## 4 Discussion
In the southern pines, SG increases from pith to bark, while at the same time, microfibril angle decreases from pith to bark [70; 71]. For Douglas-fir, the microfibril angle trend is the same as the southern pines, but SG decreases from pith to bark for approximately 10 years, then it gradually increases [67]. Thus, the relationships between SG and MOE and MOR is not as strong as what was reported previously for southern pine, and they mirror results others have found for Douglas-fir [72]. The presence of defects in lumber, particularly knots, will reduce the prediction accuracy of models in which defect information is not accounted for [73; 74]. Here, we have no information on the defects within the lumber, and thus our models depend on the relative contributions of earlywood and latewood to estimate whole-board properties. Given the lack of information on knots in this study, the relationships reported here for the NIR benchtop and NIR-HSI calibrations may be approaching what can reasonably be expected for the NIR prediction of MOE or MOR. If the wood is very high quality (few knots, straight grain), then the calibration/prediction statistics may improve (e.g., [PERSON] et al. [30]); however, if the wood has numerous, large knots that detract greatly from the measured MOE and MOR, calibrations will suffer. Here, we used No. 2 grade lumber; if we had collected select structural lumber, we would likely have obtained better prediction statistics. Incorporation of defect information and the negative impact it has on wood properties could improve our prediction accuracy. [PERSON] et al. [25] created a matrix which combined measured SG values with NIR data and created models using both measurements, which resulted in the improved accuracy of predictions of MOE (\(\text{R}_{\text{p}}{}^{2}\) = 0.70) and MOR (\(\text{R}_{\text{p}}{}^{2}\) = 0.52) compared to the NIR measurement alone for MOE (R\({}_{\rm p}\)\({}^{2}\) = 0.58) and MOR (R\({}_{\rm p}\)\({}^{2}\) = 0.4) for southern pine. A similar idea could be explored with knot data, and it is likely that without it, NIR spectra (regardless of the face scanned) will fail to provide statistics that are much stronger than those reported here. A strength of the NIR-HSI approach is that it allows the prediction of properties across the scanned surface, assuming an appropriate model exists. Here, we used our average spectra for whole-lumber PLS models for SG, MOE, and MOR to predict these properties of each pixel across the transverse surface of the scanned samples. This approach allowed us to visualize the variation in these properties within and among rings to be observed, particularly for samples with relatively wide rings, and we also observed the area at the pith as being of very low stiffness (Figure 8). For samples with narrow rings (Figure 9), it was more difficult to observe ring features owing to the similarity between ring and pixel size or the rings being smaller than the pixels. While this may be a concern for slowly grown conifers sourced from natural forests, it is not an issue for plantation-sourced trees that demonstrate much faster growth rates. The latter material is also where segregation becomes much more critical, because lumber pieces can consist entirely of corewood. We expect to see increasing mechanical properties from pith to bark, and the prediction variation we expect, as observed in Figures 8 and 9, i.e., very low MOE earlywood and very high MOE latewood, would be difficult to validate. Different approaches could be explored to help validate the data, for example measuring very small samples that contained only earlywood or latewood would help validate the general ranges predicted here; however, the approach would be extremely time-consuming and costly. Assuming we had the appropriate wood property data, a wide range of additional properties, such as wood chemistry information, as demonstrated by [PERSON] et al. [44; 45], could have been generated.