TY - JOUR TI - Periodic elements of wheat and grapevine pathosystems AU - Savary, S. AU - Willocquet, L. AU - DeWolf, E. AU - Magarey, R. T2 - Phytopathology DA - 2006/// PY - 2006/// VL - 96 IS - 6 ER - TY - RPRT TI - Climate and Host Risk Map for Sudden Oak Death Risk (Phytophthora ramorum) AU - Magarey, Roger AU - Glenn Fowler, B.R. DA - 2006/// PY - 2006/// ER - TY - JOUR TI - An introduction to the periodic table for plant pathogens AU - Magarey, R. AU - DeWolf, E. AU - Savary, S. AU - Willocquet, L. T2 - Phytopathology DA - 2006/// PY - 2006/// VL - 96 IS - 6 ER - TY - CONF TI - Climate-host mapping of Phytophthora ramorum, causal agent of sudden oak death AU - Fowler, Glenn AU - Magarey, R. AU - Colunga, M. A2 - Frankel, Susan J. A2 - Shea, Patrick J. A2 - Haverty, Michael I. C2 - 2006/// C3 - Proceedings of the sudden oak death second science symposium: the state of our knowledge DA - 2006/// SP - 329-332 PB - Pacific Southwest Research Station, Forest Service, US Department of Agriculture ER - TY - JOUR TI - Grape canopy surface wetness: Simulation versus visualization and measurement AU - Magarey, R.D. AU - Seem, R.C. AU - Russo, J.M. T2 - Agricultural and Forest Meteorology AB - Surface wetness is commonly measured with electronic sensors but simulation is a promising alternative, although these methods have rarely been compared statistically using visual observations as a truth data set. In this study, these comparisons are made in two vineyards in New York and one in Australia using simulations from the Surface Wetness Energy Balance (SWEB) model run from canopy-collected atmospheric inputs. The fraction of canopy wet surface area was visually observed on 45 leaves (Wobs), measured using a 15-sensor array (Wsen), and simulated using the SWEB model (Wsim). Both the measurements and visual observations were made in five canopy positions. Observations of wetness included periods of rain, dew and near dew. Overall the SWB model was slightly more effective (r2 = 0.73) than the sensor (r2 = 0.6), although there was substantial variation between sites. Since most applications use surface wetness duration, an additional comparison was based on the Canopy Surface Wetness Duration (CSWD). The mean absolute error (MAE) of the SWEB model varied from 0.7 to 1.5 h at the three sites, while for sensors the MAE varied from 1.1 to 1.9 h. Both SWEB and the sensor performed more poorly in dew than in rain. The sensor did not perform as well as in past studies, possibly because it was validated under a wider range of conditions or because of degradation of the painted surface. In what is likely to be one of the first rigorous comparisons of measurement and simulation, the SWEB model performed as well as a commonly used sensor for estimation of canopy wet surface area and surface wetness duration. DA - 2006/10// PY - 2006/10// DO - 10.1016/j.agrformet.2006.08.015 VL - 139 IS - 3-4 SP - 361-372 J2 - Agricultural and Forest Meteorology LA - en OP - SN - 0168-1923 UR - http://dx.doi.org/10.1016/j.agrformet.2006.08.015 DB - Crossref KW - leaf wetness KW - standardization KW - canopy model KW - surface energy balance ER - TY - JOUR TI - The comparison of four dynamic systems-based software packages: Translation and sensitivity analysis AU - Rizzo, D AU - Mouser, P AU - Whitney, D AU - Mark, C AU - Magarey, R AU - Voinov, A T2 - Environmental Modelling & Software AB - Dynamic model development for describing complex ecological systems continues to grow in popularity. For both academic research and project management, understanding the benefits and limitations of systems-based software could improve the accuracy of results and enlarge the user audience. A Surface Wetness Energy Balance (SWEB) model for canopy surface wetness has been translated into four software packages and their strengths and weaknesses were compared based on ‘novice’ user interpretations. We found expression-based models such as Simulink and GoldSim with Expressions were able to model the SWEB more accurately; however, stock and flow-based models such as STELLA, Madonna, and GoldSim with Flows provided the user a better conceptual understanding of the ecologic system. Although the original objective of this study was to identify an ‘appropriate’ software package for predicting canopy surface wetness using SWEB, our outcomes suggest that many factors must be considered by the stakeholders when selecting a model because the modeling software becomes part of the model and of the calibration process. These constraints may include user demographics, budget limitations, built-in sensitivity and optimization tools, and the preference of user friendliness vs. computational power. Furthermore, the multitude of closed proprietary software may present a disservice to the modeling community, creating model artifacts that originate somewhere deep inside the undocumented features of the software, and masking the underlying properties of the model. DA - 2006/10// PY - 2006/10// DO - 10.1016/j.envsoft.2005.07.009 VL - 21 IS - 10 SP - 1491-1502 J2 - Environmental Modelling & Software LA - en OP - SN - 1364-8152 UR - http://dx.doi.org/10.1016/j.envsoft.2005.07.009 DB - Crossref KW - model comparison KW - dynamic simulation KW - system-based models KW - canopy surface energy balance ER - TY - JOUR TI - Simulation of surface wetness with a water budget and energy balance approach AU - Magarey, R.D. AU - Russo, J.M. AU - Seem, R.C. T2 - Agricultural and Forest Meteorology AB - Surface wetness plays an important role in environmental studies. In particular, it is a major variable for plant disease prediction. Surface wetness is commonly measured with electronic sensors but simulation with a surface wetness model is an alternative. Recently, the increased use of interpolation procedures and atmospheric models to produce site-specific weather products has created a greater need for reliable surface wetness models. However, surface wetness models have not been widely used operationally because they are often highly complex, do not simulate both dews and rain or do not adapt well to a new spatial scale or crop. Other models estimate surface wetness in units that are cumbersome to observe in the field. In addition, few models have been calibrated to observed surface wetness over a wide range of atmospheric variables and plant leaf properties under controlled environmental conditions. The objective of this study was to develop a surface wetness model that would be appropriate for operational use in site-specific weather products for grapes. For this purpose, we developed the surface wetness energy balance (SWEB) model based on a ‘big leaf’. The SWEB model consists of four sub-modules describing: (i) surface water distribution based on an observed wet fraction; (ii) canopy water budget; (iii) energy balance module based on a combination equation developed by Tanner and Fuchs; (iv) a transfer function based on Bird et al.'s generic transfer coefficient that was previously calibrated to surface wetness under controlled conditions. The SWEB model can be adapted to the physical characteristics of a particular crop by adjusting four plant parameters: leaf area index (LAI), maximum fraction of canopy allowed as wet surface area (Wmax), crop height and maximum water storage. The SWEB model is most sensitive to LAI and Wmax. The SWEB model is close to the required criteria for a suitable surface wetness model including simplicity, utility, scalability, easily observable output units and in addition, it has been calibrated under controlled conditions. The SWEB model was validated in a vineyard and in a companion study, compared to a widely used sensor. The overall objective of these studies was to develop a theoretical standard for surface wetness measurement. DA - 2006/10// PY - 2006/10// DO - 10.1016/j.agrformet.2006.08.016 VL - 139 IS - 3-4 SP - 373-381 J2 - Agricultural and Forest Meteorology LA - en OP - SN - 0168-1923 UR - http://dx.doi.org/10.1016/j.agrformet.2006.08.016 DB - Crossref KW - simulation KW - leaf wetness KW - standardization KW - canopy model KW - grapes ER -