title: Ecophysiological Modeling of Grapevine Water Stress in Burgundy Terroirs by a Machine-Learning Approach.
abstract: In a climate change scenario, successful modeling of the relationships between plant-soil-meteorology is crucial for a sustainable agricultural production, especially for perennial crops. Grapevines (Vitis vinifera L. cv Chardonnay) located in eight experimental plots (Burgundy, France) along a hillslope were monitored weekly for 3 years for leaf water potentials, both at predawn (Ψpd) and at midday (Ψstem). The water stress experienced by grapevine was modeled as a function of meteorological data (minimum and maximum temperature, rainfall) and soil characteristics (soil texture, gravel content, slope) by a gradient boosting machine. Model performance was assessed by comparison with carbon isotope discrimination (δ(13)C) of grape sugars at harvest and by the use of a test-set. The developed models reached outstanding prediction performance (RMSE < 0.08 MPa for Ψstem and < 0.06 MPa for Ψpd), comparable to measurement accuracy. Model predictions at a daily time step improved correlation with δ(13)C data, respect to the observed trend at a weekly time scale. The role of each predictor in these models was described in order to understand how temperature, rainfall, soil texture, gravel content and slope affect the grapevine water status in the studied context. This work proposes a straight-forward strategy to simulate plant water stress in field condition, at a local scale; to investigate ecological relationships in the vineyard and adapt cultural practices to future conditions.
authors.affiliation: Viticulture Research Center, Council for Agricultural Research and EconomicsConegliano, Italy; UMR CNRS/uB 6282 Biogéosciences, Université de BourgogneDijon, France.
authors.forename: Luca
authors.initials: L
authors.lastname: Brillante
authors.affiliation: UMR CNRS/uB 6282 Biogéosciences, Université de Bourgogne Dijon, France.
authors.forename: Olivier
authors.initials: O
authors.lastname: Mathieu
authors.affiliation: UMR CNRS/uB 6282 Biogéosciences, Université de Bourgogne Dijon, France.
authors.forename: Jean
authors.initials: J
authors.lastname: Lévêque
authors.affiliation: UMR CNRS/uB 6282 Biogéosciences, Université de BourgogneDijon, France; Institut Universitaire de la Vigne et du Vin "Jules Guyot," Université de BourgogneDijon, France.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Bois
keywords: carbon isotope discrimination δ13C; gradient boosting machine (GBM); grapevine (Vitis vinifera L.); machine-learning; plant-soil water relationships; temperature; water balance; water stress