جواد طایی

This study aimed to develop a new approach for predicting budburst and flowering with no dependency on GDD coupled with a GDD model to predict veraison with the capability of application for site-specific prediction of phenphases using geospatial functions. The budburst and flowering phases were predicted using probability statistical models based on threshold temperature occurrence and the chill requirement supplement was considered as a Boolean function. The field phenological data were recorded from irrigated vineyards with the same management systems. Based on the climatic conditions, four growing patterns of grapevine were defined over the study area, and phenological models were separately fitted for each growth pattern. The spatial analysis was performed by ArcGIS9x using linear models that fitted between the predicted phenophase and digital elevation model (DEM, grid cell 75 m). The results of the model indicated normalized RMSE and model efficiency of 12.5 %–0.93, 6.3 %–0.95, and 18.9 %–0.79, respectively for budburst, flowering, and veraison phases, with the absolute error of 1.12–1.75, which indicated high accuracy in the phenology estimation. The validated models of phenophase occurrence probability used historical weather data under different climatic conditions. The changes in probability during the days of the year were analyzed using different regression models. The bestfitted model for the budburst probability followed the sigmoid model under climatic conditions with mild (R2=0.98) or cool (R2=0.95) winter whereas the quadratic models (R2=0.98) were best-fitted under cold winter. The flowering model followed the quadratic model (R2=0.97) under all climatic conditions. The maps of phenophase characterized the site-specific time of budburst, flowering, and veraison.