Residual feed intake and gain alters the performance and carcass traits in Nellore cattle.

The residual intake and gain (RIG) aims to select animals that present low feed intake in relation to the expected. This study aimed to evaluate the associations of selection for RIG with performance and carcass traits in Nellore cattle. Initially, residual feed intake (RFI) and residual gain (RG) were determined. From this, the RIG was calculated, and the animals were classified as efficient and inefficient for RIG. The efficient animals for RIG showed higher daily weight gain (DWG), Longissimus muscle area (LMA), and mid-test metabolic body weight (MMBW) than the inefficient ones. No significant correlations were found between subcutaneous fat thickness (SFT), marbling (MAR), LMA, MMBW, and the RIG. Thus, it's concluded that the RIG is a measure that can be used to identify and select animals with higher rates of DWG and LMA without changes in the dry matter intake (DMI), SFT, and MAR. However, this index should still be observed with caution, as it may be dependent on body size. Based on the findings, the selection of animals according to the RIG can be an important factor to generate phenotypic evolution in characteristics such as weight gain and rib eye area without adverse effects on the carcass fat deposition.

Dynamic Changes in the Nutrient Digestibility, Rumen Fermentation, Serum Parameters of Perinatal Ewes and Their Relationship with Rumen Microbiota.

Changes in physiological and biochemical parameters are crucial for the reproductive performance and health of perinatal ewes. This study investigated the temporal variations in feed intake, nutrient digestibility, serum parameters, and ruminal fermentation on days 21, 14, and 7 before lambing (Q21, Q14, and Q7) and days 3, 7, and 14 after lambing (H3, H7, and H14). The results showed that dry matter intake (DMI) and glucose (Glu) gradually decreased (p < 0.05) before lambing and increased (p < 0.05) after lambing. The digestibility of dry matter (DMD), crude protein (CPD), and acid detergent fiber (ADFD) increased (p < 0.05) before lambing, then decreased (p < 0.05) on day H3, and then increased (p < 0.05) on day H14. The rumen pH, NH3-N, and triglycerides (TG) gradually increased (p < 0.05) before lambing and were higher (p < 0.05) on day Q7 than after lambing. The concentrations of acetate, butyrate, and total volatile fatty acids (T-VFA) were lower (p < 0.05) on day Q7 than those on days Q21 and Q14, then increased (p < 0.05) after lambing. Total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) concentrations gradually decreased (p < 0.05) in perinatal ewes. BHBA and NEFA concentrations were lower (p < 0.05) on day Q21 than those from days Q14 to H14. The rumen microbiota compositions were different (p < 0.05) in perinatal ewes, and g_Anaerovibrio, g_Lachnobacterium, and g_Schwartzia were positively correlated (p < 0.05) with DMI, Glu, acetate, propionate, and T-VFA, and negatively correlated (p < 0.05) with LDL-C. g_Bacillus was negatively correlated (p < 0.05) with DMI, Glu, acetate, propionate, butyrate, and T-VFA, but positively correlated (p < 0.05) with rumen pH and LDL-C. In summary, the DMI, nutrient digestibility, rumen fermentation, and serum parameters changed during the perinatal period of ewes, and the changes in DMI, serum glucose, acetate, propionate, and T-VFA were related to the rumen bacteria.

Forage yields and nutritive values of oat and triticale pastures for grazing sheep in early spring.

Background: Small-grain winter cereals can be utilized as early spring pastures in temperate climates to relieve grazing pressure and potentially mitigate feed shortages. This study was conducted to determine the effects of triticale and oat cereal pastures grazed by sheep during early spring on forage yields, nutritive values, and nutritional requirements of sheep. Methods: The research was carried out over three consecutive years, from 2015 to 2017, at the Sheep Research Institute in Bandirma-Balikesir, located in the Marmara region of Türkiye. The treatments were arranged in a completely randomized block design, with the two forage species, triticale and oat, randomized within each of three blocks. The animal material for the study consisted of 24 Karacabey Merino sheep, each 2 years old, with an average live weight of 57.6 ± 0.5 kg, all in the late lactation stage. In each replication, four sheep were included, resulting in a total of 12 sheep grazing in each of the triticale and oat pastures. The sheep grazed exclusively on the cereal pastures without any additional feed, and had unrestricted access to water throughout the entire period of the experiment. The dry matter yields (DMY), dry matter intakes (DMI), nutritive values, and mineral contents of the cereal species were determined. Results: The DMY showed significant differences over the years (P < 0.05). No differences in DMY were observed between pastures, with oats yielding 11.99 t ha-1 and triticale yielding 11.08 t ha-1. During the grazing period, the change in DMY was significant in all years (P < 0.05). The average DMI of the sheep was 2,003.5 g d-1 for triticale and 2,156.6 g d-1 for oat, respectively, and DMI exhibited no significant differences across pastures. Although there was no difference in DMI between 2015 and 2016, the lowest consumption occurred in 2017 (P < 0.05). Additionally, while DMI showed different trends each year based on the periods, it generally decreased by the end of the grazing period. While both pastures provided similar nutritive values, significant differences were observed in the crude protein (CP), acid detergent fiber (ADF), in vitro true DM digestibility (DDM), and metabolisable energy (ME) values across the years. Over the years, as the grazing period progressed, CP levels decreased while neutral detergent fiber (NDF), ADF, and acid detergent lignin (ADL) increased, resulting in reduced DDM and ME values. The phosphorus (P) content in triticale was higher than in oats, but there were no differences in the content of other minerals between them. Between the years, significant differences were observed in the levels of phosphorus (P) and iron (Fe), while changes in other elements were insignificant. The variation in mineral content during the grazing process differed over the three years. Study results indicated that the nutritional values of triticale and oat pastures are similar, and both can effectively be used to provide sufficient feed to meet the early spring forage requirements for sheep.

What the hay: predicting equine voluntary forage intake using a meta-analysis approach.

To properly formulate diets, the ability to accurately estimate feed intake is critical as the amount of feed consumed will influence the amount of nutrients delivered to the animal. Inaccurate intake estimates may lead to under- or over-feeding of nutrients to the animal. Individual differences in equine forage intake are well-known, but predictive equations based on animal and nutritional factors are not comprehensive. The objective of the present study was to consolidate the current body of knowledge in the published literature on voluntary forage DM intake (VFDMI) in equines and conduct a meta-analysis to identify driving factors, sources of heterogeneity, and develop predictive equations. Therefore, a systematic literature search was applied and identified 61 publications which met the inclusion criteria. From each study, the outcomes of interest (e.g., forage intake), diet composition (e.g., forage information, nutrient composition), and animal factors (e.g., sex, age, breed, BW, exercise level) were extracted. Forage intake was analyzed as two different outcome variables: (1) VFDMI in kg/d and (2) VFDMI in g/kg BW. Linear mixed model analysis treating study as a random effect was applied, using a backward-stepping approach to identifying potential driving variables for VFDMI (both units) where all terms have P < 0.1. The best fitting models for VFDMI included similar factors (also across kg/d and g/kg BW) such as forage quality (i.e., neutral detergent fiber or CP content), forage type (i.e., grass, legume, or mixed), the animals' size category (i.e., horses vs ponies), and some management factors (i.e., pasture access). As anticipated, forage intake increased when higher quality forages were fed (i.e., lower neutral detergent fiber or higher CP), potentially due to improved digestibility. Additionally, VFDMI increased as BW increased but ponies increased their VFDMI more per every kg increase in BW compared to horses. Lastly, pasture access (i.e., grazing) may influence VFDMI such that pastured animals consume less than stalled animals, possibly due to the time it takes to graze forage. In conclusion, equations to predict equine VFDMI with high accuracy and precision (concordance correlation coefficient  = 0.82 - 0.95; root mean squared error RMSE = 0.82-5.49) were developed which could be applied in practice by equine nutritionists or owners and managers. The results of this meta-analysis confirm that animal traits and forage quality have a significant impact on the VFDMI of equines and should be accounted for when formulating diets to ensure nutritional requirements are met.

Validation of the Residual Feed Intake Model in Brangus Heifers: Determination of the Optimal Days on Feed Interval to Estimate Dry Matter Intake and Average Daily Gain.

Brangus cattle are gaining popularity in the Southeast U.S. due to the desirable heat tolerance from their Brahman influence combined with the superior carcass merit aspects of Angus genetics. However, little is known about the optimal evaluation conditions for this hybrid breed when placed on test for Residual Feed Intake (RFI), a heritable measure of feed efficiency that allows improvement in performance without altering carcass traits. To address this, dry matter intake (DMI) was measured on Brangus heifers for 70-d to determine the optimal days on feed required to estimate feed intake and ADG and assess if inclusion of ultrasound measures of carcass merit into the model impact RFI rankings for this breed. The 56-d test period had a regression coefficient of 0.96 (p < 0.0001), R2 = 0.94, rp = 0.97 (p < 0.0001), and rs = 0.97 (p < 0.0001), indicating little change in rank of cattle for DMI compared to a 70-d test. ADG was the limiting factor in determining test duration. Based upon examining only heifers that calved, ultrasound backfat measures should be included in the RFI model to normalize for differences in heifer maturity. Results from this study indicate that a test duration of 56-d is sufficient to accurately estimate DMI in this population. This data indicates on-test duration can be shortened, enhancing the rate of genetic change by reducing cost and increasing the number of animals that can be tested annually.

The effect of replacing conventional alfalfa hay with lower-lignin alfalfa hay on feed intake, nutrient digestibility, and energy utilization in lactating Jersey cows.

Lower-lignin (LoL) varieties of alfalfa (Medicago sativa L.) have been developed in recent years, and have the potential to positively impact animal performance. The objective of this study was to evaluate the effects of increasing the proportion of LoL alfalfa hay in diets fed to lactating dairy cows. Research plots were planted with a conventional variety (CON; Dairyland Hybriforce 3400), and 2 LoL varieties (LLG; 54HVX42 and LLB; Aflorex HiGest 460). After harvest, the LoL varieties were blended in equal proportions for feeding. Twelve multiparous Jersey cows (100 ± 4 d in milk) were used in a 3 × 3 Latin square with 3 periods of 28 d. Cows were assigned to 3 diets containing 0 (CNTRL), 16.1 (MdLL), and 32.2% (HiLL) of the diet DM as LoL alfalfa hay, which replaced CON. The CON alfalfa had average CP, NDF, and lignin contents (DM basis) of 20.5 ± 1.15, 42.1 ± 1.37, and 6.81 ± 0.57%, respectively, while the LoL alfalfa averaged 19.8 ± 0.75, 39.9 ± 1.56, and 6.07 ± 0.28%, respectively. No difference was observed in DMI (20.4 ± 0.61 kg/d). No difference in milk yield was observed, averaging 31.0 ± 1.02 kg/d across treatments. Similarly, no difference was observed in ECM yield (averaging 36.2 ± 1.43 kg/d). Feed conversion (ECM/DMI) tended to increase linearly with LoL alfalfa inclusion (1.74 to 1.80 ± 0.03). No difference was observed for milk fat yield and content (1.39 ± 0.075 kg/d and 4.51 ± 0.219%) or milk protein yield and content (1.06 ± 0.041 kg/d and 3.43 ± 0.096%). Total methane production quadratically decreased from CNTRL to MdLL then increased to HiLL (441, 389, 412 ± 18.2 L/d, respectively). No differences were observed on total-tract digestibility of DM (averaging 67.2 ± 0.55%) and NDF (averaging 50.9 ± 1.56%). No difference was observed in the concentration of DE, ME or NEL was observed averaging 2.82 ± 0.021, 2.51 ± 0.027, and 1.72 ± 0.030 Mcal/kg respectively. Our results suggest that replacing CON alfalfa with LoL alfalfa has no effects on milk production, milk composition, or nutrient digestibility but may improve feed efficiency.

Intake profile, milk production, and energy balance of early-lactation spring-calving Holstein Friesian and Jersey × Holstein Friesian dairy cows in high-utilization pasture-based systems.

Early lactation is a critical period for dairy cows, as energy requirements rapidly increase with the onset of lactation; however, early-lactation DMI in pasture-based systems are under measured. The objectives of this study were (1) to measure and profile total DMI (TDMI) and animal performance of dairy cows during early lactation in a pasture-based system, (2) to investigate early-lactation energy balance in pasture-based systems, and (3) to examine production efficiencies, including TDMI and milk solids production per 100 kg of BW. Eighty spring-calving dairy cows were allocated to a grazing group as they calved over a 2-yr period (2021 and 2022). Cows were offered a daily herbage allowance to achieve a postgrazing sward height of 4 cm, with silage supplementation when necessary due to inclement weather. Total DMI was measured using the n-alkane technique over a 12-wk period from February 1, 2021, to April 23, 2022. Total DMI and daily milk yield were significantly affected by parity with both variables being greatest for third-parity animals (17.7 kg of DM and 26.3 kg/cow per day, respectively), lowest for first parity (13.2 kg of DM and 19.6 kg/cow per day, respectively) and intermediate for second-parity animals (16.8 kg of DM and 24.1 kg/cow per day, respectively). Peak TDMI was reached on wk 10 for first-parity animals (14.6 kg of DM), wk 11 for second parity animals (19.3 kg of DM) and wk 12 for third-parity animals (19.9 kg of DM). Parity also had a significant effect on unité fouragère lait (UFL; feed units for milk) feed balance as first-parity animals experienced a greater degree of negative energy balance (-3.2 UFL) compared with second- and third-parity animals (-2.3 UFL). Breed and parity had an effect on production efficiencies during the first 12 wk of lactation as Jersey × Holstein Friesian cows had greater TDMI/100 kg of BW and milk solids/100 kg of BW compared with Holstein Friesian cows.

Assessing different cross-validation schemes for predicting novel traits using sensor data: An application to dry matter intake and residual feed intake using milk spectral data.

Feed efficiency is important for economic profitability of dairy farms; however, recording daily DMI is expensive. Our objective was to investigate the potential use of milk mid-infrared (MIR) spectral data to predict proxy phenotypes for DMI based on different cross-validation schemes. We were specifically interested in comparisons between a model that included only MIR data (model M1); a model that incorporated different energy sink predictors, such as body weight, body weight change, and milk energy (model M2); and an extended model that incorporated both energy sinks and MIR data (model M3). Models M2 and M3 also included various cow-level variables (stage of lactation, age at calving, parity) such that any improvement in model performance from M2 to M3, whether through a smaller root mean squared error (RMSE) or a greater squared predictive correlation (R2), could indicate a potential benefit of MIR to predict residual feed intake. The data used in our study originated from a multi-institutional project on the genetics of feed efficiency in US Holsteins. Analyses were conducted on 2 different trait definitions based on different period lengths: averaged across weeks versus averaged across 28 d. Specifically, there were 19,942 weekly records on 1,812 cows across 46 experiments or cohorts and 3,724 28-d records on 1,700 cows across 43 different cohorts. The cross-validation analyses involved 3 different k-fold schemes. First, a 10-fold cow-independent cross-validation was conducted whereby all records from any one cow were kept together in either training or test sets. Similarly, a 10-fold experiment-independent cross-validation kept entire experiments together, whereas a 4-fold herd-independent cross-validation kept entire herds together in either training or test sets. Based on cow-independent cross-validation for both weekly and 28-d DMI, adding MIR predictors to energy sinks (model M3 vs. M2) significantly (P < 10-10) reduced average RMSE to 1.59 kg and increased average R2 to 0.89. However, adding MIR to energy sinks (M3) to predict DMI either within an experiment-independent or herd-independent cross-validation scheme seemed to demonstrate no merit (P > 0.05) compared with an energy sink model (M2) for either R2 or RMSE (respectively, 0.68 and 2.55 kg for M2 in herd-independent scheme). We further noted that with broader cross-validation schemes (i.e., from cow-independent to experiment-independent to herd-independent schemes), the mean and slope bias increased. Given that proxy DMI phenotypes for cows would need to be almost entirely generated in herds having no DMI or training data of their own, herd-independent cross-validation assessments of predictive performance should be emphasized. Hence, more research on predictive algorithms suitable for broader cross-validation schemes and a more earnest effort on calibration of spectrophotometers against each other should be considered.

Investigating relationships between the host genome, rumen microbiome, and dairy cow feed efficiency using mediation analysis with structural equation modeling.

The rumen microbiome is crucial for converting feed into absorbable nutrients used for milk synthesis, and the efficiency of this process directly affects the profitability and sustainability of the dairy industry. Recent studies have found that the rumen microbial composition explains part of the variation in feed efficiency traits, including dry matter intake, milk energy, and residual feed intake. The main goal of this study was to reveal relationships between the host genome, the rumen microbiome, and dairy cow feed efficiency using structural equation models. Our specific objectives were to (1) infer the mediation effects of the rumen microbiome on feed efficiency traits, (2) estimate the direct and total heritability of feed efficiency traits, and (3) calculate the direct and total breeding values of feed efficiency traits. Data consisted of dry matter intake, milk energy, and residual feed intake records, SNP genotype data, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows from 2 research farms. We implemented structural equation models such that the host genome directly affects the phenotype (GP → P) and the rumen microbiome (GM → P), and the microbiome affects the phenotype (M → P), partially mediating the effect of the host genome on the phenotype (G → M → P). We found that 7% to 30% of microbes within the rumen microbial community had structural coefficients different from zero. We classified these microbes into 3 groups that could have different uses in dairy farming. Microbes with heritability <0.10 but significant causal effects on feed efficiency are attractive for external interventions. On the other hand, 2 groups of microbes with heritability ≥0.10, significant causal effects, and genetic covariances and causal effects with the same or opposite sign to feed efficiency are attractive for selective breeding, improving or decreasing the trait heritability and response to selection, respectively. In general, the inclusion of the different microbes in genomic models tends to decrease the trait heritability rather than increase it, ranging from -15% to +5% depending on the microbial group and phenotypic trait. Our findings provide more understanding to target rumen microbes that can be manipulated, either through selection or management interventions, in order to improve feed efficiency traits.

Feed intake in housed dairy cows: validation of a three-dimensional camera-based feed intake measurement system.

Measuring feed intake accurately is crucial to determine feed efficiency and for genetic selection. A system using three-dimensional (3D) cameras and deep learning algorithms can measure the volume of feed intake in dairy cows, but for now, the system has not been validated for feed intake expressed as weight of feed. The aim of this study was to validate the weight of feed intake predicted from the 3D cameras with the actual measured weight. It was hypothesised that diet-specific coefficients are necessary for predicting changes in weight, that the relationship between weight and volume is curvilinear throughout the day, and that manually pushing the feed affects this relationship. Twenty-four lactating Danish Holstein cows were used in a cross-over design with four dietary treatments, 2 × 2 factorial arranged with either grass-clover silage or maize silage as silage factor, and barley or dried beet pulp as concentrate factor. Cows were adapted to the diets for 11 d, and for 3 d to tie-stall housing before camera measurements. Six cameras were used for recording, each mounted over an individual feeding platform equipped with a weight scale. When building the predictive models, four cameras were used for training, and the remaining two for testing the prediction of the models. The most accurate predictions were found for the average feed intake over a period when using the starting density of the feed pile, which resulted in the lowest errors, 6% when expressed as RMSE and 5% expressed as mean absolute error. A model including curvilinear effects of feed volume and the impact of manual feed pushing was used on a dataset including daily time points. When cross-validating, the inclusion of a curvilinear effect and a feed push effect did not improve the accuracy of the model for neither the feed pile nor the feed removed by the cow between consecutive time points. In conclusion, measuring daily feed intake from this 3D camera system in the present experimental setup could be accomplished with an acceptable error (below 8%), but the system should be improved for individual meal intake measurements if these measures were to be implemented.

Methane output across life stages in sheep, how it differs from lambs to adult ewes using portable accumulation chambers.

Methane (CH4) produced from enteric fermentation is a potent greenhouse gas produced by ruminant animals. Multiple measurements are required across life stages to develop an understanding of how CH4 output changes throughout the animal's lifetime. The objectives of the current study were to estimate CH4 output across life stages in sheep and to investigate the relationship between CH4 output and dry matter (DM) intake (DMI). Data were generated on a total of 266 female Suffolk and Texel animals. Methane and carbon dioxide (CO2) output, estimated using portable accumulation chambers, and DMI, estimated using the n-alkane technique outdoors and using individual penning indoors, were quantified across the animal's life stage; as lambs (<12 mo), nulliparous hoggets (12 to 24 mo) and ewes (primiparous or greater; > 24 mo). Ewes were further classified as pregnant, lactating, and dry (non-pregnant and non-lactating). Multiple measurements were taken within and across the life stages of the same animals. A linear mixed model was used to determine if CH4 and CO2 output differed across life stages and using a separate linear mixed model the factors associated with CH4 output within each life stage were also investigated. Methane, CO2 output, and DMI differed by life stage (P < 0.05), with lactating ewes producing the greatest amount of CH4 (25.99 g CH4/d) and CO2 (1711.6 g CO2/d), while also having the highest DMI (2.18 kg DM/d). Methane output differed by live-weight of the animals across all life stages (P < 0.001). As ewe body condition score increased CH4 output declined (P < 0.05). Correlations between CH4 output measured across life stages ranged from 0.26 (SE 0.08; lambs and lactating ewes) to 0.59 (SE 0.06; hoggets and pregnant ewes), while correlations between CO2 output measured across life stages ranged from 0.12 (SE 0.06; lambs and hoggets) to 0.65 (SE 0.06; hoggets and lactating ewes). DMI was moderately correlated with CH4 (0.44; SE 0.04) and CO2 output (0.59; SE 0.03). Results from this study provide estimates of CH4 output across life stages in a pasture-based sheep production system and offer valuable information for the national inventory and the marginal abatement cost curve on the optimum time to target mitigation strategies.

Obtaining accurate estimates of methane (CH4) output across life stages is important to assess how CH4 output changes throughout the production cycle in pasture-based sheep production systems. This study investigated the factors associated with CH4 output at each life stage (lambs, hoggets, pregnant, lactating, and dry ewes), the relationship between CH4 output measured across life stages and the relationship between CH4 output and dry matter intake (DMI) in an Irish lowland sheep production system. Methane and carbon dioxide (CO2) output and DMI were measured on 266 purebred Suffolk and Texel females across their lifetime. Lactating ewes produced the highest CH4 and CO2 output, along with having the highest DMI. Across all life stages, CH4 output increased with increasing live weight while CH4 output decreased as body condition score increased. Weak to moderate relationships were found between CH4 output measured across life stages, with the strength of the relationship decreasing as the time between life stages increased. A positive relationship was found between DMI and CH4 output. Results from this study lead to the development of a profile of CH4 output across the production cycle of a pasture-based sheep system.

Predicting feed intake in confined beef cows.

Six existing equations (three for nonlactating and three for lactating; NRC, 1987, Predicting feed intake of food-producing animals. Washington, DC: The National Academies Press, National Academy of Science; doi: 10.17226/950; NRC, 1996, Nutrient requirements of beef cattle, 7th Revised Edition: Update 1996. Washington, DC: The National Academies Press; doi: 10.17226/9791; Hibberd and Thrift, 1992. Supplementation of forage-based diets. J. Anim. Sci. 70:181. [Abstr]) were evaluated for predicting feed intake in beef cows. Each of the previously published equations are sensitive to cow-shrunk BW and feed energy concentration. Adjustments in feed intake prediction are provided for level of milk yield in NRC (1987. Predicting feed intake of food-producing animals. Washington, DC: The National Academies Press, National Academy of Science; doi: 10.17226/950) and NRC (1996 Nutrient requirements of beef cattle, 7th Revised Edition: Update 1996. Washington, DC: The National Academies Press; doi: 10.17226/9791) equations. The equation published in 1996 used data generated between 1979 and 1993. Our objectives were to validate the accuracy of the published equations using more recent data and to propose alternative prediction models. Criteria for inclusion in the evaluation dataset included projects conducted or published since 2002, direct measurement of feed intake, adequate protein supply, and pen feeding (no metabolism crate data). After removing outliers, the dataset included 53 treatment means for nonlactating cows and 32 treatment means for lactating cows. Means for the nonlactating dataset were dry matter intake (DMI) = 13.2 ±â€…2.9 kg/d, shrunk body weight (SBW) = 578 ±â€…83.9 kg, body condition score = 5.7 ±â€…0.73, and Mcal net energy for maintenance (NEm)/kg of feed = 1.27 ±â€…0.15 Mcal/kg. Means for the lactating dataset were DMI = 14.6 ±â€…2.24 kg/d, SBW = 503 ±â€…73.4 kg, body condition score = 4.7 ±â€…0.58, and Mcal NEm/kg feed = 1.22 ±â€…0.16. Simple linear regression was used to determine slope, intercept, and bias when observed DMI (y) was regressed against predicted DMI (x). The NRC (1996. Nutrient requirements of beef cattle, 7th Revised Edition: Update 1996. Washington, DC: The National Academies Press; doi: 10.17226/9791) nonlactating equation underestimated feed intake in diets moderate to high in energy density with intercept differing from 0 and slope differing from one (P ≤ 0.01). Average deviation from observed values was 2.4 kg/d. Similarly, when the NRC (1996. Nutrient requirements of beef cattle, 7th Revised Edition: Update 1996. Washington, DC: The National Academies Press; doi: 10.17226/9791) equation was used to predict DMI in lactating cows, the slope differed from one (P < 0.01) with average deviation from observed values of 3.0 kg/d. New models were developed by pooling the two datasets and including a categorical variable for stage of production (0 = nonlactating and 1 = lactating). Continuous variables included study-average SBW0.75 and diet NEm, Mcal/kg. The best-fit empirical model accounted for 68% of the variation in daily feed intake with standard error of the estimate Sy root mean squared error = 1.31. The proposed equation needs to be validated with independent data.

Effect of monensin and live-cell yeast supplementation on lactation performance, feeding behavior, and total-tract nutrient digestibility in dairy cows.

The objective of this study was to evaluate the effects of supplementing monensin (19.8 g/Mg DM TMR; MON) and Saccharomyces cerevisiae CNCM I-1077 live-cell yeasts (Saccharomyces cerevisiae CNCM I-1077; 1 × 1010 cfu/head per day; LCY) on lactation performance, feeding behavior, and total-tract nutrient digestibility of high-producing dairy cows. Sixty-four multiparous Holstein cows (3.2 ± 1.5 lactations; 97 ± 16 DIM, and 724 ± 68 kg of BW at covariate period initiation) and 32 gate feeders were enrolled in a study with a completely randomized design and a 2 × 2 factorial arrangement. Cows and gate feeders were randomly assigned to treatments (16 cows and 8 gate feeders per treatment). Cows were allowed 2 wk to acclimate to feeding gates followed by a 2-wk covariate period. During the acclimation and covariate periods, all cows were fed a diet containing MON and LCY. Following the covariate period, cows were enrolled in a 10-wk treatment period during which cows were randomly assigned to 1 of 4 treatments: (1) a combination of MON and LCY (MON-LCY), (2) MON-CON, (3) CON-LCY, or (4) neither MON nor LCY (CON-CON). Data were analyzed using a mixed model with week as a repeated measure and fixed effects of MON, LCY, week, and all their interactions. Cow (treatment) was included as a random effect. The average covariate period value of each variable was used as a covariate. Three-way interactions were observed for DMI and feed efficiency. Dry matter intake decreased from wk 4 to 5 and wk 8 to 10 in MON-LCY cows compared with CON-CON. No treatment differences were observed for actual or component-corrected milk yield or milk components, except for a tendency for LCY to decrease milk fat yield. Feed efficiency was greater for MON-LCY relative to CON-CON in 4 of 10 wk. Interactions between MON and LCY were observed for dry matter and organic matter digestibility, where both were lower for CON-CON than other treatments. Under the conditions of the present study, feeding dairy cows in a high feed bunk density a combination of MON and LCY can decrease intake and improve feed efficiency without affecting milk production or components. Additionally, monensin and live-cell yeasts may each improve total-tract digestibility based on improvements in DM and OM digestibility.

Consistency of dry matter intake in Holstein cows: Heritability estimates and associations with feed efficiency.

Resilience can be defined as the capacity to maintain performance or bounce back to normal functioning after a perturbation, and studying fluctuations in daily feed intake may be an effective way to identify resilient dairy cows. Our goal was to develop new phenotypes based on daily dry matter intake (DMI) consistency in Holstein cows, estimate genetic parameters and genetic correlations with feed efficiency and milk yield consistency, and evaluate their relationships with production, longevity, health, and reproduction traits. Data consisted of 397,334 daily DMI records of 6,238 lactating Holstein cows collected from 2007 to 2022 at 6 research stations across the United States. Consistency phenotypes were calculated based on the deviations from expected daily DMI for individual cows during their respective feeding trials, which ranged from 27 to 151 d in duration. Expected values were derived from different models, including simple average, quadratic and cubic quantile regression with a 0.5 quantile, and locally estimated scatterplot smoothing (LOESS) regression with span parameters 0.5 and 0.7. We then calculated the log of variance (log-Var-DMI) of daily deviations for each model as the consistency phenotype. Consistency of milk yield was also calculated, as a reference, using the same methods (log-Var-Milk). Genetic parameters were estimated using an animal model, including lactation, days in milk and cohort as fixed effects, and animal as random effect. Relationships between log-Var-DMI and traits currently considered in the US national genetic evaluation were evaluated using Spearman's rank correlations between sires' breeding values. Heritability estimates for log-Var-DMI ranged from 0.11 ± 0.02 to 0.14 ± 0.02 across models. Different methods (simple average, quantile regressions, and LOESS regressions) used to calculate log-Var-DMI yielded very similar results, with genetic correlations ranging from 0.94 to 0.99. Estimated genetic correlations between log-Var-DMI and log-Var-Milk ranged from 0.51 to 0.62. Estimated genetic correlations between log-Var-DMI and feed efficiency ranged from 0.55 to 0.60 with secreted milk energy, from 0.59 to 0.63 with metabolic body weight, and from 0.26 to 0.31 with residual feed intake (RFI). Relationships between log-Var-DMI and the traits in the national genetic evaluation were moderate and positive correlations with milk yield (0.20 to 0.21), moderate and negative correlations with female fertility (-0.07 to -0.20), no significant correlations with health and longevity, and favorable correlations with feed efficiency (-0.23 to -0.25 with feed saved and 0.21 to 0.26 with RFI). We concluded that DMI consistency is heritable and may be an indicator of resilience. Cows with lower variation in the difference between actual and expected daily DMI (more consistency) may be more effective in maintaining performance in the face of challenges or perturbations, whereas cows with greater variation in observed versus expected daily DMI (less consistency) are less feed efficient and may be less resilient.

Energy balance of dairy cows predicted by mid-infrared spectra data of milk using Bayesian approaches.

Information on dry matter intake (DMI) and energy balance (EB) at the animal and herd level is important for management and breeding decisions. However, routine recording of these traits at commercial farms can be challenging and costly. Fourier-transform mid-infrared (FT-MIR) spectroscopy is a noninvasive technique applicable to a large cohort of animals that is routinely used to analyze milk components and is convenient for predicting complex phenotypes that are typically difficult and expensive to obtain on a large scale. We aimed to develop prediction models for EB and use the predicted phenotypes for genetic analysis. First, we assessed prediction equations using 4,485 phenotypic records from 167 Holstein cows from an experimental station. The phenotypes available were body weight (BW), milk yield (MY) and milk components, weekly-averaged DMI, and FT-MIR data from all milk samples available. We implemented mixed models with Bayesian approaches and assessed them through 50 randomized replicates of a 5-fold cross-validation. Second, we used the best prediction models to obtain predicted phenotypes of EB (EBp) and DMI (DMIp) on 5 commercial farms with 2,365 phenotypic records of MY, milk components and FT-MIR data, and BW from 1,441 Holstein cows. Third, we performed a GWAS and estimated heritability and genetic correlations for energy content in milk (EnM), BW, DMIp, and EBp using the genomic information available on the cows from commercial farms. The highest correlation between the predicted and observed phenotype (ry,y^) was obtained with DMI (0.88) and EB (0.86), while predicting BW was, as anticipated, more challenging (0.69). In our study, models that included FT-MIR information performed better than models without spectra information in the 3 traits analyzed, with increments in prediction correlation ranging from 5% to 10%. For the predicted phenotypes calculated by the prediction equations and data from the commercial farms the heritability ranged between 0.11 and 0.16 for EnM, DMIp and EBp, and 0.42 for BW. The genetic correlation between EnM and BW was -0.17, with DMIp was 0.40 and with EBp was -0.39. From the GWAS, we detected one significant QTL region for EnM, and 3 for BW, but none for DMIp and EBp. The results obtained in our study support previous evidence that FT-MIR information from milk samples contribute to improve the prediction equations for DMI, BW, and EB, and these predicted phenotypes may be used for herd management and contribute to the breeding strategy for improving cow performance.

The Resilient Dairy Genome Project-A general overview of methods and objectives related to feed efficiency and methane emissions.

The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries (i.e., Australia, Canada, Denmark, Germany, Spain, Switzerland, and United States) contribute with genotypes and phenotypes including DMI and CH4. However, combining data are challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis.

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

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

Short communication: Impact of corn grain particle size on production performance of lactating dairy cows.

Corn grain particle size has the potential to influence the performance of lactating dairy cows and the overall profitability of a dairy farm. The objective of this study was to evaluate the productive performance of lactating cows fed diets containing finely or coarsely ground corn grain. Fifty lactating Holstein cows (n = 50; 10 primiparous and 40 multiparous), averaging (mean ± standard deviation, SD) 658 ± 64 kg of BW, 38.8 ± 7.3 kg of milk/d, and 155 ± 80 DIM, were fed diets with finely ground corn grain (FGC) or coarsely ground corn grain (CGC) in a randomized block design with a 28-d treatment period. Finely and coarsely ground corn grain had an average particle size of 660 and 915 µm, respectively. Dry matter intake (DMI) was reduced (p < 0.01) for cows fed FGC (22.1 vs. 21.2 kg d-1). Milk yield and efficiency were not affected by treatments (37.9 vs. 36.8 kg d-1; p = 0.12 and 1.78 vs. 1.79; p = 0.15). Concentrations of milk protein and fat, as well as other milk solids, were unaffected (p > 0.05) by treatments. Fecal starch (FS) concentrations were greater (p < 0.01) for cows fed CGC (7.0 vs. 4.9%), whereas plasma concentrations of D-lactate were greater (p < 0.05) for cows fed FGC (98.5 vs. 79.7 µM). Overall, feeding finely ground corn grain increased total-tract starch digestibility and reduced DMI while maintaining milk yield.

A three-year comparison of once-a-day and twice-a-day milking in seasonal-calving pasture-based systems.

Globally, the majority of dairy cows are milked twice a day (TAD); however, in pasture-based production systems, such as in Ireland, the idea of milking once a day (OAD) is being considered for reasons such as improved work-life balance. The immediate effects within a lactation, as well as the multilactation consequences of OAD, compared with TAD milking, require understanding. The objective of this randomized experiment was to compare OAD and TAD milking, over a 3-yr period, by examining the differences in milk production and composition, body weight (BW), body condition score (BCS), dry matter intake (DMI), udder characteristics, locomotion score, and milking time. Over the 3-yr period, 83 cows were enrolled in the experiment; 32, 44, and 48 cows in yr 1, 2, and 3 of the experiment, respectively. Each year, 23% of the herds were primiparous animals, while the remainder were second lactation or greater in parity. All cows were milked in the morning at 0700 h; only cows milked TAD were milked a second time each day at 1600 h. Cows rotationally grazed pastures for the duration of the lactating period and were housed during the nonlactating period. Milking cows OAD reduced cumulative milk yield by 26%, and milk solids yield (kg of fat + kg of protein) by 21%, across the 3 yr of the experiment when compared with cows milked TAD which produced 4,126 and 365 kg/cow, respectively. A contributory factor to the reduced production was a shorter lactation length (9.7 d) of the cows milked OAD compared with TAD (294 d). Milk fat percent of cows milked TAD was similar for all 3 yr of the study (5.05%), whereas milk fat percent of the cows milked OAD increased year on year, with each year being greater than the previous year (5.02%, 5.32%, and 5.70% for yr 1, 2, and 3; respectively). Milk protein percent was greater (+0.19%) for cows milked OAD compared with TAD which was 3.78%. Compared with cows milked TAD, total DMI for cows milked OAD was 22% less at the start of lactation (<167 d), but as the lactation progressed (>167 d) we observed no difference in DMI between treatments. Similar to the literature, milking cows OAD significantly increased average somatic cell score, both during (+16%) and at the end of lactation (+19%), compared with milking cows TAD which were 4.69 and 4.79, respectively. We detected positive aspects associated with OAD milking such as greater BW, BCS, and fertility performance. Milking OAD reduced both milking time per cow per day (reductions ranged from 34% in the first 4 mo of lactation to 43% during mo 5-9 of lactation) and milking time per liter of milk (-3.5 s/L) throughout lactation, leading to less labor inputs on-farm which can have positive implications for farmer work-life balance. The significant time saving and potential savings in costs (e.g., electricity) need to be considered in conjunction with the milk production reduction when considering OAD milking for the entire lactation.

Association of dry matter intake, milk yield, and days to first ovulation with cytological endometritis in Holstein cows.

Dry matter intake (DMI, kg/d) is closely related to the magnitude of negative energy and protein balance during the transition period, and the metabolic adaptations to support lactation in dairy cows. Thus, DMI might affect the development of cytological endometritis in the early postpartum period. Difficulty to adapt to these metabolic changes is related to impaired immune function and increased occurrence of reproductive disorders. We aimed to examine the association of pre- and postpartum DMI, body weight (BW), body condition score, milk yield and milk composition, and days to first ovulation with cytological endometritis at 15 (CYT15) and 30 DIM (CYT30). A second objective was to understand the association of vaginal discharge with CYT15 and CYT30 and performance. We conducted a pooled statistical analysis of 5 studies, including data from 280 multiparous Holstein cows. Based on the cutoffs for the percentage of uterine polymorphonuclear cells (PMN), determined by taking the median value of the data set for 15 and 30 DIM, cows were categorized as follows: LOW15 (PMN % at 15 DIM ≤24%; n = 125), HIGH15 (PMN % at 15 DIM >24%; n = 125), LOW30 (PMN % at 30DIM ≤7%; n = 141); and HIGH30 (PMN % at 30DIM >7%; n = 139). Cows in HIGH15 consumed an average of 1.97 ± 0.5 kg/d less DM than cows in LOW15 during prepartum, and 3.01 ± 0.5 kg/d less DM during postpartum. Dry matter intake (as a percentage of BW) was higher for cows in LOW15 during pre- and postpartum than for cows in HIGH15. Moreover, cows in HIGH15 tended to have lower milk yield than cows in LOW15 from the third until the fifth week postpartum. Although DMI was not associated with CYT30, DMI (as a percentage of BW) was lower for cows in LOW30 pre- and postpartum than for cows in HIGH30. There was no association between CYT30 and milk yield. Cows in LOW15 had greater days to first ovulation than cows in HIGH15, while cows in LOW30 also had greater days to first ovulation than cows in HIGH30. Simple regression analyses demonstrated linear associations of increased DMI, particularly postpartum, with decreased uterine PMN percentage and lower vaginal discharge score. Additionally, increased units of vaginal discharge score and increased percentage units of uterine PMN were linearly associated with decreased milk yield. Corroborating with the notion of the ovarian function being associated with uterine inflammatory status, cows in HIGH15 and HIGH30 ovulated on average 3 d before than cows in LOW15 and LOW30, respectively. Cytological endometritis at 15 DIM was associated with lower DMI from 4 wk before calving until 4 wk postpartum and was associated with lower milk yield. The association of vaginal discharge with cytological endometritis was variable and dependent on the day of evaluation.