Archive for September, 2012

PhytoSim used in wood thermal diffusivity research

Tuesday, September 18th, 2012

A recently published paper on wood thermal diffusivity used PhytoSim to calibrate a theoretical model in order to determine both axial and tangential diffusivity in sapwood. The PhytoSim sensitivity and identifiability analysis was also used to better understand the model and learn which parameters most influenced the end-results.

Reference:

Vandegehuchte M.W. and Steppe K. (2012). A triple-probe heat-pulse method for measurement of thermal diffusivity in trees. Agricultural and Forest Meteorology, 160, 90–99.
http://www.sciencedirect.com/science/article/pii/S0168192312000998

Abstract:

Although thermal diffusivity is a crucial parameter for sap flow calculations in both the heat field deformation and the heat ratio method, it is seldom measured on a routine basis. This paper presents a theory based on thermodynamic principles to determine both axial and tangential diffusivity in sapwood. By measuring the temperature response after application of a heat pulse at a short axial and tangential distance from a line heater, axial and tangential thermal conductivity as well as volumetric heat capacity of the sapwood can be derived from a theoretical model. From these parameters, axial and tangential diffusivity can easily be determined. Sensitivity analysis and results of an experiment on European beech (Fagus sylvatica L.) confirm the applicability of the method. The obtained thermal diffusivities ranged from 2.7 × 10−7 m2 s−1 to 2.2 × 10−7 m2 s−1 for a relative water content (moisture per dry weight) ranging from 0.47 to 0.90, respectively. This was on average 22% lower than when applying the common methodology based on wood core sampling.

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PhytoSim used in leaf temperature modelling research

Tuesday, September 4th, 2012

A recently published paper on leaf temperature monitoring and modelling used PhytoSim as the main modelling and simulation software. The PhytoSim built-in sensitivity, identifiability and uncertainty analysis was used to help construct a model for online plant stress detection at the leaf level.

Reference:

Vermeulen K, Aerts J-M, Dekock J, Bleyaert P, Berckmans D, Steppe K. (2012). Automated leaf temperature monitoring of glasshouse tomato plants by using a leaf energy balance model. Computers and Electronics in Agriculture 87: 19–31. http://www.sciencedirect.com/science/article/pii/S0168169912001172

Abstract:

In order to detect biotic and abiotic stress at leaf level thermal indices based on leaf temperature measurements have been commonly used. The application of these indices within glasshouse crops is, however, restricted due to the specific humid conditions and the large spatial variability of irradiance and air temperature inside a glasshouse. In this study, a novel diagnostic algorithm is proposed as an alternative method to automatically monitor the leaf temperature of a glasshouse tomato crop based on the ecophysiological interactions between a leaf and its surrounding microclimate. Given that this algorithm is intended to be implemented as a software tool in glasshouse climate control systems, a critical overview of all relevant equations found in literature was first given. Next, the most appropriate equations were selected by using two objective criteria, i.e. the commonly used R2 and the less conventional Young Information Criterion, which also takes into account the complexity of an algorithm, so that the most feasible algorithm for automated monitoring purposes was built. Our results also showed that an in situ calibration of the selected algorithm was needed, for which a novel procedure was proposed. Once calibrated, this algorithm successfully simulated the leaf temperature of a well-watered tomato plant during several days given that the environmental conditions in its microclimate were accurately measured. Finally, the 95% confidence limits on the leaf temperature simulations provided the requested dynamic thresholds necessary for an effective automated monitoring tool. It was demonstrated that by using this novel diagnostic algorithm unexpected and likely harmful stomatal closure can be detected before visual signs of turgidity loss are observed.

 

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