Abstract
Leaf wetness duration (LWD) is a key parameter in agrometeorology because it is related to plant disease occurrence. As LWD is seldomly measured in a standard weather station it must be estimated to run warning systems for schedule chemical disease control. The objective of the present study was to estimate LWD over turfgrass considering different models with data from a standard weather station, and to evaluate the correlation between estimated LWD over turfgrass and LWD measured in a 'Niagara Rosada' vineyard, cultivated in a hedgerow training system, in Jundiaí, São Paulo State, Brazil. The wetness sensors inside the vineyard were located at the top of the plants, deployed at an inclination angle of 45º and oriented southwest, with three replications. The methods used to estimate LWD were: number of hours with relative humidity above 90% (NHRH > 90%), dew point depression (DPD), classification and regression tree (CART) and Penman-Monteith (PM). The CART model had the best performance to estimate LWD over turfgrass, with a good precision (R² = 0.82) and a high accuracy (d = 0.94), resulting in a good confidence index (c = 0.85). The results from this model also presented a good correlation with measured LWD inside the vineyard, with a good precision (R² = 0.87) and a high accuracy (d = 0.96), resulting in a high confidence index (c = 0.93), showing that LWD in a 'Niagara Rosada' vineyard can be estimated with data from a standard weather station.
Highlights
Leaf wetness duration (LWD) is defined as the period of time when free water, caused by dew, rain or irrigation, remains deposited on plants tissues
Standard weather data were used to estimate LWD by four different models: number of hours with relative humidity above 90% - NHRH > 90% (Sentelhas, 2004), dew point depression - DPD (Gillespie et al, 1993), classification and regression tree - CART (Gleason et al, 1994) and Penman-Monteith - PM (Sentelhas, 2006), as follows: NHRH > 90%: RH = 90% was considered as limit for the beginning of dew deposition (Sentelhas, 2004)
The best LWD estimate model over turfgrass was CART (Figure 2d), presenting a good precision (R2 = 0.8225) and high accuracy (d = 0.9363), resulting in a good confidence index (c = 0.8492). This performance was better than that found by Kim et al (2004), using the CART model in 15 places of the United States, from May to September of 1998 and 1999 on the other hand, PM model (Figure 2a) obtained the worst performance, showing low accuracy (d = 0.8517) and precision (R2 = 0.6911), resulting in the lowest confidence index (c = 0.7080)
Summary
Leaf wetness duration (LWD) is defined as the period of time when free water, caused by dew, rain or irrigation, remains deposited on plants tissues. LWD is a very important variable for epidemiology of plant diseases, since the majority of the pathogens, mainly fungus and bacteria, requires wetness on the plants tissues for infection, which include germination and penetration processes. LWD is seldom measured in conventional or automatic weather stations. Even when LWD measurements are available, they often fail to represent places that are distant from a meteorological station, due to the spatial variability of the wetness occurrence (Rao et al, 1998). Some models have been developed to estimate LWD using meteorological variables, such as air temperature, vapor pressure and wind speed (Francl & Panigrahi, 1997; Kim et al, 2002; Dalla Marta et al, 2005). Due to the shortage of LWD measurements, both in standard weather stations and in crop fields, the objective of the present study was to assess four different models to estimate LWD over turfgrass and in a ‘Niagara Rosada’ vineyard
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