Variation of greenhouse gas emissions and identification of their drivers during the fattening of Belgian Blue White bulls based on a LCA approach
Michaël Mathot A E , Eric Elias B , Edouard Reding C , Amélie Vanlierde D , Werne Reuter B , Viviane Planchon A and Didier Stilmant A
+ Author Affiliations
- Author Affiliations
A Farming Systems, Territory and Information Technologies Unit, Walloon Agricultural Research Centre, rue du Serpont 100, B-6800 Libramont, Belgium.
B DUMOULIN s.a., Parc Industriel 18, B-5300 Seilles, Belgium.
C AWE asbl., Département des Services aux Éleveurs, Rue des Champs Elysées 4, B-5590 Ciney, Belgium.
D Agricultural Product Technology Unit, Walloon Agricultural Research Centre, Rue de Liroux 8, B-5030 Gembloux, Belgium.
E Corresponding author. Email: m.mathot@cra.wallonie.be
Animal Production Science 56(3) 322-329 https://doi.org/10.1071/AN15592
Submitted: 15 September 2015 Accepted: 5 December 2015 Published: 9 February 2016
Abstract
Greenhouse gas emission intensity (GHGI; kilograms carbon dioxide equivalents/kilograms liveweight gain) have to be reduced so as to limit the impact of human activities on global warming while furnishing food to human. In this respect, performances of 654 Belgian Blue double-muscled bulls (BBdm) during their fattening phase were recorded. On this basis, their greenhouse gas emissions were modelled to estimate variation in GHGI and investigate mitigation options at that level. The relevance of theses option is discussed, taking into account the whole life and production system scales. Large variations (mean (s.d.)) were observed (from 7.2 (0.4) to 10.0 (0.7) kg carbon dioxide equivalents/kg liveweight gain) for, respectively, the 1st- and 4th-quantile groups defined for GHGI. Early culling, low liveweight and age at start of the fattening phase of the bulls would lead to a reduction of GHGI. Nevertheless, more than 32% of the variation remained unexplained. However, decision leading to reduction of GHG intensity at this stage of the life may be compensated in the early stage of BBdm. Attention is drawn on the necessity to encompass the whole life of BBdm for investigating mitigation options and on the sensitivity of the results on models and methodological choices.
Additional keywords: life-cycle assessment, mitigation, system approach.
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