2008
DOI: 10.1016/j.ecolmodel.2007.10.046
|View full text |Cite
|
Sign up to set email alerts
|

A mechanistic model simulating primary infections of downy mildew in grapevine

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

1
55
0

Year Published

2010
2010
2024
2024

Publication Types

Select...
6
2

Relationship

1
7

Authors

Journals

citations
Cited by 85 publications
(59 citation statements)
references
References 20 publications
1
55
0
Order By: Relevance
“…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%
“…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%
“…In these countries, epidemics are caused by primary infection, in which an increase in air temperature may accelerate oospore germination (Rossi et al, 2008). With these results, it can be inferred that there will be an increase in the production cost of the grapevine, due to the need for changes in the schedule and the number of fungicide applications (Salinari et al, 2006(Salinari et al, , 2007.…”
Section: Resultsmentioning
confidence: 79%
“…Thus, the temperature increase delayed the appearance of the grapevine downy mildew in seedlings of the analyzed cultivars. According to Rossi et al (2008), in the field, an increase in the number of days between infection and sporangium production may reduce the rate of disease progression.…”
Section: Resultsmentioning
confidence: 99%
“…More recent studies have demonstrated that oospores represent a source of inoculum for P. viticola infection throughout the season (Gessler et al, 2003;Gobbin et al, 2006;Rossi et al, 2013) and in some cases, their contribution to the epidemic as a whole is higher than that of secondary infections, due to sporangia. This new conception of the P. viticola life cycle was recently incorporated in a mechanistic model for the dynamic simulation of primary infections of P. viticola (Rossi et al, 2008), linked to a life-cycle model . The model of Rossi et al (2008) was evaluated in more than 100 vineyards in northern, southern, and insular Italy (from 1995 to 2007) as well as with potted grapevine plants exposed to the inoculum (from 2006 to 2008) , and also in the environmental conditions in the province of Quebec, Eastern Canada, by comparing the time of lesion emergence predicted by the model with field observations (Caffi et al, 2011a,b).…”
Section: Modelling Downy Mildewmentioning
confidence: 99%
“…This new conception of the P. viticola life cycle was recently incorporated in a mechanistic model for the dynamic simulation of primary infections of P. viticola (Rossi et al, 2008), linked to a life-cycle model . The model of Rossi et al (2008) was evaluated in more than 100 vineyards in northern, southern, and insular Italy (from 1995 to 2007) as well as with potted grapevine plants exposed to the inoculum (from 2006 to 2008) , and also in the environmental conditions in the province of Quebec, Eastern Canada, by comparing the time of lesion emergence predicted by the model with field observations (Caffi et al, 2011a,b). This model always showed very high accuracy ) and when used to schedule fungicide application against downy mildew, allowed a reduction from 50 to 66% in pesticide applications, corresponding to an average saving of 174 and 224 V/ha, respectively .…”
Section: Modelling Downy Mildewmentioning
confidence: 99%