Cow-level risk factors associated with the increase in somatic cell count and the occurrence of subclinical mastitis in Brazilian Holstein and Jersey dairy cows.

Mastitis, a multifactorial disease influenced by both cow and herd-level factors, results in significant losses throughout the dairy chain. We aimed to evaluate the relationship between milking frequency (MF), parity order (PO), days in milk (DIM), and milk yield (MY) on somatic cell count (SCC) and the odds of a cow having subclinical mastitis (SCM) in Brazilian Holstein and Jersey dairy cows. Our dataset consisted of 747,520 test-day records from 52,954 cows, including 49,089 Holstein cows and 3865 Jersey cows and 498 herds. The SCC was evaluated using a generalized linear mixed model, whereas SCM occurrence was evaluated using a logistic regression model. A case of SCM was defined when a cow had >200×103 cells/mL. Our results indicated that the SCC increases with higher PO and DIM and decreases in cows milked three times a day and those with higher MY in both breeds (>40 and >25 L/d for Holstein and Jersey, respectively). Increasing MF from two to three times a day reduced the chances of a Holstein and Jersey cow having SCM by 10 and 20 %, respectively. For Holstein and Jersey cows, those with ≥quadriparous had 3.9 times and 2.2 times higher chances, respectively, of having SCM compared to primiparous cows. Cows with >305 DIM had 2.0 times greater chances of having SCM for both, Holstein and Jersey cows, compared to cows with ≤105 DIM. Holstein cows yielding ≥40 L/d had a 75 % lower chance of having SCM compared to those yielding <20 L/d, while Jersey cows with ≥25 L/d had a 60 % lower chance compared to those yielding <15 L/d. In conclusion, higher PO and DIM pose risks, whereas a MF of three times a day and higher MY are protective factors against increases in SCC and SCM occurrence in Brazilian Holstein and Jersey cows.

Thermoregulatory response of black or red lactating Holstein cows in the hot and cold season in southern Brazil.

Dairy cows in pasture-based systems are more susceptible to heat stress. Holstein cows have the black or red phenotypes, the latter having lower absorbance of solar radiation. Therefore, the study's objective was to evaluate whether cows with red (R) coats are more resistant than black (B) cows to hot weather in a subtropical climate. R and B lactating Holstein cows were evaluated during the cold and hot seasons for internal and surface temperature and sweating rate. In the cold season, body temperature (n = 9/group) did not differ between groups, but the average superficial temperature (n = 13/group) was lower in R cows (B: 30.9 ± 0.3 °C; RW: 29.6 ± 0.3 °C; p = 0.02). In the hot season, under mild to moderate heat stress, mean body temperature (n = 9/group) of R cows was lower (B: 38.75 ± 0.01 °C; R: 38.62 ± 0.1 °C; p=<0.0001), whereas no difference was observed in superficial temperature (n = 17/group). The maximum internal temperature and sweating rate (n = 11/group), measured in the hot season, and the number of evaluations in hyperthermia in both seasons did not differ. Therefore, there were differences in thermoregulation between phenotypes under mild to moderate heat stress conditions. However, considering that only discrete differences were observed, the red and white coat is unlikely to benefit the Holstein cow's welfare under mild to moderate thermal stress.

Factors Influencing Milk Quality and Subclinical Mastitis in Dairy Herds Housed in Compost-Bedded Pack Barn System.

The compost-bedded pack barn (CBPB) system has been increasingly adopted by dairy farms due to its ability to enhance animal comfort and milk production. This study evaluated the associations among bedding characteristics, milk quality and composition, and subclinical mastitis (SCM) occurrence in dairy herds housed in CBPB systems. Over a period of six months, data related to milk quality and udder health and bedding sampling were collected from eight dairy farms. Monthly measurements of the bedding temperature and wind speed inside the CBPB were taken, while temperature and relative humidity data inside the CBPB were recorded using a datalogger. Bedding samples were subjected to analysis of moisture, pH, microbiological count, and carbon/nitrogen ratio. Data on milk composition (fat, protein, milk urea nitrogen, and total solids) and quality (somatic cell count and standard plate count) of bulk tank milk were obtained from DHIA results. Canonical correlation analyses were used to evaluate the association between the analyzed group variables, and linear regression models were used to identify associations between bedding characteristics and SCM occurrence in the studied herds. The bedding characteristics that most influenced milk composition and quality were moisture, temperature at 30 cm depth (T30), and bedding pH. Environmental variables played an important role in bedding composting, as they were closely related to the surface temperature and pH. Overall, 62.71% of the variation in milk quality and composition could be explained by the bedding variables, and 77.50% of the variation in the bedding variables was associated with environmental variables. Median SCM prevalence and incidence were 28.6 and 13.8%, respectively. An increase of 1 °C for T30 resulted in a 0.6% reduction in the prevalence of SCM. Additionally, the bedding surface temperature at 22.3 °C resulted in the highest incidence of SCM (~18.1%). Our results demonstrate the importance of controlling microclimatic conditions in the CBPB to optimize the bedding composting process and milk quality.

Body growth of replacement dairy heifers from 3 distinct genetic groups from commercial Brazilian dairy herds.

Few studies have been published on the body growth of replacement dairy heifers from Jersey (JER) and Holstein × Gyr (H × G) breeds, as most of them have focused on Holstein (HOL) heifers. In addition, HOL genetics vary significantly across countries. Our goal was to study the body growth curves of 3 distinct genetic groups of heifers (HOL, H × G, and JER) using data from Brazilian commercial dairy herds. Heart girth [to estimate body weight (BW)], hip height (HH), and withers height (WH) were measured. Weights (heifers and cows) and heights (only heifers) were collected from animals in several herds for each genetic group to model and describe the growth rates, mature body weight (MBW), weights, and heights for the recommended age at first breeding (RAFB) and first calving (RAFC). The RAFB values for HOL, H × G, and JER cattle were 15, 18, and 13 mo, respectively. The RAFC values for HOL, H × G, and JER cattle were 24, 27, and 22 mo, respectively. Data were obtained from 18 dairy farms located in 4 Brazilian states and analyzed using nonlinear modeling. Data were collected from 2,266 animals: 878 HOL, 610 H × G, and 778 JER cattle. We observed different body growth patterns in each genetic group. Jersey cattle matured earlier than HOL and H × G, especially for BW and HH. Mature BW of the HOL, H × G, and JER cattle was 681, 607, and 440 kg, respectively. All genetic groups reached the recommended BW at RAFB. However, the genetic groups did not reach the recommended BW at RAFC. Average daily weight gain from weaning to RAFB was 0.84, 0.53, and 0.54 kg/d for HOL, H × G, and JER cattle, respectively. Average daily gain from RAFB to RAFC was 0.53, 0.42, and 0.48 kg/d for HOL, H × G, and JER cattle, respectively. The HH at RAFB and RAFC were 130 and 139 cm for HOL, 130 and 137 cm for H × G, and 114 and 124 cm for JER. Withers height at RAFB and RAFC were 125 and 134 cm, 125 and 134 cm, and 110 and 121 cm for HOL, H × G, and JER cattle, respectively. In general, the rearing practices were adequate to reach the recommended BW at RAFB but below the recommended BW at RAFC for all genetic groups. In addition, each genetic group demonstrated different body growth patterns, especially for BW.

Feed Intake of Growing Dairy Heifers Raised under Tropical Conditions: A Model Evaluation Using Meta-Analysis.

Several models for predicting dry matter intake (DMI) of replacement dairy heifers have been developed; however, only a few have been evaluated using data from heifers of different breeds raised under tropical conditions. Thus, the objective of this study was to evaluate the DMI equations for dairy heifers managed under tropical conditions. A total of 230 treatment means from 61 studies using dairy heifers (n = 1513 heifers, average body weight = 246 kg) were used. The animals were grouped into two groups based on their genetics: (1) Bos taurus (Holstein, Jersey, Brown Swiss, and Holstein × Jersey) and (2) crossbred (Bos taurus × Bos indicus). Seven previously published DMI equations (HH, HHJ, QUI, STA, 2001 NRC, OFLin, and OFNLin) for heifers were evaluated using mean bias, slope bias, mean squared prediction errors (MSPE) and its decomposition, and other model evaluation statistics. For Bos taurus heifers, our results indicated that OFNLin and HHJ had lower mean bias (0.13 and 0.16 kg/d, respectively) than other models. There was no significant slope or mean bias for HHJ and OFNLin (p > 0.05), indicating agreement between the observed and predicted DMI values. All other models had a significant mean bias (p < 0.05), whereas the QUI model also presented a significant slope bias (p < 0.02). For crossbred heifers, the STA equation was the only one that did not present mean and slope bias significance (p > 0.05). All other DMI models had significant mean bias when evaluated using crossbred data (p < 0.04), and QUI, OFLin, and OFNLin also presented significant slope bias (p < 0.01). Based on our results, predictions from OFNLin and HHJ best represented the observed DMI of Bos taurus heifers (MSPE ≤ 1.25 kg2/d2, mean bias ≤ 0.16 kg/d), whereas STA was the best model for crossbred heifers (MSPE = 1.25 kg2/d2, mean bias = 0.09 kg/d). These findings indicate that not all available models are adequate for estimating the DMI of dairy heifers managed under a tropical climate, with HHJ and OFNLin for Bos taurus and STA for crossbreds being the most suitable models for DMI prediction. There is evidence that models from Bos taurus heifers could be used to estimate the DMI of heifers under tropical conditions. For heifer ration formulation is necessary to consider that DMI is influenced by breed, diet, management, and climate. Future work should also include animal genetic and environmental variables for the prediction of DMI in dairy heifers.

Relationship between thermal environment and morphophysiological, performance and carcass traits of Brahman bulls raised on tropical pasture: A canonical approach to a set of indicators.

The purpose of this study was to understand and discuss the relationships of the thermal environment, morphophysiological response, performance, and carcass traits of Brahman bulls from weaning at 18 months exposed to grazing conditions based on the physiological and evolutionary rationale behind the different proposals for the thermal adaptation of zebu cattle. Fifty-three uncastrated and clinically healthy bulls with birth and weaning weight of 34.0 ± 3.32 kg and 215.5 ± 44.75 kg, respectively, were evaluated. Fifteen canonical correlations were estimated, but only six were significant: thermal environment × thermoregulatory responses (rc = 0.4635; P = 0.0413); thermal environment × performance (rc = 0.4338; P = 0.0218); thermoregulatory responses × performance (rc = 0.5119; P = 0.0071); hair coat characteristics × performance (rc = 0.4939; P = 0.0273); hormone × carcass traits (rc = 0.5408; P = 0.0698); and performance × carcass traits (rc = 0.9644; P < 0.0001). Thermal environment, thermoregulatory responses and hair coat morphology influence of 18.81%, 21.49% and 24.40%, respectively, were found in the performance. We also concluded that (i) rectal temperature (RT) is a homoeothermic indicator; (ii) sweating rate (SR) is an important heat dissipation mechanism to explain the adaptation of zebu animals in the tropics and is related to weight at 550 days (W550); (iii) coat morphology, especially length (HL), diameter (HD) and hair density (ND) are related to animal performance; and (iv) hormonal profile, mainly T4, influences the carcass traits (yield, weight, subcutaneous fat and marble meat).

Production of nutrients in dual-purpose wheat pastures managed with different doses of nitrogen as topdressing - exponential model.

The aims of this study were to evaluate the yield and composition of dual-purpose wheat pasture BRS Tarumã managed with various urea nitrogen (N) doses and validate an exponential model and compare nutrient production costs. The completely randomized design had four replications per treatment (0, 150, 250, 350, and 450 kg N ha-1). For the 350 and 450 kg ha-1 treatments, the cycle was 212 d whereas that of the control was 167 d. The control accumulated 1,771 kg ha-1 dry matter. In contrast, the 450 kg ha-1 treatment accumulated 7,011 kg ha-1 DM. Topdressing nitrogen (150, 250, 350, and 450 kg ha-1) increased the traditional average daily accumulation rate by 586% relative to the control. However, the degree-days method determined a daily accumulation rate 652% higher than the control. The levels of dry matter and other nutrients in BRS Tarumã wheat pasture were influenced by the doses of nitrogen in the topdressing under the same environmental conditions (temperature and rainfall). An exponential model explained the dynamics of nutrient accumulation and was based on the thermal sum of each nitrogen dose impacting the cost per kilogram of pasture produced.

Dietary roughage sources affect lactating Holstein x Zebu cows under experimental conditions in Brazil: a meta-analysis.

To reduce costs, lactating cows are often fed a higher proportion of roughage and alternative feeds; however, such protocols may not lead to optimal milk production. Using a meta-analysis, we evaluated the effects of dietary roughage sources on the composition, voluntary intake, and digestibility of diets fed to crossbred Holstein x Zebu cows under experimental feeding conditions in Brazil. Finally, we assessed the effects of dietary roughage sources on milk yield and composition. The database was composed of 43 experiments with 183 treatments, including 943 lactating cows. The data were obtained from studies published between January 2000 and December 2015. The data were evaluated by analysis of variance, considering the distribution of variables. The diets differed in dry matter and crude protein (CP) contents according to the roughage sources. Diets based on corn silage demonstrated higher intakes of neutral detergent fiber (NDF), CP, and total digestible nutrients, which improved the digestibility of NDF and CP, and resulted in a higher milk yield compared to the diets with one roughage source or those with a combination of two roughage sources. The present study demonstrated that feeding crossbred Holstein x Zebu lactating cows alternative roughage sources compromised milk production and that lactating cows produced higher milk yields when fed corn silage.

Multivariate analysis relating milk production, milk composition, and seasons of the year.

Our objective was to quantify the relationship between seasons of the year, milk production, and milk composition of a dairy farm based on data for 48 consecutive months, using multivariate statistical analyses. The dataset contained information on productive indexes and milk composition from the bulk tank milk, which was measured from milk samples, collected monthly and used to determine the total dry extract and defatted dry extract, lactose, fat, protein, somatic cell count, and total bacterial count. Seasons of the year and milk production/hectare were also considered. Factor, cluster, and discriminant analysis were used to study the relationships between the above-mentioned variables. A positive relationship was noted between season and total dry extract, defatted dry extract, milk fat, and protein, with higher values being observed in winter and spring. Similarly, a positive relationship was noted between season and milk production/hectare, lactose content, with an increase in milk production and lactose content in winter and spring, which was negatively related to the somatic cell count and total bacterial count. Milk production and composition varied mainly with seasons. Multivariate analyses facilitated a better understanding of the relationship between milk production and composition on this dairy farm.

Relationship between climatic variables and the variation in bulk tank milk composition using canonical correlation analysis.

A number of studies have addressed the relations between climatic variables and milk composition, but these works used univariate statistical approaches. In our study, we used a multivariate approach (canonical correlation) to study the impact of climatic variables on milk composition, price, and monthly milk production at a dairy farm using bulk tank milk data. Data on milk composition, price, and monthly milk production were obtained from a dairy company that purchased the milk from the farm, while climatic variable data were obtained from the National Institute of Meteorology (INMET). The data are from January 2014 to December 2016. Univariate correlation analysis and canonical correlation analysis were performed. Few correlations between the climatic variables and milk composition were found using a univariate approach. However, using canonical correlation analysis, we found a strong and significant correlation (rc = 0.95, p value = 0.0029). Lactose, ambient temperature measures (mean, minimum, and maximum), and temperature-humidity index (THI) were found to be the most important variables for the canonical correlation. Our study indicated that 10.2% of the variation in milk composition, pricing, and monthly milk production can be explained by climatic variables. Ambient temperature variables, together with THI, seem to have the most influence on variation in milk composition.

Month-wise variation and prediction of bulk tank somatic cell count in Brazilian dairy herds and its impact on payment based on milk quality.

BACKGROUND: Payment programs based on milk quality (PPBMQ) are used in several countries around the world as an incentive to improve milk quality. One of the principal milk parameters used in such programs is the bulk tank somatic cell count (BTSCC). In this study, using data from an average of 37,000 farms per month in Brazil where milk was analyzed, BTSCC data were divided into different payment classes based on milk quality. Then, descriptive and graphical analyses were performed. The probability of a change to a worse payment class was calculated, future BTSCC values were predicted using time series models, and financial losses due to the failure to reach the maximum bonus for the payment based on milk quality were simulated. RESULTS: In Brazil, the mean BTSCC has remained high in recent years, without a tendency to improve. The probability of changing to a worse payment class was strongly affected by both the BTSCC average and BTSCC standard deviation for classes 1 and 2 (1000-200,000 and 201,000-400,000 cells/mL, respectively) and only by the BTSCC average for classes 3 and 4 (401,000-500,000 and 501,000-800,000 cells/mL, respectively). The time series models indicated that at some point in the year, farms would not remain in their current class and would accrue financial losses due to payments based on milk quality. CONCLUSION: The BTSCC for Brazilian dairy farms has not recently improved. The probability of a class change to a worse class is a metric that can aid in decision-making and stimulate farmers to improve milk quality. A time series model can be used to predict the future value of the BTSCC, making it possible to estimate financial losses and to show, moreover, that financial losses occur in all classes of the PPBMQ because the farmers do not remain in the best payment class in all months.

Estimation of prevalence and incidence of subclinical mastitis in a large population of Brazilian dairy herds.

The objectives of this study were to estimate the prevalence and incidence of subclinical mastitis (SM) in a large population of Brazilian dairy herds and to describe how these indices changed over time. A data set comprising individual cow somatic cell counts (SCC) from 18,316 test days (TD) of 1,809 herds that participated in a Dairy Herd Improvement Association (DHIA) program between January 2011 and May 2015 was available for analysis. Only tests that had ≥10 lactating cows and that were performed at 30 ± 10-d intervals were used for analysis. The final data set included 8,285 TD from 517 herds located in 5 regions of the country. Prevalence (%) of SM was defined as the number of cows with SCC ≥200,000 cells/mL divided by the total number of tested cows on a given TD. The incidence of SM was defined as the number of cows whose SCC increased from <200,000 to ≥200,000 cells/mL over 2 consecutive TD divided by the sum of each cow's days at risk during this interval, expressed as new cases per cow month at risk. Prevalence and incidence of SM were compared among years, regions, herd size categories, and frequency of DHIA testing during the study period. The overall mean prevalence and incidence of SM including all tests performed during the study period was 46.4% and 0.17 new cases per cow month at risk, respectively. The prevalence of SM varied little from 2011 to 2015, and an increasing trend was observed over the years. Prevalence was lower in herds that performed ≥60 DHIA tests during the study period than in those that performed fewer tests and was not different among regions or herd size categories. Incidence of SM varied little over the years and was not different among the regions studied. Prevalence and incidence of SM in the 517 herds studied were high and did not improve over the years. These trends were observed across all herd size categories and regions studied. Producers who had more DHIA tests performed per herd during the study period had lower prevalence of SM. Results of this study highlight the need to establish large-scale milk quality programs in Brazil.