One of the most important objectives in efficiency analysis is to investigate the relationships between production decisions and their contextual environments like geographical regions, production time periods, modes of production, or policies and regulations. Using the measurement of technical change as a template, the study presents a general framework to better understand varying production decisions under different time periods by showing how such production heterogeneity can be attributable to the differences in time-specifc technological frontiers at industry level and the differences in the prevalence of technical inefficiency at producer level. In DEA, a leading non/semi-parametric frontier estimation method, these differences can be analyzed through decomposing Malmquist productivity index (MPI) into technical change (TC) and technical efficiency change (TEC) respectively. The decomposition approach falls into the non-Hicks-neutral TC estimation as the mean distance measures among time-specific frontiers, which is generally less restrictive than the Hicks-neutral TC estimation as an intertemporal-shift component of the frontier specification under fixed substitution patterns across time periods. The method is more generally applicable to the comparisons between any two different contextual environments, including before and after a policy intervention, by which a sample can be partitioned. To make the existing method more empirically accessible and appealing, the study proposes a regression-based MPI decomposition that overcomes its limitations, or the need of balanced panel data and the lack of control for potentially confounding non-production factors. The proposed methodology is demonstrated with an empirical application using data from the Schedule F Tax returns of 62 dairy farmers in Maryland during 1995-2009. For conventional, confinement dairy operations, the preliminary results under preferred specifications show a 26.4%/decade expansion in technological frontier, accompanied by a 6.3%/decade decline in the mean technical efficiency levels (i.e. increases in the prevalance of technical inefficiencies). The indicators for farm ownership and off-farm income are associated with a 4.5% increase and a 5.8% decrease in technical efficiency respectively. Higher seasonal rainfalls and temperatures, except for winter rainfall and summer temperature, are associated with larger technical feasibility in a given year.