TY - JOUR
T1 - A mechanistic model of Botrytis cinerea on grapevines that includes weather, vine growth stage, and the main infection pathways
AU - González-Domínguez, Elisa
AU - Caffi, Tito
AU - Ciliberti, Nicola
AU - Rossi, Vittorio
PY - 2015
Y1 - 2015
N2 - A mechanistic model for Botrytis cinerea on grapevine was developed. The model, which
accounts for conidia production on various inoculum sources and for multiple infection pathways,
considers two infection periods. During the first period (“inflorescences clearly visible”
to “berries groat-sized”), the model calculates: i) infection severity on inflorescences and
young clusters caused by conidia (SEV1). During the second period (“majority of berries
touching” to “berries ripe for harvest”), the model calculates: ii) infection severity of ripening
berries by conidia (SEV2); and iii) severity of berry-to-berry infection caused by mycelium
(SEV3). The model was validated in 21 epidemics (vineyard × year combinations) between
2009 and 2014 in Italy and France. A discriminant function analysis (DFA) was used to: i)
evaluate the ability of the model to predict mild, intermediate, and severe epidemics; and ii)
assess how SEV1, SEV2, and SEV3 contribute to epidemics. The model correctly classified
the severity of 17 of 21 epidemics. Results from DFA were also used to calculate the daily
probabilities that an ongoing epidemic would be mild, intermediate, or severe. SEV1 was
the most influential variable in discriminating between mild and intermediate epidemics,
whereas SEV2 and SEV3 were relevant for discriminating between intermediate and severe
epidemics. The model represents an improvement of previous B. cinerea models in viticulture
and could be useful for making decisions about Botrytis bunch rot control.
AB - A mechanistic model for Botrytis cinerea on grapevine was developed. The model, which
accounts for conidia production on various inoculum sources and for multiple infection pathways,
considers two infection periods. During the first period (“inflorescences clearly visible”
to “berries groat-sized”), the model calculates: i) infection severity on inflorescences and
young clusters caused by conidia (SEV1). During the second period (“majority of berries
touching” to “berries ripe for harvest”), the model calculates: ii) infection severity of ripening
berries by conidia (SEV2); and iii) severity of berry-to-berry infection caused by mycelium
(SEV3). The model was validated in 21 epidemics (vineyard × year combinations) between
2009 and 2014 in Italy and France. A discriminant function analysis (DFA) was used to: i)
evaluate the ability of the model to predict mild, intermediate, and severe epidemics; and ii)
assess how SEV1, SEV2, and SEV3 contribute to epidemics. The model correctly classified
the severity of 17 of 21 epidemics. Results from DFA were also used to calculate the daily
probabilities that an ongoing epidemic would be mild, intermediate, or severe. SEV1 was
the most influential variable in discriminating between mild and intermediate epidemics,
whereas SEV2 and SEV3 were relevant for discriminating between intermediate and severe
epidemics. The model represents an improvement of previous B. cinerea models in viticulture
and could be useful for making decisions about Botrytis bunch rot control.
KW - Botrytis cinerea
KW - epidemiology
KW - modelling
KW - Botrytis cinerea
KW - epidemiology
KW - modelling
UR - http://hdl.handle.net/10807/69951
U2 - 10.1371/journal.pone.0140444
DO - 10.1371/journal.pone.0140444
M3 - Article
SN - 1932-6203
VL - 10
SP - 1
EP - 23
JO - PLoS One
JF - PLoS One
ER -