The interaction soil-plant was evaluated using a state space approach (dynamic model) comparatively to a static regression model using both, standard and sequential estimations. Experimental soil data consisted of bulk density, macroporosity, microporosity and porosity of a dark red latosol, and plant data of root density in length per unit volume, of a forage-oat crop. Among these, only soil porosity had a good correlation with the root system density, which is the response variable of this study. A static regression model written in the state space form with a sequential estimation, gave a R² coefficient of 0.69, comparatively to a conventional (non-sequential) regression model, which gave a R² coefficient of only 0.59. This soil-plant relation was better described by a dynamic regression model, which gave a R² coefficient greater than 0.98. These results indicate the advantage of the state space approach in relation to the other more conventional regression methods.
spatial variability; dynamic regression; static regression; sequential estimation