@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{albaugh_albaugh_carter_cook_cohrs_rubilar_campoe_2021, title={Duration of response to nitrogen and phosphorus applications in mid-rotation Pinus taeda}, volume={498}, ISSN={["1872-7042"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85112486933&partnerID=MN8TOARS}, DOI={10.1016/j.foreco.2021.119578}, abstractNote={We quantified the response duration to one-time applications of 112, 224, and 336 kg ha−1 of elemental nitrogen (112 N, 224 N, and 336 N, respectively) combined with 28 or 56 kg ha−1 of elemental phosphorus in mid-rotation Pinus taeda L. stands. Post-application measurements continued for 10 years at 32 sites in the southeastern United States and one site in Argentina, and we fit a Ricker model to data from each treatment in the event that a zero growth response was not observed in our measured data. The response duration was eight (measured), 14 (modeled), and 16 (modeled) years after treatment for the respective 112 N, 224 N, and 336 N treatments. The corresponding growth response per unit of applied nitrogen estimated from fertilization to when the growth response was not different from zero (whether measured or modeled) was 0.20, 0.16, and 0.13 m3 kg−1 for the 112 N, 224 N, and 336 N treatments, respectively. We hypothesized that the mechanism controlling the response duration was related to the amount of fertilizer nitrogen remaining in the foliage over time after treatment; previous studies found that nitrogen application had large impacts on the foliage amount and foliar nitrogen content. Based on retranslocation rate estimates from the literature of 67% of fertilizer nitrogen per year, our results suggest that a good correlation exists between the growth response and the amount of fertilizer nitrogen remaining in the foliage.}, journal={FOREST ECOLOGY AND MANAGEMENT}, author={Albaugh, Timothy J. and Albaugh, Janine M. and Carter, David R. and Cook, Rachel L. and Cohrs, Chris W. and Rubilar, Rafael A. and Campoe, Otavio C.}, year={2021}, month={Oct} }
 @article{gao_gray_cohrs_cook_albaugh_2021, title={Longer greenup periods associated with greater wood volume growth in managed pine stands}, volume={297}, ISSN={["1873-2240"]}, url={https://doi.org/10.1016/j.agrformet.2020.108237}, DOI={10.1016/j.agrformet.2020.108237}, abstractNote={Increasing forest productivity is important to meet future demand for forest products, and to improve resilience in the face of climate change. Forest productivity depends on many things, but the timing of leaf development (hereafter: “plant phenology”) is especially important. However, our understanding of how plant phenology affects the productivity of managed forests, and how silviculture may in turn affect phenology, has been limited because of the spatial scale mismatch between phenological data and field experimental observations. In this study, we take advantage of a new 30 m satellite land surface phenology dataset and stand growth measurements from long-term experimental pine plantation sites in the southeastern United States to investigate the question: is stand growth related to remotely sensed phenology metrics? Multiple linear regression and random forest models were fitted to quantify the effect of phenology and silvicultural treatments on stand growth. We found that 1) Greater wood volume growth was associated with longer green up periods; 2) Fertilization elevated EVI2 measurement values during the whole growing season, especially in the winter; 3) Competing vegetation could affect remotely sensed observations and complicates interpretation of remotely sensed phenology metrics.}, journal={AGRICULTURAL AND FOREST METEOROLOGY}, publisher={Elsevier BV}, author={Gao, Xiaojie and Gray, Josh and Cohrs, Chris W. and Cook, Rachel and Albaugh, Timothy J.}, year={2021}, month={Feb} }
 @article{cohrs_cook_gray_albaugh_2020, title={Sentinel-2 Leaf Area Index Estimation for Pine Plantations in the Southeastern United States}, volume={12}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs12091406}, DOI={10.3390/rs12091406}, abstractNote={Leaf area index (LAI) is an important biophysical indicator of forest health that is linearly related to productivity, serving as a key criterion for potential nutrient management. A single equation was produced to model surface reflectance values captured from the Sentinel-2 Multispectral Instrument (MSI) with a robust dataset of field observations of loblolly pine (Pinus taeda L.) LAI collected with a LAI-2200C plant canopy analyzer. Support vector machine (SVM)-supervised classification was used to improve the model fit by removing plots saturated with aberrant radiometric signatures that would not be captured in the association between Sentinel-2 and LAI-2200C. The resulting equation, LAI = 0.310SR − 0.098 (where SR = the simple ratio between near-infrared (NIR) and red bands), displayed good performance (     R 2      = 0.81, RMSE = 0.36) at estimating the LAI for loblolly pine within the analyzed region at a 10 m spatial resolution. Our model incorporated a high number of validation plots (n = 292) spanning from southern Virginia to northern Florida across a range of soil textures (sandy to clayey), drainage classes (well drained to very poorly drained), and site characteristics common to pine forest plantations in the southeastern United States. The training dataset included plot-level treatment metrics—silviculture intensity, genetics, and density—on which sensitivity analysis was performed to inform model fit behavior. Plot density, particularly when there were ≤618 trees per hectare, was shown to impact model performance, causing LAI estimates to be overpredicted (to a maximum of      X i      + 0.16). Silviculture intensity (competition control and fertilization rates) and genetics did not markedly impact the relationship between SR and LAI. Results indicate that Sentinel-2’s improved spatial resolution and temporal revisit interval provide new opportunities for managers to detect within-stand variance and improve accuracy for LAI estimation over current industry standard models.}, number={9}, journal={Remote Sensing}, publisher={MDPI AG}, author={Cohrs, Chris W. and Cook, Rachel L. and Gray, Josh M. and Albaugh, Timothy J.}, year={2020}, month={Apr}, pages={1406} }