Between-cow variation in the components of feed efficiency

How much can performance measures explain of the between-cow variation in enteric methane?

Enteric CH4 produced from dairy cows contributes to the greenhouse gas emission from anthropogenic sources. Recent studies have shown that the selection of lower CH4 emitting cows is possible, but this would be simpler if performance measures already recorded on farm could be used, instead of measuring gas emission from individual cows. These performance measures could be used for selection of low emitting cows. The aim of this analysis was to quantify how much of the between-cow variation in CH4 production can be explained by variation in performance measures. A data set with 3 experiments, a total of 149 lactating dairy cows with repeated measures, was used to estimate the between-cow variation (the variation between cow estimates) for performance and gas measures from GreenFeed. The cow estimates were obtained with a linear mixed model with the diet within period effect as a fixed effect and the cow within experiment as a random effect. The cow estimates for CH4 production were first regressed on the performance and gas measures individually, and then performance and CO2 production measures were grouped in 3 subsets for principal component analysis and principal component regression. The variables that explained most of the between-cow variation in CH4 production were DMI (R2 = 0.44), among the performance measures, and CO2 production (R2 = 0.61), among gas measures. Grouping the measures increased the R2 to 0.53, when only performance measures were used, and to 0.66, when CO2 production was added to the significant performance measures. We found the marginal improvement to be insufficient to justify the use of grouped measures rather than an individual measure, since the latter avoid over fitting and simplify the model. Investigation of other measures that can be explored to increase explanatory power of between-cow variation in CH4 production is briefly discussed. Finally, the use of residual CH4 as a measure for CH4 efficiency could be considered by using either DMI or CO2 production as the sole predicting variables.

Effects of offering free-choice hay for the first 5 days postpartum on productivity, serum inflammatory markers, gut permeability, and colon gene expression in fresh dairy cows

The objective of the present study was to evaluate the effects of offering free-choice hay to cows during the first 5 d immediately after calving on feed intake, milk yield, plasma metabolites, serum inflammatory markers, rumination, gut permeability, and colon gene expression. It was hypothesized that cows offered free-choice hay would have lower gut permeability, lower inflammation, and higher milk production, compared with cows not offered hay. Thirty-two multiparous cows were fed a closeup total mixed ration (TMR; 21.5% starch, 32.1% forage neutral detergent fiber [NDF] on a dry matter basis) until calving. In the postpartum period, all cows were fed a fresh cow TMR (26.8% starch and 23.4% forage NDF) from calving until 21 DIM, and were assigned randomly to receive 1 of 2 treatments as follows: (1) free-choice timothy hay (61.6% NDF; 9.6% crude protein), offered outside of the TMR in a separate manger, for the first 5 d postpartum (FCH; n = 20), or 2) no free-choice hay (NH; n = 12). The FCH cows tended to have lower serum haptoglobin concentration on d 3, compared with NH (0.95 vs. 1.52 mg/mL). Within the FCH group, cows with greater hay intake had a smaller increase in serum amyloid A from d 1 to 3 after calving (r = 0.37), and tended to have a smaller increase in serum haptoglobin concentration (r = 0.36). Cows in the FCH group had a lower ratio of starch intake (kg) to forage NDF intake (kg) on d 1 and 2, compared with NH (0.91 vs. 1.14 ± 0.03), and cows that had a lower starch:forage NDF ratio tended to have a smaller increase in serum haptoglobin concentration from d 1 to 3 after calving (r = 0.32). Cows in the FCH group had lower TMR dry matter intake (DMI; 15.0 vs. 17.1 ± 0.93 kg/d) and lower total DMI (TMR + hay DMI; 15.9 vs. 17.1 ± 0.87 kg/d), from d 1 to 5 when free-choice hay was offered, compared with NH. However, the hay treatment did not affect plasma energy metabolite concentration, gut permeability, colon gene expression, milk yield, rumination time, or change in body weight or body condition score. Overall, these findings suggest that offering free-choice hay for the first 5 d after calving may reduce serum inflammatory marker concentration, but milk yield may not increase, due to lower intake.

Between-individual variability on reticulorumen digesta passage rate in goats

Identifying factors that influence an individual's ability to utilize nutrients is a strategy for enhancing livestock sustainability. Digesta passage rate (kp) is one of the most important determinants that influence nutrient utilization. However, there is limited knowledge regarding the individual's variability on kp. The objective of this study was to investigate between-individual variability of reticulorumen (RR) kp of solutes and particles in goats, and its relationship to total NDF digestibility (NDFd). A dataset consisting of 103 individual records from two studies of castrated male (n = 36), female (n = 34), and intact male (n = 33) growing Saanen goats fed ad libitum and slaughtered at around 16.62 ± 0.96 (n = 21), 23.09 ± 1.33 (n = 19), 30.67 ± 2.25 kg (n = 29), 38.02 ± 1.61 (n = 16), and 44.12 ± 1.95 (n = 18) kg BW was used. The RR kp of solutes and particles were measured using Cr-EDTA and indigestible NDF (iNDF), respectively. Using mixed-effect models, considering sex and BW as fixed effects and study as random effect, we aimed to identify meaningful between-individual variance of RR kp of solutes and particles. Given the characteristic of our dataset (i.e., one observation per individual), our approach focused on exploring the variance between individuals described by the residuals of our fitted models. The RR kp of solutes and particles showed significant (P < 0.05) between-individual variation (CV ≈ 27%). Only 10% of RR kp of solutes variance was explained by DM intake (DMI) (g/d and g/d and g/kg BW) and NDF intake (NDFI); g/d), while 51.8% of the variance in RR kp of particles was explained by DMI and NDFI (g/d and g/kg BW), RR wet pool size, and iNDF:NDF intake ratio. The between-individual variation of RR kp of particles explained 19.5% of the between-individual variation in NDFd (P < 0.001). However, the nutritional and individual traits cited above (i.e., DMI, NDFI, RR wet pool size, and iNDF:NDF intake ratio) explained only 51.8% of the between-individual variation of RR kp of particles, while the remaining 48.2% was attributed to the individual's inherent characteristics, which in turn explained only 1.24% of the between-individual variation of NDFd (P = 0.146). The RR kp of particles and solutes showed meaningful between-individuals variance, which was partially related to both individual inherent characteristics and nutritional and animal traits. However, the NDFd was found to be unrelated to the inherent characteristics of RR kp of particles.

Dynamic exposure and body burden models for per- and polyfluoroalkyl substances (PFAS) enable management of food safety risks in cattle

With increasing global focus on planetary boundaries, food safety and quality, the presence of per- and polyfluoroalkyl substances (PFAS) in the food chain presents a challenge for the sustainable production and supply of quality assured food. Consumption of food is the primary PFAS exposure route for the general population. At contaminated sites, PFAS have been reported in a range of agricultural commodities including cattle. Consumer exposure assessments are complicated by the lack of validated modelling approaches to estimate PFAS bioaccumulation in cattle. Previous studies have shown that PFAS bioaccumulation in livestock is influenced by environmental, spatial and temporal factors that necessitate a dynamic modelling approach. This work presents an integrated exposure and population toxicokinetic (PopTK) model for cattle that estimates serum and tissue concentrations of PFAS over time. Daily exposures were estimated from intakes of water, pasture, and soil, and considered animal growth, seasonal variability (pasture moisture content and temperature) and variable PFAS concentrations across paddocks. Modelled serum and tissue estimates were validated against monitoring data from Australian and Swedish cattle farms. The models were also used to develop and test practical management options for reducing PFAS exposure and to prioritise remediation for farms. Model outputs for exposure management scenarios (testing cattle rotation and targeted supplementation of feed and water) showed potential for marked reductions in consumer exposures from cattle produce.

Dietary supplementation with xylooligosaccharides and exogenous enzyme improves milk production, energy utilization efficiency and reduces enteric methane emissions of Jersey cows

BACKGROUND

Sustainable strategies for enteric methane (CH₄) mitigation of dairy cows have been extensively explored to improve production performance and alleviate environmental pressure. The present study aimed to investigate the effects of dietary xylooligosaccharides (XOS) and exogenous enzyme (EXE) supplementation on milk production, nutrient digestibility, enteric CH₄ emissions, energy utilization efficiency of lactating Jersey dairy cows. Forty-eight lactating cows were randomly assigned to one of 4 treatments: (1) control diet (CON), (2) CON with 25 g/d XOS (XOS), (3) CON with 15 g/d EXE (EXE), and (4) CON with 25 g/d XOS and 15 g/d EXE (XOS + EXE). The 60-d experimental period consisted of a 14-d adaptation period and a 46-d sampling period. The enteric CO₂ and CH₄ emissions and O₂ consumption were measured using two GreenFeed units, which were further used to determine the energy utilization efficiency of cows.

RESULTS

Compared with CON, cows fed XOS, EXE or XOS + EXE significantly (P < 0.05) increased milk yield, true protein and fat concentration, and energy-corrected milk yield (ECM)/DM intake, which could be reflected by the significant improvement (P < 0.05) of dietary NDF and ADF digestibility. The results showed that dietary supplementation of XOS, EXE or XOS + EXE significantly (P < 0.05) reduced CH₄ emission, CH₄/milk yield, and CH₄/ECM. Furthermore, cows fed XOS demonstrated highest (P < 0.05) metabolizable energy intake, milk energy output but lowest (P < 0.05) of CH₄ energy output and CH₄ energy output as a proportion of gross energy intake compared with the remaining treatments.

CONCLUSIONS

Dietary supplementary of XOS, EXE or combination of XOS and EXE contributed to the improvement of lactation performance, nutrient digestibility, and energy utilization efficiency, as well as reduction of enteric CH₄ emissions of lactating Jersey cows. This promising mitigation method may need further research to validate its long-term effect and mode of action for dairy cows.

On-farm evaluation of models to predict herbage intake of dairy cows grazing temperate semi-natural grasslands

The objective of the present on-farm study was to evaluate the adequacy of existing models in predicting the pasture herbage DM intake (PDMI) of lactating dairy cows grazing semi-natural grasslands. The prediction adequacy of 13 empirical and semi-mechanistic models, which were predominantly developed to represent stall-fed cows or cows grazing high-quality pastures, were evaluated using the mean bias, relative prediction error (RPE), and partitioning of mean square error of prediction, where models with an RPE ≤ 20% were considered adequate. The reference dataset comprised n = 233 individual animal observations from nine commercial farms in South Germany with a mean milk production, DM intake, and PDMI (arithmetic means ± one SD) of 24 kg/d, (±5.6), 21 kg/d (±3.2), and 12 kg/d (±5.1), respectively. Despite their adaptation to grazing conditions, the behaviour-based and semi-mechanistic grazing-based models had the lowest prediction adequacy among the evaluated models. Their underlying empirical equations likely did not fit the grazing and production conditions of low-input farms using semi-natural grasslands for grazing. The semi-mechanistic stall-based model Mertens II with slight modifications achieved the highest and a satisfactory modelling performance (RPE = 13.4%) when evaluated based on the mean observed PDMI, i.e., averaged across animals per farm and period (n = 28). It also allowed for the adequate prediction of PDMI on individual cows (RPE = 18.5%) that were fed < 4.8 kg DM of supplement feed per day. Nevertheless, when used to predict PDMI of individual animals receiving a high supplementation level, the model Mertens II also did not meet the threshold for an acceptable adequacy (RPE = 24.7%). It was concluded that this lack of prediction adequacy for animals receiving greater levels of supplementation was due to a lack of modelling precision, which mainly could be related to inter-animal and methodological limitations such as the lack of individually measured supplement feed intake for some cows. The latter limitation is a trade-off of the on-farm research approach of the present study, which was chosen to represent the range in feed intake of dairy cows across the diverse low-input farming systems using semi-natural grasslands for grazing.

Genetic analysis of production traits and body size measurements and their relationships with metabolic diseases in German Holstein cattle

This study sheds light on the genetic complexity and interplay of production, body size, and metabolic health in dairy cattle. Phenotypes for body size-related traits from conformation classification (130,166 animals) and production (101,562 animals) of primiparous German Holstein cows were available. Additionally, 21,992, 16,641, and 7,096 animals were from herds with recordings of the metabolic diseases ketosis, displaced abomasum, and milk fever in first, second, and third lactation. Moreover, all animals were genotyped. Heritabilities of traits and genetic correlations between all traits were estimated and GWAS were performed. Heritability was between 0.240 and 0.333 for production and between 0.149 and 0.368 for body size traits. Metabolic diseases were lowly heritable, with estimates ranging from 0.011 to 0.029 in primiparous cows, from 0.008 to 0.031 in second lactation, and from 0.037 to 0.052 in third lactation. Production was found to have negative genetic correlations with body condition score (BCS; -0.279 to -0.343) and udder depth (-0.348 to -0.419). Positive correlations were observed for production and body depth (0.138-0.228), dairy character (DCH) (0.334-0.422), and stature (STAT) (0.084-0.158). In first parity cows, metabolic disease traits were unfavorably correlated with production, with genetic correlations varying from 0.111 to 0.224, implying that higher yielding cows have more metabolic problems. Genetic correlations of disease traits in second and third lactation with production in primiparous cows were low to moderate and in most cases unfavorable. While BCS was negatively correlated with metabolic diseases (-0.255 to -0.470), positive correlations were found between disease traits and DCH (0.269-0.469) as well as STAT (0.172-0.242). Thus, the results indicate that larger and sharper animals with low BCS are more susceptible to metabolic disorders. Genome-wide association studies revealed several significantly associated SNPs for production and conformation traits, confirming previous findings from literature. Moreover, for production and conformation traits, shared significant signals on Bos taurus autosome (BTA) 5 (88.36 Mb) and BTA 6 (86.40 to 87.27 Mb) were found, implying pleiotropy. Additionally, significant SNPs were observed for metabolic diseases on BTA 3, 10, 14, 17, and 26 in first lactation and on BTA 2, 6, 8, 17, and 23 in third lactation. Overall, this study provides important insights into the genetic basis and interrelations of relevant traits in today's Holstein cattle breeding programs, and findings may help to improve selection decisions.

Full-lactation performance of multiparous dairy cows with differing residual feed intake

Residual feed intake (RFI) is an efficiency trait underpinning profitability and environmental sustainability in dairy production. This study compared performance during a complete lactation of 36 multiparous dairy cows divided into three equal-sized groups with high (HRFI), intermediate (IRFI) or low RFI (LRFI). Residual feed intake was determined by two different equations. Residual feed intake according to the NorFor system was calculated as (RFI_NorFor) = (NE_intake)–(NE_maintenance + NE_gestation + NE_milk—NE_mobilisation + NE_deposition). Residual feed intake according to the USA National Research Council (NRC) (RFI_NRC) was calculated as: RFI = DMI − predicted DMI where predicted_s DMI = [(0.372× ECM)+(0.0968×BW^0.75)]×(1−e^{−0.192×(DIM/7+3.67)}). Cows in the HRFI_NorFor group showed higher daily CH₄ production, CH₄/ECM and CH₄ yield (g/kg DMI) than IRFI_NorFor and LRFI_NorFor cows. Cows characterized by high efficiency (LRFI_NorFor) according to the NorFor system had lower body weight. Dry matter intake and apparent dry matter digestibility were not affected by efficiency group but milk yield was lower in the low efficiency, HRFI_NorFor, group. Cows characterized by high efficiency according to the NRC system (LRFI_NRC) had lower dry matter intake while yield of CH₄ was higher. Daily CH₄ production and CH₄ g/kg ECM did not differ between RFI_NRC groups. Dairy cows characterized by high efficiency (both LRFI_NorFor and LRFI_NRC cows) over a complete lactation mobilized more of their body reserves in early lactation as well as during the complete lactation. The results also indicated great phenotypic variation in RFI between different stages the lactation.

New insights into the raw milk microbiota diversity from animals with a different genetic predisposition for feed efficiency and resilience to mastitis

The main objective of this study was to assess the microbiota diversity in milk samples collected from Holstein cows with different estimated breeding values for predicted feed efficiency, milk coagulation, resilience to mastitis, and consequently, to study its effects on milk quality. One hundred and twenty milk samples were collected in two seasons (summer and winter) from different commercial dairy farms in the Nord-east of Italy. For each trait, 20 animals divided into two groups of the high (10 cows) and the low (10 cows) were selected to study the microbiota profile using 16S rRNA metabarcoding sequencing. The alpha and beta diversity analysis revealed significant differences between the high and the low groups for feed efficiency and resilience to mastitis, while no significant difference was detected for milk coagulation. Moreover, remarkable differences among the taxa were detected between the two seasons, where the winter was more diverse than summer when applied the Chao1 index. Lastly, the linear discriminant analysis (LDA) effect size (LEfSe) indicated Aerococcus, Corynebacterium, Facklamia, and Psychrobacter taxa with more abundance in the high group of feed efficiency, whereas, in resilience to mastitis, only two genera of Mycoplana and Rhodococcus were more abundant in the low group. In addition, LEfSe analysis between the seasons showed significant differences in the abundance of Bacteroides, Lactobacillus, Corynebacterium, Escherichia, Citrobacter, Pantoea, Pseudomonas, and Stenotrophomonas. These findings indicate that the different genetic predisposition for feed efficiency and resilience to mastitis could affect the raw milk microbiota and, consequently, its quality. Moreover, we found more abundance of mastitis-associated bacteria in the milk of dairy cows with a higher feed efficiency index.

Balancing Trade-Offs in Milk Production by Making Use of Animal Individual Energy Balancing

Traditionally, the energy supply of dairy cows is based on the average performance of the herd. Because this contradicts the great variation in requirements between individual animals, the objective of the present study was to quantify both the extent and consequences of variation in the relevant sub-variables used to calculate the energy balance (EB) on an individual animal basis. Total energy supply (TES) and requirements (TER) of 28 multiparous German Holstein dairy cows fed TMR with 7.0 MJ NEL were studied between the 2nd and 15th week after calving. TES, mainly influenced by DMI, increased from 100.1 (week 2) to 152.1 MJ NEL/d (week 15; p < 0.01). Weekly coefficients of variation (CV) ranged between 0.10 and 0.16 and were similar to the CV of DMI (0.09 to 0.17). TER, as the sum of energy requirement for maintenance (body weight) and production (milk yield), decreased from 174.8 (week 2) to 164.5 MJ NEL/d (week 15; p < 0.01) and CV varied between 0.16 (week 2) and 0.07 (week 11). EB increased from −74.8 (week 2) to −12.4 MJ NEL/d (week 15; p < 0.01) and CV varied from 0.32 (week 3) to 1.01 (week 10). The results indicate that calculating EB on an individual animal basis is a prerequisite to identify animals with an increased risk of failing to cope with their energy situation, which cause failure costs that drain the profit of affected cows.

Association of Residual Feed Intake With Blood Metabolites and Reproduction in Holstein Cows

The objectives of this study were to evaluate the associations between residual dry matter (DM) intake or residual feed intake (RFI) from 1 to 15 weeks postpartum and concentrations of metabolites in plasma in early lactation and reproduction in Holstein cows. Data from 9 experiments, including 851 cows, were used. Intake of DM, milk yield, and body weight were evaluated daily, whereas milk composition and body condition were evaluated twice weekly for the first 105 days postpartum. Blood was sampled on the day of calving and again on days 7, 14, and 21 postpartum and analyzed for concentrations of non-esterified fatty acids (FA), β-hydroxybutyrate (BHB), and glucose. Reproduction was evaluated for the first 300 days postpartum. Residual DM intake was calculated as the observed minus the predicted intake, with intake predicted based on a model that accounted for major energy sinks. Cows were ranked and categorized into RFI quartiles, from the smallest (Q1) to the largest (Q4) RFI (−1.87, −0.46, 0.39, and 1.90 kg/day). Increasing efficiency (i.e., from Q4 to Q1) resulted in linear decreases in DM intake (Q1 to Q4; 18.9, 20.4, 21.3, and 22.7 kg/day), and median days open (132, 125, 135, and 147 d). Conversely, improving efficiency was associated with a linear increase in pregnancy per artificial insemination (AI, 31.4, 30.6, 31.2, and 24.5%) and quadratic increases in the 21-day cycle pregnancy rate (21.2, 21.1, 22.0, and 16.6%) and the proportion of pregnant cows (79.0, 80.7, 82.4, and 71.5%). The estimated net energy for lactation (NEL) content of diets increased linearly with improved RFI (1.88, 1.76, 1.71, and 1.58 Mcal/kg), resulting in no association between RFI and energy-corrected milk yield or body energy change. Nevertheless, increased feed efficiency was associated with a linear increase in concentrations of blood FA (0.68, 0.63, 0.60, and 0.59 mM), but a quadratic association with BHB (0.75, 0.64, 0.64, and 0.65 mM), with no association with glucose. Collectively, the most feed efficient cows ate 3.8 kg/day less DM, produced the same amount of energy-corrected milk, and had improved reproductive performance compared with the least efficient cows, thus suggesting that the underlying mechanisms responsible for improved feed efficiency might also be linked with improvements in reproduction.

A mathematical nutrition model adequately predicts beef and dairy cow intake and biological efficiency

The beef cow-calf sector accounts for 70% of feed consumed and greenhouse gases emitted for the beef industry, but there is no straightforward method to measure biological efficiency in grazing conditions. The objective of this study was to evaluate a mathematical nutrition model to estimate the feed intake and biological efficiency of mature beef cows. Data from dams (N = 160) and their second and third progeny (312 pairs) were collected from 1953 through 1980. Individual feed intake was measured at 28-d intervals year-round for dams and during 240-d lactation for progeny. Body weights of progeny were measured at 28-d intervals from birth to weaning, and of dams at parturition and weaning each production cycle. The milk yield of dams was measured at 14-d intervals. Dam metabolizable energy intake (DMEI) and milk energy yield (MEL) of each cow were predicted using the Cattle Value Discovery System beef cow (CVDSbc) model for each parity. Biological efficiency (Mcal/kg) was computed as the ratio of observed or predicted DMEI to observed calf weaning weight (PWW). Pearson correlation coefficients were computed using corr.test function and model evaluation was performed using the epiR function in R software. Average (SD) dam weight, PWW, DMEI, and observed MEL were 527 (86) kg, 291 (47) kg, 9584 (2701) Mcal/production cycle, and 1029 (529) Mcal, respectively. Observed and predicted DMEI (r = 0.93 and 0.91), and observed and predicted MEL (r = 0.58 and 0.59) were positively correlated for progeny 2 and 3, respectively. The CVDSbc model under-predicted DMEI (mean bias [MB] = 1,120 ± 76 Mcal, 11.7% of observed value) and MEL (MB = 30 ± 25 Mcal, 2.9% of observed value). Observed and predicted progeny feed intake were not correlated (r = 0.01, P-value = 0.79). Observed and predicted biological efficiency were positively correlated (r = 0.80 and 0.80, P-value ≤ 0.05) for parity 2 and 3, respectively, and the CVDSbc model under-predicted biological efficiency by 11% (MB = 3.59 ± 0.25 Mcal/kg). The CVDSbc provides reasonable predictions of feed intake and biological efficiency of mature beef cows, but further refinement of the relationship between calf feed intake and milk yield is recommended to improve predictions. Mathematical nutrition models can assist in the discovery of the biological efficiency of mature beef cows.

Inter- and Intra-Individual Variation in the Behavior of Feed Intake on Nutrient Availability in Early Lactating Dairy Cows

Simple Summary

The problem of nutrient and energy deficiency and the associated risk of disease in fresh dairy cows has been known for many years. Previous approaches to reducing the risk have been almost exclusively on a herd basis approach but have so far not been sufficiently effective. The present study revealed large variation between individual animals during the first weeks after calving, particularly with respect to dry matter intake (DMI). In addition to the large variation in intake behavior, feed intake and nutrient digestibility, interactions between parameters refute the traditional feeding regimes, based on the mean requirement values at herd level. Inter-individual variation indicates that each animal follows an individual strategy in optimizing DMI. Only if constant access to the feed bunk and balanced diets are made possible, all animals can follow their individual strategy, maximize individual DMI and come closer to the goal of an adequate supply. As there is only a low risk for excessive DMI during early lactation, feeding regimes should not be oriented towards the assumed average level of feed intake but towards the animals with a low level of dry matter intake. Otherwise, it will not be possible to improve the nutrient supply for all animals.

Abstract

Since energetic deficits in dairy cows can only be reduced at an animal level, the objective of the present study was to determine the extent of variation in intake behavior within and between animals during early lactation, to explore the magnitude of interactions between feed intake, intake behavior and nutrient digestibility, and to identify levers for maximizing feed intake at the individual animal level. Feeding behavior, intake and nutrient digestibility of 28 German Holstein dairy cows, fed TMR with 7.0 MJ NEL, were studied between the 2nd and 15th week after calving. Dry matter intake was assessed daily and nutrient digestibility weekly, with iNDF₂₄₀ as an intrinsic marker. Results showed high intra- and inter-individual variation in intake behavior parameters with coefficients of variation (CV) up to 0.58 in meal frequency. Nutrient digestibility varied only slightly with CV values up to 0.10 in crude protein. Milk yield, meal frequency, feeding time, feeding rate and meal size had significant positive effects on DMI (p < 0.01). To achieve long-term improvements in feed intake, it is important to optimize feed intake and feeding behavior of individual animals by improving feeding conditions and develop technical tools to identify animals with insufficient feed intake.

Replacement of barley with oats and dehulled oats: Effects on milk production, enteric methane emissions, and energy utilization in dairy cows fed a grass silage-based diet

Sixteen Nordic Red dairy cows, at 80 ± 4.6 d in milk and with an average body weight of 624 ± 91.8 kg, were used in a replicated 4 × 4 Latin square design to investigate the effects of different concentrate supplements on milk production, enteric CH₄ emissions, ruminal fermentation, digestibility, and energy utilization. The cows were blocked into 4 groups based on parity and milk yield and randomly assigned to 1 of 4 experimental concentrates: (1) barley, (2) hulled oats, (3) an oat mixture consisting of hulled and dehulled oats, 50:50 on dry matter basis, and (4) dehulled oats; canola meal was a protein supplement in all 4 concentrates. The cows were fed grass silage and experimental concentrate (forage-to-concentrate ratio 60:40 on dry matter basis) ad libitum. To compare the effects of barley and oats, the barley diet was compared with the overall mean of the hulled oat, oat mixture, and dehulled oat diets. To investigate the effects of gradual replacement of hulled oats with dehulled oats, linear and quadratic contrasts were specified. Milk and energy-corrected milk (ECM) yield were higher on the oat diets compared with the barley diet but were not affected by the type of oats. Concentrations of milk constituents were not affected by grain species or type of oats, except for protein concentration, which was lower on the oat diets than on the barley diet. Feeding the oat diets led to higher milk protein yield and higher milk urea N concentrations. Feed efficiency tended to be higher on the oat diets, and linearly increased with increased inclusion of dehulled oats. Methane emissions (g/d) and CH₄ yield (g/kg of dry matter intake) were unaffected by grain species but increased linearly with increasing inclusion of dehulled oats in the diet. Because of higher ECM yield, CH₄ intensity (g/kg of ECM) was on average 5.7% lower from cows on the oat diets than on the barley diet. Ruminal fermentation was not affected by dietary treatment. Total-tract apparent digestibility of organic matter, crude protein, and neutral detergent fiber was unaffected by grain species but linearly increased with increasing inclusion of dehulled oats. Gross energy content was higher on the oat diets and linearly increased with increasing inclusion of dehulled oats. Feeding the oat diets led to a lower ratio of CH₄ energy to gross energy intake, greater milk energy and heat production but no change in energy balance. Gradual replacement of hulled oats with dehulled oats linearly increased gross energy digestibility, CH₄ energy, metabolizable energy intake, heat production, and energy balance. We observed no effect of dietary treatment on efficiency of metabolizable energy use for lactation. In conclusion, replacing barley with any type of oats increased milk and ECM yield, which led to a 5.7% decrease in CH₄ intensity. In addition, dehulling of oats before feeding is unnecessary because it did not significantly improve production performance of dairy cows in positive energy balance.

Herd-level versus animal-level variation in methane emission prediction in grazing dairy cattle

In response to the increased concern over agriculture's contribution to greenhouse gas (GHG) emissions, more detailed assessments of current methane emissions and their variation, within and across individual dairy farms and cattle, are of interest for research and policy development. This assessment will provide insights into possible changes needed to reduce GHG emissions, the nature and direction of these changes, ways to influence farmer behavior and areas to maximize the adoption of emerging mitigation technologies. The objectives of this study were to (1) quantify the variation in enteric fermentation methane emissions within and among seasonal calving dairy farms with the majority of nutritional requirements met through grazed pasture; (2) use this variation to assess the potential of new individual animal emission monitoring technologies and their impact on mitigation policy. We used a large database of cow performance records for milk production and survival from 2 398 herds in New Zealand, and simulation to account for unobserved variation in feed efficiency and methane emissions per unit of feed. Results showed an average of 120 ± 31.4 kg predicted methane (CH₄) per cow per year after accounting for replacement costs, ranging 8.9-323 kg CH₄/cow per year. Whereas milk production, survival and predicted live weight were reasonably effective at predicting both individual and herd average levels of per cow feed intake, substantial within animal variation in emissions per unit of feed reduced the ability of these variables to predict variation in per animal methane output. Animal-level measurement technologies predicting only feed intake but not emissions per unit of feed are unlikely to be effective for advancing national policy goals of reducing dairy farming enteric methane output. This is because farmers seek to profitably utilize all farm feed resources available, so improvements in feed efficiency will not result in the reduction in feed utilization required to reduce methane emissions. At a herd level, average per cow milk production and live weight could form the basis of assigning a farm-level point of obligation for methane emissions. In conclusion, a comprehensive national database infrastructure that was tightly linked to animal identification and movement systems, and captured live weight data from existing farm-level recording systems, would be required to make this effective. Additional policy and incentivization mechanisms would still be required to encourage farmer uptake of mitigation interventions, such as novel feed supplements or vaccines that reduce methane emissions per unit of feed.

The effects of gradual replacement of barley with oats on enteric methane emissions, rumen fermentation, milk production, and energy utilization in dairy cows

This study evaluated the effects of gradual replacement of barley with oats on enteric CH₄ emissions, rumen fermentation, diet digestibility, milk production, and energy utilization in dairy cows fed a grass silage-based diet. Sixteen lactating Nordic Red dairy cows received a total mixed ration [58:42 forage:concentrate on dry matter (DM) basis]. Grass silage (Phleum pratense) was the sole forage with canola meal (10% of diet DM) as a protein supplement. The effects of gradual replacement of barley with oats on DM basis were evaluated using a replicated 4 × 4 Latin square design with 21 d periods. The grain supplements (30% of diet DM) consisted of 100% barley, 67% barley and 33% oats, 33% barley and 67% oats, and 100% oats. In addition to intake, milk production, and digestibility measurements, CH₄ emissions were measured by the GreenFeed system (C-Lock Inc.). The energy metabolism was estimated from the gas exchange measurements recorded by the GreenFeed unit. The last 10 d of each period were used for recordings of gas exchanges, feed intake and milk production. Dry matter intake, body weight, milk yield, and energy-corrected milk yield were not affected by gradual replacement of barley with oats in the diet. Increased inclusion of oats linearly decreased CH₄ emissions from 467 to 445 g/d, and CH₄ intensity from 14.7 to 14.0 g/kg energy-corrected milk. In addition, the ratio of CH₄ to CO₂ decreased with increasing inclusion of oats in the diet. Digestibility of organic matter, neutral detergent fiber, and potentially digestible neutral detergent fiber decreased linearly with increasing inclusion of oats. Increased inclusion of oats linearly increased fecal energy from 121 to 133 MJ/d, whereas urinary energy and heat production were not affected by dietary treatment. This resulted in a linear decrease in metabolizable energy intake. However, increased levels of oat in the diet did not significantly affect energy balance or efficiency of metabolizable energy utilization for lactation. This study concludes that barley could be replaced with oats in the diet of dairy cows fed a grass silage-based diet to mitigate CH₄ emissions without having any adverse effects on productivity or energy balance. However, the effect of replacing barley with oats on CH₄ emissions is dependent on the differences between barley and oats in the concentrations of indigestible neutral detergent fiber and fat.

The use of an upgraded GreenFeed system and milk fatty acids to estimate energy balance in early-lactation cows

Measurements of energy balance (EB) require the use of respiration chambers, which are quite expensive and laborious. The GreenFeed (GF) system (C-Lock Inc.) has been developed to offer a less expensive, user friendly alternative. In this study, we used the GF system to estimate the EB of cows in early lactation and compared it with EB predicted from energy requirements for dairy cows in the Finnish feeding standards. We also evaluated the association between milk fatty acids and the GF estimated EB. The cows were fed the same grass silage but supplemented with either cereal grain or fibrous by-product concentrate. Cows were followed from 1 to 18 wk of lactation, and measurements of energy metabolism variables were taken. Data were subjected to ANOVA using the mixed model procedure of SAS (SAS Institute Inc.). The repeatability estimates of the gaseous exchanges from the GF were moderate to high, presenting an opportunity to use it for indirect calorimetry in EB estimates. Energy metabolism variables were not different between cows fed different concentrates. However, cows fed the grain concentrate produced more methane (24.0 MJ/d or 62.9 kJ/MJ of gross energy) from increased digestibility than cows fed the by-product concentrate (21.3 MJ/d or 56.5 kJ/MJ of gross energy). Nitrogen metabolism was also not different between the diets. Milk long-chain fatty acids displayed an inverse time course with EB and de novo fatty acids. There was good concordance (0.85) between EB predicted using energy requirements derived from the Finnish feed table and EB estimated by the GF system. In conclusion, the GF can accurately estimate EB in early-lactating dairy cows. However, more data are needed to further validate the system for a wide range of dietary conditions.

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.

Assessing feed efficiency in early and mid lactation and its associations with performance and health in Holstein cows

Objectives were to evaluate the associations between residual dry matter (DM) intake (RFI) and residual N intake (RNI) in early lactation, from 1 to 5 wk postpartum, and in mid lactation, from 9 to 15 wk postpartum, and assess production performance and risk of diseases in cows according to RFI in mid lactation. Data from 4 experiments including 399 Holsteins cows were used in this study. Intakes of DM and N, yields of milk components, body weight, and body condition were evaluated daily or weekly for the first 105 d postpartum. Milk yield by 305 d postpartum was also measured. Incidence of disease was evaluated for the first 90 d postpartum and survival up to 300 d postpartum. Residual DM and N intake were calculated in early and mid lactation as the observed minus the predicted values, which were based on linear models that accounted for major energy or N sinks, including daily milk energy or N output, metabolic body weight, and daily body energy or N changes, and adjusting for parity, season of calving, and treatment within experiment. Cows were ranked by RFI and RNI in mid lactation and categorized into quartiles (Q1 = smallest RFI, to Q4 = largest RFI). Increasing efficiency in mid lactation resulted in linear decreases in RFI (depicted from Q1 to Q4; -0.93, -0.05, -0.04, and 0.98 kg/d), DMI (16.0, 16.9, 17.3, and 18.4 kg/d), net energy for lactation (NE_L) intake (26.8, 28.4, 29.0, and 30.8 Mcal/d), and NE_L balance (-9.0, -8.1, -8.2, and -5.5 Mcal/d) during early lactation, but no differences were observed in body NE_L or N changes or yield of energy-corrected milk in the first 5 wk of lactation. Residual DM intake in mid lactation was associated with RFI (Pearson r = 0.43, and Spearman ρ = 0.32) and RNI (r = 0.44, ρ = 0.36) in early lactation, and with RNI in mid lactation (r = 0.91, ρ = 0.84). Similarly, RNI in mid lactation was associated with RNI in early lactation (r = 0.42, ρ = 0.35). During the first 15 wk postpartum, more efficient cows in mid lactation consumed 3.5 kg/d less DM (Q1 = 19.3 vs. Q4 = 22.8 kg/d) and were more N efficient (Q1 = 31.6 vs. Q4 = 25.8%), at the same time that yields of milk (Q1 = 39.0 vs. Q4 = 39.4 kg/d), energy-corrected milk (Q1 = 38.6 vs. Q4 = 39.3 kg/d), and milk components did not differ compared with the quartile of least efficient cows. Furthermore, RFI in mid lactation was not associated with 305-d milk yield, incidence of diseases in the first 90 d postpartum, or survival by 300 d postpartum. Collectively, rankings of RFI and RNI are associated and repeatable across lactation stages. The most feed-efficient cows were also more N efficient in early and mid lactation. Phenotypic selection of RFI based on measurements in mid lactation is associated with improved efficiency without affecting production or health in dairy cows.

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.