We explored the relationship between tree growth, water use, and related hydraulic traits in Populus deltoides Bartr. ex Marsh.and hybrid clones, to examine potential trade-offs between growth and water use efficiency. Nine genotypes, six P. deltoides and three hybrid clones, that represented genotypes with high (Group H), intermediate (Group I), and low (Group L) growth performance were selected for study, based on year-two standing stem biomass in a replicated field trial. In year four, tree growth, transpiration (E_t), canopy stomatal conductance (G_s), whole-tree hydraulic conductance (Gp), and carbon isotope discrimination (Δ¹³C) were measured. Tree sap flux was measured continuously using thermal dissipation probes. We hypothesized that Group H genotypes would have increased growth efficiency (GE), increased water use efficiency of production (WUEp, woody biomass growth/E_t), lower Δ¹³C, and greater Gp than slower growing genotypes. Tree GE increased with relative growth rate (RGR), and mean GE in Group H was significantly greater than L, but not I. Tree WUEp ranged between 1.7 and 3.9 kg biomass m³ H₂O⁻¹, which increased with RGR. At similar levels of E_t, WUEp was significantly greater in Group H (2.45 ± 0.20 kg m⁻³), compared to I (2.03 ± 0.18 kg m⁻³) or L (1.72 ± 0.23 kg m⁻³). Leaf and wood Δ¹³C scaled positively with stem biomass growth but was not correlated with WUEp. However, at a similar biomass increment, clones in Group H and I had significantly lower leaf Δ¹³C than Group L. Similarly, Group H clones had a significantly lower wood Δ¹³C than Group L, supporting our hypothesis of increased WUE in larger trees. Tree physiological and hydraulic traits partially explain differences in WUEp and Δ¹³C, and suggest that clone selection and management activities that increase tree biomass production will likely increase tree and stand WUE. However, more research is needed to discern the underlying hydraulic mechanisms responsible for the higher WUE exhibited by large trees and distinct clones.

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.

The competitive neighbourhood surrounding an individual tree can have a significant influence on its diameter at breast height (DBH) and individual tree stem volume (SV). Distance dependent competition index metrics are rarely recorded in traditional field campaigns because they are laborious to collect and are spatially limited. Remote sensing data could overcome these limitations while providing estimation of forest attributes over a large area. We used unoccupied aerial vehicle laser scanning data to delineate individual tree crowns (ITCs) and calculated crown size and distance-dependent competition indices to estimate DBH and SV. We contrasted two methods: (i) Random Forest (RF) and (ii) backwards-stepwise, linear multiple regression (LMR). We utilized an existing experiment in Pinus taeda L. plantations including multiple planting densities, genotypes and silvicultural levels. While the tree planting density did affect the correct delineation of ITCs, between 61% and 99% (mean 86%) were correctly linked to the planting location. The most accurate RF and LMR models all included metrics related to ITC size and competitive neighbourhood. The DBH estimates from RF and LMR were similar: RMSE 3.05 and 3.13 cm (R² 0.64 and 0.62), respectively. Estimates of SV from RF were slightly better than for LMR: RMSE 0.06 and 0.07 m³ (R² 0.77 and 0.70), respectively. Our results provide evidence that ITC size and competition index metrics may improve DBH and SV estimation accuracy when analysing laser-scanning data. The ability to provide accurate, and near-complete, forest inventories holds a great deal of potential for forest management planning.

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.