Comparison of 3 mathematical models to estimate lactation performance in dairy cows

Milk yield dynamics and production performance reflect how dairy cows cope with their environment. To optimize farm management, time series of individual cow milk yield have been studied in the context of precision livestock farming, and many mathematical models have been proposed to translate raw data into useful information for the stakeholders of the dairy chain. To gain better insights on the topic, this study aimed at comparing 3 recent methods that allow one to estimate individual cow potential lactation performance, using daily data recorded by the automatic milking systems of 14 dairy farms (7 Holstein, 7 Italian Simmental) from Belgium, the Netherlands, and Italy. An iterative Wood model (IW), a perturbed lactation model (PLM), and a quantile regression (QR) were compared in terms of estimated total unperturbed (i.e., expected) milk production and estimated total milk loss (relative to unperturbed yield). The IW and PLM can also be used to identify perturbations of the lactation curve and were thus compared in this regard. The outcome of this study may help a given end-user in choosing the most appropriate method according to their specific requirements. If there is a specific interest in the post-peak lactation phase, IW can be the best option. If one wants to accurately describe the perturbations of the lactation curve, PLM can be the most suitable method. If there is need for a fast and easy approach on a very large dataset, QR can be the choice. Finally, as an example of application, PLM was used to analyze the effect of cow parity, calving season, and breed on their estimated lactation performance.

Comparative Analysis of Milking and Behavior Characteristics of Multiparous and Primiparous Cows in Robotic Systems

Robotic milking systems are successful innovations in the development of dairy cattle. The objective of this study was to analyse the milking characteristics and behavior of dairy cows of different calving orders in "milk first" robotic milking systems. The data were collected from a commercial herd located in the Midwest region of Minas Gerais (Brazil), which uses an automatic milking system (AMS TM, DeLaval). Were analysed 26,574 observations of 235 Holstein cows were available. Data were evaluated by multivariate analysis of variance and the Tukey test. - Tthe characteristics milk flow and milking efficiency were more favourable for multiparous cows (p <0.01), while the time in the stall was more favourable for primiparous females (p <0.01). The values of handling time were better in the primiparous cows (p <0.01). Primiparous cows had higher amounts of kick-off (p <0.001), and multiparous cows had higher incomplete milkings (p <0.001). The number of incomplete milkings showed a higher ratio in terms of reduction in milk production in 26.6% in primiparous cows and 26.7% in multiparous cows (p <0.01). Regarding the behavioral characteristics, primiparous cows had higher amounts of kickbacks, while multiparous cows had greater quantities of incomplete milkings.

Feed preference in lactating dairy cows for different pellet formulations

Two taste preference experiments were conducted with the same 8 multiparous lactating Jersey cattle (100 ± 7.1 DIM, 30.5 ± 4 0.06 kg of milk yield, 18.8 ± 2.52 kg of DMI in experiment 1; and 215 ± 7.1 DIM, 27.6 ± 3.98 kg of milk yield, 19.6 ± 3.03 kg of DMI in experiment 2). In experiment 1, 4 pellets were formulated and manufactured into 4.0-mm pellets. These were as follows: 45.7% alfalfa meal, 45.7% corn grain, and 8.57% wheat middlings (ALFC); 72.3% corn grain, 18.5% wheat middlings, and 9.25% dried distillers grains and solubles (ENG); a pellet containing 100% dehydrated alfalfa meal (DALF); and a pellet containing a mixture of concentrate ingredients (GMIX; 43.1% corn grain, 26.3% dried distillers grains and solubles, 13.8% wheat middlings, 7.10% dry molasses, 3.18% soybean meal, 0.93% corn oil, and 5.6% minor constituents). Cows were offered 0.50 kg of pellets in a randomized arrangement within the feed bunk. Feed preference was ranked from 1 to 4 with 1 being the most preferred and 4 the least. The resulting preference rankings were averaged (± SE) resulting in a highest (closest to 1) to lowest (furthest from 1) ranking as follows of ALFC (1.38 ± 0.164), ENG (2.13 ± 0.327), GMIX (2.88 ± 0.375), and DALF (3.13 ± 0.350). The probabilities of first choice were 70.6 ± 0.55% ALFC, 16.5 ± 0.46% ENG, 5.50 ± 0.475% DALF, and 7.48 ± 0.455% GMIX. A Z-test was conducted to determine the percentage a treatment would be chosen first differed from the value of no preference at 25%; ALFC and DALF differed from the mean value, whereas no difference was observed for ENG and GMIX. The most preferred pellet (ALFC) was used in a second study and compared against 3 other treatments in which different flavoring agents were added. In this study, 4 pellets were manufactured with ALFC: 45.7% alfalfa meal, 45.7% corn grain, 6.76% wheat middlings, and 1.81% oregano leaf (ALFCO); 45.7% alfalfa meal, 45.7% corn grain, 8.22% wheat middlings, 0.10% melon flavoring, and 0.25% BitterOff (ALFCM); and 45.7% alfalfa meal, 45.7% corn grain, 8.47% wheat middlings, and 0.10% licorice flavoring (ALFCL). The resulting preference rankings were averaged resulting in a highest to lowest ranking as follows: ALFC (1.25 ± 0.164), ALFCO (2.38 ± 0.263), ALFCM (2.63 ± 0.375), and ALFCL (3.25 ± 0.164). The probabilities of first choice were 81.9 ± 0.65% ALFC, 8.49 ± 0.46% ALFCO, 6.50 ± 0.481% ALFCM, and 3.12 ± 0.491% ALFCL. Of the pellet choices, ALFC and ALFCL differed from the mean value of no choice, whereas no difference was observed for ALFCO and ALFCM. Mixtures of corn grain and dehydrated alfalfa meal bound by wheat middlings may serve as a feeding strategy that is preferred by the animals and may be an effective reward to cows entering an automated milk system, and we were unable to improve preference by adding flavoring agents.

Genomic-based genetic parameters for milkability traits derived from automatic milking systems in North American Holstein cattle

The number of dairy farms adopting automatic milking systems (AMS) has considerably increased around the world aiming to reduce labor costs, improve cow welfare, increase overall performance, and generate a large amount of daily data, including production, behavior, health, and milk quality records. In this context, this study aimed to (1) estimate genomic-based variance components for milkability traits derived from AMS in North American Holstein cattle based on random regression models; and (2) derive and estimate genetic parameters for novel behavioral indicators based on AMS-derived data. A total of 1,752,713 daily records collected using 36 milking robot stations and 70,958 test-day records from 4,118 genotyped Holstein cows were used in this study. A total of 57,600 SNP remained after quality control. The daily-measured traits evaluated were milk yield (MY, kg), somatic cell score (SCS, score unit), milk electrical conductivity (EC, mS), milking efficiency (ME, kg/min), average milk flow rate (FR, kg/min), maximum milk flow rate (FRM, kg/min), milking time (MT, min), milking failures (MFAIL), and milking refusals (MREF). Variance components and genetic parameters for MY, SCS, ME, FR, FRM, MT, and EC were estimated using the AIREMLF90 software under a random regression model fitting a third-order Legendre orthogonal polynomial. A threshold Bayesian model using the THRGIBBS1F90 software was used for genetically evaluating MFAIL and MREF. The daily heritability estimates across days in milk (DIM) ranged from 0.07 to 0.28 for MY, 0.02 to 0.08 for SCS, 0.38 to 0.49 for EC, 0.45 to 0.56 for ME, 0.43 to 0.52 for FR, 0.47 to 0.58 for FRM, and 0.22 to 0.28 for MT. The estimates of heritability (± SD) for MFAIL and MREF were 0.02 ± 0.01 and 0.09 ± 0.01, respectively. Slight differences in the genetic correlations were observed across DIM for each trait. Strong and positive genetic correlations were observed among ME, FR, and FRM, with estimates ranging from 0.94 to 0.99. Also, moderate to high and negative genetic correlations (ranging from -0.48 to -0.86) were observed between MT and other traits such as SCS, ME, FR, and FRM. The genetic correlation (± SD) between MFAIL and MREF was 0.25 ± 0.02, indicating that both traits are influenced by different sets of genes. High and negative genetic correlations were observed between MFAIL and FR (-0.58 ± 0.02) and MFAIL and FRM (-0.56 ± 0.02), indicating that cows with more MFAIL are those with lower FR. The use of random regression models is a useful alternative for genetically evaluating AMS-derived traits measured throughout the lactation. All the milkability traits evaluated in this study are heritable and have demonstrated selective potential, suggesting that their use in dairy cattle breeding programs can improve dairy production efficiency in AMS.

Translactational associations of dry off management, milking activity, and somatic cell count in herds with automated milking systems

The objective of this study was to evaluate associations of dry off management factors, milking activity and production data, and somatic cell count prior to dry off and early in the subsequent lactation of cows milked by automated systems. Data were collected for 342 cows from five farms, for two milk tests prior to dry off, and for the two milk tests post calving. The results suggest that the post-calving milking performance of cows milked by automated systems may be more associated with individual cow traits than with their dry off management.

Association between early postpartum rumination time and peak milk yield in dairy cows

Limited information is available on the relationship between rumination time (RT) in the early postpartum period and milk production later in lactation. Therefore, the objectives of this study were to (1) investigate the association of change in RT and average RT during the immediate postpartum period with peak milk yield (PMY) in dairy cows, and (2) determine the best model based on days in milk (DIM) to evaluate this association. Cows from 33 free-flow automatic milking system farms were included in this study, where retrospective milk production and RT data were collected for 12 mo. Cows were categorized by parity number into parity 1 (P1, n = 1,538), parity 2 (P2, n = 1,354), or parity ≥3 (P3+, n = 1,770). For each cow, PMY was identified as the highest daily milk yield up to 180 DIM for P1 and 120 DIM for P2 and P3+ cows. Five change in RT variables and 5 average RT variables were created corresponding to the first 2 to 6 DIM. Change in RT variables were the slope coefficients for change in RT/d related to DIM = 1 extracted from simple linear regressions, and average RT variables were the arithmetic mean RT. Five models analyzing PMY and corresponding variables calculated over the first 2 to 6 DIM had fixed effects of average RT, change in RT, parity, average RT × parity interaction, change in RT × parity interaction, and a random intercept for farm. Peak milk yield occurred at (median) 75, 44, and 46 DIM for P1, P2, and P3+, respectively. Overall PMY was (mean ± standard deviation) 54 ± 11 kg and it increased as parity increased. A positive association was found between change in RT and PMY, and average RT and PMY for P2 and P3+ cows in all 5 models corresponding to the first 2 to 6 DIM, indicating that greater average RT and quicker increase in RT after calving are associated with greater PMY for multiparous cows. Although the model including all 6 DIM had the greatest accuracy, results indicated that rumination data collected over the first 2 DIM may also provide adequate information for the association of average RT and change in RT with PMY in P2 and P3+ cows. For each 100 min/d increase in change in RT over the first 6 DIM, PMY increased by 4.3 (95% confidence interval: 2.2-6.3) and 4.8 (95% confidence interval: 3.2-6.5) kg for P2 and P3+ cows, respectively. Peak milk yield increased by 2.3 (95% CI: 1.7-2.8) and 2.2 (95% confidence interval: 1.7-2.6) kg for each 100 min increase in average RT over the first 6 DIM for P2 and P3+ cows, respectively. No association was observed between rumination behaviors and PMY for P1 cows. Results from this study indicate that the length of time for multiparous cows to achieve a stable RT in the early postpartum period combined with average RT during the same period may be useful in predicting their overall lactation milk production.