Dynamic simulation of grape downy mildew primary infections - Assam

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Validation of a simulation model for Plasmopara viticola primary infections in different vine-growing areas across Italy Tito Caffi, Vittorio Rossi, Riccardo Bugiani, Federico Spanna, Lucio Flamini, Antonello Cossu, Camilla Nigro Istituto di Entomologia e Patologia vegetale, Università Cattolica del Sacro Cuore, via E. Parmense 84, 29100 Piacenza, Italy (e-mail: tito.caffi@unicatt.it; fax: 0039 0523 599256) ___________________________________________________________________________ Downy mildew of grape, caused by Plasmopara viticola (Berk et Curt.) Berlese et de Toni, is a disease of major importance in grape-growing areas with a temperate climate. It is a potentially destructive disease that requires repeated fungicide application during the growing season. Some epidemiological models have been elaborated to support decisions about disease control but none are accurate or robust enough to be used for scheduling fungicide application. Consequently, warning systems are still based on the empirical rule called “three tens” even if it is frequently unreliable. A new model has recently been elaborated, which can simulate the dynamics of primary inoculum and infection during the season. This model uses meteorological data (air temperature, relative humidity, rainfall, leaf wetness) to simulate, with a time step of one hour, the infection chain from oospore germination to the onset of disease symptoms, including the germination progress, survival of sporangia, zoospore ejection and survival, zoospore dispersal, infection and incubation. The model performs several simulation runs per season, considering that the overwintering oospore population overcomes dormancy gradually. In particular, the oospore population of a vineyard is composed of different cohorts that become able to germinate according to a normal distribution. When a measurable rainfall wets the leaf litter containing these oospores a simulation run starts with the beginning of oospore germination. This simulation run can be interrupted at any stage of the infection chain if the environmental conditions do not favour the fungus, or can complete the infection chain until the appearance of the disease. The model provides both tables showing the hourly progress of each simulation run and graphs showing the state of the infection cycle for each day during the primary inoculum season (Fig. 1). Validations were performed in 77 commercial vineyards throughout five regions of Italy, between 1995 and 2005 (Fig .1). Some data were provided by historical series available at the local services delegate to producing disease warnings for vine-growers in the different areas. Other, more recent data, were specifically collected for validation. In both cases, vineyards can be considered representative of the different vine-growing areas, for soil type, varieties, training systems and cropping regimes. They also contained a representative dose of overwintering inoculum because a regular fungicide schedule was applied the previous season. During winter, a plot which included several rows of vines was set apart in each vineyard and not sprayed with 112 fungicides against downy mildew till the time of first disease onset. Starting from bud burst, plots were carefully inspected at least once per week, to detect the time of appearance of the first disease symptoms such as “oil spots” on leaves. Data collection was coordinated by the team working on this paper, from the regional phytosanitary services of Emila-Romagna (R. Bugiani) and Piedmont (F. Spanna), SAR (regional agrometeorological service) of Sardinia (A. Cossu), Assam in Marche (L. Flamini), and Alsia in Basilicata (C. Nigro). In Oltrepò Pavese (Lombardy) data were collected by the first author of this work. Rain (mm) (mm) 20 10 0 Oospore germination 1/4 8/4 Zoospore release Infection Infection Infection Infection Infection Infection Infection Infection Infection Infection Infection Infection Infection Infection Infection Infection Infection Zoospore Zoospore dispersal dispersal 15/4 22/4 29/4 6/5 13/5 20/5 27/5 3/6 10/6 End of incubation 17/6 24/6 Fig. 1. Model output showing 10 simulation runs triggered by rainfall (bars). Lines show the germination course for different P. viticola oospore cohorts; dots show progress over time of the infection process, stage by stage: they are white when the infection chain aborts and black when it is successfully completed; the box shows the period of expected downy mildew appearance. 1998-2002 5 vineyards 1999-2004 19 vineyards 1999-2004 6 vineyards 2004-2005 2 vineyards 1995-2004 38 vineyards 2004-2005 7 vineyards Fig. 2. Distribution of the vineyards used in model validation.
Hourly meteorological data of air temperature, relative humidity, rainfall, and leaf wetness were collected to be used as model inputs. In Piedmont and Oltrepò Pavese, data were measured by automatic and mechanic weather stations, respectively, installed in the vineyards. In the other regions data were supplied by the regional networks for the nearest automatic station. In Emilia-Romagna, after the year 2000, the meteorological data were extracted from a data-base containing data interpolated on a grid of 5x5 km. The model was used to simulate, for each vineyard, the infection process in each cohort of oospores between the end of dormancy and the time of first disease onset in the vineyard. Total simulations were first classified as aborted or successful. A simulation was considered aborted when the infection process stopped at any stage before infection, while it was considered successful when all the stages progressed until infection was established. Both aborted and successful simulations were then classified as: i) accurate, when the model produced a successful simulated infection that actually produced symptom appearance in the vineyard, or when an aborted simulated infection did not correspond to an actual symptom onset; ii) overestimated, when the model produced a successful simulated infection but the disease did not appear; iii) underestimated, when the model did not simulate an infection that actually occurred. A possible criticism of the above mentioned classification is that there is no proof that an aborted simulation process actually occurred in the vineyard; nevertheless, from a practical point of view, the model produces accurate information in such a case, because it indicates that there is no risk of infection occurring. The model produced 736 simulations in total: the 2 test showed a significant correlation between model simulations and actual observations (Tab. 1). The model was very accurate in simulating successful infections, because all the observed disease appearances in the vineyards were correctly simulated by the model; this happened 122 times (16.6% of total simulations). In 543 simulations (73.8%) the model rightly interrupted the simulation runs because the environmental conditions did not permit the infection process to proceed; no disease symptoms were actually observed in the vineyards as a consequence of these possible infection events. In these cases, the infection process was aborted because sporangia died before zoospore release (14% of cases), zoospores died before dispersal (75%) or before infection establishment (8%) (data not shown). Considering both successful and aborted infections, there were 665 accurate simulations out of 736 (90.4%) (Tab. 1). In 71 simulation runs the model simulated an infection event that did not actually occur (9.6% of cases overestimated). On the contrary, the model never failed to simulate actual infections. Model outputs were validated in different epidemiological conditions (areas x vineyards x years) (Tab. 2). In Piedmont the model was validated in 19 different situations; it provided 153 simulations with only 4% of cases overestimated. In Oltrepò Pavese, the model was validated for a 5-year period in the same vineyard providing 26 simulations; only in one case (4%) infection was overestimated. In Emilia-Romagna, the model was validated in 38 cases that provided 365 simulations in 113 aggregate; 7% of them were overestimated while the remaining 93% were accurate. In the Marche region the model provided 104 simulations, under 7 different epidemiological conditions; it produced 88 accurate simulations, with 15% overestimation. In Basilicata the model produced 34 simulations in two different vinegrowing areas: in 91% of cases the model correctly simulated the primary infection process, with 3 (9%) unjustified alarms. In Sardinia the model was validated in the same vineyard over a six-year period; it provided 54 simulations. Also in this area the model never failed to signal an actual infection, but 19 simulations (35%) signalled an infection that actually did not occur. In conclusion, this dynamic model for primary P. viticola infections was accurate and robust: in more than 90% of the simulations performed the model correctly simulated either aborted or successful infections, under different epidemiological conditions and over a wide seasonal period, from early to late disease onsets (Fig. 3). In the remaining cases, the model overestimated the actual situation because it simulated an infection that did not actually produce disease symptoms; these cases frequently occurred in early spring, at the beginning of the primary inoculum season (data not shown). Moreover, the model never failed to simulate actual infections. Inaccuracies produced by the model were always due to false positive infections, which have a low negative impact on model performances because it simply produced unjustified warnings. Nevertheless, the model can be improved to reduce false positive prognoses. Considering that this model runs using only meteorological data and does not include information about the growth stage of the host at the time of a possible infection, there are good reasons to believe that unjustified alarm could be avoided by ignoring infections which occur before the beginning of host susceptibility. Tab. 1. Comparison between model simulations and observed occurrence of P. viticola primary infections ( 2 = 411.4 calculated using the Yate’s correction, significant at P
Page 1 and 2: Dynamic simulation of grape downy m
Page 3: Fig. 2. Example of model simulation

Dynamic simulation of grape downy mildew primary infections - Assam

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