A multidisciplinary approach to assess environmental and economic impact of conventional and innovative vineyards management systems in Northern Italy

Abstract

Viticulture is gradually shifting to more sustainable production systems and a fair number of studies aim at assessing the environmental impacts of different technologies and techniques adopted in the wine production through the Life Cycle Assessment approach. The main environmental issues identified are on water, soil and energy use, management of organic and inorganic solid waste streams, greenhouse gas emissions and use of chemicals. Precision viticulture (PV) techniques can play an important role in the sustainable use of water and fertilizers in grape production, thanks to the site-specific application of these inputs, improving yield and quality of grapes while minimizing negative effects on the environment. However, PV often implies investments and additional management costs. The objective of this study is to compare different strategies for the management of water and fertilizers in vineyards, ranging from the conventional ones to the most technologically advanced, to assess their sustainability both from an economic and an environmental point of view. Six scenarios have been explored, considering different irrigation water supply systems, and irrigation and fertilizer management strategies. A multidisciplinary approach, including Life Cycle Assessment, economic assessment and multivariate analysis was used to assess the sustainability of the different vineyard management approaches. The results show the higher economic and environmental sustainability for the scenario considering irrigation water supplied from an irrigation consortium, a variable rate drip irrigation system for irrigation and fertigation. Finally, also according to PCA results, at least for the scenarios explored in the study, the introduction of PV technologies led to the reduction of environmental impacts and to the increase in economic advantages, which showed to be inversely correlated.