Abstract
Purpose
For the development of any life cycle assessment study, the practitioner frequently integrates primary data collected on-field, with background data taken from various life cycle inventory databases which are part of most commercial LCA software packages. However, such data is often not generally applicable to all product systems since, especially concerning the agri-food sector, available datasets may not be fully representative of the site specificity of the food product under examination. In this context, the present work investigates the background, sources and methodological aspects that characterise the most known commercial databases containing agri-food data, with a focus on four agri-food supply chains (olive oil, wine, wheat products and citrus fruit), which represent an important asset for the Italian food sector.
Methods
Specifically, the paper entails a review of currently available LCI databases and their datasets with a twofold scope: firstly, to understand how agri-food data is modelled in these databases for a coherent and consistent representation of regional scenarios and to verify whether they are also suitable for the Italian context and, secondly, to identify and analyse useful and relevant methodological approaches implemented in the existing LCI databases when regional data are modelled.
Results
Based on the aforementioned review, it is possible to highlight some problems which may arise when developing an LCI pertaining to the four Italian agri-food supply chains, namely:
1. The need for specific inventory datasets to tackle the specificities of agri-food product systems.
2. The lack of datasets, within the existing DBs, related to the Italian context and to the abovementioned supply chains. In fact, at present, in the currently available LCI DBs, there are very few (or in some cases none) datasets related to Italian wine, olive oil, wheat-based products and citrus fruit. The few available datasets often contain some data related to the Italian context but also approximate data with that of product systems representing other countries.
Furthermore, the present study allowed to identify and discuss the main aspects to be used as starting elements for modelling regional data to be included in a future Italian LCI database of the abovementioned four supply chains.
Conclusions
The results of the present study represent a starting point for the collection of data and its organisation, in order to develop an Italian LCI agri-food database with datasets which are representative of the regional specificities of four agri-food supply chains which play an important role in the Italian economy.
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