Skip to main content
SpringerLink
Log in

Genotype by environment interaction analysis of growth of Picea koraiensis families at different sites using BLUP-GGE

  • Published:
New Forests Aims and scope Submit manuscript

Abstract

This study investigated the growth (height, diameter at breast height and volume) of Picea koraiensis families at two sites and explored the genotype × environment interaction (GEI) of growth traits to provide a theoretical basis for the improvement of P. koraiensis families. Genetic variation analysis and genetic parameter estimation of growth traits were carried out using 52 families of three provenances, Muling (M), Jinshantun (J), Wuyiling (W), of P. koraiensis at Jiangshanjiao (JSJ) and Qingshan (QS) forest farms in Heilongjiang Province, China. The variance analysis of the multisite test and the type B correlation coefficient of growth traits between the two sites were less than 0.2, indicating that the growth traits of the family had extremely significant GEI, and the growth of families of provenances J and W (experienced colder conditions) at JSJ and QS was significantly better than that of provenance M (grew in relatively warm areas). The main drivers of GEI might be the annual average temperature and frost-free period. The families from provenances J and W that experienced colder conditions might have higher environmental adaptability and superior growth compared with families from provenance M. The family with the highest but unstable volume yield at JSJ and QS was M410 and W010, respectively. While, the top five families with high and stable yields were J085, J097, W037, W045 and J068 by stability analysis (BLUP-GGE) of joint-site. This study enhanced our understanding of the GEI of P. koraiensis families and benefits the selection of optimal families.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

BLUP:

Best linear unbiased prediction

MET:

Multi environment test

GEI:

Genotype by environment interaction

GGE:

Genotype and genotype × environment

PCV:

Phenotypic coefficient of variation

GCV:

Genetic coefficient of variation

References

  • Arnold GC (1992) Statistical analysis of regional yield trials: AMMI analysis of factorial designs. Comput Electron Agric 12:81–82

    Article  Google Scholar 

  • Baltunis BS, Gapare WJ, Wu HX (2010) Genetic parameters and genotype by environment interaction in radiata pine for growth and wood quality traits in Australia. Silvae Genet 59(1–6):113–124

    Article  Google Scholar 

  • Burdon R (1977) Genetic correlation as a concept for studying genotype-environment interaction in forest tree breeding. Silvae Genet 26:168–175

    Google Scholar 

  • Calleja-Rodriguez A, Andersson Gull B, Wu HX, Mullin TJ, Persson T (2019) Genotype-by-environment interactions and the dynamic relationship between tree vitality and height in northern Pinus sylvestris. Tree Genet Genomes 15(3):36

    Article  Google Scholar 

  • Carson S (1991) Genotype × environment interaction and optimal number of progeny test sites for improving Pinus radiata in New Zealand. N Z J For Sci 21:32–49

    Google Scholar 

  • Chen ZQ, Karlsson B, Wu HX (2017) Patterns of additive genotype-by-environment interaction in tree height of Norway spruce in southern and central Sweden. Tree Genet Genomes 13(1):25

    Article  Google Scholar 

  • Cheng WS, Feng ZK, Yu JX (2017) Development of generic standard volume model and derived form factor model for major tree species in China. Trans Chin Soc Agric Mach 48(3):245–252

    Google Scholar 

  • Cheng L, Zhang X, Zhang X, Zhang W, Lin Y (2018) Forestry multi-environment trial analysis based on BLUP and GGE biplot. J Northeast For Univ (Natural Sciences Edition) 46(3):87–93 (in Chinese)

    Google Scholar 

  • Clair JBS, Kleinschmit J (1986) Genotype-environment interaction and stability in ten-year height growth of Norway spruce clones (Picea abies Karst.). Silvae Genet 35(6):177–186

    Google Scholar 

  • Cullis BR, Jefferson P, Thompson R, Smith AB (2014) Factor analytic and reduced animal models for the investigation of additive genotype-by-environment interaction in outcrossing plant species with application to a Pinus radiata breeding programme. Theor Appl Genet 127(10):2193–2210

    Article  CAS  PubMed  Google Scholar 

  • De Souza M, Hodge S, White G (1992) Indirect prediction of breeding values for fusiform rust resistance of slash pine parents using greenhouse tests. For Sci 38(1):45–60

    Google Scholar 

  • Ding M, Tier B, Yan W (2008) Application of GGE biplot analysis to evaluate Genotype (G), Environment (E) and G x E interaction on P. radiata: a case study. N Z J For Sci 38(1):132–142

    Google Scholar 

  • Finlay K, Wilkinson G (1963) The analysis of adaptation in a plant-breeding programme. Aust J Agric Res 14(6):742–754

    Article  Google Scholar 

  • Gibson A, Bachelard EP, Hubick KT (1995) Relationship between climate and provenance variation in Eucalyptus camaldulensis Dehnh. Funct Plant Biol 22:453–460

    Article  Google Scholar 

  • Gilmour A, Gogel B, Cullis B, Thompson R (2011) ASReml user guide release 3.0. VSN International Ltd, Hemel Hempstead

    Google Scholar 

  • Goldblum D, Rigg LS (2005) Tree growth response to climate change at the deciduous boreal forest ecotone, Ontario, Canada. Can J For Res 35(11):2709–2718

    Article  Google Scholar 

  • Guerra FP, Richards JH, Fiehn O, Famula R, Stanton BJ, Shuren R, Sykes R, Davis MF, Neale DB (2016) Analysis of the genetic variation in growth, ecophysiology, and chemical and metabolomic composition of wood of Populus trichocarpa provenances. Tree Genet Genomes 12:6

    Article  Google Scholar 

  • Houspanossian J, Kuppel S, Nosetto M, Di Bella C, Oricchio P, Barrucand M, Rusticucci M, Jobbágy E (2016) Long-lasting floods buffer the thermal regime of the Pampas. Theor Appl Climatol 131(1–2):111–120

    Google Scholar 

  • Johnson GR, Burdon R (1990) Family-site interaction in Pinus radiata: implications for progeny testing strategy and regionalised breeding in New Zealand. Silvae Genet 39:55–62

    Google Scholar 

  • Kirchhefer A (2001) The influence of slope aspect on tree-ring growth of Pinus sylvestris L. in northern Norway and its implications for climate reconstruction. Dendrochronologia 18:27–40

    Google Scholar 

  • Klápště J, Lstibůrek M, Kobliha J (2007) Initial evaluation of half-sib progenies of Norway spruce using the best linear unbiased prediction. J For Sci 53(2):41–46

    Article  Google Scholar 

  • Li CR, Weng QJ, Chen JB, Li M, Zhou CP, Chen SK, Zhou W, Guo DQ, Lu CX, Chen JC, Xiang DY, Gan SM (2016) Genetic parameters for growth and wood mechanical properties in Eucalyptus cloeziana F. muell. New For 48:33–49

    Article  CAS  Google Scholar 

  • 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 Genet Genomes 13(3):60

    Article  Google Scholar 

  • Lu P, Parker WH, Cherry M, Colombo S, Parker WC, Man R, Roubal N (2014) Survival and growth patterns of white spruce (Picea glauca [Moench] Voss) rangewide provenances and their implications for climate change adaptation. Ecol Evol 4(12):2360–2374

    Article  PubMed  PubMed Central  Google Scholar 

  • Meason DF, Kennedy SG, Dungey HS (2016) Two New Zealand-based common garden experiments of the range-wide ‘Kuser’ clonal collection of Sequoia sempervirens reveal patterns of provenance variation in growth and wood properties. New For 47(4):635–651

    Article  Google Scholar 

  • Pagliarini MK, Kieras WS, Moreira JP, Sousa VA, Shimizu JY, Moraes MLT, Furlani E, Aguiar AV (2016) Adaptability, stability, productivity and genetic parameters in slash pine second-generation families in early age. Silvae Genet 65(1):71–82

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Raymond CA (2011) Genotype by environment interactions for Pinus radiata in New South Wales, Australia. Tree Genet Genomes 7(4):819–833

    Article  Google Scholar 

  • Rweyongeza DM (2011) Pattern of genotype–environment interaction in Picea glauca (Moench) Voss in Alberta, Canada. Ann For Sci 68(2):245–253

    Article  Google Scholar 

  • SAS System for Windows (2009) SAS/Stat Software, version 9.2. SAS Institute Inc., Cary

    Google Scholar 

  • Sixto H, Salvia J, Barrio M, Ciria MP, Cañellas I (2011) Genetic variation and genotype–environment interactions in short rotation Populus plantations in southern Europe. New Forest 42(2):163–177

    Article  Google Scholar 

  • 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. Ann For Sci 63(8):897–904

    Article  CAS  Google Scholar 

  • Wang QY, Wu YL, Cai BM, Wang HR, Liu JC (1997) The geographic variation of 15 years old provenance and the provenance selection of Picea koraiensis. J Northeast For Univ 25(3):6–8 (in Chinese)

    CAS  Google Scholar 

  • Wang Q, Jia H, Shang J (2005) Geographic variation and genetic performance of Picea koraiensis in growth and wood characteristics. J For Res 16(2):93–96

    Article  Google Scholar 

  • Wang R, Hu D, Zheng H, Yan S, Wei R (2015) Genotype × environmental interaction by AMMI and GGE biplot analysis for the provenances of Michelia chapensis in South China. J For Res 27(3):659–664

    Article  Google Scholar 

  • White TL, Hodge GR (1988) Best linear prediction of breeding values in a forest tree improvement program. Theor Appl Genet 76(5):719–727

    Article  CAS  PubMed  Google Scholar 

  • Xiang B, Li B (2003) Best linear unbiased prediction of clonal breeding values and genetic values from full-sib mating designs. Can J For Res 33(10):2036–2043

    Article  Google Scholar 

  • Xiao Y, Ma WJ, Lu N, Wang Z, Wang N, Zhai WJ, Kong LS, Qu GZ, Wang QX, Wang JH (2019) Genetic variation of growth traits and genotype-by-environment interactions in clones of Catalpa bungei and Catalpa fargesii f. duclouxii. Forests 10(1):57

    Article  Google Scholar 

  • Xie CY (2003) Genotype by environment interaction and its implications for genetic improvement of interior spruce in British Columbia. Can J For Res 33(9):1635–1643

    Article  Google Scholar 

  • Yan W (2010) Optimal use of biplots in analysis of multi-location variety test data. Acta Agronom Sin 36:1805–1819 (in Chinese)

    Article  CAS  Google Scholar 

  • Yan W, Hunt A, Sheng L, Szlavnics Q (2000) Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci 40:597–605

    Article  Google Scholar 

Download references

Acknowledgements

This work was financially supported by the Thirteenth Five-Year Plan for Key & Research Projects of China (2017YFD0600606-09).

Author information

Authors and Affiliations

  1. State Key Laboratory of Forest Genetics and Tree Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, People’s Republic of China

    Juanjuan Ling, Yao Xiao, Jiwen Hu, Fangqun Ouyang & Junhui Wang

  2. Forestry Research Institute of Heilongjiang Province, Harbin, People’s Republic of China

    Fude Wang

  3. Arthur Temple College of Forestry and Agriculture, Stephen F. Austin State University, Nacogdoches, TX, 75962, USA

    Yuhui Weng

  4. State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, People’s Republic of China

    Hanguo Zhang

Authors
  1. Juanjuan Ling
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yao Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiwen Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fude Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fangqun Ouyang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Junhui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yuhui Weng
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hanguo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Fangqun Ouyang or Junhui Wang.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (XLSX 15 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ling, J., Xiao, Y., Hu, J. et al. Genotype by environment interaction analysis of growth of Picea koraiensis families at different sites using BLUP-GGE. New Forests 52, 113–127 (2021). https://doi.org/10.1007/s11056-020-09785-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11056-020-09785-3

Keywords

Search

Navigation