Milk mid-infrared spectral data as a tool to predict feed intake in lactating Norwegian Red dairy cows

Mid-infrared (MIR) spectroscopy of milk was used to predict dry matter intake (DMI) and net energy intake (NEI) in 160 lactating Norwegian Red dairy cows. A total of 857 observations were used in leave-one-out cross-validation and external validation to develop and validate prediction equations using 5 different models. Predictions were performed using (multiple) linear regression, partial least squares (PLS) regression, or best linear unbiased prediction (BLUP) methods. Linear regression was implemented using just milk yield (MY) or fat, protein, and lactose concentration in milk (Mcont) or using MY together with body weight (BW) as predictors of intake. The PLS and BLUP methods were implemented using just the MIR spectral information or using the MIR together with Mcont, MY, BW, or NEI from concentrate (NEIconc). When using BLUP, the MIR spectral wavelengths were always treated as random effects, whereas Mcont, MY, BW, and NEIconc were considered to be fixed effects. Accuracy of prediction (R) was defined as the correlation between the predicted and observed feed intake test-day records. When using the linear regression method, the greatest R of predicting DMI (0.54) and NEI (0.60) in the external validation was achieved when the model included both MY and BW. When using PLS, the greatest R of predicting DMI (0.54) and NEI (0.65) in the external validation data set was achieved when using both BW and MY as predictors in combination with the MIR spectra. When using BLUP, the greatest R of predicting DMI (0.54) in the external validation was when using MY together with the MIR spectra. The greatest R of predicting NEI (0.65) in the external validation using BLUP was achieved when the model included both BW and MY in combination with the MIR spectra or when the model included both NEIconc and MY in combination with MIR spectra. However, although the linear regression coefficients of actual on predicted values for DMI and NEI were not different from unity when using PLS, they were less than unity for some of the models developed using BLUP. This study shows that MIR spectral data can be used to predict NEI as a measure of feed intake in Norwegian Red dairy cattle and that the accuracy is augmented if additional, often available data are also included in the prediction model.     

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

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

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.     

A meta-analysis examining the relationship among dietary factors, dry matter intake, and milk and milk protein yield in dairy cows

This meta-analysis was undertaken to determine the impact of dietary components on dry matter intake (DMI), milk yield (MY), and milk protein yield (MPY) in Holstein dairy cows. Diets (n=846) from 256 feeding trials published in Volumes 73 through 83 of the Journal of Dairy Science were evaluated for nutrient composition using 2 diet evaluation models: CPM Dairy (a computer program based on the principles of the Cornell Net Carbohydrate and Protein System) and NRC (2001). Data were analyzed with and without the effect of stage of lactation as a dummy variable (<100 d in milk or > or =100 d in milk). A mixed model regression analysis was used to completely investigate the potential relationships among composition variables and DMI, MY, and MPY. Protein and carbohydrate fractions were the main components within the DMI models, and DMI played a dominant role in estimating MY and MPY. Inclusion of stage of lactation substantially improved the MY models but did not affect model fits or residual structure for DMI and MPY.     

Mining data from milk infrared spectroscopy to improve feed intake predictions in lactating dairy cows

Feed intake is one of the most important components of feed efficiency in dairy systems. However, it is a difficult trait to measure in commercial operations for individual cows. Milk spectrum from mid-infrared spectroscopy has been previously used to predict milk traits, and could be an alternative to predict dry matter intake (DMI). The objectives of this study were (1) to evaluate if milk spectra can improve DMI predictions based only on cow variables; (2) to compare artificial neural network (ANN) and partial least squares (PLS) predictions; and (3) to evaluate if wavelength (WL) selection through Bayesian network (BN) improves prediction quality. Milk samples (n = 1,279) from 308 mid-lactation dairy cows [127 ± 27 d in milk (DIM)] were collected between 2014 and 2016. For each milk spectra time point, DMI (kg/d), body weight (BW, kg), milk yield (MY, kg/d), fat (%), protein (%), lactose (%), and actual DIM were recorded. The DMI was predicted with ANN and PLS using different combinations of explanatory variables. Such combinations, called covariate sets, were as follows: set 1 (MY, BW^0.75, DIM, and 361 WL); set 2 [MY, BW^0.75, DIM, and 33 WL (WL selected by BN)]; set 3 (MY, BW^0.75, DIM, and fat, protein, and lactose concentrations); set 4 (MY, BW^0.75, DIM, 33 WL, fat, protein, and lactose); set 5 (MY, BW^0.75, DIM, 33 WL, and visit duration in the feed bunk); set 6 (MY, DIM, and 33 WL); set 7 (MY, BW^0.75, and DIM); set-WL (included 361 WL); and set-BN (included just 33 selected WL). All models (i.e., each combination of covariate set and fitting approach, ANN or PLS) were validated with an external data set. The use of ANN improved the performance of models 2, 5, 6, and BN. The use of BN combined with ANN yielded the highest accuracy and precision. The addition of individual WL compared with milk components (set 2 vs. set 3) did not improve prediction quality when using PLS. However, when ANN was employed, the model prediction with the inclusion of 33 WL was improved over the model containing only milk components (set 2 vs. set 3; concordance correlation coefficient = 0.80 vs. 0.72; coefficient of determination = 0.67 vs. 0.53; root mean square error of prediction 2.36 vs. 2.81 kg/d). The use of ANN and the inclusion of a behavior parameter, set 5, resulted in the best predictions compared with all other models (coefficient of determination = 0.70, concordance correlation coefficient = 0.83, root mean square error of prediction = 2.15 kg/d). The addition of milk spectra information to models containing cow variables improved the accuracy and precision of DMI predictions in lactating dairy cows when ANN was used. The use of BN to select more informative WL improved the model prediction when combined with cow variables, with further improvement when combined with ANN.     

Milk production and feed intake of Holstein cows given short (30-d) or normal (60-d) dry periods

Eighty-four Holstein cows were utilized to evaluate effects of dry period (60 d vs. 30 d), with or without estradiol cypionate (ECP) injections to accelerate mammary involution, on prepartum and postpartum dry matter intake (DMI), body weight (BW), body condition score (BCS), and subsequent milk yield (MY). Treatments were arranged in a 3 x 2 x 2 factorial design that included dry period (30 d dry, 30 d dry + ECP, and 60 d dry), prepartum and postpartum bovine somatotropin (bST; 10.2 mg/d), and prepartum anionic or cationic diets. To accelerate mammary involution, ECP (15 mg) was injected intramuscularly at dry-off. No interaction of bST or prepartum diet with dry period length was detected on BW, BCS, or MY. No significant effects of dry period length on prepartum DMI, BW, or BCS were detected. Cows with shorter dry periods maintained postpartum BCS better and tended to have greater DMI immediately postpartum. Mean daily yields of milk for dry period groups did not differ during overall lactation period (1 to 21 wk). Injection of ECP at the onset of the 30-d dry period did not affect MY. No significant differences due to dry period length were detected for milk, 3.5% FCM, or SCM yields during first 10 wk of lactation. Data indicated that a short dry period protocol can be used as a management tool with no loss in the subsequent milk production of dairy cows.     

Heritability of electronically recorded daily body weight and correlations with yield, dry matter intake, and body condition score

The objectives of this study were to estimate heritability for daily body weight (BW) and genetic correlations of daily BW with daily milk yield (MY), body condition score (BCS), dry matter intake, fat yield, and protein yield. The Afiweigh cow body weighing system records BW of every cow exiting the milking parlor. The Afiweigh system was installed at the Pennsylvania State University dairy herd in August 2001 and in July 2004 at the Virginia Tech dairy herd. The edited data included 202,143 daily BW and 290,930 daily MY observations from 575 Pennsylvania State University and 120 Virginia Tech Holstein cows. Data were initially analyzed with a series of 4-trait animal models, followed by random regression models. The models included fixed effects for age within lactation group, week of lactation, and herd-date. Random effects included animal, permanent environment, and error. The order of the polynomials for random animal and permanent environmental effects with the random regression model for daily BW was 4 and 6, respectively. Heritability estimates for daily BW ranged from 0.48 to 0.57 and were largest between 200 and 230 and smallest at 305 d of lactation. Genetic correlations were large between BW and BCS (0.60). The genetic correlation between daily BW and MY was −0.14 but was positive (0.24) after adjusting for BCS. Electronically recorded daily BW is highly heritable, and genetic evaluations of daily BW and BW change across the lactation could be used to select for less early lactation BW loss.     

The effect of day-only versus day-plus-night cooling of dairy cows

The aim of this study was to assess effects on milk yield (MY), rumen temperature, and panting score when lactating dairy cows were cooled during the day only or during the day and night. The study was conducted over 106 d during using 120 multiparous Holstein-Friesian cows assigned to 2 treatments (60 cows/treatment; 2 pens/treatment): (1) day cooling (DC): overhead sprinklers (large droplet) and fans while in the dairy holding yard only, shade and fans at the feedpad, and a shaded loafing area; and (2) enhanced day+night cooling (EDN): overhead sprinklers (large droplet) and fans in dairy holding yard, ducted air blowing onto cows during milking, plus thorough wetting (shower array) on exit from dairy; shade and fans at feedpad (turned off at night); and shaded loafing area + ducted fan-forced air blowing onto cows at night. The ducted air at night was manually activated at 2030 h when the maximum daily temperature-humidity index exceeded 75 and remained on until 0430 h the next day. The cows were fed a total mixed ration ad libitum, and feed intake was determined on a pen basis. Rumen temperature and cow activity were obtained from each cow at 10-min intervals via rumen boluses. Panting scores were obtained by direct observation 4 times a day at approximately 0430, 0930, 1530, and 2030 h. Cows were milked twice daily: 0500 to 0600 h and 1600 to 1700 h. Individual MY were obtained at each milking and combined to give individual daily totals. The EDN cows had greater daily MY (+2.05 kg/cow per day) over the duration of the study compared with DC cows. Rumen temperature during the third heat wave was lower for EDN (39.51 ± 0.01°C) than for DC (39.66 ± 0.01°C) cows. During the most severe heat wave (heat wave 3), MY for the 2 groups was similar, but over the 6 d following the heat wave, EDN cows had greater daily MY (+3.61 kg/cow per day). Rumen temperature was lower for EDN (39.58 ± 0.01°C) than for DC (40.10 ± 0.01°C) cows.     

Estimating production benefits through simulation of group and individual feeding of dairy cows.

Objectives were 1) to develop DMI and milk prediction equations, 2) to use these equations to simulate group and individual feeding of dairy herds, and 3) to estimate effects of group and individual feeding on FCM production. University of New Hampshire data were used to predict DMI from previous DMI and cow and ration characteristics. The same data were used to predict milk production from DMI and previous milk production. Feeding was simulated for 100 cows over 50 4-wk periods in a number of trials. Effects of individual feeding, additional groups, herd calving intervals, and within-herd variation of annual milk production per cow on daily FCM per cow were isolated in average and high producing herds. Changing from one group to individual feeding can increase daily FCM per cow by .5 to 1.1 kg and two groups to individual feeding by 0 to .8 kg without changing total herd nutrient intake. Reallocation of the same amount of nutrients to two groups instead of one can increase daily milk production by .15 to .8 kg of FCM per cow, reallocation to three groups instead of two by 0 to .6 kg of FCM per cow, and reallocation to four groups instead of three by 0 to .35 kg of FCM per cow.     

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.     

Genetic parameters for functional traits in dairy cattle from daily random regression models

The objective of the research was to estimate genetic parameters, such as heritabilities and genetic correlations, using daily test day data for milk yield (MY), milking speed (MS), dry matter intake (DMI), and body weight (BW) using random regression methodology. Data were from first lactation dairy cows (n = 320) from the Chamau research farm of the Swiss Federal Institute of Technology, Switzerland over the period from April 1994 to 2004. All traits were recorded daily using automated machines. Estimated heritabilities (h²) varied from 0.18 to 0.30 (mean h² = 0.24) for MY, 0.003 to 0.098 (mean h² = 0.03) for MS, 0.22 to 0.53 (mean h² = 0.43) for BW, and 0.12 to 0.34 (mean h² = 0.23) for DMI. A permanent environmental effect was included in both the univariate and bivariate models, but was assumed constant in estimating some genetic correlations because of convergence problems. Estimated genetic correlations varied from 0.31 to 0.41 between MY and MS, from −0.47 to 0.29 between MY and DMI, from −0.60 to 0.54 between MY and BW, from 0.17 to 0.26 between MS and DMI, from −0.18 to 0.25 between MS and BW, and from −0.89 to 0.29 between DMI and BW. Genetic correlations for MY, MS, DMI, and BW from calving to midlactation decreased similarly to 0.40, 0.36, 0.14, and 0.36 and, at the end of the lactation, decreased to −0.06, 0.23, −0.07, and 0.09, respectively. Daily genetic variance-covariance of many functional traits are reported for the first time and will be useful when constructing selection indexes for more than one trait based on longitudinal genetic parameters.     

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

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

Predicting the dry matter intake of grazing dairy cows using infrared reflectance spectroscopy analysis

The objective of this study was to compare mid-infrared reflectance spectroscopy (MIRS) analysis of milk and near-infrared reflectance spectroscopy (NIRS) analysis of feces with regard to their ability to predict the dry matter intake (DMI) of lactating grazing dairy cows. A data set comprising 1,074 records of DMI from 457 cows was available for analysis. Linear regression and partial least squares regression were used to develop the equations using the following variables: (1) milk yield (MY), fat percentage, protein percentage, body weight (BW), stage of lactation (SOL), and parity (benchmark equation); (2) MIRS wavelengths; (3) MIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity; (4) NIRS wavelengths; (5) NIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity; (6) MIRS and NIRS wavelengths; and (7) MIRS wavelengths, NIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity. The equations were validated both within herd using animals from similar experiments and across herds using animals from independent experiments. The accuracy of equations was greater for within-herd validation compared with across-herds validation. Across-herds validation was deemed the more suitable method to assess equations for robustness and real-world application. The benchmark equation was more accurate [coefficient of determination (R²) = 0.60; root mean squared error (RMSE) = 1.68 kg] than MIRS alone (R² = 0.30; RMSE = 2.23 kg) or NIRS alone (R² = 0.16; RMSE = 2.43 kg). The combination of the benchmark equation with MIRS (R² = 0.64; RMSE = 1.59 kg) resulted in slightly superior fitting statistics compared with the benchmark equation alone. The combination of the benchmark equation with NIRS (R² = 0.58; RMSE = 1.71 kg) did not result in a more accurate prediction equation than the benchmark equation. The combination of MIRS and NIRS wavelengths resulted in superior fitting statistics compared with either method alone (R² = 0.36; RMSE = 2.15 kg). The combination of the benchmark equation and MIRS and NIRS wavelengths resulted in the most accurate equation (R² = 0.68; RMSE = 1.52 kg). A further analysis demonstrated that Holstein-Friesian cows could predict the DMI of Jersey × Holstein-Friesian crossbred cows using both MIRS and NIRS. Similarly, the Jersey × Holstein-Friesian animals could predict the DMI of Holstein-Friesian cows using both MIRS and NIRS. The equations developed in this study have the capacity to predict DMI of grazing dairy cows. From a practicality perspective, MIRS in combination with variables in the benchmark equation is the most suitable equation because MIRS is currently used on all milk-recorded milk samples from dairy cows.     

Comparative responses of lactating cows to total mixed rations or computerized individual concentrates feeding.

A trial was conducted in a commercial dairy herd in which the concentrate part of the ration was fed individually to a group of cows through computerized self-feeders. Performance results were compared with those of a group fed TMR of 65 to 67% concentrates. Rationing of individual concentrates was according to parity, milk yield, milk yield potential, BW changes, and bunk feed-stuffs. Mean intake of concentrates per cow was about 1 kg/d lower in the individually supplemented cows. This was partly compensated for by a higher intake of bunk feedstuffs. Overall daily milk yield per cow was similar to those receiving a TMR in first parity cows, higher in second parity cows, and lower in third and greater parity cows. The higher performance of the second parity cows was achieved in all milk yield potential classes, and the lower yield in subsequent lactations was due to lower performance in low and high potential classes. The individually supplemented cows gained less BW than those in the TMR group. Milk yield per unit of BW was better than milk yield as a variable to refine individual cow supplementation strategy for allocation of concentrates. Results also suggest that the same criteria used for supplementation of concentrates can be beneficial to cows' assignments and movements among different TMR groups. Computerized dispensing of concentrates, when applied properly, can economize on consumption of concentrates when grouping and feeding different TMR are impossible.     

Effect of concentrate feed level on methane emissions from grazing dairy cows

Although the effect of nutrition on enteric methane (CH₄) emissions from confined dairy cattle has been extensively examined, less information is available on factors influencing CH₄ emissions from grazing dairy cattle. In the present experiment, 40 Holstein-Friesian dairy cows (12 primiparous and 28 multiparous) were used to examine the effect of concentrate feed level (2.0, 4.0, 6.0, and 8.0 kg/cow per day; fresh basis) on enteric CH₄ emissions from cows grazing perennial ryegrass-based swards (10 cows per treatment). Methane emissions were measured on 4 occasions during the grazing period (one 4-d measurement period and three 5-d measurement periods) using the sulfur hexafluoride technique. Milk yield, liveweight, and milk composition for each cow was recorded daily during each CH₄ measurement period, whereas daily herbage dry matter intake (DMI) was estimated for each cow from performance data, using the back-calculation approach. Total DMI, milk yield, and energy-corrected milk (ECM) yield increased with increasing concentrate feed level. Within each of the 4 measurement periods, daily CH₄ production (g/d) was unaffected by concentrate level, whereas CH₄/DMI decreased with increasing concentrate feed level in period 4, and CH₄/ECM yield decreased with increasing concentrate feed level in periods 2 and 4. When emissions data were combined across all 4 measurement periods, concentrate feed level (2.0, 4.0, 6.0, and 8.0 kg/d; fresh basis) had no effect on daily CH₄ emissions (287, 273, 272, and 277 g/d, respectively), whereas CH₄/DMI (20.0, 19.3, 17.7, and 18.1 g/kg, respectively) and CH₄-E/gross energy intake (0.059, 0.057, 0.053, and 0.054, respectively) decreased with increasing concentrate feed levels. A range of prediction equations for CH₄ emissions were developed using liveweight, DMI, ECM yield, and energy intake, with the strongest relationship found between ECM yield and CH₄/ECM yield (coefficient of determination = 0.50). These results demonstrate that offering concentrates to grazing dairy cows increased milk production per cow and decreased CH₄ emissions per unit of milk produced.     

The effect of Holstein-Friesian,Jersey × Holstein-Friesian,and Norwegian Red × (Jersey × Holstein-Friesian) cows on dry matter intake and production efficiencies in pasture-based systems

The objective of this study was to investigate the effect of cow genotype and parity on dry matter intake (DMI) and production efficiencies in pasture-based systems. Three dairy cow genotypes were evaluated over 3 yr; 40 Holstein-Friesian (HF), 40 Jersey × HF (JEX), and 40 Norwegian Red × JEX (3WAY) each year, with each genotype grazed in equal numbers on 1 of 4 grazing treatments in a 2 × 2 factorial arrangement of treatments [diploid or tetraploid perennial ryegrass (Lolium perenne L.) with or without white clover (Trifolium repens L.)]. A total of 208 individual cows were used during the experiment. The effect of parity (lactation 1, 2, and 3+) was also evaluated. Individual DMI was estimated 8 times during the study, 3 times in 2015 and in 2017, and twice in 2016, using the n-alkane technique. Days in milk at each DMI measurement period were 64, 110, and 189, corresponding to spring, summer, and autumn. Measures of milk production efficiency calculated were total DMI/100 kg of body weight (BW), milk solids (kg fat + protein; MSo)/100 kg of BW, solids-corrected milk (SCM)/100 kg of BW, and unité fourragère lait (net energy requirements for lactation equivalent of 1 kg of standard air-dry barley; UFL) available for standard (4.0% fat and 3.1% protein content) milk production after accounting for maintenance. During the DMI measurement periods HF had a greater milk yield (23.2 kg/cow per d) compared with JEX and 3WAY (22.0 and 21.9 kg/cow per d, respectively) but there was no difference in MSo yield. Holstein-Friesian and JEX, and JEX and 3WAY had similar DMI, but HF had greater total DMI than 3WAY (DMI was 17.2, 17.0, and 16.7 kg/cow per d for HF, JEX, and 3WAY, respectively). Jersey × Holstein-Friesian cows were the most efficient for total DMI/100 kg of BW, SCM/100 kg of BW, and MSo/100 kg of BW (3.63, 4.96, and 0.39 kg/kg of BW) compared with HF (3.36, 4.51, and 0.35 kg/kg of BW) and 3WAY (3.45, 4.63, and 0.37 kg/kg of BW), respectively. Unité fourragère lait available for standard milk production after accounting for maintenance was not different among genotypes. As expected, DMI differed significantly among parities with greater parity cows having higher DMI and subsequently higher milk and MSo yield. Although all 3 genotypes achieved high levels of DMI and production efficiency, JEX achieved the highest production efficiency. Some of the efficiency gains (SCM/100 kg of BW, MSo/100 kg of BW, and total DMI/100 kg of BW) achieved with JEX decreased when the third breed (Norwegian Red) was introduced.     

Modeling the effects of heat stress in animal performance and enteric methane emissions in lactating dairy cows

Heat stress (HS) negatively affects dry matter intake (DMI), milk yield (MY), feed efficiency (FE), and free water intake (FWI) in dairy cows, with detrimental consequences to animal welfare, health, and profitability of dairy farms. Absolute enteric methane (CH₄) emission, yield (CH₄/DMI), and intensity (CH₄/MY) may also be affected. Therefore, the goal of this study was to model the changes in dairy cow productivity, water intake, and absolute CH₄ emissions, yield, and intensity with the progression (days of exposure) of a cyclical HS period in lactating dairy cows. Heat stress was induced by increasing the average temperature by 15°C (from 19°C in the thermoneutral period to 34°C) while keeping relative humidity constant at 20% (temperature-humidity index peaks of approximately 83) in climate-controlled chambers for up to 20 d. A database composed of individual records (n = 1,675) of DMI and MY from 82 heat-stressed lactating dairy cows housed in environmental chambers from 6 studies was used. Free water intake was also estimated based on DMI, dry matter, crude protein, sodium, and potassium content of the diets, and ambient temperature. Absolute CH₄ emissions was estimated based on DMI, fatty acids, and dietary digestible neutral detergent fiber content of the diets. Generalized additive mixed-effects models were used to describe the relationships of DMI, MY, FE, and absolute CH₄ emissions, yield, and intensity with HS. Dry matter intake and absolute CH₄ emissions and yield reduced with the progression of HS up to 9 d, when it started to increase again up to 20 d. Milk yield and FE reduced with the progression of HS up to 20 d. Free water intake (kg/d) decreased during the exposure to HS mainly because of a reduction in DMI; however, when expressed in kg/kg of DMI it increased modestly. Methane intensity also reduced initially up to d 5 during HS exposure but then started to increase again following the DMI and MY pattern up to d 20. However, the reductions in CH₄ emissions (absolute, yield, and intensity) occurred at the expense of decreases in DMI, MY, and FE, which are not desirable. This study provides quantitative predictions of the changes in animal performance (DMI, MY, FE, FWI) and CH₄ emissions (absolute, yield, and intensity) with the progression of HS in lactating dairy cows. The models developed in this study could be used as a tool to help dairy nutritionists to decide when and how to adopt strategies to mitigate the negative effects of HS on animal health and performance and related environmental costs. Thus, more precise and accurate on-farm management decisions could be taken with the use of these models. However, application of the developed models outside of the ranges of temperature-humidity index and period of HS exposure included in this study is not recommended. Also, validation of predictive capacity of the models to predict CH₄ emissions and FWI using data from in vivo studies where these variables are measured in heat-stressed lactating dairy cows is required before these models can be used.     

The effect of Lolium perenne L. ploidy and Trifolium repens L. inclusion on dry matter intake and production efficiencies of spring-calving grazing dairy cows

The objective of this study was to investigate the effect of perennial ryegrass (Lolium perenne L.; PRG) ploidy and white clover (Trifolium repens L.) inclusion on milk production, dry matter intake (DMI), and milk production efficiencies. Four separate grazing treatments were evaluated: tetraploid PRG only, diploid PRG only, tetraploid PRG with white clover, and diploid PRG with white clover. Individual DMI was estimated 8 times during the study (3 times in 2015, 2 times in 2016, and 3 times in 2017) using the n-alkane technique. Cows were, on average, 64, 110, and 189 d in milk during the DMI measurement period, corresponding to spring, summer, and autumn, respectively. Measures of milk production efficiency were total DMI/100 kg of body weight (BW), milk solids (kg of fat + protein; MSo)/100 kg of BW, solids-corrected milk/100 kg of BW, and MSo/kg of total DMI. Perennial ryegrass ploidy had no effect on DMI; however, a significant increase in DMI (+0.5 kg/cow per day) was observed from cows grazing PRG-white clover swards compared with PRG-only swards. Sward white clover content influenced DMI as there was no increase in DMI in spring (9% sward white cover content), whereas DMI was greater in summer and autumn for cows grazing PRG-white clover swards (+0.8 kg/cow per day) compared with PRG-only swards (14 and 23% sward white clover content, respectively). The greater DMI of cows grazing PRG-white clover swards led to increased milk (+1.3 kg/cow per day) and MSo (+0.10 kg/cow per day) yields. Cows grazing PRG-white clover swards were also more efficient for total DMI/100 kg of BW, solids-corrected milk/100 kg of BW, and MSo/100 kg of BW compared with cows grazing PRG-only swards due to their similar BW but higher milk and MSo yields. The results highlight the potential of PRG-white clover swards to increase DMI at grazing and to improve milk production efficiency in pasture-based systems.     

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

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

Influence of precalving feed allowance on periparturient metabolic and hormonal responses and milk production in grazing dairy cows

Fifty-two multiparous dairy cows were allocated to 4 treatments consuming 5.4, 8.2, 10.0, or 11.0 kg/d of pasture dry matter per cow for 27 +/- 9.6 d precalving. This equated to 1.3, 1.9, 2.4, and 2.6% of body weight (BW; not including the conceptus weight). Following calving, all cows were fed ad libitum on pasture. Blood was sampled 17 d precalving, on day of calving, and on d 1, 2, 3, 4, 7, 14, 28, and 35 postcalving. Results suggest that the near-term grazing dairy cow requires 1.05 MJ of ME/kg of BW(0.75) and that previous estimates of energy requirements were underestimated. Precalving plasma concentrations of glucose, insulin-like growth factor-1, and leptin increased quadratically with increasing pasture intake. This was associated with precalving plasma concentrations of growth hormone that declined linearly, and concentrations of nonesterified fatty acids and beta-hydroxybutyrate that declined quadratically with increasing dry matter intake (DMI). Postcalving plasma concentrations of these metabolites showed no lasting effect of precalving feeding. The effect of precalving nutrition on milk production was small, and other than milk fat, was confined to wk 1 postcalving. Milk fat yield increased with increasing precalving DMI and calving body condition score until wk 3 post-calving, after which treatment effects were not evident. These results indicate that the level of feeding in grazing dairy cows during the last month before calving has only small effects on cow metabolic and hormonal status, and on milk production in the first 5 wk of lactation.