Productive potential of selected Norway spruce populations from nothern Poland (preliminary study from Testing Program)

Transcription

1 Productive potential of selected Norway spruce populations from nothern Poland (preliminary study from Testing Program) Marcin Klisz 1, Szymon Jastrzębowski 1, Krzysztof Ukalski 2, Joanna Ukalska 2, Paweł Przybylski 1 1 Forest Research Institute, Department of Silviculture and Genetics 2 Warsaw University of Life Sciences, Faculty of Applied Informatics and Mathematics, Department of Econometrics and Statistics

2 Norway spruce range of distribution Poland

3 Norway spruce in Poland Mionskowski, Zajączkowski 2015

4 Norway spruce migration Schmidt-Vogt 1977

5 Origin of Norway spruce Dering, Lewandowski 2009

6 What is the growth potential of Norway spruce in Poland?

7 Program for Testing Forest Reproductive Material Testing Program over 10 years: 105 forest distrcts 165 experimental plots 4 tree species 492 selected seed stands 1394 plus trees

8 Testing regions 1st testing region for Norway spruce Country standard

9 Sites & testing populations Czerwony Dwor Goldap Szczebra Czarna Bialostocka

10 Methods Linear mixed model: y ijk r k e j g i e j ge ij ijk y ijk trait value observed for the i-th genotype (i=1,...,g) in the j-th environment (j=1,...,e) μ - general mean g i - fixed effect of the i-th genotype e j - random effect of the j-th environment ge ij - random effect of the genotype environment interaction GE r k (e j ) - random effect of the k-th block in the j-th environment ε ijk - experimental error Multivariate stability analysis: GGE-biplot (Gabriel 1971; Yan i in. 2001) Gabriel K.R The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58, Yan W., Cornelius P.L., Crossa J., Hunt L.A Two types of GGE biplots for analyzing multienvironment trial data. Crop Sci., 41,

11 ANOVA Source of variation df MS F Pr.>F % (G+E+GEI) Genotype (G) * Environment (E) * Block (Environment) < Genotype Environment (GEI) Error * Hocking s approach PC % (G+E+GE) PC % PC1 + PC %.

12 Similarity of environments GREG-biplot (AEC method) A P ij =OA i *OB j *cos ij Pearson s correlation (upper part), α angle (bottom part). environments Czarna B Czerwony D Gołdap Szczebra Czarna B -0.31ns -0.71** -0.63** Czerwony D ns -0.54ns Gołdap ** Szczebra ** Significant at the 0.01 probability level, ns - not significant Average environment O α B Average environment

13 Environments ranking Discriminative Not discriminative Representative Szczebra Czerwony Dwór Not representative Gołdap Czarna Białostocka

14 Mega environments GGE-biplot mega environments E1 E3 E1 E2 E2 E3

15 Genotypes ranking Winners in Goldap & Czerwony Dwor (E1)

16 Genotypes ranking Winners in Czarna Bialostocka (E2)

17 Genotypes ranking Winners in Szczebra (E3)

18 Ideal genotype GGE-biplot ideal genotype The best genotype in terms of high stability & well mean performance

19 Genotypes stability Stability ver. performance Well mean performance Poor mean performance High stability Well adapted everywhere Poor adapted everywhere Low stability Specifically adapted Poor adapted to specify conditions

20 Genotypes stability Stability ver. performance Well mean performance Poor mean performance High stability 6523 Poor adapted everywhere Low stability Specifically adapted Poor adapted to specify conditions

21 Genotypes stability Stability ver. performance Well mean performance Poor mean performance High stability 6523 Poor adapted everywhere Low stability 52692, 50265, Poor adapted to specify conditions

22 Genotypes stability Stability ver. performance Well mean performance Poor mean performance High stability , 5802, 33256, 6540, 1 Low stability 52692, 50265, Poor adapted to specify conditions

23 Genotypes stability Stability ver. performance Well mean performance Poor mean performance High stability , 5802, 33256, 6540, 1 Low stability 52692, 50265, 33253, , 5821, 33259, 5801

24 Genotypes stability Stability ver. performance Well mean performance Poor mean performance High stability , 5802, 33256, 6540, 1 Low stability 52692, 50265, 33253, , 5821, 33259, 5801

25 Conclusions Most of the observed variability between populations is explained by environmental variability or the G E interaction. Only less than 7% relates to genotype The growth potential of the northern populations does not show any clear geographical pattern, The semi-natural population from Białowieża manifests a growth potential similar to the theoretical ideal genotype, The country standard for Norway spruce (the population from southern Poland) shows a lack of adaptation to growing conditions in the north of the country, Czerwony Dwór seems to be the most representative environment, while Czarna Białostocka and Szczebra have a high discriminating ability.

26 Ongoing study survival height growth survival DBH height growth survival DBH height growth survival 1, 2 and 3 YEARS 5 YEARS 10 YEARS 20 YEARS

27 Acknowledgments FRI Jan Matras Jan Kowalczyk Szymon Jastrzębowski Paweł Przybylski Marcin Mionskowski ID PAS Daniel Chmura Roman Roszkowski WULS (Forest Faculty) Włodzimierz Buraczyk Henryk Szeligowski AR in Krakow (Forest Faculty) Kinga Skrzyszewska Jacek Banach PULS (Forest Faculty) Władysław Barzdajn Wojciech Kowalkowski State Forests in Poland WULS (Faculty of Informatics and Mathematics) Joanna Ukalska Krzysztof Ukalski

28 Thank You For Attention!