Inferred equations for predicting cumulative exergy extraction throughout cradle-to-gate life cycles of Pangasius feeds and intensive Pangasius grow-out farms in Vietnam

Abstract Intensive Pangasius aquaculture in Vietnam has attracted concerns about the sustainability of its resource use. However, the assessment of the latter, using Life Cycle Assessment (LCA), is a time-consuming and complex task. To establish a simplified approach, especially relevant for certification incentives, as an alternative to a full LCA, we first highlight key parameters in resource use of the system to provide a better understanding of the variability in LCA results, and second present inferred equations that allow for an easy estimation of the resource footprint using some of these key parameters. A representative sample of 10 certified (i.e., ASC and GLOBALG.A.P.) and 10 non-certified intensive Pangasius farms in the Mekong Delta was investigated. Detailed LCA results, resulting in resource consumption in terms of the Cumulative Exergy Extraction from Natural Environment (CEENE), showed that pond water renewal and feed production, particularly agriculture-based feed ingredients, were the hotspots. Inferred equations, in fact linear regression models, were successfully set up and have proven to be useful to estimate the resource footprint of Pangasius feeds and aquaculture using parameters identified as the resource use hotspots. The CEENE over the cradle to feed mill gate per tonne feed could be predicted from the mass share of agriculture-based ingredients (R 2  ≥ 0.90, n = 12 feeds). The CEENE over the cradle to farm gate per tonne Pangasius fish in the certified farms and/or non-certified farms can be explained by the amount of water and feed inputs (R 2  ≥ 0.98, n = 20 farms), which highlights the relevance of managing these resources in intensive Pangasius farming, and subsequent equations were also set up.

[1]  Laure Nitschelm,et al.  Data strategy for environmental assessment of agricultural regions via LCA: case study of a French catchment , 2016, The International Journal of Life Cycle Assessment.

[2]  J. Dewulf,et al.  Resource consumption assessment of Pangasius fillet products from Vietnamese aquaculture to European retailers , 2015 .

[3]  Sena S. De Silva,et al.  Current status of farming practices of striped catfish, Pangasianodon hypophthalmus in the Mekong Delta, Vietnam , 2009 .

[4]  Sam Debaveye,et al.  Environmental sustainability assessments of pharmaceuticals: an emerging need for simplification in life cycle assessments. , 2014, Environmental science & technology.

[5]  Isabelle Blanc,et al.  A Simplified Life Cycle Approach for Assessing Greenhouse Gas Emissions of Wind Electricity , 2012 .

[6]  D. Little,et al.  Certifying catfish in Vietnam and Bangladesh: Who will make the grade and will it matter? , 2011 .

[7]  Jo Dewulf,et al.  Exergy-based accounting for land as a natural resource in life cycle assessment , 2013, The International Journal of Life Cycle Assessment.

[8]  Pilar Swart,et al.  Abiotic Resource Use , 2015 .

[9]  Steven De Meester,et al.  Resource use analysis of Pangasius aquaculture in the Mekong Delta in Vietnam using Exergetic Life Cycle Assessment , 2013 .

[10]  Gerald Rebitzer,et al.  The ecoinvent database system: a comprehensive web-based LCA database , 2005 .

[11]  N. Pelletier,et al.  Life Cycle Considerations for Improving Sustainability Assessments in Seafood Awareness Campaigns , 2008, Environmental management.

[12]  J. Stevens,et al.  Outliers and influential data points in regression analysis. , 1984 .

[13]  Jeroen B. Guinée,et al.  Life cycle assessment of aquaculture systems—a review of methodologies , 2011, The International Journal of Life Cycle Assessment.

[14]  D. Little,et al.  Comparison of Asian Aquaculture Products by Use of Statistically Supported Life Cycle Assessment. , 2015, Environmental science & technology.

[15]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[16]  Stijn Bruers,et al.  Exergy: its potential and limitations in environmental science and technology. , 2008, Environmental science & technology.

[17]  R. Heijungs,et al.  Product Carbon Footprints and Their Uncertainties in Comparative Decision Contexts , 2015, PloS one.

[18]  J. Dewulf,et al.  Re-evaluating Primary Biotic Resource Use for Marine Biomass Production: A New Calculation Framework. , 2015, Environmental science & technology.

[19]  Gregory A. Keoleian,et al.  Role of life cycle assessment in sustainable aquaculture , 2013 .

[20]  J Dewulf,et al.  Cumulative exergy extraction from the natural environment (CEENE): a comprehensive life cycle impact assessment method for resource accounting. , 2007, Environmental science & technology.

[21]  H. Lilliefors On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown , 1967 .

[22]  Gjalt Huppes,et al.  Thermodynamic resource indicators in LCA: a case study on the titania produced in Panzhihua city, southwest China , 2012, The International Journal of Life Cycle Assessment.

[23]  W. W. Muir,et al.  Regression Diagnostics: Identifying Influential Data and Sources of Collinearity , 1980 .

[24]  R. Bosma,et al.  Life cycle assessment of intensive striped catfish farming in the Mekong Delta for screening hotspots as input to environmental policy and research agenda , 2011 .

[25]  Ralph Horne,et al.  Life Cycle Assessment: Principles, Practice and Prospects , 2009 .

[26]  R. Bosma,et al.  Environmental impact of non-certified versus certified (ASC) intensive Pangasius aquaculture in Vietnam, a comparison based on a statistically supported LCA. , 2016, Environmental pollution.