What are the key environmental drivers for genotypes in New Zealand and Australia?

Transcription

1 What are the key environmental drivers for genotypes in New Zealand and Australia? Washy Gapare et al PLANT INDUSTRY

2 Outline What is genotype-by-environment interaction? challenge to an opportunity What are the possible drivers of G x E for growth? Australia New Zealand Analysis of most tested and deployed parents Summary conclusions

3 Definition of G x E No G x E Non-crossover G x E Crossover G x E Env. 1 Env. 2 Env. 1 Env. 2 Genotype 1 Genotype 2 Env. 1 Env. 2

4 G x E Genotype with little G x E has stability across environments One with high G x E may outperform all others in specific environments G x E implies the opportunity to fine-tune specific genotypes to specific environments for maximum genetic gain/productivity

5 Dealing with G x E: a challenge and an opportunity Ignore Use genotypic means across environments even when ge ij exists (σ ge 2 <50% σ g2 ) Avoid Group similar environments (forming mega-environments) Exploit Identify the causes and provide the opportunity to use

6 New South Wales high elevation sites Radiata pine plantations Victoria fertile ex-pasture South Australia New South Wales Victoria South Australia low elevation sandy soils Tasmania

7 STBA radiata pine trials used in G x E study Trials across SE Aust, Tasmania and Western Aust Some genotypes replicated on more than 60 sites Genetic connectedness can be improved

8 Bi-plot of climate variables for trial sites across Australia RHLT Aridity AIX (Evaporation/Precipitation) WA_ WA_9702 West Australia TAS_9610 Annual Precipitation AP MR MXTDM MTGS VPP TE MMNT NSW_ CV_9608 GL_9613 GL_9713 CV_9709 GL_ GT_9707 GT_ GT_9606 GT_9705 GT_9703 GT_9601 Victoria and South Australia TAS_ TAS_ OTW_9710 TAS_ Tasmania PGS PDQ XIX RHHT

9 Altitude (m) Trials 10. grouped Trials grouped according according to rainfall to rainfall & elevation and elevation Green Hills Sherwood Billapalloola Wirrabara Second Valley 100 Salicki Longs BongBong Rainfall (mm)

10 Initial site types based on temp & rainfall

11 Northland sand dunes Radiata pine is planted in diverse environments across New Zealand Radiata plantations Kaingaroa pumice soils Marlborough silty-clays

12 Location of 48 RPBC trials Most tested parents (n = 24) Most deployed Trait of interest is DBH

13 Multivariate Regression Trees to study G x E Statistical technique used to Explain and predict relationships between G & E Tree clusters driven by degree of genetic differentiation Each cluster is then defined as a different environment Grouping environments where genotypes will be most successful

14 Multivariate Regression Tree analysis Unconstrained clustering - maximum variance that could possibly be explained by G x E Constrained clustering by climate and soils to determine G x E drivers If there is a large % of unexplained variance, then there are some unmeasured drivers

15 % variance explained by G x E = 58% 15 Presentation title Presenter name

16 9% 6% Wettest sites Cold sites Group D Warmest sites Group C Mixed bag

17 Clustering of trials based on MAP & MinTCM 17 Presentation title Presenter name

18 Summary Significant G E 25% of G x E can be explained by precipitation and temperature Large-scale drivers of G E - mainly climatic High rainfall sites: most genotypes perform relatively good Low rainfall, cold sites need to pick specific genotypes But regional-scale drivers - soils, terrain and geology across sites and years (and programs) is paramount for future G x E studies Current deployment is not optimal industry adoption plan is being developed Climate change will influence site clustering and rankings of genotypes in different deployment zones

19 Summary Significant G E (70% of additive variance?) Large-scale drivers of G E are mainly climatic, 25% of G x E can be explained by precipitation and temperature but regional-scale drivers - soils, terrain and geology Current deployment could be improved industry adoption plan is being developed Genetic linkage across sites and years (and programs) is paramount for future G x E studies Climate change will influence site clustering and rankings of genotypes in different deployment zones

20 Project team: M Ivkovich, W Gapare, H Wu, T Jovanovic (CSIRO) P Jefferson, J Butcher (RPBC) G Dutkowski, P Buxton, T McRae (STBA, PPG) H Dungey, A Dunningham, M Heaphy, A Yanchuk (SCION) D Aurik (Timberlands Pacific) Thank you Plant Industry Washy Gapare Senior Research Scientist t e Washington.Gapare@csiro.au w PLANT INDUSTRY

21 Canonical discriminant analysis of average climate in plantation areas (dots) and trial locations (triangles)

22 Site categories Burdon (Burdon et al (1998) et al. DBH 1997) data - results Infertile clays Coastal dunes Volcanic plateau Central Southern S.I CSIRO Plant Industry