Presentation at a workshop convened by CCAFS Learning Platform 1 in Rome, Italy, on 19-20 February 2019.

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

3. Breeding pipelines
Hybrid
Upland
Irrigated
CIAT-FLAR
Inbred
CIAT-HIAAL
Inbred Nutrition
Rice
Nutrition

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!