S3.1 Integrating selection for stress tolerance with selection for yield potential in maize

Integrating selection for stress tolerance

with selection for yield potential in maize

CIMMYT

Gary Atlin

Jill Cairns

Samuel Trachsel

Felix San Vicente

Cosmos Magorokosho

Peter Setimela

Dan Makumbi

Pichet Grudlyoma

PH Zaidi

…but yield is stress tolerance! -Duvick

-Zhang

Outline

1. How has CIMMYT made gains for

tolerance to severe stress?

2. What are the difficulties in using

managed-stress data for selection, and

how can we deal with them?

3. How can we increase yield “potential” in

tropical maize?

Where will the additional maize Asia

needs come from?

• Mainly from favorable rainfed environments

• …but even favorable environments have drought,

heat, cold, low sunlight, and waterlogging

• Farmers need high yield potential (YP), but high YP is

mainly tolerance to moderate stress

• Tolerance to moderate stress and high YP are easy to

integrate

• Tolerance to severe stress and high YP are much

harder to integrate

Temperate maize yield gains were due to

• Increased tolerance to high density

• Improved DT

• Enhanced capacity to extract nutrients from deeper soil layers

• Faster recovery from cold stress

• Improved stay-green.

• Faster dry-down

Gains were not due to

• Increased photosynthesis rate

• Increased harvest index

• Transgenics**

Lee EA and Tollenaar M. 2007. Physiological basis of

successful breeding strategies for maize grain yield. Crop Sci.

2007 47: S-202-215S.

How were Corn Belt stress tolerance gains achieved?

• No direct selection for yield under drought, low-N, flooding, heat, or

cold!

• Gains were achieved almost entirely from

• wide-scale multi-location testing in the TPE under rainfed

conditions

• Selection for plant density tolerance

• These selection techniques are very effective in productive

environments with moderate, intermittent stress

• Managed stress required when stress is frequent and severe

The CIMMYT approach to breeding for abiotic

stress tolerance

CIMMYT started MSS in 1975 to improve maize for drought,

low N via recurrent selection

Introduced the use of managed stress environments

■ NOT to simulate a farmers field

■ BUT to simulate a stress that is highly relevant in farmers’ fields

■ 60-80% yield reduction targeted due to stress

Gains from stress-tolerance breeding at

CIMMYT

• Early stress-tolerance breeding based on rapid-cycle

recurrent selection produced gains of about 100 kg ha-1

yr-1

• More recently, pedigree breeding has resulted in gains

in farmers’ fields, but has not led to breakthroughs in

stress tolerance

Drought stressed Well-watered

Pedigree

Yield

(t ha-`)

Days to

anthesis ASI

Yield

(t ha-1)

Days to

anthesis

DTPYC9-F46-1-2-1-2 2.66 72 0.7 7.35 73

La Posta Seq C7-F64-2-6-2-2 2.51 75 1.3 7.88 76

DTPWC9-F24-4-3-1 2.49 73 1.4 7.27 74

CML442/CML312SR (check) 2.09 77 6.0 7.52 80

CML442/CML444 (check) 2.00 80 3.7 7.19 77

Mean 2.13 74.5 4.3 6.90 76.2

LSD 0.81 2.0 3.7 1.26 2.5

Lines combining heat and drought tolerance identified from the DTMA association mapping panel as a result of screening under managed stress in 9 environments (J. Cairns)

Gains made for high-yield environments in

farmers’ fields in Eastern and Southern Africa:

Results of 26 farmer-managed strip trials in 2011

Year of first regional testing Name

Yield (t/ha)

2007 CZH0616 6.32

1995 SC513 4.75

SC627 5.05

Mean 5.37

n 26

H 0.83

LSD 0.67

Gains per year

under favorable

conditions:

• 110 kg/ha

• 2.8 %

Gains made for low-yield environments in

farmers’ fields in Eastern and Southern Africa:

Results of 19 farmer-managed strip trials in 2011

Year of first regional testing Name

Yield (t/ha)

2007 CZH0616 2.37

1995 SC513 1.60

SC627 2.03

Mean 2.00

n 19

H 0.62

LSD 0.44

Gains per year under

unfavorable

conditions:

• 66 kg/ha

• 4 %

• We are identifying some

hybrids combining high

stress tolerance and

yield potential!

• Where are these gains

coming from?

Selection environment Low-yield target

environment

Genetic correlation

Early maturity group

Optimal 0.80

Managed drought 0.64

Low-N 0.91

Late maturity group

Optimal 0.75

Managed drought 0.76

Low-N 0.90

Genetic correlations for yield between low-yield target

environments and optimal, managed drought, and low-N selection

environments: ESA 2001-9

• Yield in low-

yield trials is

most closely

related to

yield under

low N

The “standard” CIMMYT breeding pipeline

Stage Activity Screening environment

Reps

Rows/

plot

Optimal Drought Low N

----------- number of trials -------

Line development Unreplicated nursery 1 or 2

Stage 1 testcross

evaluation

Replicated yield trials 4-8 1-2 1-2 2 1

Stage 2 testcross

evaluation


Line x tester Replicated yield trials 8-10 1-2 1-2 2 2

Advanced hybrid

testing


Regional yield

testing


• Replication, and therefore H, is much higher for optimal than stress

trials! • How do we combine the data from optimal and stress trials?

In combining stress and nonstress trial data

we need to consider:

• How repeatable are the stress data?

• How representative are the results of stress trials of

stress in farmers’ fields?

• Do the stress trials give information that is different from

non-stress trials

• What is the frequency of occurrence of stress and non-

stress fields in the target environment?

TPE SE

rG(SE-TPE) HSE

We select in selection environments (SE) to make gains

in the target population of environments (TPE) (farmers’

fields) via correlated response

Correlated response in farmers’

fields is a function of:

• the genetic correlation between SE

and TPE

• H in the SE

The target TPE in drought-prone regions is a

mixture of stressed and non-stressed fields

TPE

Stress

Non-stress

We use stress and non-stress selection

environments (SE) to maximize gains in the TPE

via correlated response

TPE

Stress

Non-stress

SE

Gains in the TPE depend on repeatability (H) in

the two SEs, and…

TPE

Stress

Non-stress

Hstress

Hnonstress

SE

…the genetic correlations (rG ) between SEs and

stress and non-stress components of the TPE

TPE

Stress

Non-stress

rGSS

rGSN

rGNS

rGNN

Hstress

Hnonstress

SE

…the genetic correlations (rG ) between SEs and

stress and non-stress components of the TPE

TPE

Stress

Non-stress

rGSS

rGSN

rGNS

rGNN

Hstress

Hnonstress

SE

rG(SE)

TPE

Stress

Non-stress

rGSS

rGSN

rGNS

rGNN

Hstress

Hnonstress

SE

rG(SE)

The weight should also reflect the relative

frequency of stress and non-stress fields

• Usually only H’s are known

• SE – TPE correlations are assumed

to be high

TPE

Stress

Non-stress

rGSS

rGSN

rGNS

rGNN

Hstress

Hnonstress

SE

rG(SE)

Very few of these parameters

have been measured!

What do we know about these repeatabilities

and correlations?

TPE

Stress

Non-stress

rGSS

rGSN

rGNS

rGNN

Hstress

Hnonstress

SE

rG(SE)

Hnonstress > Hstress

All of the rG’s are positive

Implications for screening systems

1. Hstress is almost always << Hnon-stress in practical

breeding programs

• Breeding programs that put too much weight on low-H non-

stress trials will reduce gains in both stress and non-stress

environments

2. rG between stress and non-stress trials is almost

always positive in adapted breeding populations

• Selection for yield under normal rainfed conditions will give

some gains in yield under severe stress.

• If rG is low, weight given to stress trials should be proportional

to H and the frequency of drought in the TPE

• If rG is high (> 0.8) managed stress is not needed

Why is H always greater in non-stress than stress

environments in cultivar development programs?

σ2G

σ2G + (σ2

GE /e) + (σ2e /re)

= H



σ2G

σ2G + (σ2

GE /e) + (σ2e /re)

= H

• Genotype x trial and within-trial variability is

almost always larger in managed stress trials



σ2G

σ2G + (σ2

GE /e) + (σ2e /re)

= H

• Genotype x trial and within-trial variability is

almost always larger in managed stress trials

• Replication across environments is almost

always lower in managed-stress than in non-

stress trials

DTMA AM set: variance components, LSD and

H from the analysis over 9 DS or 7 WW trials (2

reps per trial)

Parameter DS WW

Mean 2.12 6.88

σ2G 0.07 0.51

σ2GE 0.27 0.50

σ2E 0.31 0.57

H 0.62 0.84

LSD.05 0.81 1.16

• There is GxE in managed

stress trials

• Error in managed stress

trials is always higher than

in non-stress trials

• H in managed stress trials

is therefore lower for the

same number of trials

How many managed drought trials

does a breeding program need?

No. of trials

Managed drought WW

1 0.14 0.39 2 0.24 0.57 3 0.32 0.66 4 0.39 0.72 5 0.44 0.76

10 0.61 0.87

Predicted H of yield under managed drought and

WW conditions, using DTMA variance components:

Mexico, Kenya, Zimbabwe, and Thailand 2009-11

It takes 3-4 managed

drought trials to

achieve same H as 1

non-stress trial.

Evaluation of commercial hybrids under moderate stress: Takfa,

Thailand 2007 (from Trial HT071) – P. Grudlyoma

Hybrid

Stress

yield

Non-

stress

yield ASI

Big 919 6.9 9.6 2.0

NK 48 5.7 9.8 3.4

Mean 6.3 9.7 2.7

LSD.05 1.9 1419 2.4

H 0.64 0.81 0.77

• Under moderate stress (yield reduction of 53%), hybrid Big919

performed well relative to stress tolerant hybrid NK48

Using managed stress trials to eliminate

very weak hybrids

Evaluation of commercial hybrids under severe stress:

Takfa, Thailand 2008 (from Trial AH8101)

Hybrid

Stress

yield

Non-

stress

yield ASI

Big 919 1.1 9.7 11

NK 48 4.5 10.5 6

Trial mean 2.2 8.8 7

LSD.05 0.4 0.3 5

H .87 .89 0.93

• Under severe stress (yield reduction of 75%), Big 919 collapsed.

P. Grudlyoma

Breeders must have mixed-model software that gives

the correct H and LSD for each trait used in selection!

• Breeders need to know H for every trait they are selecting

on in yield trials. Selecting on traits with low H is like

selecting based on random numbers

• Breeders need software that automatically calculates and

presents H from single and multi-location trials

• CIMMYT has incorporated R and SAS programs for this

into the Maize Fieldbook. We can help you implement this.

• CIMMYT will publish a set of SAS programs soon that

calculate H, LSD, and BLUP for all traits, any usual design

Entry

Optimal

yield

Drought

yield pER

(CML495 x CL-RCW54)-B-2-3//CML494 6.72 3.16 0.09

(CML495 x CL-RCW54)-B-18-1-

1//CML494 6.52 2.69 0.13

(CML495 x CL-RCW54)-B-17//CML494 6.47 1.79 0.09

(CML495 x CML254)-B-23-1//CML494 4.60 1.66 0.13

(CML503/CML492)//CML491 4.53 1.26 0.13

Trial Mean 5.60 1.91 0.11

LSD 0.88 1.65 0.06

Heritability 0.56 0.10 0.56

Entry variance 0.12 0.04 0.11

Entry x loc variance 0.26 0.02 0.33

Residual variance 0.65 0.63 0.36 Number of reps 2 2 2

Number of locs 6 1 6

Means of white lowland tropic stage 2 testcrosses

screened at 6 optimal and 1 drought location in 2008

Conclusions from CIMMYT’s experience of combining

data from stress and non-stress trials

Managed stress (MS) trials can give very important information, but

are often of low H due to high error and genotype x trial interaction

Selection decisions should be made on mean of 3-4 managed stress

trials, not 1.

We must check to see if MS trials are truly predictive of performance

under stress in the target environment

For most breeding programs, MS trials should be used like disease

screening trials – to throw out highly susceptible materials.

Putting too much weight on low-H trials is like throwing out replicates

from your good trials

Means for low-yield and high-yield trials should be reported

separately to identify specifically-adapted hybrids, and those that

work across yield levels

Breeders must have good data, and good analysis tools, to

make good decisions!

The biggest source of GEI in rainfed yield

trials is mean yield level

Often, in multi-location yield trials, we have a big range in

trial mean yield

If we analyze high- and low-yield trials together, the

information from the low-yield trials will be “hidden” by the

high-yield trials

It is best to analyze and present the means of high- and

low-yield trials separately.

This allows you to identify hybrids that are good at both

yield levels, or that should only be used by farmers in low-

or high-yield environments

Example: 2011 Southern African regional

trial

All trials High yield trials Low yield trials

PEX 501 PEX 501 CZH1033

SC535 X7A344W CZH0935

AS113 AS113 CZH1036

X7A344W SC535 CZH0928

AS115 AS115 CZH1031

Mean yield 4.81 6.51 2.17

H 0.88 0.89 0.75

Top 5 of 54 entries in 14 high-yield trials and 9 low-yield trials

All High

High 0.97

Low 0.57 0.36

Correlations

among yield

levels

Opportunities for increasing breeding gains

and yield potential in tropical maize

1. Increase density tolerance

2. Increase harvest index (HI)

3. Increase grain-filling period

and reduce dry-down time

4. Reduce breeding cycle

time.

Harvest Index (%)

35 40 45 50 55

Gra

in y

ield

/ p

lan

t (g

)

40

60

80

100

120

140

160

180

HN

LN

r2

= 0.58

r2

= 0.50

Relationship between yield and HI in 23

elite hybrids, AF and Tlaltizapan, 2011

F. San Vicente, S. Trachsel

Planting Density

5 7 9

Gra

in Y

ield

/ m

2 (

g)

0

200

400

600

800

1000

1200

1400

HN

LN

* * *

ab

c

ab

b

Mean response of 4 hybrids to 3 densities

at two locations in Mexico, 2011

S. Trachsel, S. San Vicente

CM

L247

/CM

L254

CM

L448

/CM

L449

CM

L494

/CM

L495

CRCw10

5/CLW

N20

1

Ha

rve

st

Ind

ex (

%)

0

10

20

30

40

50

60

1995 2007

Harvest index of old and new

hybrids, 2 locations in Mexico, 2011


• No

improvement

in HI!


Planting Density

5 7 9

Gra

in Y

ield

/ P

lan

t (g

)

0

50

100

150

200G1

G2

G3

G4

• New hybrids should

have much better

tolerance to density!

Response of 2 older and 2 newer hybrids to plant

density: 2 locations in Mexico, 2011

Reducing the breeding cycle

• Gains per year are directly proportional to the length of

the breeding cycle

• Many breeders wait too long before using promising new

lines as parents, often testing for 7-8 years.

• The best new Stage 2 lines should be immediately used

as parents.

• Breeding cycle should be 5 years maximum. Easily

achieved with DH and 2 seasons per year

Genomic selection- a new approach to

reducing the breeding cycle in maize

• Most agronomic traits in maize are highly polygenic

• Marker index selection approaches that use the effects of many

markers (thousands) can predict performance for such quantitative

trait.s

• Modern marker prediction approaches, referred to as genomic

selection (GS), incorporate all genotyped markers into a prediction of

breeding or genotypic value (GEBV), rather than a significant subset

• Selection based on markers alone can greatly reduce cycle time, if

GEBVs are accurate and remain so for several cycles

New developments in genotyping make GS possible

• Currently, high-throughput genotyping systems based on next-gen

sequencing are generating 500,000 SNPs for around $20 per DNA

sample

• Within next 1-2 years, this service should be available in China for $10 or

less

• Cost of genotyping at high density is now no higher than testing in a 3-

rep trial at 1 location.

• All CIMMYT lines entering yield testing will be genotyped

• Historical and current performance information will be used to assign

values to haplotypes using genomic selection algorithms

• Unit of selection will be the haplotype, not the line

• Most breeding procedures will change dramatically

• Costs are only low if throughput is high

• Most large seed companies now predict performance using SNPs at

moderate density

• This is a form of genomic selection (GS)

• In GS programs, you estimate haplotype effects, then select the

lines with the best haplotype for phenotyping.

GS protocol

1. Genotype all stage 2 lines at the highest density possible

2. Estimate haplotype effects using testcross data from the lines

3. Select un-phenotyped lines of the next cohort on the basis of

summed haplotype effects (GEBVs)

4. Selection based on haplotype or marker effects alone can be

done very quickly (one or two cycles per year)

5. Gains per year will depend on accuracy of GEBVs

6. Even if GEBVs are only 25% of phenotypic estimates, gains can

be at least doubled if cycle time is reduced from 5 years to 1.

Advantages of GS?

• Will allow us to select for drought tolerance even if we can’t

phenotype in a given season (just use last season’s effects)

• Will allow us to pre-select promising DH lines, once we start

producing more than we can phenotype (next year).

• Does not require extra phenotyping of lines we would normally

discard, as does MARS. Fits well in a pedigree program

• Rapid-cycle methods can increase rates of gain

Rapid cycle GS networks

“Open-source” breeding networks could provide companies with

proprietary lines, but allow haplotype effects to be shared

Rapid-cycle

marker-only

selection

Phenotyping by

Company 3 Phenotyping by

Company 1 Phenotyping by company 2

Lines with high value confirmed

by phenotyping released

commercially by partners

Lines extracted, genotyped: untested,

proprietary DH lines provided to

companies based on GEBVs

Overall conclusions on improving yield

potential and stress tolerance

• CIMMYT is making gains in both optimal and stress-prone

environments

• The key to gains is wide-scale replicated yield testing in the target

environment

• Managed stress screening is extremely useful for identifying very

weak and very tolerant material

• Care must be taken in using managed stress (and all other) data to

avoid selecting on low-H data

• Breeders need software tools that allow them to monitor H in their

trials. CIMMYT is providing these tools

• Increasing HI and density tolerance will increase yield in the tropics

• Reducing breeding cycle time is critical to increasing gains

• High-density genotyping is now available at low cost, permitting GS

• Advantage of GS is that it permits greatly reduced cycle time, and

therefore increased gains

S3.1 Integrating selection for stress tolerance with selection for yield potential in maize

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Transcript of S3.1 Integrating selection for stress tolerance with selection for yield potential in maize