The duration of leaf wetness is a very significant parameter for the development of many fungal diseases in plants. In order to make agriculture more competitive, it's important knowledge resulting from good information on the use of models and by application of remote sensing of leaf wetness and other agro-meteorological variables. So itÕs possible to reduce risk and uncertainty in decisions and increase profits. In this study we aim to estimate a statistical model formalizing the dependence of leaf wetness from atmospheric variables. In particular we have applied some specific regression models in three different agro-meteorological stations, located at different altitudes (Cesarò Vignazza, Pettineo and Patti) in Messina's province. The examined agro-meteorological variables are: air temperature, barometric pressure, precipitation, relative humidity, solar radiation and wind speed at 2 meters. The first approach was the Poisson regression, applied to the count of minutes of leaf wetness; later, we estimated an ordinal logistic regression model for classes of leaf wetness duration; after dichotomization of the response variable, we estimated a binary logistic regression model. Finally, we perform a comparison of the results obtained by the estimatedgeneralized linear models. In ouranalysis, temperature, wind speed, pressure and humidity significantly influence the leaf wetness. Although the three estimated regression models provided similar information, specific criteria suggest that the Poisson regression is the best model.

I Modelli Lineari Generalizzati per lo Studio della Dipendenza della Bagnatura Fogliare da Variabili Atmosferiche

ZIRILLI, Agata;ALIBRANDI, Angela;
2011

Abstract

The duration of leaf wetness is a very significant parameter for the development of many fungal diseases in plants. In order to make agriculture more competitive, it's important knowledge resulting from good information on the use of models and by application of remote sensing of leaf wetness and other agro-meteorological variables. So itÕs possible to reduce risk and uncertainty in decisions and increase profits. In this study we aim to estimate a statistical model formalizing the dependence of leaf wetness from atmospheric variables. In particular we have applied some specific regression models in three different agro-meteorological stations, located at different altitudes (Cesarò Vignazza, Pettineo and Patti) in Messina's province. The examined agro-meteorological variables are: air temperature, barometric pressure, precipitation, relative humidity, solar radiation and wind speed at 2 meters. The first approach was the Poisson regression, applied to the count of minutes of leaf wetness; later, we estimated an ordinal logistic regression model for classes of leaf wetness duration; after dichotomization of the response variable, we estimated a binary logistic regression model. Finally, we perform a comparison of the results obtained by the estimatedgeneralized linear models. In ouranalysis, temperature, wind speed, pressure and humidity significantly influence the leaf wetness. Although the three estimated regression models provided similar information, specific criteria suggest that the Poisson regression is the best model.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/1918101
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