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Impact of incorporating greenhouse gas emission intensities in selection indexes for sow productivity traits

Alfonso, L.
Livestock science 2019 v.219 pp. 57-61
bioeconomic models, carbon footprint, conception, crossing, emissions factor, genetic improvement, greenhouse gas emissions, greenhouse gases, litter size, markets, meat production, piglets, pork, pork industry, purebreds, ruminants, sows, weaning
Genetic improvement programmes should incorporate emerging challenges about environmental concerns into breeding goals. The large volume of pig meat production implies important greenhouse gas (GHG) emissions despite its lower carbon footprint per animal in front of ruminant productions. The different breeding goals considered by swine industry depending on different purebred lines, or line crosses adapted to different market demands and production constraints, could mask the effect of incorporating GHG emissions into selection indexes for improving sow productivity traits in nucleus populations. This paper analysed this effect following a methodological approach consisting in augmenting existing selection indexes derived from profit functions. An index previously described in the literature including litter size at birth, piglet perinatal survival, piglet survival to weaning, age at first conception and weaning to conception interval, was employed. This index was expanded to include GHG emissions calculating the emission intensities per litter, assuming a finished pig market and different scenarios and financial costs of GHG emissions. Results indicated that the inclusion of GHG emissions diminished the economic weight of litter size and piglet survival vs. the age at first conception and the interval weaning to conception, but did not affect significantly the contributions of these traits in the selection indexes. The improvement of sow productivity traits diluted relevantly the GHG emissions per piglet produced, and so, per kg of pork produced. The approach used in this study, despite its limitations in front of bio-economic models, has shown to be a simple and flexible way to analyse the effect of incorporating GHG emissions into existing selection indexes.