[
Abe N (1989) Studies on the system for thinning of Abies sachalinensis MAST planted forest. Bulletin of the Hokkaido Forest Experiment Station 26: 1-94
]Search in Google Scholar
[
Avery TE, Burkhart HE (2002) Forest measurement, 5th edition. McGraw-Hill: New York. pp. 426
]Search in Google Scholar
[
Bagchi S (2007) Relationship between size Hierarchy and density of trees in a tropical dry deciduous forest of western India. Journal of Vegetation Science 18: 389-394. https://doi.org/10.1111/j.1654-1103.2007.tb02551.x
]Search in Google Scholar
[
Burdon RD (1989) Early selection in tree breeding: principles for applying index selection and inferring input parameters. Canadian Journal of Forest Research 19: 499-504. https://doi.org/10.1139/x89-076
]Search in Google Scholar
[
Burdon RD, Bannister MH, Low CB (1992) Genetic survey of Pinus radiata. 5: Between- trait and age-age correlations for growth rate, morphology and disease resistance. New Zealand Journal of Forest Science 22: 211-227
]Search in Google Scholar
[
Cornelius J (1994) Heritabilities and additive genetic coefficient of variation in forest trees. Canadian Journal of Forest Resesarch 24: 372-379 https://doi.org/10.1139/x94-050
]Search in Google Scholar
[
Diao S, Hou Y, Xie Y, Sun X (2016) Age trends of genetic parameters, early selection and family by site interactions for growth traits in Larix kaempferi open-pollinated families. BMC Genetics 17: 104 https://doi.org/10.1186/s12863-016-0400-7493628627388017
]Search in Google Scholar
[
Falconer DS (1981) Introduction to quantitative genetics, 2nd edition. John Wiley & Sons, Inc: New York. pp. 340
]Search in Google Scholar
[
Forestry Agency (2010) Annual report on forest and forestry in Japan
]Search in Google Scholar
[
Franklin EC (1979) Model relating levels of genetic variance to stand development of four North American conifers. Silvae Genetica 28: 207-212
]Search in Google Scholar
[
Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Statistical Science 7: 457-499 https://doi.org/10.1214/ss/1177011136
]Search in Google Scholar
[
Gonçalves PdS, Bortoletto N, Cardinal ÁBB, Gouvêa LRL, Costa RBd, Moraes MLTd (2005) Age-age correlation for early selection of rubber tree genotypes in São Paulo State, Brazil. Genetics and Molecular Biology 28: 758-764 https://doi.org/10.1590/s1415-47572005000500018
]Search in Google Scholar
[
Gunnar J, Li B, Hannrup B (2003) Time trends in genetic parameters for height and optimal age for parental selection in Scots pine. Forest Science 49: 696-705
]Search in Google Scholar
[
Gwaze DP, Bridgwater FE, Byram TD, Woolliams JA, Williams CG (2000) Predicting age-age genetic correlation in tree-breeding programs: a case study of Pinus taeda L. Theoretical and Applied Genetics 100: 199-206 https://doi.org/10.1007/s001220050027
]Search in Google Scholar
[
Hallingbäck HR, Högberg KA, Säll H, Lindeberg J, Johansson M, Jansson G (2018) Optimal timing of early genetic selection for sawn timber traits in Picea abies. European Journal of Forest Research. 137: 553-564 https://doi.org/10.1007/s10342-018-1123-2
]Search in Google Scholar
[
Haapanen M (2001) Time trends in genetic parameter estimates and selection efficiency for Scot pine in relation to field testing method. Forest Genetics 8: 129-144
]Search in Google Scholar
[
Hiraoka Y, Miura M, Fukatsu E, Iki T, Yamanobe T, Kurita Kisoda M, Kubota M, Takahashi M (2019) Time trends of genetic parameters and genetic gains and optimum selection age for growth traits in sugi (Cryptomeria japonica) based on progeny tests conducted throughout Japan. Journal of Forest Research 24: 303-312. https://doi.org/10.1080/13416979.2019.1661068
]Search in Google Scholar
[
Hodge GR, White TL (1992) Genetic parameter estimates for growth traits at different ages in slash pine and some implications for breeding. Silvae Genetica 41: 252-262
]Search in Google Scholar
[
Jansson G (2007) Gains from selecting Pinus sylvestris in southern Sweden for volume per hectare. Scandinavian Journal of Forest Research 22: 185-192 https://doi.org/10.1080/02827580701330894
]Search in Google Scholar
[
Jansson G, Li B, Hannrup B (2003) Time trends in genetic parameters for height and optimum age for parental selection in Scots pine. Forest Science 49: 696-705
]Search in Google Scholar
[
Kroon J, Ericsson T, Jansson G, Andersson B (2011) Patterns of genetic parameters for height in field genetic tests of Picea abies and Pinus sylvestris in Sweden. Tree Genetics and Genomes 7: 1099-1111 https://doi.org/10.1007/s11295-011-0398-y
]Search in Google Scholar
[
Lambeth CC (1980) Juvenile-mature correlations in Pinaceae and implications for early selection. Forest Science 26: 571-580
]Search in Google Scholar
[
Lambeth CC, Dill LA (2001) Prediction models for juvenile-mature correlation for loblolly pine growth traits within, between and across test sites. Forest Genetics 8: 101-108
]Search in Google Scholar
[
Mihai G, Mirancea I (2016) Age trends in genetic parameters for growth and quality traits in Abies alba. iForest 9: 954-959 https://doi.org/10.3832/ifor1766-009
]Search in Google Scholar
[
Muñoz F, Sanchez L (2016) breedR: statistical methods for forest genetic resources Analysts. R package version 0.12-2 https://github.com/famuvie/breedR
]Search in Google Scholar
[
Naji HR, Nia MF, Kiaei M, Abdul-Hamid H, Soltani M, Faghihi A (2015) Effect of intensive planting density on tree growth, wood density and fiber properties of maple (Acer velutinum Boiss.). iForest 9: 325-329 https://doi.org/10.3832/ifor1333-008
]Search in Google Scholar
[
Nakagawa M (2015) Statistical information on weeding-duration of Japanese larch and Sakhalin fir plantation in Hokkaido. Bulletin of the Hokkaido Forestry Research Institute 52: 23-24 (in Japanese)
]Search in Google Scholar
[
R Core Team (2016) R: A language and environment for statistical computing. R Foundation for Statistical Computing: Vienna, Austria https://www.R-project.org/
]Search in Google Scholar
[
Roberts SD, Harrington CA (2008) Individual tree growth response to variable- density thinning in coastal Pacific Northwest forests. Forest Ecology and Management 255: 2771-2781 https://doi.org/10.1016/j.foreco.2008.01.043
]Search in Google Scholar
[
Rweyongeza DM (2016) A new approach to prediction of age-age correlation for use in tree breeding. Annals of Forest Science 73: 1099-1111 https://doi.org/10.1007/s13595-016-0570-5
]Search in Google Scholar
[
Takiya M (2014) Site index curve estimation of Abies sahalinensis plantation forest in Hokkaido, Japan. Bulletin of Hokkaido Forest Experiment Station 51: 7-11 (in Japanese with English summary)
]Search in Google Scholar
[
Weng YH, Tosh KJ, Park YS, Fullarton MS (2007) Age-related trends in genetic parameters for Jack pine and their implications for early selection. Silvae Genetica 56: 242-252. https://doi.org/10.1515/sg-2007-0035
]Search in Google Scholar
[
White TL, Adams WT, Neale DB (2007) Forest genetics. UK: CAB International. https://doi.org/10.1079/9781845932855.0000
]Search in Google Scholar
[
Wu HX (1998) Study of early selection in tree breeding: I, advantage of early selection through increase of selection intensity and reduction of field test size. Silvae Genetica 47: 146-155
]Search in Google Scholar
