[1]

Grattapaglia D, Silva OB Junior, Resende RT, Cappa EP, Müller BSF, et al. 2018. Quantitative genetics and genomics converge to accelerate forest tree breeding. Frontiers in Plant Science 9:1693

doi: 10.3389/fpls.2018.01693
[2]

Grattapaglia D, Resende MDV. 2011. Genomic selection in forest tree breeding. Tree Genetics & Genomes 7(2):241−255

doi: 10.1007/s11295-010-0328-4
[3]

Borthakur D, Busov V, Cao XH, Du Q, Gailing O, et al. 2022. Current status and trends in forest genomics. Forestry Research 2:11

doi: 10.48130/fr-2022-0011
[4]

Resende MDV, Resende MFR, Sansaloni CP, Petroli CD, Missiaggia AA, et al. 2012. Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytologist 194(1):116−128

doi: 10.1111/j.1469-8137.2011.04038.x
[5]

Forest Products Laboratory. 2010. Wood handbook: wood as an engineering material. FPL-GTR-190. US Department of Agriculture, Forest Service, Forest Products Laboratory, Madison, WI. doi: 10.2737/FPL-GTR-190

[6]

Zhao Y, Abid M, Xie X, Fu Y, Huang Y, et al. 2024. Harnessing unconventional monomers to tailor lignin structures for lignocellulosic biomass valorization. Forestry Research 4:e004

doi: 10.48130/forres-0024-0001
[7]

Sluiter A, Hames B, Ruiz R, Scarlata C, Sluiter J, et al. 2012. Determination of structural carbohydrates and lignin in biomass: laboratory analytical procedure. NREL/TP-510-42618. National Renewable Energy Laboratory, Golden, CO. https://research-hub.nrel.gov/en/publications/determination-of-structural-carbohydrates-and-lignin-in-biomass-l/

[8]

Sykes RW, Isik F, Li B, Kadla J, Chang HM. 2003. Genetic variation of juvenile wood properties in a loblolly pine progeny test. TAPPI Journal 2(12):3−8

[9]

Tsuchikawa S, Kobori H. 2015. A review of recent application of near infrared spectroscopy to wood science and technology. Journal of Wood Science 61(3):213−220

doi: 10.1007/s10086-015-1467-x
[10]

Sandak J, Sandak A, Meder R. 2016. Assessing trees, wood and derived products with near infrared spectroscopy: hints and tips. Journal of Near Infrared Spectroscopy 24(6):485−505

doi: 10.1255/jnirs.1255
[11]

Schimleck L, Ma T, Inagaki T, Tsuchikawa S. 2023. Review of near infrared hyperspectral imaging applications related to wood and wood products. Applied Spectroscopy Reviews 58(9):585−609

doi: 10.1080/05704928.2022.2098759
[12]

Defoirdt N, Sen A, Dhaene J, De Mil T, Pereira H, et al. 2017. A generic platform for hyperspectral mapping of wood. Wood Science and Technology 51(4):887−907

doi: 10.1007/s00226-017-0903-z
[13]

Chambi-Legoas R, Tomazello-Filho M, Vidal C, Chaix G. 2023. Wood density prediction using near-infrared hyperspectral imaging for early selection of Eucalyptus grandis trees. Trees 37(3):981−991

doi: 10.1007/s00468-023-02397-2
[14]

Xing D, Sun P, Wang Y, Jiang M, Miao S, et al. 2024. Non-destructive estimation of needle leaf chlorophyll and water contents in Chinese fir seedlings based on hyperspectral reflectance spectra. Forestry Research 4:e024

doi: 10.48130/forres-0024-0021
[15]

Rincent R, Charpentier JP, Faivre-Rampant P, Paux E, Le Gouis J, et al. 2018. Phenomic selection is a low-cost and high-throughput method based on indirect predictions: proof of concept on wheat and poplar. G3 Genes, Genomes, Genetics 8(12):3961−3972

doi: 10.1534/g3.118.200760
[16]

Zhu X, Leiser WL, Hahn V, Würschum T. 2021. Phenomic selection is competitive with genomic selection for breeding of complex traits. The Plant Phenome Journal 4(1):e20027

doi: 10.1002/ppj2.20027
[17]

Zhu X, Maurer HP, Jenz M, Hahn V, Ruckelshausen A, et al. 2022. The performance of phenomic selection depends on the genetic architecture of the target trait. Theoretical and Applied Genetics 135(2):653−665

doi: 10.1007/s00122-021-03997-7
[18]

Robert P, Auzanneau J, Goudemand E, Oury FX, Rolland B, et al. 2022. Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection. Theoretical and Applied Genetics 135(3):895−914

doi: 10.1007/s00122-021-04005-8
[19]

Li Y, Yang X, Tong L, Wang L, Xue L, et al. 2023. Phenomic selection in slash pine multi-temporally using UAV-multispectral imagery. Frontiers in Plant Science 14:1156430

doi: 10.3389/fpls.2023.1156430
[20]

Zhang D, Chen L, Li L, Bai Q, Yu Z, et al. 2026. Multi-modal proximal sensing of structural and spectral traits for clonal selection of color-leaved tree seedlings. Smart Forestry 1:e008

doi: 10.48130/smartfor-0026-0005
[21]

Bian L, Zhang H, Ge Y, Čepl J, Stejskal J, et al. 2022. Closing the gap between phenotyping and genotyping: review of advanced, image-based phenotyping technologies in forestry. Annals of Forest Science 79(1):22

doi: 10.1186/s13595-022-01143-x
[22]

Yan R, Dong Y, Li Y, Xu C, Luan Q, et al. 2024. Enhancing genomic association studies in slash pine through close-range UAV-based morphological phenotyping. Forestry Research 4:e025

doi: 10.48130/forres-0024-0022
[23]

Roberts DR, Bahn V, Ciuti S, Boyce MS, Elith J, et al. 2017. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40(8):913−929

doi: 10.1111/ecog.02881
[24]

Werner CR, Gaynor RC, Gorjanc G, Hickey JM, Kox T, et al. 2020. How population structure impacts genomic selection accuracy in cross-validation: implications for practical breeding. Frontiers in Plant Science 11:592977

doi: 10.3389/fpls.2020.592977
[25]

Caruana R. 1997. Multitask learning. Machine Learning 28(1):41−75

doi: 10.1023/A:1007379606734
[26]

Izenman AJ. 1975. Reduced-rank regression for the multivariate linear model. Journal of Multivariate Analysis 5(2):248−264

doi: 10.1016/0047-259x(75)90042-1
[27]

Hua X, Ding X, Wu S, Huang Q, Diao S, et al. 2026. Aboveground biomass models for young Pinus elliottii plantations based on various growth factors. Scientia Silvae Sinicae 62(3):211−222

doi: 10.11707/j.1001-7488.LYKX20250455
[28]

State Bureau of Technical Supervision. 1994. GB/T 2677.8-1994 Fibrous raw material: determination of acid-insoluble lignin. Beijing: Standards Press of China https://openstd.samr.gov.cn/bzgk/gb/newGbInfo?hcno=B78B4C8134B235B183BA7FB345DE2F80 (in Chinese)

[29]

Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57(1):289−300

doi: 10.1111/j.2517-6161.1995.tb02031.x
[30]

Pasquini C. 2018. Near infrared spectroscopy: a mature analytical technique with new perspectives - a review. Analytica Chimica Acta 1026:8−36

doi: 10.1016/j.aca.2018.04.004
[31]

Geladi P, Kowalski BR. 1986. Partial least-squares regression: a tutorial. Analytica Chimica Acta 185:1−17

doi: 10.1016/0003-2670(86)80028-9
[32]

Wold S, Sjöström M, Eriksson L. 2001. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58(2):109−130

doi: 10.1016/S0169-7439(01)00155-1
[33]

Henderson CR. 1975. Best linear unbiased estimation and prediction under a selection model. Biometrics 31(2):423

doi: 10.2307/2529430
[34]

Piepho HP, Möhring J, Melchinger AE, Büchse A. 2008. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161(1−2):209−228

doi: 10.1007/s10681-007-9449-8
[35]

Isik F, Holland J, Maltecca C. 2017. Genetic data analysis for plant and animal breeding. Cham: Springer International Publishing doi: 10.1007/978-3-319-55177-7

[36]

Li Y, Suontama M, Burdon RD, Dungey HS. 2017. Genotype by environment interactions in forest tree breeding: review of methodology and perspectives on research and application. Tree Genetics & Genomes 13:60

doi: 10.1007/s11295-017-1144-x
[37]

Lauer E, Sims A, McKeand S, Isik F. 2021. Genetic parameters and genotype-by-environment interactions in regional progeny tests of Pinus taeda L. in the southern USA. Forest Science 67(1):60−71

doi: 10.1093/forsci/fxaa035
[38]

Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1):55−67

doi: 10.1080/00401706.1970.10488634
[39]

Järvelin K, Kekäläinen J. 2002. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems 20(4):422−446

doi: 10.1145/582415.582418
[40]

Wolpert DH. 1992. Stacked generalization. Neural Networks 5(2):241−259

doi: 10.1016/S0893-6080(05)80023-1
[41]

van der Laan MJ, Polley EC, Hubbard AE. 2007. Super learner. Statistical Applications in Genetics and Molecular Biology 6(1):25

doi: 10.2202/1544-6115.1309
[42]

Varma S, Simon R. 2006. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 7(1):91

doi: 10.1186/1471-2105-7-91
[43]

Du M, Liu M, Li Y, Hou H, Chen B, et al. 2025. DEFGermplasm: a comprehensive digital platform for forest genomic and phenotype data integration. Forestry Research 5:e009

doi: 10.48130/forres-0025-0009
[44]

Meder R. 2015. The magnitude of tree breeding and the role of near infrared spectroscopy. NIR News 26(3):8−10

doi: 10.1255/nirn.1521
[45]

Sykes R, Li B, Isik F, Kadla J, Chang HM. 2006. Genetic variation and genotype by environment interactions of juvenile wood chemical properties in Pinus taeda L. Annals of Forest Science 63(8):897−904

doi: 10.1051/forest:2006073
[46]

Lepoittevin C, Rousseau JP, Guillemin A, Gauvrit C, Besson F, et al. 2011. Genetic parameters of growth, straightness and wood chemistry traits in Pinus pinaster. Annals of Forest Science 68:873−884

doi: 10.1007/s13595-011-0084-0
[47]

Ding X, Zhang Y, Sun J, Tan Z, Huang Q, et al. 2024. Genetic selection for growth, wood quality and resin traits of potential slash pine for multiple industrial uses. Forestry Research 4:e023

doi: 10.48130/forres-0024-0020
[48]

Walker TD, Isik F, McKeand SE. 2019. Genetic variation in acoustic time of flight and drill resistance of juvenile wood in a large loblolly pine breeding population. Forest Science 65(4):469−482

doi: 10.1093/forsci/fxz002
[49]

Grans D, Isik F, Purnell RC, Peszlen IM, McKeand SE. 2021. Genetic variation and the effect of herbicide and fertilization treatments on wood quality traits in loblolly pine. Forest Science 67(5):564−573

doi: 10.1093/forsci/fxab026
[50]

Breiman L. 1996. Stacked regressions. Machine Learning 24(1):49−64

doi: 10.1023/A:1018046112532
[51]

Fu R, Zhang H, Wang G, Zhu X, Sun H, et al. 2026. Improving the accuracy of DBH estimation in Chinese fir using multi-source data fusion and interpretable machine learning algorithms. Smart Forestry 1:e007

doi: 10.48130/smartfor-0026-0004
[52]

Sun J, Xu C, Zhao H, Ding X, Luan Q. 2025. Soil property-driven fertilization in slash pine orchards: a stacking framework with PLSR and neural networks. Smart Forestry 1:e002

doi: 10.48130/smartfor-0025-0002
[53]

Que Q, Ouyang K, Li C, Li B, Song H, et al. 2022. Geographic variation in growth and wood traits of Neolamarckia cadamba in China. Forestry Research 2:12

doi: 10.48130/FR-2022-0012
[54]

Meuwissen THE, Hayes BJ, Goddard ME. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819−1829

doi: 10.1093/genetics/157.4.1819
[55]

Robert P, Brault C, Rincent R, Segura V. 2022. Phenomic selection: a new and efficient alternative to genomic selection. In Genomic Prediction of Complex Traits. Vol. 2467. Cham: Springer. pp. 397–420 doi: 10.1007/978-1-0716-2205-6_14

[56]

Krause MR, González-Pérez L, Crossa J, Pérez-Rodríguez P, Montesinos-López O, et al. 2019. Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat. G3 Genes, Genomes, Genetics 9(4):1231−1247

doi: 10.1534/g3.118.200856
[57]

Isik F. 2014. Genomic selection in forest tree breeding: the concept and an outlook to the future. New Forests 45:379−401

doi: 10.1007/s11056-014-9422-z
[58]

Zhang Z, Wang W, Niu H, Zhao H, Ji J, et al. 2025. Growth strategies and phenotypic plasticity of improved Chinese fir families across soil types. Forestry Research 5:e024

doi: 10.48130/forres-0025-0022
[59]

Wu HX, Powell MB, Yang JL, Ivković M, McRae TA. 2007. Efficiency of early selection for rotation-aged wood quality traits in radiata pine. Annals of Forest Science 64(1):1−9

doi: 10.1051/forest:2006082
[60]

Funda T, Fundová I, Gorzsás A, Fries A, Wu HX. 2020. Predicting the chemical composition of juvenile and mature woods in Scots pine (Pinus sylvestris L.) using FTIR spectroscopy. Wood Science and Technology 54(2):289−311

doi: 10.1007/s00226-020-01159-4