Stomatal resistance of New Guinea Impatiens pot plants. Part 1: Model development for well watered plants based on design of experiments

Abstract : In greenhouses, reducing water consumption by increasing water efficiency in order to fulfil the requirements of sustainability is a challenge. To reach this goal, we need to better understand the water demand of plants. Transpiration is the main mechanism involved in water transfer, which is controlled by stomatal resistance Rs. Predictive models can be used to assess this parameter. However, few models currently exist for greenhouse plants grown in pots. The aim of this work is to develop a model of Rs based on full factorial design (FFD), and to validate it for greenhouse plants at various growth stages. FFD is based on an optimisation process to establish a polynomial relationship between Rs and radiation, humidity, and temperature. To establish the parameters of the model, a set of experiments was conducted inside a 10-m(2) growth chamber with New Guinea Impatiens grown in pots. Rs was measured with a porometer under nine climatic scenarios. Once the parameters were determined, the FFD model was validated against experimental data recorded from a greenhouse Impatiens crop, and compared with the Jarvis model. The slopes of the linear regression between measured Rs values and Rs values predicted from the FFD and Jarvis models varied within the range 0.89-1.12 for FFD and 0.45-0.54 for Jarvis. FFD was therefore able to correctly simulate Rs. Its main advantage was to only require few data for its calibration, contrary to the Jarvis model. In a next step, it will be used to predict transpiration rates. (C) 2016 IAgrE. Published by Elsevier Ltd. All rights reserved.
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Biosystems Engineering, Elsevier, 2016, 149, pp.112-124. 〈10.1016/j.biosystemseng.2016.05.004〉
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Hacene Bouhoun Ali, Pierre-Emmanuel Bournet, Patrice Cannavo, Etienne Chantoiseau, M. Sourgnes. Stomatal resistance of New Guinea Impatiens pot plants. Part 1: Model development for well watered plants based on design of experiments. Biosystems Engineering, Elsevier, 2016, 149, pp.112-124. 〈10.1016/j.biosystemseng.2016.05.004〉. 〈hal-01523676〉

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