Mapping and modeling within-tree variation for loblolly pine pulp yield and lignin content

We examined the within-tree variation of pulp yield and lignin content for loblolly pine (Pinus taeda L.) trees aged 13 and 22 years. Radial trends in pulp yield (increase) and lignin (decrease) were consistent with what would be expected for loblolly pine as were changes in properties related to maturation. Maps, based on the average of 18 trees at each age, depicting pulp yield variation within-tree were similar to loblolly pine maps reported for microfibril angle and stiffness, while lignin maps resembled the inverse of those reported for density and related properties. Mixed-effects models for both properties were developed with the base model for pulp yield explaining 64% of the observed variation, with the inclusion of tree height improving the model slightly, whereas models for lignin content explained 44% of the variability. The models could be incorporated into growth and yield prediction systems, or procurement model systems that predict within-tree wood properties based on age and tree size.

[1]  H. Mäkinen,et al.  Predicting wood and tracheid properties of Norway spruce , 2007 .

[2]  M. Defo,et al.  A method to map within-tree distribution of fibre properties using SilviScan-3 data , 2009 .

[3]  B. Gardiner,et al.  Models for predicting wood density variation in Scots pine , 2014 .

[4]  L. Schimleck,et al.  Determination of within-tree variation of kraft pulp yield using near-infrared spectroscopy , 1998 .

[5]  S. Tsuchikawa,et al.  Calibration of SilviScan data of Cryptomeria japonica wood concerning density and microfibril angles with NIR hyperspectral imaging with high spatial resolution , 2017 .

[6]  S. Tsuchikawa,et al.  Non-destructive evaluation of wood stiffness and fiber coarseness, derived from SilviScan data, via near infrared hyperspectral imaging , 2018, Journal of Near Infrared Spectroscopy.

[7]  Roger Meder,et al.  Near Infrared Hyperspectral Imaging Applied to Mapping Chemical Composition in Wood Samples , 2010 .

[8]  Robert Evans,et al.  Wood Stiffness by X‐Ray Diffractometry , 2008 .

[9]  G. Downes,et al.  Radial variation in Kraft pulp yield and cellulose content in Eucalyptus globulus wood across three contrasting sites predicted by near infrared spectroscopy , 2012 .

[10]  J. Uprichard.  Cellulose and Lignin Content in Pinus radiata D. Don. Within-Tree Variation in Chemical Composition, Density, and Tracheid Length , 1971 .

[11]  A. Scallan,et al.  The Effect of Pulping Upon the Dimensions of Wood Tracheids , 2007 .

[12]  H. Peltola,et al.  Modelling the distribution of wood properties along the stems of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) as affected by silvicultural management , 2008 .

[13]  Robert Evans,et al.  Rapid Measurement of the Transverse Dimensions of Tracheids in Radial Wood Sections from Pinus radiata , 1994 .

[14]  J. Harrington,et al.  Mapping Within-Stem Variation of Chemical Composition by near Infrared Hyperspectral Imaging , 2016 .

[15]  A. Ragauskas,et al.  From wood to fuels: Integrating biofuels and pulp production , 2006 .

[16]  Robert Evans,et al.  Juvenile Versus Mature Wood: A New Concept, Orthogonal to Corewood Versus Outerwood, with Special Reference to Pinus radiata and P. taeda , 2004, Forest Science.

[17]  Alexander Clark,et al.  Formation and Properties of Juvenile Wood in Southern Pines: A Synopsis , 2001 .

[18]  G. Downes,et al.  Towards the in-forest Assessment of Kraft Pulp Yield: Comparing the Performance of Laboratory and Hand-Held Instruments and Their Value in Screening Breeding Trials , 2011 .

[19]  Christian R. Mora,et al.  Comparison of Whole-Tree Wood Property Maps for 13- and 22-Year-Old Loblolly Pine , 2018 .

[20]  Bjørn-Helge Mevik,et al.  Partial Least Squares and Principal Component Regression [R package pls version 2.7-3] , 2020 .

[21]  David A. Ratkowsky,et al.  Handbook of nonlinear regression models , 1990 .

[22]  Hadley Wickham,et al.  ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .

[23]  Nmg Borralho,et al.  The influence of basic density and pulp-yield on the cost of eucalypt kraft pulping: a theoretical model for tree breeding , 1996 .

[24]  R. Wimmer,et al.  Radial Distribution of Heartwood Extractives and Lignin in Mature European Larch , 2007 .

[25]  Spatially-Resolved Radial Scanning of Tree Increment Cores for near Infrared Prediction of Microfibril Angle and Chemical Composition , 2010 .

[26]  R Evans,et al.  木材でのミクロフィブリル角のX線回折測定法による算定への分散近似法 | 文献情報 | J-GLOBAL 科学技術総合リンクセンター , 1999 .

[27]  J. Dahlen,et al.  Models for Predicting Specific Gravity and Ring Width for Loblolly Pine from Intensively Managed Plantations, and Implications for Wood Utilization , 2018 .

[28]  H. D. Erickson,et al.  Douglas-fir wood quality studies Part II: Effects of age and stimulated growth on fibril angle and chemical constituents , 1974, Wood Science and Technology.

[29]  G. Downes,et al.  Radial Variation in Cellulose Content and Kraft Pulp Yield in Eucalyptus Nitens using Near-Infrared Spectral Analysis of Air-Dry Wood Surfaces , 2010 .

[30]  O. Høibø,et al.  Development and variation of wood density, kraft pulp yield and fibre dimensions in young Norway spruce (Picea abies) , 2006, Wood Science and Technology.

[31]  Helena Pereira,et al.  Wood Chemistry in Relation to Quality , 2004 .

[32]  V. Harwood.  Variation in Carbohydrate Analyses in Relation to Wood Age in Pinus radiata , 1971 .

[33]  A. J. Panshin,et al.  Textbook of Wood Technology , 1964 .

[34]  P. Williams,et al.  Comparison of Commercial near Infrared Transmittance and Reflectance Instruments for Analysis of Whole Grains and Seeds , 1993 .

[35]  Y. Çöpür,et al.  The prediction of pulp yield using selected fiber properties , 2005 .

[36]  A. Clark,et al.  Regional variation in wood specific gravity of planted loblolly pine in the United States , 2008 .

[37]  Christian R. Mora,et al.  Determination of Within-tree Variation of Pinus taeda Wood Properties by Near Infrared Spectroscopy. Part 2: Whole-tree Wood Property Maps , 2009 .

[38]  J. Dahlen,et al.  Models for predicting the within-tree and regional variation of tracheid length and width for plantation loblolly pine , 2020 .

[39]  Christian R. Mora,et al.  Determination of within-tree variation of Pinus taeda wood properties by near infrared spectroscopy. Part 1: Development of multiple height calibrations. , 2009 .

[40]  Gary A. Smook,et al.  Handbook for Pulp and Paper Technologists , 1982 .

[41]  Leslie H. Groom,et al.  Within tree variation of lignin, extractives, and microfibril angle coupled with the theoretical and near infrared modeling of microfibril angle , 2007 .

[42]  Robert Evans,et al.  Rapid measurement of variation in tracheid transverse dimensions in a radiata pine tree , 1995 .

[43]  Deepayan Sarkar,et al.  Lattice: Multivariate Data Visualization with R , 2008 .

[44]  R. Evans,et al.  Radial Variation in Wood Structure and Function in Woody Plants, and Hypotheses for Its Occurrence , 2011 .

[45]  D. Cown,et al.  Modelling microfibril angle variation in New Zealand-grown radiata pine , 2014, New Zealand Journal of Forestry Science.

[46]  D. Bates,et al.  Linear and Nonlinear Mixed Effects Models [R package nlme version 3.1-149] , 2020 .

[47]  Christian R. Mora,et al.  Comparison of whole-tree wood property maps based on near-infrared spectroscopic calibrations utilizing data at different spatial resolutions , 2019 .

[48]  G. Peter,et al.  Effects of Specific Gravity and Wood Chemical Content on the Pulp Yield of Loblolly Pine , 2009 .