Explore the vast range of titles available.

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R2) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs.

Maps developed using Akima’s interpolation method were used to compare patterns of within-tree variation for Pinus taeda L. (loblolly pine) wood properties in plantation-grown trees aged 13 and 22 years. Air-dry density, microfibril angle (MFA) and modulus of elasticity (MOE) maps represented the average of 18 sampled trees in each age class. Near infrared (NIR) spectroscopy models calibrated using SilviScan provided data for the analysis. Zones of high density, low MFA and high MOE wood increased markedly in size in maps of the older trees. The proportion of wood meeting the visually graded No. 1 (11 GPa) and No. 2 (9.7 GPa) MOE design values for southern pine lumber increased from 44 to 74% and from 58 to 83% respectively demonstrating the impact of age on end-product quality. Air-dry density increased from pith to bark at all heights but lacked a significant trend vertically, while radial and longitudinal trends were observed for MFA and MOE. Changes were consistent with the asymptotic progression of properties associated with full maturity in older trees.

Aquatic invasive species have negative impacts on native biodiversity and pose a significant threat to overall ecosystem health. Successfully established non‐native species possess life history traits that are advantageous for colonization and expansion into novel environments. The reproductive traits and strategies of fish are often good predictors of invasion success. Thus, understanding reproductive dynamics of non‐native species in their introduced environments is an important component for predicting expansion and effectively managing invasive populations. The Weather Loach Misgurnus anguillicaudatus is a recently discovered introduced species in Georgia, USA, and little is known about its life history attributes where it is not native. Thus, the objectives of this study were to: (1) estimate mean batch fecundity of female Weather Loach; (2) determine timing and periodicity of spawning; and (3) evaluate whether the gonadosomatic index (GSI) is a reliable indicator of reproductive status in this species. Based on observed peaks in eggs larger than 500 µm, we identified the presumed spawning season for Weather Loach to be occurring from April through August. The highest average fecundity observed was during July (10,539 eggs) and the lowest average fecundity observed was during April (3083 eggs). The GSI was a strong predictor of fecundity and tracked the number of mature eggs present in each month of the year. Our estimates of batch fecundity and determination of the annual spawning season can help managers better understand reproductive dynamics and develop predictive population models aimed at evaluating management activities.

Near-infrared (NIR) spectroscopy and NIR hyperspectral imaging (NIR-HSI) were compared for the rapid estimation of physical and mechanical properties of No. 2 visual grade 2 × 4 (38.1 mm by 88.9 mm) Douglas-fir structural lumber. In total, 390 lumber samples were acquired from four mills in North America and destructively tested through bending. From each piece of lumber, a 25-mm length block was cut to collect diffuse reflectance NIR spectra and hyperspectral images. Calibrations for the specific gravity (SG) of both the lumber (SGlumber) and 25-mm block (SGblock) and the lumber modulus of elasticity (MOE) and modulus of rupture (MOR) were created using partial least squares (PLS) regression and their performance checked with a prediction set. The strongest calibrations were based on NIR spectra; however, the NIR-HSI data provided stronger predictions for all properties. In terms of fit statistics, SGblock gave the best results, followed by SGlumber, MOE, and MOR. The NIR-HSI SGlumber, MOE, and MOR calibrations were used to predict these properties for each pixel across the transverse surface of the scanned samples, allowing SG, MOE, and MOR variation within and among rings to be observed.

Time-domain reflectometry (TDR) can monitor the moisture content (MC) of water saturated logs stored in wet-decks where the MC exceeds the range that can be measured using traditional moisture meters (>50%). For this application to become routine, it is required that TDR monitoring of wet-decks occurs after establishment, and tools are needed that automate data collection and analysis. We developed models that predict wood MC using three-rod epoxy encased TDR probes inserted into the transverse surface of bolts (prior wet-deck studies were installed on the tangential surface). Models were developed for southern pine, sweetgum, yellow poplar, hickory, red oak, and white oak using a Campbell Scientific TDR100. For each species, at least 37 bolts were soaked for a minimum of three months and then air dried with TDR waveforms, and MC was periodically recorded. Calibrations were developed between MC and the TDR signal using nonlinear mixed effects models. Fixed effects ranged from excellent (southern pine R2 = 0.93) to poor (red oak R2 = 0.36, hickory R2 = 0.38). Independent of wood species, random effects all had a R2 greater than 0.80, which indicates that TDR detects changes in MC at the individual sample level. Use of TDR combined with a datalogger was demonstrated in an operational wet-deck that monitored changes in MC over 12 months, and in a laboratory trial where bolts were exposed to successive wet-dry cycles over 400 days. Both applications demonstrated the utility of TDR to monitor changes in wood MC in high MC environments where periodic measurement is not feasible due to operational safety concerns. Because a saturated TDR reading indicates a saturated MC, and because of the relatively accurate random effects found here, developing individual species models is not necessary for monitoring purposes. Therefore, application of TDR monitoring can be broadly applied for wet-decks, regardless of the species stored.

Abstract Logging businesses in the US South have not adopted cut-to-length harvesting systems. Adding dangle head processors on the landing of whole-tree harvesting systems may allow southern loggers to achieve some of the advantages of cut-to-length systems (i.e., precise length and diameter measurements) while maintaining high productivity and low costs per ton characteristic of current whole-tree systems. We conducted a designed study of conventional (i.e., feller-buncher, grapple skidder, loader) and processor (i.e., feller-buncher, grapple skidder, processor, loader) systems. Four harvest sites were split, with half of each site harvested by a conventional system and the other half by a processor system. Harvesting productivity was estimated using time-and-motion studies, and costs were estimated using the machine rate method. Cut-and-load costs averaged US$13.57 and US$14.67 ton–1 on the processor and conventional harvests, respectively (P > .10). Cost per ton was elevated on several conventional harvest tracts because of long skidding distances, indicating harvest planning is more important than harvesting system in determining harvesting costs. Processing and loading costs were US$1.70 ton–1 higher on processor harvests, which, combined with restrictive mill quotas being more problematic for processor crews, suggests loggers will require a logging rate premium in order to invest in processors.

Near-infrared wavelengths selected by genetic algorithm were used to optimize partial least squares (PLS) regression models for loblolly pine ( Pinus taeda L.) from the southeastern United States. Wood properties examined included density (D), microfibril angle, modulus of elasticity and tracheid coarseness (C), radial diameter (R), tangential diameter (T), and wall thickness (w)—measured by SilviScan. The optimization process was run for each property with Agenda 2020 samples utilized for PLS model development and the other sets used for prediction. The number of variables (i.e. wavelengths) varied from 10 to 100 with an optimum number identified by genetic algorithm. When compared to a full data set model (based on 700 wavelengths), calibration and prediction performance of optimized PLS regression models were superior for all properties. Importantly, representative wavelengths for each property were consistently related to recognized bond vibrations observed in specific wood components demonstrating that optimization targets wavelengths directly related to changes in wood chemistry within the examined loblolly pine samples.

With conifer plantations having an increasingly important role in meeting the fiber needs of society, an understanding of the effect of silvicultural practices on wood quality is critical. The perception of wood quality varies, making it hard to define in a single statement; however, possibly the most succinct definition is “a measure of the aptness of wood for a given use”. In general, properties that have a positive influence on a specific product assist in defining changes in wood quality. Since wood properties exhibit large variability within annual rings, within trees, and among trees in a stand, and have both genetic and environmental components (i.e., vary with different physiographical regions), it is imperative to have an understanding of wood properties at multiple levels. In this paper, we review the typical variation patterns in wood properties of conifers, with specific emphasis on loblolly pine (Pinus taeda L.), and radiata pine (Pinus radiata D.Don), two of the most common conifer plantation species globally. We also describe the impact of conventional silvicultural treatments on wood quality. Modeling efforts to predict variation in wood properties within trees, and in response to silvicultural treatments are also summarized.

Loblolly pine (Pinus taeda L.) is increasingly grown on intensively managed plantations that yield high growth rates. Wood properties, including specific gravity (SG), change with cambial age, and thus intensively managed trees contain a high proportion of low density corewood when harvested because of reduced rotation lengths. This study was undertaken to develop models of ring-level properties (SG and width) in intensively managed loblolly pine plantations. Ninety-three trees from five stands aged from 24 to 33 years were harvested, and 490 disks were obtained from in between the 5.2-m logs that were cut, and at the merchantable top. The disks were cut into pith-to-bark radial strips that were scanned on an X-ray densitometer, and the resultant data analyzed using non-linear mixed-effects models. The fixed effects of the models, which included cambial age and for some models disk height and ring width, were able to explain 56, 46, 54, 16, and 46 percent of the within-tree variation for ring SG, ring width, latewood SG, earlywood SG, and latewood percent, respectively. To assess implications for wood utilization, a modeled tree was built by using height, diameter, and taper equations and these models were linked with the developed ring SG model to produce a tree properties map. The linked information was also used to generate tree and log SG and proportion of corewood values for different rotation ages. The results from this study are a step towards integrating wood quality models into growth-and-yield modeling systems that are important for loblolly pine plantation management.

Maps developed using Akima’s interpolation method, and representing average data for trees aged 13 and 22 years, were used to compare patterns of within-tree variation for Pinus taeda L. (loblolly pine) tracheid properties: coarseness ( C ), specific surface ( S ), radial ( R ) and tangential ( T ) diameter and wall thickness ( w ). SilviScan-calibrated near-infrared (NIR) spectroscopy provided data for the analysis with C ( R c 2  = 0.85, R p 2  = 0.85), S ( R c 2  = 0.83, R p 2  = 0.76), and w ( R c 2  = 0.89, R p 2  = 0.93) models having very good calibration / prediction statistics, while those for T and R diameter were moderate ( R c 2  = 0.79, R p 2  = 0.57) and poor ( R c 2  = 0.64, R p 2  = 0.19), respectively. C , S , and w maps were similar to the density maps for P. taeda and indicate the properties increase radially at all heights. The T diameter map was similar to maps reported for microfibril angle except that T diameter increased radially and with height whereas microfibril angle decreased radially and with height. The map for R diameter increased with height and was unlike the other properties examined; caution is recommended regarding any interpretations based on the R diameter map owing to the weak statistics observed for the NIR model. Changes observed between the two ages are consistent with the asymptotic progression of properties associated with maturation.