Improvement of feed efficiency has always been a priority in pig breeding and selection programs.The feed conversion ratio(FCR),which is a simple ratio of the average daily feed intake(ADFI)to the average daily gain(ADG),was historically used to select for feed efficiency.However,efficiency of the growing pig is more complex than simply FCR and driven by the biology of the growing pig(https://genesus.com/feed-intake-growth-and-health/).Selection placed solely on FCR will not result in optimal change in both feed intake and growth,two economically important traits.
Feed intake is a primary driver of growth and the genetic correlation between ADFI and ADG is relatively high(0.32–0.84)(Hoque et al.,2009;Jiao et al.,2014).Therefore,people generally thought that the pigs have to eat more to grow faster.However,the correlation between feed intake and growth is not perfect(i.e.less than 1),and thus there is an opportunity to identify and select animals having faster growth with lower feed intake.Growth rate and feed intake both significantly impact profitability,but their economic values are not the same and are weighted in opposite directions.Compared to direct selection on FCR,an alternative selection strategy is to use a selection index that improves growth rate while limiting the change of feed intake.
In addition,feed efficiency is also affected by the rate of lean and fat deposition and energy utilization from the diet(https://genesus.com/feed-intake-growth-and-health/).Moderate to high genetic correlations between ADFI and fat and ADFI and loin depth(0.22-0.57)(Jiao et al.,2014;MacNeil&Kemp,2015)demonstrate these effects.Inclusion of these genetically correlated traits improves the accuracy of the estimated breeding values(EBV)for both growth and feed intake,and consequently increase the genetic improvement rate for feed efficiency.
Furthermore,utilizing genomic information provides advantages for improving these traits with unfavorable genetic correlations(e.g ADFI and ADG).Positively correlated traits are expected to have more genetic markers in common however not all markers affecting the traits are the same.Genomic evaluation and selection can utilise the markers not in common to effectively identify animals that go against the expected correlation thereby driving selection more quickly in the desired direction.Further,collecting individual animal feed intake data is very costly,limiting the number of animals with actual feed intake data.Utilizing genomic information,even for animals without feed intake data will result in more accurate EBV for all animals including the ones without feed intake data.More accurate EBV results in a higher rate of genetic improvement.
As a global pig breeding company,Genesus considers all these strategies within our genetic improvement program.Since 2004,we have been collecting individual feed intake along with multiple feed efficiency component traits,including growth rate(Day120,measured as age at 120kg/265lb),ultrasound and carcass fat and loin depths.Genesus has invested heavily in genomic evaluation and selection research and utilises a custom SNP(single nucleotide polymorphism,a kind of genetic marker)chip with>60K SNPs including many SNPs associated with feed efficiency component traits.
Through a genomic evaluation multiple trait model,we are able to obtain accurate genomic EBV for both ADFI and Day120,then give optimal selection emphasis on ADFI and Day120 in the selection index.In this way,we can select pigs having the genetic ability for faster growth with minimal change in feed intake and thereby resulting in improved FCR.The genetic trends for ADFI and Day120 together with the calculated FCR EBV in our Duroc population are shown in the figure below.From year 2017 onward,it shows continual improvement in growth rate(fewer days to reach 120kg/265lb)while limiting the change in ADFI when both traits were included in the selection index and appropriately emphasised.The result shows that FCR has been steadily improving through increased growth and essentially unchanged feed intake.
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