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The objective of the present study was to estimate genetic parameters across lactation for measures of energy balance (EB) and a range of feed efficiency variables as well as to quantify the genetic inter-relationships between them. Net energy intake (NEI) from pasture and concentrate intake was estimated up to 8 times per lactation for 2,481 lactations from 1,274 Holstein-Friesian cows. A total of 8,134 individual feed intake measurements were used. Efficiency traits were either ratio based or residual based; the latter were derived from least squares regression models. Residual energy intake (REI) was defined as NEI minus predicted energy requirements [e.g., net energy of lactation (NEL), maintenance, and body tissue anabolism] or supplied from body tissue mobilization; residual energy production was defined as the difference between actual NEL and predicted NEL based on NEI, maintenance, and body tissue anabolism/catabolism. Energy conversion efficiency was defined as NEL divided by NEI. Random regression animal models were used to estimate residual, additive genetic, and permanent environmental (co)variances across lactation. Heritability across lactation stages varied from 0.03 to 0.36 for all efficiency traits. Within-trait genetic correlations tended to weaken as the interval between lactation stages compared lengthened for EB, REI, residual energy production, and NEI. Analysis of eigenvalues and associated eigenfunctions for EB and the efficiency traits indicate the ability to genetically alter the profile of these lactation curves to potentially improve dairy cow efficiency differently at different stages of lactation. Residual energy intake and EB were moderately to strongly genetically correlated with each other across lactation (genetic correlations ranged from 0.45 to 0.90), indicating that selection for lower REI alone (i.e., deemed efficient cows) would favor cows with a compromised energy status; nevertheless, selection for REI within a holistic breeding goal could be used to overcome such antagonisms. The smallest (8.90% of genetic variance) and middle (11.22% of genetic variance) eigenfunctions for REI changed sign during lactation, indicating the potential to alter the shape of the REI lactation profile. Results from the present study suggest exploitable genetic variation exists for a range of efficiency traits, and the magnitude of this variation is sufficiently large to justify consideration of the feed efficiency complex in future dairy breeding goals. Moreover, it is possible to alter the trajectories of the efficiency traits to suit a particular breeding objective, although this relies on very precise across-parity genetic parameter estimates, including genetic correlations with health and fertility traits (as well as other traits).

peer-reviewed International interest in feed efficiency, and in particular energy intake and residual energy intake (REI), is intensifying due to a greater global demand for animal-derived protein and energy sources. Feed efficiency is a trait of economic importance, and yet is overlooked in national dairy cow breeding goals. This is due primarily to a lack of accurate data on commercial animals, but also a lack of clarity on the most appropriate definition of the feed intake and utilization complex. The objective of the present study was to derive alternative definitions of energetic efficiency in grazing lactating dairy cows and to quantify the inter-relationships among these alternative definitions. Net energy intake (NEI) from pasture and concentrate intake was estimated up to 8 times per lactation for 2,693 lactations from 1,412 Holstein-Friesian cows. Energy values of feed were based on the French Net Energy system where 1 UFL is the net energy requirements for lactation equivalent of 1 kg of air-dry barley. A total of 8,183 individual feed intake measurements were available. Energy balance was defined as the difference between NEI and energy expenditure. Efficiency traits were either ratio-based or residual-based; the latter were derived from least squares regression models. Residual energy intake was defined as NEI minus predicted energy to fulfill the requirements for the various energy sinks. The energy sinks (e.g., NEL, metabolic live weight) and additional contributors to energy kinetics (e.g., live weight loss) combined, explained 59% of the variation in NEI, implying that REI represented 41% of the variance in total NEI. The most efficient 10% of test-day records, as defined by REI (n = 709), on average were associated with a 7.59 UFL/d less NEI (average NEI of the entire population was 16.23 UFL/d) than the least efficient 10% of test-day records based on REI (n = 709). Additionally, the most efficient 10% of test-day records, as defined by REI, were associated with superior energy conversion efficiency (ECE, i.e., NEL divided by NEI; ECE = 0.55) compared with the least efficient 10% of test-day records (ECE = 0.33). Moreover, REI was positively correlated with energy balance, implying that more negative REI animals (i.e., deemed more efficient) are expected to be, on average, in greater negative energy balance. Many of the correlations among the 14 defined efficiency traits differed from unity, implying that each trait is measuring a different aspect of efficiency. The authors gratefully acknowledge funding from the Irish Department of Agriculture, Food and Marine (Dublin, Ireland) Research Stimulus Fund project GENCOST, and funding from the Marie Curie project International Research Staff Exchange Scheme SEQSEL

peer-reviewed The objective of the present study was to investigate the phenotypic inter- and intra-relationships within and among alternative feed efficiency metrics across different stages of lactation and parities; the expected effect of genetic selection for feed efficiency on the resulting phenotypic lactation profiles was also quantified. A total of 8,199 net energy intake (NEI) test-day records from 2,505 lactations on 1,290 cows were used. Derived efficiency traits were either ratio based or residual based; the latter were derived from least squares regression models. Residual energy intake (REI) was defined as NEI minus predicted energy requirements based on lactation performance; residual energy production (REP) was defined as net energy for lactation minus predicted energy requirements based on lactation performance. Energy conversion efficiency was defined as net energy for lactation divided by NEI. Pearson phenotypic correlations among traits were computed across lactation stages and parities, and the significance of the differences was determined using the Fisher r-to-z transformation. Sources of variation in the feed efficiency metrics were investigated using linear mixed models, which included the fixed effects of contemporary group, breed, parity, stage of lactation, and the 2-way interaction of parity by stage of lactation. With the exception of REI, parity was associated with all efficiency and production traits. Stage of lactation, as well as the 2-way interaction of parity by stage of lactation, were associated with all efficiency and production traits. Phenotypic correlations among the efficiency and production traits differed not only by stage of lactation but also by parity. For example, the strong phenotypic correlation between REI and energy balance (EB; 0.89) for cows in parity 3 or greater and early lactation was weaker for parity 1 cows at the same lactation stage (0.81), suggesting primiparous cows use the ingested energy for both milk production and growth. Nonetheless, these strong phenotypic correlations between REI and EB suggested negative REI animals (i.e., more efficient) are also in more negative EB. These correlations were further supported when assessing the effect on phenotypic performance of animals genetically divergent for feed intake and efficiency based on parental average. Animals genetically selected to have lower REI resulted in cows who consumed less NEI but were also in negative EB throughout the entire lactation. Nonetheless, such repercussions of negative EB do not imply that selection for negative REI (as defined here) should not be practiced, but instead should be undertaken within the framework of a balanced breeding objective, which includes traits such as reproduction and health. Funding from the Irish Department of Agriculture, Food and Marine (Dublin, Ireland) Research Stimulus Fund project Genetics of cost production traits (GENCOST), and funding from the Horizon 2020 Marie Curie project International Research Staff Exchange Scheme Global cooperation to develop next generation whole genome sequence selection tools for novel traits (SEQSEL) are greatly appreciated.