Terroir 2008 banner
IVES 9 IVES Conference Series 9 Vineyard soil mapping to optimise wine quality: from ‘terroir’ characterisation to vineyard management

Vineyard soil mapping to optimise wine quality: from ‘terroir’ characterisation to vineyard management

Abstract

In this study, a soil mapping methodology at subplot level (scale 1:5000) for vineyard soils was developed. The aim of this mapping method was to establish mapping units, which could be used as basic units for ‘terroir’ characterisation and vineyard management (precision viticulture). The developed methodology applied most of the criteria of the Soil Inventory of Catalonia and the Soil Survey Manual of the Department of Agriculture of United States, at very-detailed scale. The suitability of soil maps as a tool for definition of ‘terroir’ units and management units are discussed, according to our experiences. The method followed allowed good soil type discrimination at vineyard subplot level, differentiating zones with distinct soil properties important to vineyard development. However, the variability within the soil mapping unit could not be ascertained by this method. Significant differences in grape quality were found between distinct soil mapping units. Moreover, the application of variable rates of fertilizer at vine subplot level was possible using thematic maps calculated from soil maps, by means of Geographic Information Systems. 

DOI:

Publication date: December 8, 2021

Issue: Terroir 2008

Type: Article

Authors

Josep Miquel UBALDE (1), Xavier SORT (1), Rosa Maria POCH (2) and Miquel PORTA (1)

(1) Dept. of Viticulture, Miguel Torres Winery, Miquel Torres i Carbó 6, 08720 Vilafranca del Penedès, Spain
(2) Dept. of Environment and Soil Science, University of Lleida, Rovira Roure 191, 25198 Lleida, Spain 

Contact the author

Keywords

soil mapping, viticultural zoning, terroir unit, management unit, precision viticulture

Tags

IVES Conference Series | Terroir 2008

Citation

Related articles…

Monitoring vineyard canopy structure by aerial and ground-based RGB and multispectral imagery analysis

Unmanned Aerial Vehicles (UAVs) are increasingly used to monitor canopy structure and vineyard performance. Compared with traditional remote sensing platforms (e.g. aircraft and satellite), UAVs offer a higher operational flexibility and can acquire ultra-high resolution images in formats such as true color red, green and blue (RGB) and multispectral. Using photogrammetry, 3D vineyard models and normalized difference vegetation index (NDVI) maps can be created from UAV images and used to study the structure and health of grapevine canopies. However, there is a lack of comparison between UAV-based images and ground-based measurements, such as leaf area index (LAI) and canopy porosity.

Impact of Metschnikowia pulcherrima and Saccharomyces cerevisiae in mixed fermentation on volatile compounds and energy sustainability in Lugana wine

In recent years, heightened awareness of the environmental impact has led to sustainability as a key issue for the winemaking sector.

Application of non-Saccharomyces yeasts in peculiar winemaking, sparkling and sweet wines: biological acidification, prise de mousse, aroma profile. Two cases of study

In this video recording of the IVES science meeting 2025, Raffaele Guzzon (Fondazione Edmund Mach, Centro di Trasferimento Tecnologico, San Michele all’Adige (TN), Italy) speaks about the application of non-Saccharomyces yeasts in peculiar winemaking, sparkling and sweet wines (biological acidification, prise de mousse, aroma profile). This presentation is based on an original article accessible for free on OENO One.

Kinetics modeling of a sangiovese wine chemical and physical parameters during one-year aging in different tank materials

The use of different tank materials during red wine aging has become increasingly popular, but little is known about their impact on wine chemical and physical parameters.

Within-vineyard variability in grape composition at the estate scale can be assessed through machine-learning modeling of plant water status in space and time. A case study from the hills of Adelaida District AVA, Paso Robles, CA, USA

Aim: Through machine-learning modelling of plant water status from environmental characteristics, this work aims to develop a model able to predict grape phenolic composition in space and time to guide selective harvest decisions at the estate scale.