A time thermal model for predicting the

parasitism of Orobanche cumana in

sunflower - five years of field validation

HANAN EIZENBERGJ. HERSHENHORN, G. ACHDARI. AND J. E. EPHRATH

Department of Plant Pathology and Weed Research, Newe Ya’ar

Research Center, ARO, Ramat Yishay, Israel.

Background• During its initial stages of parasitism, the

broomrapes grow underground

• Predicting their developmental stages at this phase

is a necessity in order to properly apply control

measures

• This challenge can be met by using the modeling

approach, as reported for P. aegyptiaca, O. minor

and O. cumana, in tomato, red clover and sunflower,

respectively

• In those studies, the relations between parasitism

dynamics and thermal time has been described by

mathematical functions, e.g. sigmoid, logistic,

Weibull, and polynomial functions

Weed Research 50, 140–152

Weed Research 50, 140–152

b

xGDDa

YY

0

10

Y - broomrape number

b - the slope at x0

a - the upper asymptote (maximum)

Y0 - the lower asymptote (minimum)

X0 - the GDD when Y is 50% of maximum (median)

Four parameters logistic equation

Weed Research 50, 140–152

The objective of this study is to:Calibrate under field conditions an equation

that describes the parasitism dynamics of

Orobanche cumana in sunflower

To estimate the contribution of an additional

estimated parameters (lag phase) to reduce

the RMSE of the model

Fit model for

individual field

Model calibration field Trails

4 locations 4 years

Model validation field Trails

5 locations 2years

Input

Test combined

model4 locations

Does the

model consist?

Yes

No

Model test

Model adjustment

base on multi years

data

Flow chart for model development

Fit model for

individual field

Model calibration field Trails

4 locations 4 years

Model validation field Trails

5 locations 2years

Input

Test combined

model4 locations

Does the

model consist?

Yes

No

Model test

Model adjustment

base on multi years

data

Flow chart for model development

• Temperature (10 cm depth)

• Attachments (No. and size)

Minirhizotron system

50 cm

45°

9 Field experiments through 2005-2009

Model calibrationModel validation

To estimate the number of attachments related to

thermal time, the following equations were tested:

Sigmoid, Gompertz (both three parameters) and

Weibull (four parameters)

These equations are characterized with the pattern

lag, and with the log and maximal asymptote for the

number of parasite tubercles as a function of thermal

time.

Fit of equations was evaluated by RMSE, and by the

corrected Akaike Information Criterion (AIC)

Thermal time (GDD)0 300 600 900 1200 1500In

fect

ion (

LO

G10 a

ttach

ments

/tube)

0.0

0.5

1.0

1.5

2.0

2.5

Model calibration field trails

Logistic (RMSE=0.9)

Thermal time (GDD)0 300 600 900 1200 1500In

fect

ion (

LO

G10 a

ttach

ments

/tube)

0.0

0.5

1.0

1.5

2.0

2.5

Model calibration field trails

Weibull (RMSE=0.06)

Logistic (RMSE=0.09)

In the calibration studies, the number of

attachments was best fitted to thermal time using

the Weibull equation, which resulted in a great fit in

the validation studies (RMSE = 0.066; R2 = 0.99;

slope a ~ 1).

a = 2.1 P

<0.0001

σ = 331.7 P

<0.0001

g= 1.9 P

<0.0001

µ (lag) = 420 P

<0.0001

µ = lag (location)

σ = scale (63% of maximum)

λ = shape

a = maximum asymptote

Thermal time (GDD)

0 300 600 900 1200 1500

Infe

ctio

n (

LO

G1

0 a

ttach

me

nts

No

./tu

be

)

0.0

0.5

1.0

1.5

2.0

2.5

Validation test of the model

9.1

7.331

420exp11.2

GDDY

Thermal time (GDD)

0 300 600 900 1200 1500

Infe

ctio

n (

LO

G1

0 a

ttach

me

nts

No

./tu

be

)

0.0

0.5

1.0

1.5

2.0

2.5

Validation test of the modelA four parameters modified Weibull equation (estimated the lag phase) based on the parameters obtained from model calibration

This is not a fit! This curve is based on the

parameters estimated from the calibration

model

Thermal time (GDD)

0 300 600 900 1200 1500

Infe

ctio

n (

LO

G1

0 a

ttach

me

nts

No

./tu

be

)

0.0

0.5

1.0

1.5

2.0

2.5

Validation test of the modelA four parameters modified Weibull equation (estimated in the lag phase) based on the parameters estimated from model calibration

Blue circle obtained from field validation studies

Curve obtained from Calibration study

Predicted Infection (LOG10 attachments/tube)

0.0 0.5 1.0 1.5 2.0 2.5Ob

serv

ed In

fect

ion

(LO

G1

0 a

ttach

me

nts

/tub

e)

0.0

0.5

1.0

1.5

2.0

2.5

Model test

R2 = 0.99; P < 0.001

Where such a model could be applied?

Smart control of O. cumana in sunflower

200 GDD 600 GDD 1000 GDD

Weibull equation estimated the parameters:

µ (lag) = 420 and σ (63% of maximal asymptote) =

331.7

Thermal time (GDD)

0 300 600 900 1200 1500

Infe

ctio

n (

LO

G1

0 a

ttach

me

nts

No

./tu

be

)

0.0

0.5

1.0

1.5

2.0

2.5

Lag=420 s=331

Imazapic (as other imidazolinone herbicides) effectively controls broomrape when it is attached to the roots

Imazapic (4.8 g a.i. ha-

1) applied at 720 GDD

Where such a model could be applied?Smart control of O. cumana in sunflower

200 GDD 600 GDD 1000 GDD

Non herbicide treated control

Imazapic (4.8 g a.i. ha-1) applied at 720 GDD

Control Imazapic Control Imazapic0

20

40

60

80

100

120In

fect

ion

(vita

l tta

chm

ents

/tub

e) SED=5.65

Thermal time (degree days)

600 GDD 1000 GDD

Control efficacy based on the model

Conculsions (chemical control)

The example that has been given

demonstrates control efficacy of one foliar

treatment with imazapic

Further chemical treatments should be applied

according to the model but not as foliar

applications as imazapic may injure the

sunflower reproductive tissues after initiation

Herbigation may be considered for further

treatments but a protocol should be developed

Conclusions• Thermal time can robustly predict O.

cumana parasitism in sunflower using the Weibull equation

• The Weibull equation adds a biological dimension to the model, compared to the other equations, as the lag phase allows to estimate the precise timing of parasite attachment to host roots

• This information is crucial in any attempt to develop control strategies for these parasitic weeds

Taking home message

The modeling approach is essential for the

development of control strategy and

decision support systems for Orobanche

managment

However,

It could be applied in other field of studies

related to parasitic plants such as

resistance, biological aspects and

strigolactones