Pretorius pst symposium 2014

Can crop sensing be used for mapping stripe rust resistance loci in wheat?

• Wheat rust research relies heavily on accurate phenotyping

–Pathogen variability

• Virulent or avirulent

–Host reponse

• Resistant or susceptible

• R gene phenotype

Wheat Rust Phenotyping

• Cobb scale (Cobb, 1892)

– Percentages are equal to actual leaf area covered by rust

Diagrammatic Scales for Rust Assessment

1% 5% 10% 20% 50%

• Modified Cobb scale I (Melchers & Parker, 1922)

• 100% disease severity = 37% area covered by pustules

Diagrammatic Scales for Rust Assessment

5% 10% 25% 40% 65% 100%

• Modified Cobb scale II (Peterson et al., 1948)

– Retained 100% disease severity = 37% area covered

– Used a planimeter to measure

area of “pustules”

– Introduced• more equally spaced intervals

• 4 sets of different pustule sizes

– Developed for:• Puccinia graminis

• P. rubigo-vera

• P. hordei

• P. coronata

Diagrammatic Scales for Rust Assessment

“The writers do not consider these diagrams suitable for stripe rust …” Peterson et al. (1948)

Diagrammatic Scales for Rust Assessment

• Is a hand-held crop sensor sensitive enough to phenotype wheat populations in mapping stripe rust resistance QTL?

Research Question

• Population

– Francolin#1 x Avocet-YrA (developed at CIMMYT)

– Francolin#1 is a spring wheat line with pedigree Waxwing2*/Vivitsi

• Locality

– Redgates Research Station, Pannar, Greytown, South Africa

• Plot layout

– 198 F5 RIL entries planted in 1 m rows spaced 75 cm apart

– Two replications of Francolin#1 and Avocet-YrA included

– JIC871 served as susceptible check at regular intervals

• Natural infection by Pst race 6E22A+

– Experiment was part of a larger stripe rust nursery with spreaders and sufficient disease pressure

Materials and Methods

• 4 October 2013:

–Visual disease severity and host response

• Modified Cobb scale (0-100%)

• R > RMR > MR > MRMS > MS > MSS > S – (0.1 - 0.7 transformation)

–NDVI (scan 1)

• 10 October 2013:

–NDVI (scan 2)

Disease Assessment

• NDVI (Pask et al. 2012 – CIMMYT Field Guide)

–Normalized Difference Vegetation Index • Calculated from measurements of light reflectance in

the red and near-infrared regions of the spectrum

• Regularly used in crop canopy characterisation

– Leaf area index, biomass, nutrient status

– Healthy green leaves absorb most of the red light and reflect most of the NIR light

– NDVI = (RNIR – RRed) / (RNIR + RRed)

Disease Assessment

Trimble GreenSeeker™ (model HCS-100) crop sensor

• Relationships between

– Severity and host response

–NDVI and severity

–NDVI and host response

–Used means per response class

• Population reduced to 180 (eliminating mixtures)

Statistical Analysis

• 141 RILs were genotyped with 581 DArT, SSR markers

• Phenotyped in Mexico and China

QTL Mapping

• Uniform and severe stripe epidemic prevailed

• Avocet-YrA = 100S

• Francolin#1 = TR

Results

Entry 1622 Entry 1620

Entry 1586

R

RMR

MR

MRMSMS

MSS

S

Y = 0.0063X+0.0483R² = 0.97

0.00

0.20

0.40

0.60

0.80

0 20 40 60 80 100

Stri

pe

ru

st r

esp

on

se t

ype

Stripe rust severity (%)

• First scan

0.36 to 0.76• Avocet = 0.48

• Francolin#1 = 0.67

• Second scan

0.34 to 0.79• Avocet = 0.45

• Francolin#1 = 0.72

NDVI Range

4 OctY = 439.66-614.36X

R² = 0.95

10 OctY = 259.10-348.84X

R² = 0.99

0

20

40

60

80

100

0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80

Stri

pe

ru

st s

eve

rity

(%

)

NDVI

4 OctY =3.11-4.26X

R² = 0.93

10 OctY = 1.87-2.47X

R² = 0.99

0.00

0.20

0.40

0.60

0.80

1.00

0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80

Stri

pe

ru

st r

esp

on

se t

ype

NDVI

QTL Mapping

• Lan et al. (2014)

–Mean Final Disease Scores identified QTL on

• 1BL (Francolin#1)

• 2BS (Francolin#1)

• 3BS (Francolin#1)

• 6AL (Avocet)

QTL Mapping with SA Data

Trait name QTL Position Left marker Right marker LOD PVE(%) AddResistance

source

YRDS QYr.cim-1BL 20 wPt-1770 wPt-9028 11.61 18.67 14.37 Francolin#1

YRDS QYr.cim-2BS 77 YrF wmc474 12.87 27.88 17.56 Francolin#1

YRDS QYr.cim-3BS 30 wPt-741331 wPt-741750 3.37 5.19 7.71 Francolin#1

YRDS QYr.CIM-6AL 31 gwm356.1 wPt-743476 3.63 5.52 -8.19 Avocet

SCAN1 QYr.cim-1BL 8 csLV46 gwm140 3.48 13.7 -2.24 Francolin#1

SCAN1 QYr.cim-2BS 70 wPt-6174 wmc344 7.3 14.09 -2.2 Francolin#1

SCAN1 QYr.cim-3BS 29 wPt-0302 wPt-741331 3.1 5.03 -1.35 Francolin#1

SCAN1 QYr.CIM-6AL 35 wPt-743476 wPt-744881 2.88 4.57 1.26 Avocet

SCAN2 QYr.cim-1BL 20 wPt-1770 wPt-9028 5.43 7.85 -2.89 Francolin#1

SCAN2 QYr.cim-2BS 67 barc55 wPt-8548 11.21 22.3 -4.88 Francolin#1

SCAN2 QYr.CIM-6AL 29 wPt-741026 gwm356.1 4.37 6.7 2.83 Avocet

• GreenSeeker™ technology and visual disease severity scores identified the same chromosome regions

• Data were comparable with published mapping studies using multi-location phenotyping of the same population

• Less variation was explained by NDVI data

Conclusions

• Some differences in marker regions occurred for the respective traits

• Timing of assessments is important considering the optimal expression windows of different QTL

• A uniform epidemic is required

• Take measurements at same time of day and during similar weather conditions

Conclusions

• Standardise procedures for distance, angle, trigger time, number of samples per entry

• Non-subjective crop sensing is suitable for detecting stripe rust resistance loci in the field– Works well for Pst where total leaf damage is most

indicative of host response

– More experiments with different populations will be conducted in 2014

Conclusions

– Ravi Singh – FxA population

– Caixia Lan – mapping

– Cornel Bender – disease scores

– Neal McLaren – statistical analyses

– Rikus Kloppers and Vicky Knight – field facilities and NDVI data

Acknowledgements