Parametric regression models assume a linear relationship between variables, while nonparametric models are used when this relationship is unknown. Semiparametric models combine both approaches to handle linear and nonlinear relationships. In this study, researchers analyzed different regression models in agriculture. They found that certain factors like milking unit and quarantine area were statistically significant in parametric models, while factors like operation area and distance to the center were important in nonparametric models.