Variational data assimilation (VAR-DA) has been implemented to estimate the unknown input parameters of a new agricultural subsurface drainage model (SIDRA-RU) through assimilating discharge observations. The adjoint model of SIDRA-RU has been successfully generated by means of the automatic differentiation tool (TAPENADE). First, the adjoint model is used to explore the local and global adjoint sensitivities of the valuable function defined over the drainage discharge simulations with respect to model input parameters. Next, the most influential parameters are estimated by applying the VAR-DA embedded into a simple stochastic procedure in order to achieve the global minimum. The performed sensitivity analysis shows that the most influential parameters on drainage discharge are those controlling the dynamics of the water table; the second most influential parameters manage the starting date of the drainage season. Compared to an alternative gradient-free calibration performance, the estimation of these governing parameters by the VAR-DA method improves the overall quality of the drainage discharge prediction, in particular in terms of the cumulative water volume. Improved parameters generate less than 5 mm (1%) of the discrepancy between simulated and observed water volume, based on the five years of daily discharge observations on the Chantemerle agricultural parcels (36 ha). Preliminary numerical tests allow identifying the potential presence of local minima as well as equifinality issues. The latter can be highlighted by the self-compensation of both the physical soil parameters and the main conceptual parameters. Moreover, the proposed techniques may be applied to a panel of hydrological and water quality models.