Angiosperm trees reorient their woody stems by asymmetrically producing a specialized xylem tissue, tension wood, which exerts a strong contractile force resulting in negative gravitropism of the stem. Here, we show, in Populus trees, that initial gravity perception and response occurs in specialized cells through sedimentation of starch-filled amyloplasts and relocalization of the auxin transport protein, PIN3. Gibberellic acid treatment stimulates the rate of tension wood formation and gravibending and enhances tissue-specific expression of an auxin-responsive reporter. Gravibending, maturation of contractile fibers, and gibberellic acid (GA) stimulation of tension wood formation are all sensitive to transcript levels of the Class I KNOX homeodomain transcription factor-encoding gene ARBORKNOX2 (ARK2). We generated genome-wide transcriptomes for trees in which gene expression was perturbed by gravistimulation, GA treatment, and modulation of ARK2 expression. These data were employed in computational analyses to model the transcriptional networks underlying wood formation, including identification and dissection of gene coexpression modules associated with wood phenotypes, GA response, and ARK2 binding to genes within modules. We propose a model for gravitropism in the woody stem in which the peripheral location of PIN3-expressing cells relative to the cambium results in auxin transport toward the cambium in the top of the stem, triggering tension wood formation, while transport away from the cambium in the bottom of the stem triggers opposite wood formation.

& Context Wood quality traits are important to balance the negative decline of wood quality associated with selection for growth attributes in gymnosperm breeding programs. Obtaining wood quality estimates quickly is crucial for successful incorporation in breeding programs.

Multivariate statistical models capable of rapidly and accurately predicting the surface moisture content in Tsuga heterophylla were developed based on near infrared (NIR) spectra of small specimens precisely conditioned within the hygroscopic range. In an initial research phase, the applicability of NIR as predictor of surface moisture was investigated. The first derivative spectra in the range of 1300–2100 nm yielded the best results. The partial least squares regression (PLS-1) model had coefficient of determination (R2) of 0.98, root mean square error of cross validation (RMSECV) of 0.97%, root mean square error of prediction (RMSEP) of 1.05%, and ratio of performance to deviation (RPD) of 7.25. In a subsequent phase, an inline pilot-plant NIR system combined with this PLS-1 model was constructed. The prediction ability of the NIR system was tested with line speeds of 0, 100, 200, and 400 mm s-1 on kiln-dried full-length lamination boards classified as “wets” after conventional kiln drying. In a calibrated range of moisture content (0–25.4%), the NIR system demonstrated R2 values of 0.79 and 0.74, RMSEP values of 3.13 and 3.28, and RPD values of 2.18 and 1.67 at a line speed of 0 and 100 mm s-1, respectively, regardless of the presence of knots and surface roughness. These results demonstrate that the NIR system at a line speed of 0–100 mm s-1 could be used to provide entire surface moisture distribution and to detect local moisture peaks that indicate surface wet-pockets in kiln-dried lumber destined for lamination.

An optimum artificial neural network and a partial least square with discriminant analysis regression were developed and tested for accuracy in distinguishing two wood species by using near-infrared (NIR) spectrum. A mixed population of kiln-dried wood boards of western hemlock (Tsuga heterophylla (Raf.) Sarg.) and amabilis fir (Abies amabilis (Dougl.) Forbes) were scanned by NIR and then a random sub-set was water saturated under vacuum conditions and scanned again. This design aimed to capture the effect of moisture content above the fibre saturation point on the separation algorithms. Our results revealed that both modelling techniques can be effective tools for species recognition achieving correct identification of over 86% for fir and 94% for hemlock on either kiln-dried or fully saturated boards.