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Integrating selection for stress tolerance
with selection for yield potential in maize

                     …but yield is stress tolerance!
                                            -Duvick
CIMMYT
                                            -Zhang
Gary Atlin
Jill Cairns
Samuel Trachsel
Felix San Vicente
Cosmos Magorokosho
Peter Setimela
Dan Makumbi
Pichet Grudlyoma
PH Zaidi
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
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)
                                  Drought stressed       Well-watered
                              Yield Days to           Yield    Days to
  Pedigree                    (t ha-`) anthesis ASI   (t ha-1) 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
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                 Yield   Gains per year
  regional testing   Name      (t/ha)   under favorable
          2007       CZH0616    6.32    conditions:
          1995       SC513      4.75
                     SC627      5.05    • 110 kg/ha
                                        • 2.8 %
                     Mean      5.37

                     n          26
                     H         0.83
                     LSD       0.67
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                 Yield   Gains per year under
  regional testing   Name      (t/ha)   unfavorable
          2007       CZH0616    2.37    conditions:
          1995       SC513      1.60
                     SC627      2.03    • 66 kg/ha
                                        • 4%
                     Mean      2.00
                                        • We are identifying some
                     n          19        hybrids combining high
                     H         0.62       stress tolerance and
                     LSD       0.44       yield potential!

                                        • Where are these gains
                                          coming from?
Genetic correlations for yield between low-yield target
environments and optimal, managed drought, and low-N selection
environments: ESA 2001-9
  Selection environment    Low-yield target
                            environment


                            Genetic correlation
  Early maturity group
  Optimal                       0.80
  Managed drought               0.64
  Low-N                         0.91              • Yield in low-
                                                    yield trials is
  Late maturity group                               most closely
  Optimal                       0.75                related to
  Managed drought               0.76                yield under
  Low-N                         0.90                low N
The “standard” CIMMYT breeding pipeline
Stage               Activity                  Screening environment
                                         Optimal       Drought Low N Reps       Rows/
                                         ----------- number of trials -------   plot
Line development    Unreplicated nursery 1 or 2

Stage 1 testcross   Replicated yield trials 4-8      1-2        1-2     2       1
evaluation
Stage 2 testcross   Replicated yield trials 8-10     1-2        1-2     2       2
evaluation
Line x tester       Replicated yield trials 8-10     1-2        1-2     2       2

Advanced hybrid     Replicated yield trials 8-10     1-2        1-2     3       2
testing
Regional yield      Replicated yield trials 15-30    1-2        1-2     3       2
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?
We select in selection environments (SE) to make gains
in the target population of environments (TPE) (farmers’
fields) via correlated response




                      rG(SE-TPE)
  HSE

           Correlated response in farmers’
           fields is a function of:

 SE        • the genetic correlation between SE
                                                  TPE
             and TPE
           • H in the SE
The target TPE in drought-prone regions is a
mixture of stressed and non-stressed fields


                                      Stress




                                     Non-stress




                                     TPE
We use stress and non-stress selection
environments (SE) to maximize gains in the TPE
via correlated response

                                     Stress




                                    Non-stress




SE                                  TPE
Gains in the TPE depend on repeatability (H) in
the two SEs, and…


                                       Stress
 Hstress




                                      Non-stress
Hnonstress




SE                                    TPE
…the genetic correlations (rG ) between SEs and
stress and non-stress components of the TPE


                rGSS                  Stress
 Hstress
                rGSN

                rGNS

                rGNN                 Non-stress
Hnonstress




SE                                   TPE
…the genetic correlations (rG ) between SEs and
         stress and non-stress components of the TPE


                         rGSS                  Stress
          Hstress
                         rGSN

rG(SE)                   rGNS

                         rGNN                 Non-stress
         Hnonstress




         SE                                   TPE
The weight should also reflect the relative
            frequency of stress and non-stress fields


                               rGSS                          Stress
          Hstress
                               rGSN

rG(SE)                         rGNS

                               rGNN                         Non-stress
         Hnonstress



                      • Usually only H’s are known
                      • SE – TPE correlations are assumed
         SE             to be high                          TPE
rGSS                  Stress
          Hstress
                               rGSN

rG(SE)                          rGNS

                                rGNN                 Non-stress
         Hnonstress




         SE           Very few of these parameters   TPE
                      have been measured!
What do we know about these repeatabilities
         and correlations?


                               rGSS                   Stress
          Hstress
                               rGSN

rG(SE)                         rGNS

                               rGNN                  Non-stress
         Hnonstress

                      Hnonstress > Hstress

         SE           All of the rG’s are positive
                                                     TPE
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
             H =
                   σ2G + (σ2GE /e) + (σ2e /re)
Why is H always greater in non-stress than stress
environments in cultivar development programs?


                                  σ2G
                H =
                       σ2G + (σ2GE /e) + (σ2e /re)


  •   Genotype x trial and within-trial variability is
      almost always larger in managed stress trials
Why is H always greater in non-stress than stress
environments in cultivar development programs?


                                  σ2G
                H =
                       σ2G + (σ2GE /e) + (σ2e /re)


  •   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     • There is GxE in managed
                                 stress trials
Mean             2.12   6.88
                               • Error in managed stress
                                 trials is always higher than
σ2G              0.07   0.51     in non-stress trials
σ2GE             0.27   0.50
σ2E              0.31   0.57   • H in managed stress trials
                                 is therefore lower for the
                                 same number of trials
H                0.62   0.84

LSD.05           0.81   1.16
How many managed drought trials
does a breeding program need?
  Predicted H of yield under managed drought and
  WW conditions, using DTMA variance components:
  Mexico, Kenya, Zimbabwe, and Thailand 2009-11

         No. of   Managed             It takes 3-4 managed
         trials   drought     WW      drought trials to
                                      achieve same H as 1
           1        0.14      0.39
                                      non-stress trial.
           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
Using managed stress trials to eliminate
very weak hybrids
Evaluation of commercial hybrids under moderate stress: Takfa,
Thailand 2007 (from Trial HT071) – P. Grudlyoma

                                      Non-
                          Stress     stress
 Hybrid                    yield      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
Evaluation of commercial hybrids under severe stress:
 Takfa, Thailand 2008 (from Trial AH8101)

                                              Non-
                                  Stress     stress
          Hybrid                   yield      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
Means of white lowland tropic stage 2 testcrosses
screened at 6 optimal and 1 drought location in 2008
                                     Optimal Drought
 Entry                               yield     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
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

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

                      High yield                       Correlations
         All trials   trials       Low yield trials    among yield
                                                       levels
         PEX 501      PEX 501      CZH1033
         SC535        X7A344W      CZH0935                    All     High
         AS113        AS113        CZH1036             High 0.97
         X7A344W      SC535        CZH0928             Low    0.57    0.36
         AS115        AS115        CZH1031

Mean
yield    4.81         6.51         2.17

H        0.88         0.89         0.75
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.
Relationship between yield and HI in 23
elite hybrids, AF and Tlaltizapan, 2011


                          180

                                                                               HN
                          160                                                  LN
                                      2
                                     r = 0.58
                          140
Grain yield / plant (g)




                          120


                          100
                                                                     2
                                                                    r = 0.50
                          80


                          60


                          40
                                35        40           45           50          55


                                                Harvest Index (%)


                                                                         F. San Vicente, S. Trachsel
Mean response of 4 hybrids to 3 densities
                           at two locations in Mexico, 2011
                           1400

                                  HN
                           1200   LN


                           1000                                c
     Grain Yield/ m2 (g)




                           800
                                                b
                                   a
                           600
                                                                       b
                           400
                                                        b
                                       *a           *              *
                           200


                             0
                                       5            7              9


                                            Planting Density
S. Trachsel, S. San Vicente
Harvest index of old and new
          hybrids, 2 locations in Mexico, 2011
                                60



                                50
            Harvest Index (%)




                                40



                                30



                                20
                                                                                                      • No
                                                                                                        improvement
                                10
                                                                                                        in HI!
                                0
                                               54




                                                              49




                                                                             95




                                                                                                  1
                                                                                                20
                                             L2




                                                            L4




                                                                           L4




                                                                                               N
                                            M




                                                           M




                                                                          M




                                                                                           LW
                                          /C




                                                         /C




                                                                        /C




                                                                                          /C
                                        47




                                                       48




                                                                      94




                                                                                        05
                                      L2




                                                     L4




                                                                    L4




                                                                                      w1
                                     M




                                                    M




                                                                   M
                                     C




                                                    C




                                                                   C




                                                                                      C
                                                                                  R
                                                                                  C




                                         1995                                                     2007
S. Trachsel, S. San Vicente
Response of 2 older and 2 newer hybrids to plant
  density: 2 locations in Mexico, 2011

                           200
                                                                   G1
                                                                   G2
                                                                   G3
                                                                   G4
  Grain Yield/ Plant (g)




                           150




                           100


                                                        • New hybrids should
                                                          have much better
                           50
                                                          tolerance to density!


                            0
                                 5          7                  9

                                     Planting Density

S. Trachsel, S. San Vicente
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
                                 Rapid-cycle
     “Open-source” breeding networks could provide companies with
                                 marker-only
                                 selection
     proprietary lines, but allow haplotype effects to be shared



                  Lines extracted, genotyped: untested,
                  proprietary DH lines provided to
                  companies based on GEBVs




Phenotyping by            Phenotyping by company 2        Phenotyping by
Company 1                                                 Company 3


                      Lines with high value confirmed
                      by phenotyping released
                      commercially by partners
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

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

  • 1. Integrating selection for stress tolerance with selection for yield potential in maize …but yield is stress tolerance! -Duvick CIMMYT -Zhang Gary Atlin Jill Cairns Samuel Trachsel Felix San Vicente Cosmos Magorokosho Peter Setimela Dan Makumbi Pichet Grudlyoma PH Zaidi
  • 2. 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?
  • 3. 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
  • 4. 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.
  • 5. 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
  • 6. 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
  • 7. 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
  • 8. 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) Drought stressed Well-watered Yield Days to Yield Days to Pedigree (t ha-`) anthesis ASI (t ha-1) 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
  • 9. 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 Yield Gains per year regional testing Name (t/ha) under favorable 2007 CZH0616 6.32 conditions: 1995 SC513 4.75 SC627 5.05 • 110 kg/ha • 2.8 % Mean 5.37 n 26 H 0.83 LSD 0.67
  • 10. 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 Yield Gains per year under regional testing Name (t/ha) unfavorable 2007 CZH0616 2.37 conditions: 1995 SC513 1.60 SC627 2.03 • 66 kg/ha • 4% Mean 2.00 • We are identifying some n 19 hybrids combining high H 0.62 stress tolerance and LSD 0.44 yield potential! • Where are these gains coming from?
  • 11. Genetic correlations for yield between low-yield target environments and optimal, managed drought, and low-N selection environments: ESA 2001-9 Selection environment Low-yield target environment Genetic correlation Early maturity group Optimal 0.80 Managed drought 0.64 Low-N 0.91 • Yield in low- yield trials is Late maturity group most closely Optimal 0.75 related to Managed drought 0.76 yield under Low-N 0.90 low N
  • 12. The “standard” CIMMYT breeding pipeline Stage Activity Screening environment Optimal Drought Low N Reps Rows/ ----------- number of trials ------- plot Line development Unreplicated nursery 1 or 2 Stage 1 testcross Replicated yield trials 4-8 1-2 1-2 2 1 evaluation Stage 2 testcross Replicated yield trials 8-10 1-2 1-2 2 2 evaluation Line x tester Replicated yield trials 8-10 1-2 1-2 2 2 Advanced hybrid Replicated yield trials 8-10 1-2 1-2 3 2 testing Regional yield Replicated yield trials 15-30 1-2 1-2 3 2 testing • Replication, and therefore H, is much higher for optimal than stress • trials! How do we combine the data from optimal and stress trials?
  • 13. 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?
  • 14. We select in selection environments (SE) to make gains in the target population of environments (TPE) (farmers’ fields) via correlated response rG(SE-TPE) HSE Correlated response in farmers’ fields is a function of: SE • the genetic correlation between SE TPE and TPE • H in the SE
  • 15. The target TPE in drought-prone regions is a mixture of stressed and non-stressed fields Stress Non-stress TPE
  • 16. We use stress and non-stress selection environments (SE) to maximize gains in the TPE via correlated response Stress Non-stress SE TPE
  • 17. Gains in the TPE depend on repeatability (H) in the two SEs, and… Stress Hstress Non-stress Hnonstress SE TPE
  • 18. …the genetic correlations (rG ) between SEs and stress and non-stress components of the TPE rGSS Stress Hstress rGSN rGNS rGNN Non-stress Hnonstress SE TPE
  • 19. …the genetic correlations (rG ) between SEs and stress and non-stress components of the TPE rGSS Stress Hstress rGSN rG(SE) rGNS rGNN Non-stress Hnonstress SE TPE
  • 20. The weight should also reflect the relative frequency of stress and non-stress fields rGSS Stress Hstress rGSN rG(SE) rGNS rGNN Non-stress Hnonstress • Usually only H’s are known • SE – TPE correlations are assumed SE to be high TPE
  • 21. rGSS Stress Hstress rGSN rG(SE) rGNS rGNN Non-stress Hnonstress SE Very few of these parameters TPE have been measured!
  • 22. What do we know about these repeatabilities and correlations? rGSS Stress Hstress rGSN rG(SE) rGNS rGNN Non-stress Hnonstress Hnonstress > Hstress SE All of the rG’s are positive TPE
  • 23. 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
  • 24. Why is H always greater in non-stress than stress environments in cultivar development programs? σ2G H = σ2G + (σ2GE /e) + (σ2e /re)
  • 25. Why is H always greater in non-stress than stress environments in cultivar development programs? σ2G H = σ2G + (σ2GE /e) + (σ2e /re) • Genotype x trial and within-trial variability is almost always larger in managed stress trials
  • 26. Why is H always greater in non-stress than stress environments in cultivar development programs? σ2G H = σ2G + (σ2GE /e) + (σ2e /re) • 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
  • 27. 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 • There is GxE in managed stress trials Mean 2.12 6.88 • Error in managed stress trials is always higher than σ2G 0.07 0.51 in non-stress trials σ2GE 0.27 0.50 σ2E 0.31 0.57 • H in managed stress trials is therefore lower for the same number of trials H 0.62 0.84 LSD.05 0.81 1.16
  • 28. How many managed drought trials does a breeding program need? Predicted H of yield under managed drought and WW conditions, using DTMA variance components: Mexico, Kenya, Zimbabwe, and Thailand 2009-11 No. of Managed It takes 3-4 managed trials drought WW drought trials to achieve same H as 1 1 0.14 0.39 non-stress trial. 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
  • 29. Using managed stress trials to eliminate very weak hybrids Evaluation of commercial hybrids under moderate stress: Takfa, Thailand 2007 (from Trial HT071) – P. Grudlyoma Non- Stress stress Hybrid yield 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
  • 30. Evaluation of commercial hybrids under severe stress: Takfa, Thailand 2008 (from Trial AH8101) Non- Stress stress Hybrid yield 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
  • 31. 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
  • 32. Means of white lowland tropic stage 2 testcrosses screened at 6 optimal and 1 drought location in 2008 Optimal Drought Entry yield 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
  • 33. 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!
  • 34. 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
  • 35. Example: 2011 Southern African regional trial Top 5 of 54 entries in 14 high-yield trials and 9 low-yield trials High yield Correlations All trials trials Low yield trials among yield levels PEX 501 PEX 501 CZH1033 SC535 X7A344W CZH0935 All High AS113 AS113 CZH1036 High 0.97 X7A344W SC535 CZH0928 Low 0.57 0.36 AS115 AS115 CZH1031 Mean yield 4.81 6.51 2.17 H 0.88 0.89 0.75
  • 36. 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.
  • 37. Relationship between yield and HI in 23 elite hybrids, AF and Tlaltizapan, 2011 180 HN 160 LN 2 r = 0.58 140 Grain yield / plant (g) 120 100 2 r = 0.50 80 60 40 35 40 45 50 55 Harvest Index (%) F. San Vicente, S. Trachsel
  • 38. Mean response of 4 hybrids to 3 densities at two locations in Mexico, 2011 1400 HN 1200 LN 1000 c Grain Yield/ m2 (g) 800 b a 600 b 400 b *a * * 200 0 5 7 9 Planting Density S. Trachsel, S. San Vicente
  • 39. Harvest index of old and new hybrids, 2 locations in Mexico, 2011 60 50 Harvest Index (%) 40 30 20 • No improvement 10 in HI! 0 54 49 95 1 20 L2 L4 L4 N M M M LW /C /C /C /C 47 48 94 05 L2 L4 L4 w1 M M M C C C C R C 1995 2007 S. Trachsel, S. San Vicente
  • 40. Response of 2 older and 2 newer hybrids to plant density: 2 locations in Mexico, 2011 200 G1 G2 G3 G4 Grain Yield/ Plant (g) 150 100 • New hybrids should have much better 50 tolerance to density! 0 5 7 9 Planting Density S. Trachsel, S. San Vicente
  • 41. 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
  • 42. 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
  • 43. 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
  • 44. • 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.
  • 45. 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
  • 46. Rapid cycle GS networks Rapid-cycle “Open-source” breeding networks could provide companies with marker-only selection proprietary lines, but allow haplotype effects to be shared Lines extracted, genotyped: untested, proprietary DH lines provided to companies based on GEBVs Phenotyping by Phenotyping by company 2 Phenotyping by Company 1 Company 3 Lines with high value confirmed by phenotyping released commercially by partners
  • 47. 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