ciency and productivity change for a sample of large-scale arable farms in Germany. For this, the paper applies input-oriented Data Envelopment Analysis (DEA) and Malmquist Index (MI) methods in combi-nation with bootstrapping to a balanced five-year panel data set of 86 German large-scale arable farms over a time period from 2012/2013 to 2016/2017. The DEA results of the original sample show a mean in-put-saving potential of 9.2 % across farms and time periods. The bootstrapped confidence intervals indi-cate no statistically significant difference among the mean scores for individual years, however significant differences exist between individual farms. The results of the MI analysis of the original sample suggest a mean annual growth in total factor productivity of 5.4 %. This progress was driven by technical change (6.5 %) and happened despite a small average deteri-oration in change in technical efficiency (1.1 %). The progress in total factor productivity as well as tech-nical change is statistically underpinned through the bootstrapped confidence intervals. The result of change in technical efficiency computed from the original sample cannot be confirmed statistically as the corresponding confidence interval includes unity.