Classification of Croatian winegrowing regions based on bioclimatic indices

Abstract

The aim of this work was to classifie vine growing regions of Croatia using bioclimatic indices. For the analysis of climatic conditions, linear trends of bioclimatic indices were determined using meteorological observations for all avaliable climatological stations located in vine growing regions of Croatia. Analysis were performed for two different climatological periods: 1961-1990 and 1988-2017. Four commonly used bioclimatic indices were determined: the Winkler index, the Huglin index, Cool night index and Growing season average temperature.

Geographical Discrimination of Croatian Wines by Stable Isotope Ratios and Multielemental Composition Analysis

“…However, the positioning of three microvinified samples from CII zone and one commercial sample from zone B outside of the 95% confidence interval can be noticed. This could be the result of specific microclimatic and pedologic characteristics of individual vineyards locations and it can be supported by the research of Croatian winegrowing regions ( 92 ) where it was found that both Western and Eastern continental Croatian regions, ranging from 1,323.9 to 1,652.5 GDD for the observed climatologic period (1988–2017) belong to the Winkler Regions I and II (zone A and B). In regards to Coastal Croatia in the same period, values ranged from 1,496.5 to 2,483.5 GDD, which is Winkler Region II to V (zones B, CI, CII, and CIII).…”

“…GDA showed somewhat weaker separation (Figure 4) of the zones B and CI in comparison to the excellent discrimination of continental and coastal areas. This can be explained by incompliance between official borders of the zones (Figure 1) (63) and actual situation presented by previous research (92), which established that within the zone B exist a smaller area corresponding to the Winkler Region I (zone A) and that Slavonia and Croatian Danube fall into Region II, which is zone B and not CI as stated by the current EU division system.…”

“…The analysis of bioclimatic indices in Croatian winegrowing regions ( 92 ) would enable more accurate interpretation of isotopic and multielement data found in this research as the tools for Croatian wine geographical origin determination. Furthermore, current administrative division of the zones established by the EU legislation ( 63 ) is defining the limits and conditions for certain oenological practices (enrichment limits/increase in the natural alcoholic strength) where climatic conditions have made it necessary in certain winegrowing zones.…”

Climate change impacts on viticulture in Croatia: Viticultural zoning and future potential

“…The set, consisting of the average growing season temperature (GST; Jones, 2006), growing degree‐days (GDD; Winkler, Cook, Kliewer, & Lider, 1974), Huglin index (HI; Huglin, 1978), dryness index (DI; Riou et al, 1994; Tonietto & Carbonneau, 2004) and cool night index (CI; Tonietto, 1999), is usually analysed. Although many authors have calculated these indices for the specific locations (e.g., Jones, Duff, Hall, & Myers, 2010; Koufos et al, 2018; Orlandini et al, 2009), the gridded meteorological dataset created from measurements and/or regional climate models (RCMs) has enabled more recently a spatial analysis of these indices that has been used for viticultural zoning of the large(r) regions (e.g., Fraga, Malheiro, Moutinho‐Pereira, & Santos, 2013; Fraga et al, 2014; Karoglan et al, 2018; Malheiro et al, 2010; Ruml et al, 2012; Santos, Malheiro, Pinto, & Jones, 2012). However, in most studies, only one RCM forced by one global climate model (GCM) has typically been used.…”

Advanced seasonal predictions for vine management based on bioclimatic indicators tailored to the wine sector

“…GST is the 7-month average of daily mean temperatures over the entire growing season from 1st April to 31st October. Since many of the most used grapevine varieties have been classified according to their optimum ranges of GST, this intuitive indicator is frequently used in the wine sector (Karoglan et al, 2018) at least in two ways. On the one hand, for a given location, the anomalies in the predicted GST can be used to plan the possible outcomes of the season in terms of grape quality and phenology.…”