Application of the IML Resistograph to the infield assessment of basic density in plantation eucalypts

Downes, GM; Lausberg, Marco; Potts, Brad M.; Pilbeam, DL; Bird, Michael G.; Bradshaw, Ben

doi:10.1080/00049158.2018.1500676

Cited by 45 publications

(48 citation statements)

References 19 publications

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“…Each profile was checked immediately after drilling on the tool's screen and the measurement was repeated when necessary. Custom software is available from a web URL (https://forestquality.shinyapps.io/EucalyptResiProcessor/) (Downes et al 2018). Finally, the mean resistance (resistograph (Resi)) values were provided from the data profile.…”

Section: Measurement Of the Resistographmentioning

confidence: 99%

Advantage of clonal deployment in Norway spruce (Picea abies (L.) H. Karst)

Chen

Hai

Helmersson

et al. 2020

Annals of Forest Science

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& Key message There is considerable genetic gain of tree volume from clonal deployment in Norway spruce (Picea abies (L.) H. Karst) and clonal deployment will have at least 50% more or double genetic gain than the seedling deployment. & Context Genetic parameters and genetic gains for wood quality and growth traits were estimated in six large clonal progeny trials. & Aims Develop the optimal clonal deployment strategy of Norway spruce in Sweden. & Methods Wood quality and growth traits were measured in all clonal trials and additive and non-additive genetic variances are partitioned. & Results Additive and non-additive genetic variances were equally important for growth traits while non-additive variance was small or not significant for wood quality trait. The genetic gain predicted for clonal deployment was greater than any of the other four deployment strategies. Selecting the top 1% of tested clones (clonal forestry) would have 48.4% and 134.6% more gain than the gain predicted for the seedling deployment of selected full-sib families and half-sib family (family forestry), respectively, at the same selection intensity. & Conclusion This study highlights that testing of 30-40 clones per family would maximize the realized genetic gain for different clonal selection scenarios, either selecting the best ten or 20 clones without any co-ancestry restrictions or selecting the best single clone from each of the best ten or 20 families (e.g., co-ancestry restriction). Clonal mean selection and vegetative deployment are the most effective.

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Section: Measurement Of the Resistographmentioning

confidence: 99%

Advantage of clonal deployment in Norway spruce (Picea abies (L.) H. Karst)

Chen

Hai

Helmersson

et al. 2020

Annals of Forest Science

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“…(n = 4), and Eucalyptus nitens (Deane & Maiden) Maiden (n = 1)) located in Northwest Tasmania (n = 2) and Western (n = 2) and South-Eastern Australia (n = 1). Deploying a simple linear model similar in specificity to that employed in this study for the five separate plantations, Downes [61] reported statistically significant (p ≤ 0.05) mean amplitude-wood density relationships, which explained a moderate to high level of core-based density (e.g., coefficients of determination ranging from 0.66 to 0.87). Although intercept and slope values were generally stable and of similar magnitude across the sampling sites, slight differences were observed, particularly with regard to the slope term.…”

Section: Non-destructive Wood Density Estimation and Consequences On mentioning

confidence: 76%

“…However, the portion of variability explained was lower than what was determined for jack pine (27% for red pine versus 51% for jack pine). Downes [61] reported viable relationships for five eucaplypt plantations (Eucalyptus globulus Lambill. (n = 4), and Eucalyptus nitens (Deane & Maiden) Maiden (n = 1)) located in Northwest Tasmania (n = 2) and Western (n = 2) and South-Eastern Australia (n = 1).…”

Section: Non-destructive Wood Density Estimation and Consequences On mentioning

confidence: 99%

“…Although intercept and slope values were generally stable and of similar magnitude across the sampling sites, slight differences were observed, particularly with regard to the slope term. Downes [61] suggested that instrumentation-induced variation and sampling conditions were plausible determinates underlying these differences.…”

Section: Non-destructive Wood Density Estimation and Consequences On mentioning

confidence: 99%

“…Tables 5 and 6). Although recent advancements in automating the processing of Resistograph profiles in terms of generating mean amplitude values have reduced the computational burden (i.e., web-based computational software [61]), the acoustic-based approach would be the most applicable for jack pine given that it requires the least amount of effort with regard to logistical requirements and data processing, provides a real-time on site density estimate, and is only marginally inferior in terms of predictive precision. Other low impact tools and techniques such as the mechanical-based Pilodyn impact device [63] and near infra-red spectrometers [64] could also be potentially used to estimate wood density non-destructively.…”

Section: Non-destructive Wood Density Estimation and Consequences On mentioning

confidence: 99%

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Acoustic-Based Prediction of End-Product-Based Fibre Determinates within Standing Jack Pine Trees

Newton

2019

Forests

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The objective of this study was to specify, parameterize, and evaluate an acoustic-based inferential framework for estimating commercially-relevant wood attributes within standing jack pine (Pinus banksiana Lamb) trees. The analytical framework consisted of a suite of models for predicting the dynamic modulus of elasticity (m e ), microfibril angle (m a ), oven-dried wood density (w d ), tracheid wall thickness (w t ), radial and tangential tracheid diameters (d r and d t , respectively), fibre coarseness (c o ), and specific surface area (s a ), from dilatational stress wave velocity (v d ). Data acquisition consisted of (1) in-forest collection of acoustic velocity measurements on 61 sample trees situated within 10 variable-sized plots that were established in four mature jack pine stands situated in boreal Canada followed by the removal of breast-height cross-sectional disk samples, and (2) given (1), in-laboratory extraction of radial-based transverse xylem samples from the 61 disks and subsequent attribute determination via Silviscan-3. Statistically, attribute-specific acoustic prediction models were specified, parameterized, and, subsequently, evaluated on their goodness-of-fit, lack-of-fit, and predictive ability. The results indicated that significant (p ≤ 0.05) and unbiased relationships could be established for all attributes but d t . The models explained 71%, 66%, 61%, 42%, 30%, 19%, and 13% of the variation in m e , w t , s a , c o , w d , m a , and d r , respectively. Simulated model performance when deploying an acoustic-based wood density estimate indicated that the expected magnitude of the error arising from predicting d t , c o , s a , w t , m e, and m a prediction would be in the order of ±8%, ±12%, ±12%, ±13%, ±20%, and ±39% of their true values, respectively. Assessment of the utility of predicting the prerequisite w d estimate using micro-drill resistance measures revealed that the amplitude-based w d estimate was inconsequentially more precise than that obtained from v d (≈ <2%). A discourse regarding the potential utility and limitations of the acoustic-based computational suite for forecasting jack pine end-product potential was also articulated. Keywords: wood quality; dilatational stress wave velocity; absolute and relative error intervals; ResistographForests 2019, 10, 605 2 of 29 however, a priori knowledge of the internal fibre attributes, which are among the principal determinates underlying end-product quality (sensu [4]), are not readily observable or measurable within standing trees prior to harvest nor within the derived logs following harvest. Furthermore, forecasting end-product potential based on correlative relationships between external morphological tree characteristics and internal fibre attributes have yielded mixed results of generally limited utility [5]. Thus, apart from the implementation of a logistically-challenging destructive-based field sampling initiative combined with the subsequent expensive and time-consuming laboratory processing of extracted wood samples...

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