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|Title:||Multi metric evaluation of leaf wetness models for large-area application of plant disease models|
|Authors:||BREGAGLIO Simone; DONATELLI Marcello; CONFALONIERI Roberto; ACUTIS Marco; ORLANDINI Simone|
|Citation:||AGRICULTURAL AND FOREST METEOROLOGY vol. 151 no. 9 p. 1163-1172|
|Publisher:||ELSEVIER SCIENCE BV|
|Type:||Articles in periodicals and books|
|Abstract:||Leaf wetness (LW) is one of the most important input variables of disease simulation models 3 because of its fundamental role in the development of the infection process of many fungal 4 pathogens. The low reliability of LW sensors and/or their rare use in standard weather stations has 5 led to an increasing demand for reliable models that are able to estimate LW from other 6 meteorological variables. When working on large databases in which data are interpolated in grids 7 starting from weather stations, LW estimation is often penalized by the lack of hourly inputs (e.g., 8 air relative humidity and air temperature), leading researchers to generate such variables from the 9 daily values of the available weather data. 10 Although it is possible to find several papers about models for the estimation of LW, the behavior 11 and reliability of these models were never assessed by running them with inputs at different time 12 resolutions aiming at large-area applications. Furthermore, only a limited number of papers have 13 assessed the suitability of different LW models when used to provide inputs to simulate the 14 development of the infection process of fungal pathogens. In this paper, six LW models were 15 compared using data collected at 12 sites across the U.S. and Italy between 2002 and 2008 using an 16 integrated, multi metric and fuzzy-based expert system developed ad hoc. The models were 17 evaluated for their capability to estimate LW and for their impact on the simulation of the infection 18 process for three pathogens through the use of a potential infection model. This study indicated that 19 some empirical LW models performed better than physically based LW models. The classification 20 and regression tree (CART) model performed better than the other models in most of the conditions 21 tested. Finally, the estimate of LW using hourly inputs from daily data led to a decline of the LW 22 models performances, which should still be considered acceptable. However, this estimate may 23 require further work in data collection and model evaluation for applications at finer spatial 24 resolutions aimed at decision support systems.|
|JRC Institute:||Sustainable Resources|
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