More information can be found here:

http://www.sapflowworkshop.info

and the conference flyer can be downloaded here:

http://www.sapflowworkshop.info/documents/flyer_9thsapflowworkshop.pdf

The HortiModel 2012 conference was the Fourth International Symposium on Models for Plant Growth, Environmental Control and Management in Protected Cultivation. It covered a wide range of topics related to horticulture:

• Crop and Climate Management
• Water, Nutrient and Energy Management
• Decision Support and Farm Management
• Product Quality
• Systems Biology, Gene-plant-crop Modelling
• 3D-models, Architectural Models
• Methodological Issues, Aggregation and Scale
• Combination of Models and Sensors

Thanks to all the participants who came by our booth!

The new version can be downloaded here (including a demo version).

New features:

General

• NEW “visualise module”: custom graphs, display data from multiple sensors and files (even non-ICT International data).
• Value inspector slider on all graphs: shows numerical value of all curves at the slider position.
• Option to show DOY on graph x-axis.
• Ability to search in the user manual.

Files window

• Import any data file (sap flow calculations still limited to ICT International .csv and .bin files).
• Quick view graph: plot the currently selected item in the Files window.
• Batch import of multiple data files + drag & drop in the Files window.
• Batch export of multiple sensors.
• Auto-reimport exported data.
• Merge data from the same sensor in different files.
• Files window filter: show only items that match certain criteria.
• Move files up and down in the Files window.

Visualise module

• Custom graphs, display data from multiple sensors and files.
• Apply data filters to any curve on the graph.
• Export all data from the custom graphs.
• New example: sample 6, shows the use of custom graphs and non-sap flow data.

Overview video:

Reference:

Vandegehuchte M.W. and Steppe K. (2012). Sapflow+: a four-needle heat-pulse sap flow sensor enabling nonempirical sap flux density and water content measurements. New Phytologist, 196(1), 306-317. http://onlinelibrary.wiley.com/doi/10.1111/j.1469-8137.2012.04237.x/abstract

Abstract:

• To our knowledge, to date, no nonempirical method exists to measure reverse, low or high sap flux density. Moreover, existing sap flow methods require destructive wood core measurements to determine sapwood water content, necessary to convert heat velocity to sap flux density, not only damaging the tree, but also neglecting seasonal variability in sapwood water content.
• Here, we present a nonempirical heat-pulse-based method and coupled sensor which measure temperature changes around a linear heater in both axial and tangential directions after application of a heat pulse. By fitting the correct heat conduction–convection equation to the measured temperature profiles, the heat velocity and water content of the sapwood can be determined.
• An identifiability analysis and validation tests on artificial and real stem segments of European beech (Fagus sylvatica L.) confirm the applicability of the method, leading to accurate determinations of heat velocity, water content and hence sap flux density.
• The proposed method enables sap flux density measurements to be made across the entire natural occurring sap flux density range of woody plants. Moreover, the water content during low flows can be determined accurately, enabling a correct conversion from heat velocity to sap flux density without destructive core measurements.

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.

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.

Download it here (WARNING: Mac OS X Snow Leopard is no longer supported). Install the new version and update the modules in the Module Manager.

[UPDATE] If you are experiencing problems updating the Simulation module, delete the PhytoSimSimulation folder in: “My Documents/PhytoSim/Modules” and continue updating the other modules.

For this occasion, all trial accounts have been reset enabling everyone to try out the new release.

Key new features:

• User Guide search (Help > User Guide; search field is located at the top).
• The current workspace can now be closed (File > Close).

• Data I/O
• Titles and units of input columns can now also be edited by clicking a settings button in the header of a selected input column.

• Modelling:
• Added support for unit substances, e.g. cm^3[H2O].g^-1[leaf].
• New model: CarbohydrateSupplyDemand, carbohydrate supply and demand model for vegetative growth, Gent and Seginer (2012).
• New function: valueat( variable, at ), returns the value of a variable at any past time.
• New function: average( variable, from, to ), returns the average of a variable between two points in time.
• New function: integral( variable, from, to ), returns the integral of a variable between two points in time.
• New function: uniform( minimum, maximum ), returns a uniformly distributed random number.
• New function: triangular( minimum, mode, maximum ), returns a triangularly distributed random number.
• New function: normal( mean, stddev ), returns a normally distributed random number.
• New function: poisson( mean ), returns a poisson distributed random number.
• Parentheses matching in the model editor.
• Units in equations are no longer automatically converted: stricter but less confusing! As a consequence, unit conversion warnings no longer show up. WARNING: If you relied on automatic unit conversions in your models, they will not be valid anymore. Inserting the proper unit conversion factors will fix this.

• Simulation:
• New solver: CVODE stiff/non-stiff differential equation solver (Settings > Solver).
• New example: Stiff, shows off the benefits of the CVODE solver for stiff differential equations (in Examples/PhytoSimSimulation/Misc).
• New example: CarbohydrateSupplyDemand, simulation example for the new CarbohydrateSupplyDemand model (in Examples/PhytoSimSimulation/Plant Physiology).
• New example: Random, illustrates the random number generation functions (in Examples/PhytoSimSimulation/Misc).
• New example: ExpDecay, illustrates the valueat, average and integral functions (in Examples/PhytoSimSimulation/Misc).
• Option to turn off curve painting optimization (Graph Settings > Curves). Only needed in some rare cases.
• A warning is given when no plot interval (Settings > Graphs) is set: protects against too many curve points.

• Calibration:
• New global optimizer: Shuffled Complex Evolution.
• New global optimizer: Simulated Annealing.
• New global optimizer: Random Search.
• New example: SixHumpCamelBack, global optimizer test function (in Examples/PhytoSimCalibration/SixHumpCamelBack).

• Uncertainty:
• New uncertainty distribution type: normal distribution.

• Sensitivity:
• Data variables can now also be used as source components (like parameters and initial values of derived variables).
• Setting to turn off numerical error checking (workspace specific).

What’s new in this version:

To install the new modules start PhytoSim, head to the Module Manager and update the Simulation and Calibration modules. Also make sure you have upgraded to the latest PhytoSim version (v1.2.2).

The new version can be found at the PhytoSim download page.