Allometric and biometric equations were combined with growth and yield models to estimate C stocks and dynamics for loblolly pine plantations in the southeast U.S. We used published loblolly pine growth and yield equations that could be broadly applied to Lower Coastal Plain, Upper Coastal Plain and Piedmont regions [16-18]. These equations predicted stand growth in basal area (BA, m²·ha⁻¹), dominant height (H_d, m), number of surviving trees per hectare (N_ha, trees·ha⁻¹), quadratic mean diameter (QMD, the diameter of the trees of mean BA, cm) and total stem volume (V, m³·ha⁻¹), using as inputs number of trees planted per hectare (N_ha), site index (SI, the height (m) reached by the stand's dominant trees at a reference age of 25 years), type of site preparation (no soil preparation or bedding), weed control treatment (age and method of application, broadcast or banded), fertilization treatment (age and amount of N and P applied), and thinning timing and intensity (as a removal percentage from the surviving trees before thinning).

At each age, allometric equations (Table 1) [19-21] were used to estimate coarse root, stem, crown and total aboveground stand biomass from QMD and N_ha simulated by the growth and yield model. Biomass estimations were corrected using log-bias correction following back-transformations [22]. Fine root biomass was estimated from fine root/needle mass ratios reported for stands younger than 5 years [23], and for stands 5 years and older [21]. Loblolly pine projected leaf area index (LAI, the ratio of leaf surface area supported by a plant to its corresponding horizontal projection on the ground, m²·m⁻²) and litterfall (B_L; Mg·ha⁻¹·year⁻¹) were estimated from the model reported by Gonzalez-Benecke et al. [24]. Forest floor biomass accumulation (B_F; Mg·ha⁻¹) was determined as in Gonzalez-Benecke et al. [24] as the sum of yearly litterfall inputs corrected for decay loss using an equation to estimate decay rate of the forest floor [25]. This equation used site coordinates (latitude and longitude, in decimal units) as inputs to estimate decay rate and was in good agreement with decay rates reported for southern pines in the southeastern U.S. [26-28]. Understory biomass accumulation (B_U; Mg·ha⁻¹) was estimated from published equations [14] that used a 17-year chronosequence of understory and litterfall biomass [29].

Standing dead trees, estimated from mortality equations [19], were incorporated into the dead component of total biomass. A large fraction of the stand mortality occurred in diameter classes below the median, due to the effects of resource competition of suppressed and weakened trees [4,30]. Using diameter distribution models reported for unthinned and thinned plantations [16], the diameters at the 25th percentile (cm) were determined for each age. This diameter class was assumed to better represent the observed diameter class of dying trees [30]. Biomass of dying trees was computed in the same way as standing biomass, but diameters at the 25th percentile from the previous year were used instead of QMD to estimate individual tree biomass.

At the time of thinning, reductions in loblolly pine LAI were set to be proportional to reductions in BA due to thinning and, therefore, litterfall, forest floor and understory biomass were affected due to their LAI-dependence [24]. At thinning and final harvest (clear-cut), logging slash (root and crown biomass plus stem residues) from harvested tress were also included into flux calculations and incorporated into the dead biomass pool. Stem residues were obtained by assuming a harvest efficiency of 88% of V [31-34]. It was assumed that C storage in soil was not affected by forest management in southern pines plantations [7,35-38], therefore the model did not include changes in soil C. Carbon mass (MgC·ha⁻¹) was calculated by using an average C content of 50% for loblolly pine and understory biomass components [6,39,40].

Validation of model results was carried out by comparing model outputs against data reported in the peer-reviewed literature for: (i) total above-plus belowground C accumulation in loblolly pine biomass (T_C, MgC·ha⁻¹); (ii) aboveground C accumulation in live loblolly pine biomass (AG_C, MgC·ha⁻¹); (iii)belowground C accumulation in live loblolly pine biomass (BG_C, MgC·ha⁻¹); and (iv) net ecosystem production (NEP; MgC·ha⁻¹·year⁻¹). Results reported in 15 publications including both above- and belowground loblolly pine biomass, for stands between 3 and 48 years old, were considered in our approach. On each validation study, the authors estimated biomass directly using inventory and local biomass functions. For each literature value, the reported initial stand characteristics such as planting density, SI and management activities (site preparation, weed control, fertilization and thinning) were used as model inputs. Loblolly pine's T_C, AG_C and BG_C were validated in two ways, depending on the availability of information: (i) whole-model validation, using reports that included planting density, SI and management activities [25,41-48]; or (ii) allometry validation, using reports where SI was not reported [27,49-53]. The former enabled us to run the model from planting year to age when biomass was determined. For allometry validation from papers which did not report initial stand characteristics, initial model inputs were adjusted to achieve the final stand characteristics of each report, such as H_d, DBH, BA, N_ha. The latter approach can be considered a validation of the biomass equations used by the model. Even though two of the studies used for validation [21,23] utilized some of the equations included in the model (see Table 1), we contend that the independent estimation of N_ha, DBH and all other C pool components validate the inclusion of those cases. The values of NEP reported by Oren et al. [54] for 16 to 21-year old stands were compared with our model estimates. A summary table of all data used for C accumulation validations is reported in Appendix 1. Locations of validation sites are also shown in Figure 1. Validation of litterfall and forest floor outputs were developed/tested in related research by Gonzalez-Benecke, et al. 2011 [24]. Validation simulations were performed with the growth and yield functions appropriate for the physiographic region of each validation study.

Depending on stem DBH and merchantable diameter, harvested roundwood (from thinnings or clear-cuts) was assigned to three main product classes: sawtimber (ST), chip-and-saw (CNS) and pulpwood (PW), using the model proposed by Harrison and Borders [16]. Merchantable outside-bark stem volume was calculated at each stand age and product volume was transformed to biomass (Mg· ha⁻¹) by multiplying the volume by the average specific gravity (SG) at a given age from equations derived from the model [7]. The exponential model that correlates SG and age is shown in Table 1. A C content of 50% was used to calculate C mass of each product type.

Similar to Gonzalez-Benecke et al. [14], industrial conversion efficiencies of 65%, 65% and 58% were assigned to ST, CNS and PW, respectively [25,40,55]. In addition, all the product types were divided into four life span categories (Table 2) [12,56] and adapted to loblolly pine utilization patterns in the southeastern U.S. [57-60].

The effects of silvicultural treatments and rotation lengths on C sequestration were analyzed by simulating C flux under four different scenarios for standard conditions of loblolly pine plantations established in the southeastern US lower Coastal Plain. Initial variables were set as follow: base SI = 22 m, N_ha = 1500 trees·ha⁻¹, bedding, weed control at planting and at age 1, and NP fertilization at age 5 (135 Kg·ha⁻¹ N + 28 Kg·ha⁻¹ P), 10 (225 Kg·ha⁻¹ N + 28 Kg·ha⁻¹ P) and 15 years (225 Kg·ha⁻¹ N + 28 Kg·ha⁻¹ P). After fertilization and weed control treatments, the model estimated that the effective SI was increased to 24.3 m. Initial C accumulated from the previous rotation in coarse root debris (∼21.6 MgC·ha⁻¹; [41,42,49]) and the forest floor and aboveground coarse woody debris (∼33.83 MgC·ha⁻¹; [61]) was assumed to be 55.4 MgC·ha⁻¹. This value was similar to the amount of 60.0 MgC·ha⁻¹ reported for a slash pine plantation following a clearcut harvest [62]. Thinning intensity removal was set at 33% of the living trees. Details of the four management scenarios used in the modeling effort are shown in Table 3. Silvicultural-scenario simulations were performed with the growth and yield functions for the lower Coastal Plain physiographic region.

Emissions of C by silvicultural activities were determined from Markewitz [63] and White et al. [64]. Carbon release in transportation of raw material from the forest to the mill was estimated according to White et al. [64], assuming an average haul distance of 100 km from the forest to the mill, load per logging truck of 24 m³, and fuel economy of the diesel logging truck of 2.6 km 1⁻¹. Details of C emissions are presented in Table 4.

Sensitivity analyses were carried out to determine the effects of changes in key parameter estimates on total C balance. The effects of site quality were assessed by evaluating the model under contrasting SI values of 15 and 30 m, which corresponded to the full range of site qualities observed in loblolly pine plantations in the southeastern US. After fertilization and weed control treatments, the model estimated that the effective SI increased from 15 to 18.2 m, and from 30 to 32.3 m. Initial stand density effects were evaluated by running the model under contrasting planting densities of 750 and 2250 trees·ha⁻¹. Forest floor accumulation and total net C stock were evaluated by using decomposition rates of 10 and 20%; values beyond the extremes of 12 and 17% reported for this region [26,27,65]. In addition to the silvicultural management scenarios tested, rotation length effects were assessed by evaluating the model under the PP scenario for 18 and 35 years. Average product life span was evaluated by changing the proportion of products in different life span classes. In the case of ST and CNS, the proportion of products in the long life classes (50 years) were changed by 25% (step up and down), distributing the residual proportion in equal parts to the rest of the life span classes. Sensitivity analyses to industrial conversion efficiencies were not considered due to their low impact on ex situ C stocks [14].

The effects of thinning (as a percentage of living trees removed) were assessed by evaluating the model under different combinations of thinning age (8, 12 and 16 years) and intensity (20, 40 and 60% of living trees removal). Stand and management characteristics were set as those described previously for ST1 (i.e., base SI = 22 m, N = 1500 trees·ha⁻¹, bedding, weed control at planting and at age 1, and NP fertilization at age 5–10–15 years).

We compared estimates of loblolly pine C stocks with those of slash pine using a previously reported model [14] that was updated with relationships used to estimate LAI, litterfall and forest floor accumulation [24]. Initial parameter estimates such as site index, planting density, site preparation, weed control, fertilization and rotation length were set equal for both species. An analysis for unthinned stands with rotations length of 22 and 35 years was performed. For both species, two NP fertilizations were set at ages 5 and at age 10 years (120 kg·ha⁻¹ diamonium phosphate + 210 kg·ha⁻¹ urea, and 120 kg·ha⁻¹ diamonium phosphate + 430 kg·ha⁻¹ urea, respectively).

Net C stock was defined as: Net C stock = Total C in situ (C stored in living loblolly pine trees + understory + forest floor + coarse woody debris + standing dead trees) + Total C ex situ (C stored in wood products ST + CNS + PW) − Total C cost (silvicultural activities, including transportation of supplies). For all simulations, estimates of average C stock were reported as the average of all yearly values from the first ∼200 years of management, stopping the simulation at the end of the rotation closest to the 200 year endpoint (not stopping the simulations midway into a rotation). For the scenarios with rotation length of 18, 22 and 35 years, the number of rotations simulated was 11, 9 and 6, respectively. This simulation length was chosen to be sufficiently long to approach steady state values for ex situ pools, while remaining within plausible bounds for consideration of future forest management scenarios. While it is likely that “true” steady state would be achieved for simulations of 500–1000 years, these time periods are far beyond the time horizon over which forest management planning occurs.

Four measures of accuracy were used to evaluate the “goodness of fit” between observed and predicted (simulated) values for each variable from the dataset obtained in the model validation [66-70]: (i) Mean absolute error (MAE); (ii) Root mean square error (RMSE); (iii) Mean bias error (Bias); and (iv) Pearson product-moment correlation coefficient (R).

There was no statistical difference between average NEP modeled (NEP_EST) and measured NEP (NEP_EC) from ages 15 to 21 years (P = 0.77), averaging 4.46 and 4.63 MgC·ha⁻¹·year⁻¹ for NEP_EST and NEP_EC, respectively (Table 5; Figure 2). Predicted values were in the mid-range of variation of observed values.

Estimations of C stock in aboveground (AG_C), belowground (BG_C) and total (T_C) live pine were well correlated with values reported for stands of different ages and productivity (R² > 0.94; Figure 3a, b and c). In all cases, the intercept and slope of these relationships were not different from zero and one, respectively (P > 0.27).

All model performance tests showed that NEP, T_C, AG_C and BG_C estimations agreed well with measured values (Table 5). For the whole-model validation of C stock estimations, MAE and RMSE ranged between 15.3 to 16.7%, and 12.8 to 14.3% of the observed C stock values, respectively. On the other hand, MAE and RMSE for the validation of the allometric equations for biomass estimation (that was carried out with sites were SI was not reported), ranged between 11.1 to 23.2%, and 8.6 to 21.4% of the observed C stock values, respectively. In all cases, BG_C estimations presented the larger differences between the observed and predicted values. The Bias of the validation of allometry ranged between 1.5% under-estimations for AG_C to 6.5% over-estimations for BG_C, whereas Bias for the whole-model validation ranged between 0.1% under-estimations for AG_C to 8.5% over-estimations for BG_C, with no clear tendency to under- or over-estimate the results. Estimated and observed values of C stock were highly correlated, with R values greater than 0.94. In the case of NEP, the model was less accurate than the C stock estimates, even though the mean was not different between the observed and predicted values. MAE and RMSE were 28.1 and 30.5 % of the observed values, respectively. The Bias showed a 3.7% underestimation.

One-thinned or two-thinned stands with longer rotation ages (35 years) stored only 4% or 1% more C, respectively, than unthinned stands harvested at age 22 years. Under conditions used in the simulations, average net C stock, which corresponded to total C in situ (loblolly pine + understory + forest floor + coarse woody debris + standing dead trees) plus total C ex situ (C in woody products ST + CNS + PW) minus total C cost of silvicultural activities (including transport), averaged 176,179,183 and 178 MgC·ha⁻¹ for PP, ST1, ST2 and ST3, respectively, for a 200 year simulation period (Figure 4). In situ C stock accounted for between 62% and 71% of the gross C sequestration (not including silvicultural C costs) across silvicultural regimes. The magnitude of emissions associated with silvicultural activities (including transportation) was between 1.7% and 1.8% of the gross C stock. The relative impact on C sequestration for the different woody products depended on the silvicultural regimes; ST accounted for 40%, 60%, 75% and 74% of total C ex situ for PP, ST1, ST2 and ST3, respectively. In contrast, CNS followed an opposite trend, accounting for 56%, 37%, 23% and 23% for the PP, ST1, ST2 and ST3 silvicultural regimes, respectively. Across different silvicultural management systems, the forest floor + dead trees + coarse woody debris (FF_D) components averaged 42 MgC·ha⁻¹ (between 31% to 39% of total in situ C stock) and the understory averaged 1.0 MgC·ha⁻¹ (less than 1% of total in situ C stock).

For a 22-year rotation length, removing 33% of the living trees by thinning at age 10 (ST1) had an almost null effect on average net C stock, increasing C storage by about 2.7 MgC·ha⁻¹. Even though there was a 5.8 MgC·ha⁻¹ reduction in living loblolly pine C stock, there was a 9.1 MgC·ha⁻¹ increment in woody products (mostly due to larger ST production). Extending the rotation age to 35 years with one thinning at age 10 (ST2) increased average net C stock by 4.3 MgC·ha⁻¹ when compared with ST1. The similar C stock between both scenarios was caused mainly by the counteracting effects of larger accumulations in living loblolly pine biomass (22.4 MgC·ha⁻¹ increment) and by a reduction in ex situ C (15.4 MgC·ha⁻¹ decrease). If a second thinning was carried out at age 17 years with the extended rotation scenario (ST3), average net C stock decreased by 5.2 MgC·ha⁻¹, compared with ST2. Most of this decrease was due to lower C accumulation in living loblolly pine (7.8 MgC·ha⁻¹ reduction), which was partially counteracted by an increment in the ex situ C stock of 3.6 MgC·ha⁻¹.

From a total silviculture C cost perspective, fertilization accounted for 0.92 MgC·ha⁻¹ (three fertilizations), representing between 28% and 30% of the total silvicultural C emissions. Harvest and transportation of woody products accounted for between 1.42 and 1.84 MgC·ha⁻¹, representing about 47 to 55 % of the total silvicultural C emissions.

In general, after ∼200 years, C flux in the woody products converged to stable values, reaching quasi-equilibrium minimum and maximum values. At their respective rotation ages, in situ C stocks were 166, 161, 169 and 158 MgC·ha⁻¹ for the PP, ST1, ST2 and ST3 scenarios, respectively (Figure 5). From that total, the C stock in living loblolly pine and the understory was 128, 126, 131 and 124 MgC·ha⁻¹ for the same silvicultural regimes, respectively (data not shown). Total woody products C stock increased each rotation from 80, 77, 95 and 90 MgC·ha⁻¹ during the first rotation, up to 142, 150, 153 and 150 MgC·ha⁻¹ at the end of the rotation closest to the 200 year endpoint, for the PP, ST1, ST2 and ST3 scenarios, respectively (Figure 5). This result implies that in the long term, for extended rotations, C storage fluxes in woody products were similar than the amount of C stored in living loblolly pine.

Differences in tree size (diameter and height) and number of trees remaining due to different thinning and rotation age scenarios created different woody products pools that had different life spans. While PW represented 4.2%, 3.4%, 2.3% and 2.6% of the total C extracted in products at harvest, ST accounted for 40%, 60%, 75% and 74% of that C for the PP, ST1, ST2 and ST3 scenarios, respectively. In general, C stored in products derived from PW (e.g., paper, packing material, office supplies, etc.) had a negligible effect on net C sequestration; between harvest events (thinning or clear cutting) the amount of C stored in products derived from pulpwood diminished towards zero, while C stored in CNS and ST increased between harvests (Figure 6).

With all other parameters held constant, site quality (or potential productivity) reflected in SI of the stand, was the major factor controlling C sequestration (Table 6). For example, on low productivity sites (e.g., base SI = 15 m), average net C stocks were about 28% lower than with the default site quality (base SI = 22 m). In contrast, for high quality sites (e.g., base SI = 30 m), C stocks across silvicultural regimes averaged about 38% greater than base SI = 22 (Table 6). When SI was set equal to 15 m, ex situ C stocks were largely reduced for sawtimber-oriented silvicultural regimes. In situ C stocks were reduced 21.6, 20, 25.6 and 24 MgC·ha⁻¹, and ex situ C stocks were reduced 28, 30, 26.4 and 26.8 MgC·ha⁻¹, for PP, ST1, ST2 and ST3, respectively. By comparison, for base SI = 30 m, in situ C stocks increased 25, 23, 30 and 28 MgC·ha⁻¹, and ex situ C stock augmented 44.6, 44.7, 39.3 and 40 MgC·ha⁻¹, for the PP, ST1, ST2 and ST3 management regimes, respectively. The silvicultural treatments included in our analysis, that improved SI by 3.2, 2.3 and 2.3 m, for stands with base Si of 15, 22 and 30 m, respectively, produced an increase in net C stock of 12.5, 12.4 and 12.8 MgC·ha⁻¹, respectively, across management regimes (Table 6).

The effects of planting density were largely reflected in in situ rather than ex situ C pools. Reducing the initial planting density decreased net C stocks up to 10%, while increasing the planting density enhanced net C stocks by about 6% among silvicultural regimes. By lowering planting density from 1500 trees·ha⁻¹ to 750 trees·ha⁻¹, the average net C stocks decreased 16.6,17.3,19 and 18.9 MgC·ha⁻¹ for the PP, ST1, ST2 and ST3 management systems, respectively. This reduction was explained principally by a decrease in the in situ C stocks of 11.7, 10.5, 10.7 and 10.3 MgC·ha⁻¹ for the same silvicultural regimes. The effects on ex situ C stocks were substantially smaller, producing gains of 2.4 MgC·ha⁻¹ for the PP, but reductions of 0.1, 2.2 and 3.1 MgC·ha⁻¹ for the ST1, ST2 and ST3 management systems, respectively. By increasing the initial planting density from 1500 trees·ha⁻¹ to 2250 trees·ha⁻¹, average net C stocks were increased 10.7, 9.5, 11.2 and 11 MgC·ha⁻¹ compared to the default initial planting density for the PP, ST1, ST2 and ST3 treatments, respectively. For the high initial planting density, in situ C stocks were increased 8.1, 7.2, 6.8 and 6.6 MgC·ha⁻¹ for the PP, ST1, ST2 and ST3 silvicultural management systems, respectively. The C storage in woody products was reduced by 2.7, 2.4 MgC·ha⁻¹ for the PP and ST1 management regimes, but was increased by 0.2 and 0.7 MgC·ha⁻¹ for the ST2 and ST3 scenarios, respectively.

Shortening the rotation length to 18 years under the PP scenario (unthinned), which corresponded to the biological rotation age for that silvicultural scenario (i.e., the time when mean annual increment equals periodic annual increment), reduced the average net C stock to 164.2 MgC·ha⁻¹. This 7% decrease, when compared with the default 22-year-rotation, was caused mainly by reductions in the in situ rather than ex situ C stocks (10.8 and 1.4 MgC·ha⁻¹ reductions, respectively). When the rotation age was extended to 35 years, average net C stocks increased to 183.2 MgC·ha⁻¹.

When the C decay rate was reduced to 10% or increased to 20%, average net C stocks increased or decreased between 7.5 and 6.6%, respectively, across silvicultural regimes (Table 6). Assuming a decay rate of 10%, C storage in the FF_D increased to about 13.3 MgC·ha⁻¹, across silvicultural regimes, compared to a decay rate of 15%. When the decay rate was increased up to 20%, the C stocks in the FF_D were reduced to about 11.8 MgC·ha⁻¹.

Variations in average life span of ST and CNS woody products affected net C stock. On the other hand, paper product life span had a smaller effect on net C storage (Table 6). When the average life span of ST was increased by increasing the product proportion in the long-lived class (half-life 50 years, Table 2) from 50% to 75%, average net C stocks were increased 7.3, 12.6, 11.9 and 12.6 MgC·ha⁻¹ for the PP, ST1, ST2 and ST3 management regimes, respectively; this represented an increment of between 4.1 and 7.1% (Table 6). On the other hand, when the ST proportion of long life span products was stepped-down to 25%, reductions in C increments of the same magnitude were observed. The impact of the CNS half-life on net C stocks was more important in the PP than the sawtimber-oriented regimes, changing average net C stocks by 12.2, 9.2, 4.3 and 4.6 MgC·ha⁻¹ for the PP, ST1, ST2 and ST3 management scenarios, respectively (Table 6). When all PW products were set to have a life span of one year, average net C stocks was reduced between 0.42 and 0.86 MgC·ha⁻¹. Conversely, when the PW products life span was increased by assuming that 67% of PW products would have a life span of four years, the average net C stock was increased between 0.45 and 0.89 MgC·ha⁻¹.

In general, for a base SI = 22 m stand with 1500 trees·ha⁻¹ and managed under a 22-year rotation, thinning had a positive on net C stock. The negative effect of thinning on the in situ C stock was largely counteracted by increasing the ex situ C stock, producing an increase in average net C stock (Figure 7).

For any given thinning intensity, there was a small effect due to the age of thinning on the in situ C stock (Figure 7a). For example, using a thinning intensity of 20% at ages 8, 12 and 16 years reduced the in situ C stock by 3.1, 3.4 and 3.1 MgC·ha⁻¹, respectively (Figure 7a). Increments in thinning intensity produced a quasi-constant decline in in situ C stock, independent of thinning age. For instance, in situ C stocks were reduced 3.2, 7.9 and 13 MgC·ha⁻¹, when thinning intensities were set at 20, 40, and 60% removal of living trees, respectively (Figure 7b). Ex situ C storage had an opposite response to thinning; the more intensive the thinning regime, the more gain in woody products C storage. Ex situ C storage was slightly increased by thinning age for any given thinning intensity. For example, when thinning age was set at 8,12 and 16 years, with a thinning intensity of 20%, the ex situ C stock was increased by 5.6, 5.2 and 4.7 MgC·ha⁻¹, respectively; when thinning intensity was set to 60%, ex situ C stocks were increased 19.4, 19.9 and 17.7 MgC·ha⁻¹, respectively (Figure 7c). Increments in thinning intensity produced a quasi-constant increment in ex situ C stock independent of thinning age. Ex situ C stocks were increased 5.2, 11.5 and 19 MgC·ha⁻¹, when thinning intensities were set at 20, 40 and 60% removal of living trees, respectively (Figure 7d). Due to the larger positive effects on ex situ C storage, rather than smaller negative effects on in situ C storage, net C stocks were slightly increased by thinning (Figure 7e and f).

Under the default parameters used for simulations in unthinned loblolly pine stands harvested at age 22 years, loblolly and slash pine net C stocks were generally similar, albeit on an absolute basis loblolly pine accumulated 1.3 Mg·ha⁻¹ more C than slash pine (Figure 8). This 1% difference in favor of loblolly pine was explained mainly by increases in stored C in the living tree biomass (2.8 MgC·ha⁻¹) and FF_D (3.8 MgC·ha⁻¹) pools. It should be noted, however, that a 4.6 MgC·ha⁻¹ decrease in the loblolly pine woody products pool partially counteracted that gain. Understory biomass was 0.7 MgC·ha⁻¹ larger in slash pine than in loblolly pine stands. When the rotation length was increased to 35 years without thinning, slash pine C stocks exceeded loblolly pine, averaging 11.9 MgC·ha⁻¹ more net C stock. This difference was explained mainly through increases in net C stored in the living tree (7.8 MgC·ha⁻¹) and ex situ biomass (5.3 MgC·ha⁻¹) pools. Understory C accumulation was 0.9 MgC·ha⁻¹ larger in slash pine than in loblolly pine stands, and the FF_D was 2.1 MgC·ha⁻¹ larger in loblolly than in slash pine stands.