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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: andreas.soteriades@sruc.ac.uk
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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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References

Adler, N, Friedman, L and Sinuany-Stern, Z 2002. Review of ranking methods in the data envelopment analysis context. European Journal of Operational Research 140, 249–265.Google Scholar
Basset-Mens, C, Ledgard, S and Boyes, M 2009. Eco-efficiency of intensification scenarios for milk production in New Zealand. Ecological Economics 68, 1615–1625.Google Scholar
Bava, L, Sandrucci, A, Zucali, M, Guerci, M and Tamburini, A 2014. How can farming intensification affect the environmental impact of milk production? Journal of Dairy Science 97, 4579–4593.CrossRefGoogle ScholarPubMed
Béguin, E, Bonnet, J, Dollé, J-B, Charroin, T and Ferrand, M 2008. Les différents postes de consommation en énergie et les pistes d'économie en élevage bovin laitier. Rencontres Recherches Ruminants 15, 217–220.Google Scholar
Beltrán-Esteve, M, Picazo-Tadeo, A and Reig-Martínez, E 2014. A metafrontier directional distance function aproach to assessing eco-efficiency. Journal of Productivity Analysis 41, 69–83.Google Scholar
Berre, D, Blancard, S, Boussemart, JP, Leleu, H and Tillard, E 2014. Finding the right compromise between productivity and environmental efficiency on high input tropical dairy farms: a case study. Journal of Environmental Management 146, 235–244.CrossRefGoogle ScholarPubMed
Bogetoft, P and Otto, L 2011. Benchmarking with DEA, SFA, and R. Springer, New York, NY, USA.CrossRefGoogle Scholar
Brockett, PL, Cooper, WW, Golden, LL, Rousseau, JJ and Wang, YY 2004. Evaluating solvency versus efficiency performance and different forms of organization and marketing in US property – liability insurance companies. European Journal of Operational Research 154, 492–514.CrossRefGoogle Scholar
Brockett, PL and Golany, B 1996. Using rank statistics for determining programmatic efficiency differences in data envelopment analysis. Management Science 42, 466–472.Google Scholar
Casey, JW and Holden, NM 2005. The relationship between greenhouse gas emissions and the intensity of milk production in Ireland. Journal of Environmental Quality 34, 429–436.Google Scholar
Charroin, T, Palazon, R, Madeline, Y, Guillaumin, A and Tchakerian, E 2005. Le système d’information des Réseaux d’Elevage français sur l’approche globale de l’exploitation. Intérêt et enjeux dans une perspective de prise en compte de la durabilité. Rencontres Recherches Ruminants 12, 335–338.Google Scholar
Chatellier, V and Pflimlin, A 2006. Dairy systems in the European regions of the Atlantic area. In Proceedings of the Final Seminar of the Green Dairy Project, 13 to 14 December 2006, Rennes, France pp. 11–30.Google Scholar
Chen, PC, Yu, MM, Chang, CC, Hsu, SH and Managi, S 2015. The enhanced Russell-based directional distance measure with undesirable outputs: numerical example considering CO2 emissions. Omega 53, 30–40.CrossRefGoogle Scholar
Conover, WJ 1999. Practical nonparametric statistics, 3rd edition. John Wiley & Sons Inc, New York, NY, USA.Google Scholar
Cooper, WW, Park, KS and Pastor, JT 1999. RAM: a range adjusted measure of inefficiency for use with additive models, and relations to other models and measures in DEA. Journal of Productivity Analysis 11, 5–42.Google Scholar
Cooper, WW, Seiford, LM and Tone, K 2007. Data envelopment analysis: a comprehensive text with models, applications, references and DEA-Solver software, 2nd edition. Springer Science+Business Media, LLC, New York, NY, USA.Google Scholar
CORPEN- Comité d’orientation pour de pratiques agricoles respectueuses de l’environnement 1999. Estimation des flux d’azote, de phosphore et de potassium associés aux vaches laitières et à leur système fourrager : influence de l’alimentation et du niveau de production. Ministère de l'Écologie, de l'Énergie, du Développement durable et de la Mer, Ministère de l’Agriculture, France.Google Scholar
Despotis, DK 2002. Improving the discriminating power of DEA: focus on globally efficient units. Journal of the Operational Research Society 53, 314–323.CrossRefGoogle Scholar
EMEP/CORINAIR- European Monitoring and Evaluation Programme/Core Inventory of Air Emissions in Europe 2002. Atmospheric emission inventory guidebook, 3rd edition. European Environment Agency, Copenhagen, Denmark.Google Scholar
ESCAP- Economic and Social Commission for Asia and the Pacific 2009. Eco-efficiency indicators: measuring resource-use efficiency and the impact of economic activities on the environment. ESCAP, Bangkok, Thailand.Google Scholar
Färe, R and Grosskopf, S 2000. Theory and applications of directional distance functions. Journal of Productivity Analysis 13, 93–103.Google Scholar
Faverdin, P, Baratte, C, Perbost, R, Thomas, S, Ramat, E and Peyraud, J-L 2014. A web tool to simply assess feed autonomy and nitrogen excretion of dairy herds: CowNex. Rencontres Recherches Ruminants 21, 391–394.Google Scholar
Foresight 2011. The future of food and farming. The Government Office for Science, London, UK.Google Scholar
Gac, A, Deltour, L, Cariolle, M, Dollé, J-B, Espagnol, S, Flénet, F, Guingand, N, Lagadec, S, Le Gall, A, Lellahi, A, Malaval, C, Ponchant, P and Tailleur, A 2010a. GES’TIM, Guide méthodologique pour l’estimation des impacts des activités agricoles sur l’effet de serre. Version 1.2. Institut de l’Elevage, Paris, France.Google Scholar
Gac, A, Manneville, V, Raison, C, Charroin, T and Ferrand, M 2010b. L’empreinte carbone des élevages d’herbivores : présentation de la méthodologie d'évaluation appliquée à des élevages spécialisés lait et viande. Rencontres Recherches Ruminants 17, 335–342.Google Scholar
Guerci, M, Bava, L, Zucali, M, Sandrucci, A, Penati, C and Tamburini, A 2013. Effect of farming strategies on environmental impact of intensive dairy farms in Italy. Journal of Dairy Research 80, 300–308.Google Scholar
Guinée, JB, Gorrée, M, Heijungs, R, Huppes, G, Kleijn, R, Koning de, A, Oers van, L, Wegener Sleeswijk, A, Suh, S, Udo de Haes, HA, Bruijn de, H, Duin van, R and Huijbregts, MAJ 2002. Handbook on life cycle assessment. Operational guide to the ISO standards. I: LCA in perspective. IIa: Guide. IIb: Operational annex. III: Scientific background. Kluwer Academic Publishers, Dordrecht, the Netherlands.Google Scholar
IPCC- Intergovernmental Panel on Climate Change 2007. 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, UK.Google Scholar
Jan, P, Dux, D, Lips, M, Alig, M and Dumondel, M 2012. On the link between economic and environmental performance of Swiss dairy farms of the alpine area. International Journal of Life Cycle Assessment 17, 706–719.CrossRefGoogle Scholar
Kuosmanen, T and Kortelainen, M 2005. Measuring eco-efficiency of production with data envelopment analysis. Journal of Industrial Ecology 9, 59–72.CrossRefGoogle Scholar
LEAP- Livestock Environmental Assessment and Performance Partnership 2015. Developing sound tools for transition to sustainable food and agriculture. LEAP partnership life cycle assessment guidelines on livestock supply chains: methodological notes. Draft version 1 for public review. LEAP, Rome, Italy.Google Scholar
Markovits-Somogyi, R 2011. Ranking efficient and inefficient decision making units in data envelopment analysis. International Journal for Traffic and Transport Engineering 1, 245–256.Google Scholar
Nemecek, T and 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, Zurich, Switzerland.Google Scholar
Pérez Urdiales, M, Oude Lansink, A and Wall, A 2015. Eco-efficiency among dairy farmers: the importance of socio-economic characteristics and farmer attitudes. Environmental and Resource Economics, doi:10.1007/s10640-015-9885-1, Published online by Springer Netherlands 20 February 2015.Google Scholar
Ramli, NA and Munisamy, S 2015. Eco-efficiency in greenhouse emissions among manufacturing industries: a range adjusted measure. Economic Modelling 47, 219–227.Google Scholar
Rotz, CA, Montes, F and Chianese, DS 2010. The carbon footprint of dairy production systems through partial life cycle assessment. Journal of Dairy Science 93, 1266–1282.CrossRefGoogle ScholarPubMed
Sheskin, DJ 1997. Handbook of parametric and nonparametric statistical procedures. CRC Press LLC, Boca Raton, FL, USA.Google Scholar
Soteriades, AD, Faverdin, P, March, M and Stott, AW 2015. Improving efficiency assessments using additive data envelopment analysis models: an application to contrasting dairy farming systems. Agricultural and Food Science 24, 235–248.Google Scholar
van Calker, KJ, Berentsen, PBM, Giesen, GWJ and Huirne, RBM 2008. Maximising sustainability of Dutch dairy farming systems for different stakeholders: a modelling approach. Ecological Economics 65, 407–419.Google Scholar
Vázquez-Rowe, I and Iribarren, D 2015. Review of life-cycle approaches coupled with data envelopment analysis: launching the CFP. The Scientific World Journal. doi.org/10.1155/2015/813921.Google Scholar