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

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.

A Bayesian model for control strategy selection against Plasmopara viticola infections

Plant pathogens pose a persistent threat to grape production, causing significant economic losses if disease management strategies are not carefully planned and implemented. Simulation models are one approach to address this challenge because they provide short-term and field-scale disease prediction by incorporating the biological mechanisms of the disease process and the different phenological stages of the vines. In this study, we developed a Bayesian model to predict the probability of Plasmopara viticola infection in grapevines, considering various disease management approaches. To aid decision-making, we introduced a multi-attribute utility function that incorporated a sustainability index for each strategy. The data used in this study were derived from trials conducted during the production years 2018-2020, involving the application of five disease management strategies: conventional Integrated Pest Management (IPM), conventional organic, IPM with substantial fungicide reduction combined with host-defense inducing biostimulants, organic management with biostimulants, and the use of biostimulants only. Two scenarios were considered, one with medium pathogen pressure (Average) and another with high pathogen pressure (Severe). The results indicated that when sustainability indexes were not considered, the conventional IPM strategy provided the most effective disease management in the Average scenario. However, when sustainability indexes were included, the utility values of conventional strategies approached those of reduced fungicide strategies due to their lower environmental impact. In the Severe scenario, the application of biostimulants alone emerged as the most effective strategy. These results suggest that in situations of high disease pressure, the use of conventional strategies effectively combats the disease but at the expense of a greater environmental impact. In contrast to mechanistic-deterministic approaches recently published in the literature, the proposed Bayesian model takes into account the main sources of heterogeneity through the two group-level effects, providing accurate predictions, although precise estimates of random effects may require larger samples than usual. Moreover, the proposed Bayesian model assists the agronomist in selecting the most effective crop protection strategy while accounting for induced environmental side effects through customizable utility functions.

Modeling the dynamic changes in Plasmopara viticola sporangia concentration based on LSTM and understanding the impact of relative factor variability

Reliable disease management can guarantee healthy plant production and relies on the knowledge of pathogen prevalence. Modeling the dynamic changes in spore concentration is available for realizing this purpose. We present a novel model based on a time-series modeling machine learning method, i.e., a long short-term memory (LSTM) network, to analyze oomycete Plasmopara viticola sporangia concentration dynamics using data from a 4-year field experiment trial in North China. Principal component analysis (PCA)-based high-quality input screening and simulation result calibration were performed to ensure model performance, obtaining a high determination coefficient (0.99), a low root mean square error (0.87), and a low mean bias error (0.55), high sensitivity (91.5%), and high specificity (96.5%). The impact of the variability of relative factors on daily P. viticola sporangia concentrations was analyzed, confirming that a low daily mean air temperature restricts pathogen development even during a long period of high humidity in the field.

Current Trends and Perspectives on Predictive Models for Mildew Diseases in Vineyards

Environmental and economic costs demand a rapid transition to more sustainable farming systems, which are still heavily dependent on chemicals for crop protection. Despite their widespread application, powdery mildew (PM) and downy mildew (DM) continue to generate serious economic penalties for grape and wine production. To reduce these losses and minimize environmental impacts, it is important to predict infections with high confidence and accuracy, allowing timely and efficient intervention. This review provides an appraisal of the predictive tools for PM and DM in a vineyard, a specialized farming system characterized by high crop protection cost and increasing adoption of precision agriculture techniques. Different methodological approaches, from traditional mechanistic or statistic models to machine and deep learning, are outlined with their main features, potential, and constraints. Our analysis indicated that strategies are being continuously developed to achieve the required goals of ease of monitoring and timely prediction of diseases. We also discuss that scientific and technological advances (e.g., in weather data, omics, digital solutions, sensing devices, data science) still need to be fully harnessed, not only for modelling plant-pathogen interaction but also to develop novel, integrated, and robust predictive systems and related applied technologies. We conclude by identifying key challenges and perspectives for predictive modelling of phytopathogenic disease in vineyards.

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.

Evaluation of the Characteristics and Infectivity of the Secondary Inoculum Produced by Plasmopara viticola on Grapevine Leaves by Means of Flow Cytometry and Fluorescence-Activated Cell Sorting

Plasmopara viticola, the oomycete causing grapevine downy mildew, is one of the most important pathogens in viticulture. P. viticola is a polycyclic pathogen, able to carry out numerous secondary cycles of infection during a single vegetative grapevine season, by producing asexual spores (zoospores) within sporangia. The extent of these infections is strongly influenced by both the quantity (density) and quality (infectivity) of the inoculum produced by the pathogen. To date, the protocols for evaluating all these characteristics are quite limited and time-consuming and do not allow all the information to be obtained in a single run. In this study, a protocol combining flow cytometry (FCM) and fluorescence-activated cell sorting (FACS) was developed to investigate the composition, the infection efficiency and the dynamics of the inoculum produced by P. viticola for secondary infection cycles. In our analyses, we identified different structures within the inoculum, including degenerated and intact sporangia. The latter have been sorted, and single sporangia were directly inoculated on grapevine leaf discs, thus allowing a thorough investigation of the infection dynamics and efficiency. In detail, we determined that, in our conditions, 8% of sporangia were able to infect the leaves and that on a susceptible variety, the time required by the pathogen to reach 50% of total infection is about 10 days. The analytical approach developed in this study could open a new perspective to shed light on the biology and epidemiology of this important pathogen. IMPORTANCE P. viticola secondary infections contribute significantly to the epidemiology of this important plant pathogen. However, the infection dynamics of asexual spores produced by this organism are still poorly investigated. The main challenges in dissecting the grapevine-P. viticola interaction in vitro are attributable to the biotrophic adaptation of the pathogen. This work provides new insights into the infection efficiency and dynamics imputable to P. viticola sporangia, contributing useful information on grapevine downy mildew epidemiology. Moreover, future applications of the sorting protocol developed in this work could yield a significant and positive impact in the study of P. viticola, providing unmatched resolution, precision, and accuracy compared with the traditional techniques.

A public decision support system for the assessment of plant disease infection risk shared by Italian regions

Integrated pest management (IPM) practices proved to be efficient in reducing pesticide use and ensuring economic farming sustainability. Digital decision support systems (DSS) to support the adoption of IPM practices from plant protection services are required by European legislation. Available DSSs used by Italian plant protection services are heterogeneous with regards to disease forecasting models, datasets for their calibration, and level of integration in operational decision-making. This study presents the MISFITS-DSS, which has been jointly developed by a public research institution and nine regional plant protection services with the objective of harmonizing data collection and decision support for Italian farmers. Participatory approach allowed designing a predictive workflow relying on specific domain expertise, in order to explicitly match actual user needs. The DSS calibration entailed the risk of grapevine downy mildew infection (5-point scale from very low to very high), and phenological observations in 2012-2017 as reference data. Process-based models of primary and secondary infections have been implemented and tested via sensitivity analysis (Morris method) under contrasting weather conditions. Hindcast simulations of grapevine phenology, host susceptibility and disease pressure were post-processed by machine-learning classifiers to predict the reference infection risk. Results indicate that IPM principles are implemented by plant protection services since years. The accurate reproduction of grapevine phenology (RMSE = 4-14 days), which drove the dynamic of host susceptibility, and the use of weather forecasts as model inputs contributed to reliably predict the reference infection risk (88% balanced accuracy). We did a pioneering effort to homogenize the methodology to deliver decision support to Italian farmers, by involving plant protection services in the DSS definition, to foster a further adoption of IPM practices.

LAMP for in-field quantitative assessments of airborne grapevine downy mildew inoculum

AIMS

Cheap, rapid tools for measuring emissions of Plasmopara viticola sporangia directly in the field are required to protect grapevines efficiently and sustainably against downy mildew. To this end, we adapted an existing loop-mediated isothermal amplification (LAMP) protocol based on ITS2 sequences, coupled with a rotating-arm sampler and simple cell lysis, for the in-field measurement of airborne sporangia of P. viticola.

METHODS AND RESULTS

We estimated the sensitivity and specificity of the molecular reaction with an unpurified DNA template in controlled conditions, using the Droplet Digital PCR (ddPCR) as a reference. We show that the LAMP lower limit of quantification is 3.3 sporangia.m^-3 air sampled. Cell lysis in KOH solution was less efficient than CTAB for DNA extraction, but the repeatability of the method was good. We tested this protocol directly in a plot at Chateau Dillon (Blanquefort, France) in which we monitored P. viticola sporangia concentrations from March to October 2020 (88 samples which revealed concentrations ranging from 0 to 243 sporangia.m^-3 ). There was a significant quantitative correlation (R² = 0.52) between ddPCR and LAMP results.

CONCLUSION

LAMP analysis of an unpurified DNA matrix is a simple and reliable method for in-field estimations of the concentration of airborne P. viticola sporangia.

SIGNIFICANCE AND IMPACT OF STUDY

This study constitutes a first step towards the development of a regional grapevine downy mildew monitoring network in the vineyards of Bordeaux.

Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology

One major problem that affecting grape production is that of infestations by fungal pathogens, among which Plasmopara viticola is one of the worst, causing grapevine downy mildew. This can cause substantial damage to a vineyard, which leads to economic losses. Methods of predicting disease outbreak rely on the monitoring of meteorological parameters. With the recent development of Internet of Things (IoT) technologies, in situ data can be efficiently collected on a large scale. In this paper, a new model with early warning system implementation for grapevine downy mildew based on Narrow Band IoT (NB-IoT) and energy harvesting is presented. Models of downy mildew warning systems have evolved from the early temperature-based (and later, humidity-based) models to the latest mechanistic models which include rainfall/leaf wetness and hourly monitoring. We added parameters such as ’favorable night condition’ and ’wind speed’ as critical for sporangia spreading. The comparison of the model with the commercial iMetos® warning system and the latest mechanistic model for three specific vineyard locations indicates a high correlation between alarms.