Acoustic time of flight and drill resistance (surrogates for wood stiffness and density, respectively) were measured on 11,097 standing trees from 269 pollen-mix families of loblolly pine (Pinus taeda L.) in 6- to 9-year-old progeny tests at eight sites across the southeastern United States. Specific gravity was measured on two test sites. The phenotypic correlation between specific gravity and drill resistance was moderate (r = 0.68), whereas the genetic correlation was very strong (r_g = 0.96). Narrow-sense heritabilities for acoustic time of flight and drill resistance were around 0.35 for individual trees and very strong (0.90) for family means. High genetic correlations (>0.80) between pairs of sites suggested a low genotype-by-environment interaction for both traits. Genetic correlations between wood quality traits and other economic traits (growth and stem straightness) were low except for a moderate correlation between acoustic time of flight and tree slenderness (r_g = –0.64). The checklot ranked near the middle for both wood quality traits, implying no inadvertent selection occurred in this population that has been selected intensively for volume productivity. This study is the first to apply these tools in a large breeding program, and results suggest they are effective for selecting genotypes for wood quality.

The use of genomic markers in forest tree breeding is expected to improve the response to selection, especially within family. To evaluate the potential improvements from genotyping, we analyzed a large Pinus taeda L. clonal population (1,831 cloned individuals) tested in multiple environments. Of the total, 723 clones from five full-sib families were genotyped using 10,337 single-nucleotide polymorphism markers. Single-step models with genomic and pedigree-based relationships produced similar heritability estimates. Breeding value predictions were greatly improved with inclusion of genomic relationships, even when clonal replication was abundant. The improvement was limited to genotyped individuals and attributable to accounting for the Mendelian sampling effect. Reducing clonal replication by omitting data indicated that genotyping improved breeding values similar to clonal replication. Genomic selection predictive ability (masking phenotypes) was greater for stem straightness (0.68) than for growth traits (0.41 to 0.44). Predictive ability for a new full-sibling family was poorer than when full-sibling relationships were present between model training and validation sets. Species that are difficult to propagate clonally can use genotyping to improve within-family selection. Clonal testing combined with genotyping can produce breeding value accuracies adequate to graft selections directly into deployment orchards without progeny testing.

Loblolly pine is an economically important timber species in the United States, with almost 1 billion seedlings produced annually. The most significant disease affecting this species is fusiform rust, caused by Cronartium quercuum f. sp. fusiforme. Testing for disease resistance in the greenhouse involves artificial inoculation of seedlings followed by visual inspection for disease incidence. An automated, high-throughput phenotyping method could improve both the efficiency and accuracy of the disease screening process. This study investigates the use of hyperspectral imaging for the detection of diseased seedlings. A nursery trial comprising families with known in-field rust resistance data was conducted, and the seedlings were artificially inoculated with fungal spores. Hyperspectral images in the visible and near-infrared region (400–1000 nm) were collected six months after inoculation. The disease incidence was scored with traditional methods based on the presence or absence of visible stem galls. The seedlings were segmented from the background by thresholding normalized difference vegetation index (NDVI) images, and the delineation of individual seedlings was achieved through object detection using the Faster RCNN model. Plant parts were subsequently segmented using the DeepLabv3+ model. The trained DeepLabv3+ model for semantic segmentation achieved a pixel accuracy of 0.76 and a mean Intersection over Union (mIoU) of 0.62. Crown pixels were segmented using geometric features. Support vector machine discrimination models were built for classifying the plants into diseased and non-diseased classes based on spectral data, and balanced accuracy values were calculated for the comparison of model performance. Averaged spectra from the whole plant (balanced accuracy = 61%), the crown (61%), the top half of the stem (77%), and the bottom half of the stem (62%) were used. A classification model built using the spectral data from the top half of the stem was found to be the most accurate, and resulted in an area under the receiver operating characteristic curve (AUC) of 0.83.

Recently published hazard maps for fusiform rust (Cronartium quercuum f. sp. fusiforme) using US Department of Agriculture Forest Service Forest Inventory and Analysis data appear to be biased because of incorrect assumptions about genetic improvement in rust resistance for loblolly pine (Pinus taeda) plantations in the southeastern United States. We used progeny tests to estimate the regional hazard and genetic gain in resistance based on rust incidence in local nonimproved trees. The largest genetic gains occurred in regions having higher hazard. The Coastal Georgia/Florida region had the greatest gains, with 11% lower mean incidence for second-cycle families when compared with the local nonimproved trees. In northern regions of Tennessee and Virginia, where rust incidence was low, no significant gains in resistance were realized. The recently published rust hazard maps overestimate hazard, especially for regions with low incidence, because those maps ignored plantations with low incidence and assumed that they were planted with resistant genotypes.

The most important climatic variable influencing growth and survival of loblolly pine is the yearly average minimum winter temperature (MWT) at the seed source origin, and it is used to guide the transfer of improved seed lots throughout the species’ distribution. This study presents a novel approach for the assessment of freeze-induced damage and prediction of MWT at seed source origin of loblolly pine seedlings using hyperspectral imaging. A population comprising 98 seed lots representing a wide range of MWT at seed source origin was subjected to an artificial freeze event. The visual assessment of freeze damage and MWT were evaluated at the family level and modeled with hyperspectral image data combined with chemometric techniques. Hyperspectral scanning of the seedlings was conducted prior to the freeze event and on four occasions periodically after the freeze. A significant relationship (R² = 0.33; p < .001) between freeze damage and MWT was observed. Prediction accuracies of freeze damage and MWT based on hyperspectral data varied among seedling portions (full-length, top, middle, and bottom portion of aboveground material) and scanning dates. Models based on the top portion were the most predictive of both freeze damage and MWT. The highest prediction accuracy of MWT [RPD (ratio of prediction to deviation) = 2.12, R² = 0.78] was achieved using hyperspectral data obtained prior to the freeze event. Adoption of this assessment method would greatly facilitate the characterization and deployment of well-adapted loblolly pine families across the landscape.

Since 2009, deployment of full-sib families of loblolly pine (Pinus taeda L.) has gained prominence in the southeastern United States. To produce full-sib seed, a pollination bag is used to isolate female strobili from outside pollen contamination, and a known pollen is applied at the time of maximum female strobilus receptivity. The goal of this study was to compare prototype pollination bags made by PBS International to the industry standard kraft paper pollination bag with and without a support wire for female strobili survival and to assess their efficiency for mass production of controlled cross loblolly pine seed. A multiyear study compared 13 pollination bag types at more than nine seed orchard sites across the southeastern United States. There were significant differences among bag types for conelet survival at the time of bag removal that persisted until cone harvest 18 months later. Female strobili bagged in prototype PBS-I2 were over three times more likely to survive to cone harvest than strobili inside the traditional kraft pollination bag. Two of the PBS bag types had the highest estimated filled seed per bag. One PBS bag was faster to install and remove than the kraft paper bag with a support wire.