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Advanced Modeling for the Identification of Different Pathogen Tolerant Vines to Reduce Fungicides and Energy Consumption

Author

Listed:
  • Francesca Cecchini

    (Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA)—Centro di Ricerca Viticoltura ed Enologia—Via Cantina Sperimentale, 1, 00049 Velletri (Rome), Italy)

  • Maria Cecilia Serra

    (Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA)—Centro di Ricerca Viticoltura ed Enologia—Via Cantina Sperimentale, 1, 00049 Velletri (Rome), Italy)

  • Noemi Bevilacqua

    (Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA)—Centro di Ricerca Viticoltura ed Enologia—Via Cantina Sperimentale, 1, 00049 Velletri (Rome), Italy)

  • Corrado Costa

    (Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA)—Centro di ricerca Ingegneria e Trasformazioni agroalimentari—Via della Pascolare, 16, 00015 Monterotondo (Rome), Italy)

  • Roberto Valori

    (STAPHYT, contract research organization (CRO) in the fields of Agrosciences and Regulatory Affairs. Main facility Italy, Via della Meccanica, 28, 04011 Aprilia (LT), Italy)

  • Federico Pallottino

    (Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA)—Centro di ricerca Ingegneria e Trasformazioni agroalimentari—Via della Pascolare, 16, 00015 Monterotondo (Rome), Italy)

  • Giorgio Casadei

    (Agenzia Regionale per lo Sviluppo e l’Innovazione dell’Agricoltura del Lazio (ARSIAL)—Via Rodolfo Lanciani, 38, 00162 Rome, Italy)

  • Paolo Menesatti

    (Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA)—Centro di ricerca Ingegneria e Trasformazioni agroalimentari—Via della Pascolare, 16, 00015 Monterotondo (Rome), Italy)

  • Francesca Antonucci

    (Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA)—Centro di ricerca Ingegneria e Trasformazioni agroalimentari—Via della Pascolare, 16, 00015 Monterotondo (Rome), Italy)

Abstract

The aim of this study is the application of advanced modeling techniques to identify powdery mildew tolerant cultivars and reduce fungicides and energy consumption. The energy savings resulting from the increased efficiency of the use of fungicides is an innovative aspect investigated within the project AGROENER researching on energy efficiency. In this preliminary study, investigations through phenotyping methods could represent a potential solution, especially if they are used in combination with tools and algorithms able to extract and convert a large amount of data. Twelve different grapevine cultivars were tested. The construction of an artificial model, characterized by absolute optima of response to a pathogen (i.e., low values of disease incidence and severity and first day of the pathogen appearance), allowed us to cover the potential variability of a real dataset. To identify the cultivars that tolerate powdery mildew the most, two Soft Independent Modeling of Class Analogy (SIMCA) models were built. The modeling efficiencies, indicated by sensitivity value, were equal to 100%. These statistical multivariate classifications identified some of these tolerant cultivars, as the best responding to the pathogen.

Suggested Citation

  • Francesca Cecchini & Maria Cecilia Serra & Noemi Bevilacqua & Corrado Costa & Roberto Valori & Federico Pallottino & Giorgio Casadei & Paolo Menesatti & Francesca Antonucci, 2020. "Advanced Modeling for the Identification of Different Pathogen Tolerant Vines to Reduce Fungicides and Energy Consumption," Sustainability, MDPI, vol. 12(5), pages 1-15, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1900-:d:327535
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    References listed on IDEAS

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    1. Giovanni Abramo & Corrado Costa & Ciriaco Andrea D’Angelo, 2015. "A multivariate stochastic model to assess research performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1755-1772, February.
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    Cited by:

    1. Roberto Valori & Corrado Costa & Simone Figorilli & Luciano Ortenzi & Rossella Manganiello & Roberto Ciccoritti & Francesca Cecchini & Massimo Morassut & Noemi Bevilacqua & Giorgio Colatosti & Giovann, 2023. "Advanced Forecasting Modeling to Early Predict Powdery Mildew First Appearance in Different Vines Cultivars," Sustainability, MDPI, vol. 15(3), pages 1-17, February.

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