A public decision support system for the assessment of plant disease infection risk shared by Italian regions. (1st September 2022)

• Record Type: Journal Article
• Authors: Bregaglio, Simone; Savian, Francesco; Raparelli, Elisabetta; Morelli, Danilo; Epifani, Rosanna; Pietrangeli, Fabio; Nigro, Camilla; Bugiani, Riccardo; Pini, Stefano; Culatti, Paolo; Tognetti, Danilo; Spanna, Federico; Gerardi, Marco; Delillo, Irene; Bajocco, Sofia; Fanchini, Davide; Fila, Gianni; Ginaldi, Fabrizio; Manici, Luisa M.
• Is Part Of: Journal of environmental management. Volume 317(2022)
• Publication Date: 2022-09-01
• Subjects: Process-based modelling -- Machine learning -- Sustainable agriculture -- Plant protection -- Participatory approach; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2022.115365
• Languages: English
• ISSNs: 0301-4797

Abstract

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. Highlights: MISFITS-DSS is a decision support system for plant disease risk assessment. Participatory approach has been implemented to design the methodological workflow. Epidemiological models and machine learning were integrated in the predictive system. Random Forest classifiers were trained with disease and host susceptibility model outputs. IPM principles in decision-making from public officers were reflected by MISFITS-DSS.