Model Fitting of Growth Pattern of Sunflower Head in Lakomka and Progress Varieties Under Dryland Condition

Considering the importance of sunflower as one of the most important plants in the production of edible oils, present study was developed in order to determine the best nonlinear regression function which can quantify growth of diameter of sunflower head to time. At the present study in order to fit the best regression model explaining relationship between increasing of sunflower head diameter of Lakomka and Progress varieties and time, an experiment was performed at 60 kilometers of Amlash city in rainfed condition. At first, the head diameter measurement since the beginning of growth maturity and harvest was recorded. Then different regression models including exponential, power, logarithmic, logistic, schnute and gompertz were used. Then to determine the most efficient model, different parameters of evaluation of model fitting were used. The results revealed that schnute and logistic models are the best model for explaining of the head diameter variation of Lakomka and Progress varieties to time. Schnute model with highest adjusted coefficient of determination (0.99 and 0.98) and lowest root error mean of squares was determined as the best model in explaining of growth pattern of head diameter to time. It is expected that using these models could be used for prediction of head diameter of Lakomka and Progress varieties with high precision.