Knowledge gaps on grape sour rot inferred from a systematic literature review.

Sour rot (SR) is one of the major diseases affecting grapevine berries, causing severe yield losses and deterioration of wine quality. SR is caused by an etiologic complex of microorganisms, including yeasts, bacteria, and filamentous fungi. This systematic review focuses on the etiology, epidemiology, and control of SR. A total of 74 papers published between 1986 and 2023 were assessed in this review. Description of disease symptoms was quite consistent across the papers, including oxidation of the grape skin, disaggregation of the internal tissues, and detachment of the rotten berries from the pedicel. The affected bunches are characterized by the smell of acetic acid and ethyl acetate that attracts fruit flies (Drosophila spp.). However, several knowledge gaps and/or inconsistencies were identified with respect to SR etiology, epidemiology, and control. Overall, 146 microorganisms were isolated from the affected berries (44.5% yeasts, 34.3% bacteria, and 21.2% filamentous fungi); however, the selected papers could not definitively clarify which species are primarily involved in the etiology of the disease. A general inconsistency was also observed in the methods used to assess the incidence and severity of SR in vineyards, making inter-study comparisons extremely challenging. Inconsistencies were also found in the methods used for pathogenicity assessment in artificial inoculation studies. Furthermore, gaps were detected in terms of SR epidemiology, with a focus on environmental conditions affecting the disease development. The SR management options are limited, and efficacy trials often result in poor, variable, and inconsistent levels of control, which might be attributed to the lack of knowledge on disease epidemiology. These knowledge gaps and inconsistencies were analyzed in this review to inform future research activities.

A Systematic Map of the Research on Disease Modelling for Agricultural Crops Worldwide.

In this work, we developed a systematic map to identify and catalogue the literature pertaining to disease modelling for agricultural crops worldwide. Searches were performed in 2021 in the Web of Science and Scopus for papers reporting any type of disease model for 103 crops. In total, 768 papers were retrieved, and their descriptive metadata were extracted. The number of papers found increased from the mid-1900s to 2020, and most of the studies were from North America and Europe. More disease models were retrieved for wheat, potatoes, grapes, and apples than for other crops; the number of papers was more affected by the crop's economic value than by its cultivated area. The systematic map revealed an underrepresentation of disease models for maize and rice, which is not justified by either the crop economic value or by disease impact. Most of the models were developed to understand the pathosystem, and fewer were developed for tactical disease management, strategic planning, or scenario analysis. The systematic map highlights a variety of knowledge gaps and suggests questions that warrant further research.

A Weather-Driven Model for Predicting Infections of Grapevines by Sporangia of Plasmopara viticola.

A mechanistic model was developed to predict secondary infections of Plasmopara viticola and their severity as influenced by environmental conditions; the model incorporates the processes of sporangia production and survival on downy mildew (DM) lesions, dispersal and deposition, and infection. The model was evaluated against observed data (collected in a 3-year vineyard) for its accuracy to predict periods with no sporangia (i.e., for negative prognosis) or with peaks of sporangia, so that growers can identify periods with no/low risk or high risk. The model increased the probability to correctly predict no sporangia [P(P-O-) = 0.67] by two times compared to the prior probability, with fewer than 3% of the total sporangia found in the vineyard being sampled when not predicted by the model. The model also correctly predicted peaks of sporangia, with only 1 of 40 peaks unpredicted. When evaluated for the negative prognosis of infection periods, the model showed a posterior probability for infection not to occur when not predicted P(P-O-) = 0.87 with only 9 of 108 real infections not predicted; these unpredicted infections were mild, accounting for only 4.4% of the total DM lesions observed in the vineyard. In conclusion, the model was able to identify periods in which the DM risk was nil or very low. It may, therefore, help growers avoid fungicide sprays when not needed and lengthen the interval between two sprays, i.e., it will help growers move from calendar-based to risk-based fungicide schedules for the control of P. viticola in vineyards.

Can Spore Sampler Data Be Used to Predict Plasmopara viticola Infection in Vineyards?

Grapevine downy mildew (DM) is caused by the dimorphic oomycete Plasmopara viticola, which incites epidemics through primary and secondary infection cycles that occur throughout the season. The secondary infection cycles are caused by the sporangia produced on DM lesions. The current research examined the relationship between numbers of airborne sporangia and DM development on grape leaves to determine whether spore sampler data can be useful to predict the potential for secondary infections of P. viticola. Three years (2015-2017) of spore sampler data confirmed that sporangia are a common component of the airborne microflora in a DM-infested vineyard and that their numbers depend on weather conditions. For a total of 108 days, leaf samples were collected from the vineyard at 2- to 3-day intervals and incubated under optimal conditions for P. viticola infection. The numbers of airborne sporangia sampled on 1 to 7 days before leaf sampling were significantly correlated with the numbers of DM lesions on the leaves. The best correlation (r=0.59), however, was found for the numbers of viable airborne sporangia (SPV), which were assessed by using equations driven by the vapour pressure deficit. In Bayesian and ROC curve analyses, SPV was found to be a good predictor of P. viticola infection of grape leaves, with AUROC=0.821 and false positive predictions mainly occurring at low SPV. A binary logistic regression showed that a threshold of 2.52 viable sporangia m-3 air day-1 enables a prediction of no infection with a posterior probability of 0.870, which was higher than the prior probability of 0.574. Numbers of viable sporangia in the vineyard air is therefore a useful predictor of infection and especially of no infection. The predictor missed some observed infections, but these infections were not severe (they accounted for only 31 of 374 DM lesions).

Biocontrol of Botrytis cinerea on Grape Berries as Influenced by Temperature and Humidity.

Six commercial biocontrol agents (BCAs, containing Aureobasidium pullulans, Bacillus amyloliquefaciens, Bacillus amyloliquefaciens plantarum, Bacillus subtilis, Pythium oligandrum, or Trichoderma atroviride) were applied to ripening berries that were then incubated at one of four temperatures (T, 15, 20, 25, and 30°C) and one of four relative humidity levels (RH, 60, 80, 90, and 100%). After 1 to 13 days of incubation (BCA colonization period), the berries were inoculated with conidia of Botrytis cinerea and kept at 25°C and 100% RH for 7 days, at which time Botrytis bunch rot (BBR) was assessed. The response of BBR control to T/RH conditions and BCA colonization period differed among BCAs; the coefficients of variation among the BCAs ranged from 44.7 to 72.4%. An equation was developed that accounted for the combined effects of T, RH, and BCA colonization period on BBR control. The equation, which had an R2>0.94, could help farmers select the BCA to be used for a specific application based on weather conditions at the time of treatment and in the following days.