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Abstract Using extensive legacy and new datasets, two equations of whole-tree green density, defined as the ratio of the green weight of stem wood with bark (GWob) divided either by the stem outside-bark volume (Vob) or by the stem inside-bark volume (Vib), were developed along with individual tree Vob, Vib, and GWob equations for loblolly and slash pines. The green density equations indicated that the GWob/Vob ratio increases while the GWob/Vib ratio decreases with an increase in tree size for both species. The transition from low-intensity management to intensive management has a notable impact on tree green weight characteristics. Generally, trees from older established plantations exhibited a higher GWob/Vob ratio compared with trees from more recently established plantations, spanning both loblolly and slash pines. Study Implications: Derived whole-tree green density equations, alongside updated stem green weight and volume equations, are valuable tools for estimating the volume and green weight of entire stem boles, facilitating volume-to-green-weight conversion for specific sections for loblolly and slash pines, the primary commercial timber species in the southern United States. 

Abstract Over the last three decades, many growth and yield systems developed for the southeast USA have incorporated methods to create a compatible basal area (BA) prediction and projection equation. This technique allows practitioners to calibrate BA models using both measurements at a given arbitrary age, as well as the increment in BA when time series panel data are available. As a result, model parameters for either prediction or projection alternatives are compatible. One caveat of this methodology is that pairs of observations used to project forward have the same weight as observations from a single measurement age, regardless of the projection time interval. To address this problem, we introduce a variance–covariance structure giving different weights to predictions with variable intervals. To test this approach, prediction and projection equations were fitted simultaneously using an ad hoc matrix structure. We tested three different error structures in fitting models with (i) homoscedastic errors described by a single parameter (Method 1); (ii) heteroscedastic errors described with a weighting factor $${w}_t$$ (Method 2); and (iii) errors including both prediction ($$\overset{\smile }{\varepsilon }$$) and projection errors ($$\tilde{\varepsilon}$$) in the weighting factor $${w}_t$$ (Method 3). A rotation-age dataset covering nine sites, each including four blocks with four silvicultural treatments per block, was used for model calibration and validation, including explicit terms for each treatment. Fitting using an error structure which incorporated the combined error term ($$\overset{\smile }{\varepsilon }$$ and $$\tilde{\varepsilon}$$) into the weighting factor $${w}_t$$ (Method 3), generated better results according to the root mean square error with respect to the other two methods evaluated. Also, the system of equations that incorporated silvicultural treatments as dummy variables generated lower root mean square error (RMSE) and Akaike’s index values (AIC) in all methods. Our results show a substantial improvement over the current prediction-projection approach, resulting in consistent estimators for BA. 

Growth and yield models are essential tools in modern forestry, especially for intensively managed loblolly pine plantations in the southeastern United States. While model developers often have a good idea of where these models should be used with respect to geographic location, determining geographic bounds for model usage can be daunting. Such bounds provide suitable areas where model predictions are likely to behave as expected or identify areas where models may do a poor job of characterizing the growth of a resource. In this research, we adapted a niche model methodology, commonly used to identify suitable spots for species occurrence (maximum entropy), to identify areas for using growth and yield models built from plots established in the Lower Coastal Plain and Piedmont/Upper Coastal Plain in the southeastern United States. The results from this analysis identify areas with similar climatic envelopes and soil properties to the areas where data was collected to fit these growth and yield models. These areas show notable overlap with the areas prescribed for use by the evaluated growth and yield models and support practitioners use of these models throughout these regions. Furthermore, this methodology can be applied to different forest models built using large regional extents as long as climatic and soil values are available for each site. 

Abstract Mid-rotation silvicultural treatments (MRT) are commonly applied to loblolly pine (Pinus taeda L.) plantations in the southeastern United States to improve pine productivity. Competing vegetation is often present in operational plantations and limits site resource availability. The benefits of MRT for pine productivity are well known, but competing vegetation growth has not been extensively studied. Pine and competing vegetation growth within two regions of the southeastern United States was monitored for 8 years following a one-time post-thin application of either fertilization (224 kg ha-1 of nitrogen plus 28 kg ha-1 phosphorus), chemical herbicide (0.8 oz glyphosate and 0.8 oz triclopyr L-1 of water) or their combination. Fertilization significantly increased pine volume growth in the Lower Coastal Plain (LCP, 2.67-4.01 m3ha-1yr-1) and the Upper Coastal Plain/ Piedmont (UCPIE, 0.20-3.72 m3ha-1yr-1). Chemical herbicide application in both the LCP (0.34-4.87 m3 ha-1yr-1) and UCPIE (0.89-1.97 m3 ha-1yr-1) also significantly increased pine volume. Chemical herbicide application, individually and combined, did not result in significant decreases in herbaceous vegetation, but reduced woody vegetation by up to -2.40 m3 ha-1yr-1 in the LCP and -5.67 m3 ha-1yr-1 in the UCPIE. Consequently, we suggest that competing vegetation response should be considered within site-specific management plans aimed at maximizing pine productivity.