Beating the heat for rice

Integrated pipeline to generate varieties adapted to climate variability at a faster rate

M.C RebolledoE. PetroA.PenaC.ErazoD.JimenezS.DelerceE.Torres

• Climate variability explains ~32% of rice yield variability globally.

• 25% to 38% in Latin America (precipitation and temperature variability).

Rice production is highly sensitive to climate conditions event under current climate scenarios

Ray et al, 2015

Climate variability and rice production

Our strategy:

1.Environment characterization “through the eyes of the crop”

2.Trait dissection for specific environments

3.Unlocking the gene bank to increase the adaptation for specific environments

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.

Boxplots of conditional permutation based VI scores using CIF on cultivar F733 subset (Jimenez and Delerce)

1.Environment characterization “through the eyes of the crop”: Big data analysis of commercial data

Saldana :Yields limited by low radiation accumulated during the maturity stage

Saldana: yields limited by high night temperature during the reproductive stage (Tmin >23°C)

Unpublished data

DIC.05.2013

OCT.07.2014

JUL.1

5.2014

FEB.05.2014

JUL.2

4.2014

ABR.29.20130

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Yields (kg/ha) Saldana Tolima

CT21375FED2000FED733

Saldaña Yopal

VillavicencioAipe

Montería

1.Environment characterization “through the eyes of the crop”: Multi-environmental trials

-Same management, same soil, just different sowing dates and a decrease of almost 50% on grain yields

Unpublished data

22.4 22.6 22.8 23 23.2 23.4 23.6 23.8 240

100020003000400050006000700080009000

f(x) = 256.179995978212 x² − 14307.8662682421 x + 200985.556993471R² = 0.732122505217476

f(x) = − 2147.95234407272 x² + 97892.7280803253 x − 1108049.70583879R² = 0.621568274227083

f(x) = 1713.6089078921 x² − 81520.1955516284 x + 975214.927079472R² = 0.588305597797857

yield vs. Average Tmin Reproductive stage

CT21375Polynomial (CT21375)FED2000Polynomial (FED2000)FED733Polynomial (FED733)

Average Min Temperature ( C )

An increase (1 °C) in night temperature during reproductive stage will result in major crop losses

1. Environment characterization “through the eyes of the crop”: Validation of the main crop limiting factors

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f(x) = 4.88241549647006E-05 x² − 0.994608531371649 x + 9760.82075339526R² = 0.809501235311529

f(x) = − 5.10342079174885E-05 x² + 2.06596336529365 x − 13787.1301406539R² = 0.580306507982456

f(x) = 5.69305013038043E-05 x² − 1.29936654131452 x + 12398.5179168898R² = 0.85259183861016

yield vs. accumulated radiation maturity

CT21375Polynomial (CT21375)FED2000Polynomial (FED2000)FED733Polynomial (FED733)

Radiation accumulated at ripening stage Cal/cm2/day

A decrease in solar radiation during maturity stage will result in major crop losses

Peng S et al. PNAS 2004;101:9971-9975

High night temperatures AND low radiation occur together in the fieldcausing grain yield losses even under current climates

Unpublished data

-High night temperatures will increase respiration rates

-Low radiation will decrease the photosynthetic rate

NightDay

Photosynthesis RespirationCo2 Co2

Role of non structural carbohydrate Reserves ? STARCH

Co2Loss

Co2Assimilation

Vegetative Reproductive Maturity

Rate of STARCH decrease?Contribution to yield under high night temperature and low radiation?

2.Trait dissection to increase the adaptation of rice varieties to specific climatic conditions

Negative balance for CO2 in the plant

1 14 27 40 53 66 79 92 105 118 131 144 157 170 183 196 209 222 235 248 261 2740

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starch contribution to grains (mg/gDM)

Traits, genes and promising parental lines that will confer higher yield under high night temperatures and low light in Saldana Tolima

High throughput phenotypic tools for breeding

Unlocking the gene bankPromising parental lines for breeding

3.Unlocking the gene bank to increase the adaptation of rice varieties to specific climatic conditions

New genes conferring tolerance to low light and high night temperatures for breeding

Site characterization“through the eyes of the crop”-Climate-Soils-cropping system-management-End use of the crop

Traits of interest/ promising parental lines- Trait dissection- Genetic resources

Genes- Genotyping and

phenotyping tools- Local genetic

background

DATADATA

DATADATA

DATADATA

DATADATA

DATA

DATADATA

DATADATA

DATADATA Varieties adapted to climate

change

New plant types for climate variability

Empirical andMechanistic modelling + Future spatial and temporal climate (CCAFS)

Breeding

1.Environment characterization

2.Trait Dissection

3.Unlocking the gene bank

Breeding

Change breeding focus

Provide breeding tools

GRISP II ?

Unpublished data