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

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

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.     

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

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

Development and evaluation of models to predict the feed intake of dairy cows in early lactation

Inaccurate prediction of dry matter intake (DMI) limits the ability of current models to anticipate the technical and economic consequences of adopting different strategies for production management on individual dairy farms. The objective of the present study was to develop an accurate, robust, and broadly applicable prediction model and to compare it with the current NRC model for dairy cows in early lactation. Among various functions, an exponential model was selected for its best fit to DMI data of dairy cows in early lactation. Daily DMI data (n = 8,547) for 3 groups of Holstein cows (at Illinois, New Hampshire, and Pennsylvania) were used in this study. Cows at Illinois and New Hampshire were fed totally mixed diets for the first 70 d of lactation. At Pennsylvania, data were for the first 63 d postpartum. Data from Illinois cows were used as the developmental dataset, and the other 2 datasets were used for model evaluation and validation. Data for BW, milk yield, and milk composition were only available for Illinois and New Hampshire cows; therefore, only these 2 datasets were used for model comparisons. The exponential model, fitted to the individual cow daily DMI data, explained an average of 74% of the total variation in daily DMI for Illinois data, 49% of the variation for New Hampshire data, 67% of the variation for Pennsylvania data, and 64% of the variation overall. Based on all model selection criteria used in this study, the exponential model for prediction of weekly DMI of individual cows was superior to the current NRC equation. The exponential model explained 85% of the variation in weekly mean DMI compared with 42% for the NRC equation. Compared with the relative prediction error of 6% for the exponential model, that associated with prediction using the NRC equation was 14%. The overall mean square prediction error value for individual cows was 5-fold higher for the NRC equation than for the exponential model (10.4 vs. 2.0 kg²/d²). The consistently accurate and robust prediction of DMI by the exponential model for all data-sets suggested that it could safely be used for predicting DMI in many circumstances.     

Predicting average feed intake of lactating Holstein cows fed totally mixed rations

A dry matter intake (DMI) prediction equation was estimated by using a data file that contained 124 treatment means collected from published studies. Animal factors considered for inclusion in the prediction model were body weight (BW) and its natural logarithm, BW(0.75), milk yield (MY) and its natural logarithm, milk fat and protein yields, month of lactation and its square, as well as its natural logarithm. The dietary factors considered were the percentages of neutral detergent fiber, acid detergent fiber, crude protein and hemicellulose in the ration dry matter together with the square of all these predictors. The multiple regression model selected by using the maximum R2 method include both animal and dietary factors as independent variables. The accuracy of this DMI prediction equation was evaluated and compared with that of five other equations previously published by using three independent datasets also containing treatment means collected from literature. Even though the latest NRC equation was slightly more accurate than the equation proposed in this study with the three evaluation datasets, the latter can be used for some applications for which the NRC equation is not appropriate.     

Short communication: Added value of rumination time for the prediction of dry matter intake in lactating dairy cows

The objective of the current study was to quantify the change in the prediction of dry matter intake (DMI) resulting from the inclusion of rumination time (RT) in the 2001 National Research Council (NRC) DMI prediction model. Forty-one Holstein cows fed the same total mixed ration were involved in a 10-wk study. Individual DMI were measured daily. The accuracy and precision of the original NRC prediction model, based on body weight, fat-corrected milk, and week of lactation as independent variables, was compared with the accuracy and precision of the same model with RT as an additional independent variable. The RT estimate was significant in the model developed but had a low value (0.031 kg/h). Root mean square prediction errors were very similar in the 2 models (1.70 and 1.68 kg/d) as were the other indicators (R², linear bias, random error, and concordance correlation coefficient) selected to compare the models in this study. These results indicate no gain in DMI prediction precision or accuracy when RT is included in the NRC model.     

Comparison of techniques for estimating herbage intake of grazing dairy cows

For estimating herbage intake during grazing, the traditional sward cutting technique was compared in grazing experiments in 2002 and 2003 with the recently developed n-alkanes technique and with the net energy method. The first method estimates herbage intake by the difference between the herbage mass before and after grazing and the regrowth between the 2 points in time. The second technique estimates herbage intake by the ratio of a dosed even-chain synthetic n-alkane (C32) and a naturally occurring odd-chain n-alkane (C31 or C33) in the herbage and feces. The third technique calculated the intake from the animal's energy requirements for milk production and maintenance. The sward cutting technique estimated herbage intake with the highest coefficient of variation and had different results in the 2 experimental years. The n-alkanes method yielded less variable results, whereas the net energy method gave the least variable results. In 2002, the estimates of the alkane ratio C32:C33 were best related with estimations of the net energy method. In 2003, the estimates of the alkane ratio C32:C31 were best related. The estimate based on the alkane ratio C32:C33 had a lower coefficient of variation than the one based on the alkane ratio C32:C31. Therefore, the C32:C33 alkane method was considered to be a better direct estimator for herbage intake by grazing lactating dairy cows.     

A meta-analysis of feed digestion in dairy cows. 2. The effects of feeding level and diet composition on digestibility

A meta-analysis based on published experiments with lactating dairy cows fed mainly grass silage-based diets was conducted to study the effects of intake, diet composition, and digestibility at a maintenance level of feeding on the apparent total diet digestibility. A data set that included a total of 497 dietary treatment means from 92 studies was collected and analyzed using mixed model regression analysis with a random study effect. Diet organic matter digestibility (OMD) in dairy cows at a production level (OMD_p) was positively associated with OMD at maintenance (OMD_m), but the slope was less than 1 (0.69). Diet OMD_p decreased as feed intake increased, and diets with high OMD_m exhibited greater depressions in digestibility with increased intake than did diets with low OMD_m. Digestibility of organic matter and neutral detergent fiber (NDF) increased as dietary crude protein concentration increased, whereas increased concentrate fat decreased digestibility. Replacement of grass silage with whole-crop cereal silage was associated with a quadratic decrease in diet digestibility. Metabolic fecal output, defined as fecal organic matter minus NDF, averaged 95.8 (SE = 0.65) g/kg of dry matter intake, and it was not influenced by intake or diet composition. Variation in OMD_p in cows fed grass silage-based diets was therefore attributable to variation in dietary NDF concentration and NDF digestibility. Depression in digestibility of organic matter with increased intake was less than predicted by the National Research Council and Cornell Net Carbohydrate and Protein systems. The following 2-parameter model indicates that the difference between OMD estimated in sheep fed at maintenance compared with dairy cows at production level is related both to dry matter intake and digestibility at maintenance level: OMD_p = 257 (±43) + 0.685 (±0.054) × OMD_m (g/kg of dry matter) - 2.6 (±0.44) × dry matter intake (kg/d); adjusted residual mean square error = 8.4 g/kg. It was concluded that diet digestibility in dairy cows can be predicted accurately and precisely from digestibility estimated at maintenance intake in sheep by using regression models including animal and dietary factors.     

Effect of feed restriction on reproductive and metabolic hormones in dairy cows

The objective of this trial was to evaluate the effects of feed restriction (FR) on serum glucose, nonesterified fatty acids, progesterone (P4), insulin, and milk production in dairy cows. Eight multiparous Holstein cows, 114 ± 14 d pregnant and 685 ± 39 kg of body weight, were randomly assigned to a replicated 4 × 4 Latin square design with 14-d periods. During the first 8 d of each period, cows in all treatments were fed for ad libitum feed intake. Beginning on d 9 of each period, cows received 1 of 4 treatments: ad libitum (AL), 25% feed restriction (25FR), 50% feed restriction (50FR), and 50% of TMR replaced with wheat straw (50ST). Daily feed allowance was divided into 3 equal portions allocated every 8 h with jugular blood samples collected immediately before each feeding through d 14. In addition, on d 12 of each period, blood samples were collected before and at 60, 120, 180, 240, 300, 360, 420, and 480 min after morning feeding. The conventional total mixed ration and total mixed ration with straw averaged 15.1 and 10.8%, 32.1 and 50.5%, and 26.8 and 17.0% for concentrations of crude protein, neutral detergent fiber, and starch, respectively. Cows that were feed and energy restricted had reduced dry matter intake, net energy for lactation intake, circulating glucose concentrations, and milk production, but greater body weight and body condition score losses than AL cows. Circulating concentrations of insulin were lower for cows fed 50FR (8.27 μIU/mL) and 50ST (6.24 μIU/mL) compared with cows fed AL (16.65 μIU/mL) and 25FR (11.16 μIU/mL). Furthermore, the greatest plasma nonesterified fatty acids concentration was observed for 50ST (647.7 μEq/L), followed by 50FR (357.5 μEq/L), 25FR (225.3 μEq/L), and AL (156.3 μEq/L). In addition, serum P4 concentration was lower for cows fed AL than cows fed 50ST and 25FR. Thus, FR reduced circulating glucose and insulin but increased P4 concentration, changes that may be positive in reproductive management programs.     

Short communication: Effects of subacute ruminal acidosis on free-choice intake of sodium bicarbonate in lactating dairy cows

The effect of inducing subacute ruminal acidosis (SARA) on the free-choice intake of sodium bicarbonate (SB) was investigated in four midlactation Holstein cows in a switchover experiment with four 1-wk periods. The SARA was induced by replacing 25% of the ad libitum intake of total mixed ration (TMR) with pellets containing 50% ground wheat and 50% ground barley and restricting access to TMR from 0700 to 1700 h. Control consisted of feeding TMR ad libitum. Powdered SB was provided for ad libitum consumption. Rumen pH was measured continuously using indwelling pH probes. Induction of SARA reduced (P < 0.05) the average daily rumen pH from 6.08 to 5.87, increased (P < 0.05) the average duration of rumen pH below 6 from 547 min x d(-1) to 916 min x d(-1), and increased (P < 0.05) the average duration of rumen pH below 5.6 from 132 min x d(-1) to 397 min x d(-1) (P < 0.05) but did not significantly affect SB intake. Average intake of SB was 26.8 g x d(-1) during SARA and 34.5 g x d(-1) during control. These low SB intakes must not have substantially affected rumen pH. Sodium bicarbonate intake differed significantly (P < 0.05) between cows. These data indicate that cows did not select SB in order to attenuate SARA.     

Time of feed delivery affects the feeding and lying patterns of dairy cows

The objective of this experiment was to determine whether it is the return from milking or delivery of fresh feed that has the greater effect on the daily patterns of feeding and lying behavior of dairy cattle. Forty-eight lactating Holstein cows were subjected to each of 2 treatments in a 2 x 2 cross-over design replicated over time. The treatments were 1) milking and feed delivery times coinciding and 2) feed delivery 6 h after milking. Cows were milked twice daily at 0500 and 1700 h. An electronic monitoring system was used to measure the time spent at the feed alley. Time-lapse video was used to quantify the lying time and incidence of aggressive displacements of the cows at the feed alley. Cows increased their total daily feeding time by 12.5% when fed 6 h after milking. This change was driven by an 82% increase in feeding time during the first hour immediately following the delivery of fresh feed and a 26% decrease in feeding time during the first hour after milking. The delivery of feed 6 h after milking did not change the daily lying time of the cows, but did decrease the latency to lie down after milking by 20 min. The reduction in feeding time after milking and decreased latency to lie down resulted in a tendency for less aggressive interactions at the feed alley after the cows returned from milking. These results indicate that the delivery of fresh feed has a greater impact on stimulating feeding behavior than does the return from milking and that changes in feeding management can affect both the feeding and lying behavior of dairy cows.     

The effects of supplementation with a blend of cinnamaldehyde and eugenol on feed intake and milk production of dairy cows

Plant extracts (PE) are naturally occurring chemicals in plants, and many of these molecules have been reported to influence production efficiency of dairy and beef animals. Two experiments were conducted to determine the effect of a PE additive (CE; an encapsulated blend of cinnamaldehyde and eugenol) on the milk production performance of lactating dairy cows across a range of doses. In experiment 1, 32 Holstein multi- and primiparous dairy cows in mid-lactation were assigned to no additive or supplementation with CE (350 mg/d; n = 16 cows/treatment) for 6 wk. In experiment 2, 48 Holstein multi- and primiparous dairy cows were assigned to no additive or supplementation with CE (200, 400, or 600 mg/d; n = 12 animals/treatment) for 8 wk. A 1-wk covariate period was included in both experiments. In both experiments, individual dry matter intake (DMI), milk production, milk composition, and somatic cell count were recorded daily. In experiment 1, CE was associated with an increase in DMI in both parity groups but an increase in milk production of multiparous cows only. In experiment 2, milk yield of multiparous cows was decreased at the 2 highest doses, whereas milk yield of primiparous cows was increased at the low and high doses of CE. These responses were accompanied by similar changes in DMI; therefore, CE did not affect feed efficiency. We observed no effect of CE on SCC or milk composition; however, treatment by parity interactions were detected for each of these variables that have not been described previously. Based on the results of these experiments, we conclude that a blend of cinnamaldehyde and eugenol can increase DMI and milk production in lactating dairy cows. In addition, environmental factors appear to influence the response to CE, including dose and parity, and these should be explored further.     

Frequency of feed delivery affects the behavior of lactating dairy cows

The objectives of this study were to examine how frequency of feed delivery affects 1) the behavior of group-housed and group-fed dairy cows and 2) the extent of feed sorting. These objectives were tested in two experiments. In each experiment, 48 lactating Holstein cows, split into groups of 12, were subjected to each of 2 treatments (over 10-d periods) in a cross-over design. The treatments for the first experiment were 1) delivery of feed once per day (1×) and 2) delivery of feed twice per day (2×). Treatments for the second experiment were 1) delivery of feed 2× and 2) delivery of feed four times per day (4×). For the 1×, 2×, and 4× treatments, feed was pushed up 3, 2, and 0 times per day, respectively. For both experiments, cows had 0.6 m of feeding space; one cows was allowed per lying stall. Time-lapse video was used to quantify the feeding and lying behavior, as well as the aggressive behavior displayed at the feed bunk by the cows. Changes in NDF content of the TMR throughout the day were used to determine the extent of feed sorting by the cows. In both experiments, increased frequency of feed provision increased, as well as changed, the distribution of daily feeding time. The changes in distribution of feeding time resulted in cows having more equal access to feed throughout the day. Frequency of feed delivery had no effect on the daily lying time of the cows or the daily incidence of aggressive interactions at the feed bunk. However, subordinate cows were not displaced as frequently when fed more often. For all treatments, in both experiments, the NDF content of the TMR present in the feed bunk increased throughout the day, indicating that sorting of the feed had occurred. Further, the amount of sorting of the feed was reduced by increasing the frequency of feed delivery from 1× to 2×. These results indicate that frequent delivery of feed improves access to feed for all cows, particularly during peak feeding periods when fresh feed is provided, and reduces the amount of feed sorting.     

Prediction and validation of residual feed intake and dry matter intake in Danish lactating dairy cows using mid-infrared spectroscopy of milk

The present study explored the effectiveness of Fourier transform mid-infrared (FT-IR) spectral profiles as a predictor for dry matter intake (DMI) and residual feed intake (RFI). The partial least squares regression method was used to develop the prediction models. The models were validated using different external test sets, one randomly leaving out 20% of the records (validation A), the second randomly leaving out 20% of cows (validation B), and a third (for DMI prediction models) randomly leaving out one cow (validation C). The data included 1,044 records from 140 cows; 97 were Danish Holstein and 43 Danish Jersey. Results showed better accuracies for validation A compared with other validation methods. Milk yield (MY) contributed largely to DMI prediction; MY explained 59% of the variation and the validated model error root mean square error of prediction (RMSEP) was 2.24 kg. The model was improved by adding live weight (LW) as an additional predictor trait, where the accuracy R² increased from 0.59 to 0.72 and error RMSEP decreased from 2.24 to 1.83 kg. When only the milk FT-IR spectral profile was used in DMI prediction, a lower prediction ability was obtained, with R² = 0.30 and RMSEP = 2.91 kg. However, once the spectral information was added, along with MY and LW as predictors, model accuracy improved and R² increased to 0.81 and RMSEP decreased to 1.49 kg. Prediction accuracies of RFI changed throughout lactation. The RFI prediction model for the early-lactation stage was better compared with across lactation or mid- and late-lactation stages, with R² = 0.46 and RMSEP = 1.70. The most important spectral wavenumbers that contributed to DMI and RFI prediction models included fat, protein, and lactose peaks. Comparable prediction results were obtained when using infrared-predicted fat, protein, and lactose instead of full spectra, indicating that FT-IR spectral data do not add significant new information to improve DMI and RFI prediction models. Therefore, in practice, if full FT-IR spectral data are not stored, it is possible to achieve similar DMI or RFI prediction results based on standard milk control data. For DMI, the milk fat region was responsible for the major variation in milk spectra; for RFI, the major variation in milk spectra was within the milk protein region.     

Effect of frequency of feed delivery on the behavior and productivity of lactating dairy cows

The objective of this study was to determine the effect of feed delivery frequency on the behavioral patterns and productivity of lactating dairy cows. Twelve freestall-housed, lactating Holstein dairy cows, including 6 primiparous (PP) and 6 multiparous (MP), milked 3×/d (at 1400, 2200, and 0600h), were exposed to each of 3 treatments (over 21-d periods) in a replicated Latin square design. Treatments included feed delivery frequency of (1) 1×/d (at 1400 h), (2) 2×/d (at 1400 and 2200 h), and (3) 3×/d (at 1400, 2200, and 0600 h). Milk production as well as feeding, lying, and rumination behaviors were electronically monitored for each animal for the last 7 d of each treatment period. Milk samples were collected for the last 3 d of each period for milk component analysis. Dry matter intake (DMI) varied with feed delivery frequency, with greatest DMI observed in cows fed 3×/d (27.8 kg/d) compared with those fed 2×/d (27.0 kg/d) or 1×/d (27.4 kg/d). Treatment had no effect on milk yield (41.3 kg/d) or efficiency of production (1.54 kg of milk/kg of DMI). Cows that did not receive delivery of feed following the 2200 h milking (treatment 1) and 0600 h milking (treatments 1 and 2) had lower DMI during the first hour after milking than those that received feed at all milkings (treatment 3). Total feeding time and meal frequency, size, and duration did not vary by treatment, but PP cows consumed smaller meals at a slower rate, resulting in lower DMI compared with MP cows. Primiparous cows consumed 50.1% and 26.1% less dry matter than MP cows during the first meal following the first and second milkings, respectively. Lying time did not vary by treatment, but PP cows spent more time lying (10.3 vs. 8.3 h/d) than MP cows. Under 3×/d milking schedules, greater feed delivery frequency resulted in greater DMI as a function of increased DMI following the return from milking and the delivery of feed.     

Occurrence of mycotoxins in feedstuffs of dairy cows and estimation of total dietary intakes

A survey was conducted to determine the occurrence of mycotoxins in feedstuffs of dairy cows in the Netherlands and to estimate total dietary intakes of these compounds. Twenty-four dairy farms were visited twice and samples taken of all diet ingredients. Feed intake data were collected by means of questionnaires. A total of 169 feed samples were collected and analyzed for 20 mycotoxins using a liquid chromatography tandem mass spectrometry multimethod. Silage and compound feed were the main diet ingredients, representing on average 67 and 23% of dry matter intake, respectively. Deoxynivalenol (DON), zearalenone, roquefortine C, and mycophenolic acid were the mycotoxins with the highest incidence. The incidence of DON in silage, compound feed, and feed commodity samples was 38 to 54%. The incidence of zearalenone in silage, compound feed, and feed commodity samples was 17 to 38%. The DON and zearalenone had a low incidence in forage samples and were not detected in ensiled by-product samples. Roquefortine C and mycophenolic acid were only detected in silage and ensiled by-product samples (incidence 7 to 19%). Fumonisins B₁ and B₂ were detected in 2 compound feed samples and one feed commodity sample. Aflatoxins B₁, B₂, G₁, and G₂, ochratoxin A, T-2 and HT-2 toxin, 3-acetyl-DON, 15-acetyl-DON, diacetoxyscirpenol, sterigmatocystin, fusarenon-X, ergotamine, and penicillinic acid were not detected in any of the samples. Average concentrations of DON, zearalenone, roquefortine C, and mycophenolic acid in complete diets were 273, 28, 114, and 54 μg/kg, respectively. Maximum concentrations were 969, 203, 2,211, and 1,840 μg/kg, respectively. Calculated average daily intakes of these mycotoxins were 5.0, 0.5, 2.0, and 0.9 mg/animal, respectively, and maximum daily intakes 19.3, 3.5, 38.9, and 32.3 mg/animal, respectively. Corn silage was the major source of all 4 of these mycotoxins in the diet. Extremely high concentrations of roquefortine C and mycophenolic acid (up to 45 and 25 mg/kg, respectively) were detected in visibly molded areas in surface layers of corn silage. These areas appeared to be the main source of roquefortine C and mycophenolic acid in the diet. Because carry-over of DON, zearale-none, roquefortine C, and mycophenolic acid into milk is negligible, their occurrence in feedstuffs is not considered of significant concern with respect to the safety of dairy products for consumers. Potential implications for animal health are discussed.     

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

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

High-density genome-wide association study for residual feed intake in Holstein dairy cattle

Improving feed efficiency (FE) of dairy cattle may boost farm profitability and reduce the environmental footprint of the dairy industry. Residual feed intake (RFI), a candidate FE trait in dairy cattle, can be defined to be genetically uncorrelated with major energy sink traits (e.g., milk production, body weight) by including genomic predicted transmitting ability of such traits in genetic analyses for RFI. We examined the genetic basis of RFI through genome-wide association (GWA) analyses and post-GWA enrichment analyses and identified candidate genes and biological pathways associated with RFI in dairy cattle. Data were collected from 4,823 lactations of 3,947 Holstein cows in 9 research herds in the United States. Of these cows, 3,555 were genotyped and were imputed to a high-density list of 312,614 SNP. We used a single-step GWA method to combine information from genotyped and nongenotyped animals with phenotypes as well as their ancestors' information. The estimated genomic breeding values from a single-step genomic BLUP were back-solved to obtain the individual SNP effects for RFI. The proportion of genetic variance explained by each 5-SNP sliding window was also calculated for RFI. Our GWA analyses suggested that RFI is a highly polygenic trait regulated by many genes with small effects. The closest genes to the top SNP and sliding windows were associated with dry matter intake (DMI), RFI, energy homeostasis and energy balance regulation, digestion and metabolism of carbohydrates and proteins, immune regulation, leptin signaling, mitochondrial ATP activities, rumen development, skeletal muscle development, and spermatogenesis. The region of 40.7 to 41.5 Mb on BTA25 (UMD3.1 reference genome) was the top associated region for RFI. The closest genes to this region, CARD11 and EIF3B, were previously shown to be related to RFI of dairy cattle and FE of broilers, respectively. Another candidate region, 57.7 to 58.2 Mb on BTA18, which is associated with DMI and leptin signaling, was also associated with RFI in this study. Post-GWA enrichment analyses used a sum-based marker-set test based on 4 public annotation databases: Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Reactome pathways, and medical subject heading (MeSH) terms. Results of these analyses were consistent with those from the top GWA signals. Across the 4 databases, GWA signals for RFI were highly enriched in the biosynthesis and metabolism of amino acids and proteins, digestion and metabolism of carbohydrates, skeletal development, mitochondrial electron transport, immunity, rumen bacteria activities, and sperm motility. Our findings offer novel insight into the genetic basis of RFI and identify candidate regions and biological pathways associated with RFI in dairy cattle.