Partial modelling of the perennial crop cycle misleads LCA results in two contrasted case studies

Purpose

As highlighted in recent reviews, there is a need to harmonise the way life cycle assessment (LCA) of perennial crops is conducted. In most published LCA on perennial crops, the modelling of the agricultural production is based on data sets for just one productive year. This may be misleading since performance and impacts of the system may greatly vary year by year. The purposes of this study are to analyse how partial modelling of the perennial cycle through non-holistic data collection may affect LCA results and to make recommendations.

Methods

Three modelling choices for the perennial crop cycle were tested in parallel in two contrasted LCA case studies: oil palm fruits from Indonesia, and small citrus from Morocco. Modelling choices tested were as follows: (i) a chronological modelling over the complete crop cycle of orchards, (ii) a 3-year average from the productive phase, and (iii) various single years from the productive phase. In both case studies, the system boundary was a cradle-to-farm gate with a functional unit of 1 kg fresh fruits. LCA midpoint impacts were calculated with ReCiPe 2008 in Simapro©V.7. We first analysed how inputs, yields and potential impacts varied over time. We then analysed process contributions in the baseline model, i.e. the chronological modelling, and finally compared LCA results for the various perennial modelling choices.

Results and discussion

Agricultural practices, yields and impacts varied over the years especially during the first 3–9 years depending on the case study. In both case studies, the modelling choices to account or not for the whole perennial cycle drastically influenced LCA results. The differences could be explained by the inclusion or not of the yearly variability and the accounting or not of the immature phase, which contributed to 7–40 or 6.5–29 % of all impact categories for oil palm fruit and citrus, respectively.

Conclusions

The chosen approach to model the perennial cycle influenced the final LCA results for two contrasted case studies and deserved specific attention. Although data availability may remain the limiting factor in most cases, assumptions can be made to interpolate or extrapolate some data sets or to consolidate data sets from chronosequences (i.e. modular modelling). In all cases, we suggest that the approach chosen to model the perennial cycle and the representativeness of associated collected data should be made transparent and discussed. Further research work is needed to improve the understanding and modelling of perennial crop functioning and LCA assessment.