摘要：Climate indices based on heat accumulation, e.g. the Winkler index, are widely used to define the
climatic niches for vines. In this study, we investigate how a combined use of high-resolution (25 m) climate
index maps and phenology records from 30 vineyards in South Tyrol (in the Italian Alps) can help to (i) assess
viticultural suitability across a mountainous landscape and (ii) estimate the timing of physiological processes of
Pinot Noir development (ripening and must weight) at various sites across the region. First, the best
interpolation method is chosen (from multiple linear regression (MLR), regression kriging or support vector
regression) to create maps of climate indices averaged over the time period 1991-2010. Second, correlation is
calculated between the timing of phenological stages of Pinot Noir for the year 2017 and various climate
indicators, such as temperature-based indices (Winkler, Huglin, Biologically Active Degree Days, Cool Night,
Fregoni) and average (GST), minimum and maximum temperature over the growing season. The MLR method
is shown to yield the best interpolations of the climate indices across the complex terrain of the study area. The
Winkler and GST indices correlate most precisely with the late-season phenological events of the study sites,
and are thus the most predictive. These findings demonstrate the potential of climatic maps to effectively
define suitable areas for grape growing and estimate ripening dates in South Tyrol.
其他摘要：Climate indices based on heat accumulation, e.g. the Winkler index, are widely used to define the climatic niches for vines. In this study, we investigate how a combined use of high-resolution (25 m) climate index maps and phenology records from 30 vineyards in South Tyrol (in the Italian Alps) can help to (i) assess viticultural suitability across a mountainous landscape and (ii) estimate the timing of physiological processes of Pinot Noir development (ripening and must weight) at various sites across the region. First, the best interpolation method is chosen (from multiple linear regression (MLR), regression kriging or support vector regression) to create maps of climate indices averaged over the time period 1991-2010. Second, correlation is calculated between the timing of phenological stages of Pinot Noir for the year 2017 and various climate indicators, such as temperature-based indices (Winkler, Huglin, Biologically Active Degree Days, Cool Night, Fregoni) and average (GST), minimum and maximum temperature over the growing season. The MLR method is shown to yield the best interpolations of the climate indices across the complex terrain of the study area. The Winkler and GST indices correlate most precisely with the late-season phenological events of the study sites, and are thus the most predictive. These findings demonstrate the potential of climatic maps to effectively define suitable areas for grape growing and estimate ripening dates in South Tyrol.
