Bayesian network: a simplified approach for environmental similarity studies on maize

Abstract

The current methodologies used to evaluate environmental similarities do not allow the simultaneous analysis and categorization of the environments. The objective of this study was to verify the possibility of using the Bayesian network (BN) to detect similarities between environments for plant height, lodging, and grain yield in maize. Thirteen experimental varieties were grown in six environments to measure the traits plant height, lodging, and grain yield. The BN was constructed for each trait, using the Hill-Climbing algorithm. Results were compared with the simple part of the genotypes x environments interaction, clustering by the Lin's method and by simple correlation between environments. The Lin's method clustered environments with predominance of complex interaction for all traits. The BN is efficient to analyze environmental similarity for plant height and grain yield since it detected the highest correlations. The BN revealed no connections among the environments that presented predominance of complex interaction.