Background During its initial stages of parasitism, the broomrapes grow underground

1. A time thermal model for predicting the parasitism of Orobanche cumana in sunflower - five years of field validation HANAN EIZENBERG J. HERSHENHORN, G. ACHDARI. AND J. E. EPHRATH Department of Plant Pathology and Weed Research, NeweYa’ar Research Center, ARO, Ramat Yishay, Israel.

4. 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

5. Weed Research 50, 140–152

6. Weed Research 50, 140–152

7. Four parameters logistic equation Y - broomrape number a - the upper asymptote (maximum) Y0 - the lower asymptote (minimum) X0 - the GDD when Y is 50% of maximum (median) b - the slope at x0

8. Weed Research 50, 140–152

9. The objective of this study is to: Calibrate under field conditions an equation that describes the parasitism dynamics of Orobanche cumanain sunflower To estimate the contribution of an additional estimated parameters (lag phase) to reduce the RMSE of the model

10. Flow chart for model development No Does the model consist? Input Fit model for individual field Test combined model 4 locations Yes Model adjustment base on multi years data Model test Model validation field Trails 5 locations 2years Model calibration field Trails 4 locations 4 years

11. Flow chart for model development No Does the model consist? Input Fit model for individual field Test combined model 4 locations Yes Model adjustment base on multi years data Model test Model validation field Trails 5 locations 2years Model calibration field Trails 4 locations 4 years

13. Minirhizotron system

14. 50 cm 45°

18. 9 Field experiments through 2005-2009 Model calibration Model validation

19. 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)

20. Model calibration field trails Logistic (RMSE=0.9)

21. Model calibration field trails Logistic (RMSE=0.09) Weibull (RMSE=0.06)

22. 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 = maximum asymptote a = 2.1 P <0.0001 σ = 331.7P <0.0001 g= 1.9 P <0.0001 µ (lag) = 420 P <0.0001 σ = scale (63% of maximum) λ = shape µ = lag (location)

23. Validation test of the model

24. Validation test of the model A 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

25. Validation test of the model A 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

26. Model test R2= 0.99; P < 0.001

27. Where such a model could be applied? Smart control of O. cumanain sunflower 200 GDD 600 GDD 1000 GDD Weibull equation estimated the parameters: µ (lag) = 420 and σ (63% of maximal asymptote) = 331.7

28. 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 Lag=420 s=331

29. Where such a model could be applied? Smart control of O. cumanain sunflower Non herbicide treated control Imazapic (4.8 g a.i. ha-1) applied at 720 GDD 200 GDD 600 GDD 1000 GDD

30. Control efficacy based on the model SED=5.65 600 GDD 1000 GDD Thermal time (degree days)

31. 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

32. Conclusions • Thermal time can robustly predict O. cumanaparasitism 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

33. 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