Assessing the impact of uncertainty on benchmarking the eco-efficiency of dairy farming using fuzzy data envelopment analysis. (10th July 2018)

• Record Type: Journal Article
• Authors: Mu, W.; Kanellopoulos, A.; van Middelaar, C.E.; Stilmant, D.; Bloemhof, J.M.
• Is Part Of: Journal of cleaner production. Volume 189(2018)
• Page Start: 709
• Page End: 717
• Publication Date: 2018-07-10
• Subjects: Environmental indicator -- Multi-criteria -- Frontier analysis -- Ranking -- Farm practice -- DEA; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2018.04.091
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: The dairy sector is challenged to increase its eco-efficiency, which means, minimizing environmental impacts, while maintaining economic viability. To quantify eco-efficiency, multiple environmental and economic indicators are needed. Data envelopment analysis (DEA) has been used to evaluate the eco-efficiency of agricultural systems accounting for multiple indicators simultaneously. In practice, however, data used to calculate the economic and environmental performance of dairy farms can contain high levels of uncertainty. Standard DEA is deterministic and does not consider data uncertainty. Fuzzy DEA is a useful approach to account for uncertainties when benchmarking the eco-efficiency of dairy farming. In this study we therefore demonstrate how fuzzy DEA can be used to evaluate the eco-efficiency of dairy farming. We used a case study of 55 dairy farms from different regions across Western Europe. We used N surplus, P surplus, land use, energy use as the environmental indicators and gross margin as the economic indicator. We found that accounting for uncertainty around the value of environmental and economic indicators can affect substantially the eco-efficiency of evaluated farms. In addition, fuzzy DEA identified different set of peers compared to the peers of the standard DEA. All the aforementioned findings showed the importance of taking uncertainty into consideration in the benchmarking process, and how fuzzy DEA can be used to do so. Highlights: We evaluate eco-efficiency of dairy farming systems using DEA. We evaluate eco-efficiency based on multiple environmental indicators. Uncertainty on model parameters are taken into account. We show that accounting for uncertainty improves quantification of eco-efficiency. Possibilities for eliminating inefficiencies are identified.