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Maddalena G, Marone Fassolo E, Bianco PA, Toffolatti SL. Disease Forecasting for the Rational Management of Grapevine Mildews in the Chianti Bio-District (Tuscany). PLANTS (BASEL, SWITZERLAND) 2023; 12:285. [PMID: 36678997 PMCID: PMC9865324 DOI: 10.3390/plants12020285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/27/2022] [Accepted: 01/05/2023] [Indexed: 06/17/2023]
Abstract
Downy and powdery mildews are major grapevine diseases. In organic viticulture, a few fungicides with protectant activities (copper and sulphur in particular) can be used, and their preventative application frequently leads to unneeded spraying. The adoption of an epidemiological disease forecasting model could optimise the timing of treatments and achieve a good level of disease protection. In this study, the effectiveness of the EPI (Etat Potentiel d'Infection) model in predicting infection risk for downy and powdery mildews was evaluated in nine organic vineyards located in Panzano in Chianti (FI), over a 2-year period (2020-2021). The reliability of the EPI model was investigated by comparing the disease intensities, the number of fungicide sprayings, the quantities of the fungicides (kg/ha), and the costs of the treatment achieved, with or without the use of the model, in a vineyard. The results obtained over two seasons indicated that, in most cases, the use of the EPI model accurately signalled the infection risk and allowed for a reduction in the frequency and cost of spraying, particularly for powdery mildew control (-40% sprayings, -20% costs compared to the farmer's schedule), without compromising crop protection. The use of the EPI model can, therefore, contribute to more-sustainable disease management in organic viticulture.
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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. Appl Environ Microbiol 2022; 88:e0101022. [PMID: 36250698 PMCID: PMC9642012 DOI: 10.1128/aem.01010-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
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.
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Marone Fassolo E, Lecchi B, Marcianò D, Maddalena G, Toffolatti SL. Pathogen Adaptation to American ( Rpv3-1) and Eurasian ( Rpv29) Grapevine Loci Conferring Resistance to Downy Mildew. PLANTS (BASEL, SWITZERLAND) 2022; 11:2619. [PMID: 36235481 PMCID: PMC9571346 DOI: 10.3390/plants11192619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/22/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Durable resistance is a key objective in genetic improvement for disease resistance in grapevines, which must survive for years in the field in the presence of adaptable pathogen populations. In this study, the adaptation of 72 Northern Italian isolates of Plasmopara viticola, the downy mildew agent, has been investigated into Bianca, possessing Rpv3-1, the most frequently exploited resistance locus for genetic improvement, and Mgaloblishvili, a Vitis vinifera variety possessing the newly discovered Rpv29 locus. Infection parameters (latency period, infection frequency, and disease severity) and oospore production and viability were evaluated and compared to those of Pinot noir, the susceptible reference. The expected levels of disease control were achieved by both resistant cultivars (>90% on Bianca; >25% on Mgaloblishvili), despite the high frequency of isolates able to grow on one (28%) or both (46%) accessions. The disease incidence and severity were limited by both resistant cultivars and the strains able to grow on resistant accessions showed signatures of fitness penalties (reduced virulence, infection frequency, and oospore density). Together, these results indicate an adequate pathogen control but suitable practices must be adopted in the field to prevent the diffusion of the partially adapted P. viticola strains to protect resistance genes from erosion.
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Bregaglio S, Savian F, Raparelli E, Morelli D, Epifani R, Pietrangeli F, Nigro C, Bugiani R, Pini S, Culatti P, Tognetti D, Spanna F, Gerardi M, Delillo I, Bajocco S, Fanchini D, Fila G, Ginaldi F, Manici LM. A public decision support system for the assessment of plant disease infection risk shared by Italian regions. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 317:115365. [PMID: 35642822 DOI: 10.1016/j.jenvman.2022.115365] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/05/2022] [Accepted: 05/17/2022] [Indexed: 06/15/2023]
Abstract
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.
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Affiliation(s)
- Simone Bregaglio
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy.
| | - Francesco Savian
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
| | - Elisabetta Raparelli
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
| | - Danilo Morelli
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
| | - Rosanna Epifani
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
| | - Fabio Pietrangeli
- Regional Agrometeorological Centre, Abruzzo Region, Contrada Colle Comune Scerni I-66020, Chieti CH, Italy
| | - Camilla Nigro
- Lucana Agency for Development and Innovation in Agriculture, Basilicata Region, Via Annunziatella, 64, I-75100 Matera MT, Italy
| | - Riccardo Bugiani
- Plant Protection Service, Emilia-Romagna Region, Via Saliceto 81, I-40128, Bologna BO, Italy
| | - Stefano Pini
- Servizi Alle Imprese Agricole e Florovivaismo, CAAR (Centro Agrometeorologia Applicata Regionale), Laboratori Regionali Analisi Terreni-Produzioni Vegetali e Fitopatologico, I-19038 Sarzana SP, Liguria Region, Italy
| | - Paolo Culatti
- Regione Lombardia, Plant Protection Service, I-20124 Milan MI, Italy
| | - Danilo Tognetti
- Centro Operativo Agrometeo ASSAM, Marche Region, Via Cavour, 29, I-62010 Treia MC, Italy
| | - Federico Spanna
- Regional Phytosanitary Service, Piemonte Region, Agrometeo Sector, I-10144, Torino, TO, Italy
| | - Marco Gerardi
- LAORE Sardegna, Regional Agency for Agriculture Development, Via Caprera 8, I-09123 Cagliari CA, Italy
| | - Irene Delillo
- ARPAV. Dipartimento Regionale per La Sicurezza Del Territorio. U.O.C. Meteorologia e Climatologia, Veneto Region, Via Marconi 55, I-35037 Teolo, PD, Italy
| | - Sofia Bajocco
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
| | - Davide Fanchini
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
| | - Gianni Fila
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
| | - Fabrizio Ginaldi
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
| | - Luisa M Manici
- CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, I-40128 Bologna, I-00184 Rome, Italy
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Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology. ELECTRONICS 2022. [DOI: 10.3390/electronics11030356] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
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.
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