Estimating environmental impacts of agricultural systems with LCA using data from the French Farm Accountancy Data Network (FADN)

被引:11
|
作者
Samson, Elisabeth [1 ,2 ]
Van der Werf, Hayo M. G. [3 ,4 ]
Dupraz, Pierre [1 ,2 ]
Ruas, Jean-Francois [1 ,2 ]
Corson, Michael S. [3 ,4 ]
机构
[1] INRA, UMR SMART 1302, F-35000 Rennes, France
[2] Agrocampus Ouest, UMR SMART 1302, F-35000 Rennes, France
[3] INRA, UMR Sol Agro & Hydrosyst Spatialisat 1069, F-35042 Rennes, France
[4] Agrocampus Ouest, UMR Sol Agro & Hydrosyst Spatialisat 1069, F-35042 Rennes, France
关键词
agriculture; climate change; databases; energy for agriculture; life cycle analysis;
D O I
10.1684/agr.2012.0581
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
This work developed environmental indicators from the economic and agricultural database of Farm Accountancy Data Network (FADN). Two environmental impacts, climate change and non-renewable energy use, were estimated from the database according to the life cycle assessment (LCA) approach. The large variability observed seems to be linked to production orientation and geographic location. Livestock production was strongly correlated with the impact of climate change, particularly cattle production, due to its high emissions of enteric methane. Farms specialized in cereals and cash crops showed high non-renewable energy use, linked with intensive use of mineral fertilizers. Some mixed farm holdings, whether livestock-oriented or crop-and livestock-oriented, had impacts similar to those of the specialized farming system which each of them resembled most. Impacts varied according to geographic location in France. For farms specialized in cereals or dairy production, direct greenhouse gas emissions and indirect energy consumption predominated. Impacts for specialized dairy farms varied greatly per unit of agricultural area ( hectares) but less per 1,000 euros of revenue. In contrast, impact variability for specialized cereal farms was lower per hectare but considerably greater per 1,000 euros. Continuing the study will be useful for improving methods and results of these farming systems. It will also be interesting to consider more detailed methods to understand mixed crop-livestock farms, as these systems are widespread.
引用
收藏
页码:248 / 257
页数:10
相关论文
共 50 条
  • [31] Using Artificial Intelligence on environmental data from Internet of Things for estimating solar radiation: Comprehensive analysis
    Kosovic, Ivana Nizetic
    Mastelic, Toni
    Ivankovic, Damir
    JOURNAL OF CLEANER PRODUCTION, 2020, 266
  • [32] Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers
    Green, P. L.
    Maskell, S.
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2017, 93 : 379 - 396
  • [33] Estimating leaf area index from MODIS and surface meteorological data using a dynamic Bayesian network
    Zhang, Yuzhen
    Qu, Yonghua
    Wang, Jindi
    Liang, Shunlin
    Liu, Yan
    REMOTE SENSING OF ENVIRONMENT, 2012, 127 : 30 - 43
  • [34] Mapping Potential Environmental Impacts from Tourists Using Data from Social Media: A Case Study in the Westfjords of Iceland
    Hale, Brack W.
    ENVIRONMENTAL MANAGEMENT, 2018, 62 (03) : 446 - 457
  • [35] Mapping Potential Environmental Impacts from Tourists Using Data from Social Media: A Case Study in the Westfjords of Iceland
    Brack W. Hale
    Environmental Management, 2018, 62 : 446 - 457
  • [36] Estimating the efficiency of complex marine systems in China's coastal regions using a network Data Envelope Analysis model
    Sun, Caizhi
    Wang, Song
    Zou, Wei
    Wang, Zeyu
    OCEAN & COASTAL MANAGEMENT, 2017, 139 : 77 - 91
  • [37] Estimating the prevalence of hematological malignancies and precursor conditions using data from Haematological Malignancy Research Network (HMRN)
    Li, Jinlei
    Smith, Alex
    Crouch, Simon
    Oliver, Steven
    Roman, Eve
    CANCER CAUSES & CONTROL, 2016, 27 (08) : 1019 - 1026
  • [38] Estimating global downward shortwave radiation from VIIRS data using a transfer-learning neural network
    Wang, Dongdong
    Li, Ruohan
    Liang, Shunlin
    Jia, Aolin
    Wang, Zhihao
    REMOTE SENSING OF ENVIRONMENT, 2022, 274
  • [39] Estimating the prevalence of hematological malignancies and precursor conditions using data from Haematological Malignancy Research Network (HMRN)
    Jinlei Li
    Alex Smith
    Simon Crouch
    Steven Oliver
    Eve Roman
    Cancer Causes & Control, 2016, 27 : 1019 - 1026
  • [40] Estimating Roadway Horizontal Alignment from Geographic Information Systems Data: An Artificial Neural Network-Based Approach
    Bartin, Bekir
    Jami, Mojibulrahman
    Ozbay, Kaan
    JOURNAL OF SURVEYING ENGINEERING, 2023, 149 (04)