Temporal Dynamics and Dispersal Patterns of the Primary Inoculum of Coniella diplodiella, the Causal Agent of Grape White Rot.

Grape white rot can cause considerable yield losses in viticulture areas worldwide and is principally caused by Coniella diplodiella. The fungus overwinters in berry mummies on the soil surface or on the trellis and produces pycnidia and conidia that serve as primary inoculum. However, little is known about the temporal dynamics and dispersal pattern of C. diplodiella conidia. In this study, we investigated the production and dispersal of C. diplodiella conidia from a primary inoculum source, namely, affected mummified berries that overwintered in two vineyards in northern Italy in 2021 and 2022. Conidia of C. diplodiella were repeatedly produced in berry mummies from the budburst of vines to harvesting, with approximately 50 and 75% of the total conidia in a season being produced before fruit set and véraison, respectively. The production dynamics of C. diplodiella conidia over time were described by a Weibull equation in which the thermal time is the independent variable, with a concordance correlation coefficient of ≥0.964. A rainfall cutoff of ≥0.2 mm provided an overall accuracy of ≥0.86 in predicting conidial dispersal through rain splashes from berry mummies on the soil surface, with the number of dispersed conidia increasing with the amount of rainfall. The dispersal of conidia from mummies on the trellis by washing with rain required at least 6.1 mm of rain. The proposed mathematical equations and rain cutoffs can be used to predict periods with a high dispersal risk of C. diplodiella.

Role of rain in the spore dispersal of fungal pathogens associated with grapevine trunk diseases.

Grapevine trunk diseases are caused by a complex of fungi that belong to different taxa, which produce different spore types and have different spore dispersal mechanisms. It is commonly accepted that rainfall plays a key role in spore dispersal, but there is conflicting information in the literature on the relationship between rain and spore trapping in aerobiology studies. We conducted a systematic literature review, extracted quantitative data from published papers, and used the pooled data for Bayesian analysis of the effect of rain on spore trapping. We selected 17 papers covering 95 studies and 8,778 trapping periods, concerning a total of 26 fungal taxa causing Botryosphaeria dieback (BD), Esca complex (EC), and Eutypa dieback (ED). Results confirmed the role of rain in the spore dispersal of these fungi, but revealed differences among the different fungi. Rain was a good predictor of spore trapping for ED (AUROC = 0.820) and BD (0.766) but not for the Ascomycetes involved in EC (0.569) and not for the only Basidiomycetes, Fomitiporella viticola, studied as for spore discharge (AUROC not significant). Prediction of spore trapping was more accurate for negative than for positive prognosis; a rain cutoff of ≥0.2 mm provided an overall accuracy ≥0.61 for correct prognoses. Spores trapped in rainless periods accounted for only <10% of the total spores. Our analysis had some drawbacks, which were mainly caused by knowledge gaps and limited data availability; these drawbacks are discussed to facilitate further research.

Seasonal periodicity of the airborne spores of fungi causing grapevine trunk diseases: an analysis of 247 studies published worldwide.

Grapevine trunk diseases (GTDs) are among the most devastating grapevine diseases globally. GTDs are caused by multiple fungi from various taxa, which release spores into the vineyard and infect wood tissue, mainly through wounds caused by viticultural operations. The timing of operations to avoid infection is critical concerning the periodicity of GTD spores in vineyards, and many studies have been conducted in different grape-growing areas worldwide. However, these studies provide conflicting and fragmented information. To synthesize current knowledge, we performed a systematic literature review, extracted quantitative data from published papers, and used these data to identify trends and knowledge gaps to be addressed in future studies. Our database included 26 papers covering 247 studies and 3,529 spore sampling records concerning a total of 29 fungal taxa responsible for Botryosphaeria dieback (BD), Esca complex (EC), and Eutypa dieback (ED). We found a clear seasonality in the presence and abundance of BD spores, with a peak from fall to spring, more in the northern than in the southern hemisphere, but not for EC and ED. Spores of these fungi were present throughout the growing season in both hemispheres, possibly due to higher variability in spore types, sporulation conditions, and spore release mechanisms in EC and ED fungi compared to BD. Our analysis has limitations due to knowledge gaps and data availability for some fungi (e.g., Basidiomycetes, causing EC). These limitations are discussed to facilitate further research.

Plant Resistance Inducers Affect Multiple Epidemiological Components of Plasmopara viticola on Grapevine Leaves.

Plant resistance inducers (PRIs) harbor promising potential for use in downy mildew (DM) control in viticulture. Here, the effects of six commercial PRIs on some epidemiological components of Plasmopara viticola (Pv) on grapevine leaves were studied over 3 years. Disease severity, mycelial colonization of leaf tissue, sporulation severity, production of sporangia on affected leaves, and per unit of DM lesion were evaluated by inoculating the leaves of PRI-treated plants at 1, 3, 6, 12, and 19 days after treatment (DAT). Laminarin, potassium phosphonate (PHO), and fosetyl-aluminium (FOS) were the most effective in reducing disease severity as well as the Pv DNA concentration of DM lesions on leaves treated and inoculated at 1 and 3 DAT; PHO and FOS also showed long-lasting effects on leaves established after treatment (inoculations at 6 to 19 DAT). PRIs also prevented the sporulation of Pv on lesions; all the PRI-treated leaves produced fewer sporangia than the nontreated control, especially in PHO-, FOS-, and cerevisane-treated leaves (>75% reduction). These results illustrate the broader and longer effect of PRIs on DM epidemics. The findings open up new perspectives for using PRIs in a defense program based on single, timely, and preventative field interventions.

Development of a model for Colletotrichum diseases with calibration for phylogenetic clades on different host plants.

Fungi in the genus Colletotrichum cause serious pre- and post-harvest losses to several agricultural crops worldwide. Through a systematic literature review, we retrieved the published information on Colletotrichum anthracnose diseases on different host plants and developed a mechanistic model incorporating the main stages of the pathogen's life cycle and the effect of weather. The model predicts anthracnose progress during the growing season on the aerial organs of different crops, and was parameterized for seven Colletotrichum clades (acutatum, dematium, destructivum, gloeosporioides, graminicola, and orbiculare) and the singleton species, C. coccodes. The model was evaluated for the anthracnose diseases caused by fungi belonging to five clades on six hosts by using data from 17 epidemics that occurred in Italy, the USA, Canada, and Japan. A comparison of observed versus predicted data showed a concordance correlation coefficient of 0.928 and an average distance between real data and the fitted line of 0.044. After further validation, the model could be used to support decision-making for crop protection.

Susceptibility of pruning wounds to grapevine trunk diseases: A quantitative analysis of literature data.

Introduction: Pruning wounds are the main entry points for fungi causing grapevine trunk diseases (GTDs). Several studies identified factors influencing the temporal dynamics of wound susceptibility, which include the fungal species and inoculum dose, weather conditions, grape variety, pruning date, and so forth. Here, we conducted a quantitative analysis of literature data to synthesise outcomes across studies and to identify the factors that most affect the length of pruning wound susceptibility. Methods: We extracted data on the frequency at which the inoculated wounds showed GTD symptoms or an inoculated pathogen was reisolated following artificial inoculation at the time of pruning or in the following days. A negative exponential model was fit to these data to describe changes in wound susceptibility as a function of time since pruning, in which the rate parameter changed depending on specific factors. Results and Discussion: The results show that wound susceptibility is high at the time of pruning, and they remain susceptible to invasion by GTD fungi for months after pruning. Infection incidence on wounds was higher for fungi associated with Botryosphaeria dieback than those associated with Eutypa dieback or Esca complex, and wound susceptibility decreased faster for Eutypa dieback than for other GTD agents. Grapevine variety and pruning season also affected the wound susceptibility period. Sauvignon Blanc remains susceptible to GTDs longer than other varieties. We also found that the time of pruning can affect infection dynamics, especially for more susceptible varieties. The results increase our understanding of GTD epidemiology and should help growers control infections.

Plant Disease Models and Forecasting: Changes in Principles and Applications over the Last 50 Years.

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.

Effects of Temperature and Moisture Duration on Spore Germination of Four Fungi that Cause Grapevine Trunk Diseases.

Grapevine trunk diseases (GTDs) are serious threats worldwide and are difficult to control, in part because the environmental requirements for epidemiological processes of the causal fungi are poorly understood. Therefore, we investigated the effects of temperature and moisture duration on spore germination of four fungi associated with two GTDs (esca complex and Eutypa dieback): Phaeomoniella chlamydospora, Phaeoacremonium minimum, Cadophora luteo-olivacea, and Eutypa lata. Conidia of Phaeomoniella chlamydospora, Phaeoacremonium minimum, and C. luteo-olivacea were similar: conidia of these fungi germinated profusely (>90%) between 20 and 30°C; Phaeomoniella chlamydospora and Phaeoacremonium minimum tended to germinate at higher temperatures (up to 40°C for P. minimum), and C. luteo-olivacea at lower temperatures (as low as 5°C). E. lata ascospores germinated between 10 and 30°C. The required duration of moist periods for germination was shortest for C. luteo-olivacea (about 6 h), followed by P. minimum and E. lata (about 12 h) and Phaeomoniella chlamydospora (about 24 h). Further research on the environmental requirements of GTD fungi may increase our ability to predict infection periods and, thereby, improve disease control.

Temperature-Dependent Growth and Spore Germination of Fungi Causing Grapevine Trunk Diseases: Quantitative Analysis of Literature Data.

Grapevine trunk diseases (GTDs) are serious threats in all viticultural areas of the world, and their management is always complex and usually inadequate. Fragmented and inconsistent information on the epidemiology and environmental requirements of the causal fungi is among the reasons for poor disease control. Therefore, we conducted a quantitative analysis of literature data to determine the effects of temperature on mycelial growth and the effects of temperature and moisture duration on spore germination. Using the collected information, we then developed mathematical equations describing the response of mycelial growth to temperature, and the response of spore germination to temperature and moisture for the different species and disease syndromes. We considered 27 articles (selected from a total of 207 articles found through a systematic literature search) and 116 cases; these involved 43 fungal species belonging to three disease syndromes. The mycelial growth of the fungi causing Botryosphaeria dieback (BD) and the esca complex (EC) responded similarly to temperature, and preferred higher temperatures than those causing Eutypa dieback (ED) (with optimal temperature of 25.3, 26.5, and 23.3°C, respectively). At any temperature, the minimal duration of the moist period required for 50% spore germination was shorter for BD (3.0 h) than for EC (17.2 h) or ED (15.5 h). Mathematical equations were developed accounting for temperature-moisture relationships of GTD fungi, which showed concordance correlation coefficients ≥0.888; such equations should be useful for reducing the risk of infection.

A Mechanistic Model Accounting for the Effect of Soil Moisture, Weather, and Host Growth Stage on the Development of Sclerotinia sclerotiorum.

The fungus Sclerotinia sclerotiorum causes serious losses to several agricultural crops worldwide. By using systems analysis, we retrieved the available knowledge concerning S. sclerotiorum from the literature and then analyzed and synthesized the data to develop a mechanistic, dynamic, weather-driven model for the prediction of epidemics on different crops. The model accounts for i) the production and survival of apothecia; ii) the production, dispersal, and survival of ascospores; iii) infection by ascospores; and iv) lesion onset. The ability of the model to predict the occurrence of apothecia was evaluated for epidemics observed with different climates, soil types, and host crops (soybean, white bean, and carrot) using independent data obtained from trials conducted in Ontario (Canada) in 1981, 1982, and from 1999 to 2002; in Michigan (U.S.A.) in 2015 and 2016; and in Wisconsin (U.S.A.) in 2016. The model showed 0.82 accuracy and 0.73 specificity in predicting the presence of apothecia, with a posterior probability of correctly predicting apothecia to be present or absent of 0.804 and 0.876, respectively. The model was also validated for its ability to predict disease progress on soybean and sunflower in Ontario in 1981 and 1982, in Manitoba (Canada) in 2001 and 2002, and in Michigan in 2015 and 2016. Comparison of model output with observations showed a concordance correlation coefficient of 0.948, and a root mean square error of 0.122. The model represents an improvement of previous S. sclerotiorum models and could be useful for making decisions on disease control.

Information on disease resistance patterns of grape varieties may improve disease management.

Resistance to downy mildew (DM) and powdery mildew (PM) contributes to sustainable vineyard management by reducing the diseases and the need for fungicide applications. Resistant varieties vary in their degree of resistance to DM and PM, and in their susceptibility to other diseases. As a consequence, fungicide use may differ among varieties depending on their "resistance patterns" (i.e., the resistance level of a variety toward all of the diseases in the vineyard). The resistance patterns of 16 grapevine varieties to DM, PM, black rot (BR), and gray mold (GM) were evaluated over a 4-year period under field conditions. Disease severity was assessed on leaves and bunches, and the AUDPC (Area Under Disease Progress Curve) was calculated to represent the epidemic progress. GM was found only on bunches and only at very low levels, irrespective of the year or variety, and was therefore excluded from further analyses. The varieties were then grouped into four resistance patterns: i) low resistance to DM and PM, intermediate resistance to BR; ii) high resistance to DM, intermediate resistance to PM, low resistance to BR; iii) intermediate resistance to DM and BR, low resistance to PM; and iv) high resistance to DM, PM, and BR. AUDPC values on leaves were positively correlated with AUDPC values on bunches for susceptible varieties but not for resistant ones, with the exception of PM. Therefore, bioassays with leaves can be used to predict the resistance of bunches to DM and BR for susceptible varieties but not for resistant ones. These results may facilitate both strategic and tactical decisions for the sustainable management of grapevine diseases.

Temperature requirements of Colletotrichum spp. belonging to different clades.

The fungal genus Colletotrichum includes plant pathogens that cause substantial economic damage to horticultural, ornamental, and fruit tree crops worldwide. Here, we conducted a systematic literature review to retrieve and analyze the metadata on the influence of temperature on four biological processes: (i) mycelial growth, (ii) conidial germination, (iii) infection by conidia, and (iv) sporulation. The literature review considered 118 papers (selected from a total of 1,641 papers found with the literature search), 19 Colletotrichum species belonging to eight clades (acutatum, graminicola, destructivum, coccodes, dematium, gloeosporioides, and orbiculare), and 27 host plants (alfalfa, almond, apple, azalea, banana, barley, bathurst burr, blueberry, celery, chilli, coffee, corn, cotton, cowpea, grape, guava, jointvetch, lentil, lupin, olive, onion, snap bean, spinach, strawberry, tomato, watermelon, and white bean). We used the metadata to develop temperature-dependent equations representing the effect of temperature on the biological processes for the different clades and species. Inter- and intra-clades similarities and differences are analyzed and discussed. A multi-factor cluster analysis identified four groups of clades with similar temperature dependencies. The results should facilitate further research on the biology and epidemiology of Colletotrichum species and should also contribute to the development of models for the management of anthracnose diseases.

Development and Validation of a Mechanistic, Weather-Based Model for Predicting Puccinia graminis f. sp. tritici Infections and Stem Rust Progress in Wheat.

Stem rust (or black rust) of wheat, caused by Puccinia graminis f. sp. tritici (Pgt), is a re-emerging, major threat to wheat production worldwide. Here, we retrieved, analyzed, and synthetized the available information about Pgt to develop a mechanistic, weather-driven model for predicting stem rust epidemics caused by uredospores. The ability of the model to predict the first infections in a season was evaluated using field data collected in three wheat-growing areas of Italy (Emilia-Romagna, Apulia, and Sardinia) from 2016 to 2021. The model showed good accuracy, with a posterior probability to correctly predict infections of 0.78 and a probability that there was no infection when not predicted of 0.96. The model's ability to predict disease progress during the growing season was also evaluated by using published data obtained from trials in Minnesota, United States, in 1968, 1978, and 1979, and in Pennsylvania, United States, in 1986. Comparison of observed versus predicted data generated a concordance correlation coefficient of 0.96 and an average distance between real data and the fitted line of 0.09. The model could therefore be considered accurate and reliable for predicting epidemics of wheat stem rust and could be tested for its ability to support risk-based control of the disease.

Modeling the Effects of the Environment and the Host Plant on the Ripe Rot of Grapes, Caused by the Colletotrichum Species.

Ripe rot caused by Colletotrichum spp. is a serious threat in many vineyards, and its control relies mainly on the repeated use of fungicides. A mechanistic, dynamic model for the prediction of grape ripe rot epidemics was developed by using information and data from a systematic literature review. The model accounts for (i) the production and maturation of the primary inoculum; (ii) the infection caused by the primary inoculum; (iii) the production of a secondary inoculum; and (iv) the infection caused by the secondary inoculum. The model was validated in 19 epidemics (vineyard × year combinations) between 1980 and 2014 in China, Japan, and the USA. The observed disease incidence was correlated with the number of infection events predicted by the model and their severity (ρ = 0.878 and 0.533, respectively, n = 37, p ≤ 0.001). The model also accurately predicted the disease severity progress during the season, with a concordance correlation coefficient of 0.975 between the observed and predicted data. Overall, the model provided an accurate description of the grape ripe rot system, as well as reliable predictions of infection events and of disease progress during the season. The model increases our understanding of ripe rot epidemics in vineyards and will help guide disease control. By using the model, growers can schedule fungicides based on the risk of infection rather than on a seasonal spray calendar.

A Mechanistic Weather-Driven Model for Ascochyta rabiei Infection and Disease Development in Chickpea.

Ascochyta blight caused by Ascochyta rabiei is an important disease of chickpea. By using systems analysis, we retrieved and analyzed the published information on A. rabiei to develop a mechanistic, weather-driven model for the prediction of Ascochyta blight epidemics. The ability of the model to predict primary infections was evaluated using published data obtained from trials conducted in Washington (USA) in 2004 and 2005, Israel in 1996 and 1998, and Spain from 1988 to 1992. The model showed good accuracy and specificity in predicting primary infections. The probability of correctly predicting infections was 0.838 and the probability that there was no infection when not predicted was 0.776. The model's ability to predict disease progress during the growing season was also evaluated by using data collected in Australia from 1996 to 1998 and in Southern Italy in 2019; a high concordance correlation coefficient (CCC = 0.947) between predicted and observed data was obtained, with an average distance between real and fitted data of root mean square error (RMSE) = 0.103, indicating that the model was reliable, accurate, and robust in predicting seasonal dynamics of Ascochyta blight epidemics. The model could help growers schedule fungicide treatments to control Ascochyta blight on chickpea.