Increasing yield potential and improving crop adaptation ...€¦ · Average genetic gains at 556...
Transcript of Increasing yield potential and improving crop adaptation ...€¦ · Average genetic gains at 556...
Increasing yield potential and improving crop adaptation to climate change:
Strategies and genetic gains
Matthew P. Reynolds, Gemma Molero, Maria Tattaris, C. Mariano Cossani, Sivakumar Sukumaran
EPPN PLANT PHENOTYPING SYMPOSIUM
November 12, 2015, Barcelona
International Wheat Improvement Network (IWIN) Coordinated by CIMMYT since 1960s
Latin
America
Africa Middle
East
South &
East Asia
CIMMYT distributes 1,000 new wheat genotypes annually targeted to a range of environments
Average genetic gains at 556 international sites: ~1% per year from 1996-2010
Manes et al. 2012 .Genetic yield gains of CIMMYT international semi-arid
wheat yield trials from 1994 to 2010. Crop Science 52:1543-1552.
Complementary strategies to increase
genetic gains
1) Identify crop characteristics conferring adaptation
2) Precision and high throughput phenotyping
3) Exploration of genetic resources for adaptive traits
4) Inter-specific hybridization to broaden the crop
genepool
5) Genomics to increase breeding efficiency
6) Strategic crossing to achieve cumulative gene action
1) Identify crop characteristics conferring adaptation
2) Precision and high throughput phenotyping
3) Exploration of genetic resources for adaptive traits
4) Inter-specific hybridization to broaden the crop
genepool
5) Genomics to increase breeding efficiency
6) Strategic crossing to achieve cumulative gene action
Complementary strategies to increase
genetic gains
Conceptual models
DROUGHT
YIELD = WU x WUE x HI
HEAT
YIELD = LI x RUE x HI
YIELD POTENTIAL
YIELD = LI x RUE x HI
Water Use (RUE)
•Roots match evaporative
demand
•Regulation of transpiration
(VPD; ABA)
Partitioning (HI)
•Spike fertility (meiosis, pollen, etc)
•Stress signaling (e.g. ethylene)
regulating
•senescence rate
•floret abortion
•Grain filling (starch synthase)
•Stem carbohydrate storage &
remobilization
Photo-Protection (RUE)
•Leaf morphology
(display, wax)
•Down regulation
•Pigment composition
• Chl a:b
• Carotenoids
•Antioxidants
Conceptual Model of Heat-Adaptive Traits YIELD = LI x RUE x HI
Efficient metabolism (RUE)
•CO2 fixation •CO2 conductance •Rubsico (>>)
•Canopy photosynthesis •spike photosynthesis
•Respiration
Light interception (LI)
•Rapid ground cover
•Functional stay-green
Cossani and Reynolds, 2012. Physiological traits for improving heat tolerance in wheat. Plant Physiology 160 1710-1718
G x E? G x G?
1) Identify crop characteristics conferring adaptation
2) Precision and high throughput phenotyping
3) Exploration of genetic resources for adaptive traits
4) Inter-specific hybridization to broaden the crop
genepool
5) Genomics to increase breeding efficiency
6) Strategic crossing to achieve cumulative gene action
Complementary strategies to increase
genetic gains
Plant selection tools
Visual selection ++
(Molecular markers) Spectral reflectance
Canopy temperature
Phenotyping is not just about tools!
►Design experimental
populations to avoid
confounding agronomic
traits
Seri/Babax
population
Representative phenotyping platforms (e.g. IWYP-PLAT)
Located at heart of high yield
wheat agro-ecosystem (Yaqui
Valley NW Mexico)
• Production >1 m tons
• Farm yields avg. 6.5 t/ha
• Maximum yields ~10 t/ha
• Research and breeding
conducted side by side,
encouraging maximum
accountability of both.
Canopy temperature shows consistent
association with yield under drought and heat
Flintham et al. 1997
GIDDINGS SOIL CORER
TO SAMPLE ROOTS
& MEASURE SOIL MOISTURE
Deeper roots under drought confer
stress adaptation
140
190
240
290
340
390
440
490
0 10 20 30 40 50 60 70
Root DW 60-120 cm (gm-2
)
Yie
ld (
gm
-2)
0
5
10
15
20
25
30
35
40
CT
gf (o
C)
CT=-0.20x+34.3, R2=0.88
Yield=2.07x+254.9, R2=0.35
Lopes MS and Reynolds MP, 2010. Partitioning of assimilates to deeper roots is associated with cooler
canopies and increased yield under drought in wheat. Functional Plant Biology 37:147-156
Aerial remote sensing
Ground v Airborne: UAV & Blimp
r=0.76 r_G=0.76
25
26
27
28
29
30
125 150 175 200
CT
GR
OU
ND
Thermal Index UAV
Thermal Index UAV VS CT Ground Heat_1
r=0.75 r_G=0.73
0.38
0.43
0.48
2.5 2.9 3.3 3.7
CT
Gro
un
d*
Thermal Index UAV*
Thermal Index UAV VS CT Ground Heat_2
r=0.84 r_G=0.93
0.50
0.60
0.70
0.80
0.50 0.60 0.70 0.80
ND
VI
GR
OU
ND
MSAVI BLIMP
MSAVI BLIMP VS NDVI Ground Drought_1
r=-0.86 r_G=0.86
0.50
0.60
0.70
0.80
0.10 0.20 0.30 0.40
ND
VI
GR
OU
ND
NCPI BLIMP
NCPI BLIMP VS NDVI Ground Drought_1
In most cases data from airborne platforms explains genetic
variation in yield etc. better than with ground based readings
Airborne UAV VS Yield, Biomass
Trial NDVI Ground
Yield (g/m2) Biomass (g/m2)
CIMCOG_H_1 NDVI UAV 0.85 0.77 0.79 NDVI GROUND 0.63 0.58
CIMCOG_H_2 NDVI UAV 0.89 0.79 0.72 NDVI GROUND 0.74 0.64
SEED_SEL NDVI UAV 0.82 0.67 - NDVI GROUND 0.43 -
DIVERSITY SET NDVI UAV 0.71 0.64 0.76 NDVI GROUND 0.63 0.66
FIGS NDVI UAV 0.90 0.60 0.69 NDVI GROUND 0.58 0.66
Trial CT Ground (oC)
Yield (g/m2) Biomass (g/m2)
Diversity_Set Thermal Index (UAV) 0.59 -0.57 -0.64
CT Ground (oC) -0.56 -0.60
CIMCOG_H_1 Thermal Index (UAV) 0.76 -0.73 -0.78 CT Ground (oC) -0.55 -0.61
CIMCOG_H_2 Thermal Index (UAV) 0.73 -0.74 -0.78
CT Ground (oC) -0.62 -0.67
Tattaris et al., unpublished
Additional Traits: Height
Fritz et al. 2013
Derivation of a 3D image (x, y, z coordinates) from overlapping RGB
images taken at an angle
• Important to obtain throughout the growing cycle, for
multiple years, across environments.
Things to think about…
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
50 60 70 80 90 100 110Co
rre
lati
on
Co
eff
icie
nt
Days after Emergence
NDVI VS YLD Irrigated
Ground VS YLD
UAV VS YLD
Average Days to
Anthesis ± STDEV
0
0.2
0.4
0.6
0.8
1
20 25 30 35 40 45 50 55 60 65 70
Co
rre
lati
on
Co
eff
icie
nt
Days after Emergence
NDVI VS YLD Heat
GROUND VSYLD
UAV VS YLD
Average Days to
Heading ± STDEV
1) Identify crop characteristics conferring adaptation
2) Precision and high throughput phenotyping
3) Exploration of genetic resources for adaptive traits
4) Inter-specific hybridization to broaden the crop
genepool
5) Genomics to increase breeding efficiency
6) Strategic crossing to achieve cumulative gene action
Complementary strategies to increase
genetic gains
Genetic resources:
~ 0.5 million
accessions of
wheat genetic
resources
in collections worldwide
The World
Wheat Collection at
CIMMYT has
~170,000
Wheat ‘landraces’ in Oaxaca
70,000
wheat genetic
resources screened
under drought and heat,
Sonora, Mexico, 2011-2013
FIGS drought set, Sonora, 2013 Focused Identification of Germplasm Strategy (http://www.figs.icarda.net/)
A
A B
FIGS drought set, Sonora, 2013 Focused Identification of Germplasm Strategy (http://www.figs.icarda.net/)
1) Identify crop characteristics conferring adaptation
2) Precision and high throughput phenotyping
3) Exploration of genetic resources for adaptive traits
4) Inter-specific hybridization to broaden the crop
genepool
5) Genomics to increase breeding efficiency
6) Strategic crossing to achieve cumulative gene action
Complementary strategies to increase
genetic gains
T. durum
AABB
T. tauschii
DD
Hexaploid synthetic
AABBDD
Wide crossing with close relatives
e.g. “Synthetics”
► Sources of disease
resistance
►Redistribution of
roots to deeper soil
profiles under water
stress
X =
0
5
10
15
20
25
30
35
Check
1,000 New primary synthetics screened for
biomass –heat environment-
# lines
Dry weight (g)
1) Identify crop characteristics conferring adaptation
2) Precision and high throughput phenotyping
3) Exploration of genetic resources for adaptive traits
4) Inter-specific hybridization to broaden the crop
genepool
5) Genomics to increase breeding efficiency
6) Strategic crossing to achieve cumulative gene action
Complementary strategies to increase
genetic gains
Canopy temp as a surrogate for root function
.
CTAMVEGCTPMVEGCTAMGFCTPMGF
0
50
100
150
200
250
300
350
400
450
500
18 20 22 24 26 28 30
y = -0 .003x + 21.54, r2 = 0.61y = -0 .004x + 25.904, r2 = 0.68y = -0 .005x + 24.545, r2 = 0.64y = -0 .006x + 27.98, r2 = 0.62
YIE
LD
(g/m
2)
CANOPY TEMPERATURE ( oC)
Figure1. Association of yield performance (g/m2) and canopy temperature ( oC)of Seri-Babax population under drought (cycle Y01/02).
CT is robustly associated with performance under
heat and drought stress
CANOPY TEMPERATURE (0C)
R2 = 0.47
200
250
300
350
400
450
27.0 28.0 29.0 30.0 31.0 32.0
CT-boot
Yiel
d
Drought stress
Heat stress
Olivares-Villegas et al, 2007. FPB
Cossani et al, unpublished
Consistent QTL identified in the Seri/Babax Population
1B-a.aac/caa-41B-a.wPt-14031B-a.wPt-52811B-a.aca/cac-51B-a.gwm2731B-a.wPt-01701B-a.aac/ctg-41B-a.wPt-75291B-a.agg/cat-41B-a.acc/cat-41B-a.act/ctc-71B-a.agg/cat-111B-a.barc0651B-a.gwm4131B-a.agg/ctg-51B-a.wPt-34651B-a.aac/cta-51B-a.agg/cat-181B-a.gwm1311B-a.agg/cac-31B-a.agc/cta-91B-a.agc/cta-21B-a.agc/cta-61B-a.agc/cag-51B-a.aag/ctg-141B-a.wPt-89301B-a.act/ctc-91B-a.aca/cta-91B-a.gwm5821B-a.gwm301b1B-a.wPt-17811B-a.aag/ctc-61B-a.wPt-20521B-a.aca/cac-21B-a.wPt-78331B-a.acc/ctc-41B-a.acg/cta-21B-a.act/ctc-51B-a.wPt-86161B-a.aca/cag-51B-a.aca/caa-31B-a.agg/ctg-31B-a.aac/ctc-6
Yie
ld
GM
2
ND
VIv
CT
v
CT
g
CH
Lg
1B-a
2B-a.wPt-96682B-a.aac/cta-12B-a.wPt-73202B-a.wPt-06152B-a.aag/ctc-32B-a.wPt-64772B-a.acc/ctc-22B-a.acc/ctg-42B-a.acc/ctc-102B-a.wPt-77502B-a.aag/ctg-52B-a.agg/cat-72B-a.agg/cac-102B-a.agc/cag-42B-a.aag/ctg-152B-a.agg/cac-52B-a.gwm3882B-a.acg/cta-12B-a.gwm191a2B-a.aca/ctg-12B-a.aag/ctg-122B-a.act/ctc-112B-a.wPt-56802B-a.wPt-97362B-a.aca/caa-42B-a.agg/cta-32B-a.agg/cac-132B-a.agg/ctg-22B-a.act/ctc-1
ND
VIg
CT
v
CT
g
2B-a
3B-b.wPt-82383B-b.aag/ctc-93B-b.gwm644
3B-b.aca/ctg-53B-b.gdm0083B-b.wPt-60473B-b.aac/cac-53B-b.wPt-19403B-b.aag/ctc-1
3B-b.agg/cta-63B-b.wPt-53583B-b.wPt-71863B-b.acc/ctg-53B-b.wPt-03843B-b.wPt-44283B-b.aca/cag-9
3B-b.wPt-1804
3B-b.wPt-00213B-b.gwm301e3B-b.aca/caa-93B-b.acc/ctg-113B-b.wPt-80213B-b.acc/ctc-8
3B-b.wPt-44123B-b.wPt-4370
Yie
ld
GM
2
CT
v
CT
g
3B-b
4A-a.gwm3974A-a.act/cag-54A-a.act/cag-34A-a.wmc048d4A-a.agg/cta-124A-a.aac/ctg-3
4A-a.wmc048c
Yie
ld GM
2
ND
VIg
CT
v
4A-a
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
Common QTL identified for heat and drought adaptation
Empty bars: Drought specific QTL
Lined bars: Stress QTL specific for DRT & HOT environments
Solid bars: Robust QTL identified under stress and irrigated environments
Pinto et al , 2010 . Heat and drought adaptive QTL in a
wheat population designed to minimize confounding agronomic effects.
TAG 121:1001–21
Root distribution in Seri/Babax ‘iso-QTL’ lines
0
40
80
120
160
200
COOL-Drt HOT-Drt COOL-Heat HOT-Heat
0-30 cm 30-60 cm 60-90 cm 90-120 cm
Root
s (g
/m2 )
46%
35%
16%
56%
33%
8%
79%
16%
5%
82%
13%
4%
T-tests for COOL v HOT genotypes: DROUGHT 30-120 cm (p=0.002) ; HEAT 30-90 cm (p=0.0025)
Pinto & Reynolds, 2015. TAG
QTLs for Spike Photosynthesis
Elite spring
bread wheat
spikes
intercept up
to 45% of
sun light
wPt_34350.0
wPt_498623.5wPt_7665 wPt_911635.8wPt_511836.9wPt_516843.4wPt_795944.3wPt_005446.1wPt_697158.2wPt_092770.7wPt_321387.9wPt_893195.7wPt_332996.9
wPt_3492 wPt_3083141.8wPt_3289165.3wPt_3012194.5wPt_8163195.1wPt_10193195.8wPt_7167199.9wPt_4936218.3wPt_6531 tPt_3719242.4wPt_3457243.0wPt_9300 wPt_3763wPt_5928
267.8
wPt_1733268.6wPt_2453269.6wPt_0103293.0wPt_2539300.6wPt_0929 wPt_1951310.0wPt_7848316.8wPt_4703325.3wPt_3461 wPt_7079325.9wPt_4736339.5tPt_8942340.2wPt_3085355.3wPt_5120 wPt_9569355.8wPt_0168356.1wPt_7029356.5wPt_9724361.7wPt_3055390.5wPt_6136391.1
qS
PC
GW
SP
5B
.1-Y
P-2
Yrs
qS
PC
GW
5B
.1-Y
P-2
Yrs
qS
PC
GW
5B
.1-Y
P-2
012
qS
PC
GW
SP
5B
.1-Y
P-2
012
qS
PC
GW
5B
.1-H
t-2013
qS
PC
GW
SP
5B
.1-H
t-2013
qS
PC
GW
5B
.1-H
t-2014
qD
TA
5B
.1-Y
P-2
012-1
4
qD
TM
5B
.1-Y
P-2
012-1
4
qP
H5B
.1-Y
P-2
012-1
4
qP
H5A
.1-H
t-2013-1
4
5B
wPt_97740.0wPt_51811.8wPt_580416.6wPt_179220.9wPt_801622.2
wPt_4408 wPt_566047.0
wPt_465862.5wPt_778472.5wPt_653080.2wPt_438492.6wPt_8797115.8wPt_9592121.3wPt_2465137.6wPt_6654148.9wPt_9897151.6wPt_7951157.1wPt_6941160.3wPt_5941162.5wPt_8172170.5wPt_8478172.6wPt_1720177.7wPt_5776190.2wPt_4735191.5wPt_4666198.0wPt_3870 wPt_1692199.3wPt_8770200.8wPt_9752202.4wPt_4652213.4wPt_4676215.9wPt_6993218.9
wPt_4361294.7wPt_8245 wPt_7746300.3wPt_2861302.7wPt_3571303.3
qS
PC
GW
SP
1A
.1-Y
P-2
Yrs
qS
PC
GW
1A
.2-4
En
vs
qS
PC
GW
1A
.1-Y
P-2
012
qS
PC
GW
SP
1A
.1-4
En
vs
qY
LD
1A
.1-Y
P2012-1
4q
SP
CG
W1A
.1-H
t2014
qS
PC
GW
SP
1A
.1-Y
P2012
qS
L1A
.1-H
t2013-1
4
qS
PC
GW
1A
.1-4
En
vs
qS
PC
GW
1A
.1-Y
P2014
qD
TA
1A
.1-Y
P2012-1
4
qS
L1A
.1-Y
P2012-1
4
qS
L1A
.1-4
En
vs qP
H1A
.1-4
En
vs
qY
LD
1A
.2-Y
P2012-1
4
qP
H1A
.1-H
t2013-1
4
1A
wPt_1225219.1wPt_8915234.6wPt_7478237.3wPt_4127240.7wPt_11160242.2wPt_6020272.4wPt_10514278.6wPt_7229279.2wPt_4597280.3wPt_5716281.5wPt_3078282.7wPt_6445285.8wPt_4364304.9wPt_2119332.9wPt_4782333.5wPt_11017 wPt_10341334.1wPt_6834338.5wPt_6961 wPt_2416340.3wPt_8386346.3wPt_7514346.9wPt_4483347.9wPt_10306349.7wPt_7614350.3wPt_0343350.4wPt_0668350.9wPt_8959353.5wPt_6299362.1wPt_0021 wPt_11082wPt_4194 wPt_5947
364.4
wPt_6000364.5wPt_8184366.4wPt_10057 wPt_11078368.5wPt_5452401.6wPt_3725402.7wPt_7968404.3wPt_9577405.1wPt_8056410.1wPt_11301 wPt_10785410.8wPt_7635411.4wPt_7145419.0wPt_0644423.6
qS
PC
GW
SP
3B
.1-Y
P-2
Yrs
qS
PC
GW
3B
.1-Y
P-2
Yrs q
TG
W3B
.1-4
En
vs
qS
PC
GW
3B
.1-Y
P2012
qS
PC
GW
SP
3B
.1-Y
P2012
qS
PC
GW
SP
3B
.1-Y
P2014
qS
PC
GW
SP
3B
.1-H
t2013
qT
GW
3B
.1-Y
P2012-1
4
qS
PC
GW
3B
.1-H
t2014
qD
TH
3B
.1-H
t2013-1
4
3B.2RILs Atil x T. Dicoccum
Molero et al., unpublished
QTLs for Spike Photosynthesis
1) Identify crop characteristics conferring adaptation
2) Precision and high throughput phenotyping
3) Exploration of genetic resources for adaptive traits
4) Inter-specific hybridization to broaden the crop
genepool
5) Genomics to increase breeding efficiency
6) Strategic crossing to achieve cumulative gene action
Complementary strategies to increase
genetic gains
Strategic crossing to achieve
cumulative gene action
WUE: Transpiration
Efficiency
•Efficient leaf
photosynthesis (CID)
Strategic Crossing to Combine Adaptive Traits
DROUGHT YIELD = WU x WUE x HI
Partitioning (HI) • Stem carbohydrate
storage
WUE: Photo-Protection • Leaf wax
• Pigments
Water Uptake (WU) •Ground cover
•Access to water by roots
First new generation of lines based on
physiological crosses & selection, (2007)
Yield distribution of 3 years mean drought trials
(Cd Obregon, Mexico)
0
5
10
15
20
25
30
35
40
45
85%
<= 9
0%
90%
<= <
95%
95%
<= <
100%
100%
<= <
105%
105%
<= <
110%
110%
<= <
115%
115%
<= <
120%
% of check
%
Conventionalcrosses
Physiologicaltrait crosses
Reynolds et al., 2009 AAB
12
16
24
30
22
41
20 20
5
0
5
10
15
20
25
30
35
40
45
83-89 90-94 95-99 100-104 105-109 110-114 115-119 120-129 130-133
Nu
mb
er
of
lin
es
Yield as % of drought adapted check Vorobey
70% of new lines
outyield the check Check 3.5 t/ha (Vorobey)
New lines based on physiological criteria, 2012
Yield traits considered in strategic crosses: YIELD = LI x RUE x HI
SINKS pre-grainfill:
•Spike fertility •grain number
•kernel weight potential •avoid floret abortion
•Development pattern
•long juvenile spike phase
SINK (grain-filling)
•Harvest Index (HI) •tiller survival •grain growth rate
SOURCE (pre-grainfill):
• Light interception (LI)
• Growth rate • Canopy temperature
SOURCE (grain-filling):
• Canopy photosynthesis (RUE/LI) •Leaf conductance
•Carbohydrate storage in stem •stay green
26 international
sites of the 2nd
WYCYT
35 new (PT) lines
7 elite checks
Abbreviation Site Country
BGLD J BARI Joydebpur Bangladesh
BGLD D BARI Dinjpur Bangladesh
BGLD R BARI Rajshahi Bangladesh
China L LAOMANCHENG China
Egypt A Assiut Egypt
India D Delhi India
India L Ludhiana India
India V Varanasi India
India K Karnal India
India H Dharwad India
India I Indore India
India U Ugar India
Iran D DARAB-HASSAN-ABAD Iran
Iran Z ZARGAN Iran
Iran SP SPII - KARAJ Iran
Iran S SAFIABAD AGRIC. RES. CENTER Iran
MEX Bajio INIFAP-Bajio Mexico
MEX CM CIMMYT CENEB Mexico
MEX BC INIFAP-Mexicali Baja California Mexico
MEX JAL INIFAP-Tepatitlan Jalisco Mexico
MEX SIN INIFAP-Valle del Fuerte, Sinaloa Mexico
MEX SON INIFAP_Valle del Yaqui Mexico
Nepal B Bhairahawa Nepal
PAK I Islamabad Pakistan
PAK F Faisalabad Pakistan
PAK P Pirsabak Pakistan
Mean yield of 7 elite checks: 2nd WYCYT, 2014 Y
ield
g/m
2
Mean yields of 35 new PT lines v 7 elite checks:
(average 7% advantage of new lines)
Yie
ld g
/m2
CROP
DESIGN
GENETIC RESOURCES
PHENO-
TYPING
GENETIC
ANALYSIS
BREEDING
DELIVERY
through
IWIN
Physiological Breeding Pipeline
INFORMATICS
Determine
traits/genes
needed to
adapt crops to
target
environments
•Landraces
•Wild
relatives
•Advanced
lines
•Transgenics
•High thru-put
remote sensing
•Precision
phenotyping
Strategic
crossing
Select best
progeny using
state-of-the-
art
phenotyping
/molecular
tools
QTL
identified
and MAS
systems
developed
Standard Phenotyping Protocols
http://libcatalog.cimmyt.org/download/cim/96140.pdf
http://libcatalog.cimmyt.org/download/cim/96144.pdf
Conclusions
• Investment in understanding the ‘phenome’ and trade-offs
between traits facilitate breeding decisions
• Genetic resources represent a vast and largely untapped
opportunity for crop improvement, if evaluated using
appropriate screens: – Aerial high throughput approaches on large numbers
– Precision phenotyping approaches on selected material
– Molecular markers (especially for hard to phenotype traits)
• Strategic trait-based crossing increases genetic gains compared
with crossing the best x best yielding lines
• Phenomic and genomic technologies can deliver genetic gains in farmers’ fields; sooner when integrated with proven
techniques
Acknowledgements
Carolina Saint Pierre, Alistair Pask, Ravi
Valluru, Marc Ellis, Yann Manes ,
Richard Trethowan…
Thank you
for your
interest!