Effect of weather factors on fluctuations of spore population of Pyricularia grisea and the occurrence of the disease was considered. During growing seasons of 2006-2007, paddy fields were chosen in distance of five kilometers from weather stations of Rasht, Lahijan and Anzali in Guilan province and spore population (Ps) were measured daily using sporetraps. Weather data including precipitation (P), daily maximum and minimum temperature (Tmax, Tmin), daily maximum and minimum relative humidity (RHmax, RHmin) and sunny hours (SH) were obtained from weather stations. The relationship between spore population fluctuations and weather data was analyzed and the most important weather factors affecting spore population and predicting blast were determined. Accordingly, weather factors such as P, Tmax, RHmin and SH are the most important factors predicting rice blast in Guilan and enough precipitation, increased daily RHmin, decreased daily Tmax and SH result in increased spore population and blast occurrence during next 7-10 days. To predict final leaf blast severity (Yflbs) and neck blast index (Ynbi), factors such as Tmax, Tmin, T, RHmax, RHmin, RH, P and SH and Ps were used for modeling. For leaf blast, these factors were considered for June and July and for neck blast, the same factors used for August. Step wise regression was applied for modeling. Statistics like r, R2, aR2, SE, F and Durbin-Watson were applied for evaluating the models. Finally, the two quantitative models: Yflbs = -2.41-2.80 Tmin+0.68RHmin-0.015Ps-0.014P+0.052SH (R2 = 96.73%) and Ynbi = -24.11+0.08Tmax+0.19 RHmax+0.034Ps-0.015P+0.016SH (R2 = 73.97%), were introduced for predicting final leaf blast severity and neck blast index, respectively. Related to effects of amount of applied N fertilizer (F) and date (D) and space (S) of transplanting, the results showed high correlation between F and Yflbs and Ynbi, but such high correlation was not observed for D and S. The best function for predicting Yflbs was Y = 4.46-4.12F+1.93F2 (R2 = 96.37). The best equation for predicting Ynbi acquired when F, D and S were applied in multiple regression, Y = 2.06+0.33F+0.10D-0.03S(R2 = 54.40).
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