Assessing Greenhouse Gas Emissions from Conventional Farms Based on the Farm Accountancy Data Network
Alina Syp1, Dariusz Osuch2
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1Department of Bioeconomy and Systems Analysis, Institute of Soil Science and Plant Cultivation,
State Research Institute, 8 Czartoryskich Str., 24-100 Puławy, Poland
2Agricultural Accountancy Department, Institute of Agricultural and Food Economics, National Research Institute,
20 Świętokrzyska Str., 00-002 Warszawa, Poland
Submission date: 2017-07-06
Final revision date: 2017-08-25
Acceptance date: 2017-08-26
Online publication date: 2018-02-06
Publication date: 2018-03-12
Pol. J. Environ. Stud. 2018;27(3):1261–1268
Our paper uses the Intergovernmental Panel on Climate Change (IPCC) guidelines in combination with the Farm Accountancy Data Network (FADN) to estimate agricultural greenhouse gas emissions at the farm level. The study adopts a cross-cutting approach that combines emissions related to different categories (agriculture and energy/fuel). Overall, the aim was to assess the intensities of emissions from conventional farms classified according to production type, economic size, and utilized agricultural area (UAA). The results show that large variations in farms justify the micro approach to farm evaluation. Applying the methodology revealed that conventional dairy farm types, medium-small (25≤€<50) and medium-large (20<=UAA<30), were characterized by the highest GHG emissions intensity indexes compared to other farm types and sizes. The FADN originally was developed for evaluating the income of agricultural holdings and the impact of the Common Agricultural Policy (CAP). However, our study demonstrates that the current FADN database could also be used to provide indirect information on environmental farm performance, identify differences between farm types, and give insight into the environmental impact caused by the agricultural sectors in European countries. These results may also be useful for farm advisors to benchmark some aspects of farm environmental performance using farm financial data.