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Environmental life cycle assessment of grain maize production: An analysis of factors causing variability.

Authors
  • Boone, Lieselot1
  • Van Linden, Veerle2
  • De Meester, Steven3
  • Vandecasteele, Bart2
  • Muylle, Hilde2
  • Roldán-Ruiz, Isabel2
  • Nemecek, Thomas4
  • Dewulf, Jo5
  • 1 Research Group EnVOC, Department of Sustainable Organic Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Gent B-9000, Belgium; Institute for Agricultural and Fisheries Research (ILVO), Burgemeester Van Gansberghelaan 92, B-9820 Merelbeke, Belgium. , (Belgium)
  • 2 Institute for Agricultural and Fisheries Research (ILVO), Burgemeester Van Gansberghelaan 92, B-9820 Merelbeke, Belgium. , (Belgium)
  • 3 Research Group EnVOC, Department of Sustainable Organic Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Gent B-9000, Belgium. , (Belgium)
  • 4 Agroscope, Institute for Sustainability Sciences, LCA Group, Reckenholzstrasse 191, 8046 Zurich, Switzerland. , (Switzerland)
  • 5 Research Group EnVOC, Department of Sustainable Organic Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Gent B-9000, Belgium. Electronic address: [email protected]. , (Belgium)
Type
Published Article
Journal
The Science of the total environment
Publication Date
May 15, 2016
Volume
553
Pages
551–564
Identifiers
DOI: 10.1016/j.scitotenv.2016.02.089
PMID: 26938318
Source
Medline
Keywords
License
Unknown

Abstract

To meet the growing demand, high yielding, but environmentally sustainable agricultural plant production systems are desired. Today, life cycle assessment (LCA) is increasingly used to assess the environmental impact of these agricultural systems. However, the impact results are very diverse due to management decisions or local natural conditions. The impact of grain maize is often generalized and an average is taken. Therefore, we studied variation in production systems. Four types of drivers for variability are distinguished: policy, farm management, year-to-year weather variation and innovation. For each driver, scenarios are elaborated using ReCiPe and CEENE (Cumulative Exergy Extraction from the Natural Environment) to assess the environmental footprint. Policy limits fertilisation levels in a soil-specific way. The resource consumption is lower for non-sandy soils than for sandy soils, but entails however more eutrophication. Farm management seems to have less influence on the environmental impact when considering the CEENE only. But farm management choices such as fertiliser type have a large effect on emission-related problems (e.g. eutrophication and acidification). In contrast, year-to-year weather variation results in large differences in the environmental footprint. The difference in impact results between favourable and poor environmental conditions amounts to 19% and 17% in terms of resources and emissions respectively, and irrigation clearly is an unfavourable environmental process. The best environmental performance is obtained by innovation as plant breeding results in a steadily increasing yield over 25 years. Finally, a comparison is made between grain maize production in Flanders and a generically applied dataset, based on Swiss practices. These very different results endorse the importance of using local data to conduct LCA of plant production systems. The results of this study show decision makers and farmers how they can improve the environmental performance of agricultural systems, and LCA practitioners are alerted to challenges due to variation.

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