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Our aims were to evaluate changes in body characteristics, milk yield and milk constituents as well as to determine the relationship between the thermal environment and production characteristics during the first lactation of dairy Gyr cows managed on pasture. Between 2013 and 2015, forty-five primiparous dairy Gyr cows were evaluated from prepartum to 10 months of lactation in Southeast of Brazil. Body weight, body condition score (BCS), subcutaneous fat thickness (SFT), milk yield (305 d), and milk constituents were collected monthly and progesterone was collected weekly. Additionally, we determined the temperature humidity index (THI) based on microclimate data. Overall, the cows lost body weight until six months of lactation and there was a progressive decrease in BCS, SFT, milk yield and milk lactose as the months in lactation progressed. In contrast, there was an increase in milk fat, milk protein and milk solids. The thermal environment did not pose a consistent heat challenge, nevertheless, we found a positive correlation between the average THI two days before milk collection with milk yield, fat and lactose contents, but in contrast a negative correlation was found with total solids and protein. In conclusion, the THI and months of lactation affected the yield and constituents of milk. However, more studies are necessary to understand the impacts of body characteristics and thermal environment on yield and milk constituents throughout the productive life of Gyr dairy cows.

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In dairy systems with Zebu breeds, calves are not immediately separated from their dams after calving; consequently, maternal care and protective behavior are important, influencing both productive performance and stockpeople's safety. Our objectives were to: (1) investigate the effects of a training protocol involving pre-calving positive stimulation, delivered prior to calving, on the maternal care of primiparous Gyr cows; and (2) evaluate the effects of this training protocol on maternal protective behavior towards handlers during the first calf handling. Primiparous dairy Gyr cows (n = 37) were allocated into two groups: training (n = 16) and control (n = 21). Animal behaviors were recorded in three periods: post-calving, first calf handling, and post-handling. Maternal protective behavior during calf handling was assessed from measures of aggressiveness, attention, displacement, and agitation. Calf latency to stand up (p < 0.01) and sex (p < 0.01) differed between the training and control groups. The training group had less touching (p = 0.03), more time not interacting with the calf (p = 0.03), tended to be less protective (p = 0.056), and moved less (p < 0.01) during the first handling of their calves. In conclusion, the primiparous dairy Gyr cows subjected to pre-calving training protocol displayed less maternal care and displacement during the first handling of their calves and tended to be less protective.

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The maternal care of cows can influence both the milk production and the performance of their calves, making this a topic of important relevance for the production industry that uses zebu cattle. The aims of this study were to 1) investigate the effects of parity on the behaviors of Gyr cows during the peripartum period; 2) characterize the maternal defensiveness of primiparous and multiparous cows towards handlers during the first handling of their calves; and 3) evaluate the relationships between cows' behaviors at the peripartum period and maternal defensiveness. Thirty-one Gyr cows (primiparous and multiparous), from Empresa de Pesquisa Agropecuária de Minas Gerais (Brazil), were used. The animals were placed in a maternity paddock monitored by video cameras. The behaviors of the animals were collected in four periods: Pre-calving, Post-calving, First handling of calf and Post-handling. Primiparous cows presented more pain signs, reflected in arched spine (P = 0.05), and tended to move more (P = 0.07) than the multiparous in the Pre-calving period. Trends were observed for both Maternal Composite Score (P = 0.06) and Maternal Protective Behavior score (P = 0.06), indicating that both primiparous and multiparous were protective, but only multiparous cows were aggressive toward the caretakers on the first handling of their calves. The most protective cows spent more time eating during the prepartum period (P = 0.03), while the least attentive cows spent more time lying down (P = 0.02) in the prepartum period. The cows who nursed and stimulated their calves more were also calmer (P = 0.02) and more attentive (P = 0.01). In conclusion, the peripartum behaviors of Gyr cows were related to maternal care and maternal defensiveness. Multiparous cows tended to be more aggressive than primiparous cows at the time of the first handling of their calves.

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The aim of this study was to estimate genetic parameters for cow weight at calving (CW) and cumulative 305-day milk yield (MY305) in dairy Gyr cattle by two-trait analysis. The study used 1,847 CW records, in which 418 females presented more than one measure, and 4,048 MY305 records, wherein 1068 females provided repeated measures, from 2,339 females belonging to three herds, which calved between 1986 and 2019. Variance components were estimated by the restricted maximum likelihood method (REML) using a two-trait animal model. The model included direct additive genetic, permanent environmental and residual effects as random effects and the fixed effects of contemporary group, formed by animals that had calved on the same farm year and season, and age of cow at calving as covariates (fitted as linear and quadratic effect). The heritability estimates for CW and MY305 were 0.21 ± 0.06 and 0.29 ± 0.04, respectively, and repeatability estimates were 0.49 ± 0.03 and 0.49 ± 0.02. The genetic correlation between CW and MY305 was positive and of low magnitude (0.33 ± 0.18), indicating that selection for MY305 will cause little genetic change in the weights of dairy Gyr animals. The genetic trends of breeding values of analyzed traits showed marked genetic gains in MY305, with little changes in CW of dairy Gyr cows over the years in the herds studied, which is an important result considering the production systems adopted in the tropics.

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The objectives of this study were to assess the effects of heat stress on the milk yield and investigate the presence of genotype × environment interaction (G × E) in Brazilian Murrah buffaloes reared under tropical conditions. With this, 58,070 test-day (TD) records for milk yield from 3,459 first lactations of buffaloes collected between 1987 and 2018 were evaluated. A mixed model considering days in milk (DIM) and temperature-humidity index (THI) was applied to quantify milk yield losses due to heat stress. The most detrimental effect of THI on TD milk yield was observed in the mid-stages of lactation, after lactation peak, in DIM 105-154 and 155-204 days (-0.020 and -0.015 kg/day per THI, respectively). The least-squares means of TD milk yield were used to identify a heat stress threshold using a piecewise linear regression model. A substantial reduction in TD milk yield due to heat stress was observed for THI values above 77.8 (-0.251 kg/day per increase of 1 THI unit). An analysis using a single-trait random regression animal model was carried out to estimate variance components and genetic parameters for TD milk yield over THI and DIM values. Increased additive genetic variance and heritability estimates were observed for extreme THI values (THI = 60 and 80) combined with mid-lactation stages. The lowest genetic correlation (0.50) was observed between TD records at opposite extremes of the THI scale (THI = 60 vs. THI = 80). The genetic trends observed for the regression coefficients related to the general level of production (0.02) and specific ability to respond to heat stress (-0.002) indicated that selection to increase milk yield did not affect the specific ability to respond to heat stress until the present moment. These trends reflect the low genetic correlation between these components (0.05 ± 0.14). In this sense, monitoring trends of genetic components related to response to heat stress is recommended.

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Technological devices are increasingly present in livestock activities, such as identifying the reproductive status of cows. For this, predictive models must be accurate and usable in the productive context. The aims of this study were to evaluate estrus-associated changes in reticulo-rumen temperature (RRT) and activity (ACT) in Dairy Gyr heifers provided by reticulo-rumen boluses and to test the ability of different models for estrus prediction. The RRT and ACT of 45 heifers submitted to estrus synchronization were recorded using reticulo-rumen boluses. The means of RRT and ACT at different time intervals were compared between the day before and the day of estrus manifestation. An analysis of variance of RRT and ACT was performed using mixed models. A second approach employed logistic regression, random forest, and linear discriminant analysis models using RRT, ACT, time of day, and the temperature-humidity index (THI) as predictors. There was an increase in RRT and ACT at estrus (p < 0.05) compared to the same period on the day before and on the day after estrus. The random forest model provided the best performance values with a sensitivity of 51.69% and specificity of 93.1%. The present results suggest that RRT and ACT contribute to the identification of estrus in Dairy Gyr heifers.

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The objective of this study was to compare the standard multi-trait model and five reduced-rank models fitted to the first principal components and genetic parameter estimates in order to determine the most appropriate method to model the covariance structure of reproductive and productive traits in Brazilian Holstein cows. Individual records of the following traits from 5217 cows were analyzed: 305-day milk yield (MY305), peak yield, milk yield per day of calving interval, days from calving to first estrus, days from calving to last service (CLS), calving interval (CI), and gestation length. Schwarz's Bayesian information criterion was used to compare the different models. The results indicated that four principal components were necessary to model the genetic (co)variance structure, reducing the number of parameters to be estimated. Analysis of genetic and phenotypic correlations showed that milk production-related traits were strongly correlated with each other (ranging from 0.74 to 0.99), while the correlation of these traits with the reproductive traits was weak (ranging from - 0.14 to 0.27). Heritability estimates for the traits ranged from 0.03 to 0.18. The reproductive traits CLS and CI and the production trait MY305 should be included as selection criteria in dairy cattle breeding programs because they are correlated with the first two principal components, retaining 91% of the genetic variability of the data.

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The objective of this study was to evaluate the influence of thermal stress indices and minimum temperature on the semen quality of dairy Gyr bulls and to determine whether the minimum night temperature is able to balance the thermal stress that occurred during the day. A total of 922 semen samples were collected from 391 bulls aged between 15 and 49 months, which participated in progeny pretest trials, from December 2011 to April 2017. Semen characteristics (ejaculate volume, gross motility, sperm motility, sperm vigor, sperm concentration, and percentage of total defects) and scrotal circumference were analyzed. Meteorological data were recorded daily by the National Institute of Meteorology. The analyses were carried out using mixed models. The following fixed effects were included in the model: test, month of sample collection, scrotal circumference, the age of the bull at sampling as covariate (linear regression), and the climatic variables (equivalent temperature index [ETI], temperature and humidity index [THI], or minimum temperature). The effect of the bull nested within the test was included in the model as a random effect. Two periods were considered to evaluate the effect of the climatic variables on sperm quality: 7 days before collection (period 1) and the day of collection (period 2). There was an effect of ETI on sperm vigor and concentration in both periods and on volume and percentage of total defects only in the second period. The THI exerted a significant effect on volume in both periods and on sperm motility and concentration only in the second period. There was an effect of the minimum temperature on ejaculate volume, sperm motility, sperm concentration, and percentage of total defects only in the second period, which corresponds to the day of semen collection. Taken together, the results show that there was sufficient thermal stress to negatively affect semen quality. However, the minimum temperature during the night was a significant factor that balanced these negative effects of THI and ETI on semen traits of Gyr bulls.

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This study aimed to estimate the genetic parameters for somatic cell count (SCC) and the genetic association between SCC and milk production traits using two different methods of SCC normalization. The dataset contained information on 8870 lactation records of 6172 Guzerá dairy cows selected for dual-purpose from 95 herds. The lactation means of SCC were normalized in two ways: (a) SCC1 = log10 (SCC) and (b) SCC2 = log2 (SCC/100) + 3. Multivariate analyses were performed considering milk production traits over the course of 305 days of lactation. Estimates of the variance components and genetic parameters were carried out by the Bayesian inference method, applying Gibbs sampling. Single chains of 2,000,000 iterations were used, with sampling discards of the first 5000 chains and a sampling period of every 50 iterations. The deviation of information criteria (DIC) was used to evaluate the best transformation for standardization of the SCC data, comparing analysis 1 (milk production traits over 305 days and SCC1) with analysis 2 (milk production traits over 305 days and SCC2). According to the data structure of this study, SCC1 normalization was the most efficient method and produced better estimates than normalization by the SCC2 method. The heritability estimates for SCC were low regardless of the transformation method used, indicating a small possibility of expressive genetic gains from the direct selection of these traits. However, the repeatability indicated the potential for increasing heritability estimates if the effects of the permanent environment were reduced. The genetic correlations between the milk yield and SCC traits do not indicate the possibility of a correlated genetic gain from the direct selection of one trait. However, concomitant selection for milk production traits and SCC will likely not affect the individual response either.

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Multivariate procedures are used for the extraction of variables from the correlation matrix of phenotypes in order to identify those traits that explain the largest proportion of phenotypic variation and to evaluate the relationship structure between these traits. The reproductive traits (days from calving to first estrus [CFE], days from calving to last service [CLS], calving interval [CI] and gestation length [GL]) and milk production traits (milk yield at 305 days of lactation [MY305], peak yield [PY] and milk yield per day of calving interval [MYCI]) of 5,217 Holstein females (primiparous and multiparous) were measured. Principal component analysis (PCA) and factor analysis of the correlation matrix were used to estimate the correlation between traits. Analysis grouped the seven traits into three principal components and four latent factors that retained approximately 81.5% and 88.9% of the total variation of the data, respectively. The production variables exhibited positive phenotypic correlation coefficients of high magnitude (>.67). The phenotypic correlation estimates between the productive and reproductive traits were low, ranging from .13 to .22. A strong association (.99) was observed between CLS and CI. Our results indicate that multivariate analysis was effective in generating correlations between the traits studied, grouping the seven traits in a smaller number of variables that retained approximately 81% of the total variation of the data.

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