Associations between feed efficiency and aspects of lactation curves in primiparous Holstein dairy cattle

Abstract

Genetic selection for improved feed efficiency in dairy cattle has received renewed attention over the last decade to address the needs of a growing global population. As milk yield is a critical component of feed efficiency metrics in dairy animals, our objective was to evaluate the associations between feed efficiency in primiparous Holstein cattle and parameters of a mathematical model describing individual lactation curves. The Dijkstra lactation curve model was fit to individual lactation records from 34 Holstein heifers with previously estimated measures of feed efficiency. We found that the optimal fit of the Dijkstra model was achieved using daily milk yield records up to 21 d in milk to capture the rise to peak milk yield and using monthly dairy herd improvement records for the remainder of lactation to accurately characterize lactation persistency. In the period of lactation before peak milk yield, improved feed efficiency was associated with a faster increase in daily milk yield over a shorter period of time at the expense of increased mobilization of body reserves; this serves to reinforce the concept that dairy cattle are primarily capital breeders versus income breeders. Feed efficiency in the period following peak lactation, as measured by gross feed efficiency, return over feed costs, and net energy efficiency of lactation, was positively associated with higher peak milk yield. The findings in early lactation suggest that estimates of feed efficiency could be improved by evaluating feed efficiency relative to conception, rather than parturition and lactation, to better account for the energy stored and released from body reserves in capital breeding.

Effect of hydrogenated fat-embedded calcium gluconate on lactation performance in dairy cows

“…Lactation curve parameter estimates, while not statistically different between treatment groups, were similar to those previously reported for both primiparous and multiparous Holstein dairy cattle (Dematawewa et al, 2007, Dijkstra et al, 2010, Seymour et al, 2021a. It is of note, however, that the model evaluated in the present study does not account for changes in milk component yields.…”

“…It is of note, however, that the model evaluated in the present study does not account for changes in milk component yields. The model proposed by Dijkstra et al (1997) has previously been found to poorly fit yields of FCM and ECM in primiparous animals due to the changes in milk component concentrations over time; the high content of fat and protein in early lactation with decreasing concentrations as lactation progresses serves to flatten the ECM and FCM curves of primiparous animals, resulting in issues generating accurate parameter estimates from these curves (Seymour et al, 2021a). The response, or lack thereof, in primiparous animals is currently unclear; as this study was not designed to properly evaluate potential response differences between parity groups, these findings are likely the result of insufficient statistical power due to either the small number of primiparous animals used, responses too small to detect given the variation in the primiparous group, or a combination of these factors.…”

“…(kg/d) on day in milk t; M 0 is the theoretical milk yield (kg/d) at the onset of lactation (i.e., t = 0); µ T is the specific rate of secretory cell proliferation (d -1 ) at parturition (i.e., t = 0); k 2 is the specific rate of decay of cell proliferation (d -1 ); and λ is the specific rate of secretory cell apoptosis (d -1 ), which is also indicative of the terminal decline in daily milk yield in late lactation (% d -1 ; Seymour et al, 2021a). Parameters were estimated using the NLIN procedure of SAS v 9.4 (SAS Institute Inc., Cary, NC) using the Marquardt estimation method with a maximum of 3,000 iterations and 300 subiterations.…”

“…Parameters were estimated using the NLIN procedure of SAS v 9.4 (SAS Institute Inc., Cary, NC) using the Marquardt estimation method with a maximum of 3,000 iterations and 300 subiterations. A range of initial values were used for each parameter as follows: 0.5 to 30 by 5 for M 0 ; 0.01 to 0.1 by 0.01 for both µ T and k 2 ; and 0.001 to 0.01 by 0.001 for λ (Pot, 2020, Seymour et al, 2021a. All parameter estimates were constrained to be greater than zero.…”

Genetic Analysis of Methane Emission Traits in Holstein Dairy Cattle

“…The same pattern has been observed with methane emissions, where heritability estimates fluctuate throughout the lactation [ 28 ]. In the current study, cows were measured for methane emissions in mid-lactation (120 to 150 DIM) for a period of five consecutive days as part of a larger project that aims to increase feed efficiency after the peak of lactation, i.e., after 60 DIM [ 13 , 42 ]. Additionally, it is worth noting that despite repeated measures having added value, if the tests are separated by longer periods (i.e., days to weeks) their repeatability could be higher compared to shorter term measurements (i.e., minutes to few hours).…”