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Analysis of enteric methane emissions due to extreme variations in management practices of dairy-production systems

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  • Senga Kiessé, Tristan
  • Corson, Michael S.
  • Eugène, Maguy
  • Aubin, Joël
  • Wilfart, Aurélie

Abstract

This study concerns variability in agrosystem inputs that affect greenhouse gas emissions at the farm scale, when management practices differ from average practices. Many studies of agrosystems assume average management practices. However, existence of a wide variety of farm-management practices may result in extreme variations in estimated environmental impacts. This large variation raises the need to use statistical tools to model extreme situations and their consequences on agrosystems. For instance, methane emissions generated by enteric fermentation in dairy cattle are particularly studied because they are important at a global scale. We investigated how extreme variations in feeding practices in dairy production affect predicted methane emissions, using a methane-emission model based on existing equations that is easy to apply at the farm scale. In this study, extreme variations in the time that cattle spent grazing were propagated through three different dairy-production systems. For an intensive dairy farm, predicted methane emissions decreased up to 15% (ca. 5% on average) and increased up to 11% (ca. 1% on average) under extremely long or short times spent grazing, respectively, compared to those from average variations in the time spent grazing. The range of variation in time spent grazing also reflects changes in feed rations. Farm-management practices require deep investigation to both evaluate and decrease risks of environmental impacts in dairy production.

Suggested Citation

  • Senga Kiessé, Tristan & Corson, Michael S. & Eugène, Maguy & Aubin, Joël & Wilfart, Aurélie, 2019. "Analysis of enteric methane emissions due to extreme variations in management practices of dairy-production systems," Agricultural Systems, Elsevier, vol. 173(C), pages 449-457.
    • Handle: RePEc:eee:agisys:v:173:y:2019:i:c:p:449-457
    DOI: 10.1016/j.agsy.2019.03.024
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