Measuring and Modeling Crop Loss of Wheat Caused by Septoria Leaf Blotch in Seven Cultivars and Lines in Iran

Septoria leaf blotch caused by Septoria tritici, is one of the most important diseases of wheat worldwide including Iran. To determine yield reduction caused by this disease in Golestan province, field experiments were carried out in randomized complete block design with four replications and five wheat cvs. Tajan, Zagros, Shiroodi, Koohdasht, Shanghai and two lines N-80-6 and N-80-19 at Gorgan Research Station. Artificial inoculation was performed using spore suspension at three growth stages (Zadoks scale) including tillering (GS 37), stem elongation (GS 45) and flag leaf opening (GS 53). Control plots were sprayed with water. In this study, the 1 000 kernel weight (TKW), grain yield and area under disease progress curve (AUDPC) during growth season were measured. Statistical analysis showed that the levels of yield reduction was different in various studied wheat cultivars and lines and was reduced by 30 to 50%. The highest losses were observed for cvs. Zagros and Tajan with 48.86% and 47.41% of grain yield reduction, respectively. There was a positive correlation between grain yield reduction and AUDPC. The results of crop loss modelling using integral and multiple point regression models showed that the integral model (L = 1230.91+1.37AUDPC) in which AUDPC and crop loss percentages were independent and dependent variables, respectively, could explain more than 95% of AUDPC variations in relation to crop loss in all cultivars in two years. In the study of integral model for each cultivar, cv. Shiroodi showed the highest fitness. In multiple point models, disease severity at various dates was considered as independent variables and crop loss percentage as dependent variable. This model with the highest coefficient of determination had the best fitness for crop loss estimation. Besides, the results showed that the disease severity at GS37, GS53 and GS91 stages (Zadok's scale) was more important for crop loss prediction than that in other phenological stages.