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In breeding programs, estimation of increase in yield based on changes in effective plant traits is of great importance, which can be identified using regression modeling. The regression model refers to the prediction of the value of a dependent variable from the values of one or more independent variables. This study evaluated morphological traits of maize (FAO410) on six treatments of fertilizer in 2 years in Debrecen University by the regression model. This experiment was RCBD with four replications in the Latokep zone. Treatments were included in different levels of fertilizer: nitrogen, phosphor, and potassium. The regression model was significant at one percent that showed morphological traits have a straight effect on the yield of maize in FAO410 and FAO340. Grain yield had a positive correlation with plant height, outer ear diameter, the weight of ear, weight of cob, number of leaves, weight of all seeds in each ear, the weight of one thousand seeds on FAO410, and grain yield had a positive correlation with plant height, stem diameter, outer ear diameter, the weight of ear, weight of cob, number of seeds in each column, weight of all seeds in each ear, weight of the fresh plant in a hectare, the weight of one thousand seeds on FAO340 too. Cluster analysis showed the traits classification on two groups on hybrids. Reach maximum grain yield require the evaluation of yield components and their effect.

Evaluation of plant characteristics related to grain yield of FAO410 and FAO340 hybrids using regression models