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An approach to holistically assess (dairy) farm eco-efficiency by combining Life Cycle Analysis with Data Envelopment Analysis models and methodologies

Published online by Cambridge University Press:  29 April 2016

A. D. Soteriades ,
P. Faverdin ,
S. Moreau ,
T. Charroin ,
M. Blanchard  and
A. W. Stott
A. D. Soteriades*
Affiliation:
Scotland’s Rural College, Future Farming Systems Group, Edinburgh, UK
P. Faverdin
Affiliation:
INRA, UMR 1348 PEGASE, F-35590 St-Gilles, France Agrocampus-Ouest, UMR 1348 PEGASE, F-35000 Rennes, France
S. Moreau
Affiliation:
Institut de l’Elevage, F-75000 Paris, France
T. Charroin
Affiliation:
Institut de l’Elevage, F-75000 Paris, France
M. Blanchard
Affiliation:
CIRAD, UMR 0868 SELMET, Bobo-Dioulasso, Burkina Faso
A. W. Stott
Affiliation:
Scotland’s Rural College, Future Farming Systems Group, Edinburgh, UK
*
†E-mail: [email protected]
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Abstract

Eco-efficiency is a useful guide to dairy farm sustainability analysis aimed at increasing output (physical or value added) and minimizing environmental impacts (EIs). Widely used partial eco-efficiency ratios (EIs per some functional unit, e.g. kg milk) can be problematic because (i) substitution possibilities between EIs are ignored, (ii) multiple ratios can complicate decision making and (iii) EIs are not usually associated with just the functional unit in the ratio’s denominator. The objective of this study was to demonstrate a ‘global’ eco-efficiency modelling framework dealing with issues (i) to (iii) by combining Life Cycle Analysis (LCA) data and the multiple-input, multiple-output production efficiency method Data Envelopment Analysis (DEA). With DEA each dairy farm’s outputs and LCA-derived EIs are aggregated into a single, relative, bounded, dimensionless eco-efficiency score, thus overcoming issues (i) to (iii). A novelty of this study is that a model providing a number of additional desirable properties was employed, known as the Range Adjusted Measure (RAM) of inefficiency. These properties altogether make RAM advantageous over other DEA models and are as follows. First, RAM is able to simultaneously minimize EIs and maximize outputs. Second, it indicates which EIs and/or outputs contribute the most to a farm’s eco-inefficiency. Third it can be used to rank farms in terms of eco-efficiency scores. Thus, non-parametric rank tests can be employed to test for significant differences in terms of eco-efficiency score ranks between different farm groups. An additional DEA methodology was employed to ‘correct’ the farms’ eco-efficiency scores for inefficiencies attributed to managerial factors. By removing managerial inefficiencies it was possible to detect differences in eco-efficiency between farms solely attributed to uncontrollable factors such as region. Such analysis is lacking in previous dairy studies combining LCA with DEA. RAM and the ‘corrective’ methodology were demonstrated with LCA data from French specialized dairy farms grouped by region (West France, Continental France) and feeding strategy (regardless of region). Mean eco-efficiency score ranks were significantly higher for farms with <10% and 10% to 30% maize than farms with >30% maize in the total forage area before correcting for managerial inefficiencies. Mean eco-efficiency score ranks were higher for West than Continental farms, but significantly higher only after correcting for managerial inefficiencies. These results helped identify the eco-efficiency potential of each region and feeding strategy and could therefore aid advisors and policy makers at farm or region/sector level. The proposed framework helped better measure and understand (dairy) farm eco-efficiency, both within and between different farm groups.

Keywords

eco-efficiencycomposite indicatorsmanagerial inefficiencyuncontrollable factorsFrench dairy farm data
Type
Research Article
Information
animal , Volume 10 , Issue 11 , November 2016 , pp. 1899 - 1910
Copyright
© The Animal Consortium 2016 

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