Download - Rice for comunity
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Unpublished data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Unpublished data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
10000 12000 14000 16000 18000 200000
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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
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DATA
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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