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Loblolly pine (Pinus taeda L.) is a commercially important timber species planted across a wide temperature gradient in the southeastern U.S. It is critical to ensure that the planting stock is suitably adapted to the growing environment to achieve high productivity and survival. Long-term field studies, although considered the most reliable method for assessing cold hardiness of loblolly pine, are extremely resource-intensive and time-consuming. The development of a highthroughput screening tool to characterize and classify freeze tolerance among different genetic entries of seedlings will facilitate accurate deployment of highly productive and well-adapted families across the landscape. This study presents a novel approach using hyperspectral imaging to screen loblolly pine seedlings for freeze tolerance. A diverse population of 1549 seedlings raised in a nursery were subjected to an artificial mid-winter freeze using a freeze chamber. A customassembled hyperspectral imaging system was used for in-situ scanning of the seedlings before and periodically after the freeze event, followed by visual scoring of the frozen seedlings. A hyperspectral data processing pipeline was developed to segment individual seedlings and extract the spectral data. Examination of the spectral features of the seedlings revealed reductions in chlorophylls and water concentrations in the freeze-susceptible plants. Because the majority of seedlings were freeze-stressed, leading to severe class imbalance in the hyperspectral data, a cost-sensitive learning technique that aims to optimize a class-specific cost matrix in classification schemes was proposed for modeling the imbalanced hyperspectral data, classifying the seedlings into healthy and freeze-stressed phenotypes. Cost optimization was effective for boosting the classification accuracy compared to regular modeling that assigns equal costs to individual classes. Full-spectrum, costoptimized support vector machine (SVM) models achieved geometric classification accuracies of 75% to 78% before and within 10 days after the freeze event, and up to 96% for seedlings 41 days after the freeze event. The top portions of seedlings were more indicative of freeze events than the middle and bottom portions, leading to better classification accuracies. Further, variable selection enabled significant reductions in wavelengths while achieving even better accuracies of up to 97% than full-spectrum SVM modeling. This study demonstrates that hyperspectral imaging can provide tree breeders with a valuable tool for improved efficiency and objectivity in the characterization and screening of freeze tolerance for loblolly pine. [ABSTRACT FROM AUTHOR]

The economic consequence of continuing or increasing the tree improvement efforts for loblolly pine (Pinus taeda L.) in the southern United States is immense. For the more than one million acres planted each year with germplasm from the North Carolina State University Cooperative Tree Improvement Program, the present value of continuing current tree breeding efforts and deploying the genetic gains to landowners is estimated to be more than $1.7 billion at current prices. The present value of increasing the rate of genetic gain from 1% per year to 1.1% per year is $211 million. These analyses can be used to justify maintaining and even increasing efforts in tree improvement. With the aggressive fourth-cycle breeding program underway and plans for fifth-cycle breeding and deployment strategies being developed, we have every reason to believe that this trend for at least 1% gain per year will continue for decades, provided the resources to continue tree improvement efforts remain available. Even a modest increase in genetic gain per year would be justification for stakeholders to invest more than $12 million per year to realize this gain. [ABSTRACT FROM AUTHOR]

Abstract: Leptocybe invasa is an insect pest causing gall formation on oviposited shoot tips and leaves of Eucalyptus trees leading to leaf deformation, stunting, and death in severe cases. We previously observed different constitutive and induced terpenes, plant specialized metabolites that may act as attractants or repellents to insects, in a resistant and susceptible clone of Eucalyptus challenged with L. invasa. We tested the hypothesis that specific terpenes are associated with pest resistance in a Eucalyptus grandis half‐sib population. Insect damage was scored over 2 infestation cycles, and leaves were harvested for near‐infrared reflectance (NIR) and terpene measurements. We used Bayesian model averaging for terpene selection and obtained partial least squares NIR models to predict terpene content and L. invasa infestation damage. In our optimal model, 29% of the phenotypic variation could be explained by 7 terpenes, and the monoterpene combination, limonene, α‐terpineol, and 1,8‐cineole, could be predicted with an NIR prediction ability of  .67. Bayesian model averaging supported α‐pinene, γ‐terpinene, and iso‐pinocarveol as important for predicting L. invasa infestation. Susceptibility was associated with increased γ‐terpinene and α‐pinene, which may act as a pest attractant, whereas reduced susceptibility was associated with iso‐pinocarveol, which may act to recruit parasitoids or have direct toxic effects. [ABSTRACT FROM AUTHOR]