Results from the trap nurseriesIsaac K. Abuley & Jens G. Hansen, Aarhus Universitet

EUROBLIGHT Conference, 2019

Contributors

• Guillaume Saubeau, Association des Créateurs de Variétés Nouvellesde Pomme de terre (ACVNPT)

• Alison Lees, James Hutton Institute• Håvard Eikemo, NIBIO• Riinu Kiiker and Britt Puidet, Estonian University of Life Sciences

(EULS)

In field , live virulence monitoring: trap nurseries

Aims:• Monitoring of the stability of PLB resistance in time and

space for Europe. • ”Hunting the new” – sampling / genotyping / phenotyping• Test of mega cultivars. ”How to control (with less

chemicals) new more res. cultivars”• Input to IPMBlight2.0 DSSs

Content Late blight development in the Trap nurseries. Implementing EUCABLIGHT protocol for the Trap nursery

data Estimating the Resistance scores (1-9 scale) Conclusion remarks

Late blight development in Denmark

Late blight development in Estonia

Late blight development in Scotland

What are stability of the reference cultivars?Denmark Scotland Wales

2004

2008

2012

2016

2004

2008

2012

2016

2004

2008

2012

2016

0.0

0.2

0.4

0.6

0.8

Year

rAU

DPC

CultivarBintje

Sarpo Mira

Scotland

2005

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

20180.0

0.2

0.4

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Year

rAU

DPC

Cultivar Bintje Sarpo Mira

• EU_13_A2 dominates the scotish P. infestans poulation• EU_6_A1 started increasin from 2009• Other factors(e.g. Agronomic?)

Could the pathogen population be responsible?

Denmark

2004

2005

2006

2017

0.0

0.2

0.4

0.6

Year

rAUD

PC

Cultivar Bintje Sarpo Mira

Could the pathogen populatiob be responsible?

EU_41_A2 is dominating in DK since 2013

Implementation of the EUCABIGHT methodolgoy

• Apparent infection rate (AIR).• Time to reach 1 and 5%• AIR is not estimated when

• Severity values < 3 in the range 0.5-99%• Slope <0 • Rsuared < 0.5

• AUDPC is not calculated if first or last reading, or two consecutive readings are missing for one or more replications

• AUDPC is converted to rAUDPC• rAUDPC = AUDPC ÷ Duration of epidemic ÷ 100

Determining the type of resistance with Delta plot2004 data from GE, DK, WA, EE and FI

Delta a: AIR (Logistic), Cultivar tested minus Bintje

-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4

Del

ta t:

Day

s un

til 1

%,

Cul

tivar

test

ed m

inus

Bin

tje

-15

-10

-5

0

5

10

15KurasRobijnAlphaEscortBintjeGloriaErstling

RS + RNS (or RS not overcome)

Race-non-specific (RNS) Susceptible (none)

Race Specific (RS)

Lower AIR than Bintje

Days until 1% dislater than Bintje

Estimation of 1-9 Scale

• Implemented with the scoring formula from JHI with rAUDPC• 2 reference or anchor cutltivars

• Bintje-Susceptible reference = 3 (y2)• Sarpo Mira- resistant reference=8 (y1)

• R score (y) for a test cultivar with rAUDPC (x) is calculated as• 𝑦𝑦 = 𝑦𝑦1 + 𝑥𝑥 − 𝑥𝑥1 × 𝑦𝑦2−𝑦𝑦1

𝑥𝑥2−𝑥𝑥1

• X1 and x2 are the rAUDPC of Sarpo Mira and Bintje, respectively

R scores in Denmark

Variability of R scores over the years?

Comparison of R scores in 2017

0

2

4

6

8Al

ouet

te

Bint

je

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olus

Coq

uine

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igs

Snow

-Whi

te

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R4

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Kelly

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land

Ace

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ijn

Sarp

o M

ira

Cultivar

Res

ista

nce

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e

CountryDenmark

Estonia

France

Scotland

R scores from JHI Versus Dowley et al. (199)Denmark Scotland Wales

Bint

je

Car

a

Dés

irée

Eers

telin

g

Rob

ijn

Bint

je

Car

a

Dés

irée

Eers

telin

g

Rob

ijn

Bint

je

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a

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irée

Eers

telin

g

Rob

ijn

2

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Cultivar

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ista

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scor

e

Score_esti

Score_ref

Concluding remarks• Resistance of most cultivars are not stable across countries and

between years.• Different pathogen population• Different agronomic practices.

• Resistance is based on the interaction between the pathogen and the cultivars grown in a certain region

• Collaboration with breeders and help to understand epidemiological and evolutionary mechanisms to optimize the breeding of new cultivars.

• The inclusion of more than reference of cultivars to calculate R scores.

• Development of a common Resistance grading systems.

Concluding remarks

• In the future, deployment of resistance will be the key component in PLB DSSs

• Discuss how resistance is included most effectively in our DSSs and how we can monitor (and predict) the stability of resistance across years and regions

• Protecting R genes by augmenting with fungicide.

IPMBlight 2.0 – partners

NAES

Thanks for your attention