Understanding the factors that define a given interaction is important in agricultural, agronomic, and plant breeding research, where agronomic treatments or genotypes are evaluated under several environmental conditions and where interactions usually complicate a researcher’s decisions. We give examples of how interactions, in common agricultural experiments, can be examined and studied to make use of the rich information available on the interaction term of the model. Examples with different levels of interaction complexity are used to illustrate how to analyze and interpret interactions and how interaction components can be partitioned into comparisons with sensible biological interpretations. It will offer researchers a greater understanding of how to exploit interaction information beyond the standard statistical tests performed in the usual analysis of variance. Simple SAS codes for performing standard interaction contrasts and defining interaction covariables are provided.