Analysis of lactating cows in commercial Austrian dairy farms: diet composition, and influence of genotype, parity and stage of lactation on nutrient intake, body weight and body condition score

[Relationship between body condition of dairy cows in the peripartum period and selected metabolic parameters in consideration of different breeds]

OBJECTIVE

The results of this study describe the relationship between the body condition of dairy cows and selected metabolic parameters during the peri- and post-partum period with special consideration of 3 local dairy cow breed in Upper Bavaria and the Allgau.

MATERIAL AND METHODS

Three local dairy cattle breeds (Swiss Brown (BV), Simmental (FL), Holstein Friesian (HF)) were examined on 68 farms in southern Germany for 7 consecutive weeks. In dry cows as well as lactating cows (5.-65. day in milk), following blood parameters were investigated: beta-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), creatinine, aspartate aminotransferase, gamma-glutamyltransferase, glutamate dehydrogenase, total protein, albumin, creatine kinase. In addition, body condition (body condition score [BCS] and back fat thickness [BFT]) were recorded. Exploratory and descriptive statistics were used for data analysis.

RESULTS

Concerning the difference in condition before and after calving, the FL showed the smallest difference in RFD. For FL and BV a trend towards higher BFT values could be seen in first lactating cows. For FL and HF, the NEFA values of the later lactating cows were below those of the first lactating cows. The higher lactating cows of BV and FL had higher BHB values. The correlation between BFT and BCS showed the highest R2 (0.53) in the HF cows. BV and FL were below at 0.42 and 0.37. BCS and BFT could not be predicted by the variables NEFA, BHB and liver enzymes. BHB levels of all 3 breeds increased at weeks 2-4 post-partum. The NEFA values for all 3 breeds increased primarily in the 1st-3rd week p.p. in parallel to when the BFT p.p. decreased. NEFA values were highest when body condition declined and therefore when fat mobilization peaked. In BV and HF, there was a constant increase in GLDH when the p.p. BCS difference was there.

CONCLUSION AND CLINICAL RELEVANCE

Body condition assessment (BCS at herd and animals` level, BFT at animal level) is an important tool for animal health monitoring. Due to the recognizable breed specificity, the dairy herds can be dealt with more explicitly. The aim is to optimally influence the energy balance of the cow during early lactation in order maintain the health of the animal and its organ systems.

Feed Efficiency and Physiological Parameters of Holstein and Crossbred Holstein × Simmental Cows

This study aimed to compare the feed efficiency (FE) and physiological parameters of Holstein and crossbred Holstein × Simmental cows in a confinement system during winter and summer. The study was conducted in a dairy farm in southern Brazil by including a total of 48 multiparous cows. The cows were studied for 21 days in two periods, summer and winter, and their daily dry matter intake (DMI), milk yield (MY), rectal temperature (RT), respiratory rate (RR), body weight, and body condition score were recorded. An analysis of variance was conducted using the SAS statistical package. The results showed that crossbred Holstein × Simmental cows have a similar FE as Holstein cows in a high-production system (1.83 × 1.81 kg DMI/kg MY, respectively), and they can achieve the same production levels as purebred Holstein cows (43.8 vs. 44.5 milk/cow/day). Our findings indicated a difference for the period as both genetic groups achieved higher FE in winter than in summer (1.98 vs. 1.67 DMI/kg MY, respectively). In addition, we found evidence that crossbred cows are better at dissipating body heat during heat-stress situations, as they have higher RR in summer compared to purebred cows, while Holstein cows have higher RT in summer afternoons than crossbred cows. Therefore, using crossbred Holstein × Simmental cows is an alternative for high-production systems.

Effects of genotype, parity, season and their interactions on milk yield in crossbred dairy cattle

The crossbred dairy cattle (CDC) have been gaining popularity in the tropical countries for their increased milk yield within a short period of time because of heterogenic additive gene action. Hence, we aimed to investigate whether genotype, parity, season and their interactions had any effect on average daily milk yield (ADMY) of the CDC in a dairy farm at Chattogram district, Bangladesh, for a period of 3 years from January 2016 to December 2019. Total 16,425 retrospective lactation records of 150 multiparous Sahiwal × Friesian₁ (Sahiwal = 50%, HF = 50%), Local × Friesian₁ (Local = 50%, HF = 50%) and Local × Friesian₂ (Local = 25%, HF = 75%) CDC from the first to the third parities (50 for each parity) were collected from the farm records. The generalized linear model and principal component analysis identified substantial impacts of genotype, parity, season and their interactions on ADMY of the CDC. The herd level least squared ADMY was 11.22 ± 0.04 kg/days on a 305-days typical lactation period. The Sahiwal × Friesian₁ CDC produced 7.2% and 5.5% more milk than the Local × Friesian₁ and Local × Friesian₂ respectively. The CDC produced maximum milk in the second parity, which was 7.8% and 0.34% more than the first and third parities. Similarly, the highest ADMY was recorded in the spring, which was 10.8%, 7.3% and 6.6% more than the fall, summer and winter respectively. It was concluded that the Sahiwal × Friesian₁ crossbred produced maximum milk in the spring season at the second parity while other determinants remained constant. The changing patterns of milk yield in different genotype, parity and seasons provided scientific evidence for improving feeding strategy to optimize herd level milk yield of CDC in the commercial dairy farms under tropical perspective.

Integrating diverse data sources to predict disease risk in dairy cattle-a machine learning approach

Livestock farming is currently undergoing a digital revolution and becoming increasingly data-driven. Yet, such data often reside in disconnected silos making them impossible to leverage their full potential to improve animal well-being. Here, we introduce a precision livestock farming approach, bringing together information streams from a variety of life domains of dairy cattle to study whether including more and diverse data sources improves the quality of predictions for eight diseases and whether using more complex prediction algorithms can, to some extent, compensate for less diverse data. Using three machine learning approaches of varying complexity (from logistic regression to gradient boosted trees) trained on data from 5,828 animals in 165 herds in Austria, we show that the prediction of lameness, acute and chronic mastitis, anestrus, ovarian cysts, metritis, ketosis (hyperketonemia), and periparturient hypocalcemia (milk fever) from routinely available data gives encouraging results. For example, we can predict lameness with high sensitivity and specificity (F1 = 0.74). An analysis of the importance of individual variables to prediction performance shows that disease in dairy cattle is a product of the complex interplay between a multitude of life domains, such as housing, nutrition, or climate, that including more and diverse data sources increases prediction performance, and that the reuse of existing data can create actionable information for preventive interventions. Our findings pave the way toward data-driven point-of-care interventions and demonstrate the added value of integrating all available data in the dairy industry to improve animal well-being and reduce disease risk.

A systematic approach to analyse the impact of farm-profiles on bovine health

In this study we present systematic framework to analyse the impact of farm profiles as combinations of environmental conditions and management practices on common diseases in dairy cattle. The data used for this secondary data analysis includes observational data from 166 farms with a total of 5828 dairy cows. Each farm is characterised by features from five categories: husbandry, feeding, environmental conditions, housing, and milking systems. We combine dimension reduction with clustering techniques to identify groups of similar farm attributes, which we refer to as farm profiles. A statistical analysis of the farm profiles and their related disease risks is carried out to study the associations between disease risk, farm membership to a specific cluster as well as variables that characterise a given cluster by means of a multivariate regression model. The disease risks of five different farm profiles arise as the result of complex interactions between environmental conditions and farm management practices. We confirm previously documented relationships between diseases, feeding and husbandry. Furthermore, novel associations between housing and milking systems and specific disorders like lameness and ketosis have been discovered. Our approach contributes to paving a way towards a more holistic and data-driven understanding of bovine health and its risk factors.

The Capacity of Holstein-Friesian and Simmental Cows to Correct a Negative Energy Balance in Relation to Their Performance Parameters, Course of Lactation, and Selected Milk Components

A significant factor in improving the performance of dairy cows is their physiological ability to correct a negative energy balance (NEB). This study, using Simmental (SIM) and Holstein-Friesian (HF) cows, aimed to assess changes in NEB (non-esterified fatty acid; body condition score; and C16:0, C18:0, and C18:1) and its effect on the metabolic efficiency of the liver (β-hydroxybutyrate and urea). The effects of NEB on daily yield, production at peak lactation and its duration, and changes in selected milk components were assessed during complete lactation. Up to peak lactation, the loss of the body condition score was similar in both breeds. Subsequently, SIM cows more efficiently restored their BCS. HF cows reached peak lactation faster and with a higher milk yield, but they were less able to correct NEB. During lactation, their non-esterified fatty acid, β-hydroxybutyrate, C16:0, C18:0, C18:1, and urea levels were persistently higher, which may indicate less efficient liver function during NEB. The dynamics of NEB were linked to levels of leptin, which has anorectic effects. Its content was usually higher in HF cows and during intensive lactogenesis. An effective response to NEB may be exploited to improve the production and nutritional properties of milk. In the long term, it may extend dairy cows' productive life and increase lifetime yield.

Energy Balance Indicators during the Transition Period and Early Lactation of Purebred Holstein and Simmental Cows and Their Crosses

Simple Summary

Dairy cows undergo a very challenging time between the weeks immediately before calving and the start of lactation after calving. In particular, high yielding dairy cows, such as purebred Holstein cows, have to cope with a severe negative energy balance. In comparison to the feed (energy) intake, they produce a great surplus of milk energy. The energy deficit is supposed to be smaller in dual-purpose breeds, such as (German) Simmental. Therefore, crossbreeding of both breeds, with the aim of using the advantageous characteristics of both breeds, and the expected advantage of crossbred cows, might reduce the negative effects of the metabolic and physiologic challenges by improving the production efficiency of dairy herds. After calving, Simmental cows and cows with greater Simmental proportions decreased less in the body condition score, had lower concentrations of ketone bodies, and nonesterified fatty acids in the blood, which are common indicators of metabolic disorders during the transition period. In particular, first generation (F1) crossbred cows produced more energy corrected milk (ECM) than purebred Holstein cows, while the other crossbred generations still showed positive heterosis effects for ECM. That means, they produced more ECM than the average of both parental breeds.

Abstract

Crossbreeding in dairy cattle has been used to improve functional traits, milk composition, and efficiency of Holstein herds. The objective of the study was to compare indicators of the metabolic energy balance, nonesterified fatty acids (NEFA), beta-hydroxybutyrate (BHBA), glucose, body condition score (BCS) back fat thickness (BFT), as well as milk yield and milk composition of Holstein and Simmental cows, and their crosses from the prepartum period until the 100th day of lactation at the Livestock Center of the Ludwig Maximilians University (Munich, Germany). In total, 164 cows formed five genetic groups according to their theoretic proportion of Holstein and Simmental genes as follows: Holstein (100% Holstein; n = 9), R1-Hol (51–99% Holstein; n = 30), first generation (F1) crossbreds (50% Holstein, 50% Simmental; n = 17), R1-Sim (1–49% Holstein; n = 81) and Simmental (100% Simmental; n = 27). The study took place between April 2018 and August 2019. BCS, BFT blood parameters, such as BHBA, glucose, and NEFA were recorded weekly. A mixed model analysis with fixed effects breed, week (relative to calving), the interaction of breed and week, parity, calving year, calving season, milking season, and the repeated measure effect of cow was used. BCS increased with the Simmental proportion. All genetic groups lost BCS and BFT after calving. Simmental cows showed lower NEFA values. BHBA and glucose did not differ among genetic groups, but they differed depending on the week relative to calving. Simmental and R1-Sim cows showed a smaller effect than the other genetic groups regarding changes in body weight, BCS, or back fat thickness after a period of a negative energy balance after calving. There was no significant difference for milk yield among genetic groups, although Simmental cows showed a lower milk yield after the third week after calving. Generally, Simmental and R1-Simmental cows seemed to deal better with a negative energy balance after calving than purebred Holstein and the other crossbred lines. Based on a positive heterosis effect of 10.06% for energy corrected milk (ECM), the F1, however, was the most efficient crossbred line.

Analysis of lactating cows on commercial Austrian dairy farms: the influence of genotype and body weight on efficiency parameters

The aim of this study was twofold: first, to evaluate the influence of body weight on the efficiency of dairy cows, and second, to analyze the current state of dairy cattle populations as part of the Austrian Cattle Breeding Association's Efficient Cow project.

Data of Fleckvieh (FV, dual-purpose Simmental), Fleckvieh×Red Holstein (FV×RH), Holstein (HF) and Brown Swiss (BS) dairy cows (161 farms, 6098 cows) were collected at each performance recording during the year 2014.

In addition to routinely recorded data (e.g., milk yield, fertility), body weight, body measurements, body condition score (BCS) and individual feed information were also collected. The following efficiency traits were considered: body weight efficiency as the ratio of energy-corrected milk (ECM) to metabolic body weight, feed efficiency (kilogram ECM per kilogram dry-matter intake) and energy efficiency expressed as the ratio of energy in milk to energy intake.

The relationship of milk yield to body weight was shown to be nonlinear. Milk yield decreased in cows above the 750 kg body weight class for HF, BS and FV×RH with 68 % RH genes, but less dramatically and later for FV at 800 kg. This resulted in an optimum body weight for feed and energy efficiency. BS and HF had the highest efficiency in a narrower and lighter body weight range (550–700 kg) due to a stronger curvature of the parabolic curve. Contrary to this, the efficiency of FV did not change as much as it did in the dairy breeds with increasing body weight, meaning that FV had a similar feed and energy efficiency in a range of 500–750 kg. The breed differences disappeared when body weight ranged between 750 and 800 kg.

The average body weight of the breeds studied (FV 722 kg, BS 649 and HF 662 kg) was in the optimum range. FV was located at the upper end of the decreasing segment.

In conclusion, an optimum body weight range for efficiency does exist, due to the nonlinear relationship of milk yield and body weight. Specialized dairy breeds seem to respond more intensively to body weight range than dual-purpose breeds, due to the stronger curvature. Cows with medium weights within a population are the most efficient. Heavy cows (>750 kg) produce even less milk. A further increase in dairy cows' body weights should therefore be avoided.

Body weight prediction using body size measurements in Fleckvieh, Holstein, and Brown Swiss dairy cows in lactation and dry periods

The objective of this study was to predict cows' body weight from body size measurements and other animal data in the lactation and dry periods. During the whole year 2014, 6306 cows (on 167 commercial Austrian dairy farms) were weighed at each routine performance recording and body size measurements like heart girth (HG), belly girth (BG), and body condition score (BCS) were recorded. Data on linear traits like hip width (HW), stature, and body depth were collected three times a year. Cows belonged to the genotypes Fleckvieh (and Red Holstein crosses), Holstein, and Brown Swiss. Body measurements were tested as single predictors and in multiple regressions according to their prediction accuracy and their correlations with body weight. For validation, data sets were split randomly into independent subsets for estimation and validation. Within the prediction models with a single body measurement, heart girth influenced relationship with body weight most, with a lowest root mean square error (RMSE) of 39.0 kg, followed by belly girth (39.3 kg) and hip width (49.9 kg). All other body measurements and BCS resulted in a RMSE of higher than 50.0 kg. The model with heart and belly girth (ModelHGBG) reduced RMSE to 32.5 kg, and adding HW reduced it further to 30.4 kg (ModelHGBGHW). As RMSE and the coefficient of determination improved, genotype-specific regression coefficients for body measurements were introduced in addition to the pooled ones. The most accurate equations, ModelHGBG and ModelHGBGHW, were validated separately for the lactation and dry periods. Root mean square prediction error (RMSPE) ranged between 36.5 and 37.0 kg (ModelHGBGHW, ModelHGBG, lactation) and 39.9 and 41.3 kg (ModelHGBGHW, ModelHGBG, dry period). Accuracy of the predictions was evaluated by decomposing the mean square prediction error (MSPE) into error due to central tendency, error due to regression, and error due to disturbance. On average, 99.6 % of the variance between estimated and observed values was caused by disturbance, meaning that predictions were valid and without systematic estimation error. On the one hand, this indicates that the chosen traits sufficiently depicted factors influencing body weight. On the other hand, the data set was very heterogeneous and large. To ensure high prediction accuracy, it was necessary to include body girth traits for body weight estimation.