Models of fungal pathogens
Informative and operational guide on forecasting models and annexed DSS software dedicated to fungal pathologies
Previsional model: Downy mildew of grapevine
Input data
Air temperature, relative humidity, leaf wetness, rainfall.
Dati di output
Temporal evolution of the risk level relating to primary infections, divided into successive phases: germination phase of the soil oospore (sporangia formation), release of zoospores from sporangia, dispersion of zoospores on leaves and vine shoots, infection of leaf blades by part of zoospores (date of primary infection), incubation period (period of formation of oil spots on leaves).
Primary infection rate (associated with each infection detected).
Dates of secondary infections, identified as the onset dates of infectious sporulations.
Secondary infection rate (associated with each infection detected).
References
The present model was developed starting from various modeling solutions for the reproduction of the Plasmopara viticola cycle, and in particular it is inspired by the basic structure of the model by Rossi et al (2008) originally tested and evaluated over a ten-year period in areas to different epidemiological and phytoiatric conditions.
The Primo Principio development team worked to update and improve the model with respect to the original bibliographic formulations through a series of modeling decisions and expedients which concerned the choice of algorithms that were able to better represent the natural biological mechanisms of the pathogen according to field observations and in accordance with the purpose of the tool itself, which is the operational management of the vineyard.
The Peronospora model has therefore been recently validated on the agroclimatic conditions of recent years through a design collaboration between the Primo Principio team, the Consorzio di Tutela dei Vini del Collio and the Slovenian University of Primorska and will be periodically revised to the state of the art and examined according to its application within new territorial realities.
Modeling functionality and additional information
The model is able to improve the timing and number of treatments by acting on the following considerations:
identifying symptoms in the field takes time;
the moment of infection of the zoospores on the leaves (key moment of disease development) anticipates the appearance of the first symptoms;
the first symptoms are difficult to detect in their entirety
early or delayed treatments have little or no effect on the spread of the pathogen (Costa and Rosa, 1998).
However, it should be remembered that the model represents a simplified simulation of reality with the aim of providing decision support. Therefore, it is not designed to replace field observations.
The graphical interface of the model in the WiForAgri platform is shown below.
The model (Figure 1) takes into account the coverage period declared for the treatments carried out, reporting it as a green color band on the primary graph.
Description of the model
Primary infections
The biological cycle of Peronospora (Plasmopora viticola) starts from the survival of the oospore which overwinter in the soil inside the leaf particles fallen the previous year. The model is able to determine when, in correspondence with the first spring rains and in the presence of certain temperature and humidity conditions, the oospores are able to germinate, forming sporangia which in turn will give rise to infecting spores (zoospores).
The model calculates, in consideration of recent developments, the germination of multiple groups of oospores, called cohorts, capable of originating multiple primary infections throughout the growing season, which flank, overlap and, possibly, dominate the secondary infections.
Following the formation of the zoospores released by the sporangia of the oospores, the advent of the so-called "infectious rain" causes the dispersion of the zoospores which move from the water in the soil to the leaves of the plants, through the so-called splash effect (squirt) caused by rainfall.
At this point the model calculates whether the primary infection will be able to complete, i.e. whether the zoospores will be able to germinate or penetrate the leaf epidermis, based on a minimum combination between the number of hours in which the leaves remain wet and the average temperature during this interval.
Once this infectious phase has begun, the fungus will attack the leaf tissues without any symptoms appearing. After this phase the model will pass to the calculation of the incubation period on the basis of the temperature, at the end of which the time interval in which the symptoms of the infection will appear (oil stains) is estimated.
Everything is graphically summarized in the example of a graphical interface shown below. The various cohort germination cycles (blue lines - cohort germination) are graphically shown in the example of the graphical interface displayed below.
The state variables plotted by the model for primary infections are shown below in tabular form.
Pre-germination phase (not displayed): The germination phase of the actual oospores is preceded by a pre-germination phase (not displayed in the model graph), i.e. their maturation from the initial quiescent state.
Dose of overwintering oospores: represents the starting inoculum source.
Morphologically mature oospores: The model assumes that the entire oospores population has been in this state since January 1st.
Physiologically mature oospores: Oospores exit the dormancy phase and this change is governed by the variable DOR (progress of breaking dormancy). DOR is calculated every hour using a complex equation which depends on HT (where HT is a hydro-thermal curve).
Germination phase (visualized-GER): Every time a precipitation event occurs, a group of physiologically mature oospores begins the germination process ending with the formation of sporangia (GER). The progress of the germination process over time is graphically represented by the blue lines.
Secondary infections
Once the conditions for the primary infection have been completed, the model starts the secondary infection phase (without however stopping the calculation of the primary infections which will continue throughout the season). From a biological point of view, secondary infection consists in the formation of further sporangia from the primary infectious mycelium. The sporangia generated (sporulation), carried by the wind, infect new portions of the vineyard by affecting leaves, shoots and berries. In view of the fact that sporulation is inhibited by day light, the model considers the conditions for secondary infections only at night (between 8.00 pm and 7.00 am). For this reason, a good indication is to consult the status of secondary infections in the morning.
Through the alarm function, available at the bottom of the web page of the downy mildew model (Figure 5), it is possible to activate the mail/sms alarms corresponding to the single status variables.
Alarm GER: It warns that a group of oospores has finished germinating (100%) and there are conditions for the release of zoospores into the soil.
Alarm ZRE: It warns that zoospores have been released into the soil.
Alarm ZDI: It warns that the zoospores have been dispersed from the soil to the leaves of the host plants.
Alarm ZIN: It warns that the primary infection has occurred.
Alarm SEC: Warns that secondary infection has occurred.
Decision support diagrams Downy mildew
Below are two decision support diagrams to be consulted in order to adopt treatment times consistent with the predictive logic of the model. The specific evaluation of the treatments (type of product and dosage) must always be carried out in accordance with field indications and according to the indications of a competent qualified technician.
In particular, the forecasting system will allow the following indications to be provided:
Time interval windows (short-term) in which it will be possible to enter the field in a preventive manner with respect to infections (optimal timing of preventive interventions).
Time interval windows (short term) in which it will be necessary to act with the curative products to block the infection (optimal timing of post-infection interventions).
Identification of the infectious cycles of the pathogen that do not give rise to any infection, for the identification of the periods in which it is not necessary to treat (reduction of treatments).
The first diagram in Figure 5 provides decision support regarding primary infections. The second diagram in Figure 6 provides them for secondary infections.
Step to follow for reading the diagram (PRIMARY)
At the beginning of the season,it monitors the state of vegetative recovery of the plants in the field, observing in particular the phenological phase 'first extended leaf' (BBCH=11)che which is indicated as a fundamental phenological phase for the evaluation of the onset of sensitivity of the plants to peronosporic infections.
Steps to follow for reading the diagram (SECONDARY)
At the beginning of the season, it monitors the state of vegetative recovery of the plants in the field, observing in particular the phenological phase "first extended leaf" (BBCH=11) which is indicated as a fundamental phenological phase for the evaluation of the onset of sensitivity of the plants to peronosporic infections.
Washout of downy mildew treatments
Premise
It is advisable to use the Cumulative indices function to accurately check the mm of rain that has fallen since the day of the last treatment carried out. Furthermore, the mail/sms alarms, activated on the cumulative indexes, are useful for being updated on the daily cumulative rainfall recorded (these alarms are activated in the event of the daily exceeding of the configured alarm threshold).
Copper roofing products
The use of these products is of a preventive type (cover) and therefore, following the indications of the model and the weather forecasts, these must be positioned before the infectious rains. The most common commercial formulations have a persistence of action of 4-7 days in relation to rainfall and vegetative growth. The resistance to washout is about 20-40mm of rain depending on the formulation (30mm for most products). To find out the persistence and leaching of a specific product, check the label/brochure or contact the manufacturer. From the experiences acquired in recent years it appears that the effectiveness of treatments with copper-based products does not depend on the formulation but on the moment of use: the timing of the treatments is therefore fundamental, as suggested by the model and the decision diagram (at the following link: Models of fungal diseases ).
Roofing chemicals (non-copper)
They are non-copper products, of chemical synthesis, used for preventive coverage purposes. Their main advantage is resistance to washout. Generally the molecules that act by contact are easily washed away even with rain of normal intensity (on average, the copper compounds are washed away after 30 mm of rain). This leads to the removal of the product or its accumulation in peripheral areas of the vegetation. Some synthetic roofing products resist washout even beyond 50mm of rain providing lasting protection that allows lengthening the intervals between applications also, and especially, when in association with endotherapeutic molecules.
Endotherapeutic products (systemic, cytotropic-translaminar, others)
These are chemical synthesis products used mainly post-infection for curative purposes (endotherapeutic action) and at the same time preventive for new infections (superficial preventive action). They can be used together with covering products to strengthen the preventive action especially in vigorous vineyards subject to prolonged wetting (generally starting from the flowering phase). There is a large variety of products on the market based on different active ingredients, belonging to different chemical groups. The average frequency of treatments with these products is 8-12 days (average persistence) taking care to treat on dry vines anticipating any rain. To find out the persistence of action of a specific product, check the label/brochure or contact the manufacturer. Endotherapeutic treatments are not subject to leaching once absorbed by the vegetation. The pure formulations must always be used in a mixture with partners (copper, fosetyl, phosphites) for a correct management of the anti-resistance strategy. To reduce the risk of resistance, it is important to use products belonging to the same chemical group for a maximum of 3 treatments per season, except for more restrictive indications on the label or provided for by the product specification.
Anti-resistance strategies
At European level there is a committee of experts from the various multinationals in the agrochemicals sector (FRAC: Fungicide Resistance Action Committee) which monitors, studies and draws up suggestions and strategies for managing the resistance of pathogens to fungicides. Over time, this action group has become one of the important and recognized references on the theme of resistance management and we take some food for thought from its official website (www.frac.info). Most of the systemic products act on the vital processes of downy mildew in a single site or with a single mechanism of action (monosite products) and for this reason this category of fungicides is more susceptible to developing resistance by the fungus. Contact fungicidal products that act in different areas on the vital processes of the fungus or with different mechanisms of action are called multisite (eg copper). This means making the development of a resistant strain of downy mildew unlikely. To try to counteract resistance phenomena, it is recommended to:
reduce the number of consecutive treatments with systemic products (or with other single-site products);
alternate active substances with different mechanism of action (different chemical groups);
mix single-site systemic substances with a multi-site product (e.g. copper).
Management of downy mildew in organic farming
In organic viticulture, the most used and most effective product to date for defense against downy mildew is copper (acid clays are certainly less effective). Knowledge of the vineyard and the biology of the disease, accompanied by careful observation of the untreated witnesses and of the forecast model, are fundamental elements for setting up an effective defense. Prevention is of fundamental importance throughout the period of susceptibility to the fungus. At the beginning of the season, the interventions are weighted according to the vegetative growth and the pre-infectious forecasts of the model; the contribution of copper metal is usually low, around 20 g/hl. Continuing with the season, the interventions must be weighted according to the pressure of the disease (model forecasts) taking care to always anticipate rainy events. In anticipation of intense and prolonged rainy events, the dose of copper can be increased up to 50 g/hl. A further and interesting aid in the management of downy mildew could include the use of different copper formulations according to the pressure of the disease and the current season, also playing on the contributions of copper metal.
Bibliographical references
Ash, G. 2000. Downy mildew of grape. 2000. The plant health instructor. DOI: 10.1094/PHI-I-2000-1112- 01. Updated 2017. URL: https://www.apsnet.org/edcenter/intropp/lessons/fungi/Oomycetes/Pages/DownyMildewGrape.aspx
Bettiga, L. J. (2013). Grape Pest Management, Third Edition: University of California, Agriculture and Natural Resources.
Blaeser, M., & Weltzien, H. C. (1979). Epidemiologische Studien an Plasmopara viticola zur Verbesserung der Spritzterminbestimmung. Journal of Plant Disease and Protection, 86, 489-498.
Boso, S., Alonso-Villaverde, V., Gago, P., Santiago, J. L., & Martínez, M. C. (2014). Susceptibility to downy mildew (Plasmopara viticola) of different Vitis varieties. Crop Protection, 63(0), 26- 35. https://doi.org/http://dx.doi.org/10.1016/j.cropro.2014.04.018
Buck, A. L. (1981). New equations for computing vapor pressure and enhancement factor. Notes, 20, 1527-1532.
Caffi, T., Rossi, V., & Bugiani, R. (2010). Evaluation of a Warning System for Controlling Primary Infections of Grapevine Downy Mildew. Plant Dis, 94, 709-716
Caffi, T., Gilardi, G., Monchiero, M., & Rossi, V. (2012a). Production and Release of Asexual Sporangia in Plasmopara viticola. Phytopathology, 103(1), 64-73. https://doi.org/10.1094/PHYTO-04-12-0082-R
Caffi, T., Legler, S. E., González-Domínguez, E., & Rossi, V. (2015). Effect of temperature and wetness duration on infection by Plasmopara viticola and on post-inoculation efficacy of copper. European Journal of Plant Pathology, 144(4), 737-750. https://doi.org/10.1007/s10658-015-0802-9
Carisse, O. (2015). Development of grape downy mildew (Plasmopara viticola) under northern viticulture conditions: influence of fall disease incidence. European Journal of Plant Pathology, 144(4), 773-783. https://doi.org/10.1007/s10658-015-0748-y
Dalla Marta, A., Magarey, R. D., & Orlandini, S. (2005). Modelling leaf wetness duration and downy mildew simulation on grapevine in Italy. Agricultural and Forest Meteorology, 132(1-2), 84-95. https://doi.org/10.1016/j.agrformet.2005.07.003
Gessler, C., Pertot, I., & Perazzolli, M. (2011). Plasmopara viticola: a review of knowledge on downy mildew of grapevine and effective disease management. Phytopathol. Mediterr, 20, 3-44.
Kennelly, M. M., Gadoury, D. M., Wilcox, W. F., Magarey, P. A., & Seem, R. C. (2007). Primary Infection, Lesion Productivity, and Survival of Sporangia in the Grapevine Downy Mildew Pathogen Plasmopara viticola. Phytopathology, 97(4), 512-522. https://doi.org/10.1094/PHYTO-97-4-0512
Lybbert, T. J., Magnan, N., & Gubler, W. D. (2012). Multi-dimensional responses to risk information: how do winegrape growers respond to disease forecasts & to what environmental effect? RMI-CWE Working Paper Number 1203
Menesatti, P., Antonucci, F., Costa, C., Mandala, C., Battaglia, V., & La Torre, A. (2013). Multivariate forecasting model to optimize management of grape downy mildew control. Vitis, 52(3), 141-148.
Odile, Carisse, O., David-Mathieu, Tremblay, Tristan, Jobin, . . . Walker. (2010). Disease Decision Support Systems: Their Impact on Disease Management and Durability of Fungicide Effectiveness.
OEPP/EPPO. (2001). EPPO Standard PP 2/23 (1) Good plant protection practice. Bulletin OEPP/EPPO Bulletin, 32, 371-392.
Pertot, I., Caffi, T., Rossi, V., Mugnai, L., Hoffmann, C., Grando, M. S., . . . Anfora, G. (2017). A critical review of plant protection tools for reducing pesticide use on grapevine and new perspectives for the implementation of IPM in viticulture. Crop Protection, 97, 70-84. https://doi.org/10.1016/j.cropro.2016.11.025
Rossi, V., Caffi, T., Bugiani, R., Spanna, F., Valle, D. D., & D, B. (2007). Estimating the germination dynamics of Plasmopara viticola oospores using hydro-thermal time. Plant Pathology, 57, 216-226.
Rossi, V., Caffi, T., Giosuè, S., & Bugiani, R. (2008). A mechanistic model simulating primary infections of downy mildew in grapevine. Ecological Modelling, 212(3-4), 480-491. https://doi.org/10.1016/j.ecolmodel.2007.10.046
Rossi, V., Caffi, T., Gobbin, D. 2013. Contribution of molecular studies to botanical epidemiology and disease modelling: grapevine downy mildew as a case-study. Eur J Plant Pathol, 135, 641-654.
Rossi, V., Salinari, F., Poni, S., Caffi, T., & Bettati, T. (2014). Addressing the implementation problem in agricultural decision support systems: the example of vite.net®. Computers and Electronics in Agriculture, 100(Supplement C), 88-99. https://doi.org/https://doi.org/10.1016/j.compag.2013.10.011
Rumbolz, J., Wirtz, S., Kassemeyer, H., Guggenheim, R., Schäfer, E., & Büche, C. (2002). Sporulation of Plasmopara viticola: Differentiation and Light Regulation. Plant Biology, 4(3). https://doi.org/10.1055/s-2002-32342
Previsional Model: powdery mildew of grapevine
Input data
Air temperature, relative humidity, hourly leaf wetness, hourly rainfall. Upon user input: phytosanitary treatments, phenological phases (necessary for the model to be reliable).
Output data
Dates of primary infections, starting instants of germination of the ascospores on the leaf surface.
Temporal evolution of the epidemiological risk index of secondary infections (propagation of conidia) in the two phases of the disease: 1) Risk phase, ie preparatory to the development of secondary infection; 2) Post-infection treatment phase, reports the temporal evolution of the disease propagation pressure on the vineyard (phase in which phytosanitary treatments must be applied).
Bibliographical references
The model implemented in the WiFor platform is based on a revision of the original UC Davis Powdery Mildew forecasting model, originally developed at the University of California at Davis by pathologist Dr. Doug Gubler and tested in the fight against powdery mildew by a group of Californian farmers over an of time of 30 years.
This modeling solution is proposed in the WiForAgri service in interactive mode (possibility of entering the treatments that influence the danger of the pathogen in real time) and has been revisited by our agronomic development team through the selection and calibration of the most representative algorithms. In addition, the model has been adapted to the phenological phases of local vine varieties, in application to the phytoclimatic conditions of the Italian peninsula.
Modeling functionality and additional information
The model is based on a daily risk index between 0 and 100 that can determine the disease pressure. On the basis of this risk index, it will be possible to size the recommended treatment interval according to the severity of the infection, according to a perspective of maximum ergonomics in the administration of pesticides. Testing it in the field, it has been verified that the use of the model allows saving on average from two to three treatments a year, against an equal or better control of the disease (Gubler et al 2003).
During periods of low disease pressure, growers and particularly those treating at minimum intervals, through the use of the model may be able to lengthen treatment intervals and save costs, whilst yielding acceptably inferior crop coverage.
However, it is during periods of high disease pressure that the greatest benefits are seen, as the model is able to warn the user to shorten the treatment shift and avoid significant economic losses. This advantage is substantial as powdery mildew epidemics can sometimes get out of the control of winemakers.
Finally, the added value of the model is that of improving the control of the disease in the first and sensitive stages of the disease settling through the calculation of primary infections when these are not yet visible to the naked eye. The symptoms that appear between mid-May and the consequent damage to the grapes in the subsequent stages of disease development are the consequence of infections that occur right from the initial stages of budding but which generally go unnoticed and therefore are not properly treated.
It is important to consider that powdery mildew can be at "economically harmful levels" even when the model indicates low disease pressure, especially if the previous treatment was ineffective due to factors including atmospheric factors, therefore it is advisable to always monitor the vineyard looking for symptoms of the disease, in conjunction with the interactive use of the model.
By more accurately synchronizing the timing of treatments with powdery mildew population dynamics, use of the model can also reduce the accumulation of fungicide-resistant strains within the pathogen gene pool, which mainly occurs when growers collectively they tend to treat at minimal intervals.
Model description
The model is able to determine the diffusion pressure of the pathogen in the two phases related to its development: the initial phase of diffusion of the ascospores (primary infections) and the subsequent phase of diffusion through the conidia, spores originating via asexual way (secondary infections).
The fungus overwinters in the perulas of the dormant buds in the form of mycelium or in the crevices of the bark of the branches in the form of cleistothecia (fruiting bodies containing the ascospores). Depending on whether the climatic conditions of the last months of September-October of the previous year are more or less favorable to the disease and the presence of leaching rainfall, the quantity of cleistothecia may be more or less abundant at the beginning of the growing season.
In correspondence with spring climatic conditions favorable to the hatching of the reproductive organs and the release of the ascospores, i.e. relatively high temperatures and humidity, there is an early development of the disease, with the release of the ascospores on the basal leaves closest to the rhytidoma (advent of the primary infection)
To determine the risk levels of primary ascospore infection, the model analyzes the leaf wetness periods starting from the activation date, measuring their duration and the average air temperature.
Activation date: During the autumn-winter period the model is inactive. It is activated either by a calendar date (90th day of the year - March 31st) or by the user entering a specific phenological phase (04-Bud opening) in the logbook of the lot in question. In order to obtain greater precision in estimating the development of the disease, it is recommended to start the model by entering the phenological stage of bud opening in the field log of the lot under consideration.
If the number of hours of leaf wetness (or high humidity) and the mean temperature measured during that wet period match a certain combination, primary ascospore infection will begin. This instant of settlement of the primary infection, highlighted by the first (larger) red triangle reported in the upper status bar of the model graph (Figure 2 below) is fundamental as it determines the timing of the subsequent stages of disease development. Since the sporulation process of the ascospores is scalar, several primary infections may occur in spring, in conjunction with the secondary ones (see figure below - subsequent primary infections). The calculation phase of the primary infections extends from the model to the beginning of flowering, identified by entering the phenological phase "BBCH 10 - beginning of flowering" from the notebook or automatically from the calendar. Once the first primary infection of the season has been determined, the model moves on to the next stage (risk stage): Once a day, a risk index is calculated which indicates the disease pressure.
POWDERY MILDEY MODEL
Risk phase
Once the first primary infection is over (germination of the ascospores on the leaves and invasion of the leaf epidermis), the calculation of a thermal summation begins in the risk phase. Beyond a certain threshold there will be a secondary infection, consisting in the spread of conidia (agamic spores) on non-infected portions of the vineyard. In this phase the model considers the temperature as the only variable for calculating the risk index. Generally, temperatures between 21 and 30 °C are ideal for the development of secondary infections; temperatures above 35 °C, on the other hand, slow down the disease.
The risk index starts from the value 0 on the starting day of the risk phase.
When the index reaches the value of 60 (high index) for the first time in the season, secondary infections begin and the next phase of treatment is carried out.
Treatment phase
The secondary infection has established itself on the vineyard; the model, through the daily calculation of the risk index, indicates the pressure of the disease allowing the interval and eventually the doses of the treatments to be adjusted accordingly. The risk index varies in the interval between 0 and 100 within which three risk ranges are distinguished: low, medium, high (indicates greater than or equal to 60).
For the correct use of the model, each anti-powdery treatment must be indicated in the treatment agenda: the treatment carried out is in fact considered by the model and will result in the zeroing of the risk index.
Indications for treatment and use of the Risk Index
After the first primary infection indicated by the model, it is important to periodically monitor the presence of the disease in the vineyard, especially when the risk index is medium-high. The model is in fact a decision-making support tool that absolutely must not completely replace field control.
The model suggests the first annual treatment in conjunction with the first primary infection. However, especially in the years following those with low powdery mildew infection, it is possible to postpone this treatment by concentrating on protecting the plants especially during the subsequent phenological phases (separate floral buds).
From the Treatment Phase onwards, the risk index informs the user about the reproduction rate of the pathogen. Table 1 and the decision diagram shown below guide the user in choosing the interval of days between two successive treatments, according to the type of product used and the risk index indicated by the model.
The index between 60-100 indicates a high pressure of the disease: the pathogen is reproducing every 5 days.
The index between 40-50 indicates an intermediate pressure: reproduction typically every 8-11 days (maximum every 15 days)
Index between 0-30 indicates low pressure: frequency of reproduction approximately every 15 days or nothing.
POWDERY MILDEW
Table 1 - Powdery mildew: recommended treatment intervals
Powdery mildew model alarm functionality: At the bottom of the powdery mildew model web page, it is possible to activate the mail/sms alarms corresponding to the single status variables of the model.
Risk Index Alarm: This alarm warns the user when the index moves to the high risk phase. Attention: the "low" and "moderate" risk classes will not be signaled by the alarm, but these classes also presuppose the administration of pesticides with a certain frequency, in accordance with Table 1.
Primary Infection Alarm: This alarm warns the user of the occurred primary infection: it is suggested to deactivate this alarm after the notification of the first primary infection of the year which signals the start of the defense against Oidium; subsequent notifications are not useful for the purposes of defense which is instead based on the observation of the Risk Index.
Decision support diagram Powdery mildew
Below is a decision support diagram to be consulted in order to adopt treatment times consistent with the predictive logic of the model. It should be noted that the table of treatments (Table 1) shows generally recommended time intervals; these intervals must be subject to personalized variations according to the microclimate (more or less persistent rain, different humidity and wetting) and according to the type/dose of products used. The evaluation of the intervals between treatments must always be carried out in accordance with the field indications collected by the company technician.
Step to follow for reading the diagram
It begins at the first decision-making node by identifying the appearance of the cottony buds at the beginning of the season, a phenological phase that just precedes the opening of the buds.
Consultation of the class of plant protection product based on the type of product used is very important for determining the frequency of treatments. You can write to our agronomic staff for further information on the use of the plant protection product and for general information on the use of the decision diagram.
The class of demethylation inhibitors (DMI) plant protection products shown in Table 1 also refers to the IBE systemic plant protection products (ergosterol synthesis inhibitors) which act by preventing the biosynthesis of sterol, a fundamental component of the fungal cell wall.
The maximum number of treatments per year recommended for BEI refers to the active substances which act by inhibiting the biosynthesis of ergosterol and therefore to the molecules belonging to the following chemical groups: imidazoles, morpholines, piperazines, pyrimidines and triazoles.
Washout of powdery mildew treatments
Premise
In many companies, for reasons of practical organization (optimization of time, machinery and human resources), the treatments against powdery mildew are associated with those against downy mildew and are therefore carried out at the same time. In these cases it is generally the anti-downy mildew treatment that dictates the times based on the pressure of the disease as per the indications of the model. The beginning of the defense against powdery mildew generally coincides with that against downy mildew. However, the anti-downy mildew and anti-powdery products may have different persistence times and leaching conditions which should be evaluated individually in order to restore the relative coverage in time.
It is advisable to use the Cumulative indices function to accurately check the mm of rain that has fallen since the day of the last treatment carried out. Furthermore, the mail/sms alarms, activated on the cumulative indexes, are useful for being updated on the daily cumulative rainfall recorded (these alarms are activated in the event of the daily exceeding of the configured alarm threshold).
It is reiterated that almost all the products that can be used against powdery mildew (not only sulphur) have an effective action exclusively in prevention. Some endotherapeutic products have a stopping but not really curative action. The correct execution of the preventive treatments (speed, pressures, volumes of water and doses) is therefore fundamental. The green operations (thinning of the shoots, leaf stripping, etc.), performed promptly, are a good adjuvant for the containment of the disease, also allowing a better penetration of the treatments. In conditions of strong disease pressure (as indicated by the forecast model) it is advisable to mix the sulfur with the specific chemical product. In the final treatments (near veraison) it is generally sufficient to treat only with wettable sulfur. In general, it is always suggested to interrupt the anti-mildew defense after veraison since the berries are no longer sensitive to the fungus.
Coverage products
In addition to sulphur, there are chemical synthesis products used for preventive coverage purposes. One of their advantages over sulfur is resistance to washout. Sulphur, especially in powder form, is a product that can be washed away even with a single rain of a few mm. On the market there are formulations of liquid sulfur that guarantee greater persistence and an interesting effectiveness. Some synthetic roofing products resist washout even over 50mm of rain providing lasting protection that allows you to lengthen the intervals between applications. There is a variety of products on the market based on different active ingredients, belonging to different chemical groups. To find out about the leaching and persistence of action of a specific product, check the label/brochure or contact the manufacturer.
Endotherapeutic products (systemic, cytotropic-translaminar, others)
These are chemical synthesis products absorbed by the plant which, as regards powdery mildew, have almost exclusively preventive efficacy. The average frequency of treatments with these products is 8-12 days (average persistence). To find out about the leaching and persistence of action of a specific product, check the label/brochure or contact the manufacturer. Endotherapeutic treatments are not subject to leaching once absorbed by the vegetation. To reduce the risk of resistance, it is important to use products belonging to the same chemical group for a maximum of 3 treatments per season, except for more restrictive indications given on the label or in the product specification.
Management of organic powdery mildew
In organic farming, the products that can be used are limited and the defense against powdery mildew, as well as the use of sulphur, must be based on careful agronomic management of the vineyard (early leaf removal, vegetative balance). A preventive defense is essential throughout the period of susceptibility to the fungus; in the most affected areas and with very sensitive varieties, it might be advisable to start treatments with powdered sulfur at the phenological stage of 2-3 leaves, with temperatures above 15°C. The defense then continues with wettable sulfur, shortening the interval between treatments in case of strong disease pressure. In critical years and in periods of greater susceptibility of the bunch (end of flowering and pre-closing) it may be advisable to intervene with powdered sulfur which offers greater protection than other formulations. Sulphur, especially in powder form, is a product that can be washed away even with a single rain of a few mm. In critical periods of the disease, it is therefore important to intervene promptly after the rains to restore coverage. Its effectiveness is mainly conditioned by the dosage but the coverage is always short (4-7 days) and sensitive to washout. On the market there are also formulations of liquid sulfur which guarantee greater persistence and an interesting efficacy.
Reference bibliography
Blake, C. (2009). Update underway for powdery mildew forecast model in grapes. Retrieved from: http://www.westernfarmpress.com/grapes/update-underway-powdery-mildew- forecast-model-grapes
Caffi, T., Rossi, V., Legler, S. E., & Bugiani, R. (2011). A mechanistic model simulating ascosporic infections by Erysiphe necator, the powdery mildew fungus of grapevine. Plant Pathology, 60(3), 522-531. https://doi.org/10.1111/j.1365-3059.2010.02395.x
Caffi, T., Legler, S. E., Rossi, V., & Bugiani, R. (2012b). Evaluation of a Warning System for Early- Season Control of Grapevine Powdery Mildew. Plant Disease, 96(1), 104-110. https://doi.org/10.1094/PDIS-06-11-0484
Caffi, T., Legler, S. E., Bugiani, R., & Rossi, V. (2013). Combining sanitation and disease modelling for control of grapevine powdery mildew. European Journal of Plant Pathology, 135(4), 817-829. https://doi.org/10.1007/s10658-012-0124-0
Calonnec, A., Cartolaro, P., Naulin, J. M., Bailey, D., & Langlais, M. (2008). A host-pathogen simulation model: powdery mildew of grapevine. Plant Pathology, 57(3), 493-508. https://doi.org/10.1111/j.1365-3059.2007.01783.x
Carisse, O., Bacon, R., & Lefebvre, A. (2009). Grape powdery mildew (Erysiphe necator) risk assessment based on airborne conidium concentration. Crop Protection, 28(12), 1036- 1044. https://doi.org/10.1016/j.cropro.2009.06.002
Carrol, J. E., & Wilcox, W. F. (2003). Effects of Humidity on the Development of Grapevine Powdery Mildew. Epidemiology, 93(9), 1137-1144. https://doi.org/https://doi.org/10.1094/PHYTO.2003.93.9.1137
Grape Powdery Mildew. (2015). Retrieved from https://www2.gov.bc.ca/assets/gov/farming-natural-resources-and-industry/agriculture-and-seafood/animal-and-crops/planthealth/grape_powdery_mildew.pdf
Gubler, D. (2013) Options to Control Powdery Mildew Evaluated Annual field day looks at efficacy of fungicide treatments/Interviewer: J. Tourney. Retreived from https://www.winesandvines.com/news/article/120436/Options-to-Control-Powdery- Mildew-Evaluated
Gubler, D. (2018). Grapevine Powder Mildew: Biology, Epidemiology, and Management in California. Retrieved from http://cecentralsierra.ucanr.org/files/116782.pdf
Gubler, W. D., Rademacher, M. R., & Vasquez, S. J. (1999). Control of Powdery Mildew Using the UC Davis Powdery Mildew Risk Index. APSnet Features. https://doi.org/10.1094/APSnetFeature-1999-0199
Gubler, W. D. (2012). Biology, epidemiology, adn control of powdery mildew: use of the UC Davis Powdery Mildew Risk Index.
Haall W., T. (2000). Epidemiology of Grape Powdery Mildew, Uncinula necator, in the Willamette Valley. Master of Science. Retrieved from https://ir.library.oregonstate.edu/downloads/s7526g82g
Haviland, D., & Bettiga, L. (2017). Powdery mildew. In T. A. Martin (Ed.), Pest management guidelines for agriculture (pp. 198): University of California Agriculture and Natural Resources.
Lybbert, T. J., Magnan, N., & Gubler, W. D. (2012). Multi-dimensional responses to risk information: how do winegrape growers respond to disease forecasts & to what environmental effect? RMI-CWE Working Paper Number 1203
Moyer, M. M., Gadoury, D. M., Wilcox, W. F., & Seem, R. C. (2014). Release of Erysiphe necator Ascospores and Impact of Early Season Disease Pressure on Vitis vinifera Fruit Infection. American Journal of Enology and Viticulture, 65(3), 315-324. https://doi.org/10.5344/ajev.2014.13111
Odile, Carisse, O., David-Mathieu, Tremblay, Tristan, Jobin, . . . Walker. (2010). Disease Decision Support Systems: Their Impact on Disease Management and Durability of Fungicide Effectiveness.
OEPP/EPPO. (2001). EPPO Standard PP 2/23 (1) Good plant protection practice. Bulletin OEPP/EPPO Bulletin, 32, 371-392.
Peduto, F., Backup, P., Hand, E. K., Janousek, C. N., & Gubler, W. D. (2013). Effect of High Temperature and Exposure Time on Erysiphe necator Growth and Reproduction: Revisions to the UC Davis Powdery Mildew Risk Index. Plant Dis, 97, 1438-1447. https://doi.org/10.1094/PDIS-01-13-0039-RE
Pertot, I., Caffi, T., Rossi, V., Mugnai, L., Hoffmann, C., Grando, M. S., . . . Anfora, G. (2017). A critical review of plant protection tools for reducing pesticide use on grapevine and new perspectives for the implementation of IPM in viticulture. Crop Protection, 97, 70-84. https://doi.org/10.1016/j.cropro.2016.11.025
Rossi, V., Caffi, T., & Legler, S. E. (2010). Dynamics of Ascospore Maturation and Discharge in Erysiphe necator, the Causal Agent of Grape Powdery Mildew. Phytopathology, 100(12), 1321-1329. https://doi.org/10.1094/PHYTO-05-10-0149
Rossi, V., Salinari, F., Poni, S., Caffi, T., & Bettati, T. (2014). Addressing the implementation problem in agricultural decision support systems: the example of vite.net®. Computers and Electronics in Agriculture, 100(Supplement C), 88-99. https://doi.org/https://doi.org/10.1016/j.compag.2013.10.011
Thiessen, L. D. (2016). Overwintering of Erysiphe necator and Inoculum Monitoring for Decision Aids. (Doctoral dissertation), Oregon State University. Retrieved from http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/xk81jn88w
Thiessen, L. D., Neill, T. M., & Mahaffee, W. F. (2017). Timing Fungicide Application Intervals Based on Airborne Erysiphe necator Concentrations. Plant Disease, 101(7), 1246-1252. https://doi.org/10.1094/PDIS-12-16-1727-RE
Previsional model: Apple scab
Input Data
Daily Average Temperatures (°C)
Daily Leaf Wetting (“leaf wetness” sensor)
Daily Cumulative Rains (mm)
Daily Relative Humidity (%)
Calibratable dose of ascospores (default values 0-1)
Output Data
Ascospore issue risk start date
Percentage of ascospores emitted at each rain event
Level of risk of primary infections
Incubation duration of the disease
Expected date of onset of symptoms
Introduction
The ascomycete fungus Venturia inaequalis, cryptogamic agent of the disease commonly known as the "apple scab", represents the most important fungal agent affecting the apple tree. If left untreated, it is able to proliferate and cause serious damage to leaves, fruits and flowers (sometimes even to twigs), which in the worst cases result in the loss of a good portion of the production (for example, losses of 15-20% have been reported, in the worst years, in the apple orchards of Valsugana, Trentino).
Bibliographical references
The development of modeling solutions for apple scab started in the 1980s and focused on the mathematical transposition of concepts known in the bibliography, obtained from the study of the pathogen in the field. The models were then improved and updated over time through further field studies and annexed calibration and validation procedures. Among the best known models for apple scab are: the Biomat model by Hofmaier (1994), the METY model by Boshuizen & Verheyden (1994), the VENTEM model by Santen & Butt (1993) and the RIMpro model, developed at the early 1990s.
The WiForAgri scab model was born from an assessment of the international state of the art and integrates, updating and personalizing them, the best mathematical sub-models known in the literature with the results of studies and instrumental investigations carried out in the field in Italian apple orchards. The model has the following features:
It is based on recent and updated studies (2007) on the mechanisms of development of the fungus, supported by experimental tests in the field.
It is based on the original combination of several algorithms on which there is a large supporting publication and on which other successful models are based.
It can be trained on different microclimatic realities thanks to the personalization (calibration) of different parameters of the model by our technical staff.
Modeling functionality and additional information
The WiForAgri model has been validated and improved in the field through the use of a professional spore trap capable of monitoring the level of fungal particles that are emitted by the litter. Various litters from untreated apple orchards were sampled and placed in plastic containers and the quantity of ascospores (source of primary inoculum) emitted by the litter was compared daily with those simulated by the model, perfecting the modeling function. Predicted infections were monitored in the field for validation and calibration.
Description of the model
The raw functioning of the model is shown below according to the modeling steps shown in the flow chart in Figure 1:
The model is initialized on February 1st by setting the dose of annual ascospores (psudothecia) present in the apple orchard (in the litter) to a value of 1. This standard value represents the normalized dose of ascospores normally present in commercial apple orchards managed with normal anti-inflammatory -cryptogamic. This value (annual dose of ascospores) can be modified (calibrated) in order to be able to represent with greater accuracy the dose of inoculum present in apple orchards managed/defended through unconventional techniques; the correct calibration allows for a more accurate description of the severity of the individual infections predicted by the model.
The model calculates the beginning of the primary inoculum season, through a simulation of the exit of the ascospores (pseudothecia) from the winter dormancy period. it is therefore possible to know with certainty the start date of the season in which there will be releases of the ascospores which will lead to primary infections. The model informs the user of the start of the primary inoculum season: the treatments carried out previously are superfluous.
During the primary inoculum season, the maturation of the ascospores is simulated daily on the basis of the hygrothermal conditions of the litter. The increase is faster the higher the ambient temperature. However, the maturation process proceeds exclusively in the hours characterized by wet litter. The model uses a combination of leaf wetness, air humidity and rainfall sensor data to determine if the litter is wet. The dynamics of ascospore maturation is calculated indirectly, by computing the proportion of ascospores that mature during the day and that can be released into the air during release events. The maturation of the ascospores is calculated daily on the basis of the accumulation of degree days (thermal summaries) exclusively in the hours in which the litter is wet. On days with a large number of hours of leaf wetness and with a higher atmospheric temperature, there will be a greater maturation of the ascospores. Conversely, if in a day there are 0 wet hours, then no ascospore will have matured.
The release events (mature ascospores are released into the air) are triggered by single rain events: the "splash" effect of the water droplets allows the release of the mature ascospores from the litter into the air. The dose of ascospores released into the air is equivalent to the entire dose of ascospores matured starting from the previous rain event: in each rain event all the mature ascospores are released into the air and the mature dose inside the litter returns to be nothing.
In the primary inoculation season, if we are within a period of repeated rains, the first rain event is the fundamental one as it will discharge the entire dose of mature ascospores accumulated into the air.
Since it has been observed that most ascospores can only be released during daylight hours, nocturnal rain events produce ascospore releases delayed to dawn (7:00 AM) in the model. Some special cases are exceptions: considerable dose of mature ascospores accumulated (more than 30% - relevant infection); nearing the end of the primary inoculum season (more than 80% of the annual ascospore dose already released).
After each release, primary infections are calculated based on a minimum combination of consecutive hours of leaf wetness and temperatures.
Following the primary infection events, the model calculates the incubation periods based on the temperature, which determine the speed with which the fungal mycelium propagates inside the leaves and causes the classic symptoms to appear.
Reference Bibliography
Bugiani R., Giosuè S., Marinelli M., Ponti I., Rossi V. (1999). Field evaluation of some models estimating the seasonal pattern of air-borne ascospores of the Venturia inaequalis. Journal of Phytopathology 147: 567-575
Bugiani R., Giosuè S., Marinelli M., Ponti I., Rossi V. (2000). A new model estimating the seasonal pattern of air-borne ascospores of Venturia inaequalis (Cooke) Wint. in relation to weather conditions. Journal of Plant Pathology. 82: 111-118
Bugiani R., Giosuè S., Ponti I., Rossi V. (2000). Estimating the dinamic of air-borne ascospores of the apple scab pathogen Venturia inaequalis. OEPP/EPPO Bulletin 30: 137-142
Bugiani R., Giosuè S., Marinelli M., Ponti I., Rossi V. (2001). Environmental factors influencing the dispersal of Venturia inaequalis ascospores in the orchard air. Journal of Phytopathology 149: 11-19
Bugiani R., Giosuè S., Ponti I., Rossi V. (2001). Accuracy of a model simulating the dynamic of apple scab primary inoculum in the orchard. Integrated Fruit Production IOBC /wprs Bulletin Vol. 24 (5): 283-288
Bugiani R, Giosuè S., Rossi V. (2007). A‐scab (Apple‐scab), a simulation model for estimating risk of Venturia inaequalis primary infections*. EPPO Bulletin. 37: 300-308
Cotroneo A., Galliano A., Mancini G. (1984). Evaluation of two models for predicting ascospore maturation of Venturia inaequalis. Piedmont (NW Italy). Rivista di Patologia Vegetale, S. IV: 25-37.
Gadoury D.M., MacHardy W.E. (1989). A revision of Mill's criteria for predicting apple scab infection period. Phytopathology 79: 304-310
James JR., Sutton TB. (1982b). A model for predicting ascospore maturation of Venturia inaequalis. Phytopathology 72: 1081-1085.
Mills W.D. (1944). Efficient use of sulfur dusts and sprays during rain to control apple scab. Cornell Extension Bullettin 630: 1-4
Stensvand A, Eikemo H, Gadoury DM & Seem RC. (2005). Use of a rainfall frequency threshold to adjust a degree-day model of ascospore maturity of Venturia inaequalis. Plant Disease 89, 198–202.
Bibliographic links (all models)
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