The fungal genus Venturia Sacc. (anamorph Fusicladium Bonord.) includes plant pathogens that cause substantial economic damage to fruit crops worldwide. Although Venturia inaequalis is considered a model species in plant pathology, other Venturia spp. also cause scab on other fruit trees. Relative to the substantial research that has been conducted on V. inaequalis and apple scab, little research has been conducted on Venturia spp. affecting other fruit trees. In this review, the main characteristics of plant-pathogenic species of Venturia are discussed with special attention to V. inaequalis affecting apple, V. pyrina affecting European pear, V. nashicola affecting Asian pear, V. carpophila affecting peach and almond, Fusicladium oleagineum affecting olive, F. effusum affecting pecan, and F. eriobotryae affecting loquat. This review has two main objectives: (i) to identify the main gaps in our knowledge regarding the biology and epidemiology of Venturia spp. affecting fruit trees; and (ii) to identify similarities and differences among these Venturia spp. in order to improve disease management. A thorough review has been conducted of studies regarding the phylogenetic relationships, host ranges, biologies, and epidemiologies of Venturia spp. A multiple correspondence analysis (CA) has also been performed on the main epidemiological components of these Venturia spp. CA separated the Venturia spp. into two main groups, according to their epidemiological behavior: the first group included V. inaequalis, V. pyrina, V. nashicola, and V. carpophila, the second F. oleagineum and F. eriobotryae, with F. effusum having an intermediate position. This review shows that Venturia spp. affecting fruit trees are highly host-specific, and that important gaps in understanding the life cycle exist for some species, including V. pyrina; gaps include pseudothecia formation, ascospore and conidia germination, and mycelial growth. Considering the epidemiological information reviewed, this paper shows that the use of Mills tables to predict infection periods should be avoided for Venturia spp. other than V. inaequalis.

This review gives a perspective of selected advances made since the middle of the 20th century in plant disease modeling, and the associated increase in the number of models published during that time frame. This progress can be mainly attributed to advances in (i) sensors and automatic environmental data collection technology, (ii) instrumentation and methods for studying botanical epidemiology, and (iii) data analytics and computer science. We review the evolution of techniques for developing data-based (empirical) models and process-based (mechanistic) models using the wheat rusts as a case study. We also describe the increased importance of knowledge about biological processes for plant disease modeling by using apple scab as a second case study. For both wheat rusts and apple scab, we describe how the models have evolved over the last 50 years by considering certain milestones that have been achieved in disease modeling. Finally, we describe how plant disease models are used as part of a multi-modeling approach to develop decision-making tools in the application of integrated pest management.

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.

Although the fungus Phaeomoniella chlamydospora is the most commonly detected causal agent of Petri disease and esca, two important fungal grapevine trunk diseases, little is known about the dispersal patterns of P. chlamydospora inoculum. In this work, we studied the dispersal of P. chlamydospora airborne inoculum from 2016 to 2018 in two viticultural areas of eastern (Ontinyent) and northern (Logroño) Spain. The vineyards were monitored weekly from November to April using microscope slide traps, and P. chlamydospora was detected and quantified by a specific real-time quantitative (qPCR) method set up in this work. The method was found to be sensitive, and a good correlation was observed between numbers of P. chlamydospora conidia (counted by microscope) and DNA copy numbers (quantified by qPCR). We consistently detected DNA of P. chlamydospora at both locations and in all seasons but in different quantities. In most cases, DNA was first detected in the last half of November, and most of the DNA was detected from December to early April. When rain was used as a predictor of P. chlamydospora DNA detection in traps, false-negative detections were observed, but these involved only 4% of the total. The dispersal pattern of P. chlamydospora DNA over time was best described (R²= 0.765 and concordance correlation coefficient = 0.870) by a Gompertz equation, with time expressed as hydrothermal time (a physiological time accounting for the effects of temperature and rain). This equation could be used to predict periods with a high risk of dispersal of P. chlamydospora.