Benefit of ancillary data acquired at the cooperative level to study soil type and climatic zone influence on berry composition: a case study in Rioja appellation
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Urtzi Leibar [2] ; Olatz Unamunzaga [2] ; María José Fernández-Gómez [1] ; Purificación Galindo-Villardón [1] ; Cesar Castro [3] ; Ana Aizpurua [2]
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[1] Universidad de Salamanca
Universidad de Salamanca
Salamanca, España
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[2] Neiker-Tecnalia
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[3] Bodegas y Viñedos Labastida
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- Localización: OENO ONE: Journal international des sciences de la vigne et du vin = International journal of vine and wine sciences, ISSN 1151-0285, Vol. 52, Nº 2, 2018, págs. 117-133
- Idioma: inglés
- Enlaces
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Resumen
Aim: The main objective of this study was to evaluate the influence of soil type and climate on must qualitative parameters in a winegrower’s cooperative at Rioja appellation.
Methods and results: The study was conducted from 2009 to 2011 with data collected routinely before harvest by the technician of a cooperative with a total surface area of 525 ha. Soils were classified using an existing soil map (1:50.000 scale) according to their water-holding capacity (WHC), and two climatic zones were differentiated based on the Huglin index. Effects of soil and climate on berry composition were evaluated using HJ-Biplot statistical analysis. High WHC soils produced musts with high total acidity, mainly due to malic acid. Must K concentrations were lower in soils with lower K and clay content. Soils with lower WHC were the only ones able to produce musts with high anthocyanin concentration and higher colour intensity. The climatic zones established only resulted in small differences in grape composition.
Conclusion: It is possible to differentiate berry composition parameters according to soil type considering soil WHC, but less clear differences were observed among climatic zones considering a 50 km2 area and a difference of approximately 200 m in elevation between the two zones.
Significance and impact of the study: Many wineries have access to soil, climate and grape composition data. Therefore, these data could be used to make a grape composition classification at harvest that could be assessed every year using simple statistical tools.
