Streamlining life cycle inventory data generation in agriculture using traceability data and information and communication technologies - part II: application to viticulture

Agricultural systems are increasingly subjected to environmental life cycle assessment (LCA) but generating life cycle inventory (LCI) data in agriculture remains a challenge. In Part I, it was suggested that traceability data are a good basis for generating precise LCI with reduced effort, especially when collected by efficient information and communication technologies (ICTs). The aim of this paper is to demonstrate this for wine grape production and generate a list of data to be collected for streamlined LCI generation. The study is carried out in the South of France, on a viticultural farm implementing electronic traceability of each cultivation operation, i.e. tillage, fertilisation, crop protection, weeding, canopy management and harvesting (no irrigation is needed at this vineyard). For each operation, specific emission models which satisfy the trade-off between accuracy and need for data have been identified. Traceability data must be supplemented with data related to the plot, equipment and inputs to feed the models. The sensitivity of the LCA outputs to plot soil type and year of cultivation was studied. Consistent with previous agricultural studies, the results show that operations such as pesticide spraying and fertilising have large environmental impacts in this Mediterranean vineyard. Notable variations occur in life cycle impact assessment indicators, principally due to variations in crop yield; however, the influence of secondary factors such as soil type and agricultural practices is also evident and this contribution allows us to better characterise the variability of grape production and to show that streamlined LCI can be created using traceability data. Ultimately, this paper delivers two results. It provides simple models, and relevant data and methodology to enable viticultural LCAs to be undertaken. Additionally, it demonstrates that accurate LCIs can be built based on data already collected for traceability when supplemented with other easily collectable data (weather and farm structural data). Overall, this work paves the way for streamlined LCI in agriculture.