This paper develops a meta-regression analysis to explain the variation of mean technical efficiency (PETP) measurements from a total of 65 frontier studies that report technical efficiency (ET) measurements at the dairy farm level in the literature published in English and Spanish. The analysis includes the effect of methodology on ET measurements, as well as the effect of the econometric procedure on the meta-regression estimates. Eight models were estimated, and two of these were selected: a fixed effects specification with dummy variables for the most significant studies without geographical effects (EFS), and a specification where the multiple observations are averaged and geographical effects included (OP). Based on model performance, the EFS option is chosen for the analysis. The results of the EFS model suggested that non-parametric deterministic models generate higher PETP estimates than the parametric cases (stochastic and deterministic frontier models). In addition, the Cobb-Douglas and translog forms yield higher average PETP than all other functional forms, cross-sectional data produce higher ET estimates than panel data, and the PETP is higher when the study is input-oriented. The primal approach implies a higher ET estimate than the dual analysis, and when more variables are included in the model, the PETP value is higher.