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Beating the heat for rice
Integrated pipeline to generate varieties adapted to
climate variability at a faster rate
M.C Rebolledo
E. Petro
A.Pena
C.Erazo
D.Jimenez
S.Delerce
E.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)
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
DIC.05.2013 OCT.07.2014 JUL.15.2014 FEB.05.2014 JUL.24.2014 ABR.29.2013
Yields (kg/ha) Saldana Tolima
CT21375
FED2000
FED733
Saldaña
Yopal
Villavicencio
Aipe
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
y = 1713.6x2 - 81520x + 975215
R² = 0.5883
y = -2148x2 + 97893x - 1E+06
R² = 0.6216
y = 256.18x2 - 14308x + 200986
R² = 0.7321
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
22.5 23 23.5 24
Average Min Temperature ( C )
yield vs. Average Tmin Reproductive stage
CT21375
FED2000
FED733
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
y = 6E-05x2 - 1.2994x + 12399
R² = 0.8526
y = -5E-05x2 + 2.066x - 13787
R² = 0.5803
y = 5E-05x2 - 0.9946x + 9760.8
R² = 0.8095
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000 12000 14000 16000 18000 20000
Radiation accumulated at ripening stage Cal/cm2/day
yield vs. accumulated radiation maturity
CT21375
FED2000
FED733
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 field
causing grain yield losses even under
current climates
-High night temperatures
will increase respiration
rates
-Low radiation will
decrease the
photosynthetic rate
NightDay
Photosynthesis Respiration
Co2 Co2
Role of non
structural
carbohydrate
Reserves ?
STARCH
Co2
Loss
Co2
Assimilation
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
0
20
40
60
80
100
120
140
160
180
200
1
11
21
31
41
51
61
71
81
91
101
111
121
131
141
151
161
171
181
191
201
211
221
231
241
251
261
271
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
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA Varieties adapted to climate
change
New plant
types for
climate
variability
Empirical and
Mechanistic modelling +
Future spatial and
temporal climate
(CCAFS)
Breeding
1.Environment
characterization
2.Trait
Dissection
3.Unlocking
the gene
bank
Breeding
GRISP II ?

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Rice for comunity

  • 1. Beating the heat for rice Integrated pipeline to generate varieties adapted to climate variability at a faster rate M.C Rebolledo E. Petro A.Pena C.Erazo D.Jimenez S.Delerce E.Torres
  • 2. • 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
  • 3. 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.
  • 4. 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)
  • 5. 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 DIC.05.2013 OCT.07.2014 JUL.15.2014 FEB.05.2014 JUL.24.2014 ABR.29.2013 Yields (kg/ha) Saldana Tolima CT21375 FED2000 FED733 Saldaña Yopal Villavicencio Aipe 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
  • 6. y = 1713.6x2 - 81520x + 975215 R² = 0.5883 y = -2148x2 + 97893x - 1E+06 R² = 0.6216 y = 256.18x2 - 14308x + 200986 R² = 0.7321 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 22.5 23 23.5 24 Average Min Temperature ( C ) yield vs. Average Tmin Reproductive stage CT21375 FED2000 FED733 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 y = 6E-05x2 - 1.2994x + 12399 R² = 0.8526 y = -5E-05x2 + 2.066x - 13787 R² = 0.5803 y = 5E-05x2 - 0.9946x + 9760.8 R² = 0.8095 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 12000 14000 16000 18000 20000 Radiation accumulated at ripening stage Cal/cm2/day yield vs. accumulated radiation maturity CT21375 FED2000 FED733 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 field causing grain yield losses even under current climates
  • 7. -High night temperatures will increase respiration rates -Low radiation will decrease the photosynthetic rate NightDay Photosynthesis Respiration Co2 Co2 Role of non structural carbohydrate Reserves ? STARCH Co2 Loss Co2 Assimilation 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
  • 8. 0 20 40 60 80 100 120 140 160 180 200 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 231 241 251 261 271 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
  • 9. 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 DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA Varieties adapted to climate change New plant types for climate variability Empirical and Mechanistic modelling + Future spatial and temporal climate (CCAFS) Breeding 1.Environment characterization 2.Trait Dissection 3.Unlocking the gene bank Breeding GRISP II ?

Editor's Notes

  1. -
  2. In association with The Site Specific Agriculture group in CIAT, we developed empirical models in order to identify the main climate factors limiting yields: As an example, with commercial data aggregated by the local partner Fedearroz, statistical analysis revealed that low radiation accumulated during the maturity stage and high night temperatures above 23 degrees during the reproductive stage, explained 3 almost 35% of the observed yield variability in saldana
  3. In the same site, Saldana, we performed 6 trials varying the sowing date and we observed: a decrease in yields from 8 t/ha to almost 4t/ha comparing different sowings from april to December. We are sensing the environement, measuring the varieties response to climate (=yield)
  4. With our MET we validated: T min during reproductive stage (high night temperatures) has a negative effect on yields But this comes together with low radiation during maturity stage , that also has a negative effect on yields Also this was validated in controlled field experiments, evaluating historical data from breeding trials , at IRRI. The same increase in minimum temperatures (1 degree), associated with a decrease in radiation.
  5. Together low radiation and high night temperatures will decrease the amount of C available in the plant to fill the grains, and produce final grain yields Therefore we taught that the rate of decrease of starch (the form of C accumulated as reserves in rice stems) will have a role maintaining yields under limiting conditons We measure it for two contrasting sowing dates and we observed that the rate of starch decrase contribute to the mantainance of the number of grains per panicle, thus grain yield.