Set-membership identification of an agro-ecosystem from a small data set: The case of ammonia volatilisation in a flooded rice field

Abstract

Uncertainty analysis is an essential part of systems modelling. Preferably, the uncertainty analysis should start with a parameter estimation step. However, for agro-ecosystems, available data sets are typically small. Therefore, probabilistic parameter estimation techniques are not adequate. Alternatively, a set-membership or bounded-error approach can be used. In this paper, we demonstrate a set-membership approach to estimate the unknown parameters in a biochemical model from a small data set. As a case study, a biochemical model developed to estimate ammonia volatilisation in a flooded rice field was used. A total of 741 feasible parameter-vectors were found from 4000 simulated parameter- vectors. Eigenvalue decomposition of an ellipsoidal outer-bounding set indicated that the model was most sensitive to the growth rate of the rice plants, β. Visual inspection of the feasible parameter vectors showed that the estimates of the nitrogen uptake rate Kwupt are hyperbolically related to the estimates of β.