Estimation of genetic parameters for feed efficiency traits using random regression models in dairy cattle.

Feed efficiency has become an increasingly important research topic in recent years. As feed costs rise and the environmental impacts of agriculture become more apparent, improving the efficiency with which dairy cows convert feed to milk is increasingly important. However, feed intake is expensive to measure accurately on large populations, making the inclusion of this trait in breeding programs difficult. Understanding how the genetic parameters of feed efficiency and traits related to feed efficiency vary throughout the lactation period is valuable to gain understanding into the genetic nature of feed efficiency. This study used 121,226 dry matter intake (DMI) records, 120,500 energy-corrected milk (ECM) records, and 98,975 metabolic body weight (MBW) records, collected on 7,440 first-lactation Holstein cows from 6 countries (Canada, Denmark, Germany, Spain, Switzerland, and the United States), from January 2003 to February 2022. Genetic parameters were estimated using a multiple-trait random regression model with a fourth-order Legendre polynomial for all traits. Weekly phenotypes for DMI were re-parameterized using linear regressions of DMI on ECM and MBW, creating a measure of feed efficiency that was genetically corrected for ECM and MBW, referred to as genomic residual feed intake (gRFI). Heritability (SE) estimates varied from 0.15 (0.03) to 0.29 (0.02) for DMI, 0.24 (0.01) to 0.29 (0.03) for ECM, 0.55 (0.03) to 0.83 (0.05) for MBW, and 0.12 (0.03) to 0.22 (0.06) for gRFI. In general, heritability estimates were lower in the first stage of lactation compared with the later stages of lactation. Additive genetic correlations between weeks of lactation varied, with stronger correlations between weeks of lactation that were close together. The results of this study contribute to a better understanding of the change in genetic parameters across the first lactation, providing insight into potential selection strategies to include feed efficiency in breeding programs.

Symposium review: Comparisons of feed and milk nitrogen efficiency and carbon emissions in organic versus conventional dairy production systems.

Evaluation of feed efficiency (FE; calculated as energy-corrected milk yield/dry matter intake) and milk nitrogen efficiency (MNE; calculated as milk N yield/N intake) is needed to help farmers make decisions regarding the economic and environmental sustainability of dairy farms. Our primary objective was to compare FE and MNE data obtained from studies conducted with organic versus conventional dairy cows. Specifically, 3 data sets were constructed to meet this goal: (1) the organic Jersey data set (ORG-JE) built with studies (n = 11) done at the University of New Hampshire Burley-Demeritt Organic Dairy Research Farm (Lee, NH), (2) the conventional Jersey data set (CON-JE) constructed using 19 experiments reported in the literature, and (3) the organic non-Jersey-breed (mostly Holstein, Swedish Red, and Norwegian Red) data set (ORG-NJE) created with 11 published studies. Comparisons were made between ORG-JE and CON-JE and between ORG-JE and ORG-NJE. A second objective was to compare the enteric methane (CH4) emission data set from studies using organic Jerseys (n = 5) with those using conventional Jerseys (n = 4). Cows used in the ORG-JE data set had lower FE (-16%) and MNE (-15.5%) than cows used in the CON-JE counterpart, possibly because dry matter intake increased by an average of 10.4% in organic cows. Feed efficiency and MNE computed from cows belonging to the ORG-NJE data set were intermediate between ORG-JE and CON-JE. Measured CH4 intensity (g/kg of energy-corrected milk) from cows in the ORG-JE CH4 data set increased by 71% compared with that from cows in the CON-JE CH4 data set. Estimated FE and enteric CH4 emissions revealed that Wisconsin organic dairies with the heaviest reliance on forage sources and longest grazing time during the summer were the least feed efficient and emitted the greatest amount of CH4 per kilogram of energy-corrected milk at the animal and whole-farm levels. Overall, the comparisons of FE, MNE, and enteric CH4 emissions between organic and conventional dairies and within organic systems made in this symposium review should be interpreted cautiously because they are based on study means and small data sets. Research is needed to better characterize the performance, efficiency, profitability, and carbon emissions of forage-based organic dairies in the United States, including the fast-growing "grass-fed" segment, which relies exclusively on forage diets. The effect of large organic dairies on the economic and social sustainability of small and mid-size organic dairy operations nationwide also deserves further investigation.

Between-cow variation in the components of feed efficiency.

A meta-analysis based on an individual-cow data set was conducted to investigate between-cow variations in the components and measurements of feed efficiency (FE) and to explore the associations among these components. Data were taken from 31 chamber studies, consisting of a total of 841 cow/period observations. The experimental diets were based on grass or corn silages, fresh grass, or a mixture of fresh grass and straw, with cereal grains or by-products as energy supplements, and soybean or canola meal as protein supplements. The average forage-to-concentrate ratio across all diets on a dry matter basis was 56:44. Variance component and repeatability estimates of FE measurements and components were determined using diet, period, and cow within experiment as random effects in mixed procedures of SAS (SAS Institute Inc., Cary, NC). The between-cow coefficient of variation (CV) in gross energy intake (GE; CV = 0.10) and milk energy (El) output as a proportion of GE (El/GE; CV = 0.084) were the largest among all component traits. Similarly, the highest repeatability estimates (≥0.50) were observed for these 2 components. However, the between-cow CV in digestibility (DE/GE), metabolizability [metabolizable energy (ME)/GE], methane yield (CH4E/GE), proportional urinary energy output (UE/GE), and heat production (HP/GE), as well as the efficiency of ME use for lactation (kl), were rather small. The least repeatable component of FE was UE/GE. For FE measurements, the between-cow CV in residual energy-corrected milk (RECM) was larger than for residual feed intake (RFI), suggesting a greater possibility for genetic gain in RECM than in RFI. A high DE/GE was associated with increased CH4E/GE (r = 0.24), HP/GE (r = 0.12), ME/GE (r = 0. 91), energy balance as a proportion of GE (EB/GE; r = 0.35), and kl (r = 0.10). However, no correlation between DE/GE and GE intake or UE/GE was observed. Increased proportional milk energy adjusted to zero energy balance (El(0)/GE) was associated with increases in DE/GE, ME/GE, EB/GE, and kl but decreases in UE/GE, CH4E/GE, and HP/GE, with no effect on GE intake. In conclusion, several mechanisms are involved in the observed differences in FE among dairy cows, and reducing CH4E yield (CH4E/GE) may inadvertently result in reduced GE digestibility. However, the selection of dairy cows with improved energy utilization efficiencies offers an effective approach to lower enteric CH4 emissions.

Analysis of the genetic architecture of energy balance and its major determinants dry matter intake and energy-corrected milk yield in primiparous Holstein cows.

The focus of modern dairy cow breeding programs has shifted from being mainly yield based toward balanced goals that increasingly consider functional traits such as fertility, metabolic stability, and longevity. To improve these traits, a less pronounced energy deficit postpartum is considered a key challenge. On the other hand, feed efficiency and methane emissions are gaining importance, possibly leading to conflicts in the design of breeding goals. Dry matter intake (DMI) is one of the major determinants of energy balance (EB), and recently some efforts were undertaken to include DMI in genomic breeding programs. However, there is not yet a consensus on how this should be achieved as there are different goals in the course of lactation (i.e., reducing energy deficit postpartum vs. subsequently improving feed efficiency). Thus, the aim of this study was to gain more insight into the genetic architecture of energy metabolism across lactation by genetically dissecting EB and its major determinants DMI and energy-corrected milk (ECM) yield at different lactation stages applying random regression methodology and univariate and multivariate genomic analyses to data from 1,174 primiparous Holstein cows. Daily heritability estimates ranged from 0.29 to 0.49, 0.26 to 0.37, and 0.58 to 0.68 for EB, DMI, and ECM, respectively, across the first 180 d in milk (DIM). Genetic correlations between ECM and DMI were positive, ranging from 0.09 (DIM 11) to 0.36 (DIM 180). However, ECM and EB were negatively correlated (rg = -0.26 to -0.59). The strongest relationship was found at the onset of lactation, indicating that selection for increased milk yield at this stage will result in a more severe energy deficit postpartum. The results also indicate that EB is more affected by DMI (rg = 0.71 to 0.81) than by its other major determinant, ECM. Thus, breeding for a higher DMI in early lactation seems to be a promising strategy to improve the energy status of dairy cows. We found evidence that genetic regulation of energy homeostasis is complex, with trait- and lactation stage-specific quantitative trait loci suggesting that the trajectories of the analyzed traits can be optimized as mentioned above. Especially from the multivariate genomic analyses, we were able to draw some conclusions on the mechanisms involved and identified the genes encoding fumarate hydratase and adiponectin as highly promising candidates for EB, which will be further analyzed.

Effect of a Garlic and Citrus Extract Supplement on the Lactation Performance and Carbon Footprint of Dairy Cows under Grazing Conditions in Chile.

Two trials were conducted to evaluate the effect of a garlic and citrus extract supplement (GCE) on the milk production performance and carbon footprint of grazing dairy cows in a Chilean commercial farm. A total of 36 early- to mid-lactation and 54 late-lactation Irish Holstein-Friesian cows were used in Trial 1 and Trial 2, respectively. In both trials, the cows were reared under grazing conditions and offered a supplementary concentrate without or with GCE (33 g/cow/d) for 12 weeks. The concentrate was fed in the afternoon when the cows visited the milking parlour. Consequently, the results of milk production performance in these trials were used to determine the effect of feeding with GCE on the carbon footprint (CFP) of milk using a life cycle assessment (LCA) model. In Trial 1 and Trial 2, feeding with GCE increased estimated dry matter intake (DMI, kg/d) by 8.15% (18.4 vs. 19.9) and 15.3% (15.0 vs. 17.3), energy-corrected milk (ECM, kg/d) by 11.4% (24.5 vs. 27.3) and 33.5% (15.5 vs. 20.7), and feed efficiency (ECM/DMI) by 3.03% (1.32 vs. 1.36) and 17.8% (1.01 vs. 1.19), respectively. The LCA revealed that feeding with GCE reduced the emission intensity of milk by 8.39% (1.55 vs. 1.42 kg CO2-eq/kg ECM). Overall, these results indicate that feeding with GCE improved the production performance and CFP of grazing cows under the conditions of the current trials.

Effect of lactation trimester and parity on eating behavior, milk production and efficiency traits of dairy cows.

There is absence knowledge about the effects of lactation trimester and parity on eating behavior, production and efficiency of dairy cows. Objective of this study was to identify and characterize in 340 dairy cows, the 20% high efficient (HE), 20% low efficient (LE) and 60% mid efficient (ME) cows according to their individual residual feed intake (RFI) values, within and between lactation trimesters and between 1st and 2nd parities. Efficiency effect within each lactation trimester, was exhibited in daily dry matter intake (DMI), eating rate and meal size, that were the highest in LE cows, moderate in the ME cows and lowest in the HE group. Daily eating time, meal frequency, yields of milk and energy-corrected milk (ECM) and BW were similar in the three efficiency groups within each trimester. The lower efficiency of the LE cows in each trimester attributes to their larger metabolic energy intake, heat production and energy losses. In subgroup of 52 multiparous cows examined along their 1st and 2nd trimesters, milk and ECM production, DMI, eating behavior and efficiency traits were similar with high Pearson's correlation (r=0.78 to 0.89) between trimesters. In another subgroup of 42 multiparous cows measured at their 2nd and 3rd trimesters, milk and ECM yield, DMI and eating time were reduced (P<0.01) at the 3rd trimester, but eating rate, meal frequency and meal size remained similar with high Pearson's correlation (r=0.74 to 0.88) between trimesters. In subgroup of 26 cows measured in 1st and 2nd parities, DMI, BW, milk and ECM yield, and ECM/DMI increased in the 2nd lactation, but eating behavior and RFI traits were similar in both parities. These findings encourage accurate prediction of DMI based on a model that includes eating behavior parameters, together with individual measurement of ECM production. This can be further used to identify HE cows in commercial herd, a step necessary for potential genetic selection program aimed to improve herd efficiency.

Modelling the interactions between C and N farm balances and GHG emissions from confinement dairy farms in northern Spain.

There is world-wide concern for the contribution of dairy farming to global warming. However, there is still a need to improve the quantification of the C-footprint of dairy farming systems under different production systems and locations since most of the studies (e.g. at farm-scale or using LCA) have been carried out using too simplistic and generalised approaches. A modelling approach integrating existing and new sub-models has been developed and used to simulate the C and N flows and to predict the GHG burden of milk production (from the cradle to the farm gate) from 17 commercial confinement dairy farms in the Basque Country (northern Spain). We studied the relationship between their GHG emissions, and their management and economic performance. Additionally, we explored some of the effects on the GHG results of the modelling methodology choice. The GHG burden values resulting from this study (0.84-2.07 kg CO2-eq kg(-l) milk ECM), although variable, were within the range of values of existing studies. It was evidenced, however, that the methodology choice used for prediction had a large effect on the results. Methane from the rumen and manures, and N2O emissions from soils comprised most of the GHG emissions for milk production. Diet was the strongest factor explaining differences in GHG emissions from milk production. Moreover, the proportion of feed from the total cattle diet that could have directly been used to feed humans (e.g. cereals) was a good indicator to predict the C-footprint of milk. Not only were some other indicators, such as those in relation with farm N use efficiency, good proxies to estimate GHG emissions per ha or per kg milk ECM (C-footprint of milk) but they were also positively linked with farm economic performance.