The outcomes of on-farm experiments can support farmers’ decision-making processes, while inappropriate procedures would result in incorrect interpretations. Conventional statistical approaches (e.g., ordinary least squares regression) may not be appropriate for on-farm experiments because they are not capable of accurately accounting for the underlying spatial variations in a particular response variable (e.g., yield data). A combination of a repeated design and an anisotropic model is required to improve the precision of the experiments. [Tanaka,T.S.T. 2020. Assessment of research frameworks for on-farm experimentation through a simulation study of wheat yield in Japan . Preprint 12741.]