Partial modelling of the perennial crop cycle misleads LCA results in two contrasted case studies
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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.
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.
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.
KeywordsChronological modelling Citrus LCA Oil palm fruit Perennial crop
The authors would like to thank the French National Research Agency (ANR) for its support to fieldwork in Indonesia, within the frame of the SPOP project (http://spop.cirad.fr/) Agrobiosphere program. They are also very grateful to their local partners in Morocco and Indonesia who provided the data used in these studies. In particular, the authors want to thank Mr. Albertus Magnus C.K. and Mr. Rudy Harto Widodo for their fieldwork support. Finally, the authors would like to thank the anonymous reviewers whose comments allowed for improving the quality of the paper.
- Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration—guidelines for computing crop water requirements—FAO irrigation and drainage paper 56. Food and Agriculture Organization of the United Nations, Rome, Italy. ISBN 92-5-104219-5Google Scholar
- Audsley E (Coord.), Alber S, Clift R, Cowell S, Crettaz P, Gaillard G et al (1997) Harmonisation of environmental life cycle assessment for agriculture. Final Report. Concerted Action AIR3-CT94-2028. European Commission. DG VI Agriculture. SRI, Silsoe, UKGoogle Scholar
- Bouwman AF, Boumans LJM, Batjes NH (2002) Modeling global annual N2O and NO emissions from fertilized fields. Glob Biogeochem Cycles 16:11Google Scholar
- Caliman JP (1992) Oil palm and water deficit: production, adapted cropping techniques. Oléagineux 47(5):205–216Google Scholar
- Carron MP, Pierrat M, Snoeck D et al (2015) Temporal variability in soil quality after organic residue application in mature oil palm plantations. Soil Res 53:205–215Google Scholar
- Dufour O, Frere JL, Caliman JP, Hornus P (1988) Description of a simplified method of production forecasting in oil palm plantations based on climatology. Oléagineux 43(7):271–278Google Scholar
- Freiermuth R (2006) Modell zur Berechnung der Schwermetallflüsse in der Landwirtschaftlichen Ökobilanz. Agroscope FAL Reckenholz, 42 pp. www.art.admin.ch
- Goedkoop M, Heijungs R, Huijbregts M, De Schryver A, Struijs J, Van Zelm R (2013) ReCiPe 2008, A life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level - First edition (version 1.08), Report I: Characterisation. Ministry of Housing, Spatial planning, and Environment, the Netherlands, pp 126Google Scholar
- IPCC (2006) IPCC guidelines for national greenhouse gas inventories. In Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K (eds) The national greenhouse gas inventories programme, IGES, JapanGoogle Scholar
- IPCC (2007) Summary for Policymakers. In Solomon SD, Qin M, Manning Z, Chen M, Marquis KB, Averyt M, Tignor, Miller HL (eds) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 18 pGoogle Scholar
- Khalid H, Zin ZZ, Anderson JM (2000) Decomposition processes and nutrient release patterns of oil palm residues. J Oil Palm Res 12:46–63Google Scholar
- Kheong LV, Rahman ZA, Mohamed HM, Aminudin H (2010) Empty fruit bunch application and oil palm root proliferation. J Oil Palm Res 22:750–757Google Scholar
- Mithraratne N, McLaren S, Barber A (2008) Carbon footprinting for the Kiwifruit supply chain—report on methodology and scoping study. Landcare research Contract Report LC0708/156, prepared for New Zealand Ministry of Agriculture and Forestry, New Zealand, p 61Google Scholar
- Nemecek T, Kägi T (2007) Life cycle inventories of Swiss and European agricultural production systems. Final report ecoinvent V2.0 No. 15a. Agroscope Reckenholz-Taenikon Research Station ART, Swiss Centre for Life Cycle Inventories, Zürich and Dübendorf, Switzerland, retrieved from: www.econivent.ch
- Pallas B, Soulié J-C, Aguilar G et al (2013) X-Palm, a functional structural plant model for analysing temporal, genotypic and inter-tree variability of oil palm growth and yield. In: of the Seventh International Conference on Functional Structural Plant ModelGoogle Scholar
- Payen S (2015) Toward a consistent accounting of water as a resource and a vector of pollution in the LCA of agricultural products: Methodological development and application to a perennial cropping system. PhD thesis, University of Montpellier, Montpellier, France, 186 ppGoogle Scholar
- Prasuhn V (2006) Erfassung der PO4- Austräge für die Ökobilanzierung SALCA Phosphor. Agroscope Reckanholz-Tänikon ART, Switzerland, 20 pGoogle Scholar
- Steduto P, Hsiao TC, Fereres E, Raes D (2012) Crop yield response to water. FAO Irrigation and Drainage paper 66. Food and Agriculture Organization of the United Nations, Rome, Italy. ISBN 978-92-5-107274-5Google Scholar
- Vannière H (1992) Essai porte-greffe nutrition du clémentinier en Corse (Station de Recherches Agronomiques de San Giuliano INRA CIRAD – IRFA). Fruits 47(1)Google Scholar
- Zulkifli H, Halimah M, Mohd Basri W, Choo YM (2009) Life cycle assessment for FFB production. PIPOC Conference 2009 Palm oil— balancing ecologies with economics, MPOB, Malaysian Palm Oil Board, Kuala Lumpur, Malaysia, 9-12 November 2009Google Scholar