LCA Food 2012 in Saint Malo, France! - Manifestations et colloques ...

PARALLEL SESSION 3C: SHEEP AND DAIRY PRODUCTION SYSTEMS 8 th Int. Conference on LCA in the
Agri-Food Sector, 1-4 Oct 2012
A limited mitigation potential of below 1% of total GHG emissions (Table 3) can be associated with the
measures ‘shade trees on pastures’, ‘energy-efficient milk cooling devices’, ‘concentrate-free feeding rations’,
‘application of eco drive mode’, ‘optimisation of machines and tractors’, ‘use of solar heat’ and ‘reduced
tillage’. The relative effects of these measures are limited, as they address predominantly energy use,
while CH4 emissions from enteric fermentation dominate the emission profile of the selected farms. Furthermore,
some of the measures may perform better in a different context. For instance, we assumed that
under local farm conditions on the mixed farm, a full conversion to reduced tillage is not feasible due to
weed control problems. However, from field and on farm-trials in other locations in Switzerland we know
that if a full conversion is undertaken, soil carbon stocks build up, improving the GHG balance of production
(Berner et al., 2008; Gadermaier et al., 2010).
The low performance of the measure ‘concentrate-free feeding rations’ depends on the fact, that organic
farms in Switzerland are restricted already to use only 10% concentrates in their feeding rations. Furthermore,
soy used in Swiss organic feeding rations at present does not originate from regions where direct land
use change from rain forest or savannah to arable land is relevant.
Table 3. Optimisation potential of selected GHG-reduction measures on two typical Swiss organic farms (kg
CO2-eq / per farm and year)
Specialised dairy Mixed farm
farm
(dairy/arable)
Potential reduction of GHG emissions Absolute Relative Absolute Relative Potential impact on productivity
Total GHG emissions 139,066 100.00% 277,911 100.00%
Composting livestock manure -4429 -3.18% -12,128 -4.36%
Increased number of lactations of dairy cows -7,788 -5.60% -8,677 -3.12%
slightly reduced
expected
productivity
Use of dual-purpose cattle breeds -3,977 -2.86% -7,357 -2.65%
reduced milk production but increased
meat production expected
Use of photovoltaics (on total roof area) -4,073 -2.93% -6,153 -2.21%
Conversion to full-grazing system -4,672 -3.36% -6,128 -2.21%
Estimated reduction in milk production
11-19%
Optimisation of machine life -2,206 -1.59% -4,237 -1.52%
Shade trees on pastures -226 -0.16% -753 -0.27%
< 1% reduced pasture productivity
expected
Energy-efficient milk cooling devices -235 -0.17% -518 -0.19%
Concentrate-free feeding rations -371 -0.27% -343 -0.12%
0-10% reduced productivity
expected (Notz et al., 2012)
Application of Eco drive mode -728 -0.52% -2,206 -0.79%
Optimisation of machines and tractors -111 -0.08% -1,935 -0.70%
Use of solar heat (for process water on farm) -139 -0.10% -262 -0.09%
Reduced tillage* - -564 -0.20%
+/- 10% productivity changes
expected (Berner et al., 2008)
Potential GHG savings if all measures are
implemented
-28,955 -20.82% -51,261 -18.45%
* calculations do not take into account potential gains in carbon stocks
4. Discussion and conclusions
Our model results demonstrate that the 13 measures for GHG mitigation have a cumulative potential of
about 20.8% (dairy farm) and 18.4% (mixed farm), respectively. However, the effectiveness of the optimisation
measures depends on farm-specific characteristics. For instance, the effectiveness installing a photovoltaic
plant may be limited if the roof exposition is sub-optimal. Furthermore, if the mitigation potential is
calculated for the functional unit ‘milk production’, implementing measures that reduce productivity could
decrease the effectiveness of the measures or even lead to negative impacts on the global warming potential.
Possible productivity losses depend, however, on farm-specific characteristics and are difficult to anticipate.
It is important to understand that the mitigation potential must not be interpreted as optimisation potential.
The comparison of the two farms – one on favourable and one on marginal land – illustrates that geographical
conditions can influence the GHG efficiency of production substantially. As we have shown, the mitigation
potential of these two farms is limited, even if all measures are applied. Especially on organic farms, the
input-side is in most cases already optimised to a large extent. Optimisation potential on organic farms with
respect to GHG efficiency lies largely in improving farm management in order to increase productivity.
Finally, it is important to note, that we only analysed the impacts of the measures on GHG emissions. We
regard the occurrence of trade-offs to other environmental impact categories with implementing the measures
as unlikely. Therefore, the implementation of the analysed optimisation measures can be recommended from
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