Abstract
Temperature is a driving climate variable for grapevine development and grape ripening kinetics. The current study first reports interpolation of daily minimum and maximum temperature data by a weather station network from 2001 to 2005 in the Bordeaux (France) region by means of regression kriging using terrain, satellite and land-cover derived covariates. Second it analyses the interpolation procedure errors in agroclimatic indices by means of cross validation and then it compares the field observations of grapevine phenology to temperature-based predicted phenology applied to interpolated data. Finally it proposes a simple method to perform a zoning of Bordeaux vineyards based upon the spatialized prediction of the day on which grape sugar content reaches 200 g.L-1. The zoning performed shows large potential differences in grape maturity date (up to 20 days) induced by temperature spatial variability in a low relief area.
Highlights
Climate is one of the most important viticultural variables affecting grapevine development and the enological potential of grapes (van Leeuwen et al, 2004)
Most spatial analyses of temperature are mainly performed using point data, that is, weather stations, or gridded data at 1-km resolution or more (Jones et al, 2009; Jones et al, 2010). Such resolutions are relevant for small spatial and temporal scale zoning. They do not consider local variations in temperature, which may lead to significant differences in the physicochemical composition of the grapes (Neethling et al, 2011)
Considering the performance of the four interpolation methods, temperature estimations have been calculated using RK in any point of the study area (i.e. 50-m resolution DEM grid located within the Bordeaux winegrowing region)
Summary
Climate is one of the most important viticultural variables affecting grapevine development and the enological potential of grapes (van Leeuwen et al, 2004). Most spatial analyses of temperature are mainly performed using point data, that is, weather stations (see, for example, Gladstones, 1992; Tonietto and Carbonneau, 2004), or gridded data at 1-km resolution or more (Jones et al, 2009; Jones et al, 2010). Such resolutions are relevant for small spatial and temporal scale zoning (macroclimatic characterization, monthly time steps). They do not consider local variations in temperature, which may lead to significant differences in the physicochemical composition of the grapes (Neethling et al, 2011)
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