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Integrating selection for stress tolerancewith selection for yield potential in maize …but yield is stress tolerance! -DuvickCIMMYT -ZhangGary AtlinJill CairnsSamuel TrachselFelix San VicenteCosmos MagorokoshoPeter SetimelaDan MakumbiPichet GrudlyomaPH Zaidi
Outline1. 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 Asianeeds 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-downGains 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 abioticstress 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 atCIMMYT• 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 DTMAassociation mapping panel as a result of screening under managedstress 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 infarmers’ 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 infarmers’ 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 targetenvironments and optimal, managed drought, and low-N selectionenvironments: 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 pipelineStage Activity Screening environment Optimal Drought Low N Reps Rows/ ----------- number of trials ------- plotLine development Unreplicated nursery 1 or 2Stage 1 testcross Replicated yield trials 4-8 1-2 1-2 2 1evaluationStage 2 testcross Replicated yield trials 8-10 1-2 1-2 2 2evaluationLine x tester Replicated yield trials 8-10 1-2 1-2 2 2Advanced hybrid Replicated yield trials 8-10 1-2 1-2 3 2testingRegional yield Replicated yield trials 15-30 1-2 1-2 3 2testing• 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 datawe 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 gainsin 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 amixture of stressed and non-stressed fields Stress Non-stress TPE
We use stress and non-stress selectionenvironments (SE) to maximize gains in the TPEvia correlated response Stress Non-stressSE TPE
Gains in the TPE depend on repeatability (H) inthe two SEs, and… Stress Hstress Non-stressHnonstressSE TPE
…the genetic correlations (rG ) between SEs andstress and non-stress components of the TPE rGSS Stress Hstress rGSN rGNS rGNN Non-stressHnonstressSE TPE
…the genetic correlations (rG ) between SEs and stress and non-stress components of the TPE rGSS Stress Hstress rGSNrG(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 rGSNrG(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 rGSNrG(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 rGSNrG(SE) rGNS rGNN Non-stress Hnonstress Hnonstress > Hstress SE All of the rG’s are positive TPE
Implications for screening systems1. 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 environments2. 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 stressenvironments in cultivar development programs? σ2G H = σ2G + (σ2GE /e) + (σ2e /re)
Why is H always greater in non-stress than stressenvironments 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 stressenvironments 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 andH from the analysis over 9 DS or 7 WW trials (2reps per trial)Parameter DS WW • There is GxE in managed stress trialsMean 2.12 6.88 • Error in managed stress trials is always higher thanσ2G 0.07 0.51 in non-stress trialsσ2GE 0.27 0.50σ2E 0.31 0.57 • H in managed stress trials is therefore lower for the same number of trialsH 0.62 0.84LSD.05 0.81 1.16
How many managed drought trialsdoes 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 eliminatevery weak hybridsEvaluation 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 givesthe 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 testcrossesscreened 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 combiningdata 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 yieldtrials 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 regionaltrialTop 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 CZH1031Meanyield 4.81 6.51 2.17H 0.88 0.89 0.75
Opportunities for increasing breeding gains and yield potential in tropical maize1. Increase density tolerance2. Increase harvest index (HI)3. Increase grain-filling period and reduce dry-down time4. Reduce breeding cycle time.
Relationship between yield and HI in 23elite hybrids, AF and Tlaltizapan, 2011 180 HN 160 LN 2 r = 0.58 140Grain 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 DensityS. 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 2007S. 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 DensityS. 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 protocol1. Genotype all stage 2 lines at the highest density possible2. Estimate haplotype effects using testcross data from the lines3. 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 GEBVs6. 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 GEBVsPhenotyping by Phenotyping by company 2 Phenotyping byCompany 1 Company 3 Lines with high value confirmed by phenotyping released commercially by partners
Overall conclusions on improving yieldpotential 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