The relationships between early lactation energy status indicators and endocrine fertility traits in dairy cows

The relationships between dairy cow milk-based energy status (ES) indicators and fertility traits were studied during periods 8 to 21, 22 to 35, 36 to 49, and 50 to 63 d in milk. Commencement of luteal activity (C -LA) and interval from calving to the first heat (CFH), based on frequent measurements of progesterone by the management tool Herd Navigator (DeLaval), were used as fertility traits. Energy status indicator traits were milk beta-hydroxybutyrate (BHB) concentration provided by Herd Navigator and milk fat: protein ratio, concen-tration of C18:1 cis-9, the ratio of fatty acids (FA) C18:1 cis-9 and C10:0 in test-day milk samples, and predicted plasma concentration of nonesterified fatty acids (NEFA) on test days. Plasma NEFA predictions were based either directly on milk mid-infrared spectra (MIR) or on milk fatty acids based on MIR spectra (NEFAmir and NEFAfa, respectively). The average (standard deviation) C-LA was 39.3 (+/- 16.6) days, and the average CFH was 50.7 (+/- 17.2) days. The correlations between fertility traits and ES indicators tended to be higher for multiparous (r < 0.28) than for primiparous (r < 0.16) cows. All correlations were lower in the last period than in the other periods. In period 1, correlations of C-LA with NEFAfa and BHB, respectively, were 0.15 and 0.14 for primiparous and 0.26 and 0.22 for multiparous cows. The associations between fertility traits and ES indicators indicated that negative ES during the first weeks postpartum may delay the onset of luteal activity. Milk FPR was not as good an indicator for cow ES as other indicators. According to these findings, predictions of plasma NEFA and milk FA based on milk MIR spectra of routine test-day samples and the frequent measurement of milk BHB by Herd Navigator gave equally good predictions of cow ES during the first weeks of lactation. Our results indicate that routinely measured milk traits can be used for ES evaluation in early lactation.Peer reviewe

The effects of dietary resin acid inclusion on productive, physiological and rumen microbiome responses of dairy cows during early lactation

Dairy cows have intense fluctuations in digestive, metabolic and hormonal systems around calving which predispose them to various disorders and health problems. The aim of the current experiment was to investigate feed and nutrient intake, rumen fermentation, rumen bacterial communities, milk production, milk fatty acid composition and plasma biomarker profiles of dairy cows to assess the modulation of these functions by in-feed resin acid inclusion. Thirty-six Nordic Red cows were used in a continuous feeding trial starting 3 weeks prepartum and lasting for 10 weeks into the lactation. The cows were fed grass silage ad libitum and the dietary treatments were 1) control with basal concentrate (CON), 2) CON supplemented with tall oil fatty acids (TOFA; 90 % fatty acids and 9% resin acids) at 7.0 g/cow/day and 3) CON supplemented with resin acid concentrate (RAC; 37.5% resin acids) at 1.7 g/cow/day. The mixture of resin acids in TOFA and RAC, consisting mostly of abietic and dehydroabietic acids, originated from coniferous tree species Pinus sylvestris L. and Picea abies L. Feed intake and milk production were measured throughout the experimental period. Milk and blood samples were collected at weeks 2, 3, 6 and 10, and rumen fluid was sampled at weeks 2 and 10 of lactation to analyse rumen fermentation and rumen bacterial communities. The dynamics in feed intake and milk production with progressing lactation showed typical curvilinear trends (P for timePeer reviewe

Animal board invited review: Genomic-based improvement of cattle in response to climate change

Climate change brings challenges to cattle production, such as the need to adapt to new climates and pressure to reduce greenhouse emissions (GHG). In general, the improvement of traits in current breeding goals is favourably correlated with the reduction of GHG. Current breeding goals and tools for increasing cattle production efficiency have reduced GHG. The same amount of production can be achieved by a much smaller number of animals. Genomic selection (GS) may offer a cost-effective way of using an efficient breeding approach, even in low- and middle-income countries. As climate change increases the intensity of heatwaves, adaptation to heat stress leads to lower efficiency of production and, thus, is unfavourable to the goal of reducing GHG. Furthermore, there is evidence that heat stress during cow pregnancy can have many generation-long lowering effects on milk production. Both adaptation and reduction of GHG are among the difficult-to-measure traits for which GS is more efficient and suitable than the traditional non-genomic breeding evaluation approach. Nevertheless, the commonly used within-breed selection may be insufficient to meet the new challenges; thus, cross-breeding based on selecting highly efficient and highly adaptive breeds may be needed. Genomic introgression offers an efficient approach for cross-breeding that is expected to provide high genetic progress with a low rate of inbreeding. However, well-adapted breeds may have a small number of animals, which is a source of concern from a genetic biodiversity point of view. Furthermore, low animal numbers also limit the efficiency of genomic introgression. Sustainable cattle production in countries that have already intensified production is likely to emphasise better health, reproduction, feed efficiency, heat stress and other adaptation traits instead of higher production. This may require the application of innovative technologies for phenotyping and further use of new big data techniques to extract information for breeding

Accuracy of genomic prediction of dry matter intake in Dutch Holsteins using sequence variants from meta-analyses

We evaluated the accuracy of biology informed genomic prediction for dry matter intake in 2,162 Dutch Holstein cows. Sequence variants were selected from meta-analyses including GWAS summary statistics for QTL and metabolomic QTL in several dairy and crossbred beef populations. Selected variants were prioritized in GBLUP models in a five-fold cross-validation. The accuracies were compared to genomic prediction based on routine 50k genotype data. The average accuracy for the 50k scenario was 0.683. Adding selected sequence variants in the GBLUP model did not improve the accuracies for dry matter intake. Next steps will include testing Bayesian variable selection methods to prioritize variants in genomic prediction for dry matter intake

Longitudinal modeling of residual carbon dioxide and residual feed intake in the Nordic Red dairy cattle

Feed utilization efficiency is an important trait in dairy production playing a significant role in reducing feed costs and lowering methane emission. One of the metrics used to measure feed efficiency in dairy cows is residual feed intake (RFI). This metric requires routine measurement of feed intake. Since there is a positive high correlation between heat production and carbon dioxide (CO2) production on the one hand and heat production and efficiency on the other hand, residual carbon dioxide (RCO2) might be a useful metric to improve feed efficiency. The objectives of this study were to model the trajectories of RCO2 and RFI as well as to estimate their repeatabilities and correlations at different stages of lactation. Daily CO2 output and feed intake were recorded from 46 primiparous Nordic Red dairy cows using two Greenfeed Emissions Monitoring™ systems from 2 to 305 days in milk (DIM). Edited data comprised 5 995 daily averages. To calculate predicted values of CO2 and DM intake (DMI), prediction models were developed by fitting multiple regression models to observations. Subsequently, RCO2 and RFI were calculated by subtracting predicted values of CO2 and DMI from their corresponding actual observations. A random regression bivariate model was fitted to estimate repeatabilities and animal correlations within lactation at different DIMs between RCO2 and RFI traits. The model fitted included fixed effects of year-month of recording, lactation month, fixed regressions as well as random regressions for the animal effect. The residual variance was considered to be heterogeneous. Repeatabilities and animal correlations of RCO2 and RFI between selected DIM (for every 30 DIM i.e., 6, 36,…, 246 and 276) were calculated. Repeatability of RCO2 was high at the beginning of lactation (0.72 at DIM 6) and decreased around the peak of milk production (0.27 at DIM 96) and again increased gradually toward the end of lactation. Similarly, RFI also had high repeatability at the beginning (0.86 at DIM 6); however, it decreased in mid-lactation (0.37 at DIM 156) and then increased toward the end of lactation. Animal correlations between RCO2 and RFI were moderate to high on the same DIM and ranged from 0.37 to 0.88. Overall, we found that animals with higher CO2 production than expected also consume more DMI than expected, but the moderate correlation between RCO2 and RFI found in this study calls for more research to assess the potential of RCO2 to become a new feed efficiency metric

Comparison of multiplicative heterogeneous variance adjustment models for genetic evaluations

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Genetic heterogeneity of feed intake, energy-corrected milk, and body weight across lactation in primiparous Holstein, Nordic Red, and Jersey cows

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Incorporation of observations with different residual error variances into existing complex test-day models

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Genetic analyses of metabolic body weight, carcass weight and body conformation traits in Nordic dairy cattle

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