State space models (SSMs) are successfully used in many areas of science to describe time series and/or dynamical systems. In this work, we revisit the data augmentation algorithm introduced by Tanner and Wong (1987) for bayesian parameter estimation in SSMs. We propose to employ sequential Monte-Carlo and adaptive Monte-Carlo Markov chain methods to improve the performance of the algorithm. We provide a first numerical example that allows us to evaluate the convergence of the posterior estimate to the true posterior distribution. Our objective is to evaluate the performance of the proposed method to nonlinear/non-Gaussian models, with a special interest to plant growth models.