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The inclusion of feed intake and efficiency traits in dairy cow breeding goals can lead to increased risk of metabolic stress. An easy and inexpensive way to monitor postpartum energy status (ES) of cows is therefore needed. Cows' ES can be estimated by calculating the energy balance from energy intake and output and predicted by indicator traits such as change in body weight (Delta BW), change in body condition score (Delta BCS), milk fat:protein ratio (FPR), or milk fatty acid (FA) composition. In this study, we used blood plasma nonesterified fatty acids (NEFA) concentration as a biomarker for ES. We determined associations between NEFA concentration and ES indicators and evaluated the usefulness of body and milk traits alone, or together, in predicting ES of the cow. Data were collected from 2 research herds during 2013 to 2016 and included 137 Nordic Red dairy cows, all of which had a first lactation and 59 of which also had a second lactation. The data included daily body weight, milk yield, and feed intake and monthly BCS. Plasma samples for NEFA were collected twice in lactation wk 2 and 3 and once in wk 20. Milk samples for analysis of fat, protein, lactose, and FA concentrations were taken on the blood sampling days. Plasma NEFA concentration was higher in lactation wk 2 and 3 than in wk 20 (0.56 +/- 0.30, 0.43 +/- 0.22, and 0.13 +/- 0.06 mmol/L, respectively; all means +/- standard deviation). Among individual indicators, C18:1 cis-9 and the sum of C18:1 in milk had the highest correlations (r = 0.73) with NEFA. Seven multiple linear regression models for NEFA prediction were developed using stepwise selection. Of the models that included milk traits (other than milk FA) as well as body traits, the best fit was achieved by a model with milk yield, FPR, Delta BW, Delta BCS, FPR x Delta BW, and days in milk. The model resulted in a cross-validation coefficient of determination (R(2)cv) of 0.51 and a root mean squared error (RMSE) of 0.196 mmol/L. When only milk FA concentrations were considered in the model, NEFA prediction was more accurate using measurements from evening milk than from morning milk (R(2)cv = 0.61 vs. 0.53). The best model with milk traits contained FPR, C10:0, C14:0, C18:1 cis-9, C18:1 cis-9 x C14:0, and days in milk (R(2)cv = 0.62; RMSE = 0.177 mmol/L). The most advanced model using both milk and body traits gave a slightly better fit than the model with only milk traits (R(2)cv = 0.63; RMSE = 0.176 mmol/L). Our findings indicate that ES of cows in early lactation can be monitored with moderately high accuracy by routine milk measurements.

Currently, routine recordings of dry matter intake (DMI) in commercial herds are practically nonexistent. Recording DMI from commercial herds is a prerequisite for the inclusion of feed efficiency (FE) traits in dairy cattle breeding goals. To develop future on-farm phenotyping strategies, recording strategies that are low cost and less demanding logistically and that give relatively accurate estimates of the animal's genetic merit are therefore needed. The objectives of this study were (1) to estimate genetic parameters for daily DMI and FE traits and use the estimated parameters to simulate daily DMI phenotypes under different DMI recording scenarios (SCN) and (2) to use the simulated data to estimate for different scenarios the associated reliability of estimated breeding value and accuracies of genomic prediction for varying sizes of reference populations. Five on-farm daily DMI recording scenarios were simulated: once weekly (SCN1), once monthly (SCN2), every 2 mo (SCN3), every 3 mo (SCN4), and every 4 mo (SCN5). To estimate reliability of estimated breeding values, DMI and FE observations and true breeding values were simulated based on variance components estimated for daily observations of Nordic Red cows. To emulate realistic on-farm recording, 5 data set replicates, each with 36,037 DMI and FE records, were simulated for real pedigree and data structure of 789 Holstein cows. Observations for the 5 DMI recording scenarios were generated by discarding data in a step-wise manner from the full simulated data per the scenario's definitions. For each of these scenarios, reliabilities were calculated as correlation between the true and estimated breeding values. Variance components and genetic parameters were estimated for daily DMI, residual feed intake (RFI), and energy conversion efficiency (ECE) fitting the random regression model. Data for variance components were from 227 primiparous Nordic Red dairy cows covering 8 to 280 d in milk. Lactation-wise heritability for DMI, RFI, and ECE was 0.33, 0.12, and 0.32, respectively, and daily heritability estimates during lactation ranged from 0.18 to 0.45, 0.08 to 0.32, and 0.08 to 0.45 for DMI, RFI, and ECE, respectively. Genetic correlations for DMI between different stages of lactation ranged from −0.50 to 0.99. The comparison of different on-farm DMI recording scenarios indicated that adopting a less-frequent recording scenario (SCN3) gave a similar level of accuracy as SCN1 when 17 more daughters are recorded per sire over the 46 needed for SCN1. Such a strategy is less demanding logistically and is low cost because fewer observations need to be collected per animal. The accuracy of genomic predictions associated with the 5 recording scenarios indicated that setting up a relatively larger reference population and adopting a less-frequent DMI sampling scenario (e.g., SCN3) is promising. When the same reference population size was considered, the genomic prediction accuracy of SCN3 was only 5.0 to 7.0 percentage points lower than that for the most expensive DMI recording strategy (SCN1). We concluded that DMI recording strategies that are sparse in terms of records per cow but with slightly more cows recorded per sire are advantageous both in genomic selection and in traditional progeny testing schemes when accuracy, logistics, and cost implications are considered.

The main objective of this study was to assess the genetic differences in metabolizable energy efficiency and efficiency in partitioning metabolizable energy in different pathways: maintenance, milk production, and growth in primiparous dairy cows. Repeatability models for residual energy intake (REI) and metabolizable energy intake (MEI) were compared and the genetic and permanent environmental variations in MEI were partitioned into its energy sinks using random regression models. We proposed 2 new feed efficiency traits: metabolizable energy efficiency (MEE), which is formed by modeling MEI fitting regressions on energy sinks [metabolic body weight (BW0.75), energy-corrected milk, body weight gain, and body weight loss] directly; and partial MEE (pMEE), where the model for MEE is extended with regressions on energy sinks nested within additive genetic and permanent environmental effects. The data used were collected from Luke's experimental farms Rehtijarvi and Minkio between 1998 and 2014. There were altogether 12,350 weekly MEI records on 495 primiparous Nordic Red dairy cows from wk 2 to 40 of lactation. Heritability estimates for REI and MEE were moderate, 0.33 and 0.26, respectively. The estimate of the residual variance was smaller for MEE than for REI, indicating that analyzing weekly MEI observations simultaneously with energy sinks is preferable. Model validation based on Akaike's information criterion showed that pMEE models fitted the data even better and also resulted in smaller residual variance estimates. However, models that included random regression on BW0.75 converged slowly. The resulting genetic standard deviation estimate from the pMEE coefficient for milk production was 0.75 MJ of MEI/kg of energy-corrected milk. The derived partial heritabilities for energy efficiency in maintenance, milk production, and growth were 0.02, 0.06, and 0.04, respectively, indicating that some genetic variation may exist in the efficiency of using metabolizable energy for different pathways in dairy cows.

Residual feed intake (RFI) is a candidate trait for feed efficiency in dairy cattle. We investigated the influence of lactation stage on the effect of energy sinks in defining RFI and the genetic parameters for RFI across lactation stages for primiparous dairy cattle. Our analysis included 747 primiparous Holstein cows, each with recordings on dry matter intake (DMI), milk yield, milk composition, and body weight (BW) over 44 lactation weeks. For each individual cow, energy-corrected milk (ECM), metabolic BW (MBW), and change in BW (∆BW) were calculated in each week of lactation and were taken as energy sinks when defining RFI. Two RFI models were considered in the analyses; RFI model [1] was a 1-step RFI model with constant partial regression coefficients of DMI on energy sinks (ECM, MBW, and ∆BW) over lactation. In RFI model [2], data from 44 lactation weeks were divided into 11 consecutive lactation periods of 4 wk in length. The RFI model [2] was identical to model [1] except that period-specific partial regressions of DMI on ECM, MBW, and ∆BW in each lactation period were allowed across lactation. We estimated genetic parameters for RFI across lactation by both models using a random regression method. Using RFI model [2], we estimated the period-specific effects of ECM, MBW, and ∆BW on DMI in all lactation periods. Based on results from RFI model [2], the partial regression coefficients of DMI on ECM, MBW, and ∆BW differed across lactation in RFI. Constant partial regression coefficients of DMI on energy sinks over lactation was not always sufficient to account for the effects across lactation and tended to give roughly average information from all period-specific effects. Heritability for RFI over 44 lactation weeks ranged from 0.10 to 0.29 in model [1] and from 0.10 to 0.23 in model [2]. Genetic variance and heritability estimates for RFI from model [2] tended to be slightly lower and more stable across lactation than those from model [1]. In both models, RFI was genetically different over lactation, especially between early and later lactation stages. Genetic correlation estimates for RFI between early and later lactation tended to be higher when using model [2] compared with model [1]. In conclusion, partial regression coefficients of DMI on energy sinks differed across lactation when modeling RFI. Neglect of lactation stage when defining RFI could affect the assessment of RFI and the estimation of genetic parameters for RFI across lactation. Key words: residual feed intake, dairy cattle, energy sink, lactation stage, genetic parameter

Increased availability of automated weighing systems have made it possible to record massive amounts of body weight (BW) data in a short time. If the BW measurement is unbiased, the changes in BW reflect the energy status of the cow and can be used for management or breeding purposes. The usefulness of the BW data depends on the reliability of the measures. The noise in BW measurements can be smoothed by fitting a parametric or time series model into the BW measurements. This study examined the accuracy of different models to predict BW of the cows based on daily BW measurements and investigated the usefulness of modeling in increasing the value of BW measurements as management and breeding tools. Data included daily BW measurements, production, and intake from 230 Nordic Red dairy cows. The BW of the cows was recorded twice a day on their return from milking. In total, the data included 50,594 daily observations with 98,418 BW measurements. A clear diurnal change was present in the BW of the cows even if they had feed available 24 h. The daily average BW were used in the modeling. Five different models were tested: (1) a cow-wise fixed second-order polynomial regression model (FiX) including the exponential Wilmink term, (2) a random regression model with fixed and random animal lactation stage functions (MiX), (3) MiX with 13 periods of weighing added (PER), (4) natural cubic smoothing splines with 8 equally spaced knots (SPk8), and (5) spline model with no restriction on knots but a smoothing parameter corresponding to a fit of 5 degrees of freedom (SPdf5). In the original measured BW data, the within-animal variation was 6.4% of the total variance. Modeling decreased the within animal variation to levels of 2.9 to 5.1%. The smallest day-to-day variation and thereafter highest day-to-day repeatabilities were with PER and MiX models. The usability of modeled BW as energy balance (EB) indicator were evaluated by estimating relationships between EB, or EB indicators, and modeled BW change. In all cases the modeling increased the correlation and thus the reliability of the BW measurements. From all of the tested models, the best predictive value was attained by the random regression model with fixed and random animal lactation stage functions. Based on results, modeling of BW significantly increases the usefulness of BW as an EB predictor and management indicator.

Forty Finnish Ayrshire cows, 16 primiparous and 24 multiparous, were randomly assigned to 1 of 2 treatments (FF1 or FF5). Total mixed ration (TMR) was fed once a day on the FF1 treatment and 5 times a day on the FF5 treatment. The experiment began at calving and continued to wk 28 of lactation. The TMR consisted of a grass silage and concentrate mix. The amount of concentrate in the TMR was 51% on a DM basis. The feeding frequency had no effect on milk or energy-corrected milk yields or on milk composition. The average energy-corrected milk yield was 32.8 kg/d on the FF1 treatment and 32.5 kg/d on the FF5 treatment. The less frequent feeding increased the dry matter intake (DMI) of cows. The average DMI during the experiment was 20.9 kg/d on the FF1 treatment and 19.9 kg/d on the FF5 treatment. The difference in DMI was due to the differences in DMI of the mature cows. Energy and protein conversion tended to be lower with feeding once a day compared with feeding 5 times a day. The cows' feeding behavior was also observed. Cows fed 5 times a day tended to eat quite evenly after each delivery, whereas on the FF1 treatment there were 2 clear feeding peaks in the evening after the feed delivery. The time spent eating during the observation period was longer on FF5 than on FF1. The cows fed once a day spent more time lying than the cows fed 5 times a day. Based on the observations of feeding behavior, feeding a TMR 5 times a day seemed to be too frequent based on the increased restlessness and decreased lying time of the cows.

The aim of this paper was to study the genetic parameters for feed intake, milk production, and energy balance in Nordic Red dairy cattle from an experimental data set. The data were collected at the MTT Agrifood Research Finland Rehtijarvi experimental farm in 4 feeding trials between 1998 and 2008, and included lactation wk 2 to 30 for 291 Nordic Red nucleus heifers descending from 72 different sires. The studied traits included weekly averages for energy-corrected milk yield (ECM, kg/d), dry matter intake (kg/d), body weight (BW, kg), body condition score (BCS, score 1 to 5), and energy balance (EB, MJ of metabolizable energy/d). The data were analyzed with both fixed and random regression models. The heritabilities of ECM and BCS were moderate to high and remained fairly constant over the entire lactation period, whereas the heritabilities of BW and EB were the highest in early lactation (0.47 and 0.37, respectively) and declined later on. The heritabilities of DMI were highest (0.33) around lactation wk 5 and again at lactation wk 30, and were somewhat lower at the beginning of the lactation and in the middle period. The genetic correlations between the traits differed considerably between early and later lactation periods, especially for the trait pairs ECM-dry matter intake, ECM-EB, BW-EB, and BCS-EB, being negative or close to zero in lactation wk 2 to 5 but turning moderate to strong and positive by lactation wk 10. The results suggest that the lactating cows express their genetic potential for feed intake and energy utilization most clearly between lactation wk 2 to 10. The best candidate trait for selection might be EB in lactation wk 2 to 5 because it has a moderate heritability and is not genetically correlated with BW or BCS in that period.

Existing variation in energy efficiency and its relationship with milk yield and milk composition, body weight and body condition, feed intake, and energy status was studied in primiparous Nordic Red dairy cattle with data including 3,752 weekly records from 145 cows. Energy efficiency was defined as energy conversion efficiency (ECE) and as residual energy intake (REI) estimated based on Finnish feeding standards (REI₁) or from the current data (REI₂). The results indicated true phenotypic variation in energy efficiency of the cows. The proportion of total variance due to the animal was 0.35 for REI₁, 0.30 for REI₂, and 0.50 for ECE. The high efficiency based on ECE was associated with increased mobilization of body reserves (r = -0.50) and decreased dry matter intake (r = -0.51). With REI as an energy efficiency measure, the increased efficiency was associated with a large decrease in feed intake (REI₁: r = 0.60; REI2: r = 0.74) without any effect on body weight change (REI₁: r = 0.13; REI2: r = 0.00). Increased efficiency based on ECE and REI₁ was associated with increased milk yield (ECE: r = 0.58; REI₁: r = -0.41). A clear effect of stage of lactation on REI was found, which could be caused by true differences in utilization of metabolizable energy during lactation. However, it might also be related, in part, to the lack of knowledge of the composition of body weight change in the beginning of lactation.

Soybean meal, fish meal, meat and bone meal, and two animal by-product blends (A, B) were compared as nitrogen sources for corn silage diets for growing dairy heifers of different ages. Supplement A (62% CP) was a blend of meat and bone, meat, poultry, blood, and feather meals. Supplement B (71% CP) included the same ingredients as A but had a higher amount of blood meal and also a low concentration of fish meal. Holstein heifers were divided into five age groups with initial age of 210, 263, 314, 462, and 524 d in age groups 1, 2, 3, 4, and 5, respectively. Animals were fed a corn silage-based diet for ad libitum intake. Average intakes and gains (kg/d) were 5.66, 1.10; 6.76, 1.04; 7.45, .93; 8.51, .83; and 8.68, .80 for age groups 1, 2, 3, 4, and 5, respectively. No significant interactions among protein meal and age group were found for any measurement. Among protein sources no significant differences in growth, DM intake, or feed efficiency were found. However, the DM intake tended to be lower for heifers fed fish meal diets than for heifers fed other diets.

Eighty primiparous cows were assigned to either soybean meal, fish meal, or A and B treatments. Commercial supplement A was a blend of meat and bone, meat, poultry, blood, and feather meals. Commercial supplement B included the same ingredients as A but had a higher amount of blood meal and also a low concentration of fish meal. In vitro undegradabilities of protein were 32.0, 37.8, 38.8, and 42.4% for soybean meal, fish meal, A, and B rations, respectively. Milk yields and body weight changes were not significantly affected by treatments. Because of the lower milk fat content on fish meal diet, FCM yield was lower for cows fed fish meal than soybean meal or B supplements. Milk protein content was 3.19, 3.19, 3.03, and 3.09% on soybean meal, fish meal, A, and B diets, respectively. The calculated protein efficiency (100* output/input) on soybean meal, fish meal, A, and B diets were 47.8, 51.6, 45.9, and 48.7, respectively. The undegraded protein in the fish meal after an 18-h in vitro incubation with protease enzyme (Streptomyces griseus, type V) had a higher concentration of methionine and lysine than the undegraded protein of other supplements. Flavor of milk produced on the soybean and fish meal diets was evaluated. No flavor differences were detected.