Planting criteria of new vineyards should comply with rational and sustainable criteria, taking into account the potential mechanisability of existing viticultural areas. However, an established methodology for this assessment is still lacking. This study aimed at analysing the parameters which influence the vineyard mechanisability, with the objective to propose a new mechanisability index. The mechanisability index proposed was based on GIS-analysis of landscape and management parameters such as mean slope, shape of the vineyard block, length-width ratio, headland size, training system and row spacing. We identified a sample of 3686 vineyards in Italy. Based on the above-mentioned parameters, vineyards were categorised by their level of mechanisability (l.m.) into four classes. Moreover, we analysed the correlation between l.m. and economic indicators (area planted with vineyard and wine production). Results showed that the main factors limiting the mechanisability potential of some Italian regions are the elevated slopes, horizontal training systems and narrow vine spacings. The l.m. showed a moderate positive correlation with the size of vineyards and the volume and value of production. The methodology presented in this study may be easily applied to other viticultural areas around the world, serving as a management decision-making tool.

A GIS-Based Multicriteria Index to Evaluate the Mechanisability Potential of Italian Vineyard Area

Alessia Cogato
;
Andrea Pezzuolo;Marco Sozzi;Francesco Marinello
Supervision
2020

Abstract

Planting criteria of new vineyards should comply with rational and sustainable criteria, taking into account the potential mechanisability of existing viticultural areas. However, an established methodology for this assessment is still lacking. This study aimed at analysing the parameters which influence the vineyard mechanisability, with the objective to propose a new mechanisability index. The mechanisability index proposed was based on GIS-analysis of landscape and management parameters such as mean slope, shape of the vineyard block, length-width ratio, headland size, training system and row spacing. We identified a sample of 3686 vineyards in Italy. Based on the above-mentioned parameters, vineyards were categorised by their level of mechanisability (l.m.) into four classes. Moreover, we analysed the correlation between l.m. and economic indicators (area planted with vineyard and wine production). Results showed that the main factors limiting the mechanisability potential of some Italian regions are the elevated slopes, horizontal training systems and narrow vine spacings. The l.m. showed a moderate positive correlation with the size of vineyards and the volume and value of production. The methodology presented in this study may be easily applied to other viticultural areas around the world, serving as a management decision-making tool.
2020
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3358618
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