Rearing performances and environmental assessment of sea cage farming in Tunisia using life cycle assessment (LCA) combined with PCA and HCPC

  • Khaled Abdou
  • Frida Ben Rais Lasram
  • Mohamed Salah Romdhane
  • François Le Loc’h
  • Joël Aubin
CHALLENGES AND BEST PRACTICE IN LCAS OF SEAFOOD AND OTHER AQUATIC PRODUCTS

Abstract

Purpose

The present study aims to understand the influence of rearing practices and the contributions of production phases of fish farming to their environmental impacts and determine which practices and technical characteristics can best improve the farms’ environmental performance. Another objective is to identify the influence of variability in farming practices on the environmental performances of sea cage aquaculture farms of sea bass and sea bream in Tunisia by using principal component analysis (PCA) and hierarchical clustering on principal components (HCPC) methods and then combining the classification with life cycle assessment (LCA).

Methods

The approach consisted of three major steps: (i) of the 24 aquaculture farms in Tunisia, 18 were selected which follow intensive rearing practices in sea cages of European sea bass (Dicentrarchus labrax) and gilthead sea bream (Sparus aurata) and then a typology was developed to classify the studied farms into rearing practice groups using HCPC; (ii) LCA was performed on each aquaculture farm and (iii) mean impacts and contributions of production phases were calculated for each group of farms. Impact categories included acidification, eutrophication, global warming, land occupation, total cumulative energy demand and net primary production use.

Results and discussion

Results revealed high correlation between rearing practices and impacts. The feed-conversion ratio (FCR), water column depth under the cages and cage size had the greatest influence on impact intensity. Rearing practices and fish feed were the greatest contributors to the impacts studied due to the production of fish meal and oil and the low efficiency of feed use, which generated large amounts of nitrogen and phosphorus emissions. It is necessary to optimise the diet formulation and to follow better feeding strategies to lower the FCR and improve farm performance. Water column depth greatly influenced the farms’ environmental performance due to the increase in waste dispersion at deeper depths, while shallow depths resulted in accumulation of organic matter and degradation of water quality. Cage size influences environmental performances of aquaculture farms. Thus, from an environmental viewpoint, decision makers should grant licences for farms in deeper water with larger cages and encourage them to improve their FCRs.

Conclusions

This study is the first attempt to combine the HCPC method and the LCA framework to study the environmental performance of aquacultural activity. The typology developed captures the variability among farms because it considers several farm characteristics in the classification. The LCA demonstrated that technical parameters in need of improvement are related to the technical expertise of farm managers and workers and to the location of the farm.

Keywords

Environmental impact Life cycle assessment (LCA) Marine aquaculture Tunisia Typology 

Notes

Acknowledgements

The authors would like to acknowledge valuable financial support from the ‘Institut de Recherche pour le Développement’ (JEAI GAMBAS project). This study was also partially funded by ‘LabexMer’.

Supplementary material

11367_2017_1339_MOESM1_ESM.docx (31 kb)
ESM 1 (DOCX 30 kb).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Khaled Abdou
    • 1
    • 2
  • Frida Ben Rais Lasram
    • 3
  • Mohamed Salah Romdhane
    • 1
  • François Le Loc’h
    • 2
  • Joël Aubin
    • 4
  1. 1.UR 03AGRO1 Ecosystèmes et Ressources Aquatiques, Institut National Agronomique de Tunisie (INAT)Université de CarthageTunisTunisia
  2. 2.UMR 6539 Laboratoire des Sciences de l’Environnement Marin (CNRS/UBO/IRD/Ifremer)Institut Universitaire Européen de la Mer (IUEM)PlouzanéFrance
  3. 3.UMR 8187, LOG, Laboratoire d’Océanologie et de GéosciencesUniv. Littoral Cote d’Opale, Univ. Lille, CNRSWimereuxFrance
  4. 4.UMR 1069, Sol Agro et hydrosystème Spatialisation, INRA AGROCAMPUS OUESTRennes CEDEXFrance

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