCE
1.      y = 0.0189+2.0411x Where, y=Above ground biomass, x=LAI values, LAI=leaf area index	Mandal et al. (2005)
2.      NPP = 20/(1+ exp [1.57716 - 0.0003026(T × PR)]) F = 416.1356, r= 0.8369, n= 180, P < 0.0001 Where, T=Mean annual air temperature (8°C), PR= Mean annual precipitation (mm), F=Statistical value of F distribution, r=Correlation coefficient, n=Number of sample points, P=Significance level.	Luo et al. (2002)
3.      Y = n + b. X Where, Y=Dry weight of a component (kg), X=dbh above ground level (cm per tree)	Lodhiyal & Lodhiyal (1997)
4.      ln Y=a + b ln X Where, Y=The dry weight of the component, X=The DBH (cm), a=The Y intercept, b=The slope or regression coefficient, ln=Natural log.	Singh & Adhikari (1995)
5.      lnY = a + blnX Where, Y=Dry weight, X=Circumference at breast height, a=Intercept and b=Slope of the regression line	Singh & Singh  (1993)
6.      y =12.78x – 15.37 r2=0.73, P<0.001 7.      Dbh= 41.087 × (1 – e(-0.077*Age)) r2=0.50 Height=27.169 × (1 – e(-0.139*Age)) r2=0.49 Where, y=Total tree standing biomass (t/ha),  x=Age (yr)	Zomer & Menke  (1993)
8.      Foliage biomass                   n LBj = D [(∑ti . Cd )/ n]                i=1 Where, LBj= Foliage biomass for branch ‘j’(kg), D= Number of twigs in branch ‘j’, n=Number of twigs sampled for branch’j’, ti= fresh weight of leaves in twig ‘i’(kg), Cd=Coefficient of dry weight (td / tf), td=Dry weight of the foliage sample (kg), tf=Fresh weight of the foliage sample (kg)	Tiwari  (1992)
9.      Foliage biomass               ln Y = a + b lnX. Where, Y=Foliage biomass (kg per tree), X= Circumference of branch at the base (cm)	Tiwari  (1992)
10.      Foliage biomass               ln Y = a + b1 lnX1 + b2 ln X2 Where, Y=Foliage biomass (kg per tree), X1= Circumference of branch at the base (kg), X2= Length of branch (cm)	Tiwari  (1992)
11.      Foliage biomass               n       Ymajor= ∑ Yi                 i=1Yminor= DYi Where, Ymajor= Total foliage biomass for major branches (kg), Yi=Foliage biomass major branch ‘i’ (kg), Yminor= foliage biomass of all the minor branches of the tree (kg), D=Total number of minor branches of the tree Yi= Average foliage biomass per minor branch (kg)	Tiwari  (1992)
12.      Twig biomass                  n TBj = D [(∑ti . Cd )/ n]                  i=1 Where, TBj=Twig biomass for branch ‘j’(kg), D=Total number of twigs in branch ‘j’, n=Number of twigs sampled for branch’j’, ti=Fresh weight of twig ‘i’(kg), Cd= coefficient of dry weight (td / tf), td=Dry weight of the twig sample (kg), tf=Fresh weight of the twig sample (kg)	Tiwari  (1992)
13.      Twig biomass               ln Y = a + b lnX. Where, Y=Twig biomass (kg per tree), X=Circumference of branch at the base (cm)	Tiwari  (1992)
14.      Twig biomass               ln Y = a + b1 lnX1 + b2 ln X 2. Where, Y=Twig biomass (kg per tree), X1=Circumference of branch at the base (kg), X 2=length of branch (cm)	Tiwari  (1992)
15.      Twig biomass                 n       Ymajor= ∑Yi              i=1 Where, Ymajor=Total twig biomass for major branches (kg), Yi=Twig biomass major branch ‘i’(kg)	Tiwari  (1992)
16.      Bole biomass Bole biomass(kg)= V × Cd V = cbh 2 × L / 12 П Where, V=Volume of bole (cm3), Cbh=Circumference at breast height (cm), L=Length of bole (cm), Cd=Coefficient of dry weight=Bs / Vs, Bs=Dry weight for sample wood of bole (kg), Vs=Volume of sample wood of bole (cm3)	Tiwari  (1992)
17.      Branch biomass Y=Cd [Vmajor + Vminor] Vmajor= C2i * Li / 12 П Where, Vmajor=Total volume of major branches (cm 3), Vminor=Total volume of minor branches (cm 3), Y=Total branch biomass of tree (kg), Ci=Circumference of branch ‘i’(cm), Cd= Coefficient of dry weight= Bs / Vs, Li=Length of branch’i’, Vminor= D *  C2i * Li / 12 П D=Total number of minor branches in the tree, Bs =Dry weight for sample wood of bole (kg),  Vs=Volume of sample wood of bole (cm 3)	Tiwari  (1992)
18.      ln Y = a + b ln X Where Y=Biomass of various tree component (kg tree-1)       X=Circumference at breast height (cm) Bole: r2=0.801, a=2.1906, b=1.0193 Branch: r2=0.896, a=1.0372, b=0.8259 Twigs: r2=0.726, a=0.7446, b=0.7852 Leaves: r2=0.695, a=0.5024, b=0.7338 Total above ground: r2=0.899, a=2.1091, b=0.9406.	Tiwari  (1992)
19.      Log10 Y=b1+b2 Log10 X Where, Y=Biomass (100kg m-2), X=Crown cover (percentage of ground covered by canopy).	Tiwari & Singh  (1984)
20.      The total herb-layer production was calculated by using given formula. Where, LS and DS are the positive increments in the biomass and necromass of herbaceous live shoots and dead shoots.	Singh 1975. Did not get full article of this reference.

Reference

1. Adhikari B.S., Rawat Y.S. and Singh S.P., 1995. Structure and function of high altitude forests of central Himalaya I. Dry matter dynamics, Ann. Bot., 75: pp 237–248.
2. Hiigglund, B., 1981. Evaluation of forest site productivity. Forestry Abstracts 42(11): pp 515-526.
3. Lodhiyal L.S. and Lodhiyal Neelu, 1997. Variation in biomass and net primary productivity in short rotation high density central Himalayan poplar plantations, Forest Ecology and Management, 98: pp 167- I79.
4. Luo Tianxiang, Li Wenhua, and Zhu Huazhong, 2002. Estimated biomass and productivity of natural vegetation on the Tibetan plateau, Ecological Applications, 12(4): pp 980–997.
5. Singh Lalji & Singh, J.S., 1993. Importance of short-lived components of a dry tropical forest for biomass production and nutrient cycling. J. Veg. Sci., 4: pp 681-686.
6. Singh R.P., 1979. Primary production and energy dynamics of tropical dry deciduous forests in Chandraprabha region, Varanasi. PhD Thesis, Banaras Hindu University, Varanasi.
7. Singh S.P. and Singh J.S., 1991. Analytical conceptual plan to reforest central Himalaya for sustainable development. Environ. Manage. 15(3): pp 369–379.
8. Tiwari A. K. and Singh J. S., 1984. Mapping forest biomass in India through aerial photograph and non destructive field sampling, Applied Geography, 4: pp 151–165.
9. Tiwari A. K., 1992. Component-wise biomass models for trees. A nonharvest technique. Indian For., 118: pp 405–410.
10. Zomer Robert & John Menke, 1993. Site Index and Biomass Productivity Estimates for Himalayan Alder-Large Cardamom Plantations: A Model Agroforestry System of the Middle Hills of Eastern Nepal, Mountain Research and Development, 13(3): pp 235-255.

TABLE II: HIMALAYAN FOREST (SPECIES AND COMPONENTWISE)

SPECIES	REGRESSION EQUATION	REFERENCE
21.      Acer cappadocicum Bole wood         Bole bark Branch Twig Foliage Total above ground Stump root Lateral root Small root Total below ground Total (TAG + TBG)	ln Y = a + b ln X a= - 3.5691, b=1.9690, r2=0.949 a= - 6.5686, b=2.0288, r2=0.942 a= - 5.4372, b=2.101, r2=0.940 a= - 6.3664, b=2.0641, r2=0.967 a= - 7.3873, b= 2.0375, r2=0.935 a= - 3.3384, b= 2.0067, r2=0.945 a= - 4.9453, b=1.9990, r2=0.935 a= - 6.4184, b= 2.042, r2=0.962 a= - 8.2417, b= 2.1589, r2=0.945 a= - 3.0398, b=1.6453, r2=0.951 a= - 3.1185, b=2.0098, r2=0.949 Where, Y=Biomass of tree components (kg tree−1), X=Circumference at breast height (cm), TAG=Total above-ground biomass,  TBG=Total below-ground biomass	Garkoti  (2007)
22.      Meliosma dilleniaefolia Bole wood Bole bark Branch Twig Foliage Total above ground Stump root Lateral root Small root Total below ground Total (TAG + TBG)	ln Y = a + b ln X a= - 5.189, b=2.4068, r2=0.905 a= - 9.5460, b=2.7845, r2=0.901 a= - 8.0113, b=2.8132, r2=0.928 a= - 9.2389, b=2.7782, r2=0.910 a= - 9.9792, b=2.8272, r2=0.904 a= - 5.4984, b=2.5298, r2=0.915 a= - 6.0724, b=2.3659, r2=0.826 a= - 8.9794, b=2.7827, r2=0.900 a= - 10.125, b=2.8365, r2=0.903 a= - 6.5767, b=2.5028, r2=0.862 a= - 5.2281, b=2.5303, r2=0.908 Where, Y=Biomass of tree components (kg tree−1), X=Circumference at breast height (cm), TAG=Total above-ground biomass,  TBG=Total below-ground biomass	Garkoti  (2007)
23.      Guna Chautara (pure oak forest)	y = 0.0189+2.0411x r2=0.55 Where, y=Above ground biomass, x=LAI values, LAI=Leaf area index.	Mandal & Laake (2005)
24.      Dhaili  (Adense mixed forest)	y = - 0.6168+2.2928x r2=0.54 Where, y=Above ground biomass, x=LAI values, LAI=Leaf area index.	Mandal & Laake (2005)
25.      Abies  pindrow Whole tree (above ground) Stem (wood only) Stem(bark wood) Foliage total Twigs total Coarse stump roots Coarse lateral roots Fine roots Roots total	ln biomass = a + b × dia + c × (ln(dia d)) a=2.0656, b=0, c=0.9781, d=1, r2=0.98 a=1.538, b=0, c=1.0088, d=1, r2=0.97 a= - 0.1066, b=0, c=0.8876, d=1, r2=0.92 a=0.2464, b=0, c=0.6429, d=1, r2=0.74 a= - 0.0146, b=0, c=0.8374, d=1, r2=0.84 a= - 0.4874, b=0, c=1.0909, d=1, r2=0.95 a= - 0.651, b=0, c=0.9947, d=1,r2=0.86 a=1.0137, b=0, c=0.4604, d=1, r2=0.72 a=0.5244, b=0, c=0.998, d=1, r2=0.96	Jenkins et al. (2003)
26.      Poplar (Populus deltoides) Bole wood Bole bark Branch Twig Foliage Stump root Lateral root Fine root	Y = a + bx Where, Y=Biomass (kg tree-1), X = Diameter at breast height (cm) For age=1year: a= - 0.0157, b=1.4043, r2=0.997, P< 0.01 For age=2year: a= - 0.0075, b=2.1843, r2=0.990, P< 0.01 For age=3year: a= - 0.0939, b=2.7312, r2=0.994, P< 0.01 For age=4year: a= - 1.0568, b=3.9097, r2=0.996, P< 0.01 For age=1year: a= - 0.0022, b=0.1805, r2=0.997, P< 0.01 For age=2year: a= - 0.0013, b=0.2827, r2=0.990, P< 0.01 For age=3year: a= - 0.0129, b=0.3617, r2=0.994, P< 0.01 For age=4year: a= - 0.1412, b=0.5237, r2=0.996, P< 0.01 For age=1year: a= - 0.0201, b=0.4911, r2=0.948, P< 0.01 For age=2year: a= - 0.0024, b=0.7717, r2=0.966, P< 0.01 For age=3year: a= - 0.2786, b=0.7803, r2=0.944, P< 0.01 For age=4year: a= - 0.3461, b=1.0078, r2=0.957, P< 0.01 For age=1year: a= - 0.0084, b=0.1382, r2=0.911, P< 0.01 For age=2year: a= - 0.0083, b=0.2479, r2=0.930, P< 0.01 For age=3year: a= - 0.0792, b=0.2822, r2=0.881, P< 0.01 For age=4year: a= - 0.0511, b=0.3505, r2=0.932, P< 0.01 For age=1year: a= - 0.0050, b=0.5487, r2=0.994, P< 0.01 For age=2year: a= - 0.0913, b=0.6568, r2=0.953, P< 0.01 For age=3year: a= - 0.1569, b=0.62 19, r2=0.954, P< 0.01 For age=4year: a= - 0.2254, b=0.8036, r2=0.970, P< 0.01 For age=1year: a= - 0.0109, b=0.3916, r2=0.987, P< 0.01 For age=2year: a= - 0.0169, b=0.5727, r2=0.974, P< 0.01 For age=3year: a= - 0.0043, b=0.6343, r2=0.952, P< 0.01 For age=4year: a= - 0.3371, b=0.8813, r2=0.971, P< 0.01 For age=1year: a= - 0.0048, b=0.0677, r2=0.966, P< 0.01 For age=2year: a= - 0.0355, b=0.2285, r2=0.870, P< 0.01 For age=3year: a= - 0.1579, b=0.3283, r2=0.912, P< 0.01 For age=4year: a= - 0.2485, b=0.4733, r2=0.925, P< 0.01 For age=1year: a= - 0.0109, b=0.0342, r2=0.885, P< 0.01 For age=2year: a= - 0.0104, b=0.05 12, r2=0.858, P< 0.01 For age=3year: a= - 0.0149, b=0.0706, r2=0.824, P< 0.01 For age=4year: a= - 0.0444, b=0.0993, r2=0.777, P< 0.05	Lodhiyal and Lodhiyal  (1997)
27.      Quercus lamellosa	y = exp( - 0.948 + 0.826 ln D2H) P<0.001, r=0.947, d.f.=27, E=1.077 Where, y=Woody biomass (bole and branch)(kg), D=Diameter at breast height (cm), H=Tree height (m)	Sundriyal and Sharma (1996)
28.      Castanopsis tribuloides	y = exp( 0.807 + 0.595 ln D2H) P<0.001, r=0.908, d.f.=38, E=1.049   Where, y=Woody biomass (bole and branch)(kg), D=Diameter at breast height (cm), H=Tree height (m)	Sundriyal and Sharma (1996)
29.      Symplocos theaefolia	y = exp( 0.520 + 0.594 ln D2H) P<0.001, r=0.935, d.f.=17, E=1.066     Where, y=Woody biomass (bole and branch)(kg), D=Diameter at breast height (cm), H=Tree height (m)	Sundriyal and Sharma (1996)
30.      Eurya acuminata	y = exp( l.165 + 0.514 ln D2H) P<0.001, r=0.860, d.f.=19, E=1.073  Where, y=Woody biomass (bole and branch)(kg), D=Diameter at breast height (cm), H=Tree height (m)	Sundriyal and Sharma (1996)
31.      Alnus nepalensis	y = exp( - 2.847 + 0.839 ln D2H ) P<0.001, r=0.967, d.f.=8, E=1.030  Where, y=Woody biomass (bole and branch)(kg), D=Diameter at breast height (cm), H=Tree height (m), d.f =Degree of freedom, r=Coefficient of correlation, E=Relative error calculated as antilog of the standard error of the natural logarithm of the y-value.	Sundriyal and Sharma (1996)
32.      Other species group	y = exp( - 0.427 + 0.719 ln D2H) P<0.001, r=0.915, d.f.=24, E=1.120               Where, y=Woody biomass (bole and branch)(kg), D=Diameter at breast height (cm), H=Tree height (m), d.f =Degree of freedom, r=Coefficient of correlation, E=Relative error calculated as antilog of the standard error of the natural logarithm of the y-value.	Sundriyal and Sharma (1996)
33.      Total species	y = exp( - 0.695 + 0.780 ln D2H S) P<0.001, r=0.909, d.f.=143, E=1.049 Where, y=Woody biomass (bole and branch) (kg), D=Diameter at breast height (cm), H=Tree height (m), S=Specific wood density (gcm-3), d.f =Degree of freedom, r=Coefficient of correlation, E=Relative error calculated as antilog of the standard error of the natural logarithm of the y-value.	Sundriyal and Sharma (1996)
34.      Larix forest	y = 1.6481x 0.84788            P=0.001, R=0.9530, N=34 Where, y=Live biomass (MgDM/ha), x= Stem volume (m3/ha)	Li and Luo (1996) and Luo (1996)
35.      Picea–Abies forest	y= 50.8634 + 0.5406x   P=0.001, R=0.9330, N=26 Where, y=Forest live biomass (MgDM/ha), x=Stem volume (m3/ha)	Li and Luo (1996) and Luo (1996).
36.      Montane pine forest	y = 23.9124 + 0.5232x P=0.001, R=0.9646, N=23 Where, y=Forest live biomass (MgDM/ha), x=Stem volume (m3/ha)	Li and Luo (1996) and Luo (1996).
37.      Cupressus forest	Y  = -  2.8232 + 0.9268x    P=0.001, R=0.9915, N=20 Where, y=Forest live biomass (MgDM/ha), x=Stem volume (m3/ha)	Li and Luo (1996) and Luo (1996).
38.      Evergreen broad-leaved forest	y= -  1.5306 +1.1595x P=0.001, R=0.9255, N=50 Where, y=Forest live biomass (MgDM/ha), x=Stem volume (m3/ha)	Li and Luo (1996) and Luo (1996).
39.      Quercus forest	y = 1.8409x 0.89262 P=0.001, R=0.9469, N=49 Where, y=Forest live biomass (MgDM/ha), x=Stem volume (m3/ha)	Li and Luo (1996) and Luo (1996).
40.      Populus–Betula forest	y = 2.3727x 0.79024           P=0.001, R=0.8976, N=42 Where, y=Forest live biomass (MgDM/ha), x=Stem volume (m3/ha)	Li and Luo (1996) and Luo (1996).
41.      Castanopsis tribuloides	y = exp[0.511 + 0.7631n(D2H)] n=12, r=0.940, P<0.001                  Where, y=Wood biomass including branch and bole (kg), D=Diameter at breast height (cm), H=Height (m), S=Specific wood density (Mg/m3), exp=Exponential	Sundriyal et al. (1994)
42.      Castanopsis indica	y = exp[0.204 + 0.7691n(D2H)] n=52, r=0.906, P<0.001                                Where, y=Wood biomass including branch and bole (kg), D=Diameter at breast height (cm), H=Height (m), S=Specific wood density (Mg/m3), exp=Exponential	Sundriyal et al. (1994)
43.      Shorea robusta	y = exp[-1.768 + 0.9451n(D2H)]  n=26, r=0.904, P<0.001 Where, y=Wood biomass including branch and bole (kg), D=Diameter at breast height (cm), H=Height (m), S=Specific wood density (Mg/m3), exp=Exponential	Sundriyal et al. (1994)
44.      Schima wallichii	y = exp[-1.064 +0.8881n(D2H)]     n=32, r=0.960, P<0.001      Where, y=Wood biomass including branch and bole (kg), D=Diameter at breast height (cm), H=Height (m), S=Specific wood density (Mg/m3), exp=Exponential	Sundriyal et al. (1994)
45.      Other species	y = exp[-0.277 + 0.906ln(D2H)]                     n=13, r=0.618, P<0.050 Where, y=Wood biomass including branch and bole (kg), D=Diameter at breast height (cm), H=Height (m), S=Specific wood density (Mg/m3), exp=Exponential	Sundriyal et al. (1994)
46.      Total species	y = exp[1.741 + 0.6151n(D2HS)] n=135, r=0.615, P<0.001 Where, y=Wood biomass including branch and bole (kg), D=Diameter at breast height (cm), H=Height (m), S=Specific wood density (Mg/m3), exp=Exponential	Sundriyal et al. (1994)
47.      Shorea  robusta Bole Branch Twigs Leaves Total above ground	ln Y = a + b ln X a=1.2396, b=1.1459, Sy.X=0.051, r2=0.962 a=0.5898, b=1.0361, Sy.X=0.034, r2=0.953 a=1.2893, b=0.7209, Sy.X=0.063, r2=0.872 a=0.9131, b=0.6775, Sy.X=0.072, r2=0.795 a=2.1133, b=1.0429, Sy.X=0.071, r2=0.936 Where, Y=Biomass of various tree component (kg tree-1), X=Circumference at breast height (cm)	Tiwari  (1992)
48.      A. latifolia Bole Branch Twigs Leaves Total above ground	ln Y = a + b ln X a=1.425, b=1.0241, Sy.X=0.043, r2=0.975 a= - 0.1301, b=1.1352, Sy.X=0.061, r2=0.899 a=0.6596, b=0.8596, Sy.X=0.071, r2=0.821 a=1.8665, b=0.5125, Sy.X=0.053, r2=0.831 a=2.3399, b=0.9581, Sy.X=0.068, r2=0.913 Where Y=Biomass of various tree component (kg tree-1), X=Circumference at breast height (cm)	Tiwari  (1992)
49.      Terminalia tomentosa Bole Branch Twigs Leaves Total above ground	ln Y = a + b ln X a=0.6489, b=1.1065, Sy.X=0.069, r2=0.895 a=1.1591, b=0.7670, Sy.X=0.052, r2=0.912 a=0.9058, b=0.7536, Sy.X=0.091, r2=0.821 a= - 0.0232, b=0.8260, Sy.X=0.081, r2=0.851 a=1.8161, b=0.8161, Sy.X=0.075, r2=0.933 Where Y=Biomass of various tree component (kg tree-1), X=Circumference at breast height (cm)	Tiwari  (1992)
50.      Terminalia chebula Bole Branch Twigs Leaves Total above ground	ln Y = a + b lnX a=1.5228, b=0.8937, Sy.X=0.072, r2=0.962 a=1.9849, b=0.5449, Sy.X=0.039, r2=0.916 a=0.0941, b=0.6803, Sy.X=0.057, r2=0.812 a= -1.1703, b=1.0552, Sy.X=0.055, r2=0.713 a=2.7889, b=0.7198, Sy.X=0.063, r2=0.952 Where Y=Biomass of various tree component (kg tree-1), X=Circumference at breast height (cm)	Tiwari  (1992)
51.      Mallotus philippensis Bole Branch Twigs Leaves Total above ground	ln Y = a + b lnX a=0.6234, b=0.8912, Sy.X=0.059, r2=0.899 a=1.2549, b=0.6517, Sy.X=0.066, r2=0.812 a= -1.2571, b=1.0849, Sy.X=0.053, r2=0.712 a= - 0.5915, b=0.8811, Sy.X=0.084, r2=0.782 a=0.3557, b=1.1551, Sy.X=0.063, r2=0.911 Where Y=Biomass of various tree component (kg tree-1), X=Circumference at breast height (cm)	Tiwari  (1992)
52.      Alnus  nepalensis	Ln (Biomass) = -3.408 + 2.744 Ln (dbh) Where, dbh=Diameter at breast height, Biomass will be in kg.	Thapa et al. (1989)
53.      Shorea robusta Bole Branch Twig Leaf Total	Ln y = a + b ln x a= - 2.83, b=1.98, r2=0.98, Syx=0.12, P<0.01 a= - 2.04, b=1.50, r2=0.92, Syx=0.19, P<0.01 a= - 2.69, b=1.46, r2=0.98, Syx=0.09, P<0.01 a= - 1.74, b=1.18, r2=0.96, Syx=0.15, P<0.01 a= - 1.79, b=1.89, r2=0.98, Syx=0.11, P<0.01 Where y=Biomass of tree components (kg tree-1), x=GBH (cm)	Rana et al. (1989)
54.      Mallotus philippensis Bole Branch Twig Leaf Total	a= - 2.14, b=1.40, r2=0.92, Syx=0.30, P<0.01 a= - 2.28, b=1.22, r2=0.96, Syx=0.13, P<0.01 a= - 2.33, b=0.81, r2=0.92, Syx=0.16, P<0.01 a= - 3.86, b=1.07, r2=0.90, Syx=0.25, P<0.01 a= - 1.24, b=1.28, r2=0.96, Syx=0.14, P<0.01	Rana et al. (1989)
55.      Interspecies of sal forest Bole Branch Twig Leaf Total	a= - 5.03, b=2.33, r2=0.79, Syx=0.86, P<0.05 a= - 5.21, b=2.08, r2=0.83, Syx=0.68, P<0.05 a= - 4.63, b=1.68, r2=0.48, Syx=1.24, P<0.05 a= - 4.96, b=1.68, r2=0.42, Syx=1.42, P<0.05 a= - 4.31, b=2.21, r2=0.69,  Syx=0.67, P<0.05	Rana et al. (1989)
56.      Quercus leucotrichophora Bole Branch Twig Leaf Stump root Lateral root Fine root	a= - 0.52, b=1.37, r2=0.99, Syx=0.03, P<0.01 a= - 0.72, b=1.30, r2=0.97, Syx=0.01, P<0.01 a= - 0.07, b=0.90, r2=0.90, Syx=0.07, P<0.01 a= - 0.98, b=0.85, r2=0.30, Syx=0.44, P<0.01 a= - 0.98, b=0.90, r2=0.61, Syx=0.11, P<0.01 a= - 0.31, b=0.81, r2=0.57, Syx=0.14, P<0.01 a= - 1.33, b=0.50, r2=0.49, Syx=0.20, P<0.01	Rana et al. (1989)
57.      Quercus  floribunda Bole Branch Twig Leaf Stump root Lateral root Fine root	a= - 1.11, b=1.52, r2=0.91, Syx=0.14, P<0.01 a= - 0.99, b=1.38, r2=0.93, Syx=0.11, P<0.01 a= - 1.13, b=1.27, r2=0.81, Syx=0.18, P<0.01 a= - 1.23, b=1.38, r2=0.79, Syx=0.22, P<0.01 a= - 0.25, b=1.11, r2=0.80, Syx=0.12, P<0.01 a= - 1.59, b=1.00, r2=0.71, Syx=0.17, P<0.01 a= - 1.05, b=0.25, r2=0.61, Syx=0.12, P<0.01	Rana et al. (1989)
58.      Rhododendron arboreum Bole Branch Twig Leaf Stump root Lateral root Fine root	a=1.12, b=0.70, r2=0.87, Syx=0.19, P<0.01 a=1.11, b=0.61, r2=0.61, Syx=0.35, P<0.01 a=1.16, b=0.37, r2=0.33, Syx=0.38, P<0.01 a=1.19, b=0.17, r2=0.10, Syx=0.36, P<0.01 a= - 0.12, b=0.87, r2=0.61, Syx=0.12, P<0.01 a= - 1.75, b=0.98, r2=0.60, Syx=0.17, P<0.01 a= - 0.01, b=0.41, r2=0.57, Syx=0.20,   P<0.01	Rana et al. (1989)
59.      Pinus  roxburghii Bole First order branch Other branches Leaf Stump root Lateral root Fine root	a= - 6.42, b=2.60, r2=0.99, Syx=0.06, P<0.01 a= - 9.83, b=2.98, r2=0.98, Syx=0.09, P<0.01 a= - 9.34, b=2.63, r2=0.96, Syx=0.11, P<0.01 a= - 6.11, b=1.87, r2=0.95, Syx=0.09, P<0.01 a= - 7.22, b=2.45, r2=0.98, Syx=0.10, P<0.01 a= - 9.16, b=2.59, r2=0.97, Syx=0.11, P<0.01 a= - 9.10, b=2.07, r2=0.94, Syx=0.14,  P<0.01	Rana et al. (1989)
60.      Interspecies of other forest Bole Branch Twig Leaf Stump root Lateral root Fine root	a= - 0.86, b=1.43, r2=0.92, Syx=0.06, P<0.01 a= - 0.91, b=1.33, r2=0.91, Syx=0.05, P<0.01 a= - 0.51, b=1.03, r2=0.80, Syx=0.09, P<0.01 a= - 1.11, b=1.04, r2=0.76, Syx=0.10, P<0.01 a= - 0.10, b=0.95, r2=0.79, Syx=0.11, P<0.01 a= - 2.25, b=1.00, r2=0.72, Syx=0.14, P<0.01 a= - 2.07, b=0.53, r2=0.72, Syx=0.20, P<0.01	Ran    Rana et al. (1989)

Reference:

1. Garkoti S. C., 2007. Estimates of biomass and primary productivity in a high-altitude maple forest of the west central Himalayas, Ecol Res DOI 10.1007/s11284-007-0355-2.
2. Jenkins Jennifer C., Chojnacky David C., Heath Linda S. and Birdsey Richard A., 2003.  Comprehensive Database of Diameter-based Biomass Regressions for North American Tree Species, United States Department of Agriculture Forest Service Northeastern Research Station, General Technical Report NE-319.
3. Li, W.-H., and T.-X. Luo, 1996. Biomass and productivity of forests in China. Pages 80–133 in W.-H. Li and F. Li, editors. Research of forest resources in China. In Chinese. Chinese Forestry Publisher, Beijing, China.
4. Luo, T.-X., 1996. Patterns of net primary productivity for Chinese major forest types and their mathematical models. [In Chinese.] Dissertation. Commission for integrated survey of natural resources, Chinese Academy of Sciences, Beijing, China.
5. Lodhiyal L.S. and Lodhiyal Neelu, 1997. Variation in biomass and net primary productivity in short rotation high density central Himalayan poplar plantations, Forest Ecology and Management, 98: pp167- I79.
6. Mandal R. A. & Laake P. van, 2005. Carbon sequestration in community forests: an eligible issue for CDM (A case study of Nainital, India), Banko Janakari, 15(2): pp 53-61.
7. Rana B.S., Singh S.P. and Singh R.P., 1989. Biomass and Net Primary Productivity in Central Himalayan Forests along an Altitudinal Gradient, Forest Ecology and Management, 27: pp 199-218.
8. Singh Arvind, Jha A. K. and Singh J. S., 1997. Influence of a Developing Tree Canopy on the Yield of Pennisetum pedicellatum Sown on a Mine Spoil, Journal of Vegetation Science, 8(4): pp 537-540.
9. Sundriyal R.C., Sharma E., Rai L.K. & Rai S.C., 1994. Tree structure, regeneration and woody biomass removal in a sub-tropical forest of Mamlay watershed in the Sikkim Himalaya, Vegetatio 113: pp 53—63.
10. Sundriyalss R.C and Sharma E., 1996. Anthropogenic pressure on tree structure and biomass in the temperate forest of Mamlay watershed in Sikkim, Elsevier Forest Ecology and Management, 81: pp 113-134.
11. Thapa B., Upadhaya M. P., Wallace D., and Sherpa S. L., 1989. Prediction of Biomass in a Semi-Natural Stand of Utis (Alnus nepalensis) at Pakhribas Agricultural Centre. Pakhribas Agricultural Centre, c/o BTCO, PO Box 106, Kathmandu, Nepal, PAC Technical Paper No. 115.
12. Tiwari A.K., 1992. Component – wise biomass models for trees: A non-Harvest technique, Indian for., 118: pp 405-410.

TABLE III:  BIOMASS MODELS - TEMPERATE FOREST

EQUATIONS	REFERENCE
Y=aDBHb Where, y=Above-ground biomass of the model tree, DBH =Stem diameter at breast height (cm), a and b the parameter estimates.	Uri et al. (2007)
AGB 1/3= ─0.677+1.874×ln(height)+0.014(crown closure) r2=0.70, RMSE=33.7 t/ha	Hall et al. (2006)
Volume1/3=─ 3.252+3.089×ln(height)+0.02(crown closure) r2=0.71, RMSE=74.7 m3/ha	Hall et al. (2006)
Y= exp{─2.173+0.868 ln(D2 TH)+0.0939/2} r2=0.90 Where, Y=Total dry weight (kg), D=Diameter at breast height (cm), TH=Total height (m).	Cairns et al. (2003)
log10 biomass = a + b × (log10(diac))	Jenkins et al. (2003)
biomass = a × (exp(b + (c × ln(dia)) + (d × dia)))	Jenkins et al. (2003)
biomass = a + ((b × (diac))/((diac) + d))	Jenkins et al. (2003)
ln biomass = a + b × dia + c × (ln(diad))	Jenkins et al. (2003)
ln biomass = a + b × ln(dia) + c × (d + (e × ln(dia)))	Jenkins et al. (2003)
biomass = a + b × dia + c × (diad)	Jenkins et al. (2003)
biomass = a + (b × dia) + c × (dia2) + d × (dia3)	Jenkins et al. (2003)
log100 biomass = a + (b × log10(dia))	Jenkins et al. (2003)
ln biomass = ln(a) + (b × ln(dia))	Jenkins et al. (2003)
AB = 0.342+MCH2+2.086×COVCHPX R2=84%, P<0.0001 Where, AB=Above-ground biomass (Mg ha-1), MCH2= Mean canopy height (m) squared, COVCHPX =Product of mean cover and mean canopy height	Lefsky et al. (2002)
AB = 0.378 ×MCH2 (R2=84%, P<0.0001) Where, AB=Above-ground biomass (Mg ha-1), MCH2= Mean canopy height (m) squared, COVCHPX =Product of mean cover and mean canopy height	Lefsky et al. (2002)
y = ba × 6.6 t dry weight m-2 Where, y=Total above ground biomass (t dry weight m−2) and ba=Basal area over bark (m2 ha−1).	Snowdon  (2000)
61.      AGBM=4.236+0.200×CHP_H_X+13.325×OLIGO+ 24.300×CHP_Q_SD r2=0.87, P<0.0001, RMSE=118.5 Mgha-1 Where AGBM units are Mg ha−1, CHP_H_X=Mean height of the forest canopy (m), OLIGO=Volume of shaded foliage and woody biomass in the canopy (m3), CHP_Q_SD= standard deviation of the quadratic mean height of the canopy (m).	Lefsky et al. (1999) &  Lefsky et al. (2005)
BGBD=exp{─1.059+0.884×Ln(AGBD)+0.284} r2=0.84, n=151 Where, BGBD=Belowground biomass density ( fine and coarse roots), AGBD = Aboveground biomass density of the tree component (Mg ha−1)	Cairns et al. (1997)
For Tertiary Mediterranean ecosystems        AGB=0.0551+DBH 2.7157	Fernandez-Palacios (1991)
For each arable crop: AGB=(Y+(Y×E))×D×C×(L/365)×A AGB= Average annual above ground carbon stock (tCha-1) Y=Crop yield (tha-1) E=Expansion factor of yield to non-harvested biomass (residue/harvested crop) D=Proportion of dry matter C=Proportion of carbon content of dry matter L=Length of growing season A=Averaging coefficient for converting final standing biomass to average annual standing biomass (0.6 for crops, 0.75 for orchard crops).	Adger & Subak  (1996)

REFERENCE:

1. Adger W. Neil and Subak Susan, 1996. Estimating Above-Ground Carbon Fluxes from UK Agricultural Land. The Geographical Journal, 162(2): pp 191-204.
2. Cairns M.A., Brown S., Helmer E.H. and Baumgardner G.A., 1997. Root biomass allocation in the world's upland forests. Oecologia 111, pp 1–11.
3. Cairns Michael A., Olmsted Ingrid, Granados Julian and Argaez Jorge, 2003. Composition and aboveground tree biomass of a dry semi-evergreen forest on Mexico’s Yucatan Peninsula, Forest Ecology and Management, 186(1-3): pp 125-132.
4. Fernandez-Palacios J.M., Esteban J.J. Garcia, Lopez R.J. and Luzardo M.C., 1991.  Aproximación a la estima de la biomasa y producción neta aéreas en una estación dela Laurisilva tinerfeña, Vieraea, 20: pp 11–20.
5. Hall R.J., Skakun R.S., Arsenault E.J. and Case B.S., 2006. Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume, Forest Ecology Management, 225: pp 378-390.
6. Jenkins Jennifer C., Chojnacky David C., Heath Linda S. and Birdsey Richard A., 2003. Comprehensive Database of Diameter-based Biomass Regressions for North American Tree Species, United States Department of Agriculture Forest Service Northeastern Research Station, General Technical Report NE-319.
7. Lefsky M.A., Cohen W.B., Acker S.A., Spies T.A., Parker G.G. and Harding D., 1999.  Lidar remote sensing of biophysical properties and canopy structure of forest of Douglas-fir and western hemlock, Remote Sensing of Environment, 70: pp 339–361.
8. Lefsky Michael A., Cohent Warren B., Harding David J. S., Parker Geoffrey G., Ackery Steven A. and Thomas Gower S., 2002. Lidar remote sensing of above-ground biomass in three biomes, Global Ecology & Biogeography, 11: pp 393-399,
9. Lefsky M.A., Hudak A.T., Cohen W.B. and Acker S.A., 2005. Geographic variability in lidar predictions of forest stands structure in the Pacific Northwest, Remote Sensing of Environment, 95: pp 532–548.
10. Snowdon P., Eamus D., Gibbons P., Khanna P. K., Keith H., Raison J., et al., 2000. Synthesis of Allometrics, Review of Root Biomass and Design of future woody Biomass Sampling Strategies. National Carbon Accounting System technical report no. 17. Australian Green house Office.
11. Uri Veiko, Vares Aivo, Tullus Hardi and Kanal Arno, 2007. Above-ground biomass production and nutrient accumulation in young stands of silver birch on abandoned agricultural land, Biomass and bioenergy, 31(4): pp 195-204.

TABLE IV: TEMPERATE FOREST  (SPECIESWISE)

Species Name	Biomass Equations	Reference
For silver birch. (Haaslava plot)	y = aDBHb a=160.61, b=2.046, r2=0.993, S.E.=1.17, P<0.001 Where, y=Above-ground biomass of the model tree, DBH=Stem diameter at breast height (cm), a and b the parameter estimates.	Uri et al. (2007)
For silver birch (Valjakula plot)	y = aDBHb a=152.32, b=2.246, r2=0.987, S.E.=1.31, P<0.001 Where, y=Above-ground biomass of the model tree, DBH=Stem diameter at breast height (cm), a and b the parameter estimates.	Uri et al. (2007)
For silver birch (Lutsu plot)	y = aDBHb a=160.93, b=2.046, r2=0.992, S.E.=1.16, P<0.001 Where, y=Above-ground biomass of the model tree, DBH=Stem diameter at breast height (cm), a and b the parameter estimates.	Uri et al. (2007)
For silver birch (Puhatu plot)	y = aDBHb a=180.19, b=2.079, r2=0.983, S.E.=1.28, P<0.001 Where, y=Above-ground biomass of the model tree, DBH=Stem diameter at breast height (cm), a and b the parameter estimates.	Uri et al. (2007)
For silver birch (Kambja plot)	y = aDBHb a=163.86, b=2.019, r2=0.992, S.E.=1.14, P<0.001 Where, y=Above-ground biomass of the model tree, DBH=Stem diameter at breast height (cm), a and b the parameter estimates.	Uri et al. (2007)
Korean pine (Pinus koraiensis Sieb. et Zucc.) Total biomass Aboveground biomass Belowground biomass Stem biomass Total branch biomass Total foliage biomass	Log10B=2.033 + 2.469log10DBH r2=0.972, P<0.001 Log10B=1.945 + 2.467log10DBH r2=0.969, P<0.001 Log10B=2.033+ 2.469log10DBH r2=0.972, P<0.001 Log10B=1.891 + 2.406log10DBH r2=0.960, P<0.001 Log10B=0.703 + 2.855log10DBH r2=0.903, P<0.001 Log10B=0.855 + 2.135log10DBH r2=0.818, P<0.001 Where, B=Biomass (g), DBH=Diameter at breast height (cm)	Wang  (2006)
Dahurian larch (Larix gmelinii Rupr.) Total biomass Aboveground biomass Belowground biomass Stem biomass Total branch biomass Total foliage biomass	Log10B=2.033 + 2.469log10DBH r2=0.972, P<0.001 Log10B=1.945 + 2.467log10DBH r2=0.969, P<0.001 Log10B=2.033+ 2.469log10DBH r2=0.972, P<0.001 Log10B=1.891 + 2.406log10DBH r2=0.960, P<0.001 Log10B=0.703 + 2.855log10DBH r2=0.903, P<0.001 Log10B=0.855 + 2.135log10DBH r2=0.818, P<0.001 Where, B=Biomass (g), DBH=Diameter at breast height (cm)	Wang  (2006)
Mongolian oak (Quercus mongolica Fisch.) Total biomass Aboveground biomass Belowground biomass Stem biomass Total branch biomass Total foliage biomass	Log10B=2.033 + 2.469log10DBH r2=0.972, P<0.001 Log10B=1.945 + 2.467log10DBH r2=0.969, P<0.001 Log10B=2.033+ 2.469Log10DBH r2=0.972, P<0.001 Log10B=1.891 + 2.406Log10DBH r2=0.960, P<0.001 Log10B=0.703 + 2.855Log10DBH r2=0.903, P<0.001 Log10B=0.855 + 2.135Log10DBH r2=0.818, P<0.001 Where, B=Biomass (g), DBH=Diameter at breast height (cm)	Wang  (2006)
White birch (Betula platyphylla Suk.) Total biomass Aboveground biomass Belowground biomass Stem biomass Total branch biomass Total foliage biomass	Log10B=2.033 + 2.469log10DBH r2=0.972, P<0.001 Log10B=1.945 + 2.467log10DBH r2=0.969, P<0.001 Log10B=2.033+ 2.469log10DBH r2=0.972, P<0.001 Log10B=1.891 + 2.406log10DBH r2=0.960, P<0.001 Log10B=0.703 + 2.855log10DBH r2=0.903, P<0.001 Log10B=0.855 + 2.135log10DBH r2=0.818, P<0.001 Where, B=Biomass (g), DBH=Diameter at breast height (cm)	Wang  (2006)
Amur cork-tree (Phellodendron amurense Rupr.) Total biomass Aboveground biomass Belowground biomass Stem biomass Total branch biomass Total foliage biomass	Log10B=2.033 + 2.469log10DBH r2=0.972, P<0.001 Log10B=1.945 + 2.467log10DBH r2=0.969, P<0.001 Log10B=2.033+ 2.469log10DBH r2=0.972, P<0.001 Log10B=1.891 + 2.406log10DBH r2=0.960, P<0.001 Log10B=0.703 + 2.855log10DBH r2=0.903, P<0.001 Log10B=0.855 + 2.135log10DBH r2=0.818, P<0.001 Where, B=Biomass (g), DBH=Diameter at breast height (cm)	Wang  (2006)
Manchurian walnut (Juglans mandshurica Maxim.) Total biomass Aboveground biomass Belowground biomass Stem biomass Total branch biomass Total foliage biomass	Log10B=2.033 + 2.469log10DBH r2=0.972, P<0.001 Log10B=1.945 + 2.467log10DBH r2=0.969, P<0.001 Log10B=2.033+ 2.469log10DBH r2=0.972, P<0.001 Log10B=1.891 + 2.406log10DBH r2=0.960, P<0.001 Log10B=0.703 + 2.855log10DBH r2=0.903, P<0.001 Log10B=0.855 + 2.135log10DBH r2=0.818, P<0.001 Where, B=Biomass (g), DBH=Diameter at breast height (cm)	Wang  (2006)
Manchurian ash (Fraxinus mandshurica Rupr.) Total biomass Aboveground biomass Belowground biomass  Stem biomass Ttotal branch biomass Ttotal foliage biomass	Log10B=2.033 + 2.469log10DBH r2=0.972, P<0.001 Log10B=1.945 + 2.467log10DBH r2=0.969, P<0.001 Log10B=2.033+ 2.469log10DBH r2=0.972, P<0.001 Log10B=1.891 + 2.406log10DBH r2=0.960, P<0.001 Log10B=0.703 + 2.855log10DBH r2=0.903, P<0.001 Log10B=0.855 + 2.135log10DBH r2=0.818, P<0.001 B=Biomass (g), DBH=Diameter at breast height (cm)	Wang  (2006)
Aspen (Populous davidiana Dode) Total biomass Aboveground biomass Belowground biomass Stem biomass Total branch biomass Total foliage biomass	Log10B=2.033 + 2.469log10DBH r2=0.972, P<0.001 Log10B=1.945 + 2.467log10DBH r2=0.969, P<0.001            Log10B=2.033+ 2.469log10DBH r2=0.972, P<0.001 Log10B=1.891 + 2.406log10DBH r2=0.960, P<0.001 Log10B=0.703 + 2.855log10DBH r2=0.903, P<0.001 Log10B=0.855 + 2.135log10DBH r2=0.818, P<0.001 Where, B=Biomass (g), DBH=Diameter at breast height (cm)	Wang  (2006)
Maple (Acer mono Maxim.) Total biomass Aboveground biomass Belowground biomass Stem biomass Total branch biomass Total foliage biomass	Log10B=2.033 + 2.469log10DBH r2=0.972, P<0.001 Log10B=1.945 + 2.467log10DBH r2=0.969, P<0.001 Log10B=2.033+ 2.469log10DBH r2=0.972, P<0.001 Log10B=1.891 + 2.406log10DBH r2=0.960, P<0.001 Log10B=0.703 + 2.855log10DBH r2=0.903, P<0.001 Log10B=0.855 + 2.135log10DBH r2=0.818, P<0.001 Where, B=Biomass (g), DBH=Diameter at breast height (cm)	Wang  (2006)
Amur linden (Tilia amurensis Rupr.) Total biomass Aboveground biomass Belowground biomass Stem biomass Total branch biomass Total foliage biomass	Log10B=2.033 + 2.469log10DBH r2=0.972, P<0.001 Log10B=1.945 + 2.467log10DBH r2=0.969, P<0.001 Log10B=2.033+ 2.469log10DBH r2=0.972, P<0.001 Log10B=1.891 + 2.406log10DBH r2=0.960, P<0.001 Log10B=0.703 + 2.855log10DBH r2=0.903, P<0.001 Log10B=0.855 + 2.135log10DBH r2=0.818, P<0.001 Where, B=Biomass (g), DBH=Diameter at breast height (cm)	Wang  (2006)
For 10-year-old Scots pine trees (P. sylvestris L.) Stem    Branch Needle      Coarse root Small root Total	Stem: Y = a DBHb a(S.E.)=0.084 (0.019), b(S.E.)=1.985(0.107), R2adj=0.986, RMSE=0.261, P<0.001 Y = a DBHb Hc a(S.E.)=0.021(0.007), b(S.E.)=1.474(0.097), c(S.E.)=1.485(0.315), R2adj=0.977, RMSE=0.315, P<0.001 Branch: Y = a DBHb a(S.E.)=0.051 (0.011), b (S.E.)= 2.083 (0.102), R2adj= 0.989, RMSE= 0.183, P < 0.001 Y = a DBHb Hc  a(S.E.)=0.010(0.004), b(S.E.)=1.483(0.104), c(S.E.)=1.758 (0.337), R2adj= 0.976, RMSE=0.253, P<0.001 Y = a DBHb CLd a(S.E.)=0.034(0.008), b(S.E.)=1.7543(0.096), d(S.E.)=0.682(0.236), R2adj=0.966, RMSE=0.297, P<0.001 Y = a DBHb Hc CLd a(S.E.)=0.005(0.002), b(S.E.)=1.441(0.099), c(S.E.)=3.221(0.587), d(S.E.)= -1.123(0.363), R2adj=0.979, RMSE=0.234, P<0.001 Y = a BDb a(S.E.)=31.41(2.07), b(S.E.)=2.15(0.04), R2adj=0.936, RMSE=75.45, P<0.001 Y = a BDb BLc a(S.E.)=0.03(0.02)NS, b(S.E.)=1.31(0.07), c(S.E.)=1.51(0.15), R2adj=0.967, RMSE=54.53, P<0.001 Y = BDb BLc b(S.E.)=1.67(0.04), c(S.E.)=0.76(0.01), R2adj=0.960, RMSE=59.68, P<0.001 Y = a BDb BLc WPd a(S.E.)=0.05(0.05)NS, b(S.E.)=1.41(0.10), c(S.E.)=1.31(0.21), d(S.E.)=0.21(0.11)NS R2adj=0.967, RMSE=54.08, P<0.001 Y = BDb BLc WPd b(S.E.)=1.73(0.04), c(S.E.)=0.64(0.03), d(S.E.)=0.42(0.09), R2adj=0.964, RMSE=56.30, P<0.001 Needle: Y = a BDb a(S.E.)=22.86(1.57), b(S.E.)=2.36(0.04), R2adj=0.950, RMSE=68.78, P<0.001 Y = a BDb BLc a(S.E.)=0.22(0.19)NS, b(S.E.)=1.80(0.09), c(S.E.)=1.00(0.18), R2adj=0.962, RMSE=60.18, P<0.001 Y = BDb BLc b(S.E.)=1.95(0.04), c(S.E.)=0.68(0.01), R2adj=0.961, RMSE=60.81, P<0.001, Y = a BDb BLc WPd a(S.E.)=0.02(0.02)NS, b(S.E.)=1.49(0.12), c(S.E.)=1.68(0.26), d(S.E.)= - 0.56(0.13)NS, R2adj=0.966, RMSE=56.85, P<0.001 Y = BDb BLc WPd b(S.E.)=1.93(0.04), c(S.E.)=0.73(0.03), d(S.E.)= - 0.17(0.08), R2adj=0.962, RMSE=60.31, P<0.001 Y = a DBHb a(S.E.)=0.032(0.007), b(S.E.)=2.249(0.102), R2adj=0.991, RMSE=0.156, P<0.001 Y = a DBHb Hc a(S.E.)=0.007(0.002), b(S.E.)=1.698(0.090), c(S.E.)=1.583(0.283), R2adj=0.985, RMSE=0.192, P<0.001 Y = a DBHb CLd a(S.E.)=0.025(0.005), b(S.E.)=1.975(0.089), d(S.E.)=0.517(0.208), R2adj=0.977, RMSE=0.236, P<0.001 Y = a DBHb Hc CLd a(S.E.)=0.004(0.001), b(S.E.)=1.658(0.075), c(S.E.)=3.396(0.436), d(S.E.)= -1.398(0.271), R2adj=0.990, RMSE=0.157, P<0.001 Coarse root: Y = a DBHb a(S.E.)=0.007(0.003), b(S.E.)=2.897(0.193), R2adj=0.983, RMSE=0.227, P<0.001 Y = a DBHb Hc a(S.E.)=0.0001(0.00006), b(S.E.)=1.536(0.105), c(S.E.)=4.076(0.332), R2adj=0.988, RMSE=0.185, P<0.001 Small root: Y = a DBHb a(S.E.)=0.002(0.0006), b(S.E.)=2.222(0.129), R2adj=0.985, RMSE=0.012, P<0.001 Y = a DBHb Hc a(S.E.)=0.0001(0.00003), b(S.E.)=1.315(0.067), c(S.E.)=2.864(0.216), R2adj=0.992, RMSE=0.008, P<0.001 Total: Y = a DBHb a(S.E.)=0.152(0.033), b(S.E.)=2.234(0.100), R2adj=0.991, RMSE=0.705, P<0.001 Y = a DBHb Hc a(S.E.)=0.02(0.007), b(S.E.)=1.53 (0.092), c(S.E.)=2.098(0.295), R2adj=0.984, RMSE=0.892, P<0.001 Y = a DBHb CLd a(S.E.)=0.092(0.022), b(S.E.)=1.85(0.095), d(S.E.)=0.820(0.227), R2adj=0.972, RMSE=1.17, P<0.001 Y = a DBHb Hc CLd a(S.E.=0.01(0.004), b(S.E.)=1.49(0.081), c(S.E.)=3.741(0.469), d(S.E.)= -1.264(0.289) R2adj=0.988, RMSE=0.768, P<0.001 Where, BD=Branch diameter (cm), BL=Branch length (cm), WP=Whorl position, NA=Non-significant(P>0.05), DBH=Diameter at breast height (cm), H=Tree height (m), CL=Crown length (m), SE=Standard error; R2adj= Adjusted multiple coefficient of determination, RMSE=Root of mean squared error	Xiao and Ceulemans (2004)
Sassafras	Log 10 (bole + br)grams = 1.9566+2.3836 Log 10 (dbh cm) Where, oven dry weight of biomass component of tree will be in kilograms and dbh in centimeters a and b are parameters and are different for every species	Xiao and Ceulemans (2004)
Sumac, Staghorn	M=0.0825 dbh 2.4680 Where, M=Oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Xiao and Ceulemans (2004)
Witch Hazel	Log 10 (bole + br)grams = 2.025 + 2.163 Log 10 (dbh cm) Where, oven dry weight of biomass component of tree in kilograms and dbh in centimeters a and b are parameters and are different for every species	Xiao and Ceulemans (2004)
Abies spp (Fir)	CR=aDb a=5.2193.10-4, b=1.459 CR= a+bDc a=0.0060722, b=9.58.10-6, c=2.5578 Where, CR=Crown biomass (in t), D=Diameter at breast height (in cm)	Broadmeadow & Matthews  (2004)
Picea spp.	AB= a.Db.Hc D (cm)=4.9-28.6,H  (m)=4.8-15.4, n=56, r2=0.965 a=0.1334,  b=1.8716,  c=0.4386 Where, AB=Total above ground biomass (kg), D=Diameter at breast height (cm), H=Height in (m) CR= a.Db.Hc a=0.087, b=2.287, c= - 0.2897 D (cm)=4.9-28.6, H   (m)=4.8-15.4, n=56, r2=0.905 Where, CR=Crown biomass (t), D=Diameter at breast height(cm) CR= a.Db a=5.2193.10-4, b=1.459 Where, CR=Crown biomass (t), D=Diameter at breast height(cm) CR= a+bDc a=0.0060722, b=9.58.10-6, c=2.5578 Where, CR=Crown biomass (t), D=Diameter at breast height (cm) ST= a.Db.Hc D (cm)=4.9-28.6,H (m)=4.8-15.4, n=56, r2=0.981, a=0.0558, b=1.5953, c=0.9336 Where, ST=Total stem biomass (t), D=Diameter at breast height (cm)	Broadmeadow & Matthews  (2004) Snorrason & Einarsson (2004)
Acer pensylvanicum L. and Castenea dentate (Marsh.) Borkh	For each sapling: G = (∆S + ∆B + ∆F) / ∆LA Where, G=The mean annual production of above-ground biomass per unit leaf area, ∆B=Dry mass increment of all branches, ∆F=Total leaf biomass production, ∆S=Stem production, SLA=Specific surface area, which was determined from a sub sample of the current leaves for each sapling, ∆LA=Total leaf area (∆LA = SLA × ∆F)	King  (2003)
Acer pensylvanicum L. and Castenea dentata (Marsh.)Borkh	ΔF=current leaf dry mass× (current leaf no.+ leaf scar no.) / current leaf no. Where, ΔF=Total leaf biomass production	King  (2003)
Acer pensylvanicum L. and  Castenea dentata   (Marsh.)Borkh	ΔS= S×[I - ∑(Af – Ai ) / ∑Af ]+dry mass of stem extension increment Where, ΔS=Stem production, S=Current stem dry mass after removal of the upper extension increment, ∑Af=Stem cross-sectional area under the bark at harvest time, ∑Ai= stem cross-sectional area under the bark at initial time.	King  (2003)
Quercus spp. (Oak, Eiche)	Ln(AB)=a+bln(D) N=33, a= - 0.883, b=2.140 Where, AB=Total above ground biomass (kg),D=Diameter at breast height ( cm) Ln(ABW)=a+bln(D) D (cm)=4.5-52, N=20, r2=0.99, a= - 2.4232, b=2.4682 Where, ABW=Total above ground woody biomass (kg), D=Diameter at breast height (cm) Ln(ABW)=a+bln(D) D (cm)=4.3-35, N=16, r2=0.99, a= - 2.3223, b=2.4029 Where, ABW=Total above ground woody biomass (kg), D=Diameter at breast height (cm) Ln(ABW)=a+bln(D) D (cm)=3.8-11, N=15, r2=0.974, a= - 3.1404, b=2.8113 Where, ABW=Total above ground woody biomass (kg), D=Diameter at breast height (cm) Ln(ABW)=a+bln(D) D (cm)=5.7-33, N=18, r2=0.995, a= - 3.1009, b=2.6996 Where, ABW=Total above ground woody biomass (kg), D=Diameter at breast height (cm) CR=a.Db a= 2.1612.10-4 Where, CR=Crown biomass (t), D=Diameter at breast height (cm) CR=a.Db.Hc a=5.4224.10-4, b=2.35, c= - 1.022 Where, CR=Crown biomass (t), D=Diameter at breast height (cm), H=Height (m)	Hochbichler  (2002) Bunce  (1968) Bunce  (1968) Bunce  (1968) Bunce  (1968) Broadmeadow & Matthews  (2004) Broadmeadow & Matthews  (2004)
Douglas-fir (Pseudotsuga menziesii (Mirbel)Franco)	Branch: Y=a+(b×D102) Y=a+(b×D10)+(c×D102) Y=a×D102 Y=(a×D10)+(b×D102) Y=a×D10b Where, Y=Biomass or nutrient content at the branch level (needles, wood), D10 (mm)= Branch diameter 10 cm from the insertion on the stem	Ponette et al. (2001)
Douglas-fir (Pseudotsuga menziesii (Mirbel)Franco)	Stem, crown: Y =a+(b × DBH2) Y =a+(b× DBH)+(c × DBH2) Y =a × DBH2 Y =(a×DBH)+(b × DBH2) Ln Y=a+(b × ln DBH) Y=a × DBHb Where, Y=The biomass or nutrient content at the stem (wood, bark) or crown levels (needles, wood), DBH (cm)=Diameter at breast height	Ponette et al. (2001)
Ash, white	M= 0.1063 dbh 2.4798 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Basswood	M= 0.0617 dbh 2.5328 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Beech	M= 0.0842 dbh 2.5715 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Birch, black/sweet	M= 0.0629 dbh 2.6606 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Cedar, Red	M= 0.1019 dbh 2.3000 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Cherry, Black	M= 0.0716 dbh 2.6174 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Cherry, Sweet	M= 0.1556 dbh 2.1948 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Elm, American	M= 0.0629 dbh 2.6606 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Elm, slippery/red	M= 0.0629 dbh 2.6606 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Black Locust	M= 0.0792 dbh 2.6349 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Flowering dogwood	M= 0.0792 dbh 2.6349 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Hackberry	M= 0.0792 dbh 2.6349 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Hemlock	M= 0.0622 dbh 2.4500 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Hickory, mocker nut	M= 0.0792 dbh 2.6349 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Hickory, pignut	M= 0.0792 dbh 2.6349 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Hickory, shagbark	M= 0.0792 dbh 2.6349 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Hop hornbeam	M= 0.0792 dbh 2.6349 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Hornbeam	M= 0.0792 dbh 2.6349 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Maple, Red	M= 0.0910 dbh 2.5080 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Maple, Sugar	M= 0.2064 dbh 2.5300 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Oak, black	M= 0.0904 dbh 2.5143 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Oak, chestnut	M= 0.0554 dbh 2.7276 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Oak, red	M= 0.1130 dbh 2.4572 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Oak, white	M= 0.0579 dbh 2.6887 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Olive	Log 10 (bole + br)grams = 2.025 + 2.163 Log 10 (dbh cm) Where, D=Dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Pine, White	M= 0.1617dbh 2.1420 Where, M=oven dry weight of biomass component of tree in kilograms, D=dbh in centimeters, a and b are parameters and are different for every species	Methods of Ecosystem Analysis Saltonstall Ridge, East Haven, CT. (1999) http://www.yale.edu/fes519b/saltonstall/index.htm
Norway spruce (Picea abies) Stem wood Stem bark Living branches Dead branches Current year needles Older needles	Ln( element or biomass, kg) =α + β ln(DBH2×H) αtreat and βtreat for treated plot and αcont and βcont for control plot. Biomass: α= -3.24, β=0.88, R2(n=20)=0.96, P (β=0&n=20)=0.00, P(βtreat= βcont, N=20, n=5)= 0.84, P(αtreat= αcont, N=20, n=5)=0.86 N:  α= -9.62, β=0.83, R2 (n=20)= 0.70, P (β=0&n=20)=0.00, P(βtreat= βcont, N=20,n=5)= 0.21, P(αtreat= αcont, N=20, n=5)=0.66 P(CaMgPS): α= -9.51, β=0.52, R2(n=20)= 0.37, P(β=0&n=20)= 0.00, P(βtreat= βcont, N=20,n=5)= 0.02, P (αtreat= αcont,N=20, n=5)= 0.52 K:  α= -8.81, β=0.66, R2(n=20)= 0.59, P (β=0&n=20)=0.00, P(βtreat= βcont, N=20,n=5)= 0.09, P(αtreat= αcont, N=20,n=5)= 0.39 Ca: α= -9.21, β=0.73, R2(n=20)= 0.74, P (β=0&n=20)=0.00, P(βtreat= βcont, N=20,n=5)= 0.15, P(αtreat= αcont, N=20, n=5)= 0.73 Mg: α= -8.98, β=0.53, R2 (n=20)= 0.49, P (β=0&n=20)=0.00, P(βtreat= βcont, N=20, n=5)= 0.09, P(αtreat= αcont, N=20, n=5)=0.76. S: α= -11.70, β=0.75, R2 (n=20)= 0.76, P (β=0&n=20)=0.00, P(βtreat= βcont, N=20, n=5)= 0.01, P(αtreat= αcont, N=20, n=5)= 0.58. Biomass: α= -5.51, β=0.88, R2 (n=20)=0.84, P(β=0&n=20)=0.00, P(βtreat= βcont, N=20, n=5)= 0.74, P(αtreat= αcont, N=20, n=5)= 0.55. N (CaMgP. NPK): α= -10.93, β=0.90, R2 (n=20)=0.72, P(β=0&n=20)=0.00, P(βtreat= βcont, N=20, n=5)=0.02, P(αtreat= αcont, N=20, n=5)= 0.52 P (CaMgPS", CaMgPS . NPK", and CaMgP. NPK"): α= -13.08, β=0.93, R2 (n=20)= 0.65, P(β=0&n=20)=0.00, P(βtreat= βcont, N=20, n=5)= 0.00, P(αtreat= αcont, N=20, n=5)= 0.81. K (CaMgPS . NPK" and CaMgP . NPK): α= -10.70, β=0.81, R2(n=20)= 0.77, P(β=0&n=20)= 0.00, P(βtreat= βcont, N=20, n=5)=0.00, P(αtreat= αcont,  N=20, n=5)= 0.57 Ca: α= -10.30, β=0.88, R2(n=20)= 0.75, P(β=0&n=20)=0.00, P(βtreat= βcont, N=20, n=5)= 0.25, P(αtreat= αcont, N=20, n=5)=0.75. Mg: α= -10.52, β=0.68, R2(n=20)= 0.61, P(β=0&n=20)=0.00, P(βtreat= βcont, N=20, n=5)= 0.11, P(αtreat= αcont, N=20, n=5)= 0.87. S (CaMgPS . NPK" and CaMgP. NPK): α= -13.36, β=0.92, R2(n=20)=0.70, P (β=0&n=20)= 0.00,P(βtreat= βcont, N=20, n=5)= 0.01,  P(αtreat= αcont, N=20, n=5)= 0.82. Biomass: α= -5.88, β=1.02, R2(n=20)= 0.83, P(β=0&n=20)=0.00, P(βtreat= βcont, N=20, n=5)= 0.45, P(αtreat= αcont, N=20, n=5)= 0.45 N: α= -12.02, β=1.14, R2 (n=20)= 0.85, P(β=0&n=20)=0.00, P (βtreat= βcont, N=20, n=5)= 0.09, P(αtreat= αcont, N=20, n=5)= 0.93. P: α= -15.03, β=1.24, R2(n=20)= 0.85, P (β=0&n=20)=0.00, P (βtreat= βcont, N=20, n=5)= 0.44, P(αtreat= αcont, N=20, n=5)= 0.87. K: α= -13.35, β=1.16, R2(n=20)= 0.87, P (β=0&n=20)=0.00, P(βtreat= βcont, N=20, n=5)= 0.21, P (αtreat= αcont, N=20, n=5)= 0.86 Ca: α= -12.31, β=1.12, R2(n=20)= 0.79, P (β=0&n=20)=0.00, P (βtreat= βcont, N=20, n=5)= 0.51, P (αtreat= αcont, N=20, n=5)= 0.79. Mg: α= -12.75, β=0.98, R2(n=20)= 0.80, P (β=0&n=20)=0.00, P(βtreat= βcont, N=20, n=5)= 0.17, P(αtreat= αcont, N=20, n=5)= 0.97 S: α= -14.97, β=1.19, R2(n=20)= 0.86, P (β=0&n=20)=0.00, P (βtreat= βcont, N=20, n=5)= 0.29, P(αtreat= αcont, N=20, n=5)= 0.95. Biomass: α= -7.75, β=1.08, R2 (n=20)= 0.58, P (β=0&n=20)=0.00, P (βtreat= βcont, N=20, n=5)=0.08, P (αtreat= αcont, N=20, n=5)=0.50 N: α= -11.81, β=0.98, R2 (n=20)= 0.52, P(β=0&n=20)=0.00, Pβtreat= βcont, N=20, n=5)= 0.07, P(αtreat= αcont, N=20, n=5)= 0.76 P: α= -14.74, β=0.97, R2 (n=20)= 0.47, P(β=0&n=20)=0.00, P(βtreat= βcont, N=20, n=5)= 0.18, P (αtreat= αcont, N=20, n=5)= 0.74 K (CaMgPS, CaMgPS . NPK, and CaMgP . NPK): α= -14.44, β=0.98, R2 (n=20)= 0.36, P(β=0&n=20)=0.00, P (βtreat= βcont, N=20, n=5)= 0.01, P (αtreat= αcont, N=20, n=5)=0.35 Ca: α= -12.96, β=1.02, R2 (n=20)= 0.48, P (β=0&n=20)=0.00, P (βtreat= βcont, N=20, n=5)= 0.34, P (αtreat= αcont, N=20, n=5)= 0.14. Mg: α= -15.05, β=1.05, R2 (n=20)= 0.59, P (β=0&n=20)=0.00, P (βtreat= βcont, N=20, n=5)= 0.26, P (αtreat= αcont, N=20, n=5)= 0.40 S: α= -14.51, β=1.03, R2 (n=20)= 0.54, P (β=0&n=20)=0.00, P (βtreat= βcont, N=20, n=5)= 0.09, P (αtreat= αcont, N=20, n=5)= 0.94. Biomass: α= -4.24, β=0.67, R2 (n=20)= 0.46, P (β=0&n=20)=0.01, P(βtreat= βcont, N=20,n=5)= 0.17, P (αtreat= αcont, N=20,  n=5)= 0.21. N (CaMgPS . NPK and CaMgP . NPK): α= -9.00, β=0.73, R2 (n=20)= 0.43, P(β=0&n=20)= 0.00, P (βtreat= βcont, N=20, n=5)=0.03, P(αtreat= αcont, N=20, n=5)= 0.09 P(CaMgPS", CaMgPS . NPK",and CaMgP . NPK"): α= -14.49, β=1.14, R2 (n=20)= 0.76, P(β=0&n=20)= 0.01*, P(βtreat= βcont, N=20, n=5)= 0.00*,P(αtreat= αcont, N=20, n=5)= 0.09 K (CaMgPS. NPK" and CaMgP. NPK"): α= -10.37, β=0.81, R2 (n=20)=0.48, P(β=0&n=20)= 0.00, P(βtreat= βcont, N=20, n=5)= 0.04, P(αtreat= αcont, N=20, n=5)= 0.31. Ca (CaMgPS", CaMgPS. NPK",): α= -10.69, β=0.81, R2 (n=20)=0.22, P(β=0&n=20)=0.04, P (βtreat= βcont, N=20, n=5)=0.04, P(αtreat= αcont, N=20, n=5)=0.10 Mg (CaMgPS. NPK"): α= -10.16, β=0.60, R2 (n=20)=0.30, P (β=0&n=20)=0.01, P(βtreat= βcont, N=20, n=5)=0.03, P (αtreat= αcont, N=20, n=5)= 0.07. S (CaMgPS", CaMgPS. NPK” and CaMgP. NPK"): α= -11.30, β=0.70, R2 (n=20)= 0.31, P (β=0&n=20)= 0.01, P(βtreat= βcont, N=20, n=5)= 0.0, P(αtreat= αcont, N=20, n=5)= 0.06 Biomass: α= -4.85, β=0.81, R2 (n=20)=0.74, P (β=0&n=20)=0.00, P(βtreat= βcont, N=20, n=5)= 0.62, P(αtreat= αcont, N=20, n=5)= 0.37. N: α= -10.17, β=0.92, R2 (n=20)= 0.73, P (β=0&n=20)=0.00, P(βtreat= βcont, N=20, n=5)= 0.45, P (αtreat= αcont, N=20, n=5)= 0.31. P (CaMgPS", CaMgPS. NPK” and CaMgP. NPK"): α= -12.23, β=0.93, R2 (n=20)= 0.64, P (β=0&n=20)=0.00, P(βtreat= βcont, N=20, n=5)= 0.04, P (αtreat= αcont, N=20, n=5)= 0.65 K: α= -13.34, β=1.18, R2 (n=20)= 0.86, P (β=0&n=20)=0.00, P(βtreat= βcont, N=20, n=5)= 0.46, P (αtreat= αcont, N=20, n=5)= 0.35 Ca (CaMgPS", and CaMgPS. NPK"): α= -12.08, β=1.09, R2 (n=20)= 0.55, P (β=0&n=20)= 0.00, P (βtreat= βcont, N=20, n=5)= 0.01, P(αtreat= αcont, N=20, n=5)= 0.41 Mg (CaMgPS): α= -12.06, β=0.91, R2 (n=20)= 0.64, P (β=0&n=20)=0.00, P (βtreat= βcont, N=20, n=5)=0.01, P(αtreat= αcont, N=20, n=5)= 0.64 S: α= -12.31, β=0.90, R2 (n=20)= 0.67, P (β=0&n=20)=0.00, P (βtreat= βcont, N=20, n=5)= 0.10, P (αtreat= αcont, N=20, n=5)= 0.32	Ingerslev and Hallbacken (1999)
Dominant loblolly pine (age 10 to 48) Bole Needle Branch Total above ground	log(B)=b0 + b1[log(D)] r2=0.98 log(B)=b0 + b1[log(D)] r2=0.92 log(B)=b0 + b1[log(D)] r2=0.92 log(B)=b0 + b1[log(D)] r2=0.99	Naidu et al. (1998)
Suppressed loblolly pine (age 10 to 48) Bole Needle Branch Total above ground	log(B)=b0 + b1[log(D)] r2=0.98 log(B)=b0 + b1[log(D)] r2=0.85 log(B)=b0 + b1[log(D)] r2=0.86 log(B)=b0 + b1[log(D)] r2=0.98	Naidu et al. (1998)
Picea and Abies	y = a v + b a=0.4642, b=47.4990, n=13, r2=0.98 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Cunninghamia lanceolata	y = a v + b a=0.3999, b=22.5410, n=56, r2=0.95 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Cypress	y = a v + b a=0.6129, b=46.1451, n=11, r2=0.96 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Larix	y = a v + b a=0.9671, b=5.7598, n=8, r2=0.99 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Pinus koraiensis	y = a v + b a=0.5185, b=18.2200, n=17, r2=0.90 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Pinus  armandii	y = a v + b a=0.5856, b=18.7435, n=9, r2=0.91 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Pinus massoniana, Pinus yunnanesis	y = a v + b a=0.5101, b=1.0451, n=12, r2=0.92 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Pinus  sylvestris var. mongolica	y = a v + b a=1.0945, b=2.0040, n=11, r2=0.98 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Pinus  tabulaefomis	y = a v + b a=0.7554, b=5.0928, n=82, r2=0.96 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Other pines and conifer forests	y = a v + b a=0.5168, b=33.2378, n=16, r2=0.94 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Tsugn, Cryptomeria, Keteleeria	y = a v + b a=0.4158, b=41.3318, n=21, r2=0.89 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Mixed conifer and deciduous forests	y = a v + b a=0.8019, b=12.2799, n=9, r2=0.99 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Betula	y = a v + b a=0.9644, b=0.8485, n=4, r2=0.96 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Casuarina	y = a v + b a=0.9505, b=8.5648, n=3, r2=1.00 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Deciduous oaks	y = a v + b a=1.3288, b= - 3.8999, n=3, r2=0.99 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Eucalyptus	y = a v + b a=0.7893, b=6.9306, n=4, r2=0.99 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Lucidophyllous forests	y = a v + b a=1.0357,b=8.0591, n=17 ,r2=0.89 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Mixed deciduous and Sassafras	y = a v + b a=0.6255, b=91.0013, n=19, r2=0.86 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Nonmerchantable woods	y = a v + b a=0.7564, b=8.3103, n=11, r2=0.98 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Populus	y = a v + b a=0.4754, b=30.6034, n=10, r2=0.87 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Tropical forests	y = a v + b a=0.9505, b=8.5648, n=3, r2=0.99 Where, y=Stand biomass (Mg/ha), v=Stand volume (m3/ha), a and b=Constants for a specific forest type.	Fang et al. (1998)
Loblolly pine (age 9 to 41) Branch New foliage Old foliage	ln(B)=b0 +b1[ln(D)]+b2[ln(L)]+b3[ln(R)] r2=0.93 ln(B)=b0 +b1[ln(D)]+b2[ln(L)]+b3[ln(R)] r2=0.88 ln(B)=b0 + b1[ln(D)]+b2[ln(R)] r2=0.88	Baldwin et al. (1997)
Hardwoods (Based on tree diameter < 85 cm)	Biomass = (0. 5+25 000 dbh2.5) / (dbh2.5+246872) r2=0.99 Where, biomass is in units of kg per tree and diameter (dbh) is in cm.	Schroeder et al. (1997)
Conifers (Based on tree diameter < 72 cm)	Biomass=(0. 5 + 15 000dbh2.7) / (dbh2.7 + 364946) r2=0.98 Where, biomass is in units of kg per tree and diameter (dbh) is in cm.	Schroeder et al. (1997)
Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco)	Branch: Y=(a×D102)+b(D102×HREL(1-HREL)) Where, Y=Biomass or nutrient content at the branch level (needles, wood), D10 (mm)= Branch diameter 10 cm from the insertion on the stem, HREL=Height above the base of the crown divided by the length of the crown, i.e. (1 - (DINC/CL)), CL (m)=Crown length, DINC (m)=Depth into crown or tree height minus branch height.	Bartelink  (1996)
Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco)	Branch: Ln Y=a+(b × lnD10) Ln Y=a+(b×lnD10)+(c×ln DINC) LnY=a+(b×lnD10)+(c×lnDINC)+ (d×lnRelHACB) Where, Y=Biomass or nutrient content either at the branch level (needles, wood),D10 (mm)= Branch diameter 10 cm from the insertion on the stem, HREL=Height above the base of the crown divided by the length of the crown, i.e. (1 - (DINC/CL)), CL (m)=Crown length, DINC (m)=Depth into crown or tree height minus branch height, RelHACB=Relative height above crown base, i.e. (1.1 - (DINC/CL)).RelHACB is similar to HREL except it is based on a maximum ratio of 1.1 to avoid computational problems in Ln-transformed regressions.	Kershaw & Maguire   (1995)
Eucalyptus spp. (Eucalypt)	Ln (AB)= a+ b. ln(D) D(cm)=4-25, n=22, r2=0.99, a= -1.762, b=2.2644 Where, AB=Total above ground biomass (kg), D=Diameter at breast height (cm)	Menguzzato  (1988)
Loblolly pine (age 25) Foliage Live branch Dead branch Stem bark Stem wood	ln(B)= b0 + b1[ln(D)] r2=0.83 ln(B)= b0 + b1[ln(D)] r2=0.91 B = b0 + b1(D2) r2=0.62 B = b0 + b1(D2) r2=0.88 B = b0 + b1(D2) r2=0.99 Where, B=Biomass, D=Diameter	Pehl et al. (1984)
Loblolly pine (age 41) Total stem (5cm top) Branches Foliage Total above ground	ln(B)=b0 + b1[log(D)] r2=0.985 ln(B)=b0 + b1[log(D)] r2=0.953 ln(B)=b0 + b1[log(D)] r2=0.937 ln(B)=b0 + b1[log(D)] r2=0.985 Where, B=Biomass, D=Diameter	Van Lear et al. (1984)
Loblolly pine (age 2 to 8) Total above ground	log(B)=b0 + b1[log(D2H)] r2=0.99 Where, B=Biomass, D=Diameter, H=Tree height	Edwards & McNab (1979)
Non-plantation loblolly pine (age 37 to 47) Total above ground	log(B)=b0 + b1[log(D2H)] r2=0.99 Where, B=Biomass, D=Diameter, H=Tree height	Clark (III) & Taras (1976)
Slash pine (age 4 to 8) and loblolly pine (age 4 to 12) Main stem Total branch	ln(B)=b0 +b1[ln(D+1)]+b2[ln(D+1)]2+b3[ln(H)] r2=0.991 ln(B)=b0 +b1[ln(D+1)]+b2[ln(D+1)]2+b3[ln(L)]+b4[ln(H)]+b5[ln(H)]2 r2=0.947 Where, B=Biomass, D=Diameter, H=Tree height	Nemeth  (1973)

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22. Van Lear, D. H., Wade J. B., and Teuke M. J., 1984. Biomass and nutrient content of a 41-yearold loblolly pine (Pinus taeda L.) Plantation on a poor site in South Carolina. Forest Science, 30: pp 395-404.
23. Wang Chuankuan, Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests, Forest Ecology and Management,  222: pp 9–16.
24. Xiao Chun-Wang, Ceulemans R., 2004. Allometric relationships for below- and aboveground biomass of young Scots pines, Forest Ecology and Management, 203: pp 177–186.

TABLE V: TROPICAL FOREST

EQUATIONS	REFERENCE
63. For gbh 30–50 cm Volume of tree = - 0.191+0.004936×GBH+0.01222×length. r2=0.79	Ramachandran et al. (2007)
64. For gbh 51–100 cm Volume = - 0.609+0.008246×GBH+0.0409×length. r2=0.83	Ramachandran et al. (2007)
65. For gbh 101–150 cm Volume = - 2.328+0.01902×GBH+0.103×length. r2=0.80	Ramachandran et al. (2007)
66. For gbh 151–200 cm Volume = - 4.771+0.02683×GBH+0.211×length. r2=0.83	Ramachandran et al. (2007)
67. For gbh > 201 cm Volume = - 13.194+0.05515×GBH+0.368×length. r2=0.96	Ramachandran et al. (2007)
68. Wleaf =b1 (DBH )b2             Wwoody =b1 (DBH2HT)b2             Wtotal = b1 (DBH2HT)b2 N=26. Where, W=Oven-dry biomass (kg), DBH=Diameter at 1.37m, HT=Total height	Brandeis et al. (2006)
69. ln Wleaf =b’1 + b2 ln DBH             ln Wwoody =b’1 + b2 ln (DBH2HT)             ln Wtotal =b’1 + b2 ln (DBH2HT) Where ln=Natural logarithm, W=Oven-dry biomass (kg), DBH=Diameter at 1.37 m, HT=Total height, b’1= ln b1.	Brandeis et al. (2006)
70. Vstem =b1 + b2 (DBH2 HT)             Vstem =b1 + b2 (DBH) + b3 (DBH)2 Where, Vstem=The stem volume in cubic meters, DBH the diameter at 1.37 m and HT=The total height.	Brandeis et al. (2006)
71. From Permanent Growth Sample plot in BioSTRUCT (Biomass estimation from stand STRUCTure)method: AGB1/3= - 0.677+1.874×ln(height)+0.014(crown closure) R2=0.70, RMSE=33.7 tonnes/ha AGB1/3= - 0.677+1.874×ln(height)+0.014(crown closure) R2=0.71, RMSE=74.7m3/ha	Hall et al. (2006)
72. For tropical fallows: ALBage= a / 1+b exp(c × age) Where, ALB=The above-ground life biomass (MgDMha-1), age=Years since abandonment (0<age<15), a=57.154, b=844.654, c=2.006.	Jepsen  (2006)
73. For tropical fallows: y’(age) = ((-abc) / (1+b(-c × age))2)exp(-c × age) a=57.154, b=844.654, c=2.006. Where, age=Years since abandonment (0<age<15), y’(age)=Biomass accumulation rates (in MgDMha-1annum-1)	Jepsen  (2006)
74. In forest DNDC(DeNitrification-DeComposition) model: GDDWoodEff = (2.233×air_temp) / ((GDDWoodEnd −GDDWoodStart)×day water_stress) Where, day_WoodProdC=WoodC × GDDWoodEff, GDDWoodEff=Wood production efficiency (It is the fraction of the total forest C storage available for wood growth), air_temp=Air temperature, GDDWoodStart=Accumulative temperature for wood growth to start, GDDWoodEnd= Accumulative temperature for wood growth to cease, day water_stress=The daily water stress factor, WoodC=Forest C storage available for wood growth and day_WoodProdC=The daily wood production.	Miehle et al. (2006)
75. ln(AGB)=α + β1ln(D) + β2ln(H) + β3ln(ρ) Dry forest: α= - 2.680, β1=1.805, β2=1.038, β3=0.377, df=312 Moist forest: α= - 2.994, β1=2.135, β2=0.824, β3=0.809, df=1344, RSE=0.302, r2=0.996 Wet forest: α= - 2.408, β1=2.040, β2=0.659, β3=0.746,df=139 All types forest: α= - 2.801, β1=2.115, β2=0.780, β3=0.809, df=1804, RSE=0.316, r2=0.969 Where, D=Trunk diameter at 130cm above ground (cm), H=Total tree height (m), ρ=Wood specific gravity (g/cm3), RSE=Residual Standard Error.	Chave et al. (2005)
76. ln(AGB)=α + β2ln(D2Hρ) Dry forest: α= - 2.235, β2=0.916, df=314 Moist forest: α= - 3.080, β2=1.007, df=1346, RSE=0.311, r2=0.996 Wet forest: α= - 2.605, β2=0.940, df=141 All types forest: α= - 2.922, β2=0.990, df=1806, RSE=0.323, r2=0.967 Where, D=Trunk diameter at 130cm above ground (cm), H=Total tree height (m), ρ=Wood specific gravity (g/cm3), RSE=Residual Standard Error.	Chave et al. (2005)
77. ln(AGB)=α + ln(D2Hρ) Dry forest: α= - 2.843, df=316 Moist forest: α= - 3.027, df=1349, RSE=0.316, r2=0.989 Wet forest: α= - 3.024, df=143 All types forest: α= - 2.994, df=1808, RSE=0.324 Where, D=Trunk diameter at 130cm above ground (cm), H=Total tree height (m), ρ=Wood specific gravity (g/cm3), RSE=Residual Standard Error.	Chave et al. (2005)
78. ln(AGB)=a + bln(D) + c(ln(D))2 + d(ln(ρ))3 + β3ln(ρ) Dry: a= - 1.023, b=1.821, c=0.198, d= - 0.0272, β3=0.388, df=401 Moist: a= - 1.576, b=2.179, β3=1.036, df=1501, RSE=0.353, r2=0.995 Wet: a= - 1.362, b=2.013, β3=0.956, df=415 Mangrove: a= - 1.265, b=2.009, β3=1.700, df=81 All types: a= - 1.602, b=2.266, c=0.136, d= - 0.0206, β3=0.809, df=2405, RSE=0.377, r2=0.958 Where, D=Trunk diameter at 130cm above ground (cm), H=Total tree height (m), ρ=Wood specific gravity (g/cm3), RSE=Residual Standard Error.	Chave et al. (2005)
79. ln(AGB)=a + bln(D) + c(ln(D))2 + d(ln(ρ))3 + ln(ρ) Dry: a= - 0.730, b=1.784, c=0.207, d= - 0.0281, df=402 Moist: a= - 1.562, b=2.148, df=1502, RSE=0.356, r2=0.996 Wet: a= - 1.302, b=1.980, df=416 Mangrove: a= - 1.412, b=1.980, df=82 All types: a= - 1.589, b=2.284, c=0.129, d= - 0.0197, df=2408, RSE=0.377, r2=0.958 Where, D=Trunk diameter at 130cm above ground (cm), H=Total tree height (m), ρ=Wood specific gravity (g/cm3), RSE=Residual Standard Error.	Chave et al. (2005)
80. ln(AGB)=a + bln(D) + ln(ρ) Dry: a= - 1.083, b=2.266, df=402 Moist: a= - 1.864, b=2.608, df=1502, RSE=0.357, r2=0.996 Wet: a= - 1.554, b=2.420, df=416 Mangrove: a= - 1.786, b=2.471, df=82 All types: a= - 1.667, b=2.510, df=2408, RSE=0.378, r2=0.957 Where, D=Trunk diameter at 130cm above ground (cm), H=Total tree height (m), ρ=Wood specific gravity (g/cm3), RSE=Residual Standard Error.	Chave et al. (2005)
81. For dry forest stands (diameter range 5 to 63.4cm) Wtotal= 0.0112×(pDBH2HT) N=404 Where, Wtotal=Total tree aboveground biomass in oven-dry kg, DBH=Diameter at breast height outside bark, HT=Total tree height (in m), p=wood specific gravity(in g/cm3)	Chave et al. (2005)
82. For Dry forest stands <AGB>est = exp(-2.187+0.916×ln(ρD2H))                   ≡0.112×( ρD2H)0.916 <AGB>est = ρ × exp(-0.667+1.784ln(D)+0.207(ln(D))2 - 0.0281(ln(D))3) Where, D=Trunk diameter at 130cm above ground (cm), H=Total tree height (m), ρ=Wood specific gravity (g/cm3).	Chave et al. (2005)
83. For Moist forest stands: <AGB>est = exp(-2.977 + ln(ρD2H))                   ≡0.0509 ×  ρD2H <AGB>est = ρ × exp(-1.499 + 2.148ln(D) + 0.207(ln(D))2 - 0.0281(ln(D))3) Where, D=Trunk diameter at 130cm above ground (cm), H=Total tree height (m), ρ=Wood specific gravity (g/cm3).	Chave et al. (2005)
84. For Moist mangrove forest stands: <AGB>est = exp (-2.977 + ln(ρD2H))                   ≡0.0509 ×  ρD2H <AGB>est = ρ × exp (-1.349 + 1.980ln(D) + 0.207(ln(D))2 - 0.0281(ln(D))3) Where, D=Trunk diameter at 130cm above ground (cm), H=Total tree height (m), ρ=Wood specific gravity (g/cm3).	Chave et al. (2005)
86. For Wet forest stands: <AGB>est = exp (-2.557 + 0.940 × ln(ρD2H))                   ≡ 0.0776 × ρ(D2H)0.916 <AGB>est = ρ × exp(-1.239 + 1.980ln(D) + 0.207(ln(D))2 - 0.0281(ln(D))3) Where, D=Trunk diameter at 130cm above ground (cm), H=Total tree height (m), ρ=Wood specific gravity (g/cm3).	Chave et al. (2005)
87. ln(Y)= a + b ln(DBH) Where, ln=Natural logarithm, Y=Tree biomass (in kg), DBH=Diameter at breast height (in cm), H=Total height (in m), a, b, and c=Constant values.	Montero and Montagnini (2005)
88. ln(Y) = a + b ln(DBH) + c ln(H) Where, ln=Natural logarithm, Y=Tree biomass (in kg), DBH=Diameter at breast height (in cm), H=Total height (in m), a, b, and c=Constant values.	Montero and Montagnini (2005)
89. Deciduous (n=11) Y= C+m. basal area - 73.55+10.73(r2= 0.82, % error=48) Y=C+m. Ln(basal area) - 1168.66+429.63(r2=0.87, % error=24.98) Where, Y=biomass (in t/ha) and basal area is (in m2/ha)	Murali et al. (2005)
90. Deciduous (n=10) Y=C+m (basal area)+ m ln(height) 11.27+6.03+1.83 (r2=0.94, % error=49) Y=C+m ln(basal area)+ m ln(height) –766+452.19–166.66 (r2=0.94, % error=28.94) Where, Y=biomass(in t/ha) and basal area is (in m2/ha)	Murali et al. (2005)
91. Deciduous (n=10) Y=C+m (density)+ m (basal area) –160.64–0.025+15.4(r2=0.92, % error=19.5) Y=C+ m ln(density)+m ln(basal area) –957.1–44.07+460.63(r2=0.91, % error=31) Where, Y=biomass(in t/ha) and basal area is (in m2/ha)	Murali et al. (2005)
92. Deciduous (n=10) Y=C+m (density)+m (height) 227.2+0.03–3.54(r2=0.46, % error=123) Y=C+m ln(density)+ m ln(height) –439.2+40.3–134.17(r2=0.14, % error=129) Where, Y=biomass(in t/ha) and basal area is (in m2/ha)	Murali et al. (2005)
93. Deciduous (n=10) Y=C+m(basal area)+m(height)+ m(density) –56.8–0.015+13.6–4.21(r2=0.92, % error=27.8) Y=C+m ln(basal area)+ m ln(height)+ m ln(density) –760.94–6.4+455.05–155.82(r2=0.94, % error=26.62) Where, Y=biomass(in t/ha) and basal area is (in m2/ha)	Murali et al. (2005)
94. For all trees (dbh≥10 cm) and for palms:              n AGB=∑ ρi/0.58{exp[2.42(ln Di)-2.00]}            1                        n Where, BA= ∑ П (Di/2)2 Di=Diameter of tree i, ρi=Wood density of tree i, n=Number of stems per plot, BA=Plot basal area, AGB=Stand above-ground biomass (kg dry weight/ha)	Baker et al. (2004)
95. For all trees (dbh≥10 cm) and for palms:            n AGB=∑ ρi/0.67{exp[0.33(ln Di)+0.933 ln(Di)2 - 0.122(ln Di)3- 0.371]}            1 n Where BA= ∑ П (Di/2)2 Di=Diameter of tree i, ρi=Wood density of tree i, n=Number of stems per plot, BA=Plot basal area, AGB=Stand above-ground biomass (kg dry weight/ha)	Baker et al. (2004)
96. For Primary   forest 1/H = 1/(1.5D) + 1/61 n=3224, r2=0.736, P<0.0001, Standard error=0.007 ln(TAGB) = - 2.372 + 1.441 ln(MCH) r2=0.326, P<0.0001 Where, D=DBH, H=Tree height, TAGB=Total above ground biomass, MCH=Mean canopy height.	Okuda et al. (2004)
97. For logged forest 1/H = 1/(2.2D) + 1/47 n=819, r2=0.703, P<0.0001, Standard error=0.011 ln(TAGB) = - 1.983 + 1.348 ln(MCH) r2=0.439, P<0.0001 Where, D=DBH, H=Tree height, TAGB=Total above ground biomass, MCH=Mean canopy height.	Okuda et al. (2004)
98. y= - 6.386849 + 0.006265x + (- 137.02487 / x2) Where, y=Backscatter from P-band image (HH polarization) or PHH images, x=Biomass (t/ha)	Santos et al. (2003)
m                   n                       s 99. AGB= ( ∑DW1i + ∑DW2j)/AP + (∑DW1k )/AS               i=1                 j=1                   k=1 Where, D=Diameter at breast height (in cm), H=Total height (in m), DW1=Individual tree or sapling biomass (in kg) when DBH is less than 25cm, DW2=Individual tree biomass (in kg) when DBH is greater than or equal to 25cm, m=Total tree number in a plot when DBH is between 10 and 25 cm, n=Total tree number in a plot when DBH is greater than or equal to 25 cm, s=Total sapling number in a subplot area when DBH is between 2 and 10 cm, AP=Plot area (in m2), AS=Subplot area (in m2) and AGB=Above ground biomass (in kg/ m2), DW1 was calculated by using Nelson et al. (1999), DW1 was calculated by using Overman et al. (1994)	Lu et al. (2002)
100. Biomass = 122.288 - 1.078× KT1 - 128.913 × VARtm2_9 R=0.878, Beta value= - 0.28(sp), - 0.72(txt) Where, KT=Tasseled cap, VARtm2_9=Variance combined with TM2 and 9 ×9 window size, Sp=Spectral variables, Txt=texture variables	Lu et al. (2002)
101. Biomass= 64.037 - 1.651× TM4 +1.405× SKtm4_9 R=0.883, Beta value= - 0.76(sp), 0.29(txt) Where, TM=Thematic mapper, SKtm4_9=Skewness combined with TM4 and 9×9window size, Sp=Spectral variables, Txt=texture variables	Lu et al. (2002)
102. yleaf = - 3.39 - 0.3170H + 0.00033N - 0.11290S + 1.2015ln(D2H) Where, yleaf=Aerial biomass of leaf per hectare, N=Stand density, H=Average top height, S=Species richness, BA=Basal area, D=Diameter at breast height	Navar et al. (2002)
103. ybranch=1.46 + 0.00403N - 1.39287S + 0.08707 D2H Where, ybranch = Aerial biomass of branch per hectare, N=Stand density, H=Average top height, S=Species richness, BA=Basal area, D=Diameter at breast height	Navar et al. (2002)
104. ystem= 246.99 + 11.9170H - 32.8904ln(D2H)                              - 25.2786ln (N) + 37.7530ln (BA). Where, ystem=Aerial biomass of steam per hectare, N=Stand density, H=Average top height, S=Species richness, BA=Basal area, D=Diameter at breast height	Navar et al. (2002)
105. RB = 2.93 + 0.56BT r2=0.64, n=34, Sx=5.4 Mgha-1 Where, RB=Root Biomass (Mgha-1), BT=Total aboveground biomass (Mgha-1)	Navar et al. (2002)
106. ytotal = 245.06 +11.6H + 0.00436N -1.50577S - 31.6889ln(D2H) + 0.08707D2H - 25.2786 ln(N) +37.7530 ln(BA) Where, ytotal=Total above ground biomass per hectare, N=Stand density, H=Average top height, S=Species richness, BA=Basal area, D=Diameter at breast height	Navar et al. (2002)
107. Height trees<20 cm DBH (m) Agb=[(exp(0.6387+0.7988 ×ln(D))]× 1.0438 R2=0.85, N=40 Where, D=DBH (cm), Agb=Above ground biomass (in Mg on a dry basis).	Cummings et al. (2002)
108. Height trees>20 cm DBH(m) Agb=[ - 19.5873+13.2823×ln(D)]× 0.9999 R2=0.64, N=89 Where, D=DBH (cm), Agb=Above ground biomass (in Mg on a dry basis).	Cummings et al. (2002)
109. BIO1 = 698TM2 - 1158TM1 - 11393TM3 +3523TM7 - 5733WI - 45VI5 - 632952TSAVI- 10085DVI - 8403PC2+10983PC3 + 2214PC4 + 4067PC5 - 5IM + 11PA - 55T0 +19513TA + 558382NDVI - 3991 RVI -14DEM- 63682 Relation coefficient= 0.673 BIO1=Biomass of sample plot 30×30 m2, WI=Wetness index,  VI5=Vegetation index, TSAVI=Transformed soil adjusted vegetation index,  DVI=Difference vegetation index, NDVI=Normalized difference vegetation index, RVI=Ratio vegetation index, PC2=Second principle component, PC3=Third principle component, PC4=Fourth principle component, PC5=Fifth principle component.	Yang et al. (2001)
110. BIO2 = 1251266 –7314TM3 + 923TM4 –11219TM7– 7348WI–262VI5 +132147TSAVI –68242SARVI –3332 PC2+7358 PC4 –11697 PC5 –10IM + 19PA–9T0 +3355TA+53ASP. Relation coefficient= 0.725 BIO2=Biomass of sample plot 30×30 m2, WI=Wetness index,  VI5=Vegetation index, TSAVI=Transformed soil adjusted vegetation index,  SARVI=Soil adjusted ratio vegetation index, DVI=Difference vegetation index, NDVI=Normalized difference vegetation index, RVI=Ratio vegetation index, PC2=Second principle component, PC4=Fourth principle component, PC5=Fifth principle component.	Yang et al. (2001)
111. BIO3 = 1689804 + 15920TM1+10578TM2+4722TM-11072TM7 + 86VI5+851185TSAVI-7632DVI-807PC1+ 3146PC2+12261PC3-13766PC4-21048PC5-9IM + 14PA + 10T0 - 2956TA - 776122NDVI - 42127RVI - 107ASP + 35DEM. Relation coefficient= 0.733 BIO3=Biomass of sample plot 30×30 m2, WI=Wetness index,  VI5=Vegetation index, TSAVI=Transformed soil adjusted vegetation index,  DVI=Difference vegetation index, NDVI=Normalized difference vegetation index, RVI=Ratio vegetation index, PC1=First principle component, PC2=Second principle component, PC3=Third principle component, PC4=Fourth principle component	Yang et al. (2001)
112. B=rρD2+c For Sepunggur r = 0.11, c and ρ are parameters , If we use the value of k from H=kDc=2.54D0.62 then equation become B=0.042ρD2H This equations are used for trees (7.6<D<48.1cm)in mixed Secondary forest in Sepunggur and for tree in secondary forest of Sumatra for D=8-48 cm, B (kg per tree)=0.066D2.59 ρ=Average wood density for the site and a=rρ is hypothesized and a and r are parameters, H=Height, D=Diameter b=2+c	Ketterings et al. (2001)
113. If minimum dbh is 10 cm AGB = exp[- 2.00 + 2.42 In(D)] Where, D=Diameter measured at 1.30 m above ground, below irregularities, or above buttresses (in cm), AGB=Above ground biomass (in kg tree-1)	Chave et al. (2001)
114. If minimum dbh is 5 cm AGB = exp[ - 0.37+0.333ln(D)+0.933ln(D)2 - 0.122 ln(D)3] Where, D=Diameter measured at 1.30 above ground, below irregularities, or above buttresses (in cm), AGB=Above ground biomass (in kg tree-1)	Chambers et al. (2001)
115. For primary forest: ln (biomass) = - 0.370+0.333ln(DBH) + 0.933[ln(DBH)]2 - 0.122 [ln (DBH)]3 Where, DBH=Diameter at breast height.	Chambers et al. (2001)
116. For all trees (dbh≥10 cm), including palms:            n AGB=∑exp[0.33(ln Di)+0.933(ln Di)2 - 0.122(lnDi)3 -0.37]                                                                                i Where BA= ∑ П (Di/2)2, Di=Diameter of tree i, ρi=Wood density of tree i, n=Number of stems per plot, BA=Plot basal area.	Chambers et al. (2001)
117. For all trees (dbh≥10 cm), including palms:            n AGB=∑exp[2.42(ln Di)-2.00]            i Where BA= ∑ П (Di/2)2 Di=Diameter of tree i, ρi=Wood density of tree i, n=Number of stems per plot, BA=Plot basal area.	Chave et al. (2001)
118. For stands younger than 10 years Y = 53/(1 + exp(1.86 ─ 0.36t)) Where, t=Since Abandonment	Hashimotio et al. (2000)
119. Y =aXb1Є Where, Y=Total aboveground biomass (AB), commercial biomass (CB) or leaf area (LA). Commercial biomass was defined as the biomass of stem sections up to 20 cm top diameter. X1=Stem DBH, a and b=Model parameters, Є=Error term	Ares & Fownes  (2000)
120. Y =aXb1 X c2Є Where, Y=Total aboveground biomass (AB), X1=Stem DBH, X2=Total height,  a and b=Model parameters, and Є = Error term	Ares & Fownes  (2000)
121. Ln(DW1)= - 2.5202+ 2.14ln(D) +0.4644 ln(H) Where, D=Diameter at breast height (cm), H=Total height (m), DW1=Individual tree or sapling biomass (kg) when DBH is less than 25cm., DW2=Individual tree biomass (kg) when DBH is greater than or equal to 25cm.,m=Total tree number in a plot when DBH is between 10 and 25 cm, n=Total tree number in a plot when DBH is greater than or equal to 25 cm, s=Total sapling number in a subplot area when DBH is between 2 and 10 cm, AP=Plot area (m2), AS=Subplot area (m2) and AGB=Above ground biomass (kg/ m2)	Niklas et al. (1999)
122. Standing dead trees (Trees<10cm DBH) Agb = [(exp(1.1788×ln(D2)+4.4189))×1.0819] / 106 R2=0.96, N=66, Where, D=DBH (cm), Biomass is expressed in Mg on a dry basis.	Hughes et al. (1999)
123. Saplings (minimum dbh is 1 cm) AGB = ρ/ρav exp[─1.839 + 2.116 In(D)] Where, D=Diameter measured at 1.30 above ground, below irregularities or above buttresses (in cm), ρ=oven-dry wood specific gravity (in g cm-'), ρav =Mean wood specific gravity of the plot (0.54 g cm-3), H=Total tree height (in m) and AGB =Above ground biomass (in kg tree-1)	Hughes et al. (1999)
124. Tree<5 cm DBH Agb=[(exp(1.0583 × ln(D2)+4.9375))×1.143] / 106 R2=0.94, N=244 Where, D=DBH (cm), Biomass is expressed in Mg on a dry basis.	Hughes et al. (1999)
125. For secondary succession: ln (biomass) = - 1.9968 + 2.1428 ln (DBH) Where, Biomass is the above ground biomass.	Nelson et al. (1999)
126. DW = a + b(D2H) Where, DW=Above-ground biomass dry weight of tree, DBH=Diameter at breast height measured 1.3 m above-ground, H=Tree height	FAO  (1999)
127. sqrt (DW) = a + b(DBH) Where, DW=Above-ground biomass dry weight of tree, DBH=Diameter at breast height measured 1.3 m above-ground, H=Tree height	FAO  (1999)
128. DWf = FVf ×DWs / FVs Where, DWf =Dry weight of the stump, FVf =Fresh volume of stump, DWs=Dry weight of the subsample of the stem and FVs=Fresh volume of the subsample of the stem	Jarayaman  (1999)
129. DWf = FWf × DWs / FWs Where, DWff=Dry weight of the faction, FWf =Fresh weight of the faction, DWs=Dry weight of the subsample of the faction, FWs=Fresh weight of the subsample of the faction	Jarayaman  (1999)
130. Trees 5-20 cm DBH Agb = [(exp(─1.754+2.665×ln(D)))×0.604] / 103 R2=0.92, N=244 Where, D=DBH (cm), Biomass is expressed in Mg on a dry basis.	Higuchi et al. (1998)
131. For all trees (dbh≥10 cm), including palms: AGB=0.6 ×[66.92+(16.85×BA)]                      n Where BA= ∑ П (Di/2)2 Di=Diameter of tree i, ρi=Wood density of tree i, n=Number of stems per plot, BA=Plot basal area.	Philips et al. (1998)
132. Trees>20 cm DBH Agb = [(exp(-0.151+2.17×ln(D)))×0.604] / 103 R2=0.90, N=244 Where, D=DBH (cm), Biomass is expressed in Mg on a dry basis.	Higuchi et al. (1998)
133. For moist and precipitation 1500-4000 mm: Y = exp( - 2.134 + 2.530 ln(D)) Where, ln is the natural logarithm, Y=Total tree aboveground biomass in oven-dry kg, D=Diameter at breast height outside bark.	Brown  (1997)
134. lnWtotal= - 1.990+2.32 ln (D2 BH) Where ln is the natural logarithm, Wtotal=Total tree aboveground biomass in oven-dry kg, DBH=Diameter at breast height outside bark.	Brown  (1997)
135. For humid tropical forests W = 0.118 D 2.53	Brown  (1997)
136. For hardwood Biomass= 0.5+25000 dbh 2.5 /dbh 2.5 + 246872 R2=0.99	Schroeder et al. (1997)
137. For conifers Biomass=0.5+15000 dbh 2.7 /dbh 2.7 + 364946 R2=0.98	Schroeder et al. (1997)
138. For tree fresh dry biomass of Amazonian tree species: For trees < 20 cm dbh: ln (mass) = 1.754 + 2.665 ln (dbh) For trees > 20 cm dbh:  ln (mass) = - 0.151 + 2.170 ln (dbh) Dry biomass was calculated by multiplying fresh biomass by the mean dry mass: fresh mass ratio of 0.603.	Higuchi et al. (1997)
139. BGBD=exp{─1.059+0.884×ln(AGBD)+0.284} r2=0.84, n=151 Where, BGBD=Belowground biomass density and AGBD=Above ground biomass density	Cairns et al. (1997)
140. AGBM=(exp(3.323+(2.546×(ln(DBH/100)))))×600 Where, AGBM=Above ground biomass, DBH=Diameter  at breast height	Santosh  (1996)
141. B= 0.062ρ(0.1(π/4)D2H) = 0.049ρD2H Where, D=Diameter measured at 1.30 above ground, below irregularities or above buttresses (in cm),  ρ=oven-dry wood specific gravity (in g cm-1), H=Total tree height (in m)	Brown et al. (1995)
142. For trees (DBH≥25 cm) Ln(DW2)= - 3.843+ 1.035ln(D2 H) Where, D=Diameter at breast height (cm), H=Total height (m), DW2=Individual tree biomass (kg) when DBH is greater than or equal to 25cm.,	Overman et al. (1994)
143. DW = α(DBH)β For all trees: R2=0.90, δB (%)=39.4, P<0.0001, α=0.465, Standard error=0.307 β=2.202, Standard error=0.151 For trees with DBH≤45 cm: R2=0.81, δB(%)=42.8, P<0.0001, n=33 α=0.749, Standard error=0.552 β=2.011, Standard error=0.204 Where, DW=Aerial dry weight of the tree (kg), DBH= Diameter at breast height 1.30 m above ground level (cm) h=Height of the tree (m),  d=Wood density (gcm-3) δB(%)=Percentage deviation	Overman et al. (1994)
144. DW = α(DBH)2 For all trees: R2=0.94, δB(%)=62.8, P<0.0001, α=1.120, Standard error=0.040 For trees with DBH≤45 cm: R2=0.93, δB(%)=43.5, P<0.0001, n=33 α=0.780, Standard error=0.039	Overman et al. (1994)
145. ln(DW)=C+ α ln(DBH2×h×d) For all trees: R2=0.97, δB(%)=25.6, P<0.0001, α=1.242, Standard error=0.032 C= - 1.966, Standard error=0.235 For trees with DBH≤45 cm: R2=0.93, δB(%)=27.5, P<0.0001, n=33 α=1.256, Standard error=0.061 C= - 2.059, Standard error=0.061	Overman et al. (1994)
146. ln(DW)=C+ α ln(DBH2×d) For all trees: R2=0.99, δB(%)=11.2, P<0.0001, α=0.993, Standard error=0.012 C= - 2.904, Standard error=0.120 For trees with DBH≤45 cm: R2=0.98, δB(%)=12.9, P<0.0001, n=33 α=0.990, Standard error=0.024 C= - 2.885, Standard error=0.024	Overman et al. (1994)
147. ln(DW)=C+ α ln(DBH2×h) For all trees: R2=0.99, δB(%)=14.8, P<0.0001, α=1.177, Standard error=0.018 C= - 0.906, Standard error=0.125 For trees with DBH≤45 cm: R2=0.98, δB(%)=15.6, P<0.0001, n=33 α=1.229, Standard error=0.035 C= - 1.192, Standard error=0.206	Overman et al. (1994)
148. ln(DW)=C+ α ln(DBH2) For all trees: R2=0.97, δB(%)=24.3, P<0.0001, α=1.035,Standard error=0.025 C= - 3.843, Standard error=0.259 For trees with DBH≤45 cm: R2=0.94, δB(%)=26.3, P<0.0001, n=33 α=1.002, Standard error=0.045 C= - 3.555, Standard error=0.428	Overman et al. (1994)
149. ln(DW)=C+ α ln(DBH2)+ βln(d) For all trees: R2=0.99, δB(%)=14.7, P<0.0001, C= - 1.020, Standard error=0.175 β=1.185, Standard error=0.021 α=1.071, Standard error=0.114 For trees with DBH≤45 cm: R2=0.98, δB(%)=15.0, P<0.0001, n=33 C= - 1.322, Standard error=0.256 β=1.239, Standard error=0.037 α=1.106, Standard error=0.148	Overman et al. (1994)
150. B=aDbH If H=kDc then , B=akDb+c Where, H=height and D=Diameter, B=Above ground biomass.	Niklas  (1994)
151. DW = a(DBH)b ×Є ln(DW) = ln(a) + b × ln(DBH) + ln (Є) Where, DW=Above-ground biomass dry weight of tree, DBH=Diameter at breast height measured 1.3 m above-ground, Є=Standard error	Overman et al. (1994), Brown. (1997), Ketterings et al. (2001)
152. Wtotal = - 0.5352+log10(BA) Where, Wtotal=Total tree aboveground biomass in oven-dry kg, BA=Basal area.	Martinez-Yrizar et al. (1992)
153. AGB = exp{- 3.1141 + 0.9719 log(d21.3h)} n =168, r2=0.97 Where, d1.3=Diameter at breast height 1.3m (cm), h=Total tree height (m), AGB=Total above-ground biomass (kg per tree), n=Number of sample and r2=Regression coefficient	Brown et al. (1989)
154. For fallows aged 7–10 years precipitation 4000 mm year−1 Y = 17.376 - 6.042x + 0.72x2 - 0.0084x3 x = Time since abandonment	Halenda  (1989)
155. y= 34.4703 ─ 8.0671 dbh +0.6589 dbh2 y=Total above ground biomass, dbh=Diameter at breast height 1.3m (cm)	Brown et al. (1989)
156. For primary forest: biomass = 0.044 × (DBH2× height) 0.9719 Where, biomass=Above ground biomass.	Brown et al. (1989)
157. If minimum dbh is 5 cm y= exp[ - 3.1141+0.9719 ln(dbh2H)] Where, dbh=Diameter measured at 1.30 above ground, below irregularities or above buttresses (in cm),  ρ=oven-dry wood specific gravity (in g cm-1), H=Total tree height (in m), y=Above ground biomass (in kg tree-1)	Brown et al. (1989)
158. AGB=F×(ρ×(П D2 /4)×H) β β<1 may actually be a better model. Where, D=Diameter measured at 1.30 above ground, below irregularities or above buttresses (in cm),  ρ=oven-dry wood specific gravity (in g cm-1), H=Total tree height (in m), AGB =Above ground biomass (in kg tree-1)	Brown et al. (1989)
159. For secondary succession: ln Y = - 2.17 + 1.02 ln(DBH)2 + 0.39 ln(H) Where, Y=Above ground biomass, SG=Specific gravity, H=Height and DBH=Diameter at breast height	Uhl et al. (1988)
160. For DBH>10cm in forest. Precipitation:1750 mm year-1 ln Y = 0.991 ln((DBH)2 × H × SG) - 2.968 Where, Y=Above ground biomass, SG=Specific gravity, H=Height, DBH=diameter at breast height	Uhl et al. (1988)
161. Ws = 0.02903(DBH2H)0.9813    Where, Ws=Stem biomass, DBH=Diameter at breast height, H=Height of the tree	Yamakura et al. (1986)
162. Wb = 0.1192Ws1.059                  Where, Wb=Branch biomass, Ws=Stem biomass, DBH= Diameter at breast height	Yamakura et al. (1986)
163. Wl = 0.09146(Ws + Wb)0.7266  Where, Wl=Leave biomass, Ws=Stem biomass, Wb=Branch biomass, DBH=Diameter at breast height	Yamakura et al. (1986)
163.        m B = ∑ a i × (DBH) i + Є         i=0	Cunia  (1986a), Brown  (1997), De Gier  (2003)
164. DW = a + b(DBH)2 + c(DBH)3 Where, DW=Above-ground biomass dry weight of tree, DBH=Diameter at breast height, measured 1.3 m above-ground	Cunia  (1986) and Brown  (1997)
165. Y= 0.0921×(dbh)2.5899 r2=0.991 Where, Y=Above ground biomass, dbh=Diameter at breast height.	Lim  (1986) and Lim  (1988)
166. Palm>10cm dbh AGB =(4.5+(7.7(stem H))/ 103 r2=0.90, N=25	Frangi and Lugo  (1985)
167. WT = F(HG) D Where, WT=Above ground woody biomass, F=Stand form factor, H=Mean tree height, G=Stand, over bark, basal area at breast height	Cannell  (1984)
168. log y = log a+ b log x Where, x=Stem diameter and y=Biomass or productivity of some tissue.	Sprugel  (1983)
169. Ws = 0.0313(D2H)0.9733 Where, H=Tree’s height (in m), D=DBH (in cm), Ws=Weights (in kg) of the main stem.	Kato et al. (1978)
170. Wb = 0.039(D2H)1.041 Where, H=Tree’s height (in m), D=DBH (in cm), Wb=Weights (in kg) of the branches.	Kato et al. (1978)
171. 1 / Wl = 1 / 0.124Ws 0.794 + 1 / 125 Where, H=Tree’s height (in m), D=DBH (in cm), Ws=Weights (in kg) of the main stem, Wl=Weights (in kg) of the leaves.	Kato et al. (1978)
172. For all trees with DBH ≥5 cm. TAGB= Ws +Wb + Wl  Where, Ws=Weights (in kg) of the main stem, Wb=Weights (in kg) of the branches, Wl=Weights (in kg) of the leaves, TAGB=Total aboveground biomass for all trees with DBH ≥5 cm.	Kato et al. (1978)
173. AGB = cρD 2+B Where, AGB=Above ground biomass (in kg), D=Diameter of tree (in cm), c=Measure the taper of a mean tree, ρ=Oven-dry wood over green volume (g/cm3), B=Can be derived from engineering considerations.	Mc Mahon and kronauer (1976) Taken from Chave 2005
174. TB = 0.5  × BABH  × TH Where TB=Total biomass of tree, TH=Total height, BABH=Basal area at breast height	Whittaker and Marks  (1975)
175. Wood debris >76 cm dbh AGB =Sg×((П2×∑D2×s×Cs×d2)/8L) ×102 Sg sound=482 g cm-3 Sg rotten=0.342 g cm-3 Sg palm=0.327 g cm-3 Where, D=dbh (cm), H=height (m), BA=Basal area (cm2) Sg=specific gravity of wood (g cm-3), d2=quadratic mean diameter of wood particles, L=Transect length (cm), S=Secant of wood particles, N=Number of intercepted wood particles, ∑D2=Sum of wood particles diameter squared, r=radius (m), Cs=Slope correction factor ( √1+(%slope/100)2)	Van Wagner (1968), Brown and Roussopoulos (1974)
176. Wood debris 2.5 to 7.60 cm dbh AGB =Sg×((П2×N×s×Cs×d2)/8L) ×102 Sg=0.413 g cm-3 Where, D=dbh (cm), H=Height (m), BA=Basal area (cm2), Sg=Specific gravity of wood (g cm-3), d2=Quadratic mean diameter of wood particles, L=Transect length (cm), =Secant of wood particles, N=Number of intercepted wood particles, ∑D2=Sum of wood particles diameter squared, r=Radius (m), Cs=Slope correction factor ( 1+(%slope/100)2)	Van Wagner (1968), Brown and Roussopoulos (1974)
177. AGB=F×ρ×(П D2 /4)× H AGB=Above ground biomass (in kg) of a tree, D=Diameter, ρ=Wood specific gravity, BA=trunk basal area (П D2 /4), H=Total tree height.	Dawkins (1961)
178. ln AGB = α + β1 ln (D) + β2 ln (H) + β3 ln (ρ) AGB=Above ground biomass, D=Trunk diameter (in cm), H=Height of tree (in m), ρ=Wood specific gravity (in g/cm3), α, β1, β2 and β3=Parameters	Schumacher and Hall  (1933)

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TABLE VI: TROPICAL FORESTS  (SPECIESWISE)

SPECIES	MODEL	REFERENCES
Aboveground tree biomass in primary forests (D ≥ 1 cm)	ln (AB-T) = - 2.286 + 2.471 ln (D) Range in D (cm)=0.5–198, n=140, CF=0.091, r2(%)=97.90 Where, AB-T=Above ground biomass of tree, D=Diameter, H=Height	Sierra et al. (2007)
Aboveground tree biomass in secondary forests (D ≥ 1 cm)	ln (AB-T) = - 2.232 + 2.422 ln (D) Range in D (cm)=0.9–40, n=152, CF=0.083, r2(%)=97.47 Where, AB-T=Above ground biomass of tree, D=Diameter, H=Height	Sierra et al. (2007)
Coarse root biomass (primary and secondary forest)	ln (CRB) = - 4.394 + 2.693 ln (D) Range in D (cm)=1.7–64, n=649, CF=0.316, r2(%)=91.79 Where, CRB=Coarse root biomass, D=Diameter, H=Height	Sierra et al. (2007)
Aboveground biomass for Oenocarpus bataua	AB-Ob = 139.48 + 7.308H 1.133 Range in H (cm)=50–250, n=83, r2(%) =82.95 Where, AB-Ob=Above ground biomass for Oenocarpus bataua, D=Diameter, H=Height	Sierra et al. (2007)
Aboveground biomass for other palms	ln (AB-OP) = 0.360 + 1.218 ln (H) Range in H (cm)=100-150, n=37, CF=0.325, r2(%)=65.28 Where, AB-OP=Above ground biomass for other palms, D=Diameter, H=Height	Sierra et al. (2007)
Aboveground biomass for lianas	ln (AB-L) = 0.028 + 1.841 ln (D) Range in D (cm)=1-11, n=33, CF=0.133, r2(%)=87.44 Where, AB-L=Above ground biomass for lianas, D=Diameter, H=Height	Sierra et al. (2007)
Diptychandra  aurantiaca Total biomass(L) Volume(L) Trunk(L) Branches(L) Leaves(L)	ln Y = ln a + b ln D Or Y =aDb Where, Y=Dry weight or wood volume, D= Diameter at breast height, R2=Square of the correlation coefficient, E=Estimated standard error, CF=Correction factor, L=Linear equation, NL=Non linear equationD=5-35 cm, n=10, ln a= -2.119, b=2.380, r2=0.986, E=0.167, CF=1.0140 D=5-35 cm, n=10, ln a= -2.119, b=2.380,  r2=0.986, E=0.167, CF=1.0140 D=5-35 cm, n=10, ln a= -2.781, b=2.382, r2=0.988, E=0.151, CF=1.0115 D=5-35 cm, n=10, ln a= -3.314, b=2.508,  r2=0.952, E=0.329, CF=1.0556 D=5-35 cm, n=10, ln a= -3.289, b=1.575,  r2=0.795, E=0.469, CF=1.1101	Salis et al. (2006)
Protium heptaphyllum Total biomass(L) Volume(L) Trunk(L) Branches(L) Leaves(L)	D=8-36 cm, n=10, ln a= -2.083, b=2.536,  r2=0.971, E=0.250 D=8-36 cm, n=10, ln a= -8.914, b=2.266,    r2=0.974, E=0.212, CF=1.0227 D=8-36 cm, n=10, ln a= -2.065, b=2.150,     r2=0.976, E=0.191, CF=1.0184 D=8-36 cm, n=10, ln a= -3.554, b=2.868,      r2=0.911, E=0.510 D=8-36 cm, n=10, ln a= -4.319, b=2.076, r2=0.912, E=0.367, CF=1.0697	Salis et al. (2006)
Magonia pubescens Total biomass(L) Volume(L) Trunk(L) Branches(L) Leaves(L)	D=7-35 cm, n=10  ln a= -2.888, b=2.795,    r2=0.994, E=0.123 D=7-35 cm, n=10, ln a= -8.730, b=2.269, r2=0.994, E=0.100 D=7-35 cm, n=10, ln a= -2.525, b=2.411, r2=0.984, E=0.172 D=7-35 cm, n=10, ln a= -3.972, b=2.937, r2=0.987, E=0.163 D=7-35 cm, n=10, ln a= -4.998, b=2.342, r2=0.881, E=0.490	Salis et al. (2006)
Terminalia argentea Total biomass(L) Volume(L) Trunk(L) Branches(L) Leaves(L)	D=6–31 cm, n=10, ln a= -1.915, b=2.409,  r2=0.987, E=0.172, C.F.=1.0149 D=6–31 cm, n =10, ln a= -8.285, b=2.113, r2=0.949, E=0.300, C.F.=1.0460 D=6–31 cm, n=10, ln a= -1.380, b=1.984, r2=0.950, E=0.281 D=6–31 cm, n=10, ln a= -5.161, b=3.195,  r2=0.920, E=0.581, C.F.= 1.1839 D=6–31 cm, n=10, ln a= -4.074, b=1.967, r2=0.807, E=0.592, C.F.= 1.1915	Salis et al. (2006)
Licania minutiflora Total biomass(L) Volume(L) Trunk(NL) Branches(NL) Leaves(NL)	D=10–36 cm, n=10, ln a= -2.265, b=2.386, r2=0.912, E=0.322 D=10–36 cm, n=10, ln a= -9.376, b=2.439, r2=0.975, E=0.176 D=10-36 cm, n=10, ln a=0.031, b=2.556, r2=0.926 D=10-36 cm, n=10, ln a=0.140, b=2.076, r2= 0.948 D=10-36 cm, n=10, ln a=0.030, b=1.532, r2= 0.898	Salis et al. (2006)
Group of 11 species Total biomass(L) Volume(NL) Trunk(NL) Branches(NL) Leaves(NL)	D=6–27 cm, n=11, ln a= - 2.566, b= 2.533, r2=0.906, E= 0.451 D=6–27 cm, n=11, ln a=0.0005, b=1.899, r2=0.973 D=6–27 cm, n=11, ln a=0.339, b= 1.836, r2=0.950 D=6–27 cm, n=11, ln a=0.011, b=2.905, r2=0.868 D=6–27 cm, n=11, ln a=0.0001, b=3.756, r2=0.903	Salis et al. (2006)
Mixed species Bucida buceras	ln Wleaf =b’1 + b2 ln DBH ln Wwoody =b’1 + b2 ln (DBH2HT) ln Wtotal =b’1 + b2 ln (DBH2HT) Where, ln=Natural logarithm, ln W=Biomass in oven-dry kg, DBH=Diameter at 1.37 m and HT =Total height, b’1= ln b1. Leaf : n=26, b1= - 1.75242, b2= 1.71833, M.S.E=0.57199, r2=0.75980 Woody: n=26, b1= - 2.87503, b2=0.92900, M.S.E=0.27738, r2=0.92500 Total: n=26, b1= - 1.94371, b2=0.84134, M.S.E=0.25252, r2=0.91750 Leaf: n=11, b1= - 1.47983, b2=1.83142, M.S.E=0.19500, r2=0.92300 Woody: n=11 , b1= - 2.68937, b2=0.94081, M.S.E.=0.07125, r2=0.98060 Total: n=11, b1= - 1.76887, b2=0.86389, M.S.E=0.03091, r2=0.98990	Brandeis et al. (2006)
Mixed species Total volume (Inside bark portion of the tree’s stem between a 30 cm tall stump and the tip of the tree’s stem without a minimum upper diameter merchantability limit.) Merchantable volume (Inside bark portion of the tree’s stem between a 30 cm tall stump and a 10 cm upper stem diameter.)  Bucida buceras Total volume (Inside bark portion of the tree’s stem between a 30 cm tall stump and the tip of the tree’s stem without a minimum upper diameter merchantability limit.) Merchantable volume (Inside bark portion of the tree’s stem between a 30 cm tall stump and a 10 cm upper stem diameter.)  Bursera simaruba Total volume (Inside bark portion of the tree’s stem between a 30 cm tall stump and the tip of the tree’s stem without a minimum upper diameter merchantability limit.) Merchantable volume (Inside bark portion of the tree’s stem between a 30 cm tall stump and a 10 cm upper stem diameter.) Mixed species Total volume (Inside bark portion of the tree’s stem between a 30 cm tall stump and the tip of the tree’s stem without minimum upper diameter merchantability limit.) Merchantable volume (Inside bark portion of the tree’s stem between a 30 cm tall stump and a 10 cm upper stem diameter.) Bucida buceras Total volume (Inside bark portion of the tree’s stem between a 30 cm tall stump and the tip of the tree’s stem without minimum upper diameter merchantability limit.) Merchantable volume (Inside bark portion of the tree’s stem between a 30 cm tall stump and a 10 cm upper stem diameter.) Bursera simaruba Total volume (Inside bark portion of the tree’s stem between a 30 cm tall stump and the tip of the tree’s stem without minimum upper diameter merchantability limit.) Merchantable volume (Inside bark portion of the tree’s stem between a 30 cm tall stump and a 10 cm upper stem diameter.)	Vstem =b1 + b2 ln(DBH2HT) Total volume for Mixed species: Inside bark: n=17, b1= - 0.0173, b2=0.000047, M.S.E=0.0011, r2=0.9906 Outside bark: n=17, b1= - 0.0114, b2=  0.000046, M.S.E=0.0010, r2=0.9916 Merchantable volume for Mixed species: Inside bark: n=17, b1= - 0.0593, b2=0.000046, M.S.E=0.0020, r2=0.9826 Outside bark: n=17, b1= - 0.0576, b2=0.000045, M.S.E=0.0019, r2=0.9828 Total volume for Bucida buceras: Inside bark: n=9, b1=0.0001, b2=0.000047, M.S.E=0.0010, r2=0.9952 Outside bark: n=9 , b1=0.0002, b2=0.000046, M.S.E=0.0010, r2=0.9950 Merchantable volume for Bucida buceras: Inside bark: n=9 , b1= - 0.0697, b2=0.000046, M.S.E=0.0024, r2=0.9888 Outside bark: n=9 , b1= - 0.0688, b2=0.000046, M.S.E=0.0023, r2=0.9890 Total volume for Bursera simaruba Inside bark: n=8, b1= 0.0370, b2=  0.000028, M.S.E.=0.0003, r2= 0.8536 Outside bark: n=8 , b1= 0.0354, b2=  0.000030, M.S.E.=0.0004, r2= 0.8312 Merchantable volume for Bursera simaruba Inside bark: n=8 , b1= 0.2890, b2=  0.000025, M.S.E.=0.0009, r2= 0.5553 Outside bark: n=8 , b1= 0.0285, b2=  0.000023, M.S.E.=0.0009, r2= 0.5620 V stem =b1 + b2 (DBH) + b3 ln (DBH)2 Where, Vstem = the stem volume in cubic meters, DBH the diameter at 1.37 m and HT = the total height Total volume for Mixed species: Inside bark: n=17, b1= 0.1810, b2=  0.0234, b3= 0.0012, M.S.E.=0.0044, r2= 0.9662 Outside bark: n=17 , b1= 0.1726, b2= - 0.0222, b3=0.0012 M.S.E.=0.0036, r2= 0.9708 Merchantable volume for Mixed species: Inside bark: n=17 , b1= 0.0744, b2=  0.0182, b3= 0.0011, M.S.E.=0.0028, r2= 0.9771 Outside bark: n=17 , b1= 0.0729, b2= - 0.0179, b3=0.0011, M.S.E.=0.0027, r2= 0.9780 Total volume for Bucida buceras: Inside bark: n=9, b1= - 0.0182, b2= - 0.0018, b3= 0.0008, M.S.E.=0.0012, r2= 0.9951 Outside bark: n=9 , b1= 0.0007, b2= - 0.0034, b3=0.0008, M.S.E.=0.0012, r2= 0.9950 Merchantable volume for Bucida buceras: Inside bark: n=9 , b1= - 0.0991, b2= - 0.0009, b3= 0.0008, M.S.E.=0.0025, r2= 0.9897 Outside bark: n=9 , b1= - 0.0949, b2= - 0.0011, b3=0.0008, M.S.E.=0.0025, r2= 0.98990 Total volume for Bursera simaruba Inside bark: n=8, b1= - 0.2921, b2= 0.0340, b3= - 0.0006, M.S.E.=0.0004, r2= 0.8255 Outside bark: n=8 , b1= - 0.2388, b2= 0.0273, b3= - 0.0004, M.S.E.=0.0006, r2= 0.8091 Merchantable volume for Bursera simaruba Inside bark: n=8 , b1= 0.0807, b2= - 0.0126, b3= 0.0007, M.S.E.=0.0000672, r2= 0.9708 Outside bark: n=8 , b1= 0.0877, b2= - 0.0135, b3=0.0007, M.S.E.=0.000065, r2= 0.9727	Brandeis et al. (2006)
Dry forest stands	(AGB)est =Exp(-2.187+0.916×ln( ρD2H))                ≡ 0.112×ρ(D2H) 0.916 (AGB)est =  ρ ×Exp(0.667+1.784×ln(D)+0.207(ln(D))2 -0.0281(ln(D))3) Where, H=Total tree height, AGB=Total aboveground biomass ( in kg) of a tree, D=Diameter  of tree, ρ=Wood specific gravity	Chave et al. (2005)
Moist forest stands	(AGB)est =Exp(-2.977+ln( ρD2H))                 ≡0.0509×ρ(D2H) (AGB)est = ρ ×Exp(-1.499+2.148×ln(D)+2.07(ln(D))2 -0.0281(ln(D))3)          Where, H=Total tree height, AGB=Total aboveground biomass ( in kg) of a tree, D=Diameter of tree, ρ=Wood specific gravity	Chave et al. (2005)
Moist mangrove forest stands	(AGB)est =Exp(-2.977+ln( ρD2H))                 ≡0.0509×ρ(D2H) (AGB)est = ρ ×Exp(-1.349+1.980ln(D)+0.207(ln(D))2 -0.0281(ln(D))3)              Where, H=Total tree height, AGB=Total aboveground biomass ( in kg) of a tree, D=Diameter of tree, ρ=Wood specific gravity	Chave et al. (2005)
Wet forest stands:	(AGB)est =Exp(-2.557+0.940×ln( ρD2H))                 ≡0.0776× ρ(D2H) 0.940 (AGB)est = ρ ×Exp(-1.239+1.980ln(D)+0.207(ln(D))2 -0.0281(ln(D))3)              Where, H=Total tree height, AGB=Total aboveground biomass ( in kg) of a tree, D=Diameter of tree, ρ=Wood specific gravity	Chave et al. (2005)
Erica arborea	Ln AGB= a+bln DBH a=0.570, b=0.506, r2=0.97, P<0.001	Aboal et al. (2005)
Ilex canariensis	Ln AGB= a+bln DBH a=0.478, b=0.477, r2=0.97, P<0.001	Aboal et al. (2005)
Laurus azorica	Ln AGB= a+bln DBH a=1.783, b=0.202, r2=0.99, P<0.001	Aboal et al. (2005)
Myrica faya	Ln AGB= a+bln DBH a=0.570, b=0.506, r2=0.99, P<0.001	Aboal et al. (2005)
Persea indica	Ln AGB= a+bln DBH a=0.570, b=0.506, r2=0.98, P<0.001	Aboal et al. (2005)
Casuarina glauca Stem Branch Leaf Total yield     Stem Branch Leaf Total yield        Stem Branch Leaf Total yield	(i)Y = a + b · h Where, Y=Biomass (in kg), h=Plant height , FW=Fresh weight, DW=Dry weight, sb=Error of regression coefficient, F-value is Significant at 1% level Stem FW: a= - 20.64, b=0.480, sb=0.053, r2=0.460, n=98, F-value=83.11 DW: a= - 10.40, b=0.244, sb=0.020 Branch FW: a= - 3.10, b=0.083, sb=0.010, r2=0.409, n=98, F-value=66.67 DW: a= - 1.370, b=0.036, sb=0.004 Leaf FW : a= - 0.696, b=0.036, sb=0.006, r2=0.245, n= 98, F-value=31.15 DW : a= - 0.302, b=0.015, sb=0.002 Total yield FW: a= - 24.44, b=0.604, sb=0.067, r2=0.456, n= 98, F-value=80.59 DW : a = - 12.07, b=0.296, sb=0.033 (ii)Y = a + b · d2(dbh) Where, Y=Biomass (in kg), d2=Diameter, FW=Fresh weight, DW=Dry weight, sb=Error of regression coefficient, F-value is Significant at 1% level Stem FW: a=0.751, b=39.99, sb=2.03, r2=0.80, n=98, F-value=385.06 DW: a=0.378, b=20.15, sb=1.027 Branch FW: a=0.741, b=6.25, sb=0.532, r2=0.58, n=98, F-value=137.59 DW: a=0.327, b=2.750, sb=0.234 Leaf FW: a=0.768, b=3.41, sb=0.314, r2=0.55, n=98, F-value=118.0 DW: a=0.332, b=1.471, sb=0.135 Total yield FW: a=2.261, b=49.65, sb=2.677, r2= 0.78, n=98 F-value=344.0 DW: a=1.037, b=24.38, sb=1.307 (iii)Y = a + b · d2h Where, Y=Biomass (in kg), h=Plant height , d2=diameter, FW=Fresh weight, DW=Dry weight, sb=Error of regression coefficient Stem, F-value is Significant at 1% level FW: a=2.56, b=0.46, sb=0.021, r2=0.828, n=98, F-value=465.09 DW: a=1.290, b=0.231, sb=0.010 Branch FW: a=1.02, b=0.072, sb=0.005, r2=0.611, n=98, F-value=151.28 DW: a=0.450, b=0.031, sb=0.002 Leaf FW: a=0.925, b=0.039, sb=0.003, r2=0.568, n=98, F-value=126.58 DW: a=0.399, b=0.016, sb=0.001 Total yield FW: a=4.51, b=0.571, sb=0.028, r2= 0.809, n=98, F-value=408.45 DW: a=2.140, b=0.280, sb=0.010	Goel and Behl   (2005)
Byrsonima lucida	Fuel type: T (Total shrub or tree biomass i.e. plant parts of all sizes) B = a + b(CA) + c(CA2) P<0.001, r2=0.97, N=8, a or a’=0.310, b=0.672, c=0.035, FI=0.97, S.E.E.=0.297 Fuel type: F (Fine fuel shrub or tree biomass i.e. leaves) B = a + b(CA) + c(CA2) P<0.001, r2=0.97, N=8, a or a’=0.280 , b=0.272, c=0.036, FI=0.97, S.E.E.=0.175 CF (Correction factor)=exp (Sy,x2/2), FI (Fit Index)=1-(S(Yi- Ŷi)2) / Σ(Yi- Ŷi)2 Where, Yi is the ith observed value, Ŷi is the ith predicted value for Yi, and Ŷ is the mean observed value of Yi.(for comparison we use this factor) CA=Crown area in square meters, HT=Plant height in meters. Assuming an elliptical crown shape, crown area was calculated as CA = π (C L /2) (CW/2), where CL is the crown length at its widest point and CW the perpendicular crown extent at the same height.	Sah et al. (2004)
Conocarpus erectus	Fuel type: T(Total shrub or tree biomass i.e. plant parts of all sizes) B = a′CAb HTc P<0.001, r2=0.86, N=12, a or a’=0.323 , b=0.360, c=1.824, CF=1.07, FI=0.77, S.E.E.=0.816 Fuel type: F(Fine fuel shrub or tree biomass i.e. leaves) B = a′CAb HTc P<0.001, r2=0.82, N=12, a or a’=0.169, b=0.246, c=1.512, CF=1.06, FI=0.79, S.E.E.=0.195 CF (Correction factor)=exp (Sy,x2/2), FI (Fit Index)=1-(S(Yi- Ŷi)2) / Σ(Yi- Ŷi)2 Where, Yi is the ith observed value, Ŷi is the ith predicted value for Yi, and Ŷ is the mean observed value of Yi.(for comparison we use this factor) CA=Crown area in square meters, HT=Plant height in meters. Assuming an elliptical crown shape, crown area was calculated as CA = π (C L /2) (CW/2), where CL is the crown length at its widest point and CW the perpendicular crown extent at the same height.	Sah et al. (2004)
Croton linearis	Fuel type: T(Total shrub or tree biomass i.e. plant parts of all sizes) B = a + b(CA) + c(CA2) P<0.001, r2=0.99, N=10, a or a’=0.077, b= - 0.086, c=0.175, FI=0.99, S.E.E.=0.045 Fuel type: F(Fine fuel shrub or tree biomass i.e. leaves) B = a + b(CA) + c(CA2) P<0.001, r2=0.99, N=10, a or a’=0.057, b= - 0.049, c=0.111, FI=0.99, S.E.E.=0.034 CF (Correction factor)=exp (Sy,x2/2), FI (Fit Index)=1-(S(Yi- Ŷi)2) / Σ(Yi- Ŷi)2 Where, Yi is the ith observed value, Ŷi is the ith predicted value for Yi, and Ŷ is the mean observed value of Yi. (for comparison we use this factor) CA=Crown area in square meters, HT=Plant height in meters. Assuming an elliptical crown shape, crown area was calculated as CA = π (C L /2) (CW/2) where CL is the crown length at its widest point and CW the perpendicular crown extent at the same height.	Sah et al. (2004)
Guettarda scabra	Fuel type: T(Total shrub or tree biomass i.e. plant parts of all sizes) B = a′CAb HTc P<0.001, r2=0.96, N=13, a or a’=0.119 , b=0.419, c=2.159, FI=0.94, S.E.E.=0.417 Fuel type: F(Fine fuel shrub or tree biomass i.e. leaves) B = a′CAb HTc P<0.001, r2=0.90, N=13, a or a’=0.041, b=0.332, c=1.788, CF=1.12, FI=0.92, S.E.E.=0.089 CF (Correction factor)=exp (Sy,x2/2), FI (Fit Index)=1-(S(Yi- Ŷi)2) / Σ(Yi- Ŷi)2 Where, Yi is the ith observed value, Ŷi is the ith predicted value for Yi, and Ŷ is the mean observed value of Yi. (for comparison we use this factor) CA=Crown area in square meters, HT=Plant height in meters. Assuming an elliptical crown shape, crown area was calculated as CA = π (C L /2) (CW/2) where CL is the crown length at its widest point and CW the perpendicular crown extent at the same height.	Sah et al. (2004)
Myrica cerifera	Fuel type: T(Total shrub or tree biomass i.e. plant parts of all sizes) B = a′CAb HTc P<0.001, r2=0.93, N=15, a or a’=0.341, b=0.977, c=1.363, CF=1.09, FI=0.80, S.E.E.=0.701 Fuel type: F(Fine fuel shrub or tree biomass i.e. leaves) B = a′CAb HTc P<0.001, r2=0.89, N=15, a or a’=0.306, b=1.028, c=0.649, CF=1.12, FI=0.74, S.E.E.=0.354 CF (Correction factor)=exp (Sy,x2/2), FI (Fit Index)=1-(S(Yi- Ŷi)2) / Σ(Yi- Ŷi)2 Where, Yi is the ith observed value, Ŷi is the ith predicted value for Yi, and Ŷ is the mean observed value of Yi.(for comparison we use this factor) CA=Crown area (in m2), HT=Plant height (in m). Assuming an elliptical crown shape, crown area was calculated as CA = π (C L /2) (CW/2) where CL is the crown length at its widest point and CW the perpendicular crown extent at the same height.	Sah et al. (2004)
Myrsine floridana	Fuel type: T(Total shrub or tree biomass i.e. plant parts of all sizes) B = a + b(DBH)2 + c(HT) P<0.001, r2=0.86, N=19, a or a’= -0.258, b=0.124, c=0.003, FI=0.86, S.E.E.=0.167 Fuel type: F(Fine fuel shrub or tree biomass i.e. leaves) B = a + b(DBH)2 + c(HT) P<0.001, r2=0.79, N=19, a or a’= - 0.043, b=0.179, c=0.000, FI=0.84, S.E.E.=-0.062 CF (Correction factor)=exp (Sy,x2/2), FI (Fit Index)=1-(S(Yi- Ŷi)2) / Σ(Yi- Ŷi)2 Where, Yi is the ith observed value, Ŷi is the ith predicted value for Yi, and Ŷ is the mean observed value of Yi.(for comparison we use this factor) CA=Crown area in square meters, HT=Plant height in meters. Assuming an elliptical crown shape, crown area was calculated as CA = π (C L /2) (CW/2) where CL is the crown length at its widest point and CW the perpendicular crown extent at the same height.	Sah et al. (2004)
Pisonia rotundata	Fuel type: T(Total shrub or tree biomass i.e. plant parts of all sizes) B = a′CAb HTc P=0.001,  r2=0.79, N=12, a or a’= -0.379, b=0.510, c=0.983, CF=1.06, FI=0.90, S.E.E.=0.127 Fuel type: F(Fine fuel shrub or tree biomass i.e. leaves) B = a′CAb HTc P<0.001, r2=0.90, N=12,  a or a’= -0.159 , b=0.616, c=0.813, CF=1.03, FI=0.88, S.E.E.=0.061 CF (Correction factor)=exp (Sy,x2/2), FI (Fit Index)=1-(S(Yi- Ŷi)2) / Σ(Yi- Ŷi)2 Where, Yi is the ith observed value, Ŷi is the ith predicted value for Yi, and Ŷ is the mean observed value of Yi.(for comparison we use this factor) CA=Crown area in square meters, HT=Plant height in meters. Assuming an elliptical crown shape, crown area was calculated as CA = π (C L /2) (CW/2) where CL is the crown length at its widest point and CW the perpendicular crown extent at the same height.	Sah et al. (2004)
Pithecellobium guadalupense	Fuel type: T(Total shrub or tree biomass i.e. plant parts of all sizes) B = a + b(CA) + c(CA2) P<0.001, r2=0.99, N=12, a or a’= -0.373 , b=0.258, c=0.182, FI=0.99, S.E.E.=0.446 Fuel type: F(Fine fuel shrub or tree biomass i.e. leaves) B = a + b(CA) + c(CA2) P<0.001, r2=0.99, N=12, a or a’=0.373, b=0.142, c=0.043, FI=0.99, S.E.E=0.158 CF (Correction factor)=exp (Sy,x2/2), FI (Fit Index)=1-(S(Yi- Ŷi)2) / Σ(Yi- Ŷi)2 Where, Yi is the ith observed value, Ŷi is the ith predicted value for Yi, and Ŷ is the mean observed value of Yi.(for comparison we use this factor) CA=Crown area in square meters, HT=Plant height in meters. Assuming an elliptical crown shape, crown area was calculated as CA = π (C L/2) (CW/2) where CL is the crown length at its widest point and CW the perpendicular crown extent at the same height.	Sah et al. (2004)
Psidium longipes	Fuel type: T(Total shrub or tree biomass i.e. plant parts of all sizes) B = a′CAb HTc P=0.002, r2=0.76, N=12, a or a=0.672, b=0.956, c=0.872, CF=1.16, FI=0.67, S.E.E.=1.591 Fuel type: F(Fine fuel shrub or tree biomass i.e. leaves) B = a′CAb HTc P=0.006, r2=0.68, N=12, a or a=0.270, b=0.995, c=0.503, CF=1.16, FI=0.64, S.E.E.=0.505 CF (Correction factor)=exp (Sy,x2/2), FI (Fit Index)=1-(S(Yi- Ŷi)2) / Σ(Yi- Ŷi)2 Where, Yi is the ith observed value, Ŷi is the ith predicted value for Yi, and Ŷ is the mean observed value of Yi.(for comparison we use this factor) CA=Crown area in square meters, HT=Plant height in meters. Assuming an elliptical crown shape, crown area was calculated as CA = π (C L /2) (CW/2) where CL is the crown length at its widest point and CW the perpendicular crown extent at the same height.	Sah et al. (2004)
Randia aculeate	Fuel type: T(Total shrub or tree biomass i.e. plant parts of all sizes) B = a′CAb HTc P=0.004, r2=0.79, N=12, a or a’=0.017, b=0.121, c=3.222, CF=1.25, FI=0.70, S.E.E=0.117 Fuel type: F(Fine fuel shrub or tree biomass i.e. leaves) B = a′CAb HTc r2=0.79, N=12, a or a=0.022, b=0.138, c=1.980, CF=1.12, FI=0.79, S.E.E=0.032 CF (Correction factor)=exp (Sy,x2/2), FI (Fit Index)=1-(S(Yi- Ŷi)2) / Σ(Yi- Ŷi)2 Where, Yi is the ith observed value, Ŷi is the ith predicted value for Yi, and Ŷ is the mean observed value of Yi.(for comparison we use this factor) CA=Crown area in square meters, HT=Plant height in meters. Assuming an elliptical crown shape, crown area was calculated as CA = π (C L /2) (CW/2) where CL is the crown length at its widest point and CW the perpendicular crown extent at the same height.	Sah et al. (2004)
Mixed-species (shrub-like)	Fuel type: T(Total shrub or tree biomass i.e. plant parts of all sizes) B = a′CAb HTc P<0.001, r2=0.87, a or a=0.446, b=0.869, c=1.112, CF=1.15, FI=0.72, S.E.E=0.911 Fuel type: F(Fine fuel shrub or tree biomass i.e. leaves) B = a′CAb HTc P<0.001, r2=0.82, a or a=0.269, b=0.855, c=0.609, CF=1.18, FI=0.58, S.E.E=0.387 CF (Correction factor)=exp (Sy,x2/2), FI (Fit Index)=1-(S(Yi- Ŷi)2) / Σ(Yi- Ŷi)2 Where, Yi is the ith observed value, Ŷi is the ith predicted value for Yi, and Ŷ is the mean observed value of Yi.(for comparison we use this factor) CA=Crown area in square meters, HT=Plant height in meters. Assuming an elliptical crown shape, crown area was calculated as CA = π (C L /2) (CW/2) where CL is the crown length at its widest point and CW the perpendicular crown extent at the same height.	Sah et al. (2004)
Mixed-species (tree-like)	Fuel type: T(Total shrub or tree biomass i.e. plant parts of all sizes) B = a + b(DBH)2 + c(HT) P<0.001, r2=0.57, a or a=0.060, b=0.227, c=0.002, FI=0.57, S.E.E=0.437 Fuel type: F(Fine fuel shrub or tree biomass i.e. leaves) B = a + b(DBH)2 + c(HT) P<0.001, r2=0.58, a or a=0.184, b=0.067, c=0.002, FI=0.58, S.E.E=0.136 CF (Correction factor)=exp (Sy,x2/2), FI (Fit Index)=1-(S(Yi- Ŷi)2) / Σ(Yi- Ŷi)2 Where, Yi is the ith observed value, Ŷi is the ith predicted value for Yi, and Ŷ is the mean observed value of Yi.(for comparison we use this factor) CA=Crown area in square meters, HT=Plant height in meters. Assuming an elliptical crown shape, crown area was calculated as CA = π (C L /2) (CW/2) where CL is the crown length at its widest point and CW the perpendicular crown extent at the same height.	Sah et al. (2004)
Bauhinia racemosa	Y = a + b × X (X = cbh2 ×  height) N=19, a=0.0431, b=0.0025, r2=0.97, SE=3.17 Y=Above ground biomass, cbh=Circumference at breast height	Kale et al. (2004)
Ziziphus xylopyra	Log10 Y = a + b × log X (X = cbh) N=15, a= -3.20, b=2.87, r2=0.94, SE=0.12 Y=Above ground biomass, cbh=Circumference at breast height	Kale et al. (2004)
Tectona grandis	log Y = a + b ×  log X (X = cbh) N=15, a= -2.85, b=2.655 , r2=0.98, SE=0.075 Y=Above ground biomass, cbh=Circumference at breast height	Kale et al. (2004)
Lannea coromandelica	Y = a + b × X (X = cbh2 ×  height) N=15, a= -1.84, b=0.002, r2=0.98, SE=14.49 Y=Above ground biomass, cbh=Circumference at breast height	Kale et al. (2004)
Miliusa tomentosa.	Y = a + b × X (X = cbh2 * height) N=17, a= -0.68, b=0.0024, r2=0.99, SE=1.33 Y=Above ground biomass, cbh=Circumference at breast height	Kale et al. (2004)
Prosopis pallida (H. & B. ex. Willd.)	For fresh above ground woody biomass: y= ( a + b × x)2 r=0.9700, r2=0.9410, P=0.000, a=2.79320, b=0.82300, S.E.=2.3470 Where, y=Fresh above ground woody biomass, x=Diameter at the base y= a × xb r=0.9648, r2=0.9308, P=0.000, a=0.11320, b=1.11320, S.E.=0.2857 r=0.9573, r2=0.9164, P=0.000, a= - 0.3616, b=2.10750, S.E.=0.2730 r=0.9573, r2=0.9164, P=0.000, a=0.36257, b=1.05372, S.E.=0.2725 y= a + b × x r=0.9602, r2=0.9220, P=0.000, a=41.9637, b=0.59540, S.E.=120.68 r=0.9530, r2= 0.9082, P=0.000, a= - 41.963, b=0.59540, S.E.=105.96 y= Fresh above ground woody biomass x=Diameter at the base y= a + b × diameter at the base + c × crown height r2=0.9198, P=0.000, a= - 868.69, b=41.41, c=67.955, S.E.=145.298, MAE=105.447 y= a+ b × x1/2 r=0.9656, r2=0.9325, P=0.000, a= -132.588, b=3.03678, S.E.=3.03678 Where, y=Fresh above ground woody, x=(Diameter at the base)2 total height ×greatest crown diameter y= a+ b × x1/2 r=0.9721, r2=0.9450, P=0.000, a= - 448.817, b=16.6861, S.E.=124.164 Where, y=Fresh above ground woody, x=(Diameter at the base)2 ×crown height y= a+ b × x 1/2 r=0.9509, r2=0.9041, P=0.000, a= - 283.119, b=5.15510, S.E.=163.977 Where, y=Fresh above ground woody, x=(Diameter at the base)2×total height×crown height y= a + b × x r=0.9800, r2=0.9509, P=0.000, a=75.1691, b=0.08732, S.E.=117.335 Where, y=Fresh above ground woody, x=(Diameter at the base)2×total height y= a + b × x r=0.9605, r2=0.9225 ,P=0.000, a=302.500, b=4.73740, S.E.=147.436 Where, y=Fresh above ground woody, x=(Diameter at the base) × total height	Padron and  Navarro   (2004)
Prosopis pallida (H. & B. ex. Willd.)	For woody dry biomass: y= a × xb r= - 0.9684, r2=0.9379, P=0.000, a=2972.51, b=2587.65, S.E.=0.2708 r=0.9643, r2=0.9300, P=0.000, a= - 2972.5, b=2587.65, S.E.= 0.2494 Where, y=Woody dry biomass, x=Diameter at the base y= a + b × diameter at the base + c × crown height r2=0.9137, P=0.000, a= - 868.69, b=41.41, c=67.955, S.E.= 122.854, MAE=90.4698 Where, y=Woody dry biomass, x=Diameter at the base y= a + b × x r=0.9698, r2=0.9405, P=0.000, a= - 186.817, b=3.15880, S.E.=85.3203 Where, y=Woody dry biomass, x=(Diameter at the base)×total height y= a+ b × x 1/2 r=0.9619, r2=0.9252, P=0.000, a= - 174.938, b=3.44379, S.E.=95.6336 Where, y=Woody dry biomass, x=(Diameter at the base)2 total height×crown height y= a + b × x r=0.9801, r2=0.9607, P=0.000, a=66.5541, b=0.05796, S.E.=69.3366 Where, y=Woody dry biomass, x=(Diameter at the base)2×total height y= a+ b × x 1/2 r=0.9806, r2=0.9616, P=0.000, a=283.686, b=11.1153, S.E.=68.5285 Where, y=Woody dry biomass, x=(Diameter at the base)2 ×crown height	Padron and  Navarro (2004)
Prosopis pallida (H. & B. ex. Willd.)	Fresh biomass (35cm<diameter at the base≤45cm) y=a + b ×x r=0.9954, r2=0.9907, P=0.000, a=60.6685, b=0.054867, S.E.=31.3628 Where, y=Woody dry biomass, x=(Diameter at the base)2×total height	Padron and  Navarro (2004)
Prosopis pallida (H. & B. ex. Willd.)	Fresh biomass (15cm<diameter at the base≤35cm) Y = a × x b r=0.9448, r2=0.8927, P=0.000, a=0.38530, b=0.72270, S.E.=0.24490  Where, y=Woody dry biomass, x=(Diameter at the base)2×crown height	Padron and  Navarro (2004)
Elaeis guineensis (Oil palm)	Wet Weight (kg/tree)=1.5729×Palm stem height (cm)-8.2835 r2=0.9746 Dry Weight (kg/tree)=0.3747×Palm stem height (cm)+3.6334 r2=0.9804 The total weights for each tree were summed from individual components that included above ground core, leaf (dead and live), rachis (dead and live), flower, and palm nut.	Thenkabail et al. (2004)
Elaeis guineensis (Oil palm)	Biomass based on  reflectivity in IKONOS bands: Dry biomass (kg/m2) = 0.0046 e10.814×NDVI43 Dry biomass (kg/m2) =1499.3 e(-66.64×band 3 reflectance) Dry biomass (kg/m2) =1595 e(-0.0338×band 3 digital number)	Thenkabail et al. (2004)
Anacardium excelsum (2.4-18.6 cm DBH)	Four different methods to determine the carbon content of each tree sampled Method 1: Ln (C )= c+α ln (DBH) c= - 3.4931, S.E.=0.0983, r2=0.9957, Avg unsigned deviation (%)=8.9, t-value=0.0000 α=2.4843, S.E.=0.0470, r2=0.9957, Avg unsigned deviation (%)=8.9, t-value=0.0000 Ln (C)= c+α ln (DBH)+β ln(H) c= - 3.7179, S.E.=0.2227, r2=0.9962, Avg unsigned deviation (%)=8.6, t-value=0.0000 α=2.1936, S.E.=0.2633, t-value=0.0000 β=0.4132, S.E.=0.3684, t-value=0.28 (NS) Method 2: Ln (C)= c+α ln (DBH) c= - 3.4577, S.E.=0.1007, r2=0.9955, Avg unsigned deviation (%)=9.2, t-value=0.0000 α=2.4889, S.E.=0.0482, t-value=0.0000 Method 3: Ln (C)= c+α ln (DBH) c= - 3.4278, S.E.=0.1007, r2=0.9955, Avg unsigned deviation (%)=9.2, t-value=0.0000 α=2.4830, S.E.=0.0482, t-value=0.0000 Method 4: Ln (C)= c+α ln (DBH) c= - 3.4877, S.E.=0.1075, r2=0.9950, Avg unsigned deviation (%)=9.7, t-value=0.0000 α=2.5143, S.E.=0.0515, t-value=0.0000 c, α, β=Coefficients, DBH=Diameter at breast height (in cm), H=Height (in m), C=Total above ground carbon content (in kg)	Losi et al. (2003)
Dipteryx panamensis (1.8-11.2 cm DBH)	Method 1: Ln (C)= c+ α ln (DBH) c= - 2.6344, S.E.=0.0666, r2=0.9975, Avg unsigned deviation (%)=7.1, t-value=0.0000 α=2.5170, S.E.=0.0363, t-value=0.0000 Ln (C)= c+α ln (DBH)+β ln(H) c= - 2.8313, S.E.=0.1010, r2=0.9983, Avg unsigned deviation (%)=5.7, t-value=0.0000 α=2.1850, S.E.=0.1442, t-value=0.0000 β=0.4128, S.E.=0.1752, t-value=0.0380 Method 2: Ln (C)= c+ α ln (DBH) c= - 2.6362, S.E.=0.0696, r2=0.9973, Avg unsigned deviation (%)=7.5, t-value=0.0000 α=2.5339, S.E.=0.0379, t-value=0.0000 For trees 8.4- 11.2 cm Ln (C)= c+ α ln (DBH) c= - 2.2433, S.E.=0.9620, r2=0.8877, Avg unsigned deviation (%)=5.5, t-value=0.0801 α=2.3661, S.E.=0.4208, t-value=0.0049 Method 3: Ln (C)= c+ α ln (DBH) c= - 2.6203, S.E.=0.0699, r2=0.9973, Avg unsigned deviation (%)=7.5, t-value=0.0000 α=2.5327, S.E.=0.0380, t-value=0.0000 Method 4: Ln (C)= c+ α ln (DBH) c= - 3.3814, S.E.=0.1213, r2=0.9937, Avg unsigned deviation (%)=12.8, t-value=0.0000 α=2.8643, S.E.=0.0660, t-value=0.0000c, α, β=Coefficients, DBH=Diameter at breast height (in cm), H=Height (in m), C=Total above ground carbon content (in kg)	Losi et al. (2003)
Acer rubrum	Dry mass (g) = a + b(DBH (cm)) Where, DBH is diameter at breast height (3.7 m above ground). Log10 (branch biomass) =1.21264 + 2.57934 (Iog10 DBH) N=12, r2=0.91, MSE=0.01678, P=0.0001 Log10 (stem biomass) =2.04101 + 2.32487 (Iog10 DBH) N=12, r2=0.97, MSE=0.00392, P=0.0001	Elliott et al. (2002)
Liriodendron tulipifera	Dry mass (g) = a + b(DBH (cm)) Where, DBH is diameter at breast height (3.7 m above ground). Log10 (branch biomass) =0.71955 + 2.70444 (Iog10 DBH) N=16, r2=0.93, MSE=0.046 58, P=0.0001 Log10 (stem biomass) =1.90272 + 2.33725 (Iog10 DBH) N=16, r2=0.98, MSE=0.010 00, P=0.0001	Elliott et al. (2002)
Quercus prinus	Dry mass (g) = a + b(DBH (cm)) Where, DBH is diameter at breast height (3.7 m above ground). Log10 (branch biomass) =0.46597 + 3.30505 (Iog10 DBH) N=13, r2=0.86, MSE=0.01656, P=0.0001 Log10 (stem biomass) =1.73250 + 2.59378 (Iog10 DBH) N=13, r2=0.93, MSE=0.00499, P=0.0001	Elliott et al. (2002)
Robinia pseudoacacia	Dry mass (g) = a + b(DBH (cm)) Where, DBH is diameter at breast height (3.7 m above ground). Log10 (branch biomass) =1.07682 + 2.53262 (Iog10 DBH) N=23, r2=0.87, MSE=0.05386, P=0.0001 Log10 (stem biomass) =1.75911 + 2.60392 (Iog10 DBH) N=23, r2=0.98, MSE=0.00875, P=0.0001	Elliott et al. (2002)
Quercus rubra	Dry mass (g) = a + b(DBH (cm)) Where, DBH is diameter at breast height (3.7 m above ground). Log10 (branch biomass) =0.20948 + 3.70107 (Iog10 DBH) N=12, r2=0.95, MSE=0.01380, P=0.0001 Log10 (stem biomass) =2.00703 + 2.35049 (Iog10 DBH) N=12, r2=0.94, MSE=0.00744, P=0.0001	Elliott et al. (2002)
All oaks	Dry mass (g) = a + b(DBH (cm)) Where, DBH is diameter at breast height (3.7 m above ground). Log10 (branch biomass) =0.54101 + 3.28005 (Iog10 DBH) N=27, r2=0.94, MSE=0.01758, P=0.0001 Log10 (stem biomass) =2.009 99 + 2.338 66 (Iog10 DBH) N=27, r2=0.96, MSE=0.00577, P=0.0001	Elliott et al. (2002)
All tree species	Dry mass (g) = a + b(DBH (cm)) Where, DBH is diameter at breast height (3.7 m above ground). Log10 (branch biomass) =1.58238 + 2.11319 (Iog10 DBH) N=43, r2=0.77, MSE=0.08866, P=0.0001 Log10 (stem biomass) =1.90443 + 2.37735 (Iog10 DBH) N=43, r2=0.96, MSE=0.01754, P=0.0001	Elliott et al. (2002)
Bombacopsis quinata Type 1	Log 10Y=a+b log 10 DBH(cm) Y=Foliage dry biomass (kg): a= -2.148±0.549, b=2.047±0.395, r2=0.64, RMSE=0.201. Y=Branch dry biomass (kg): a= -2.665±0.432, b=3.138±0.311, r2=0.87, RMSE=0.158. Y=Stem dry biomass (kg): a= -2.143±0.186, b=2.95±0.133, r2=0.97, RMSE=0.068. Y=Total tree dry biomass (kg): a= -1.988±0.192, b=2.993±0.138, r2=0.97, RMSE=0.070. Y=Crown diameter(m): a=-0.177±0.208, b=0.689±0.149, r2=0.59, RMSE=0.076.	Cordero & Kanninen (2002)
Bombacopsis quinata            Type 2	Log 10Y=a+b log 10 crown diameter(m) Y=Foliage dry biomass (kg): a= -1.292±0.256, b=2.544±0.326, r2=0.80, RMSE=0.149	Cordero & Kanninen (2002)
Bombacopsis quinata      Type 3	Log 10Y=a+b log 10 branch dry biomass(kg) Y=Foliage dry biomass (kg): a= -0.280±0.223, b=0.575±0.128, r2=0.57, RMSE=0.219.	Cordero & Kanninen (2002)
Bombacopsis quinata     Type 4	Log 10Y=a+b log 10 DBH(cm)+c log 10 H(m) Y=Foliage dry biomass (kg): a= -1.468±0.373, b= -1.468±0.373, c= -3.148±0.646, r2=0.87, RMSE=0.122. Y=Crown diameter(m): a=0.041±0.171, b=1.405±0.239, c= -1.011±0.296, r2=0.78, RMSE=0.056.	Cordero & Kanninen (2002)
Coffee root	W=0.0074 Dprox3.14 r2=0.95 Where, W=Root Biomass(kg tree-1), D=Stem diameter at 1.35m (cm)	Noordwijk et al. (2002)
Fern Tree (Cyathea spp.)	y= a / 1+be-cx r2=0.88, y=Biomass, x=Height, a= - 4.266348 e+009, b= -2792284, c=0.31367768	Tiepolo et al. (2002)
Palm< 10 cm dbh	Agb= [[(exp(0.9285× ln(D2))+5.7236]×1.050]/106 R2=0.39, N=15 Where, D=DBH (cm), Agb=Above ground biomass (in Mg on a dry basis).	Cummings et al. (2002)
Stem less palm	Agb=(leaves × 296.54)/ 106 Where, Agb=Above ground biomass (in Mg on a dry basis).	Cummings et al. (2002)
Dicot seedlings	Agb=Seedling Count × Mean wt.(determined from sub-  sample)/ 106 Where, Agb=Above ground biomass (in Mg on a dry basis), Wt=Weight (in gram)	Cummings et al. (2002)
Forest floor	Agb=Wet wt. ×%dry wt./100(determined from sub sample)/ 106 Where, Agb=Above ground biomass (in Mg on a dry basis), Wt=Weight (in gram)	Cummings et al. (2002)
Palm <10 cm dbh	Agb=[(exp(1.5321×ln(D2)+3.2758)) ×1.0931]/ 106 R2=0.34, N=15 Where, D=DBH (cm), Agb=Above ground biomass (in Mg on a dry basis).	Cummings et al. (2002)
Palm>10 cm dbh	Agb=((П r2×H) ×sg)/ 106 sg=0.327g cm-3 Where, D=dbh (in cm), H=Height (in m), BA=Basal area (in cm2), sg=Specific gravity of wood (in g cm-3), r=Radius (in m), Wt=Weight (in gram)	Cummings et al. (2002)
Dipteryx panamensis (8.4-11.2 cm DBH)	Ln (C)= c+ α ln (DBH) c= - 2.0619, S.E.=0.6700, r2=0.9544, Avg unsigned deviation (%)=4.8, t-value=0.0543 α=2.3088, S.E.=0.2914, t-value=0.0042 Ln (C)= c+ α ln (DBH) c= - 2.7450, S.E.=0.5720, r2=0.9385, Avg unsigned deviation (%)=9.4, t-value=0.0004 α=2.6244, S.E.=0.2125, t-value=0.0000 c, α, β=Coefficients, DBH=Diameter at breast height (in cm), H=Height (in m), C=Total above ground carbon content (in kg)	Shepherd & Montagnini  (2001)
Bamboo	Y=0.131 x 2.278 r2=0.95, Y=Biomass (kg tree-1), x=Stem diameter at 1.35m (cm) Y=1.45x 0.96 r2=0.98, Y=Tree height (m), x=Stem diameter at 1.35m (cm)	Hairiah et al. (2001)
Banana	Y=0.030 x 2.13 r2=0.99, Y=Biomass (kg tree-1), x=Stem diameter at 1.35m (cm) Y=0.707x 0.6835 r2=0.81, Y=Tree height (m), x=Stem diameter at 1.35m (cm)	Hairiah et al. (2001)
Pruned Coffee	Y=0.2811 x 2.06357 r2=0.95, Y=Biomass (kg tree-1), x=Stem diameter at 1.35m (cm) Y=1.79x 0.0797 r2=0.84, Y=Tree height (m), x=Stem diameter at 1.35m (cm)	Hairiah et al. (2001)
Palms	Y=0.3999+7.907× height r2=0.75, Dbh and height range=1-33m	Brown et al. (2000)
Cecropia	Y =(- 0.48367+1.13488× (Sqr(dbh)) × Log(dbh))2 r2=0.62, Dbh and height Range=1-11m	Brown et al. (2000)
Lianas	Y=563.56×(dbh)2.6277 r2=0.89, Dbh and height range=0.3-2.5cm	Brown et al. (2000)
Fraxinus uhdei	0.0466D 2.70184 r2 =0.99, Mean squared error=0.011, DBH range=8-44(cm) Where, D = Tree DBH (in cm).	Ares & Fownes (2000)
Liana	Log(leaf mass)=0.81 log(basal area) - 0.57 r2=0.84, n=19, P<0.001 Log(mass)=0.07+2.17log(dbh) r2=0.95, n=19, P<0.001 mass=Dry mass	Gerwing & Farias (2000)
All Acacias	Dry weight = 0.0864 x (BA) 1.36 N=23, r2=0.96, P<0.01 Dry weight = 0.0067 (C1 + C2 + H)3.05 N=23, r2=0.96, P<0.01 Where, BA=Basal area (cm2), C=Crown diameter (m), H=Height (m)	Gerwing & Farias (2000)
Balanites aegyptica	Dry weight = 0.0111 x (BA)1.64 N=5, r2=0.97, P<0.01 Dry weight = 0.0007 (C1 + C2 + H)5.54 N=5, r2=0.96, P<0.01 Where, BA=Basal area (cm2), C=Crown diameter (m), H=Height (m)	Gerwing & Farias (2000)
Cordia sinensis	Dry weight = 0.0436 x (BA) 1.24  N=25, r2=0.92, P<0.01 Dry weight = 0.0062 (C1 + C2 + H)3.10 N=25, r2=0.92, P<0.01 Where, BA=Basal area (cm2), C=Crown diameter (m), H=Height (m)	Gerwing & Farias (2000)
Prosopis chilensis	Dry weight = 0.0550 x (BA)1.34 N=25, r2=0.96, P<0.01 Dry weight = 0.0018 (C1 + C2 + H)3.46 N=25, r2=0.95, P<0.01 Where, BA=Basal area (cm2), C=Crown diameter (m), H=Height (m)	Gerwing & Farias (2000)
All species combined	Dry weight = 0.0513 x (BA)1.35  N=78, r2=0.90, P<0.01 Dry weight = 0.0073 (C1 + C2 + H)2.98 N=78, r2=0.94, P<0.01 Where, BA=Basal area (cm2), C=Crown diameter (m), H=Height (m)	Gerwing & Farias (2000)
All species combined	Fresh weight = 0.0799 x (BA)1.35 N=78, r2=0.91, P<0.01 Fresh weight = 0.0109 (C1 + C2 + H)2.98 N=78, r2=0.94, P<0.01 Where, BA=Basal area (cm2), C=Crown diameter (m), H=Height (m)	Gerwing & Farias (2000)
All species combined	Canopy volume Dry weight = 0.2555 (CA x H)1.0 N=78, r2=0.94, P<0.01 Canopy area Dry weight = 0.3102 (CA x H)1.35 N=78, r2= 0.90, P<0.01	Gerwing & Farias (2000)
Eucalypt plantations	y = ba × 6.6 Where, y=Total above ground biomass (t dry weight m−2),  ba=The basal area over bark (m2 ha−1)	Snowdon et al. (2000)
Savanna (480 to 870 mm) Tree > 4m height Shrub < 4m height	DW(kg) = 0.0752 x sum BA (cm2)1.20 r2=0.94 DW(kg) = 0.0943 x sum BA(cm2)1.16 r2=0.94 Where, DW=Dry weight, BA=Basal area	Shackleton  (1997)
Temperate broadleaf  forests	Total AGB=0.5+25000D 2.5 /D 2.5+ 246872 r2=0.99, DBH range=1-85 (cm) Where, Total AGB=Total above ground biomass (in kg), D=Tree DBH (in cm)	Schroeder et al. (1997)
Pennisetun  pedicellaturm	log Y = 1.515 + 0.822 log X r2=0.777, P=0.000 Where, Y=Shoot biomass, X=Percentage of full Sunlight	Singh et al. (1997)
Pennisetun  pedicellaturm	log Y = 0.712+0.838 log X r2= 0.826, P = 0.000. Where, Y=Root biomass, X=Percentage of full Sunlight logY=0.834+0.969logX r2=0.918, P=0.000 Where, Y=Grass shoot weight, X=Grass root weight	Singh et al. (1997)
Pennisetum pedicellatum	Y = aXh Where, Y=Shoot or root weights, X= Percentage of full sunlight (estimated with the help of  digital light meter)	Singh et al. (1997)
Pennisetum pedicellatum	log Y = 1.58+0.82 log X r2= 0.792, P= 0.000 Where, Y=Total plant biomass in (g/m2), X=Percentage of full sunlight (estimated with the help of digital light meter)	Singh et al. (1997)
Fraxinus americana	Total AGB=0.1634 D 2.3480 DBH range=4-32 (cm) Where, Total AGB=Total above ground biomass (in kg), D=Tree DBH (in cm)	Perala & Alban (1994)
Savanna (Precipitation: 280      to 350 mm) A. zanzibarica A. reficiens Other species	FW (kg) = 0.153 x sum stem diameter (cm)2.48 FW (kg) = 0.158 x sum stem diameter (cm)2.44 FW (kg) = 0.160 x sum stem diameter (cm)2.45	Johansson & Kaarakka  (1992)
Savanna (Precipitation:1250 mm) Acacia erubescens Acacia karoo Acacia tortilis Acacia melifera Dichrostachys cinerea Ziziphus murcronata	FW (kg) = 0.0137 x canopy sum (m)3.86 N=38, r2= 0.91 FW (kg) = 0.0079 x canopy sum (m)3.19 N= 41, r2= 0.91 FW (kg) = 0.0096 x canopy sum (m)3.30 N=50, r2= 0.90 FW (kg) = 0.0548 x canopy sum (m)2.58 N=27, r2= 0.90 FW (kg) = 0.0029 x canopy sum (m)3.74 N=33, r2= 0.94 FW (kg) = 0.0130 x canopy sum (m)2.86 N=17, r2= 0.96 FW = fresh weight	Tietema  (1992)
Savanna (Precipitation:790 mm) Lesihwa (T. camohoratus)	FW = (0.552 stem diameter - 0.25)2 FW = Fresh weight	Young & Francombe (1991)
Savanna (Precipitation:1200 mm) Small diameter breast height < 5cm Large diameter breast height > 5cm	For Small diameter breast height < 5cm: Leaf biomass = 0.15 stem basal diameter - 0.17 N=7, r2=0.99 For Large diameter breast height > 5cm: Wood biomass=1.60 stem basal diameter - 4.00 N=7, r2=0.94 Leaf biomass = 0.51 DBH – 2.11 N=14, r2=0.98 Twig wood = 0.94 DBH – 3.34 N=20, r2=0.98 Cord wood = 22.40 DBH – 215 N=20, r2=0.91 Where, DBH=Diameter breast height	Chidumayo  (1990)
Savanna (Precipitation:150 to 800 mm) Acacia reficiens Acacia tortilis Diam. > 1.57 cm Acacia tortilis Diam. < 1.57 cm Combi basal diameter >1.57 cm	DW(g) = 0.46 × sum stem diameter (mm)2.56 N=27, r2=0.96 DW (g) = 0.10 × sum stem diameter (mm)2.95 N=100, r2=0.98 DW (g) = 5.49 × sum stem diameter (mm)3.98 N=14, r2=0.98 DW (g) = 0.2089 × sum stem diameter (mm)2.66 N=127, r2=0.98 Where, DW=Dry weight	Coughenour et al.(1990)
Dry tropical forests	Total AGB=34.4703 - 8.0671 D + 0.6589 D2 r2=0.67, Mean Squared Error=0.022 Where, Total AGB=Total above ground of tree (in kg), D=Tree DBH (in cm)	Brown et al. (1989)
Moist tropical forests	Total AGB=38.4908 - 11.7883 D + 1.1926 D2 r2=0.78, Mean Squared Error=0.062 Where, Total AGB=Total above ground of tree (in kg), D=Tree DBH (in cm)	Brown et al. (1989)
Wet  tropical    forests	Total AGB=13.2579 - 4.8945 D + 0.6713 D2 r2=0.90, Mean Squared Error=0.022 Where, Total AGB=Total above ground of tree (in kg), D=Tree DBH (in cm)	Brown et al. (1989)
Black spruce trees (Diameter 1-3cm, at 15cm. height) Needle Branch Bole wood Bole bark Aboveground Stump and root Complete tree	y = b0 + b1 d b0 = - 0.1627, b1=0.1583, r2=0.86, S2y.x=0.00143, n=25 b0 = - 0.1655, b1=0.1530 , r2=0.88, S2y.x=0.00114 , n=25 b0 = - 0.1231, b1=0.1194, r2=0.93, S2y.x=0.00037, n=25 b0 = - 0.0356, b1= 0.0362, r2=0.94, S2y.x=0.0003, n=25 b0 = - 0.4869, b1=0.4669, r2=0.92, S2y.x=0.00700, n=25 b0 = - 0.1231, b1=0.1227, r2=0.87, S2y.x=0.00082, n=25 b0 = - 0.6100, b1=0.5397, r2=0.92, S2y.x=0.01149, n=25 where y=Component oven-dry biomass (kg) d=Diameter outside bark at 15cm height (cm)	Czapowskyj et al. (1985)
Attalea sp.palm >1.78m height	Above ground biomass={[(46.1×StemH)-82.1]+[0.375×[(46.1×StemH)-82.1]]}/103 r2=0.99, N=7 Where, StemH= Stem height	Anderson  (1983)
Savanna (Precipitation:650mm) Prosopis	DW (kg) = 0.0667 × sum BA (cm2)1.28 N=63 Where, BA=Basal area, DW=Dry weight	Felker et al. (1982)
Prosopis   glandulosa	Logey= logea + b logex For Branch total Dry weight y=Branch total Dry weight, x=Diameter, All: n=73, Range(cm)=0.81-7.48, a=74.888, b=2.519, r=0.944, SEE=0.43 Tree: n=45, Range(cm)=0.81-5.62, a=66.134, b=2.676, r=0.982, SEE=0.26 Shrub: n=27, Range(cm)=1.01-7.48, a=69.205, b=2.455, r=0.970, SEE=0.30 For Leaf total Dry weight y=Leaf total Dry weight, x=Diam, Tree: n=45, Range(cm)=0.81-5.62, a=15.265, b=2.301, r=0.967, SEE=0.31 Shrub: n=28, Range(cm)=1.01-7.48, a=13.371, b=2.234, r=0.920, SEE=0.44 For Inflorescence Dry weight y=Inflorescence Dry weight, x=Diam , All: n= 22, Range(cm)=0.81-3.81, a=3.641, b=1.713, r=0.814, SEE=0.62 Tree: n=16, Range(cm)=0.81-3.81, a=2.994, b=1.671, r=0.798, SEE=0.74 For Pod total Dry weight y=Pod total Dry weight, x=Diam All: n= 21, Range(cm)=1.38-5.62, a=12.43, b=2.283, r=0.739, SEE=0.830 Tree: n=15, Range(cm)=1.38-5.62, a=13.65, b=2.380, r=0.777, SEE=0.830 For Juvenile twig Dry weight y=Juvenile twig Dry weight, x=Length, All: n=48, Range(cm)=12.5-51.5, a=0.004, b=1.740, r=0.899, SEE=0.337 Shrub: n=35, Range(cm)=10.4-40.4, a=0.011, b=1.534, r=0.914, SEE=0.24 For Juvenile leaf Dry weight y=Juvenile leaf Dry weight, x=Length, Tree: n=50, Range(cm)=8.0-40.5, a=0.018, b=1.166, r=0.741, SEE=0.425 Shrub: n=37, Range(cm)=8.0-40, a=0.182, b=0.060, r=0.844, SEE=0.303 For Bare twig Dry weight y=Juvenile leaf Dry weight, x=Length, Tree: n=75, Range (cm)=8-51.5, a=0.001, b=2.130, r=0.938, SEE=0.333 Shrub: n=35, Range (cm)=8-40, a=0.002, b=1.840, r=0.933, SEE=0.284	Sharifi et al. (1982)
Savanna	FW (kg) = 0.1638 x sum BA (cm2)1.26 Where, FW=Fresh weight and BA=Basal area	Guy  (1981)
Fraxinus americana	Total AGB=0.1535 D 2.3213 r2=0.99, DBH range=1-28 (cm) Where, Total AGB=Total above ground biomass (in kg), D=Tree DBH (in cm)	Ker  (1980)
Prosopis flexuosa	dry weight = 0.232995 × volume  1.035294	Braun et al. (1979)
Fraxinus americana	Total AGB=0.1063 D 2.4798 r2 =0.99, DBH range=5-50 (cm) Where, Total AGB=Total above ground biomass(in kg), D=Tree DBH (in cm)	Brenneman et al. (1978)

References

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TABLE VII: WESTERN GHATS

EQUATIONS	REFERENCE
Standing biomass (in Kg) =b+(aD2H) Where, D=Diameter at breast height, H=Height of the tree, a and b=Constants	Ramachandra et al. (2004)
Tree fractions Boles (B): log B = -1.39 + 2.28 log (dbh) r2=0.93, n=465 Branches (Br) log Br = -1.24 + 1.98 log (dbh) r2=0.84, n=229 Leaves (Lo) log Lv = 0.829 + 0.482 log (dbh) r2=0.17, n=229 Current year’s  twings (Tw) log Tw = 1.18 - 0.108 log (dbh) r2=0.001, n=229 Total aboveground biomass (T) log T = - 0.435 + 2.12 log (dbh) r2=0.92, n=9 Roots(≥5 cm girth) (R): log R = - 2.30 + 1.63 log (dbh) r2=0.74, n=91	Rai & Proctor  (1986)
WT = F×H×G×D Where, WT=Sum of the branch and bole biomass (g), F=Form factor, H=Tree height (cm), G=Basal area at breast height (cm2), D=Specific gravity (gm-3)	Cannell  (1984)
log y = a + b log x Where, y=Above ground biomass (kg), x=D2H (D, cm × 10, H, m), a and b=Constants.	Rai  (1984)
For over wood species log y= - 0.6150+ 0.8315log x N=105, r=0.9138, F=1521.2	Rai  (1984)
For Underwood species log y= -0.4917+ 0.7935log x N=78, r=0.9175, F=404.5	Rai  (1984)
For the whole forest log y= -0.6206+ 0.8315log x N=183, r=0.9579, F=2014.6	Rai  (1984)
y = a + b x Where, y=Above ground biomass in kg, x=D2H (D, cm × 10, H, m× 10), a and b = constants.	Rai  (1984)
For over wood species y = 250.96 + 0.000036x N=78, r=0.842, F=2.436	Rai   (1984)
For Underwood species y = 34.195 + 0.000047x N=80, r=0.953, F=9.92	Rai  (1984)
For the whole forest y = 111.293+ 0.00004x N=158, r=0.911, F=4.891	Rai  (1984)
y = a + b x Where, y=Above ground biomass in kg, x=DBH (cm × 10), a and b=constant	Rai  (1984)
For over wood species y = -755.356 + 6.023x N=78, r=0.874, F= 3.25	Rai  (1984)
For Under wood species y = - 322.13 + 3.661x N=80, r=0.935, F=6.92	Rai  (1984)
For the whole forest y = - 635.412+ 5.578x N=158, r=0.923,F= 5.789	Rai  (1984)

References:

1. Cannell M. G. R., 1984. Woody biomass of forest stands, Forest Ecology and Management, 8: pp 299-3 12.
2. Ramachandra T. V., Kamakshi G. and Shruthi B. V., 2004. Bioresource status in Karnataka, Renewable and Sustainable Energy Reviews, 8(1): pp 1-47.
3. Rai, S. N., 1984. Above ground biomass in tropical rainforests of Western Ghats, Indian For., 110: pp 754–764.
4. Rai S. N., Proctor, 1986. Ecological Studies on Four Rainforests in Karnataka, India: I. Environment, Structure, Floristic and Biomass, The Journal of Ecology, 74(2):  pp 439-454.

TABLE VIII: WESTERN GHATS  (SPECIESWISE)

Species	Biomass Equation	Reference
Calophyllum elatum	log y = a + b log x N=20, a= - 0.9942, b=0.8866, r=0.9808, F=457.8 Where, y=Above ground biomass (kg), x=D2H (D,cm × 10,H,m)	Rai  (1984)
Canarium  strictum	N=19, a= - 0.8233, b=0.8865, r=0.9646, F=227.3	Rai  (1984)
Carallia  brachiata	N=10, a= - 0.8398, b=0.8901, r=0.9664, F=116.7	Rai  (1984)
Dipterocarpus indicus	N=10, a= - 0.8493, b=0.8897, r=0.8859, F=29.2	Rai  (1984)
Holigarna spp.	N=16, a= - 0.7028, b=0.8355, r=0.9448, F=116.5	Rai  (1984)
Persea  macrantha	N=20, a= - 0.4252, b=0.8176, r=0.9235, F=104.3	Rai  (1984)
S. utilis	N=10, a= - 1.2066, b=0.9872, r=0.9790, F=184.2	Rai  (1984)
Garcinia cambogia	N=20, a= - 1.0883, b=0.9445, r=0.9581, F=404.5	Rai  (1984)
Garcinia  indica	N=20, a= - 0.3350, b=0.5624, r=0.7330, F=20.6	Rai  (1984)
L. anamallayanum	N=20, a= - 0.6158, b=0.8189, r=0.9758, F=357.7	Rai  (1984)
Euphoria  longana	N=18, a= - 0.5181, b=0.8195, r=0.9915, F=925.7	Rai  (1984)
Calophyllum elatum	y = a + b x N=19, a= - 572.09, b=4.59, r=0.961 Where, y=Above ground biomass in kg x=DBH in cm×10, a and b= constant	Rai  (1984)
Canarium  strictum	N=19, a= - 1045.18, b=7.30, r=0.954	Rai  (1984)
Dipterocarpus indicus	N=10, a= - 899.76, b=6.49, r=0.822	Rai  (1984)
Persea  macrantha	N=20, a= - 1302.60, b=8.46, r=0.916	Rai  (1984)
S. utilis	N=10, a= - 1070.5, b=7.19, r=0.962	Rai  (1984)
Garcinia cambogia	N=20, a= - 288.79, b=3.18, r=0.955	Rai  (1984)
Garcinia  indica	N=20, a= - 352.08, b=3.70, r=0.957	Rai  (1984)
L. anamallayanum	N=20, a= - 344.51, b=3.70, r=0.946	Rai  (1984)
Euphoria  longana	N=20, a= - 325.69, b=3.97, r=0.951	Rai  (1984)

Reference:

Rai S. N., 1984. Above ground biomass in tropical rainforests of Western Ghats, Indian For., 110: pp 754–764.

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