The objectives of this work were to use a Bayesian approach to fit efficiency curves relating individual essential AA export (AAExp) to duodenal digestible AA (DigAA) flow, and to evaluate the responsiveness of these curves to digestible energy (DE) intake and days in milk (DIM). A literature data set of 333 treatment means (87 studies) was collected. This data set included measures of diet composition, feed intake, duodenal or omasal protein and EAA flows, and milk protein output. Where diet composition data were not reported in the paper, they were calculated from feed library composition values. The AAExp was estimated as the sum of milk AA, scurf AA, and metabolic fecal AA output. The DigAA flow was estimated as the sum of AA from intestinally digestible microbial and rumen undegraded protein supplies minus endogenous urinary protein. Nonlinear least squares regression was used to develop informative prior parameter estimates, which were then used in a Bayesian hierarchical derivation of the relationship between individual essential AAExp and DigAA. Regressions were weighted based on the inverse of the SE for milk protein yield reported in each study. This procedure was done independently for each AA and a logistic equation form was used in all cases. Within-study parameter estimates for the asymptote, amplitude, and steepness of this logistic curve were then regressed on predicted dietary DE intake and reported DIM using simple linear regression. The DE intake influenced (P < 0.05) His, Ile, Leu, Lys, Met, Phe, Thr, and Val curve asymptote. Curve amplitude was influenced (P < 0.05) by DE intake for all EAA and was also influenced (P < 0.05) by DIM for Ile, Lys, Met, Phe, Thr, and Val. The curve steepness was affected (P < 0.05) by DIM only for Arg, and by DE only (P < 0.05) for Val. Both DE and DIM influenced (P < 0.05) the curve steepness for His, Ile, Leu, Lys, and Thr. Results suggest EAA use efficiency should be modeled as a nonlinear function dependent, in some cases, on DE and stage of lactation.