A serious outbreak of flavescence dorée (FD) was reported in Piemonte, northwestern Italy, in 1998, and since then, the disease has compromised the economy of this traditional wine-growing area, even following the application of compulsory insecticide treatments to control Scaphoideus titanus, the vector of the causal phytoplasma. Affected vines show severe symptoms, varying according to the cultivar, and are rogued to reduce disease spread. Following winter and pruning, a previously affected vine may appear symptomless and free of phytoplasmas in its aerial as well as its root system, even by nested-polymerase chain reaction assays. Such plants are considered to be "recovered". Since 1998 homogenous data on the incidence of newly infected, healthy, or recovered plants productivity, presence of vectors, and treatment schedules have been collected in seven severely affected vineyards of southern Piemonte for 5 years (1999 to 2003). Infectivity and recovery rates were also calculated each year. From 1999 to 2003, the average number of healthy plants decreased and the numbers of recovered plants and those with symptoms increased. Productivity of recovered vines, although lower than that of healthy ones, was always higher than that of vines with symptoms and was not influenced by the time elapsed from date of recovery. The relationships between the ln-transformed number of vectors trapped in the vineyards the previous year and the infection and the recovery rates were fitted by an exponential (R(2) = 0.95) and an asymptotic (R(2) = 0.93) model, respectively.

A dynamic simulation model for the risk of Fusarium head blight on wheat was elaborated based on systems analysis. The model calculates a daily infection risk based on sporulation, spore dispersal and infection of host tissue of the four main species causing the disease ( Gibberella zeae , Fusarium culmorum , Gibberella avenacea , Monographella nivalis ). Spore yield and dispersal are calculated as functions of temperature, rainfall and relative humidity, while the main factors affecting the infection rate are temperature, wetness and the host growth stage. The model also calculates a risk for mycotoxin production by G. zeae and F. culmorum in the infected head tissue. First validations against field data, collected in some wheat-growing areas in northern Italy and not used in model elaboration, produced satisfactory results.

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Four components of rate‐reducing resistance to Cercospora leaf spot in sugar beet (infection efficiency of conidia RC1, incubation period RC2, size of necrotic spots RC3 and spore yield RC4), previously measured in single infection cycle experiments, were integrated into a model simulating the chain of infection cycles under field conditions, as influenced by weather. To integrate resistance components, variables accounting for infection frequency, incubation period, affected leaf area, and infectiousness – which are computed for a susceptible cultivar – were modified by means of coefficients which reduced (RC1, RC3, RC4) or increased (RC2) them. Outputs obtained by running the model and changing resistance components actually reduced the rate of disease progress and the area under the disease progress curve of epidemics (AUDPC), as happens at field level; therefore, the approach may be considered successful. Changes in single resistance components were closely correlated with changes in AUDPC: improvements in RC1, RC3 or RC4 reduced AUDPC by the same, over the whole range of variation in infection frequency, affected leaf area, and infectiousness; on the contrary, little improvements in RC2 were more effective than stronger ones. When components acted simultaneously, each of them reduced disease progress in proportion to its magnitude; when all components were improved by the same amount, they had about the same effectiveness in slowing the epidemic. Changing more components simultaneously reduced the disease development slightly more than additively. Advantages for plant breeders in improving their selection strategies are outlined.

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.

A 6‐year study was carried out to evaluate the accuracy of some models in estimating airborne ascospores of Venturia inaequalis. The proportion of the season’s ascospores trapped on each discharge event was compared with the proportion of mature ascospores, estimated by the New Hampshire model or by some related models. The models differed from each other in the degree‐day cumulation, accounting or not for the leaf litter wetness caused by rainfall or by deposition of atmospheric humidity. The New Hampshire model did not fit spore trappings well: 59% of the actual values fell outside the range of the estimates, and 83% of them were overestimates. The wide discrepancy between reality and estimates resulted from the effect of dryness: when many consecutive rainless days occurred, the proportion of ascospores trapped was constantly lower than the model estimates, due to a slowed spore maturation. The effect of dryness was evident during the greater part of the ascospore maturity season, irrespective of the proportion of the season’s ascospores that had just matured when the dry period began. Models accounting for leaf litter wetness significantly improved estimates. Therefore, in the Po Valley, the accuracy of the New Hampshire model can be improved by accumulating degree‐days only when leaf litter is wet.

A‐scab (Apple‐scab) is a dynamic simulation model for Venturia inaequalis primary infections on apple. It simulates development of pseudothecia, ascospore maturation, discharge, deposition and infection during the season based on hourly data of air temperature, rainfall, relative humidity and leaf wetness. A‐scab produces a risk index for each infection period and forecasts the probable periods of symptoms appearance. The model was validated under different epidemiological conditions: its outputs were successfully compared with daily spore counts and actual onset and severity of the disease under orchard conditions, and neither corrections nor calibrations have been necessary to adapt the model to different apple‐growing areas. Compared to other existing models, A‐scab: (i) combines information from literature and data acquired from specific experiments; (ii) is completely ‘open’ because both model structure and algorithms have been published and are easily accessible; (iii) is not written with a specific computer language but it works on simple‐to‐use electronic sheets. For these reasons the model can be easily implemented in the computerized systems used by warning services.