Study of Genotypic and Phenotypic Correlations and Stepwise Regression Analysis in Maize Under the Influence of Combinations of Nitrogen Fertilization  and Humic Acid

Marwa  Kareem Salman Al-Mafraji; Mohammed  Ahmed Ibraihi AL-Anbari

المؤلفون

Marwa Kareem Salman Al-Mafraji Department of Field Crops, College of Agriculture, University of Kerbala, Kerbala, Iraq
Mohammed Ahmed Ibraihi AL-Anbari Department of Field Crops, College of Agriculture, University of Kerbala, Kerbala, Iraq

الكلمات المفتاحية:

Humic Acid، Maize crop، Nitrogen، Genotypic، Phenotypic Correlations

الملخص

An experiment was commducted in the fields of Ibn Al-Bitar Preparatory Vocational School in Al-Hussainiya District-Holy Karbala governorate during the spring season 2021. Split-plot arrangement with in randomized complete blocks design (RCBD) with three replications was applied.The objective of this experiment was to identify the traits most related to the grain yield and determin its predictive equation for six maize genotypes (5018, Bahouth 106, Al-Maha, Fajr 1, Al-Furat and Sarah) under the effect of four levels of fertilization (160 N kg.ha^-1, 160 N kg.ha^-1with humic acid, 320 N kg.ha^-1 and 320 N kg.ha^-1 with humic acid) where the genotypes of maize represented the subplots and fertilization as the main plots.

Several traits of growth, quality, characteristics of the crop and some of its components were studied, in addition to studying some traits of the tassel and vitality of pollen grains. The genotypic and phenotypic correlations were studied, and an equation predicting the grain yield was found through a stepwise regression analysis.

The analysis of the phenotypic and genotypic correlations showed that the values of the genotypic correlations were higher than the values of the phenotypic correlations for most traits. Grain yield was connected with a highly significant positive phenotypic correlation with the characteristics of leaf area, the 500 grain’s weight and yield efficiency, and a highly significant positive genetic correlation with the 500 grain’s weight (0.97) and yield efficiency (0.71).

It became clear from the stepwise regression analysis that the independent traits that are closely associated with the dependent variable (yield) were, according to the predictive equation for yield, the 500 grain’s weight, yield efficiency and leaf area, as these traits were responsible for 95% of the yield variance.

We conclude from the foregoing that it is possible to use the 500 grain’s weight as a selective criterion to achieve the highest genotypic correlation with grain yield, and the stepwise regression analysis confirms this