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
•...
• Cobb scale (Cobb, 1892)
– Percentages are equal to actual leaf area covered by rust
Diagrammatic Scales for Rust Assessm...
• Modified Cobb scale I (Melchers & Parker, 1922)
• 100% disease severity = 37% area covered by pustules
Diagrammatic Scal...
• Modified Cobb scale II (Peterson et al., 1948)
– Retained 100% disease severity = 37% area covered
– Used a planimeter t...
“The writers do not consider these
diagrams suitable for stripe rust …”
Peterson et al. (1948)
Diagrammatic Scales for Rus...
• Is a hand-held crop sensor sensitive enough to
phenotype wheat populations in mapping
stripe rust resistance QTL?
Resear...
• Population
– Francolin#1 x Avocet-YrA (developed at CIMMYT)
– Francolin#1 is a spring wheat line with pedigree Waxwing2*...
• 4 October 2013:
–Visual disease severity and host response
• Modified Cobb scale (0-100%)
• R > RMR > MR > MRMS > MS > M...
• NDVI (Pask et al. 2012 – CIMMYT Field Guide)
–Normalized Difference Vegetation Index
• Calculated from measurements of l...
Trimble GreenSeeker™
(model HCS-100) crop
sensor
• Relationships between
–Severity and host response
–NDVI and severity
–NDVI and host response
–Used means per response cl...
• 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
MRMS
MS
MSS
S
Y = 0.0063X+0.0483
R² = 0.97
0.00
0.20
0.40
0.60
0.80
0 20 40 60 80 100
Striperustresponsetype
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 ...
4 Oct
Y = 439.66-614.36X
R² = 0.95
10 Oct
Y = 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 0.7...
4 Oct
Y =3.11-4.26X
R² = 0.93
10 Oct
Y = 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 ...
QTL Mapping
• Lan et al. (2014)
–Mean Final Disease Scores identified QTL on
• 1BL (Francolin#1)
• 2BS (Francolin#1)
• 3BS...
QTL Mapping with SA Data
Trait name QTL Position Left marker Right marker LOD PVE(%) Add
Resistance
source
YRDS QYr.cim-1B...
• GreenSeeker™ technology and visual disease
severity scores identified the same
chromosome regions
• Data were comparable...
• Some differences in marker regions occurred
for the respective traits
• Timing of assessments is important
considering t...
• Standardise procedures for
distance, angle, trigger time, number of
samples per entry
• Non-subjective crop sensing is s...
– Ravi Singh – FxA population
– Caixia Lan – mapping
– Cornel Bender – disease scores
– Neal McLaren – statistical analyse...
Pretorius pst symposium 2014
Pretorius pst symposium 2014
Pretorius pst symposium 2014
Pretorius pst symposium 2014
Pretorius pst symposium 2014
Pretorius pst symposium 2014
Pretorius pst symposium 2014
Pretorius pst symposium 2014
Pretorius pst symposium 2014
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Pretorius pst symposium 2014

Published on: Mar 4, 2016
Published in: Business      Technology      
Source: www.slideshare.net


Transcripts - Pretorius pst symposium 2014

  • 1. Can crop sensing be used for mapping stripe rust resistance loci in wheat?
  • 2. • Wheat rust research relies heavily on accurate phenotyping –Pathogen variability • Virulent or avirulent –Host reponse • Resistant or susceptible • R gene phenotype Wheat Rust Phenotyping
  • 3. • Cobb scale (Cobb, 1892) – Percentages are equal to actual leaf area covered by rust Diagrammatic Scales for Rust Assessment 1% 5% 10% 20% 50%
  • 4. • 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%
  • 5. • 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
  • 6. “The writers do not consider these diagrams suitable for stripe rust …” Peterson et al. (1948) Diagrammatic Scales for Rust Assessment
  • 7. • Is a hand-held crop sensor sensitive enough to phenotype wheat populations in mapping stripe rust resistance QTL? Research Question
  • 8. • 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
  • 9. • 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
  • 10. • 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
  • 11. Trimble GreenSeeker™ (model HCS-100) crop sensor
  • 12. • 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
  • 13. • 141 RILs were genotyped with 581 DArT, SSR markers • Phenotyped in Mexico and China QTL Mapping
  • 14. • Uniform and severe stripe epidemic prevailed • Avocet-YrA = 100S • Francolin#1 = TR Results
  • 15. Entry 1622 Entry 1620 Entry 1586
  • 16. R RMR MR MRMS MS MSS S
  • 17. Y = 0.0063X+0.0483 R² = 0.97 0.00 0.20 0.40 0.60 0.80 0 20 40 60 80 100 Striperustresponsetype Stripe rust severity (%)
  • 18. • 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
  • 19. 4 Oct Y = 439.66-614.36X R² = 0.95 10 Oct Y = 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 Striperustseverity(%) NDVI
  • 20. 4 Oct Y =3.11-4.26X R² = 0.93 10 Oct Y = 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 Striperustresponsetype NDVI
  • 21. QTL Mapping • Lan et al. (2014) –Mean Final Disease Scores identified QTL on • 1BL (Francolin#1) • 2BS (Francolin#1) • 3BS (Francolin#1) • 6AL (Avocet)
  • 22. QTL Mapping with SA Data Trait name QTL Position Left marker Right marker LOD PVE(%) Add Resistance 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
  • 23. • 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
  • 24. • 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
  • 25. • 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
  • 26. – 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

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