of emissions in egg production. Achieving feed use efficiencies
comparable to the best performing facilities industry-wide would
much reduce aggregate emissions.
However, any such efforts need necessarily be attentive to the
GHG intensity of potential alternative feed inputs. Here, the concept
of least-environmental cost feed sourcing is of particular relevance,
and must include attention to primary production, processing, and
transportation phases. It is recommended that similar biophysical
accounting methods be applied to any potential alternative feed
input supply chains to ensure methodological consistency and
comparability with the present analysis. Our scenario analysis of
the mitigation potential of replacing ruminant by-product meal and
fat with equivalent porcine or poultry materials, or using no
animal-derived materials, suggests substantial potential emissions
reductions. This is unsurprising given the considerable resource
and emissions intensities of producing livestock, in particular ruminants.
Formulating feeds free of livestock materials would
reduce emissions a large margin, provided similar feed conversion
efficiencies were maintained. In such cases, the relative importance
of feed as a determinant of supply chain emissions decreases,
whereas managing for other facility-level resource use efficiencies
(in particular, energy use), becomes correspondingly more
important.
Managing feed supply chains for GHG mitigation must also take
into consideration nitrogen use efficiencies. N losses from poultry
manure are the second largest contributor to GHG emissions in
both pullet and layer facilities, and the upstream impacts of N
fertilizer production and use are a primary determinant of feed
input GHG intensity. Feed formulation, breeding, and selecting
manure management strategies for optimal N use efficiencies are
therefore powerful tools in supply chain carbon footprint reduction.
Here, we modeled N losses using nutrient balances and
emissions factors derived from IPCC protocols. Given the margin of
error associated with manure N sampling, we recommend using
this modeling approach. This will also maximize inter- and intracompany
and product comparability. However, we also suggest
continued efforts to improve and standardize company-level
manure-N sampling accuracy, in order to allow for differentiation
between facilities and production strategies looking forward.
We further report that egg processing and breaking stages
contribute trivial emissions compared to those associated with egg
production. However, on a concluding, cautionary note: our inventory
analysis indicates non-trivial variability in reported material
and energy use in pullet, layer, shell egg processing, and breaker
facilities. It is unclear whether this variability reflects operational
realities or discrepancies in data reporting. In case of the former,
this would indicate opportunities for streamlining production
towards the most efficient common denominator. In the latter case,
better tracking and reporting of the inventory data categories
employed in this analysis will be essential to continuing quantifying
and seeking to reduce supply chain GHG emissions moving
forward. We therefore recommend employing the inventory data
tables provided in this document and supplementary information
file as a basis for future data collection and record