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Production of greenhouse gases (GHG) from animals
and their impact on smog forming emissions and climate changes are a growing
public policy concern worldwide. In the European Union, livestock farming is
responsible for 13% of total greenhouse gas emissions (Leip et al.,
2010), in particular for methane (CH4 ) which has a global warming potential 28
times greater than CO2  (Myhre et al.,
2013). Therefore the European Union has committed
to decrease its GHG emissions by 20% by 2020 relative to 1990 (de Haas et
al., 2017). Emissions from ruminants are principally
high with due to CH4 from enteric fermentation (approximately two-thirds)  and manure handling (one-third) (Moss et al.,
2000; Olesen et al., 2006).

Cattle is considered as an important
contributor to global emissions of CH4, they typically lose from 2% to 12% of their net energy
as enteric eructated CH4 (Johnson
and Johnson, 1995; Lassey et al., 1997; de Haas et al., 2011). Therefore, CH4 represents a loss of dietary energy in
ruminants and is an important contributor to global warming (Negussie et
al., 2017). Consequently, mitigation of enteric CH4 emission in ruminants has become an
important area of research.

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There is potential for adopting genetic
selection and in the future genomic selection, for reduced CH4  emissions from ruminants , given that it has
been observed from several studies that both CH4  emissions and production (g/day) are a
heritable and repeatable trait (Pickering et
al., 2013a; Haque et al., 2015; Lassen and Løvendahl, 2016).

Besides the role of nutrition and management
in determining the level of feed efficiency (VandeHaar et
al., 2016), one of the options to reduce CH4 
emissions is to increase rumen fermentation efficiency. Indeed,
decreasing enteric CH4  emissions from
ruminants without altering animal production is desirable both as a strategy to
reduce global greenhouse gas (GHG) emissions and as a means of improving feed
conversion efficiency (Martin et
al., 2010; de Haas et al., 2017). In this purpose,  several studies showed that it is possible to
decrease CH4  emission by selecting more efficient cows (Hegarty et
al., 2007; de Haas et al., 2011; Basarab et al., 2013).

present, Greenhouse gas emissions are not part of dairy cattle breeding goals
worldwide (de Haas et
al., 2017). Actually, there is no incentive to include CH4 emissions in the breeding goal, even though
there is great interest in reducing the release of GHG (Negussie et
al., 2017). In Spain, the breeding goals  of the Frisian breed combines the quality and
quantity of milk with functional characteristics such as longevity, fertility,
morphological characters, and somatic cell count in a selection index, called
ICO combined index (Charfeddine
and Pérez-Cabal, 2014). Therefore in order to include CH4 emission as a breeding goal, one of the
first tasks is to define the economic importance of each trait included in the
aggregate genotype and his economic weighting in the current and planned
situation. Those economic values are derived from a formulated bio economic
model (profit function) that includes the breeding objectives.

This implies a change in the parameters that
define the profitability of cattle in order to derive the maximum benefit of
animals. Likewise, the breeding goals has to change and be in line with this

Nevertheless, if selection for low CH4 emission became a reality, there would be
limited consensus on the choice of phenotype to select for. This includes
direct selection for breathing measurements and indirect selection likewise, through
several indicator traits such as feed intake, milk spectral data, and rumen flora
(de Haas et
al., 2017).

An individual measurement of CH4 emission at the farm level is not an easy
task (Garcia-Rodriguez
and Gonzalez-Recio, 2017), nevertheless with genomic selection,
incorporation of CH4 emission as a breeding objective trait is attainable,
even with a limited number of records (de Haas et
al., 2017).

This study therefore, aims to highlight the
importance of including greenhouse gas emissions in breeding goals of dairy
cattle, specifically CH4 emissions, by defining the economic weight of each trait
included in the selection index and its correlative response under four
different scenarios:

Current (without regard to CH4

CH4 emissions with
carbon tax

Quota of CH4  emissions

CH4 in relation to
net energy loss

This work encompasses 3 main parts:

In the first part, a bibliographic review
will assess the knowledge acquired in the literature about greenhouse gas emissions
in relationship with livestock. Specifically CH4 emissions, its methods of measurement and
the main traits related to it in dairy cattle.

The second part presents the main and
specific objectives of the study, as well as material and methods used to
develop the bio economic model and genetic analysis.

the last part, the results obtained will be presented, analysed and discussed.
The conclusion will highlight key results obtained and provide recommendations
for further work in the future.

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