ISHS Acta Horticulturae 801: International Symposium on High Technology for Greenhouse System Management: Greensys2007
UNCERTAINTY ON ESTIMATED PREDICTIONS OF ENERGY DEMAND FOR DEHUMIDIFICATION IN A CLOSED TOMATO GREENHOUSE |
| Authors: | E. Schrevens, P. Jancsok, K. Dieussaert |
| Keywords: | closed greenhouse, dehumidification demands, uncertainty, tomato |
| DOI: | 10.17660/ActaHortic.2008.801.165 |
| Abstract:
The objective of this paper is to investigate the effects of ‘empirical’ crop parameters and their variability on the uncertainty in the estimated predictions of energy demand for dehumidification in a closed greenhouse. Hereto an integrated climate-crop model is developed. This model simulates the internal climate of the greenhouse based on external climate, greenhouse morphology, parameters defining the cover material and the environment and crop parameters. All simulations were carried out under unlimited heating, cooling and dehumidification capacities, as a result the climate set-points are realized exactly. The set-points were extracted from measurements of the inside climate of an experimental closed greenhouse. Climate set-point profiles were set along to commercial practice in a tomato crop in Belgium. The main crop growth parameters influencing the transpiration of the plant are the leaf area development per plant and per plant/stem density (LAI) and the leaf resistances. The paper concentrates on the uncertainty propagation on dehumidification demands, induced by variability on LAI profiles and the determining underlying crop parameters (the radiation- and CO2-use efficiencies, the specific leaf area and the temperature effect on the node appearance rate), and the leaf internal and external resistances. It is shown that relatively high uncertainties on the input parameters induce only relatively small uncertainties on the yearly dehumidification demands of the closed greenhouse. As a consequence, to screen and optimize different scenario’s and strategies of closed greenhouse climatization, it is not really necessary to use highly detailed crop models, which are highly over-parameterized. Robust summary crop models would do an equally good job. | |
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