Breeding foresight workshop: Presentation by CIAT-FP4 RICE
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Presentation at a workshop convened by CCAFS Learning Platform 1 in Rome, Italy, on 19-20 February 2019.
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Breeding foresight workshop: Presentation by CIAT-FP4 RICE
- 1. Ongoing and planned activities on breeding foresight CIAT-FLAR Rice breeding needs FP4 RICE – Coa 4.1 Maria Camila Rebolledo – Rice physiologist
- 2. Team, partners Rice Product Design Breeding strategy an optimization Product Development and testing #1 Product testing #2 Product multiplication Product Introduction Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Stage 6 Plant Breeding Phenomics Grain Quality Biotic resistance Abiotic resistance Molecular Markers Trait integration Trait discovery Impact assessment Plant Breeding Plant Breeding Plant Breeding Plant Breeding Grain Quality Grain Quality Biotic resistance Abiotic resistance Molecular Markers Impact assessment Phenomics Grain Quality Agronomy Agronomy Phenomics
- 4. Plant Breeding CIAT FLAR Rice Irrigated -Breeding pipeline CIAT breeders need to better define the specific agroclimatic region (TPE) and to replace a variety • Urgent TPE definition (local varieties, soil and Climate variability /climate change) , feed into the product profile. • GXE models to predict phenotypes from genetic and environmental inputs (Ideotyping) • Models to identify the most important plant traits
- 5. Product design Trait category Trait Trait description Variety benchmark Performance required (=, >, >=, >>) Plant architecture Plant type (including tillering habit) Fedearroz67 Acceptation Crop yield Yield potential Yield under optimal conditions Fedearroz67, Fedearroz2000, IRGA424, MAC18, INTA- L9, Puita, CENTA-A8 10% or more production compare with the cultivated control Grain number Number of grains per panicle Efficient tillering All tillers have a filled panicle Panicule length Biotic stress Resistance to Magnaporthe (rice blast) Rotten neck O.Llanos 5 <= 3 Resistance to Rice Hoja Blanca Virus While leaf streaks Fedearroz 2000 <= 3 Abiotic stress Lodging resistance Stem that holds after a high nitrogen fertilization Less than 5% of lodged plants under high wind or high nitrogen Value chain clients, consumers, processors Grain quality (Milling Yield, Head Rice Recovery) % of whole grain after milling and polishing Fedearroz 60 > 55% Grain quality (Amylose content) % Fedearroz 60 >= 26 Grain quality (Chalkiness) < 0.6 Grain size Fedearroz 60 > 6mm Nutritional value Zinc content in polished grain IR68144 >= 28 ppm Cooking quality Sensorial traits after cooking Product profile Irrigated / favorable rainfed nutritious rice
- 6. What is missing to accelerate breeding for climate smart varieties? • Wide Environment characterization through the eyes of the crop (adaptation for specific environments) • Understanding of the genetic control of crop adaptation to climate variability and climate change (GxExM) • Optimize the use of genetic diversity under different environments and climatic scenarios We need to provide breeders with the phenomics, genomics and environmental information, as well as target ideotypes, to generate better adapted varieties at a faster rate Coa1. Establishing a worldwide field laboratory Coa3. Genetics of rice plant interaction with the biotic environment Coa4. Discovery of genomic associations Coa2. Global phenotyping tools Coa5. Big Data integration platform FP4 RICE CRP
- 7. Coa 4.1 Establishing antenna trials (70 var) + analysis GxE Coa 4.1 Establishing Reference panels trials + analysis GxE and GWAS Coa4.1 Environmental characterization of breeders sites Coa 4.1 Modelling yields at antenna sites = guide development of new varieties Milestones translated into products/activities: Coa 4.4 Phenotype-genotype pipeline : identification of QTls at multiple sites, identification of new parental lines Coa 4.5. Data capture, storage and analysis across sites (platforms, methods, integration with platforms, B4R training) Coa 4.2 Upgrading HTP facilities (yield/ abiotic/biotic stresses)/ Facilities used in breeding programs Coa 4.4 Upgrading genotyping facilities Coa 4.3 Blast-panicle blight-hoja blanca diagnostics/mining new genes/ survey and sampling Coa 4.3 Disease monitoring at future antenna sites
- 8. • To provide a novel, powerful, and inclusive approach to understand how climate affects crop adaptation. • To offer a systematic strategy to exploit G × E interactions to enhance crop performance. • To present an effective platform to engage our partners to support on-site research. • To attract funding on a regional and global scale. CoA 4.1 Designing a Global Rice Array Main goal: to establish a multi-environment field network serving as a tool to contribute to design site-specific rice ideotypes adapted to future climates.
- 9. 34 sites in total: 21 in Asia: - 3 Philippines - 1 Vietnam - 1 in Myanmar - 5 China - 1 Bangladesh - 10 India 5 in Africa: - 1 Senegal - 1 Ivory Coast - 1 Burundi - 2 Madagascar 8 in Latin America: - 3 Colombia - 4 Brazil - 1 Uruguay Worldwide field laboratory Antenas panel:a total of 73 varieties (40 nominated by IRRI, 16 by Africa Rice, 14 by Ciat, and 3 by Cirad) of wide diversity (irrigated, rainfed, upland, …) CoA 4.1 Designing a Global Rice Array
- 10. K F CS N K F C S N Climate Management Soil Plant growth + SoilsClimate =+ Develop/use models to quantify and map the impact of abiotic/biotic factors on yield • Use climatic, crop data to identify priority constrains and traits, define extrapolation domain and design ideotypes ORYZA 2000 AgMIP network CCAFS (DSSAT) Management Crop yield Site characterization for climate variability and climate change scenarios: - trait combinations - yield constraints Pest and diseases Crop Modelling activities Crop modelling activities today are ensured only for Africa and Madagascar. For Latin America and Asia the activities will depend on the interest of the Agmip network. Data will be stored on the DataHub created in Coa 4.5 CoA 4.1 Designing a Global Rice Array
- 11. Thank you!
Editor's Notes
- => a single trait will not improve plant performance in all scenarios of climate variability, in all cropping systems a single genotype will not cope with all the existing climatic (temporal) variability define the environment , cropping system and target areas will speed up the adaptation of the ideotype Currently, we lack a systemic understanding of how environment affect the phenotypic expression of specific genotypes. This presents a bottleneck for genetic gain in rice breeding. We lack of breeding tools, markers, to accelerate the delivery of adapted varieties to climate variability We lack the exploration of diversity in rice, we have the genebank with valuable varieties, traits and genes that could be used to increase the adaptation to CC.