Field-based phenotyping for abiotic stresses in maize:

Focus on breeder’s wish-list

P.H. Zaidi*, M.T. Vinayan and K. Seetharam

Asia Regional Maize Program, CIMMYT Int., Hyderabad, India

* phzaidi@cgair.org

Breeder’s wish-list (non-exhaustive)...

• Minimize experimental error and improve heritability (≥0.4

under stress and ≥0.7 under optimal condition) .

• Precisely estimate and account for G x E in selection

• Trait(s) are heritable, & genetically (not just phenotypically)

correlated with yield & eventually adding in selection gains

• Dissect complex traits into components; useful in genomic

region discovery.

• Tools for improving precision and efficiency of data collection

on key agronomic traits

Precision Phenotyping Key of success for a genetic improvement program

Feed to breeding

program for

developing

improved products

Diverse germplasm

Phenotype

Genotyping Genotype:phenotype associations

C,C C,T T,T

Sensitive

Tolerant

Association analysis

Promising alleles

breeding program

Conventional breeding

Molecular breeding

RFLP

RAPD

SSR

SNP

GBS-SNPs and

Indels

Full genome re-

sequencing

Epigenome

Methylome

Proteome

Metabolome

RNAi

profiling

GMP Datapoints

(….until 2009)

~ 500,000

>7.0 billion and

growing…!!!

2010-2015

Genomics

evolving

rapidly…!

Source: Raman Babu, CIMMYT

"We are able to study genes and manipulate them but

the study of processes at the level of whole plant has

not been able to keep pace with these advances."

Professor Mark Tester, Univ. Adelaide, Australia

“The growing disparity between (the cost and speed of)

genomics vs. high-quality phenotypes…[is] a major

impediment to functional genomics….”

Harvey JJW, Strauss SH (2008) Towards physiological sculpture of

plants. New Phytol 181:10

Genotype

Environment

Crop management Prevailing weather

Phenotype

“Actual environment” “Noise”

Quantify & Factor G X E in phenotyping!

Phenotyping environment…

(Bruce et al., 2002)

Managed environment

8

Uneven germination

Soil crusting

Uneven thinning

Sources of field variability/error

Differences in soil depth

Differences in soil texture

Termite hills

Bird damage

Uneven weeding

Soil compaction

Nutrient deficiency

Slope

Uneven fertiliser application

Uneven irrigation

Inherent (to be managed) Introduced (to be avoided!)

►Organize trials respecting spatial variability to avoid field gradients & minimize error and use covariate analysis, as needed

(h = >0.4 under stress & >0.7 in optimal conditiins) Precision in phenotyping trials

Fieldm Operations Month Date Day/Week Tmax Tmin

Accumulated GDD (degree C)

2013-14 Planting Nov 26 1 29.3 15.5 12.4

Dec 2 Week-1 27.6 11.4 72.7 Dec 9 Week-2 29.3 13.1 150.4 Dec 16 Week-3 29.4 14.2 229.6 Dec 23 Week-4 28.3 13.5 310.6 Dec 30 Week-5 25.4 17.7 381.5 Jan 6 Week-6 29.3 13.2 456.8 Jan 13 Week-7 28.8 19.6 540.0

Last Irrigation Jan 14 48 25.3 19.4 552.4

2014-15 Planting Nov 30 1 29.3 16.4 12.9

Dec 6 Week-1 27.8 15.6 89.1 Dec 13 Week-2 27.4 12 170.8 Dec 20 Week-3 26.3 6.8 231.1 Dec 27 Week-4 27.7 11.7 290.4 Jan 3 Week-5 27.4 14.3 363.5 Jan 10 Week-6 27.8 7.5 417.7 Jan 17 Week-7 29.2 11.7 479.1 Jan 18 50 28.8 11.3 489.1 Jan 19 51 30.6 12.4 500.6 Jan 20 52 31 12.3 512.3 Jan 21 53 30.3 10.7 522.8 Jan 22 54 29.4 10.2 532.6 Jan 23 55 30.1 10.6 542.9

Last irrigation Jan 24 56 29.7 11.3 553.4

*Site and maturity group specific

(h = >0.5 in stress & >0.7 in optimal trials) Precision in phenotyping trials

►Drought phenotyping trial : When to stop irrigation?

►Drought phenotyping trial : When to resume irrigation?

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

1 2 3 4 5 6 7 8 9

So

il m

ois

ture

(V

ol.

%)

Observations

10 cm 20 cm30 cm 40 cm60 cm 100 cm

FC-36.7

PWP-16.8

(h = >0.5 in stress & >0.7 in optimal trials) Precision in phenotyping trials

Expression of genotypic variability under managed stress

Early-Feb planting

Mild heat stress Mid-March Planting

Severe heat stress

Managed heat stress by manipulating

planting time & tracking GDD

Hyderabad

Baijenki

Sabor

Ishurdi

Jessor Gazipur

Aurangabad

Ludhiana

Jalandhar

Hoshiarpur

Sahiwal

Faisalabad

Surkhet Nepalgunj

Rampur

Raichur

B’Gudi

Jabalpur

Tmax during Spring

maize flowering

HTMA phenotyping sites

Hyderabad

B’Gudi

Heat stress phenotyping site………

Tem

pera

ture

(oC

) R

H (%

) an

d ra

infa

ll (mm

)

Sabor

Ludhiana

VPD

(kPa) and M

ean G

Y of the trial (t/ha)

Tm

ax (oC

)

Phenotyping sites

400C

350C

Tmax-Reproductive Tmax-Late Veg

Expression of genotypic variability for heat stress

Secondary traits

It is not just sufficient to know a secondary trait is related to stress tolerance

early vigour

VPD

temperature

hormones

deep roots

leaf growth

WUE

stomatal

regulation

grain filling duration

and rate

grain

number

HI

nutrient

acquisition

It is important to know that breeding progress using grain yield & a given secondary trait in selection is greater than using grain yield alone

proven breeding value! √

• Genetically correlated with grain yield (relationship with

yield causal, not casual!)

• Higher heritability than grain yield itself (less G x E than

grain yield).

• Genetic variability for the trait must exist

• The trait should not be associated with poor yields (it

should confer tolerance instead avoidance)

• Measurement - rapid, non-destructive, more

economically than the yield itself and in a reliable way

(updated from….Edmeades et al. 1998)

AN IDEAL SECONDARY TRAIT

0.0

0.5

1.0

1.5

2.0

2.5

0.0 0.2 0.4 0.6 0.8 1.0

Heritab ility o f grain yield

Eff

icie

nc

y o

f in

de

x s

ele

cti

on

Direct selection for yield vs. Index selection (Secondary traits along with grain yield)

(Bänziger and Lafitte, 1997)

Optimal Severe stress

23

Phenotyping root traits:

• Functional traits (water use, utilization pattern and WUE)

• Structural traits (root length, volume, root-length density)

Rooting depth (cm)

Root dry weight (g plant-1)

Drought stress Normal moisture

Rep

rod

uct

ive

per

iod

wat

er u

se (

litre

s)

Tran

spir

atio

n e

ffic

ien

cy (

g lit

re-1

)

S. No. Pedigree WU WUE

1 DTPYC9-F46-3-9-1-2-2-1-3-B*7 27.1 9.0

2 5406-29P24STEC1HC17-4-2-1-1-B*7 26.3 7.2

3 CML-444-BB 25.7 7.8

4 [CL-G2501xCML170]-B-3-1-1-BB-1-B*5 24.5 8.1

5 CML442-B*5 23.3 6.2

6 DTPYC9-F46-3-6-1-2-2-1-1-BB-B1-B2 22.1 7.3

7 POOL 16 BNSEQ.C3 F22 x 1-3-2-2-2 20.4 8.1

8 CLQ-RCYQ41-BB-2-B*5 19.5 8.2

9 (CTS013058/(AMATLC0HS167-1-1-1-2F/R)-B*5/Nei402011)-B*8 19.4 6.4

10 ZM621A-10-1-1-1-2-B*10-1-BBB 18.9 7.1

11 CA00310/AMATLC0HS71-1-1-2-1-1-1-B*14 17.9 9.6

13 EW-DMR-G-C7-HS-(SIB)-9-B-1-B*4 14.6 7.7

14 CML448-BBB 14.3 8.1

15 CL-RCW97-B*4 14.2 7.2

16 90[SPMATC4/P500(SELY)]#-B-48-4-B*7 11.9 5.9

DTPYC9-F46-3-9-1-2-2-1-3-B*7 (right) under DT in a field trial

Thank you

for your

interest!

Photo Credits (top left to bottom right): Julia Cumes/CIMMYT, Awais

Yaqub/CIMMYT, CIMMYT archives, Marcelo Ortiz/CIMMYT, David

Hansen/University of Minnesota, CIMMYT archives, CIMMYT archives (maize),

Ranak Martin/CIMMYT, CIMMYT archives.