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The Land Variational Ensemble Data Assimilation Framework (LAVENDAR) implements the method of fourdimensional ensemble variational (4D-En-Var) data assimilation (DA) for land surface models. Four-dimensional ensemble variational data assimilation negates the often costly calculation of a model adjoint required by traditional variational techniques (such as 4D-Var) for optimizing parameters or state variables over a time window of observations. In this paper we present the first application of LAVENDAR, implementing the framework with the Joint UK Land Environment Simulator (JULES) land surface model. We show that the system can recover seven parameters controlling crop behaviour in a set of twin experiments. We run the same experiments at the Mead continuous maize FLUXNET site in Nebraska, USA, to show the technique working with real data. We find that the system accurately captures observations of leaf area index, canopy height and gross primary productivity after assimilation and improves posterior estimates of the amount of harvestable material from the maize crop by 74 %. LAVENDAR requires no modification to the model that it is being used with and is hence able to keep up to date with model releases more easily than other DA methods.