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Freshly lifted seedlings and 21-year-old trees of loblolly pine were wound-inoculated with Leptographium species recovered from the soil and/or roots of trees with loblolly decline symptoms in central Alabama. Seedlings inoculated with L. procerum in the greenhouse produced significantly fewer root initials and a smaller root mass than control seedlings. Vertical lesions produced in seedlings by L. serpens and L. terebrantis were significantly longer than in controls. Lesions produced in mature trees by L. serpens and L. lundbergii were significantly longer than in controls. Of the fungi tested, L. serpens, L. terebrantis, and L. lundbergii were the most aggressive and may pose the greatest threat to loblolly pines.

Fourier transform infrared reflectance (FTIR) spectroscopy has been used to predict properties of forest logging residue, a very heterogeneous feedstock material. Properties studied included the chemical composition, thermal reactivity, and energy content. The ability to rapidly determine these properties is vital in the optimization of conversion technologies for the successful commercialization of biobased products. Partial least squares regression of first derivative treated FTIR spectra had good correlations with the conventionally measured properties. For the chemical composition, constructed models generally did a better job of predicting the extractives and lignin content than the carbohydrates. In predicting the thermochemical properties, models for volatile matter and fixed carbon performed very well (i.e., R 2 > 0.80, RPD > 2.0). The effect of reducing the wavenumber range to the fingerprint region for PLS modeling and the relationship between the chemical composition and higher heating value of logging residue were also explored. This study is new and different in that it is the first to use FTIR spectroscopy to quantitatively analyze forest logging residue, an abundant resource that can be used as a feedstock in the emerging low carbon economy. Furthermore, it provides a complete and systematic characterization of this heterogeneous raw material.

Summary Pine decline poses a serious threat to forest sustainability in the south‐eastern United States. Complex interactions between biotic and abiotic factors are involved in the decline and include root‐feeding bark beetles and their associated fungal genera, Leptographium and Grosmannia. A study was conducted to determine the relative tolerance of loblolly pine (Pinus taeda L.) families when challenged with either Leptographium or Grosmannia species. In the first study, bare root seedlings from 23 loblolly pine families were screened with L. procerum, L. terebrantis, G. huntii and G. alacris using an artificial inoculation method. In a second study, containerized seedlings from 27 loblolly pine families were screened with G. huntii and L. terebrantis. Measured seedling responses to the inoculations included lesion length, lesion width and occlusion of vascular tissues, measured 8 weeks after inoculations. The most common host response was dark brown lesions and resinous occluded stem tissue. Seedling families had a wide range of host responses to the different Leptographium and Grosmannia species and showed that it could be possible to select the families that may be tolerant to Leptographium and Grosmannia fungal species based on these results.

This paper addresses the precision in factor loadings during partial least squares (PLS) and principal components regression (PCR) of wood chemistry content from near infrared reflectance (NIR) spectra. The precision of the loadings is considered important because these estimates are often utilized to interpret chemometric models or selection of meaningful wavenumbers. Standard laboratory chemistry methods were employed on a mixed genus/species hardwood sample set. PLS and PCR, before and after 1st derivative pretreatment, was utilized for model building and loadings investigation. As demonstrated by others, PLS was found to provide better predictive diagnostics. However, PCR exhibited a more precise estimate of loading peaks which makes PCR better for interpretation. Application of the 1st derivative appeared to assist in improving both PCR and PLS loading precision, but due to the small sample size, the two chemometric methods could not be compared statistically. This work is important because to date most research works have committed to PLS because it yields better predictive performance. But this research suggests there is a tradeoff between better prediction and model interpretation. Future work is needed to compare PLS and PCR for a suite of spectral pretreatment techniques.