Plant adaptation to climate change - Scott Chapman
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Plant adaptation to climate change - Scott Chapman
- 1. Plant adaptation to climate change – opportunities in breeding SC Chapman , MF Dreccer , S Chakraborty , SM Howden
- 2. Climate change in Queensland? CCRSPI Feb 2011 Plant adaptation to climate change - opportunities in breeding
- 19. Deploying genetics: A research framework for physiological and genetic simulation of plant breeding Trait genetics Simulate Crop Improvement Strategies Trait dissection and functional physiology Cooper et al. 2002, In Silico Biol. Software and Database Tools Genotype (AA, Aa, aa) Phenotype (P AA ,P Aa ,P aa ) Environment (climate, soil, management) Experiments –physiology and genetics APSIM
- 20. Plant adaptation to climate change – opportunities in breeding CCRSPI Feb 2011 Plant adaptation to climate change - opportunities in breeding AIM: identifying superior combinations of ‘useful’ genetic regions and re-packaging these into new varieties in new cropping systems for climate change environments GxExM Germplasm resources Non-invasive phenotyping Design of robuststrategies Genetic mapping and analysis Photo-synthesis Grain filling WSC Physiological analysis
- 22. Plant adaptation to climate change – opportunities in breeding CCRSPI Feb 2011 Plant adaptation to climate change - opportunities in breeding Breeding takes time, but new technologies accelerate it SC Chapman 1 , MF Dreccer 1 , S Chakraborty 1 , SM Howden 2 Acknowledgements K Chenu 3 , D Jordan 3 , G McLean 4 , GL Hammer 3 , M Bourgault 1 , S Milroy 1 , JA Palta-Paz 1 , KB Wockner 1 , B Zheng 1 1 CSIRO Plant Industry/Climate Adaptation Flagship, Australia 2 CSIRO Ecosystem Sciences/Climate Adaptation Flagship, Australia 3 QAAFI, The University of Queensland, Australia 4 DEEDI, Queensland Primary Industries and Fisheries, Australia
Editor's Notes
- Departure, trial, prize, return detachment, difficulty, understanding, transformation
- Some genes very important (instructions for sparkplugs). Others not.
- Breeders screen 1000s of lines; phenotypic data is expensive 2 traits, 5 genes = 3 10 genotypes… more realistically, breeders work with ca. 10 50 genotypes…. (There are about 10 18 to 10 25 grains of sand on this planet…)
- For index of frost and heat stress
- Change - economic importance - stubble diseases notes to help you with - Diseases by pathogens that can live on stubble, etc will become more important than rusts that are more severe on healthy plants- these are the necrotrophic pathogens. These are the pathogens causing wilts, scabs, blights, blotches, spots etc. with poorly understood genetics. Change – geographic distribution notes to help you with – Crops will be increasingly grown in marginal soils as cropping areas shift pole ward. New diseases and disease complexes will become important on stressed crops. Increase - risk of new races evolving notes to help you with – Extended growing season, enlarged crop canopy and other pathogen factors will accelerate pathogen evolution. We have to be prepared for new races like UG99 affecting resistances that have been effective for a very long time [30 years for UG99]. Consequence - my favourite gene is ineffective - notes to help you with – Do we know how mfg will perform under CC? Examples are temperature-sensitive rust resistance genes, high CO2 effects on some host defence genes
- Development – avoiding high temperature at flowering Faster crop development in winter crops (earlier maturing) Slower crop development in summer crops? Leaf growth and tillering Faster appearance of more tillers Trade-off for water use Biomass accumulation Lower stomatal conductance, higher photosynthetic rates, higher transpiration efficiency, lower N requirement? Partitioning/yield components Higher carbohydrate storage in stems Grain number and potential size High temperature tolerance for seedlings, pollen germination and grain set
- Link: Crop reflectance is one of the technologies available for quantitative phenotyping
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- Captures understanding Receives parameter inputs Translates knowledge from descriptive to predictive
- Drivers Molecular, GM, food quality, biofuels, climate change