  On-farm experiments can provide farmers with information on more efficient
crop management in their own fields. Developments in precision agricultural
technologies, such as yield monitoring and variable-rate application
technology, allow farmers to implement on-farm experiments. Research frameworks
including the experimental design and the statistical analysis method strongly
influences the precision of the experiment. 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 variation in a particular response variable (e.g., yield
data). The effects of experimental designs and statistical approaches on type I
error rates and estimation accuracy were explored through a simulation study
hypothetically conducted on experiments in three wheat fields in Japan.
Isotropic and anisotropic spatial linear mixed models were established for
comparison with ordinary least squares regression models. The repeated designs
were not sufficient to reduce both the risk of a type I error and the
estimation bias on their own. A combination of a repeated design and an
anisotropic model is sometimes required to improve the precision of the
experiments. Model selection should be performed to determine whether the
anisotropic model is required for analysis of any specific field. The
anisotropic model had larger standard errors than the other models, especially
when the estimates had large biases. This finding highlights an advantage of
anisotropic models since they enable experimenters to cautiously consider the
reliability of the estimates when they have a large bias.
