Streamlining life cycle inventory data generation in agriculture using traceability data and information and communication technologies - Part I: Concepts and technical basis

[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]INSPIRE

International audience

إنجليزي. Quantitative environmental assessment methodologies such as life cycle assessment demand significant time and resource inputs during the data acquisition and life cycle inventory (LCI) phase. Approaches to streamline the LCI data collection process without degrading data quality are therefore required. This requirement is especially true for agricultural products, as agricultural systems are inherently 'open' and complex. We present a two-part paper on this topic. In this first part, we examine streamlined methods for LCI data collection in agriculture by using today's voluntary or compulsory farm traceability information systems and related information and communication technologies (ICTs), with the aim of later converting them into LCI data. The second part is to examine the application of these technologies in a case study. Our hypothesis is that both traceability data and ICTs could be major drivers for generating accurate, relevant and low-cost LCI data for use in quantitative environmental assessments of agricultural product performance. To that end, we identified the types of data being collected in agriculture as a part of current business practice, especially those with relevance to LCA studies. We also examined the status and current trends in ICTs in use in agriculture to identify the potential for automating LCI data generation. The review identified considerable potential to piggy-back current trends in ICTs in agriculture with the goal of simplifying LCI data collection. This study concludes that given the increasing need to collect traceability data in modern agriculture and the parallel growing adoption of information and communication technologies, it is likely that ICTs and associated information systems will represent an important potential route for the acquisition of future LCI data.