2000
DOI: 10.1007/bf02344698
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Estimating germinability ofPlasmopara viticola oospores by means of neural networks

Abstract: Neural networks are trained to estimate the germination percentages of Plasmopara viticola oospores, overwintered in natural conditions in two viticultural areas in northern Italy, by using climatic (temperature and rainfall) data, as well as the previous germination measurement, as input variables. The 288 available patterns consist of a set of selected independent variables associated with the corresponding germination percentage. All 12 networks investigated converge to a non-linear relationship between the… Show more

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Cited by 13 publications
(12 citation statements)
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“…The particularly dry and warm weather conditions recorded during the differentiation period in 2003 in M and T vineyards probably induced many oospores to degenerate, leading to the very low germination percentages observed in both samples. Germination is also influenced by the overwintering conditions, in particular the distribution and amount of rain 19. The very similar germination percentages observed in Montorio, except for 2003, seem to be related to the comparable amount and frequency of rain recorded in the vineyard during autumn and winter.…”
Section: Discussionmentioning
confidence: 76%
See 1 more Smart Citation
“…The particularly dry and warm weather conditions recorded during the differentiation period in 2003 in M and T vineyards probably induced many oospores to degenerate, leading to the very low germination percentages observed in both samples. Germination is also influenced by the overwintering conditions, in particular the distribution and amount of rain 19. The very similar germination percentages observed in Montorio, except for 2003, seem to be related to the comparable amount and frequency of rain recorded in the vineyard during autumn and winter.…”
Section: Discussionmentioning
confidence: 76%
“…Berl. & De Toni and P. viticola 19–22. On water agar the germination percentages of oospores collected in Northern Italy vineyards varied depending on the location and the year of sampling.…”
Section: Discussionmentioning
confidence: 99%
“…To the best of our knowledge, most of the efforts to model germination dynamics have assumed that only exogenous factors affect oospore germination. Different modelling approaches, ranging from regression (Rossi et al 2002), mechanistic (Rossi et al 2008a), neural networks (Vercesi et al 2000), to fuzzy-neural models (Guglielmann et al 2002), have recently been explored, and resulted in models that reconstruct the dynamics from temporal series of environmental and climatic variables, and are exploited to simulate either downy mildew primary infections or germination rates. Such models have limited predictive performance, and do not give any insight into the complex processes responsible for germination, since they do not consider the biological mechanisms underlying oospore germination and metabolic processes needed for macrosporangium formation.…”
Section: Introductionmentioning
confidence: 99%
“…Numerous investigations, carried out by directly assessing oospore germination percentages on water agar at 20°C from November until July, suggested that macrosporangium formation persists from mid-end November until the end of May, less frequently until the middle of June and sporadically until the end of June (Toffolatti 2007). Regular rainfall distribution usually has a positive effect on oospore germination rates, but abundant rain events were seldom associated with strong decrease in oospore germination percentages (Vercesi et al 2000).…”
Section: Introductionmentioning
confidence: 99%
“…The sexual structures of the pathogen are differentiated in the infected leaves at the end of the grapevine growing season. They overwinter and germinate the following season, providing inoculum for primary infections (Vercesi et al 2000). QoI resistance is inherited maternally (Gisi et al 2002); therefore, QoI resistance can be studied in oospores collected at the end of the grapevine growing season, reflecting the result of the selection imposed by the fungicide applications made during the past season, and indicating the potential frequency of resistance in the primary inoculum for the following season.…”
Section: Introductionmentioning
confidence: 99%