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This work aims at using the Computational Fluid Dynamic (CFD) approach to study the distributed microclimate in the leaf boundary layer of greenhouse crops. Understanding the interactions in this microclimate of this natural habitat of plant pests (i.e., boundary layer of leaves), is a prerequisite for their control through targeted climate management for sustainable greenhouse production. The temperature and humidity simulations, inside the greenhouse, were performed using CFD code which has been adapted to simulate the plant activity within each mesh in the crop canopy. The air temperature and air humidity profiles within the boundary layer of leaves were deduced from the local surrounding climate parameters, based on an analytical approach, encapsulated in a Used Defined Function (UDF), and dynamically linked to the CFD solver, a work that forms an innovative and original task. Thus, this model represents a new approach to investigate the microclimate in the boundary layer of leaves under greenhouses, which resolves the issue of the inaccessibility of this area by the conventionnel measurement tools. The findings clearly showed that (i) contrarily to what might be expected, the microclimate parameters within the boundary layer of leaves are different from the surrounding climate in the greenhouse. This is particularly visible during photoperiods when the plant’s transpiration activity is at its maximum and that (ii) the climatic parameters in the leaf boundary layer are more coupled with leaf surfaces than with those of greenhouse air. These results can help developing localized intervention strategies on the microclimate within boundary layer of plant leaves, leading to improved and sustainable pest control management. The developed climatic strategies will make it possible to optimize resources use efficiency.
Hicham Fatnassi; Thierry Boulard; Christine Poncet; Nikolaos Katsoulas; Thomas Bartzanas; Murat Kacira; Habtamu Giday; In-Bok Lee. Computational Fluid Dynamics Modelling of the Microclimate within the Boundary Layer of Leaves Leading to Improved Pest Control Management and Low-Input Greenhouse. Sustainability 2021, 13, 8310 .
AMA StyleHicham Fatnassi, Thierry Boulard, Christine Poncet, Nikolaos Katsoulas, Thomas Bartzanas, Murat Kacira, Habtamu Giday, In-Bok Lee. Computational Fluid Dynamics Modelling of the Microclimate within the Boundary Layer of Leaves Leading to Improved Pest Control Management and Low-Input Greenhouse. Sustainability. 2021; 13 (15):8310.
Chicago/Turabian StyleHicham Fatnassi; Thierry Boulard; Christine Poncet; Nikolaos Katsoulas; Thomas Bartzanas; Murat Kacira; Habtamu Giday; In-Bok Lee. 2021. "Computational Fluid Dynamics Modelling of the Microclimate within the Boundary Layer of Leaves Leading to Improved Pest Control Management and Low-Input Greenhouse." Sustainability 13, no. 15: 8310.
Ole Green; Thomas Bartzanas; Mette M. Løkke; Dionysis D. Bochtis; Claus G. Sørensen; Ole J. Jørgensen; Vicent G. Tortajada. Spatial and temporal variation of temperature and oxygen concentration inside silage stacks. Biosystems Engineering 2012, 111, 155 -165.
AMA StyleOle Green, Thomas Bartzanas, Mette M. Løkke, Dionysis D. Bochtis, Claus G. Sørensen, Ole J. Jørgensen, Vicent G. Tortajada. Spatial and temporal variation of temperature and oxygen concentration inside silage stacks. Biosystems Engineering. 2012; 111 (2):155-165.
Chicago/Turabian StyleOle Green; Thomas Bartzanas; Mette M. Løkke; Dionysis D. Bochtis; Claus G. Sørensen; Ole J. Jørgensen; Vicent G. Tortajada. 2012. "Spatial and temporal variation of temperature and oxygen concentration inside silage stacks." Biosystems Engineering 111, no. 2: 155-165.
Losses during storage of biomass are the main parameter that defines the profitability of using preserved biomass as feed for animal husbandry. In order to minimize storage losses, potential changes in specific physicochemical properties must be identified to subsequently act as indicators of silage decomposition and form the basis for preventive measures. This study presents a framework for a diagnostic system capable of detecting potential changes in specific physicochemical properties, i.e., temperature and the oxygen content, during the biomass storage process. The diagnostic system comprises a monitoring tool based on a wireless sensors network and a prediction tool based on a validated computation fluid dynamics model. It is shown that the system can provide the manager (end-user) with continuously updated information about specific biomass quality parameters. The system encompasses graphical visualization of the information to the end-user as a first step and, as a second step, the system identifies alerts depicting real differences between actual and predicted values of the monitored properties. The perspective is that this diagnostic system will provide managers with a solid basis for necessary preventive measures.
Dionysis D. Bochtis; Claus G. Sørensen; Ole Green; Thomas Bartzanas. A Diagnostic System for Improving Biomass Quality Based on a Sensor Network. Sensors 2011, 11, 4990 -5004.
AMA StyleDionysis D. Bochtis, Claus G. Sørensen, Ole Green, Thomas Bartzanas. A Diagnostic System for Improving Biomass Quality Based on a Sensor Network. Sensors. 2011; 11 (5):4990-5004.
Chicago/Turabian StyleDionysis D. Bochtis; Claus G. Sørensen; Ole Green; Thomas Bartzanas. 2011. "A Diagnostic System for Improving Biomass Quality Based on a Sensor Network." Sensors 11, no. 5: 4990-5004.