Cindy Boonen

DATA–BASED MODELING OF THE SPATIOTEMPORAL TEMPERATURE DISTRIBUTION IN A REACH–IN PLANT GROWTH CHAMBER

The spatiotemporal temperature distribution in the imperfectly mixed airspace around plants has a considerable effect on the physiological plant processes. In a fully instrumented climate chamber, eight identification experiments were carried out to model the spatiodynamic temperature response around plants to variations in the supply air temperature and the sensible heat, produced by the lamps (directly related to the light intensity), as control inputs. From the experimental data, a minimally parameterized, linear, discrete–time transfer function matrix (TFM) model was identified, capturing the dominant model behavior of the dynamic process. Based on statistical considerations, a first–order TFM model came out as the best model structure. The first–order model provided a good compromise between goodness of fit (minimum Rt2 i of 0.91) and parametric efficiency (standard error), characterized the airflow behavior very well, and formed an adequate basis for model–based process control.

MODELING DYNAMIC BEHAVIOR OF LEAF TEMPERATURE AT THREE-DIMENSIONAL POSITIONS TO STEP VARIATIONS IN AIR TEMPERATURE AND LIGHT

Organisms such as plants grow as a result of the influence of their genetics and microenvironment, consisting of physical, chemical, and biological factors. The microenvironment or microclimate is the environment closely surrounding these organisms and varies in time and space. To control processes more optimally, it is necessary to understand how living organisms respond dynamically to their physical microenvironment. Most models being developed to explain this relationship through steady-state models are descriptive (deterministic) and too complex to be used for control purposes. Therefore, it is attempted in this work to develop a dynamic black box model. For this study only air temperature was considered in 3-D (three dimensions). The objective of this research was to model the dynamic response of leaf temperature to time variations in air temperature closely surrounding that leaf, and to light-dark alterations. This biosystem is modeled using an ARX model structure (black box). Three-dimensional gradients in air temperature around the plant were shown and analyzed. It is demonstrated that a complex process such as the response of leaf temperature to changes in 3-D ambient air temperature and light-dark alterations can be modeled with a mean r 2 between 92.7% and 99.9%.

A dynamic Data-based Model for the Sap Flow in a Beech Tree observed during the Solar Eclipse of 11 August 1999

Responses of crucial plant processes such as transpiration and photosynthesis rate have been often modelled by mechanistic models or static regression models. The objective of this paper was to quantify the dynamics of sap flow rate responses of a beech tree to the fast variations in short-wave solar radiation, air temperature and vapour pressure deficit using an alternative dynamic data-based modelling approach. In order to have enough dynamic information, data of the solar eclipse of 11 August 1999 were used. This permitted determination of the order of the sap flow dynamics, together with the relative contribution of short-wave radiation, air temperature and vapour pressure deficit. A multiple-input and single output transfer function was used to simulate the sap flow rate responses in three branches at 22 m, 16 m and 9 m very accurately (R2 of 0.94, 0.86 and 0.90 respectively). The appropriate model structure was the same for the three branches and was characterised by second order dynamics. An important advantage of the dynamic modelling approach presented here, was that it enabled the decomposition of the total sap flow rate response into partial responses to short wave radiation, air temperature and vapour pressure deficit, respectively.