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Composition and evaluation of a novel web-based decision support system for grape black rot control

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Abstract

Black rot on grapevine is a fungal disease caused by Phyllosticta ampelicida (syn. Guignardia bidwellii) affecting grape leaves as well as clusters. A novel black rot decision support system termed VitiMeteo Black rot was assembled based on existing sub-models and incorporated into the established VitiMeteo forecast and decision support platform. Based on local weather data and a 5-day weather forecast, VitiMeteo Black rot simulates the relative susceptibility of grape clusters, the occurrence and severity of infection events as well as the duration of incubation periods. Data sets obtained in extended international (14 case studies; eight monitoring locations; 11 cultivars; seven countries in Europe and North America) field monitoring campaigns in 2012 and 2013 were used to evaluate the model predictions of newly expressed symptoms on leaves. In the case of the Vitis vinifera cultivars, on average 26.3 disease assessments took place per season. On average, 9.9 predictions were classified as true positive, 8.0 as true negative, 5.2 as false positive and 3.2 as false negative. Model precision, sensitivity and accuracy were on average 64, 77 and 67 %. Potential reasons for false positive and false negative predictions are discussed. VitiMeteo Black rot is freely available for several locations in Germany, Luxembourg and Austria on the internet via the VitiMeteo platform and might be expanded to other regions in the future.

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Acknowledgments

The authors wish to thank S. Fischer and R. Mannes (Institut Viti-vinicole (IVV), Remich, Luxembourg) for providing experimental vineyards, M. Skaventzou (University of Florence, Italy), N. Baron, M. Pallez, L. Giustarini (LIST, Belvaux, Luxembourg), C. Rieger, P. Erhardt and M. Grundler (Staatliches Weinbauinstitut Freiburg, Germany), L. Torriani (Agroscope Cadenazzo, Switzerland), S. Garidel (IVV Remich, Luxembourg) for technical assistance in the field monitoring as well as in GIS, B. Berkelmann-Löhnertz (Geisenheim University, Germany), G.K. Hill (DLR Rheinhessen-Nahe-Hunsrück, Oppenheim, Germany), O. Viret (Agroscope Changins, Switzerland), H.-H. Kassemeyer (Staatliches Weinbauinstitut Freiburg, Germany) and R. Krause (Geosens Ingenieurpartnerschaft, Ebringen, Germany) for supporting the development and implementation of the model as well as the Institut Viti-viticole (Remich, Luxembourg) for financial support in the framework of the research project „ProVino – Pesticide reduction in viticulture“.

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Authors and Affiliations

  1. Environmental Research and Innovation (ERIN) Department, LIST – Luxembourg Institute of Science and Technology, 4422, Belvaux, Luxembourg

    Daniel Molitor, Lucien Hoffmann & Marco Beyer

  2. Department of Crop Sciences, Division of Viticulture and Pomology, University of Natural Resources and Life Sciences, 3430, Tulln, Austria

    Daniel Molitor

  3. Geosens Ingenieurpartnerschaft, 79285, Ebringen, Germany

    Barbara Augenstein

  4. Dipartimento di Scienze delle Produzioni Agroalimentari e dell’Ambiente, University of Florence, 50144, Florence, Italy

    Laura Mugnai & Pietro Antonello Rinaldi

  5. Estação de Avisos do Dão, Direcção Regional de Agricultura e Pescas do Centro, 3504-504, Viseu, Portugal

    Jorge Sofia

  6. Lake Erie Regional Grape Research and Extension Center, Pennsylvania State University, North East, PA, USA

    Bryan Hed

  7. Agroscope, Station de Recherche Changins, 1260, Nyon, Switzerland

    Pierre-Henri Dubuis

  8. Agroscope, Centro di Cadenazzo, 6594, Contone, Switzerland

    Mauro Jermini

  9. Wein- und Obstbauschule Krems, 3500, Krems, Austria

    Erhard Kührer

  10. Staatliches Weinbauinstitut Freiburg, 79100, Freiburg, Germany

    Gottfried Bleyer

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  1. Daniel Molitor
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  2. Barbara Augenstein
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  3. Laura Mugnai
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  4. Pietro Antonello Rinaldi
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  5. Jorge Sofia
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  6. Bryan Hed
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  7. Pierre-Henri Dubuis
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  8. Mauro Jermini
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  9. Erhard Kührer
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  10. Gottfried Bleyer
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  11. Lucien Hoffmann
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  12. Marco Beyer
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Correspondence to Daniel Molitor.

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Molitor, D., Augenstein, B., Mugnai, L. et al. Composition and evaluation of a novel web-based decision support system for grape black rot control. Eur J Plant Pathol 144, 785–798 (2016). https://doi.org/10.1007/s10658-015-0835-0

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  • DOI: https://doi.org/10.1007/s10658-015-0835-0

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