As climate is a key agro-ecosystem driving force, climate change could have a severe impact on agriculture. Many assessments have been carried out to date on the possible effects of climate change (temperature, precipitation and carbon dioxide concentration changes) on plant physiology. At present however, likely effects on plant pathogens have not been investigated deeply. The aim of this work was to simulate future scenarios of downy mildew (Plasmopara viticola) epidemics on grape under climate change, by combining a disease model to output from two general circulation models (GCMs). Model runs corresponding to the SRES-A2 emissions scenario, characterized by high projections of both population and greenhouse gas emissions from present to 2100, were chosen in order to investigate impacts of worst-case scenarios, among those currently available from IPCC. Three future decades were simulated (2030, 2050, 2080), using as baseline historical series of meteorological data collected from 1955 to 2001 in Acqui Terme, an important grape-growing area in the north-west of Italy. Both GCMs predicted increase of temperature and decrease of precipitation in this region. The simulations obtained by combining the disease model to the two GCM outputs predicted an increase of the disease pressure in each decade: more severe epidemics were a direct consequence of more favourable temperature conditions during the months of May and June. These negative effects of increasing temperatures more than counterbalanced the effects of precipitation reductions, which alone would have diminished disease pressure. Results suggested that, as adaptation response to future climate change, more attention would have to be paid in the management of early downy mildew infections; two more fungicide sprays were necessary under the most negative climate scenario, compared with present management regimes. At the same time, increased knowledge on the effects of climate change on host-pathogen interactions will be necessary to improve current predictions.
Airborne ascospores of Venturia pirina were trapped at two sites in northern Italy in 2002 to 2008. The cumulative proportion of ascospores trapped at each discharge was regressed against the physiological time. The best fit (R(2) = 0.90, standard error of estimates [SEest] = 0.11) was obtained using a Gompertz equation and the degree-days (>0 degrees C) accumulated after the day on which the first ascospore of the season was trapped (biofix day), but only for the days with > or =0.2 mm rain or < or =4 hPa vapor pressure deficit (DDwet). This Italian model performed better than the models developed in Oregon, United States (R(2) = 0.69, SEest = 0.16) or Victoria, Australia (R(2) = 0.74, SEest = 0.18), which consider only the effect of temperature. When the Italian model was evaluated against data not used in its elaboration, it accurately predicted ascospore maturation (R(2) = 0.92, SEest = 0.10). A logistic regression model was also developed to estimate the biofix for initiating the accumulation of degree-days (biofix model). The probability of the first ascospore discharge of the season increased as DDwet (calculated from 1 January) increased. Based on this model, there is low probability of the first ascospore discharge when DDwet < or =268.5 (P = 0.03) and high probability (P = 0.83) of discharge on the first day with >0.2 mm rain after such a DDwet threshold.
The global climate is changing. Much research has already been carried out to assess the potential impacts of climate change on plant physiology. However, effects on plant disease have not yet been deeply studied. In this paper, an empirical disease model for primary infection of downy mildew on grapevine was elaborated and used to project future disease dynamics under climate change. The disease model was run under the outputs of the General Circulation Model (GCM) and future scenarios of downy mildew primary outbreaks were generated at several sites all over the word for three future dates: 2030, 2050, 2080. Results suggested a potential general advance of first disease outbreaks, both in the Northern and Southern Hemispheres, for all three future decades considered. The advance is predicted to be from about a minimum of one day in South Africa in 2030 to a maximum of 28 days in Chile and China in 2080. The advance in the outbreak time could lead to more severe infections, due to the polycyclic nature of the pathogen. Therefore, changes in the timing and frequency of fungicide treatments could be expected in the future, with a possible increase in the costs of disease management.
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