The ability to predict cumulative bole volume to any predefined upper stem diameter on a standing tree is essential for estimating current inventory levels and making informed decisions regarding the management of forest resources. Several types of mathematical models have been developed to predict cumulative bole volume, requiring only the collection of low cost data but high accuracy tree measurements. This paper reports on the comparison of a variable-exponent taper model and a volume-ratio model for estimating inside-bark cumulative bole volume to three predefined upper stem diameters using stem analysis data Pinus occidentalis trees in La Sierra, Dominican Republic. Each sampled tree was measured at multiple points on the bole, making observations spatially correlated within the tree. Inference problems due to autocorrelation were addressed by using a nonlinear mixed-effects model fitted by restricted maximum likelihood (REML) and including two random parameter coefficients at tree level in each of the two models tested. Using an independent validation data set, the variable-exponent taper model with two random parameters demonstrated better predictive ability as compared to the volume-ratio model when estimating cumulative bole volume to the three predefined upper stem diameters. The taper model allows flexible volume estimation for the population average as well for specific trees.

Predictive equations calibrated with remeasurement data from 25 permanent plots having individually identified trees were used to project stem diameter of Pinus occidentalis Sw. in Dominican Republic. The general form of the models used to fit the growth and yield functions included fixed effect covariates related to three subsets of explanatory variables: initial tree size, stand attributes, and competition indexes. The subsets were incrementally added in a stepwise fashion for each of the three response variables and the intercept of the model was allowed to vary randomly between plots. The models evaluated included a yield function that predicted future diameter at year t (dt), a periodic annual increment model using five-year diameter increment (id5) and its natural log transformation [ln(id5+0.01)]. For trees that were not remeasured exactly 5 years after the first measurement, id5 was computed by averaging the mean annual increment nearest the 5 year point and multiplying by five. Each approach was evaluated using an independent validation data set based on seven goodness-of-fit statistics, graphical display of residuals and the variance components of each model combination. LMM, including fixed and random parameters, provided a better fit among the models tested. For estimating future diameter, accuracy of predictions is within one centimeter for a five-year projection interval, and bias is negligible. All the models had moderately improved fit statistics when random effects were included in the evaluation. The degree of improvement behaved in a similar manner for most fit statistics, with differences in the range of values for MSE, RMSE and RMSE% of 0.53, 0.23 and 1.05, respectively. The absolute difference between the extreme values for Bias and relative Bias (%) in all the models was 0.20 and 0.92. The differences in values for MAD were only 0.15 and R2 values were practically equivalent.

Although precise species-specific aboveground tree biomass equations are needed in the Dominican Republic to quantify potential carbon storage in the context of climate change and sustainable forest management, there has been a lack of available information concerning total aboveground biomass and/or carbon content for naturally occurring Pinus occidentalis trees. The objectives of the study were to develop species-specific allometric biomass models for P. occidentalis, as well as to assess variation in carbon concentration among stem plus bark, branches and foliage by means of chemical analyses. Predictor variables included diameter at breast height (DBH) and total tree height (H), alone and in combination. Model fitting methods employing linear and nonlinear regression techniques were used and evaluated based on goodness-of-fit criteria. Two competing biomass models produced similar goodness-of-fit statistics: model 4, fitted by the Weighted Non-linear Least Squares technique, , and model 6, fitted by modeling the variance component and applying the Maximum Likelihood method, . Average carbon concentration (%) was highest in foliage tissue (49.8%), followed by branches (46.37%) and, lastly, stem plus bark (45.95%). On average, each individual tree is storing 175 kg of total aboveground carbon. To our knowledge, this is the first account reporting carbon fraction for this tropical species. Calculated wood carbon fractions from the study can be easily incorporated into forest C accounting, and may reduce errors in forest C valuations, which nowadays are performed using global estimates published elsewhere.

Pinus occidentalis and Pinus caribaea are important conifers in La Sierra, Dominican Republic, used to recover degraded ecosystems. A study on their wood density obtained from stem discs at three relative heights (RH) as a composite sample showed that wood density increased with age class for both species and decreased with RH in the stem. A mixed repeated measures analysis of variance, followed by a regression approach to the effects model, indicates a statistically significant interaction between species, age class (AC), RH, [ , P ≤0.000, partial =0.814]. Simple bidirectional interactions between factors and all simple main effects are statistically significant. Basic density (BD) increased with age class for both species and decreased with RH in the stem. Pinus occidentalis had a 14.6% higher average wood density and is endemic. Therefore, efforts must be made to provide better silvicultural practices so that its population continues to be a viable asset.

Pinus occidentalis (Swartz) is the primary timber species in the Dominican Republic (DR). Despite its economic importance, studies conducted on this species are scarce, making it difficult to estimate current inventory levels. This study aims to enhance the accuracy of estimating the total bole volume of P. occidentalis in different ecological zones (EZs) within La Sierra, evaluating and comparing two established volume equations—combined variable (CV) and Schumacher and Hall (S&H) across nine modeling variants. An indicator variables analysis determined the necessity of distinct equations for two EZs. Fitting included both linear and nonlinear models. Our comprehensive statistical analysis included goodness-of-fit metrics to evaluate each model variant’s performance rigorously. The second modeling variant (SH02) for the SH equation was most effective in the Dry Ecological Zone, showing superior performance in both the fitting and validation phases. Similarly, the third modeling variant (SH03) for the SH equation emerged as the best fit for the Combined Intermediate and Humid Ecological Zones, achieving the lowest overall ranking sum among tested variants. SH02 and SH03 provide reliable and precise volume estimations, allowing for the optimization of forestry management practices for P. occidentalis trees. The SH models outperformed the CV model variants’ consistency in parameter estimation. This tailored approach ensures more accurate volume predictions, which is crucial for sustainable management and conservation efforts.

La estimación de la calidad de sitios para la producción forestal normalmente se logra aprovechando la presencia de árboles que exhiben diferentes patrones de crecimiento en altura y desarrollando ecuaciones que relacionan su altura y edad. Sin embargo, hay lugares desprovistos de árboles donde la inversión en bosques productivos es factible; más aún si se puede estimar a priori la calidad potencial de regenerar determinadas especies forestales. Se desarrollaron modelos matemáticos regresivos para estimar el índice de sitio en rodales naturales de Pinus occidentalis Sw., en las principales regiones productivas de Republica Dominicana, estableciendo también la relación entre el índice de sitio y variables edáficas y fisiográficas. Los modelos permiten evaluar el potencial productivo en lugares desprovistos de árboles. No se encontraron diferencias significativas (α 0.01) entre los índices de sitio promediados por parcela en cada localidad, pero sí se obtuvo una correlación significativa entre el índice de sitio, altura sobre el nivel del mar y exposición de la respectivamente. Una correlación significativa fue también encontrada entre índice de sitio y contenido de materia orgánica , sodio y potasio . Las variables elevación sobre el nivel del mar y azimut resultaron estadísticamente significativas y pueden utilizarse para predecir el potencial productivo de lugares donde no existen árboles. La estimación del potencial productivo mediante variables edáficas y fisiográficas para los rodales naturales de P. occidentalis Sw. en las regiones productivas, constituye una herramienta indispensable en el manejo sostenible de estas áreas boscosas.