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We evaluated three objectives for loblolly pine (Pinus taeda L.) trees age five or less: 1) how height growth varies by soil type and silvicultural intensity, 2) the accuracy of predicted base-age 25 site index (SI25) using age one to five heights, and 3) if height dominance exhibited early in the rotation is maintained throughout the rotation. Data from 42 sites across the southeastern United States with an array of soil textures and management intensities (optimal, intensive, and operational) were used. Management intensity and soils significantly affected tree height. Coarse loamy soils were the most responsive to increasing management intensity. At age four, tree heights were greatest in the optimal group (4.63 m), followed by the intensive (4.31 m), and then the operational (3.06 m). Organic soils do not appear to respond to maximum management intensity. Predictability of SI25 was high especially starting at age four, with R2 values ranging from 0.27 for the age four intensive group to 0.78 for the age four operational group. The optimal group had the greatest slope with an expected increase of 2.61, 2.75, 1.88, and 1.78 m in site index per additional meter of height at ages two, three, four, or five, respectively. Data from six different study sites indicate, the tallest (class one) and smallest (class five) trees changed percentile class the least often over time. As early as age two, over 40 % of observations in classes one and five had zero changes in class through age 13. Young tree data were effective in predicting SI25, and height dominance appeared generally set early in the rotation. 

Forest productivity is one of the most important aspects of forest management, landscape planning, and climate change assessment. However, although there are multiple elements known to affect productivity, most of them rely on the “nature” of the edaphic, climatic, and geographic conditions, and only some speci昀椀c aspects can be modi昀椀ed through forest management or “nurture”. Through evaluation of site resource availability and an understanding of the main drivers of productivity, management can present solutions to overcome site resource limitations to productivity. Therefore, understanding the implications of a speci昀椀c management regime requires understanding what drives productivity across large spatial extents and among different management regimes. In this study, we used data from over 1 million hectares of industrial forestland, covering over 6000 different soils and several management regimes of Pinus taeda L. plantations, as well as plot-based data from the Forest Inventory and Analysis (FIA) program, facilitating a comparison of planted and natural Pinus taeda stands. Combined with US Geological Survey LiDAR data, we computed site index and generated wall-to-wall productivity maps for planted Pinus taeda stands in the southeastern US, as well as point-based site index estimates for the FIA dataset. We modeled site index using a random forest algorithm considering edaphic, geologic, and physiographic province information based on the Forest Productivity Cooperative “SPOT” system, and also included climate and management history data. Our model predicted site index with an R2 of 0.701 and RMSE of 1.41 m on the industrial data and R2 of 0.417 and RMSE of 1.84 m for the FIA data. We found that year of establishment of the forest, physiographic province, and geology, were the most important drivers of site index. The soil classi昀椀cation modi昀椀er indicating root restrictions were the most important soil-speci昀椀c variable. Additionally, we found an average increase in site index of 3.05 m since the 1950s for all FIA data, and an average increase of 4.73 m for all industrial data since the 1970s. For the latest period analyzed (2000–2012), average site index in planted FIA plots was 1.2 m higher than naturally regenerated FIA plots, and site index in all industrial forestland had a site index almost 3 m greater than planted FIA plots. Overall, we believe this work sets the foundation for better understanding of forest productivity and highlights the importance of intensive silviculture to improve productivity, and as an additional tool to achieve the economic, environmental, and social objectives.