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Plant resistance inducers (PRIs) are of increasing interest for disease control in sustainable viticulture, but their efficacy in vineyards is still under investigation. We studied the effects of four commercial PRIs, of natural and microbial origin, on the development of powdery mildew (PM) on grapevine leaves over a 3-year period. Disease and sporulation severity were evaluated following the artificial inoculation of PRI-treated leaves with Erysiphe necator conidia at 1, 3, 7 and 12 days after treatment (DAT). Laminarin (LAM) and cos-oga (COS) were the most effective in reducing both disease (57.1% and 49.5%, respectively) and sporulation (85.3% and 90.2%, respectively) severity; the reduction in PM severity provided by cerevisane (CER) was lower (27%), and that of Pythium oligandrum (PYT) was intermediate (44.9%). LAM was effective from 1 DAT, while COS and PYT were more effective when E. necator was inoculated at 7 to 12 DAT. These results open new perspectives on how PRIs are used as single, preventative interventions performed 1 to 3 days before E. necator infection, depending on the product used. The effect of PRIs on the sporulation of E. necator colonies also suggests a broader and longer effect on PM epidemics.PRIs can be used when PM infection risk is considered, and based on preventative treatments applied between 1 to 3 days before Erysiphe necator infection, depending on the product.image

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 prognosis than for positive prognosis; a rain cutoff of $0.2 mm provided an overall accuracy of $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.

Crop diseases are increasingly causing devastating yield losses each year, posing a significant threat to global food security. Currently, fungicide treatment is one of the most effective measures to mitigate these disease-induced yield losses. Accurately predicting disease occurrence, onset timing, and development is critical for optimizing fungicide application schedules to achieve high efficacy. In this study, we compiled weekly disease observations, crop management, and weather data from 207 locations across China, India, and Japan. We compared six machine learning (ML) models for their performance in simulating disease severity. By mimicking disease development curves and trends at early stages, we developed a novel change detection (CD) model, named rolling linear regression (RLR), and combined it with the best-performing ML model to predict the occurrence, severity, and onset dates of four major rice diseases: leaf blast (Magnaporthe oryzae), panicle-neck blast (Magnaporthe oryzae), sheath blight (Rhizoctonia solani), and false smut (Ustilaginoidea virens). Our findings showed that the RandomForestRegressor demonstrated the highest performance in simulating disease severity, with mean absolute errors (MAE) below 0.5 % and root mean square errors (RMSE) below 2.5 %. The RLR model showed a distinct advantage over the other four widely used CD models based on five evaluation metrics. Additionally, the paired RandomForestRegressor+RLR model achieved the highest F1-score, ranging from 0.7 to 0.8, in predicting disease occurrence, outperforming 29 other ML+CD model pairs. Furthermore, the RandomForestRegressor+RLR model predicted onset dates with fewer than 6 error days and accuracies ranging from 74 % to 87 %. Combining ML with CD models not only shows robust generalization across diverse environmental conditions but also proves highly effective for large-scale disease risk forecasting in rice farming regions. The adaptability of ML techniques, when sufficient tra

Sour rot (SR) is a disease complex that affects grape berries during ripening and can cause severe yield losses and deterioration of wine quality. The etiology and epidemiology of the disease remain uncertain, which has severely limited the development of specific, targeted management strategies. In this study, a network meta-analysis was applied to data collected through a previous systematic literature review for statistically comparing the efficacy of different methods for the control of SR and some filamentous fungi isolated from rotten berries. Use of either synthetic fungicides (CHEM) and natural compounds or biocontrol microorganisms (BIO) provided partial and variable control of SR; however, the efficacy of BIO was similar to, or higher than, that provided by CHEM. Agronomic practices (AGRO) had a significant but lower effect on SR. The integration of different control methods (IPM) provided better and less variable disease control than any single method. Natural compounds, such as zeolites and bicarbonates, and microorganisms (e.g., yeasts Candida and Aureobasidium) are also promising alternatives to synthetic fungicides in SR control.

Grapevine trunk diseases (GTDs) are among the most devastating grapevine diseases globally. GTDs are caused by numerous fungi belonging to different 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 conducted a systematic literature review, extracted quantitative data from published papers, and used these data to identify trends and knowledge gaps that need 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 hemisphere 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 because of higher variability in spore types, sporulation conditions, and spore release mechanisms in EC and ED fungi than in BD. Our analysis has limitations because of knowledge gaps and data availability for some fungi (e.g., basidiomycetes, which cause EC). These limitations are discussed to facilitate further research.

White rot, caused by the fungus Coniella diplodiella, can severely reduce grapevine yields worldwide. Currently, white rot control mainly relies on fungicides applied on a calendar basis or following hailstorms that favor disease outbreaks; however, the control achieved with this strategy is often inconsistent or otherwise unsatisfactory. Realizing more rational control requires an improved understanding of white rot epidemiology. To this end, we conducted experiments with grapevine berries of two Vitis vinifera cultivars (either injured or not before artificial inoculation with a conidial suspension of C. diplodiella) to determine the effect of temperature on the length of latency (i.e., the time between infection and onset of mature pycnidia on berries) and the production of pycnidia and conidia. Sporulation occurred between 10 and 35°C, with the optimum detected at 20°C. The latency period (LP) was shorter at 25 to 35°C than at lower temperatures; the shortest LP was 120 h at 30°C on injured berries. Affected berries produced abundant conidia at 15 to 30°C (the optimum was 20°C) for more than 2 months following inoculation. Mathematical equations were developed that fit the data, with strong associations with temperature for the LP (R2 = 0.831) and for the production dynamics of secondary conidia (R2 = 0.918). These equations may contribute to the development of a risk algorithm to predict infection periods, which can inform risk-based disease control strategies rather than calendar-based disease control strategies.

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.

The Global Plant Health Assessment (GPHA) is a collective, volunteer-based effort to assemble expert opinions on plant health and disease impacts on ecosystem services based on published scientific evidence. The GPHA considers a range of forest, agricultural, and urban systems worldwide. These are referred to as (Ecoregion x Plant System), i.e., selected case examples involving keystone plants in given parts of the world. The GPHA focuses on infectious plant diseases and plant pathogens, but encompasses the abiotic (e.g., temperature, drought, and floods) and other biotic (e.g., animal pests and humans) factors associated with plant health. Among the 33 (Ecoregion x Plant System) considered, 18 are assessed as in fair or poor health, and 20 as in declining health. Much of the observed state of plant health and its trends are driven by a combination of forces, including climate change, species invasions, and human management. Healthy plants ensure (i) provisioning (food, fiber, and material), (ii) regulation (climate, atmosphere, water, and soils), and (iii) cultural (recreation, inspiration, and spiritual) ecosystem services. All these roles that plants play are threatened by plant diseases. Nearly none of these three ecosystem services are assessed as improving. Results indicate that the poor state of plant health in sub-Saharan Africa gravely contributes to food insecurity and environmental degradation. Results further call for the need to improve crop health to ensure food security in the most populated parts of the world, such as in South Asia, where the poorest of the poor, the landless farmers, are at the greatest risk. The overview of results generated from this work identifies directions for future research to be championed by a new generation of scientists and revived public extension services. Breakthroughs from science are needed to (i) gather more data on plant health and its consequences, (ii) identify collective actions to manage plant systems, (iii) e

A plant disease model is a simplified representation of the relationships between pathogens, crops, and the environment that cause the development of epidemics over time and/or space. This chapter describes the different modelling approaches, focusing on fundamental (process-based) models for application in crop protection. The development of process-based dynamic models consists of four steps: (i) definition of the intended use of the model; (ii) conceptualization of the system; (iii) development of the mathematical framework; and (iv) model evalua- tion. Plant disease models now have a key role in supporting the decision-making process in integrated pest management (IPM). The advantages of using models in IPM are linked to their ability to process and analyze complex information and to provide outputs supporting decision-making at strategic and tactical levels. In particular, simulation models can support strategic decision-making, and predictive models can assist growers in making tactical decisions. Finally, the chapter briefly considers how different models can be combined with the aim of helping growers to make correct and objective decisions about crop protection.

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.

This review gives a perspective of selected advances made since the middle of the 20th century in plant disease modelling, 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-modelling approach to develop decision-making tools in the application of Integrated Pest Management (IPM).

Plant resistance inducers (PRIs) are potential alternatives for controlling grapevine downy (DM) and powdery (PM) mildews in vineyards. In a 3-year field study, we evaluated the field efficacy of six commercial PRIs of chemical and natural origin against DM and PM diseases when applied at designated vine growth stages in a mixture with low doses of copper and sulfur, and only when advised by weather-driven disease models. The disease severity and incidence were evaluated for each season at key growth stages (i.e., the end of flowering, berries pea-sized, veraison, and pre-harvest), and areas under the disease progress curves (AUDPC) were calculated and compared with those of nontreated vines. These risk-based applications resulted in a 41% and 61% reduction of interventions against DM and PM, respectively, compared to the official advice for integrated pest management in the growing area. These applications provided a disease control efficacy of 88% for DM and 93% for PM; the disease severity on bunches never exceeded 5%. Overall, when the disease severity was expressed as AUDPC, we observed higher efficacy of all the PRIs for PM, and of laminarin and cerevisane for DM. We also found that potassium phosphonate and fosetyl-Al (commonly used against DM) were effective against PM, and cos-oga (used against PM) was effective against DM. These results broaden the application and integration of PRIs in viticulture.

The growth of four commercial biocontrol agents (BCAs: Bacillus amyloliquefaciens (BAD), Aureobasidium pullulans (APD), Metschnikowia fructicola (MFN), and Trichoderma atroviride (TAS)) was evaluated using turbidimetric assays on artificial substrates mimicking the chemical berry composition at four stages: pea-sized berries, veraison, softening, and ripe berries. The response of BCA growth differed among BCAs. Subsequently, the BCAs’ population size was assessed after 1 to 13 days of incubation on the substrate mimicking ripe berries at 15 to 35 °C. The population size of BAD increased with temperatures, while that of MFN decreased; the population sizes of APD and TAS showed bell-shaped patterns with lower growth at 15 or 35 °C. Finally, the BCAs were applied to ripe berries and then incubated at 15 to 30 °C. After 1 to 13 days, the berries were inoculated with B. cinerea and incubated for 7 days, after which the BCA control efficacy was assessed. The highest control was observed at 25 °C for BAD and APD, at 15 to 20 °C for MFN, and at 25 to 30 °C for TAS. The results confirm that the plant substrates and temperature affect the population size of the BCA following application; temperature also affects the preventative efficacy of BCA against B. cinerea.