Estimating breeding values for feed efficiency in dairy cattle by regression on expected feed intake

The efficiency with which a dairy cow utilises feed for the various physiological and metabolic processes can be evaluated by metrics that contrast realised feed intake with expected feed intake. In this study, we presented a new metric - regression on expected feed intake (ReFI). This metric is based on the idea of regressing DM intake (DMI) on expected DMI using a random regression model, where energy requirement formulations are applied for the calculation of expected DMI covariables. We compared this new metric with the metrics residual feed intake (RFI) and genetic residual feed intake (gRFI), by applying them on 18 581 feed efficiency records from 654 primiparous Nordic Red dairy cows. We estimated variance components for the three metrics and their respective genetic correlations with intake and production traits. In addition, we examined the phenotypes of superior cows. With ReFI, we estimated for feed efficiency a higher genetic variation (4.7%) and heritability (0.23) compared to applying RFI or gRFI. The ReFI metric was genetically uncorrelated with DMI and negatively correlated within energy-corrected milk (ECM), whereas the RFI metric was genetically positively correlated with DMI and metabolic BW. The gRFI metric was genetically positively correlated with DMI and uncorrelated with energy sink traits. Overall, the estimated SE were large. The ReFI metric resulted in a different ranking of cows compared to those based on RFI or gRFI and was superior in selecting the most efficient animals. When the selection was based on ReFI breeding values, then the 10% most efficient cows produced 12.3% more ECM per unit metabolisable energy intake, whereas the corresponding values were only 4.3 or 5.9% when using RFI or gRFI breeding values, respectively. Based on ReFI, superior cows had also higher milk production, whereas based on RFI or gRFI milk production either decreased or was unaffected, respectively. The superiority of the ReFI metric in selecting efficient cows was due to a better modelling of the expected feed intake. The ReFI metric simplified modelling of feed utilisation efficiency in dairy cattle and resulted in breeding values that are equal to percentages of feed saved.

Rumen Microbiota Predicts Feed Efficiency of Primiparous Nordic Red Dairy Cows

Efficient feed utilization in dairy cows is crucial for economic and environmental reasons. The rumen microbiota plays a significant role in feed efficiency, but studies utilizing microbial data to predict host phenotype are limited. In this study, 87 primiparous Nordic Red dairy cows were ranked for feed efficiency during their early lactation based on residual energy intake, and the rumen liquid microbial ecosystem was subsequently evaluated using 16S rRNA amplicon and metagenome sequencing. The study used amplicon data to build an extreme gradient boosting model, demonstrating that taxonomic microbial variation can predict efficiency (r_test = 0.55). Prediction interpreters and microbial network revealed that predictions were based on microbial consortia and the efficient animals had more of the highly interacting microbes and consortia. Rumen metagenome data was used to evaluate carbohydrate-active enzymes and metabolic pathway differences between efficiency phenotypes. The study showed that an efficient rumen had a higher abundance of glycoside hydrolases, while an inefficient rumen had more glycosyl transferases. Enrichment of metabolic pathways was observed in the inefficient group, while efficient animals emphasized bacterial environmental sensing and motility over microbial growth. The results suggest that inter-kingdom interactions should be further analyzed to understand their association with the feed efficiency of animals.

Long-term effects of early-life rumen microbiota modulation on dairy cow production performance and methane emissions

Rumen microbiota modulation during the pre-weaning period has been suggested as means to affect animal performance later in life. In this follow-up study, we examined the post-weaning rumen microbiota development differences in monozygotic twin-heifers that were inoculated (T-group) or not inoculated (C-group) (n = 4 each) with fresh adult rumen liquid during their pre-weaning period. We also assessed the treatment effect on production parameters and methane emissions of cows during their 1st lactation period. The rumen microbiota was determined by the 16S rRNA gene, 18S rRNA gene, and ITS1 amplicon sequencing. Animal weight gain and rumen fermentation parameters were monitored from 2 to 12 months of age. The weight gain was not affected by treatment, but butyrate proportion was higher in T-group in month 3 (p = 0.04). Apart from archaea (p = 0.084), the richness of bacteria (p < 0.0001) and ciliate protozoa increased until month 7 (p = 0.004) and anaerobic fungi until month 11 (p = 0.005). The microbiota structure, measured as Bray-Curtis distances, continued to develop until months 3, 6, 7, and 10, in archaea, ciliate protozoa, bacteria, and anaerobic fungi, respectively (for all: p = 0.001). Treatment or age × treatment interaction had a significant (p < 0.05) effect on 18 bacterial, 2 archaeal, and 6 ciliate protozoan taxonomic groups, with differences occurring mostly before month 4 in bacteria, and month 3 in archaea and ciliate protozoa. Treatment stimulated earlier maturation of prokaryote community in T-group before month 4 and earlier maturation of ciliate protozoa at month 2 (Random Forest: 0.75 month for bacteria and 1.5 month for protozoa). No treatment effect on the maturity of anaerobic fungi was observed. The milk production and quality, feed efficiency, and methane emissions were monitored during cow's 1st lactation. The T-group had lower variation in energy-corrected milk yield (p < 0.001), tended to differ in pattern of residual energy intake over time (p = 0.069), and had numerically lower somatic cell count throughout their 1st lactation period (p = 0.081), but no differences between the groups in methane emissions (g/d, g/kg DMI, or g/kg milk) were observed. Our results demonstrated that the orally administered microbial inoculant induced transient changes in early rumen microbiome maturation. In addition, the treatment may influence the later production performance, although the mechanisms that mediate these effects need to be further explored.

Effect of alfalfa substituted with ramie on the expression of apoptotic genes in the gastrointestinal tracts of goats

The study investigated the effect of alfalfa hay substituted with ramie silage on the expression of apoptotic genes in the gastrointestinal tract of goats. Thirty‐two goats were randomly allocated into four groups, in which the alfalfa was substituted with ramie at 0%, 35%, 75%, and 100% levels, respectively. In the rumen, the mRNA expression of Bax was significantly up‐regulated (p = .0007) when alfalfa was 100% substituted by ramie; the mRNA expression of Bcl‐2/Bax was significantly down‐regulated (p = .02) when alfalfa was 100% substituted by ramie compared with the 75% substituted treatment; the protein expression of Bcl‐xl was significantly down‐regulated (p = .03) when alfalfa was 100% substituted by ramie compared with 35% and 75% substituted treatments, respectively. In the jejunum, the mRNA expression of p53 was significantly up‐regulated (p = .01) when alfalfa was 100% substituted by ramie compared with 0% and 35% substituted treatments; the protein expression of p53 was significantly up‐regulated (p = .001) when alfalfa was 35% substituted by ramie compared with 0% and 75% substituted treatments. However, the activity of Caspase‐3 was not affected by different substituting levels of ramie in the rumen and jejunum of goats (p > .05). In conclusion, ramie with high substitution had strong antinutritional effect, which might promote the apoptosis in the gastrointestinal tract of goats in a caspase‐independent manner, thus affecting the growth and development of goat. It was suggested that ramie should not replace alfalfa more than 35% in the process of goat feeding.

Assessment of feed and economic efficiency of dairy farms based on multivariate aggregation of partial indicators measured on field

Many of the metrics used to evaluate farm performance are only partial indicators of farm operations, which are assumed to be best predictors of the whole farm efficiency. The main objective of this work was to identify aggregated multiple indexes of profitability using common partial indicators that are routinely available from individual farms to better support the short-term decision-making processes of the cattle-feeding process. Data were collected from face-to-face interviews with farmers from 90 dairy farms in Italy and used to calculate 16 partial indicators that covered almost all indicators currently used to target feeding and economic efficiency in dairy farms. These partial indicators described feed efficiency, energy utilization, feed costs, milk-to-feed price ratio, income over feed costs, income equal feed cost, money-corrected milk, and bargaining power for feed costs. Calculations of feeding costs were based on lactating cows or the whole herd, and income from milk deliveries was determined with or without considering the milk quality payment. Multivariate factor analysis was then applied to the 16 partial indicators to determine simplified and latent structures. The results indicated that 5 factors explained 70% of the variability. Each of the original partial indicator was associated with all factors in different proportions, as indicated by loading scores from the multivariate factor analysis. Based on the loading scores, we labeled these 5 factors as "economic efficiency," "energy utilization," "break-even point," "milk-to-feed price," and "bargaining power of the farm," in decreasing order of explained communality. The first 3 factors shared 83% of the total communality. Feed efficiency was similarly associated with factor 1 (53% loading) and factor 2 (66% loading). Only factor 4 was significantly affected by farm location. Milk production and herd size had significant effects on factor 1 and factor 2. Our multivariate approach eliminated the problem of multicollinearity of partial indicators, providing simple and effective descriptions of farm feeding economics. The proposed method allowed the evaluation, benchmarking, and ranking of dairy herd performance at the level of single farms and at territorial level with high opportunity to be used or replicated in other areas.

Residual carbon dioxide as an index of feed efficiency in lactating dairy cows

High feed costs make feed conversion efficiency a desirable target for genetic improvement. Residual feed intake (RFI), calculated as the difference between observed and predicted intake, is a commonly used estimate of feed efficiency. However, determination of feed efficiency in dairy herds is challenging due to difficulties in measuring feed intake of individual animals reliably. Using residual CO₂ (RCO₂) production as an estimate of feed efficiency would allow ranking the cows according to feed efficiency, provided that CO₂ production is closely related to heat production and feed intake. The objective of this study was to evaluate the potential of RCO₂ as an index of feed efficiency using data from respiration calorimetry studies (289 cow per period observations). Heat production was precisely predicted from CO₂ production [root mean square error (RMSE)] adjusted for random effects was 1.5% of observed mean]. Dry matter intake (DMI) was better predicted from energy-corrected milk (ECM) yield and CO₂ production than from ECM yield and body weight in the model (adjusted RSME = 0.92 vs. 1.39 kg/d). Residual CO₂ production estimated as the difference between actual CO₂ production and that predicted from ECM yield, metabolic body weight was closely related to RFI (adjusted RMSE = 0.42) that was calculated as the difference between actual DMI and that predicted from ECM, metabolic body weight, and energy balance (EB). When the cows were categorized in 3 groups of equal sizes on the basis of RCO₂ (low, medium, and high), low RCO₂ cows had lower DMI, RFI, methane production and intensity (g/kg ECM), and heat production, but higher efficiency of metabolizable energy utilization for lactation than high RCO₂ cows. When RFI was predicted from RCO₂, the residuals (observed - predicted) were negatively related to EB and digestibility. Predicting RFI with a 2-variable model based on RCO₂ and digestibility, adjusted RMSE decreased to 0.23 kg/d, and residuals were not significantly related to EB. The cows in low RCO₂ group had a higher energy digestibility than the cows in the high RCO₂ group, and differences in EB were observed between the groups. Error of the model predicting residual ECM production from RCO₂ was 1.41 kg/d. The residuals were positively related to ECM yield and energy digestibility. Predicting residual ECM from RCO₂ and ECM yield decreased adjusted RMSE to 1.07 kg/d, and further to 0.78 kg/d when digestibility was included in the 2-variable model. It is concluded that RCO₂ has a potential for ranking individual cows based on feed efficiency.

Modern Holstein-origin dairy cows within grassland-based systems partition more feed nitrogen into milk and excrete less in manure

The objective was to determine whether modern Holstein-origin dairy cows, when managed within grassland-based systems, partitioned more feed nitrogen (N) into milk and excreted less in manure, in comparison to an earlier population of Holstein-origin dairy cows. Data used were collated from total diet digestibility studies undertaken in Northern Ireland from 1990 to 2002 (old dataset, n = 538) and from 2005 to 2019 (new dataset, n = 476), respectively. An analysis of variance indicated that cows in the new dataset partitioned a significantly higher proportion of consumed N into milk and excreted a lower proportion in urine and total manure, compared to cows in the old dataset. A second analysis using the linear regression revealed that in comparison to the old dataset, the new dataset had a lower slope in the relationship between N intake and N excretion in urine or total manure, while a higher slope in the relationship between N intake and milk N output. A third analysis used the combined data from both datasets to examine if there was a relationship between experimental year and N utilization efficiency. Across the period from 1990 to 2019, urine N/N intake and manure N/N intake significantly decreased, while milk N/N intake increased. These results indicate that modern Holstein-origin dairy cows utilize consumed N more efficiently than earlier populations. Thus, N excretion is likely to be overestimated if models developed from the old data are used to predict N excretion for modern dairy herds. Therefore, the final part of analysis involved using the new dataset to develop prediction models for N excretion based on N intake and farm level data (milk yield, live weight and dietary N concentration). These updated models can be used to estimate N excretion from modern Holstein-origin dairy cows within grassland-based dairy systems.

The effects of energy metabolism variables on feed efficiency in respiration chamber studies with lactating dairy cows

The objective of the present study was to investigate factors related to variation in feed efficiency (FE) among cows. Data included 841 cow/period observations from 31 energy metabolism studies assembled across 3 research stations. The cows were categorized into low-, medium-, and high-FE groups according to residual feed intake (RFI), residual energy-corrected milk (RECM), and feed conversion efficiency (FCE). Mixed model regression was conducted to identify differences among the efficiency groups in animal and energy metabolism traits. Partial regression coefficients of both RFI and RECM agreed with published energy requirements more closely than cofficients derived from production experiments. Within RFI groups, efficient (Low-RFI) cows ate less, had a higher digestibility, produced less methane (CH₄) and heat, and had a higher efficiency of metabolizable energy (ME) utilization for milk production. High-RECM (most efficient) cows produced 6.0 kg/d more of energy-corrected milk (ECM) than their Low-RECM (least efficient) contemporaries at the same feed intake. They had a higher digestibility, produced less CH₄ and heat, and had a higher efficiency of ME utilization for milk production. The contributions of improved digestibility, reduced CH₄, and reduced urinary energy losses to increased ME intake at the same feed intake were 84, 12, and 4%, respectively. For both RFI and RECM analysis, increased metabolizability contributed to approximately 35% improved FE, with the remaining 65% attributed to the greater efficiency of utilization of ME. The analysis within RECM groups suggested that the difference in ME utilization was mainly due to the higher maintenance requirement of Low-RECM cows compared with Medium- and High-RECM cows, whereas the difference between Medium- and High-RECM cows resulted mainly from the higher efficiency of ME utilization for milk production in High-RECM cows. The main difference within FCE (ECM/DMI) categories was a greater (8.2 kg/d) ECM yield at the expense of mobilization in High-FCE cows compared with Low-FCE cows. Methane intensity (CH₄/ECM) was lower for efficient cows than for inefficient cows. The results indicated that RFI and RECM are different traits. We concluded that there is considerable variation in FE among cows that is not related to dilution of maintenance requirement or nutrient partitioning. Improving FE is a sustainable approach to reduce CH₄ production per unit of product, and at the same time improve the economics of milk production.

Genetic correlations between energy status indicator traits and female fertility in primiparous Nordic Red Dairy cattle

Inclusion of feed efficiency traits into the dairy cattle breeding programmes will require considering early lactation energy status to avoid deterioration in health and fertility of dairy cows. In this regard, energy status indicator (ESI) traits, for example, blood metabolites or milk fatty acids (FAs), are of interest. These indicators can be predicted from routine milk samples by mid-IR reflectance spectroscopy (MIR). In this study, we estimated genetic variation in ESI traits and their genetic correlation with female fertility in early lactation. The data consisted of 37 424 primiparous Nordic Red Dairy cows with milk test-day records between 8 and 91 days in milk (DIM). Routine test-day milk samples were analysed by MIR using previously developed calibration equations for blood plasma non-esterified FA (NEFA), milk FAs, milk beta-hydroxybutyrate (BHB) and milk acetone concentrations. Six ESI traits were considered and included: plasma NEFA concentration (mmol/l) either predicted by multiple linear regression including DIM, milk fat to protein ratio (FPR) and FAs C10:0, C14:0, C18:1 cis-9, C14:0 * C18:1 cis-9 (NEFAFA) or directly from milk MIR spectra (NEFAMIR), C18:1 cis-9 (g/100 ml milk), FPR, BHB (mmol/l milk) and acetone (mmol/l milk). The interval from calving to first insemination (ICF) was considered as the fertility trait. Data were analysed using linear mixed models. Heritability estimates varied during the first three lactation months from 0.13 to 0.19, 0.10 to 0.17, 0.09 to 0.14, 0.07 to 0.10, 0.13 to 0.17 and 0.13 to 0.18 for NEFAMIR, NEFAFA, C18:1 cis-9, FPR, milk BHB and acetone, respectively. Genetic correlations between all ESI traits and ICF were from 0.18 to 0.40 in the first lactation period (8 to 35 DIM), in general somewhat lower (0.03 to 0.43) in the second period (36 to 63 DIM) and decreased clearly (-0.02 to 0.19) in the third period (64 to 91 DIM). Our results indicate that genetic variation in energy status of cows in early lactation can be determined using MIR-predicted indicators. In addition, the markedly lower genetic correlation between ESI traits and fertility in the third lactation month indicated that energy status should be determined from the first test-day milk samples during the first 2 months of lactation.

Body and milk traits as indicators of dairy cow energy status in early lactation

The inclusion of feed intake and efficiency traits in dairy cow breeding goals can lead to increased risk of metabolic stress. An easy and inexpensive way to monitor postpartum energy status (ES) of cows is therefore needed. Cows' ES can be estimated by calculating the energy balance from energy intake and output and predicted by indicator traits such as change in body weight (ΔBW), change in body condition score (ΔBCS), milk fat:protein ratio (FPR), or milk fatty acid (FA) composition. In this study, we used blood plasma nonesterified fatty acids (NEFA) concentration as a biomarker for ES. We determined associations between NEFA concentration and ES indicators and evaluated the usefulness of body and milk traits alone, or together, in predicting ES of the cow. Data were collected from 2 research herds during 2013 to 2016 and included 137 Nordic Red dairy cows, all of which had a first lactation and 59 of which also had a second lactation. The data included daily body weight, milk yield, and feed intake and monthly BCS. Plasma samples for NEFA were collected twice in lactation wk 2 and 3 and once in wk 20. Milk samples for analysis of fat, protein, lactose, and FA concentrations were taken on the blood sampling days. Plasma NEFA concentration was higher in lactation wk 2 and 3 than in wk 20 (0.56 ± 0.30, 0.43 ± 0.22, and 0.13 ± 0.06 mmol/L, respectively; all means ± standard deviation). Among individual indicators, C18:1 cis-9 and the sum of C18:1 in milk had the highest correlations (r = 0.73) with NEFA. Seven multiple linear regression models for NEFA prediction were developed using stepwise selection. Of the models that included milk traits (other than milk FA) as well as body traits, the best fit was achieved by a model with milk yield, FPR, ΔBW, ΔBCS, FPR × ΔBW, and days in milk. The model resulted in a cross-validation coefficient of determination (R²cv) of 0.51 and a root mean squared error (RMSE) of 0.196 mmol/L. When only milk FA concentrations were considered in the model, NEFA prediction was more accurate using measurements from evening milk than from morning milk (R²cv = 0.61 vs. 0.53). The best model with milk traits contained FPR, C10:0, C14:0, C18:1 cis-9, C18:1 cis-9 × C14:0, and days in milk (R²cv = 0.62; RMSE = 0.177 mmol/L). The most advanced model using both milk and body traits gave a slightly better fit than the model with only milk traits (R²cv = 0.63; RMSE = 0.176 mmol/L). Our findings indicate that ES of cows in early lactation can be monitored with moderately high accuracy by routine milk measurements.

Reliability of breeding values for feed intake and feed efficiency traits in dairy cattle: When dry matter intake recordings are sparse under different scenarios

Currently, routine recordings of dry matter intake (DMI) in commercial herds are practically nonexistent. Recording DMI from commercial herds is a prerequisite for the inclusion of feed efficiency (FE) traits in dairy cattle breeding goals. To develop future on-farm phenotyping strategies, recording strategies that are low cost and less demanding logistically and that give relatively accurate estimates of the animal's genetic merit are therefore needed. The objectives of this study were (1) to estimate genetic parameters for daily DMI and FE traits and use the estimated parameters to simulate daily DMI phenotypes under different DMI recording scenarios (SCN) and (2) to use the simulated data to estimate for different scenarios the associated reliability of estimated breeding value and accuracies of genomic prediction for varying sizes of reference populations. Five on-farm daily DMI recording scenarios were simulated: once weekly (SCN1), once monthly (SCN2), every 2 mo (SCN3), every 3 mo (SCN4), and every 4 mo (SCN5). To estimate reliability of estimated breeding values, DMI and FE observations and true breeding values were simulated based on variance components estimated for daily observations of Nordic Red cows. To emulate realistic on-farm recording, 5 data set replicates, each with 36,037 DMI and FE records, were simulated for real pedigree and data structure of 789 Holstein cows. Observations for the 5 DMI recording scenarios were generated by discarding data in a step-wise manner from the full simulated data per the scenario's definitions. For each of these scenarios, reliabilities were calculated as correlation between the true and estimated breeding values. Variance components and genetic parameters were estimated for daily DMI, residual feed intake (RFI), and energy conversion efficiency (ECE) fitting the random regression model. Data for variance components were from 227 primiparous Nordic Red dairy cows covering 8 to 280 d in milk. Lactation-wise heritability for DMI, RFI, and ECE was 0.33, 0.12, and 0.32, respectively, and daily heritability estimates during lactation ranged from 0.18 to 0.45, 0.08 to 0.32, and 0.08 to 0.45 for DMI, RFI, and ECE, respectively. Genetic correlations for DMI between different stages of lactation ranged from -0.50 to 0.99. The comparison of different on-farm DMI recording scenarios indicated that adopting a less-frequent recording scenario (SCN3) gave a similar level of accuracy as SCN1 when 17 more daughters are recorded per sire over the 46 needed for SCN1. Such a strategy is less demanding logistically and is low cost because fewer observations need to be collected per animal. The accuracy of genomic predictions associated with the 5 recording scenarios indicated that setting up a relatively larger reference population and adopting a less-frequent DMI sampling scenario (e.g., SCN3) is promising. When the same reference population size was considered, the genomic prediction accuracy of SCN3 was only 5.0 to 7.0 percentage points lower than that for the most expensive DMI recording strategy (SCN1). We concluded that DMI recording strategies that are sparse in terms of records per cow but with slightly more cows recorded per sire are advantageous both in genomic selection and in traditional progeny testing schemes when accuracy, logistics, and cost implications are considered.