comparison of six weather based risk models for...

COMPARISON OF SIX WEATHER BASED RISK MODELS FOR THE DEVELOPMENT OF

POTATO LATE BLIGHT

Anne-Grete Roer Hjelkrem, Mladen Cucak & Jens Grønbech Hansen

EuroBlight 13.05.2019

IPMBLIGHT2.0

14.05.2019 2

WP 1 – sampling and genotyping P. infestans populations

WP 2 – phenotyping P. infestans

WP 3 – developing improved LB DSS – IPM 2.0

WP

0 –

coo

rdin

atin

gan

d d

isse

min

atin

gIP

MB

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t2

.0

Isolate sampling & Reference isolate collections

Aggregated data on individualisolatesField monitoring of

virulence

Data management and analytic tools (e.g. poppr)

New DSS simulation models/ modules

Papers and conferencepresentations

Website

Potato LateBlightToolbox

Genetic (SSR) fingerprints

Ad

viso

ryB

oar

d

Existing DSS improved and tested

Fungiciode resistancetesting

Stakeholderinteraction

EUROPEAN DECISION SUPPORT SYSTEMS

14.05.2019 3Anne-Grete Roer Hjelkrem

GOAL

– Compare European weather based sub-models for estimation of risk of potato late blight

– Four research questions:

1. Is there a seasonal effect of Blight risk?

2. Is the Blight risk different between regions?

3. Is the Blight risk different between years?

4. Is the estimation of Blight risk different between models?

14.05.2019 4User manual – NIBIOs power point template

Foto: Vinh Hong Le

OVERVIEW


Quality control

Blight Management

Nærstad Model

Irish Rules

WUR Blight

Hutton Critera

The reference model

Input data Model implementation

QUALITY CONTROL OF WEATHER DATA


Quality control

Input data

Apelsvoll

Ås

Dronninglund

Ikast

Dundee

Cambridge

Oak ParkMoore Park

EldenArcen

Viller Saint Christophe

Boigneville

QUALITY CONTROL OF WEATHER DATA


Missing values

Min and Max

Mean RH during hours with

1. precipitation > 0,5 mm

2. precipitation > 0,5 mm,

Solar radiation < 0,5 MJ/m²

and Wind speed < 5 m/s

Median RH among the 200

highest RH values each month,

sorted from highest to lowest

Ma

x [%

] to

p 2

00

Rh

by m

on

th

82

84

86

88

90

92

94

96

98

100

Jun Jul Aug May Jun Jul Aug May Jun Jul Aug

201720162015

May

Max

ho

url

yR

H [

%]

OVERVIEW


Quality control

Blight Management

Nærstad Model

Irish Rules

WUR Blight

Hutton Critera

The reference model

Input data Output dataModel implementation

OUTPUT DATA

– All models were ran simultaneously with the quality controlled weather data


Output data

BLIGHT RISK ESTIMATION

10

Dronninglund 2015


11

Dronninglund 2015


Blight Risk Estimations were summed by month for each region, year and model, and normalized.

ANOVA and post hoc Tukey’s HSD test


𝑌𝑖𝑗𝑘𝑙 = 𝜇 +𝑚𝑜𝑛𝑡ℎ𝑖 + 𝑟𝑒𝑔𝑖𝑜𝑛𝑗 + 𝑦𝑒𝑎𝑟𝑘 +𝑚𝑜𝑑𝑒𝑙𝑙 + 𝜀𝑖𝑗𝑘𝑙

5 months × 12 region × 4 year × 6 models

Foto: Vinh Hong Le

𝑌𝑖𝑗𝑘 = 𝜇 +𝑚𝑜𝑛𝑡ℎ𝑖 + 𝑟𝑒𝑔𝑖𝑜𝑛𝑗 + 𝑦𝑒𝑎𝑟𝑘 + 𝜀𝑖𝑗𝑘

SEASONAL EFFECT OF BLIGHT RISK


A significant positive effect of month number

Increased estimated Blight Risk through the season

AA

SEASONAL EFFECT OF BLIGHT RISK


AA

YEARLY BLIGHT RISK DIFFERENCES




0

5

10

15

20

May June July August September

Mean Air Temperature (°C)

2014 2015 2016 2017

0

200

400

600

800

1000

1200

1400


Sum Precipitation (mm)

2014 2015 2016 2017

65

70

75

80

85

90


Mean Relative Humidity (%)

2014 2015 2016 2017



Date

01-04 01-05 01-06 01-07 01-08 01-09

Norway

Russia

Scotland

Estonia

Lithuania

Finland

Ireland

Sweden

Poland

Serbia

England&Wales

Denmark

Belgium

Netherlands

Switzerland

Germany

France

= 2016, Attack in > 5 conventional fields = 2015, Attack in > 5 conventional fields

REGIONAL BLIGHT RISK DIFFERENCES


Vill

er S

ain

t C

hri

sto

ph

e

Bo

ign

evill

e

Ikas

t

Dro

nn

ingl

un

d

Oak

Par

l

Mo

ore

Par

k

Ap

elsv

oll Ås

Cam

bri

dge

Du

nd

ee

Eld

en

Arc

en

Esti

mat

edN

orm

aliz

edB

ligh

tR

isk

REGIONAL DIFFERENCES IN BLIGHT RISK


Low

Estimated risk levels, averaged over the models:

Medium

High

June 2015

REGIONAL/YEARLY DIFFERENCES IN BLIGHT RISK


June 2016June 2015

REGIONAL/SEASONAL DIFFERENCES IN BLIGHT RISK


August 2015June 2015

CONCLUSIONS

1. Is there a seasonal effect of Blight risk?

2. Is the Blight risk different between regions?

3. Is the Blight risk different between years?

4. Is the estimation of Blight risk different between models?


Foto: Vinh Hong Le

Increased estimated Blight Risk through the season

Generally lower and later estimated Blight Risk north

Estimated Blight Risk differ between years

Estimated Blight Risk differ between models, but the models generally agrees in estimated high and low risk periods

CONCLUSIONS

– Blight risk differ between years and regions, and decision support systems provides valuable information about the risk levels through the season

– A more robust estimate of Blight Risk can be achieved by using several models simultaneously

– Model validation including biological observations must be performed in order to compare the accuracy of the different models


Foto: Vinh Hong Le

THANK YOU FOR THE ATTENTION!

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