Grain yield and yield components of 25 starchy maize varieties (Zea mays L.) From the province of Tayacaja, Peru

Abstract

Maize is a crop of vital importance in the Peruvian Andes, where local and improved varieties represent a key source of food and income. However, information on the relationship between yield and its components in varieties created in specific regions, such as Tayacaja, is limited. The present study aimed to estimate grain yield and its relationship with yield components in 25 maize varieties from the province of Tayacaja, Huancavelica, Peru, and to develop a multiple regression model predicting yield from the components with the highest correlation. Field evaluations were conducted in 2020 in the Daniel Hernández district of Tayacaja, Peru, using a 5 × 5 alpha-lattice experimental design with three replications. A Pearson correlation analysis, path analysis and stepwise multiple linear regression analysis were performed. Preliminary results indicate that yield components explain a significant portion of the observed variability in grain yield, with grain weight and ear diameter being the most influential variables in the predictive model. The correlation, path, and multiple regression analyses showed that average grain weight was the yield component exerting the greatest direct and consistent influence on the productivity of the evaluated starchy maize varieties. These findings provide direct selection criteria for maize genetic improvement programs in the central highlands of Peru. It is also recommended that multi-environment studies be continued to validate the stability of these components as yield predictors.