دانلود مقاله ISI انگلیسی شماره 105973
ترجمه فارسی عنوان مقاله

ارزیابی تأثیر عدم قطعیت بر معیار ارزیابی عملکرد اقتصادی کشاورزی با استفاده از تجزیه و تحلیل پوشش گیری داده های فازی

عنوان انگلیسی
Assessing the impact of uncertainty on benchmarking the eco-efficiency of dairy farming using fuzzy data envelopment analysis
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
105973 2018 31 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Cleaner Production, Available online 11 April 2018

پیش نمایش مقاله
پیش نمایش مقاله  ارزیابی تأثیر عدم قطعیت بر معیار ارزیابی عملکرد اقتصادی کشاورزی با استفاده از تجزیه و تحلیل پوشش گیری داده های فازی

چکیده انگلیسی

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.