@article{cook_fox_allen_cohrs_ribas-costa_trlica_ricker_carter_rubilar_campoe_et al._2024, title={Forest soil classification for intensive pine plantation management: "Site Productivity Optimization for Trees" system}, volume={556}, ISSN={["1872-7042"]}, DOI={10.1016/j.foreco.2024.121732}, abstractNote={Forest productivity and response to silvicultural treatments are dependent on inherent site resource availability and limitations. Trees have deeper rooting profiles than agronomic crops, so evaluating the impacts of soils, geology, and physiographic province on forest productivity can help guide silvicultural management decisions in southern pine plantations. Here, we describe the Forest Productivity Cooperative’s “Site Productivity Optimization for Trees” (SPOT) system which includes: texture, depth to increase in clay content, drainage class, soil modifiers (i.e., surface attributes, mineralogy, and additional limitations such as root restrictions), geologic formations, and physiographic province. We quantified the total area for each SPOT code in the native range of loblolly pine (Pinus taeda L.), the region’s most commercially important species, and used a remotely-sensed layer to quantify SPOT code areas in managed southern pine (approximately 14 million ha). The most common SPOT code in the native range is also the most planted, a B2WekoGgPD (fine loamy, shallow depth to increase in clay, well-drained, eroded, kaolinitic, granitic, Piedmont soil), spanning 1.1 million ha total, but only 12% in managed southern pine. However, the SPOT code with the greatest percentage of managed southern pine (61%; a D4PoioAmAF, spodic, deep to increase in clay, siliceous, middle Atlantic Coastal Plain, Flatwoods soil) was the 20th most common in the native range with 474,662 ha. We used machine learning and data from decades of “Regionwide” trials to assess the variable importance of SPOT constituents, climate, planting year, and N rate on site index (base age 25 years) and found that planting year was the most important variable, showing an increase of 17 cm site index per year since 1970, followed by maximum vapor pressure deficit, and precipitation. Geology was the top-ranking SPOT variable to explain site index followed by physiographic province. The Regionwide trials represent 72 unique SPOT codes (out of over 10,000 possible in the pine plantations) and approximately one million ha (or about 7% of all soils identified as supporting managed pine). To extrapolate site index values outside of the unique soil and geologic conditions empirically represented, we created a predictive model with an R2 of 0.79 and an RMSE of 1.38 m from SPOT codes alone. With this extrapolation, the Regionwide data predicts 10.5 million ha, or 74%, of all soils under loblolly pine management in its native range. Overall, this system will allow managers to assess their current site productivity, and recommend silvicultural treatments, thus, providing a framework to optimize forest productivity in pine plantations in the southeastern US.}, journal={FOREST ECOLOGY AND MANAGEMENT}, author={Cook, Rachel and Fox, Thomas R. and Allen, Howard Lee and Cohrs, Chris W. and Ribas-Costa, Vicent and Trlica, Andrew and Ricker, Matthew and Carter, David R. and Rubilar, Rafael and Campoe, Otavio and et al.}, year={2024}, month={Mar} }
 @article{puls_cook_baker_rakestraw_trlica_2024, title={Modeling wood product carbon flows in southern us pine plantations: implications for carbon storage}, volume={19}, ISSN={["1750-0680"]}, DOI={10.1186/s13021-024-00254-4}, abstractNote={Abstract}, number={1}, journal={CARBON BALANCE AND MANAGEMENT}, author={Puls, Sarah J. and Cook, Rachel L. and Baker, Justin S. and Rakestraw, James L. and Trlica, Andrew}, year={2024}, month={Feb} }
 @article{sumnall_trlica_carter_cook_schulte_campoe_rubilar_wynne_thomas_2021, title={Estimating the overstory and understory vertical extents and their leaf area index in intensively managed loblolly pine (Pinus taeda L.) plantations using airborne laser scanning}, volume={254}, ISSN={["1879-0704"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85097899818&partnerID=MN8TOARS}, DOI={10.1016/j.rse.2020.112250}, abstractNote={Data from four discrete-return airborne laser scanning (ALS) acquisitions and three different sensor types across seven experimentally varied loblolly pine (Pinus taeda L.) plantations were used to test published and novel methodologies in quantifying forest structural attributes within stands, including height to live crown (HTLC; i.e. the lowest vertical canopy extent) of the canopy and the contributions to total plot-level leaf area from understory and overstory canopy vegetation. These ALS data were compared to in situ field measurements to develop ALS-based predictive models of these attributes. The correlation between field- and ALS-modeled HTLC data was strong, with an R2 of 0.79 (p < 0.001). We assessed the ability of eight lidar light penetration indices to estimate effective leaf area index (eLAI) in the field. The best predictor of total (sum of understory and overstory) eLAI produced an R2 of 0.88 (p < 0.001). The independent contributions of overstory and understory components could also be accurately predicted by ALS-derived canopy-only eLAI metrics (R2 = 0.71; p < 0.001) and understory-only metrics (R2 = 0.49; p < 0.001). Two new indices, calculated as the sum of return intensity for each foliar layer and correcting for transmission losses, were developed specifically for the vertical strata related to the understory (BLunder) or overstory (BLover). The estimates from BLover were equivalent to the best-performing indices for predicting canopy-only eLAI and the corresponding BLunder was superior to other indices for understory eLAI. The broad spatial and temporal extents of the data, as well as the inclusion of pine plantations with differing stand ages, planting densities, understory control, and thinning treatments, suggest the relationships generated from these methods are robust to site and seasonal variability. The results produced from the analysis of multiple acquisitions implies that the methods presented here are transferable across location, time and sensor design, without implementation-specific calibration, at least for structurally similar loblolly pine plantations.}, journal={REMOTE SENSING OF ENVIRONMENT}, author={Sumnall, Matthew J. and Trlica, Andrew and Carter, David R. and Cook, Rachel L. and Schulte, Morgan L. and Campoe, Otavio C. and Rubilar, Rafael A. and Wynne, Randolph H. and Thomas, Valerie A.}, year={2021}, month={Mar} }
 @article{trlica_cook_albaugh_parajuli_carter_rubilar_2021, title={Financial Returns for Biomass on Short-Rotation Loblolly Pine Plantations in the Southeastern United States}, volume={67}, ISSN={["1938-3738"]}, url={https://doi.org/10.1093/forsci/fxab033}, DOI={10.1093/forsci/fxab033}, abstractNote={Abstract}, number={6}, journal={FOREST SCIENCE}, publisher={Oxford University Press (OUP)}, author={Trlica, Andrew and Cook, Rachel L. and Albaugh, Timothy J. and Parajuli, Rajan and Carter, David R. and Rubilar, Rafael A.}, year={2021}, month={Dec}, pages={670–681} }