Nonlinear statistical models continue to be relevant because they have the advantages of simplicity, flexibility, and parsimony when used in the analysis and description of agricultural data. Often nonlinear models can be easier to interpret than other alternatives. These models have been applied to data ranging from leaf‐level to landscape‐level processes, such as photosynthesis, biomass accumulation, soil CO₂ efflux, and temperature responses. Our objective is to provide a multistep framework that helps with the application of these models with specific considerations in each step. Even though objectives and applications may vary, common priorities should be set when fitting nonlinear models: obtaining acceptable parameter estimates, a good model fit, and meeting standard assumptions of statistical models. The following steps are considered: (i) choose candidate models, (ii) set starting values, (iii) fit models, (iv) check convergence and parameter estimates, (v) find the “best” model among competing models, (vi) check model assumptions (residual analysis), and (vii) calculate statistical indexes and confidence intervals. In particular, we emphasize the first step (choose candidate models) by providing an extensive library of nonlinear functions (77 equations with the associated parameter interpretations) and application examples in agriculture. We hope that this contribution will clarify some of the difficulties and confusion with using nonlinear models.