Multiple-trait random regression modeling of feed efficiency in US Holsteins

Residual feed intake (RFI) and feed saved (FS) are important feed efficiency traits that have been increasingly considered in genetic improvement programs. Future sustainability of these genetic evaluations will depend upon greater flexibility to accommodate sparsely recorded dry matter intake (DMI) records on many more cows, especially from commercial environments. Recent multiple-trait random regression (MTRR) modeling developments have facilitated days in milk (DIM)-specific inferences on RFI and FS, particularly in modeling the effect of change in metabolic body weight (MBW). The MTRR analyses, using daily data on the core traits of DMI, MBW, and milk energy (MilkE), were conducted separately for 2,532 primiparous and 2,379 multiparous US Holstein cows from 50 to 200 DIM. Estimated MTRR variance components were used to derive genetic RFI and FS and DIM-specific genetic partial regressions of DMI on MBW, MilkE, and change in MBW. Estimated daily heritabilities of RFI and FS varied across lactation for both primiparous (0.05–0.07 and 0.11–0.17, respectively) and multiparous (0.03–0.13 and 0.10–0.17, respectively) cows. Genetic correlations of RFI across DIM varied (>0.05) widely compared with FS (>0.54) within either parity class. Heritability estimates based on average lactation-wise measures were substantially larger than daily heritabilities, ranging from 0.17 to 0.25 for RFI and from 0.35 to 0.41 for FS. The partial genetic regression coefficients of DMI on MBW (0.11 to 0.16 kg/kg^0.75 for primiparous and 0.12 to 0.14 kg/kg^0.75 for multiparous cows) and of DMI on MilkE (0.45 to 0.68 kg/Mcal for primiparous and 0.36 to 0.61 kg/Mcal for multiparous cows) also varied across lactation. In spite of the computational challenges encountered with MTRR, the model potentially facilitates an efficient strategy for harnessing more data involving a wide variety of data recording scenarios for genetic evaluations on feed efficiency.     

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

In this study, we aimed to estimate and compare the genetic parameters of dry matter intake (DMI), energy-corrected milk (ECM), and body weight (BW) as 3 feed efficiency–related traits across lactation in 3 dairy cattle breeds (Holstein, Nordic Red, and Jersey). The analyses were based on weekly records of DMI, ECM, and BW per cow across lactation for 842 primiparous Holstein cows, 746 primiparous Nordic Red cows, and 378 primiparous Jersey cows. A random regression model was applied to estimate variance components and genetic parameters for DMI, ECM, and BW in each lactation week within each breed. Phenotypic means of DMI, ECM, and BW observations across lactation showed to be in very similar patterns between breeds, whereas breed differences lay in the average level of DMI, ECM, and BW. Generally, for all studied breeds, the heritability for DMI ranged from 0.2 to 0.4 across lactation and was in a range similar to the heritability for ECM. The heritability for BW ranged from 0.4 to 0.6 across lactation, higher than the heritability for DMI or ECM. Among the studied breeds, the heritability estimates for DMI shared a very similar range between breeds, whereas the heritability estimates for ECM tended to be different between breeds. For BW, the heritability estimates also tended to follow a similar range between breeds. Among the studied traits, the genetic variance and heritability for DMI varied across lactation, and the genetic correlations between DMI at different lactation stages were less than unity, indicating a genetic heterogeneity of feed intake across lactation in dairy cattle. In contrast, BW was the most genetically consistent trait across lactation, where BW among all lactation weeks was highly correlated. Genetic correlations between DMI, ECM, and BW changed across lactation, especially in early lactation. Energy-corrected milk had a low genetic correlation with both DMI and BW at the beginning of lactation, whereas ECM was highly correlated with DMI in mid and late lactation. Based on our results, genetic heterogeneity of DMI, ECM, and BW across lactation generally was observed in all studied dairy breeds, especially for DMI, which should be carefully considered for the recording strategy of these traits. The genetic correlations between DMI, ECM, and BW changed across lactation and followed similar patterns between breeds.     

Three-breed rotational crossbreds of Montbéliarde,Viking Red,and Holstein compared with Holstein cows for feed efficiency,income over feed cost,and residual feed intake

Rotational 3-breed crossbred cows of Montbéliarde, Viking Red, and Holstein (CB) were compared with Holstein (HO) cows for alternative measures of feed efficiency as well as income over feed cost (IOFC) and residual feed intake (RFI) during the first 150 d of first, second, and third lactations. Primiparous and multiparous CB (n = 63 and n = 43, respectively) and HO (n = 60 and n = 37, respectively) cows were fed the same total mixed ration twice daily with refusals weighed once daily. Feed was analyzed for dry matter content, net energy for lactation, and crude protein content. Body weight (BW) was recorded twice weekly. Daily production of milk, fat, and protein were estimated from monthly test days with best prediction. Measures of efficiency from 4 to 150 d in milk (DIM) were feed conversion efficiency (FCE), defined as fat plus protein production (kg) per kilogram of dry matter intake (DMI); ECM/DMI, defined as kilograms of energy-corrected milk (ECM) per kilogram of DMI; net energy for lactation efficiency (NE_LE), defined as ECM (kg) per megacalorie of net energy for lactation intake; crude protein efficiency (CPE), defined as true protein production (kg) per kilogram of crude protein intake; and DMI/BW, defined as DMI (kg) per kilogram of BW. The IOFC was defined as revenue from fat plus protein production minus feed cost. The RFI from 4 to 150 DIM for each lactation was the residual error remaining from regression of DMI on milk energy output (Mcal), metabolic BW, and energy required for change in BW (Mcal). Statistical analysis of measures of feed efficiency and RFI for primiparous cows included the fixed effects of year of calving and breed group. For multiparous cows, statistical analysis included breed as a fixed effect and cow as a repeated effect nested within breed group. Primiparous CB cows had higher means for FCE (+5.5%), ECM/DMI (+4.0%), NE_LE (+4.0%), and CPE (+5.2%) and a lower mean DMI/BW (–5.3%) than primiparous HO cows. Primiparous CB cows ($875) also had higher mean IOFC than primiparous HO cows ($825). In addition, mean RFI from 4 to 150 DIM was significantly lower (more desirable) for primiparous CB cows than HO cows. Likewise, multiparous CB cows had higher means for FCE (+8.2%), ECM/DMI (+5.9%), NE_LE (+5.8%), and CPE (+8.1%) and a lower mean for DMI/BW (–4.8%) than multiparous HO cows. Multiparous CB cows ($1,296) also had a higher mean for IOFC than multiparous HO cows ($1,208) and a lower mean for RFI from 4 to 150 DIM than HO cows.     

Genetic parameters for energy balance, feed efficiency, and related traits in Holstein cattle

Objectives of the current study were to estimate genetic parameters in Holstein cows for energy balance (EB) and related traits including dry matter intake (DMI), body weight (BW), body condition score (BCS), energy-corrected milk (ECM) production, and gross feed efficiency (GFE), defined as the ratio of total ECM yield to total DMI over the first 150d of lactation. Data were recorded for the first half of lactation on 227 and 175 cows in their first or later lactation, respectively. Random regression models were fitted to longitudinal data. Also, each trait was averaged over monthly intervals and analyzed by single and multivariate animal models. Heritability estimates ranged from 0.27 to 0.63, 0.12 to 0.62, 0.12 to 0.49, 0.63 to 0.72, and 0.49 to 0.53 for DMI, ECM yield, EB, BW, and BCS, respectively, averaged over monthly intervals. Daily heritability estimates ranged from 0.18 to 0.30, 0.10 to 0.26, 0.07 to 0.22, 0.43 to 0.67, and 0.25 to 0.38 for DMI, ECM yield, EB, BW, and BCS, respectively. Estimated heritability for GFE was 0.32. The genetic correlation of EB at 10d in milk (DIM) with EB at 150 DIM was -0.19, suggesting the genetic regulation of this trait differs by stage of lactation. Positive genetic correlations were found among DMI, ECM yield, and BW averaged over monthly intervals, whereas correlations of these traits with BCS depended upon stage of lactation. Total ECM yield for the lactation was positively correlated with DMI, but a negative genetic correlation between total ECM yield and EB was found. However, the genetic correlation between total ECM yield and EB in the first month of lactation was -0.02, indicating that total production is not genetically correlated with EB during the first month of lactation, when negative EB is most closely associated with diminished fitness. The genetic correlation between GFE and EB ranged from -0.73 to -0.99, indicating that selection for more efficient cows would favor a lower energy status. However, the genetic correlation between EB in the first month of lactation and GFE calculated from 75 to 150 DIM was not significant, indicating that the unfavorable correlation between GFE and EB in early lactation may be minimized with alternative definitions of efficiency. Thus, EB, GFE and related traits will likely respond to genetic selection in Holstein cows. However, the impact of selection for improved feed efficiency on EB must be carefully considered to avoid potential negative consequences of further reductions in EB at the onset of lactation.     

Time required to determine performance variables and production efficiency of lactating dairy cows

Thirty-five lactating dairy cows throughout weeks of lactation (WOL) 16 to 30 were used to determine optimal time needed for reliable measurement of performance variables, and to classify the cows into high-, medium-, and low-efficiency groups. Individual performance variables [body weight (BW), dry matter intake (DMI), and milk production] were measured daily with a computerized monitoring system. Body condition was visually scored weekly and used to calculate retained or depleted body energy as a result of fat content change (RE_F). Milk composition was analyzed weekly. Body weight, DMI, and total recovered energy (RE), which represents energy in milk production plus RE_F, were summarized weekly. Efficiency was calculated as RE/DMI and as residual feed intake (RFI; i.e., the difference between actual and expected DMI), which was calculated from multiple linear regression of DMI dependence on BW^0.75 and RE. Unexpectedly, it was found that BW did not affect DMI and RE/DMI. Changes and relative changes in phenotypic coefficient of variation and correlations among data from shortened tests ranging from 1 wk (WOL 16) to a sequence of 15-wk tests were used to determine optimal test period durations for 5 traits: BW, DMI, RE, RE/DMI, and RFI. Traits were fitted into a mixed model with repeated measures. For each week, the traits were summarized as a sequence of cumulative data, starting from WOL 16 and cumulated over periods that increased in 1-wk steps up to WOL 16 to 29. Weekly cumulations were compared with those for entire test period (WOL 16 to 30). Consistency of each cow’s efficiency classification as high, medium, or low was tested by the total-agreement procedure; the kappa index P-value was used. Throughout WOL 16 to 30, the effects of increasing test period duration on between-animal coefficient of variation differed with respect to the various performance variables and RE/DMI: it tended to change with respect to BW, did not change with respect to DMI, and decreased with respect to RE and RE/DMI. In conclusion, compared with a 15-wk study, a 2-wk study can classify RFI and RE/DMI to 3 efficiency levels, with an individual correlation coefficient of 0.6. When the study was carried out over 3 wk or more, the lowest significant index of the classification was P < 0.004, the lowest individual correlation coefficient was 0.65, and its lowest significance was P < 0.01. The current study indicated that the insignificant effect of the BW of dairy lactating cows on their DMI should be validated in more studies.     

The genetic and biological basis of feed efficiency in mid-lactation Holstein dairy cows

The objective of this study was to identify genomic regions and candidate genes associated with feed efficiency in lactating Holstein cows. In total, 4,916 cows with actual or imputed genotypes for 60,671 single nucleotide polymorphisms having individual feed intake, milk yield, milk composition, and body weight records were used in this study. Cows were from research herds located in the United States, Canada, the Netherlands, and the United Kingdom. Feed efficiency, defined as residual feed intake (RFI), was calculated within location as the residual of the regression of dry matter intake (DMI) on milk energy (MilkE), metabolic body weight (MBW), change in body weight, and systematic effects. For RFI, DMI, MilkE, and MBW, bivariate analyses were performed considering each trait as a separate trait within parity group to estimate variance components and genetic correlations between them. Animal relationships were established using a genomic relationship matrix. Genome-wide association studies were performed separately by parity group for RFI, DMI, MilkE, and MBW using the Bayes B method with a prior assumption that 1% of single nucleotide polymorphisms have a nonzero effect. One-megabase windows with greatest percentage of the total genetic variation explained by the markers (TGVM) were identified, and adjacent windows with large proportion of the TGVM were combined and reanalyzed. Heritability estimates for RFI were 0.14 (±0.03; ±SE) in primiparous cows and 0.13 (±0.03) in multiparous cows. Genetic correlations between primiparous and multiparous cows were 0.76 for RFI, 0.78 for DMI, 0.92 for MBW, and 0.61 for MilkE. No single 1-Mb window explained a significant proportion of the TGVM for RFI; however, after combining windows, significance was met on Bos taurus autosome 27 in primiparous cows, and nearly reached on Bos taurus autosome 4 in multiparous cows. Among other genes, these regions contain β-3 adrenergic receptor and the physiological candidate gene, leptin, respectively. Between the 2 parity groups, 3 of the 10 windows with the largest effects on DMI neighbored windows affecting RFI, but were not in the top 10 regions for MilkE or MBW. This result suggests a genetic basis for feed intake that is unrelated to energy consumption required for milk production or expected maintenance as determined by MBW. In conclusion, feed efficiency measured as RFI is a polygenic trait exhibiting a dynamic genetic basis and genetic variation distinct from that underlying expected maintenance requirements and milk energy output.     

Comparison of 3-breed rotational crossbreds of Montbéliarde,Viking Red,and Holstein with Holstein cows fed 2 alternative diets for dry matter intake,production, and residual feed intake

The objective of this study was to compare 3-breed rotational crossbred (CB) cows of the Montbéliarde, Viking Red, and Holstein (HO) breeds with HO cows fed 2 alternative diets for dry matter intake (DMI), fat plus protein production (CFP), body weight (BW), body condition score (BCS), feed efficiency, and residual feed intake (RFI) from 46 to 150 days in milk (DIM) during first lactation. The CB cows (n = 17) and HO cows (n = 19) calved from September 2019 to March 2020. Cows were fed either a traditional total mixed ration diet (TRAD) or a higher fiber, lower starch total mixed ration diet (HFLS). The HFLS had 21% more corn silage, 47% more alfalfa hay, 44% less corn grain, and 43% less corn gluten feed than the TRAD. The 2 diets were analyzed for dry matter content, crude protein, forage digestibility, starch, and net energy for lactation. The BW and BCS were recorded once weekly. Daily milk, fat, and protein production were estimated from twice monthly milk recording with random regression. Measures of efficiency were CFP per kilogram of DMI and DMI per kilogram of BW. The RFI from 46 to 150 DIM was the residual error from regression of DMI on milk energy, metabolic BW, and the energy required for change in BW. Statistical analysis of all variables included the fixed effects of diet, breed group, and the interaction of diet and breed group. The CB cows fed HFLS had less DMI (?12%) and lower DMI/BW (?14%) compared with the HO cows fed TRAD. For CFP, CB and HO cows were not different when fed TRAD or HFLS. Furthermore, the CB cows fed HFLS had higher BW (+50 kg) compared with HO cows fed HFLS. The CB cows fed TRAD had higher BCS than HO cows fed TRAD and HO cows fed HFLS (+0.46 and +0.62, respectively). The HO cows fed TRAD had more DMI (+14%) and lower CFP per kilogram of DMI (?12%) compared with the HO cows fed HFLS. In addition, mean RFI from 46 to 150 DIM was lower and more desirable for CB cows fed HFLS (?120.0 kg) compared with HO cows fed TRAD (85.3 kg). Dairy producers may feed either TRAD or HFLS to CB cows without loss of CFP.     

Residual feed intake in peripartal dairy cows is associated with differences in milk fat yield,ruminal bacteria,biopolymer hydrolyzing enzymes,and circulating biomarkers of immunometabolism

Residual feed intake (RFI) measures feed efficiency independent of milk production level, and is typically calculated using data past peak lactation. In the current study, we retrospectively classified multiparous Holstein cows (n = 320) from 5 of our published studies into most feed-efficient (M-eff) or least feed-efficient (L-eff) groups using performance data collected during the peripartal period. Objectives were to assess differences in profiles of plasma biomarkers of immunometabolism, relative abundance of key ruminal bacteria, and activities of digestive enzymes in ruminal digesta between M-eff and L-eff cows. Individual data from cows with ad libitum access to a total mixed ration from d ?28 to d +28 relative to calving were used. A linear regression model including dry matter intake (DMI), energy-corrected milk (ECM), changes in body weight (BW), and metabolic BW was used to classify cows based on RFI divergence into L-eff (n = 158) and M-eff (n = 162). Plasma collected from the coccygeal vessel at various times around parturition (L-eff = 60 cows; M-eff = 47 cows) was used for analyses of 30 biomarkers of immunometabolism. Ruminal digesta collected via esophageal tube (L-eff = 19 cows; M-eff = 29 cows) was used for DNA extraction and assessment of relative abundance (%) of 17 major bacteria using real-time PCR, as well as activity of cellulase, amylase, xylanase, and protease. The UNIVARIATE procedure of SAS 9.4 (SAS Institute Inc.) was used for analyses of RFI coefficients. The MIXED procedure of SAS was used for repeated measures analysis of performance, milk yield and composition, plasma immunometabolic biomarkers, ruminal bacteria, and enzyme activities. The M-eff cows consumed less DMI during the peripartal period compared with L-eff cows. In the larger cohort of cows, despite greater overall BW for M-eff cows especially in the prepartum (788 vs. 764 kg), no difference in body condition score was detected due to RFI or the interaction of RFI × time. Milk fat content (4.14 vs. 3.75 ± 0.06%) and milk fat yield (1.75 vs. 1.62 ± 0.04 kg) were greater in M-eff cows. Although cumulative ECM yield did not differ due to RFI (1,138 vs. 1,091 ± 21 kg), an RFI × time interaction due to greater ECM yield was found in M-eff cows. Among plasma biomarkers studied, concentrations of nonesterified fatty acids, β-hydroxybutyrate, bilirubin, ceruloplasmin, haptoglobin, myeloperoxidase, and reactive oxygen metabolites were overall greater, and glucose, paraoxonase, and IL-6 were lower in M-eff compared with L-eff cows. Among bacteria studied, abundance of Ruminobacter amylophilus and Prevotella ruminicola were more than 2-fold greater in M-eff cows. Despite lower ruminal activity of amylase in M-eff cows in the prepartum, regardless of RFI, we observed a marked linear increase after calving in amylase, cellulase, and xylanase activities. Protease activity did not differ due to RFI, time, or RFI × time. Despite greater concentrations of biomarkers reflective of negative energy balance and inflammation, higher feed efficiency measured as RFI in peripartal dairy cows might be associated with shifts in ruminal bacteria and amylase enzyme activity. Further studies could help address such factors, including the roles of the liver and the mammary gland.     

Prediction of dry matter intake throughout lactation in a dynamic model of dairy cow performance

In the dynamic modeling of dairy cow performance over a full lactation, the difference between net energy intake and net energy used for maintenance, growth, and output in milk accumulates in body reserves. A simple dynamic model of net energy balance was constructed to select, out of some common dry matter intake (DMI) prediction equations, the one that resulted in a minimum cumulative bias in body energy deposition. Dry matter intake was predicted using the Cornell Net Carbohydrate and Protein System, Agricultural Research Council, or National Research Council (NRC) DMI equations from body weight (BW) and predicted fat-corrected milk yield. The instantaneous BW of cows at progressive weeks of lactation was simulated as the numerical integral of the BW change obtained from the predicted net energy balance. Predicted DMI and BW from each DMI equation, using either of 2 equations to describe maintenance energy expenditures, were compared statistically against observed data from 21 herd average published full lactation data sets. All DMI equations underpredicted BW and DMI, but the NRC DMI equation resulted in the minimum cumulative error in predicted BW and DMI. As a general solution to prevent predicted BW from deviating substantially over time from the observed BW, a lipostatic feedback mechanism was integrated into the NRC DMI equation as a 2-parameter linear function of the relative size of simulated body reserves and week of lactation. Residual sum of squares was reduced on average by 52% for BW predictions and by 41% for DMI predictions by inclusion of the negative feedback with parameters taken from the average of all 21 least squares fits. Similarly, root mean square prediction error (%) was reduced by 30% on average for BW predictions and by 23% for DMI predictions. Inclusion of a feedback of energy reserves onto predicted DMI, simulating lipostatic regulation of BW, solved the problem of final BW deviation within a dynamic model and improved its DMI prediction to a satisfactory level.     

Short communication: genetic relationships among daily energy balance, feed intake, body condition score, and fat to protein ratio of milk in dairy cows

Postpartum energy status is critically important to health and fertility, and it remains a major task to find suitable indicator traits for energy balance. Therefore, genetic parameters for daily energy balance (EB) and dry matter intake (DMI), weekly milk fat to protein ratio (FPR), and monthly body condition score (BCS) were estimated using random regression on data collected from 682 Holstein-Friesian primiparous cows recorded between lactation d 11 to 180. Average energy-corrected milk (ECM), EB, DMI, BCS, and FPR were 32.0 kg, 9.6 MJ of NE_L, 20.3 kg, 2.95, and 1.12, respectively. Heritability estimates for EB, DMI, BCS, and FPR ranged from 0.03 to 0.13, 0.04 to 0.19, 0.34 to 0.59, and 0.20 to 0.54. Fat to protein ratio was a more valid measure for EB in early lactation than DMI, BCS, or single milk components. Correlations between FPR and EB were highest at the beginning of lactation [genetic correlation (r_g) = −0.62 at days in milk (DIM) 15] and decreased toward zero. Dry matter intake was the trait most closely correlated with EB in mid lactation (r_g = 0.73 at DIM 120 and 150). Energy balance in early lactation was negatively correlated to EB in mid lactation. The same applied to DMI. Genetic correlations between FPR across lactation stages were all positive; the lowest genetic correlation (0.55) was estimated between the beginning of lactation and early mid lactation. Hence, to improve EB at the beginning of lactation, EB and indicator traits need to be recorded in early lactation. We concluded that FPR is an adequate indicator for EB during the energy deficit phase. Genetic correlations of FPR with ECM, fat percentage, and protein percentage showed that a reduction of FPR in early lactation would have a slightly negative effect on ECM, whereas milk composition would change in a desirable manner.     

Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks

Dry matter intake (DMI) is a fundamental component of the animal's feed efficiency, but measuring DMI of individual cows is expensive. Mid-infrared reflectance spectroscopy (MIRS) on milk samples could be an inexpensive alternative to predict DMI. The objectives of this study were (1) to assess if milk MIRS data could improve DMI predictions of Canadian Holstein cows using artificial neural networks (ANN); (2) to investigate the ability of different ANN architectures to predict unobserved DMI; and (3) to validate the robustness of developed prediction models. A total of 7,398 milk samples from 509 dairy cows distributed over Canada, Denmark, and the United States were analyzed. Data from Denmark and the United States were used to increase the training data size and variability to improve the generalization of the prediction models over the lactation. For each milk spectra record, the corresponding weekly average DMI (kg/d), test-day milk yield (MY, kg/d), fat yield (FY, g/d), and protein yield (PY, g/d), metabolic body weight (MBW), age at calving, year of calving, season of calving, days in milk, lactation number, country, and herd were available. The weekly average DMI was predicted with various ANN architectures using 7 predictor sets, which were created by different combinations MY, FY, PY, MBW, and MIRS data. All predictor sets also included age of calving and days in milk. In addition, the classification effects of season of calving, country, and lactation number were included in all models. The explored ANN architectures consisted of 3 training algorithms (Bayesian regularization, Levenberg-Marquardt, and scaled conjugate gradient), 2 types of activation functions (hyperbolic tangent and linear), and from 1 to 10 neurons in hidden layers). In addition, partial least squares regression was also applied to predict the DMI. Models were compared using cross-validation based on leaving out 10% of records (validation A) and leaving out 10% of cows (validation B). Superior fitting statistics of models comprising MIRS information compared with the models fitting milk, fat and protein yields suggest that other unknown milk components may help explain variation in weekly average DMI. For instance, using MY, FY, PY, and MBW as predictor variables produced a predictive accuracy (r) ranging from 0.510 to 0.652 across ANN models and validation sets. Using MIRS together with MY, FY, PY, and MBW as predictors resulted in improved fitting (r = 0.679–0.777). Including MIRS data improved the weekly average DMI prediction of Canadian Holstein cows, but it seems that MIRS predicts DMI mostly through its association with milk production traits and its utility to estimate a measure of feed efficiency that accounts for the level of production, such as residual feed intake, might be limited and needs further investigation. The better predictive ability of nonlinear ANN compared with linear ANN and partial least squares regression indicated possible nonlinear relationships between weekly average DMI and the predictor variables. In general, ANN using Bayesian regularization and scaled conjugate gradient training algorithms yielded slightly better weekly average DMI predictions compared with ANN using the Levenberg-Marquardt training algorithm.     

Hot topic: Selecting cattle for low residual feed intake did not affect daily methane production but increased methane yield

Reducing enteric methane (CH₄) production and improving feed conversion efficiency of dairy cows is of high importance. Residual feed intake (RFI) is one measure of feed efficiency, with low RFI animals being more efficient in feed conversion. Enteric CH₄ is an important source of digestible energy loss in ruminants and, because research in beef cattle has reported a positive relationship between RFI and daily CH₄ production, we hypothesized that low RFI dairy heifers, which are more feed efficient, would produce less CH₄/d. We measured the daily methane production (g of CH₄/d), methane yield [g of CH₄/kg of dry matter intake (DMI)], and CH₄ per kilogram of body weight (BW) gain for 56 heifers (20–22 mo old) in a 2 × 2 factorial arrangement: factors included 2 breeds (Holstein-Friesian and Jersey; n = 28/breed), with equal numbers of animals previously determined as being either high [+2.0 kg of dry matter (DM)/d] or low RFI (?2.1 kg of DM/d; n = 28/RFI category). All heifers were commingled and offered unrestricted access to the same diet of dried alfalfa cubes. Between RFI categories, heifers did not differ in BW or BW gain but low RFI heifers had 9.3 and 10.6% lower DMI and DMI/kg of BW, respectively, than high RFI heifers. Similarly, RFI category did not affect CH₄/d or CH₄/kg of BWg, but CH₄/kg of DMI was higher in low RFI heifers because of their lower DMI. These results might reflect more complete digestion of ingested feed in the more efficient, low RFI heifers, consistent with previous reports of greater apparent digestibility of organic matter. Holstein-Friesian heifers were heavier and consumed more total DM than Jersey heifers, but breed did not affect DMI/kg of BW or BWg. Jersey heifers produced less CH₄/d, but not CH₄/kg of DMI or CH₄/kg of BWg. We detected no interaction between breed and RFI category in any of the variables measured. In conclusion, differences in RFI in dairy heifers did not affect daily CH₄ production (g/d); however, low RFI heifers had a greater CH₄ yield (g/kg of DMI) on a high forage diet.     

Defining the optimal period length and stage of growth or lactation to estimate residual feed intake in dairy cows

Residual feed intake (RFI) is an estimate of animal feed efficiency, calculated as the difference between observed and expected feed intake. Expected intake typically is derived from a multiple regression model of dry matter intake on energy sinks, including maintenance and growth in growing animals, or maintenance, gain in body reserves, and milk production in lactating animals. The best period during the production cycle of a dairy cow to estimate RFI is not clear. Here, we characterized RFI in growing Holstein heifers (RFI_Growth; ∼10 to 14 mo of age; n = 226) and cows throughout a 305-d lactation (RFI_Lac-Full; n = 118). The goals were to characterize relationships between RFI estimated at different production stages of the dairy cow; determine effects of selection for efficiency during growth on subsequent lactation and feed efficiency; and identify the most desirable testing scheme for RFI_Lac-Full. For RFI_Growth, intake was predicted from multiple linear regression of metabolizable energy (ME) intake on mid-test body weight (BW)^0.75 and average daily gain (ADG). For RFI_Lac-Full, predicted intake was based on regression of BW^0.75, ADG, and energy-corrected milk yield. Mean energy intake of the least and most efficient growing heifers (±0.5 standard deviations from mean RFI_Growth of 0) differed by 3.01 Mcal of ME/d, but the groups showed no difference in mid-test BW or ADG. Phenotypic correlation between RFI_Growth and RFI of heifers estimated in the first 100 d in milk (RFI_Lac100DIM; n = 130) was 0.37. Ranking of these heifers as least (mean + 0.5 standard deviations), middle, or most efficient (mean – 0.5 standard deviations) based on RFI_Growth resulted in 43% maintaining the same ranking by RFI_Lac100DIM. On average, the most efficient heifers ate 3.27 Mcal of ME/d less during the first 100 DIM than the least efficient heifers, but exhibited no differences in average energy-corrected milk yield, ADG, or BW. The correlation between RFI_Lac100DIM and RFI_Lac-Full was 0.72. Thus, RFI_Growth may serve as an indicator trait for RFI during lactation, and selection for heifers exhibiting low RFI_Growth should improve overall herd feed efficiency during lactation. Correlation analysis between RFI_Lac-Full (10 to 305 DIM) and subperiod estimates of RFI during lactation indicated a test period of 64 to 70 d in duration occurring between 150 to 220 DIM provided a reliable approximation (r ≥ 0.90) of RFI_Lac-Full among the test periods evaluated.     

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 (r_g = ?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 (r_g = 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.     

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.     

Dietary restriction improved feed efficiency of inefficient lactating cows

The aim of this study was to reduce voluntary dry matter intake (DMI) to increase feeding efficiency of preclassified inefficient (INE) dairy cows through restricted feeding. We studied the effects of dietary restriction on eating behavior, milk and energy-corrected milk (ECM) production, in vivo digestibility, energy balance, and measures of feed efficiency [residual feed intake (RFI) and ECM/DMI]. Before the experiment, 12 pairs of cows were classified as INE. The 2 dietary treatments consisted of ad libitum feeding versus restricted feeding of the same total mixed ration containing 36.5% roughage. Inefficient cows fed the restricted total mixed ration had a shorter eating time and lower meal and visit frequency, but a similar rate of eating, meal size, and meal duration compared with INE cows fed ad libitum. Compared with the INE cows fed ad libitum, restricted INE cows had 12.8% lower intake, their dry matter and neutral detergent fiber digestibility remained similar, and their ECM yield was 5.3% lower. Feed efficiency, measured as RFI, ECM/DMI, and net energy retained divided by digestible energy intake, was improved in the restricted INE cows as compared with the ad libitum cows. Our results show that moderate DMI restriction has the potential to improve feed efficiency of preclassified INE cows.     

Genetics of grass dry matter intake, energy balance, and digestibility in grazing irish dairy cows

The objective of this study was to estimate genetic parameters for grass dry matter intake (DMI), energy balance (EB), and cow internal digestibility (IDG) in grazing Holstein-Friesian dairy cows. Grass DMI was estimated up to 4 times per lactation on 1,588 lactations from 755 cows on 2 research farms in southern Ireland. Simultaneously measured milk production and BW records were used to calculate EB. Cow IDG, measured as the ratio of feed and fecal concentrations of the natural odd carbon-chain n-alkane pentatriacontane, was available on 583 lactations from 238 cows. Random regression and multitrait animal models were used to estimate residual, additive genetic and permanent environmental (co)variances across lactations. Results were similar for both models. Heritability for DMI, EB, and IDG across lactation varied from 0.10 [8 days in milk (DIM)] to 0.30 (169 DIM), from 0.06 (29 DIM) to 0.29 (305 DIM), and from 0.08 (50 DIM) to 0.45 (305 DIM), respectively, when estimated using the random regression model. Genetic correlations within each trait tended to decrease as the interval between periods compared increased for DMI and EB, whereas the correlations with IDG in early lactation were weakest when measured midlactation. The lowest correlation between any 2 periods was 0.10, −0.36, and −0.04 for DMI, EB, and IDG, respectively, suggesting the effect of different genes at different stages of lactations. Eigenvalues and associated eigenfunctions of the additive genetic covariance matrix revealed considerable genetic variation among animals in the shape of the lactation profiles for DMI, EB, and IDG. Genetic parameters presented are the first estimates from dairy cows fed predominantly grazed grass and imply that genetic improvement in DMI, EB, and IDG in Holstein-Friesian cows fed predominantly grazed grass is possible.     

Methane production,rumen fermentation,and diet digestibility of Holstein and Jersey dairy cows being divergent in residual feed intake and fed at 2 forage-to-concentrate ratios

Improving feed efficiency of dairy cows through breeding is expected to reduce enteric methane production per unit of milk produced. This study examined the effect of 2 forage-to-concentrate ratios on methane production, rumen fermentation, and nutrient digestibility in Holstein and Jersey dairy cows divergent in residual feed intake (RFI). Before experimental onset, RFI was estimated using a random regression model on phenotypic herd data. Ten lactating Holstein and 10 lactating Jersey cows were extracted from the herd and allocated to a high or low pre-experimental RFI group of 5 animals each within breed. Cows were fed ad libitum with total mixed rations either low (LC) or high (HC) in concentrates during 3 periods in a crossover design with a back-cross and staggered approach. Forage-to-concentrate ratio was 68:32 for LC and 39:61 for HC. Cows adapted to the diets in 12 to 24 d and feces were subsequently collected on 2 d. Afterward, gas exchange was measured in respiration chambers and rumen liquid was collected once after cows exited the chambers. Pre-experimental RFI was included in the statistical analysis as a class (low and high RFI) or continuous variable. Methane per kilogram of dry matter intake (DMI) was lower for Holsteins than Jerseys and the response to increased concentrate level was more pronounced for Holsteins than Jerseys (27.2 vs.13.8%); a similar pattern was found for the acetate:propionate ratio. However, methane production per kilogram of energy-corrected milk (ECM) was unaffected by breed. Further, total-tract digestibility of neutral detergent fiber was higher for Jerseys than Holsteins. For RFI as a class variable, DMI, methane production regardless of the expression, and digestibility were unaffected by RFI. For RFI as a continuous variable, DMI was lower and methane per kilogram of DMI was higher for cows with negative (efficient) than positive (inefficient) RFI values, and neutral detergent fiber digestibility was higher for Holsteins with negative than positive RFI values, but not for Jerseys. Daily methane production and methane per kilogram of ECM were unaffected by RFI. In conclusion, methane per kilogram of DMI of Jerseys was lowered to a smaller extent in response to the HC diet than of Holsteins. When pre-experimental RFI was used as a continuous variable, higher methane per kilogram of DMI was found for cows with negative RFI than positive RFI values, but not for methane per kilogram of ECM. These findings call for validation in larger studies.