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Introduction
Most global climate action agreements have given significant attention to forests due to their potential for carbon sequestration and climate change mitigation (Federici et al., 2015; Griscom et al., 2017). One of the most important compartments in carbon fixation in forests is tree biomass, which is quantified through field measurements and the use of remote sensing or geographic information systems. However, beyond the technique used, it is essential to have high-quality biomass equations with a high degree of fit to obtain accurate estimates (Picard et al., 2012).
Typical biomass equations aim to estimate biomass at the tree or stand level based on its allometric relationship with easily measurable variables such as diameter at breast height or total tree height (Kershaw et al., 2017). Currently, there are generalized biomass equations (Chojnacky et al., 2014) or those specific to a particular biome (Alvarez et al., 2012; Chave et al., 2014). However, it has been shown that intraspecific variation affects the accuracy of estimates (Affleck, 2019; Araujo et al., 2023), due to the species-specific allometric relationship between biomass and tree dimensions (Teobaldelli et al., 2009). Therefore, it is recommended to use species-specific biomass equations that allow for high-precision estimates (Temesgen et al., 2015).
There are biomass equations for most commercial forest species, such as pines and eucalypts (Correia et al., 2018; Han & Park, 2020; Hernández-Ramos et al., 2017). Most of these equations are based on generic models commonly used for perennial woody species, such as those by Schumacher, Spurr, Husch, and Meyer, which are selected according to goodness-of-fit statistics (Loetsch et al., 1973). However, biomass equations for native species in tropical forests are scarce (Chave et al., 2014; Liu et al., 2023), especially for localized and uncommon species. This is the case of Retrophyllum rospigliosii (Pilg.) C. N. Page, a podocarp native to the tropical Andes, distributed at elevations ranging from 1 500 to 3 300 m, for which no biomass equations have been reported. The importance of understanding the amount of accumulated biomass lies in identifying its carbon sequestration potential, thereby quantifying one of the environmental services provided by these native forests. This information is critical for their conservation, restoration, and meeting the goals established in national climate change agreements.
Podocarps in Colombia have experienced a decline of nearly 30 % in recent decades due to deforestation and selective harvesting for timber production (Ortega & Muñoz, 2020). As a result, the species is classified as vulnerable on the International Union for Conservation of Nature's (IUCN) Red List of Threatened Species (Gardner & Thomas, 2013). The threatened status of R. rospigliosii underscores the need to establish restoration and reforestation programs to ensure its survival. To date, approximately 25 ha of R. rospigliosii have been established in Colombia, particularly in the southern regions, with plantings dating back to the late 1990s. In Andean forests, R. rospigliosii is a species of high ecological value due to its ability to colonize nutrient-poor soils, facilitating the establishment of various Andean species under its canopy. It also provides shelter for wildlife (Marín, 1998). Furthermore, this species produces timber that is ideal for construction and artisanal woodworking, making it a valuable resource for local communities (Marín, 1998).
In this context, the present study aimed to estimate the carbon sequestration potential of mature R. rospigliosii plantations established for restoration purposes in the Colombian Andes. To achieve this goal, biomass production was evaluated, and allometric equations were developed to determine its ecological productivity and contribution to carbon sequestration
Materials and Methods
Study area
The study was conducted in an experimental R. rospigliosii plantation established in 1999 by the Federación Nacional de Cafeteros de Colombia and Smurfit-Westrock Colombia. The plantation is located in the western region of the Cauca department (El Tambo municipality), Colombia, between coordinates 2° 28' 0"-2° 29' 40" N and 76° 48' 30"-76° 50' 0" W (Figure 1), at an elevation of about 1 775 m. According to data from the local weather station, the average annual temperature is 19.4 °C, and average annual precipitation 2 298 mm, with a bimodal distribution pattern and lower rainfall between June and September (Ramírez et al., 2021). The area is classified as premontane wet forest (bh-PM) according to Holdridge's life zone classification system (1982).
Figure 1 Location of the experimental Retrophyllum rospigliosii plantation in the Cauca department, Colombia.
The study was established using seeds collected from a natural forest in the southwestern region of the Antioquia department (Támesis municipality, Colombia) on Finca La Playa. The seedlings were produced in a nursery and transplanted into the field when they reached a height of 30 cm.
The experimental plantation covers 8 ha and was initially established at a density of 1 666 trees∙ha-1. This density has gradually decreased due to mortality and selective removal of individuals, leaving an approximate current density of 300 trees∙ha-1.
Field methods
Initially, a plantation inventory was conducted to evaluate its condition and generate diameter class distributions. The diameter at breast height (DBH) of each tree was measured using a diameter tape, and total height was measured with a Vertex IV hypsometer. Diameter distributions were established, classifying the trees into six classes based on diameter size, ranging from the smallest (0.03 m) to the largest (0.42 m). From each diameter class, five individuals were selected for aboveground biomass quantification (30 in total) and two individuals for belowground biomass quantification (12 in total). This sampling method ensured the inclusion of individuals from all size categories present in the stand.
Aboveground biomass quantification
The trees selected for aboveground biomass quantification were cut down in 2019. Branches and leaves were separated from the main stem. In the field, the fresh biomass of the stem, branches, and leaves were weighed, and samples from each component were collected for moisture content and density measurements. The main stem of each tree was divided into ten equal-length sections, and the diameter at both ends of each section was measured to calculate the volume (Picard et al., 2012).
Belowground biomass quantification
A 2 x 3 m area was marked around each tree, according to the planting density of the plantation. Fine roots were then sampled every 30 cm from the center of the stump to the edge of the sampling area (1.5 m) using cylinders with a diameter of 10 cm and a depth of 30 cm. Finally, the root system of each tree was removed to evaluate coarse roots. The roots were cleaned to remove impurities and weighed in the field. Samples of both fine and coarse roots were taken to determine moisture content in the laboratory.
Volume, biomass, and carbon content estimation
In the laboratory, the density of each R. rospigliosii tissue was measured. The volume of each sample was determined using the water displacement method. Then, the samples of the stem, branches, leaves, and roots were placed in a drying oven to obtain their dry weight and assess moisture content. The samples were dried at 70 °C until constant weight and were weighed immediately to prevent any increase in weight due to air moisture. Moisture content of each component (Ch i ) was determined based on the dry weight of the sample of component i (ms i ) and the fresh weight of the sample of component i (mf i ) using the equation: Ch i = (ms i /mf i ).
The volume of the stem sections was calculated using Smallian's equation:
where,
L = length (m) of the stem section
d in = lower diameter (m) of stem section n
d sn = upper diameter (m) of stem section n.
The biomass of each component (B i ) was calculated using the following equation:
where,
Pf i = fresh weight (kg) of component i
Ch i = moisture content (kg) of component i.
When the component was divided into parts, the dry weight was obtained by adding the dry weight of all the parts of component i.
The aboveground biomass expansion factors (BEF) were estimated
based on the dry biomass (B) of the stem, branches, and leaves.
The factors were calculated using the equation
Finally, to determine the elemental carbon content of the plant tissues of R. rospigliosii, 84 samples were taken from the 12 trees in which both aboveground and belowground biomass were quantified. The elemental carbon analyses were conducted at the Max Planck Institute for Biogeochemistry in Jena, Germany. Carbon content was measured using a carbon-nitrogen analyzer (Vario Max Cube, Elementar Gmbh, Germany), and the results were expressed as percentage.
Data processing
Aboveground and belowground biomass were treated separately and then combined to estimate total biomass. Two total biomass models were fitted; the first used the belowground biomass values from the 12 sampled trees, while the second used the estimated belowground biomass values from the 30 sampled trees based on the model obtained for this component. Data normality was assessed using the Shapiro-Wilk test, and then the variation in volume, biomass, and carbon was related to the DBH and height by fitting linear and nonlinear models from Berkhout, Kopezky, Husch, and Spurr, both in their linear and nonlinear forms as proposed by Loetsch et al. (1973) (Table 1). The selected regression models were those with the highest likelihood, meaning those with the lowest AIC (Akaike Information Criterion) and the smallest residual standard error. Additionally, the coefficient of determination (R²) and significance at 95 % confidence were used for model selection. Finally, the normality of residuals was evaluated with the Shapiro-Wilk test, and homoscedasticity was assessed using the Breusch-Pagan test. All analyses were conducted using R software version 4.3.2 (R Core Team, 2023).
Table 1 Regression models to establish allometric equations for Retrophyllum rospigliosii (according to Loetsch et al., 1973).
| Name | Model |
|---|---|
| Linear | |
| Berkhout | y = a + b DBH |
| Kopezky | Y = a + b (DBH2) |
| Spurr | y = a + b (DBH 2 H) |
| Non-linear | |
| Husch (potential) | y = aDBH b |
| Spurr | y = a (DBH 2 H)b |
In the models y can represent volume (V, m3), biomass (B, Mg), or carbon (C, Mg). DBH corresponds to diameter at breast height (1.3 m) and H to total height (m). The letters a and b correspond to the model parameters.
Results
Diameter class distribution
According to the initial sampling, trees had an average DBH of 0.22 ± 0.04 m and a height of 10.97 ± 1.25 m (Table 2). The individuals were divided into six diameter classes ranging from 3 to 42 cm, with a width of 6.67 cm, showing a typical unimodal distribution characteristic of forest plantations.
Table 2 Variables analyzed for the biomass quantification of Retrophyllum rospigliosii.
| Variables | Number of trees | Minimum | Maximum | Average per tree |
|---|---|---|---|---|
| DBH (m) | 30 | 0.03 | 0.42 | 0.22 ± 0.04 |
| Height (m) | 30 | 3.90 | 15.80 | 10.97 ± 1.25 |
| Stem volume (m3) | 30 | 0.00 | 0.81 | 0.27 ± 0.09 |
| Form factor | 30 | 0.38 | 0.78 | 0.52 ± 0.03 |
| Density (g∙cm-3) | 30 | 0.32 | 0.40 | 0.36 ± 0.01 |
| Stem biomass (Mg) | 30 | 0.00 | 0.27 | 0.09 ± 0.03 |
| Leaf biomass (Mg) | 30 | 0.00 | 0.03 | 0.01 ± 0.00 |
| Branches biomass (Mg) | 30 | 0.00 | 0.18 | 0.04 ± 0.02 |
| Coarse root biomass (Mg) | 12 | 0.00 | 0.11 | 0.04 ± 0.02 |
| Fine root biomass (Mg) | 12 | 0.00 | 0.02 | 0.01 ± 0.00 |
| Aboveground biomass (Mg) | 30 | 0.00 | 0.48 | 0.14 ± 0.05 |
| Belowground biomass (Mg) | 12 | 0.00 | 0.12 | 0.05 ± 0.02 |
| Total biomass (Mg) | 12 | 0.00 | 0.60 | 0.20 ± 0.11 |
DBH: Diameter at breast height (1.3 m). ± standard error of the mean.
Aboveground and belowground biomass
As shown in Table 2, the average volume of R. rospigliosii trees was 0.27 m³ with a form factor of 0.52. The basic wood density was 0.36 g∙cm-3. On average, 0.14 Mg of aboveground biomass and 0.05 Mg of belowground biomass were obtained per tree. The greatest variability in biomass was recorded in the stem and coarse roots, while the lowest variability was observed in the biomass of leaves.
Allometric models
The selected models showed adjustments exceeding 94 %, higher likelihood, and lower standard error compared to the other evaluated models (Table 3; Figure 2). Additionally, the residuals of the selected models were normal, generally randomly distributed, and never exceeded 0.06 Mg (Appendices 1 and 2). The Husch model provided the best fit for estimating tree volume, biomass (aboveground and belowground), and carbon sequestration. For total biomass, the best-fitting model was Spurr's nonlinear form, using belowground biomass estimates from all sampled trees. Finally, for fitting the models to estimate volume, belowground biomass, and total biomass (a), data from one individual was eliminated (N = 11 and N = 29) since was very far from the regression curve, probably because it was the tree selected with the smallest diameter and height.
Table 3 Summary of selected allometric models for estimating volume, biomass, and carbon of Retrophyllum rospigliosii.
| Model | Estimated | Standard error | t value | t probability | N | AIC | Adjusted R2 (%) | Residual standard error |
|||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Volume (m3) | Husch | V = aDBH b | a | 5.10 | 0.35 | 14.38 | < 0.001 | 29 | -125.24 | 98.86 | 0.03 |
| b | 2.07 | 0.06 | 32.83 | < 0.001 | |||||||
| Aboveground biomass (Mg) | Husch | B = aDBH b | a | 2.84 | 0.30 | 9.34 | < 0.001 | 30 | -144.40 | 97.19 | 0.02 |
| b | 2.07 | 0.10 | 22.00 | < 0.001 | |||||||
| Belowground biomass (Mg) | Husch | B = aDBH b | a | 0.36 | 0.07 | 5.49 | < 0.001 | 11 | -67.97 | 97.36 | 0.01 |
| b | 1.32 | 0.15 | 8.62 | < 0.001 | |||||||
| Total biomass a (Mg) | Spurr | B = a(DBH 2 H) b | a | 0.24 | 0.00 | 53.14 | < 0.001 | 29 | -143.49 | 99.60 | 0.02 |
| b | 0.81 | 0.03 | 29.46 | < 0.001 | |||||||
| Total biomass b (Mg) | Spurr | B = a(DBH 2 H) b | a | 0.25 | 0.01 | 21.62 | < 0.001 | 12 | -42.72 | 98.50 | 0.03 |
| b | 0.82 | 0.06 | 13.87 | < 0.001 | |||||||
| Aboveground carbon (Mg) | Husch | C a = aDBH b | a | 1.80 | 0.24 | 7.54 | < 0.001 | 12 | -75.67 | 98.46 | 0.01 |
| b | 2.34 | 0.13 | 18.57 | < 0.001 | |||||||
| Belowground carbon (Mg) | Husch | C s = aDBH b | a | 0.17 | 0.03 | 6.26 | < 0.001 | 12 | -94.71 | 94.40 | 0.00 |
| b | 1.31 | 0.13 | 9.81 | < 0.001 | |||||||
| Total carbon (Mg) | Husch | C T = aDBH b | a | 1.72 | 0.20 | 8.51 | < 0.001 | 12 | -70.96 | 98.48 | 0.01 |
| b | 2.06 | 0.11 | 18.95 | < 0.001 | |||||||
DBH: diameter at breast height (1. 3 m), H: height (m), n: number of trees, AIC: Akaike Information Criterion.
Figure 2 Models fitted for the estimation of volume, biomass, and carbon of Retrophyllum rospigliosii. DBH: Diameter at breast height, H: Height.
The model that best fitted the biomass of the stem, branches, leaves, coarse roots, and fine roots was the Husch model (Table 4; Figure 3). It was observed that the aboveground components achieved a better fit (>80 %) compared to the belowground components (ranging between 56 % and 77 %).
Table 4 Summary of statistics according to the Husch model for estimating the biomass (B) of each component (Mg) of Retrophyllum rospigliosii.
| Model | Estimated | Standard error | t value | t probability | n | AIC | Adjusted R2 (%) | Residual standard error | ||
|---|---|---|---|---|---|---|---|---|---|---|
| Stem | B = aDBH b | a | 1.38 | 0.14 | 9.71 | < 0.001 | 30 | -170.40 | 96.94 | 0.01 |
| b | 1.91 | 0.09 | 20.90 | < 0.001 | ||||||
| Branches | B = aDBH b | a | 2.12 | 0.52 | 4.07 | < 0.001 | 30 | -177.91 | 97.25 | 0.01 |
| b | 3.00 | 0.24 | 12.64 | < 0.001 | ||||||
| Leaves | B = aDBH b | a | 0.11 | 0.03 | 4.20 | < 0.001 | 29 | -242.65 | 80.89 | 0.00 |
| b | 1.69 | 0.21 | 8.20 | < 0.001 | ||||||
| Coarse roots | B = aDBH b | a | 0.27 | 0.09 | 3.00 | < 0.001 | 12 | -63.46 | 77.09 | 0.01 |
| b | 1.23 | 0.28 | 4.48 | < 0.001 | ||||||
| Fine roots | B = aDBH b | a | 0.04 | 0.02 | 2.26 | < 0.001 | 12 | -98.62 | 56.26 | 0.00 |
| b | 1.03 | 0.35 | 2.94 | < 0.001 | ||||||
DBH: diameter at breast height (1.3 m), n: number of trees, AIC: Akaike Information Criterion.
Biomass expansion factor
The average BEF was 1.50, with a minimum value of 1.16 and a maximum of 1.90 (Table 5). In general, the average values increased as the diameter class increased.
Table 5 Biomass expansion factor (BEF) for aboveground biomass of Retrophyllum rospigliosii trees per diameter class.
| BEF | Class I 2.0 - 8.7 |
Class II 8.7 - 15.3 |
Class III 15.3 - 22.0 |
Class IV 22.0 - 28.7 |
Class V 28.7 - 35.3 |
Class VI 35.3 - 42.0 |
|---|---|---|---|---|---|---|
| Average (cm) | 1.40 ± 0.17 | 1.45 ± 0.19 | 1.54 ± 0.22 | 1.53 ± 0.22 | 1.42 ± 0.04 | 1.65 ± 0.13 |
| Range (cm) | 1.16-1.62 | 1.32-1.77 | 1.25-1.78 | 1.37-1.90 | 1.39-1.47 | 1.52-1.82 |
± standard error of the mean.
Biomass per tree section
The tree section contributing the most to total biomass was the stem, with 46.65 %, followed by coarse roots at 22.67 %, branches at 20.12 %, fine roots at 5.36 %, and leaves at 5.2 % (Table 6).
Table 6 Biomass of each section of Retrophyllum rospigliosii tree.
| Biomass | Stem | Leaves | Branches | Coarse roots | Fine roots |
|---|---|---|---|---|---|
| Aboveground (%) | 67.59 ± 7.80 | 8.74 ± 3.88 | 23.67 ± 6.93 | - | - |
| Belowground (%) | - | - | - | 81.07 ± 12.42 | 18.93 ± 12.42 |
| Total (%) | 46.65 ± 8.37 | 5.20 ± 2.06 | 20.12 ± 6.76 | 22.67 ± 9.22 | 5.36 ± 8.69 |
Average value ± standard error.
Carbon per tree section
The carbon content was similar among sections (around 49 %), except for fine roots, which had 41.08 % (Figure 4). On average, each tree stores 0.09 ± 0.06 Mg C. Therefore, it is estimated that a plantation of R. rospigliosii with 1 666 trees∙ha-1 stores a total of 156.09 ± 92.80 Mg C∙ha-1 (Figure 4).
Discussion
Based on destructive sampling, the average volume per tree of R. rospigliosii was recorded as 0.27 m³, with a form factor of 0.50 and a wood basic density of 0.36 g∙cm-3. According to these results, the shape of the trees can be classified as the paraboloid dendrometric type (Kershaw et al., 2017). A study of a forest dominated by R. rospigliosii and Prumnopitys harmsiana (Pilg.) de Laub. in Ecuador reported a volume of 510 m3∙ha-1, much higher than the estimate for the study area (300 m3∙ha-1) (Yaguana et al., 2012). Regarding wood density, it can be classified as soft and light, coinciding with the basic density (0.34 g∙cm-3) of the same species reported by Portillo et al. (2019). However, these density values are much lower than those presented by Baker et al. (2004), who measured 0.57 g∙cm-3 in wood from natural forests. A similar trend was reported for 19 species planted for restoration purposes in tropical areas of Ghana, where the wood density of the species was lower compared to individuals in forests (Yeboah et al., 2014). This reduction is because individuals experience greater intraspecific competition in plantations than under natural conditions (Nguyen et al., 2014). The reduction in basic wood density of plantation species is also evidence of a possible reduction in the rate of carbon sequestration (Yeboah et al., 2014), an aspect that should be considered in the management of plantations for restoration purposes.
Regarding the biomass per R. rospigliosii tree, average values of 0.14 Mg of aboveground biomass and 0.05 Mg of belowground biomass were found. In all individuals of the plantation, aboveground biomass was greater than belowground biomass. However, the belowground component plays an important role, as this compartment contributes the most to the total biomass after the stem, as described by Rodríguez et al. (2019) in Pinus patula Schltdl. & Cham.
The Husch model provided the best fit for estimating aboveground, belowground, and total carbon, as well as for estimating aboveground and belowground biomass of R. rospigliosii, while the nonlinear Spurr model was a better fit for estimating total biomass. It is important to emphasize that, overall, all models showed optimal fits to the dataset (R2 > 94 %). The Husch and Spurr models have been widely used in forestry estimation due to their good fit and ability to generate accurate predictions (Kiviste et al., 2002).
In the present study, the biomass of each component of R. rospigliosii was estimated using the Husch model. A higher fit was observed for the models of the aboveground tree components (R2 > 80 %), while the estimation of coarse root biomass (R2 = 77 %) and fine root biomass (R2 = 56 %) showed lower fits. These results are consistent with reports on biomass and carbon estimation in other conifer species. For instance, in Cupressus lusitanica Mill. in Costa Rica, Fonseca-González et al. (2023) reported a 97 % fit for total biomass using the Husch model. In Mexico, Carrillo et al. (2014) used the Husch model to estimate total carbon in Pinus montezumae Lamb. ex Gordon & Glend, achieving fits greater than 98 %, and later obtained a 98 % fit when estimating the aboveground biomass of Pinus hartwegii Lindl. (Carrillo et al., 2016). Similarly, Rodríguez et al. (2019) estimated the root biomass of 90 Pinus patula trees with a 92 % fit and total biomass with a 97 % fit. Other plantation broadleaf species, such as Tectona grandis L. f. in Guatemala, showed fits of 78 % using the Husch model (López et al., 2018).
El average BEF for R. rospigliosii was 1.50 which is higher than the constant proposed by the Intergovernmental Panel on Climate Change (IPCC) for conifer plantations in the tropics, set at 1.30 (IPCC, 2019). In Brazil, an average BEF of 1.47 was reported for Pinus elliottii Engelm. and Pinus taeda L., established under conditions similar to those of this study (Sanquetta et al., 2011). In Costa Rica, an average BEF of 1.54 was calculated for individuals of C. lusitanica from plantations (Fonseca-González et al., 2023). These results are similar to those found for R. rospigliosii and underscore the importance of calculating specific BEFs, as relying on the IPCC constant of 1.30 could lead to underestimations in carbon estimation.
Regarding the biomass contribution per tree section for R. rospigliosii, the stem was the largest contributor to total biomass, followed by coarse roots, branches, fine roots, and finally, leaves. These findings are consistent with other conifers, where most of biomass is concentrated in the stem (Rodríguez et al., 2019). Notably, the root component of R. rospigliosii accounted for 35.35 % of the total biomass, highlighting its significance in the quantification of this species' biomass. Carbon content across components was similar (approximately 49 %), except for fine roots, where the content was lower and exhibited greater variability (41.08 % ± 5.02 %). In other planted conifer species, carbon content ranged between 44 % and 49 % for tree components, similar to those observed in R. rospigliosii (Hernández-Vera et al., 2017; Pompa-García et al., 2017).
Lastly, on average, one hectare of R. rospigliosii with 1 666 trees stores 316.26 ± 187.26 Mg of biomass and 156.09 ± 92.80 Mg C. These values are low compared to those reported for other conifer plantations and that at the end of their cycle (~15 to 20 years) can store up to 230 Mg C∙ha-1 (Cook et al., 2014). Additionally, carbon storage is lower than that reported in premontane moist forests in Colombia which is between 149 and 235 Mg C∙ha-1, with significantly lower densities (Alvarez et al., 2012; Yepes et al., 2016). Although no data were found on biomass and carbon sequestration of R. rospigliosii in natural forests in Colombia, reports on diameters and heights of individuals are considerably greater under these conditions, reaching up to 30 m in height and more than 1 m DBH (Marín, 1998).
Conclusions
Biomass accumulation and carbon storage were estimated in 20-year-old experimental plantations of Retrophyllum rospigliosii established for restoration purposes in the Colombian Andes. Individuals of this species, grown in high-density monospecific plantations, exhibited relatively low biomass values (316.26 ± 187.26 Mg∙ha-1) and carbon sequestration (156.08 ± 92.80 Mg C∙ha-1) compared to those in natural forests. The lack of biodiversity in monospecific plantations could limit productivity by reducing the potential for complementarity and facilitation. Given the large size attained by R. rospigliosii under natural conditions, this species demonstrates significant potential for carbon sequestration in stem. Therefore, restoration efforts should prioritize the enrichment of degraded forests with this species or its inclusion in agroforestry systems.
