Variance components and genetic parameters for milk production of Holstein cattle in Antioquia (Colombia) using random regression models

Background: genetic parameters of lactation curve in dairy cattle can be analyzed as longitudinal data using random regression models (RRM). Objective: the goal of the present study was to estimate variance components and genetic parameters for milk production in Holstein cattle located in Antioquia province using RRM. Methods: a total of 3,158 monthly controls corresponding to 741 first lactations of Holstein cows were evaluated. The RRM included several Legendre polynomials to estimate the population fixed-curve coefficients and to predict the direct additive genetic and the permanent environment effects. Additionally, heterogeneous residual variances were considered by grouping the days in milk into 5 and 10 classes. Eleven models with first to fourth order polynomials were used to describe the direct additive genetic and the permanent environment effects. The residue was modeled by considering five variance classes. Models were compared using Schwartz Bayesian and Akaike's information criteria. Results: the best model was obtained by fourth order Legendre polynomials to estimate the fixed curve of the population, genetic and permanent environment effects. In addition, 5 kinds of days were used to model the residual variances. The variance for the animal genetic, phenotypic, permanent environment, and residual effects decreased as days increased. Milk production heritability in early lactation was 0.36, increasing until 95 days (0.41), with subsequent decrease, reaching 0.10 at 245 days. The permanent environment variance values decreased to 125 days (0.13) and then increased to 215 days (0.21), to finish at the last stage of lactation with values of 0.05. The genetic and phenotypic correlations between milk yields at different days of lactation decreased as days intervals increased. Conclusion: the findings of this study suggest that in the first 150 days of lactation animals better express their genetic potential and that after 180 days there is greater environmental effect.Antecedentes: os parâmetros genéticos da curva de lactação em gado leiteiro podem ser analisados como dados longitudinais usando modelos de regressão aleatória (RRM). Objetivo: o objetivo deste estudo foi estimar os componentes de variância e os parâmetros genéticos para produção de leite de vacas holandesas em Antioquia, utilizando um modelo de regressão aleatória (RRM). Métodos: foram utilizados 3.158 controles mensais de 741 primeiras lactações. Usaram-se RRM com diferentes graus de polinômio ortogonal de Legendre para estimar os coeficientes da curva fixa da população e a predição dos efeitos genéticos aditivos diretos e de ambiente permanente. Consideraram-se 5 e 10 classes de variâncias residuais heterogêneas. Foram empregados 11 modelos com polinômios de primeira ate quarta ordem para descrever os efeitos genéticos aditivos diretos e de ambiente permanente. Os modelos foram comparados mediante os critérios de informação bayesiano de Schwartz e de Akaike. Resultados: o melhor modelo foi o de quarto ordem (4, 4 e 4) da curva fixa, do efeito genético aditivo e de ambiente permanente, respectivamente, e com cinco classes de variâncias heterogéneas (444.het5). A variância para os efeitos genético animal, fenotípico, de ambiente permanente e residual diminuiu com o aumento dos dias. A herdabilidade da produção de leite ao inicio da lactação foi de 0.36 e foi aumentando até os 95 dias (0.41), com posterior diminuição, chegando até 0.10 aos 245 dias. Para a trajetória da proporção de ambiente permanente os valores descenderam até os 125 dias (com 0.13), com posterior aumento até os 215 dias (com 0.21), para finalizar na última etapa da lactação com valores de 0.05. As correlaciones genéticas e fenotípicas entre produções de leite nos diferentes dias de lactação diminuíram com o aumento do intervalo dos dias. Conclusão: os resultados encontrados sugerem que nos primeiros 150 dias da lactação os animais expressaram melhor seu potencial genético.Antecedentes: los parámetros genéticos de la curva de lactancia en ganado de leche pueden ser analizados como datos longitudinales usando modelos de regresión aleatoria (RRM). Objetivo: el objetivo de este estudio fue estimar componentes de varianza y parámetros genéticos para la producción de leche en vacas Holstein en el departamento de Antioquia, utilizando RRM. Métodos: se utilizaron 3.158 controles mensuales de 741 primeras lactancias. Se usaron RRM con diferentes grados de polinomios de Legendre para estimar los coeficientes de la curva fija de la población y la predicción de los efectos genético aditivo directo y de ambiente permanente y se consideraron 5 y 10 clases de varianzas residuales heterogéneas. Se emplearon once modelos con polinomios de primer a cuarto orden, para describir los efectos genético aditivo directo y ambiente permanente. Los modelos fueron comparados mediante los criterios de información bayesiano de Schwartz y de Akaike. Resultados: el mejor modelo presentó polinomios de cuarto orden 4, 4 y 4 de la curva fija, del efecto genético aditivo y de ambiente permanente, respectivamente, y con 5 clases de varianzas heterogéneas (444.het5). La varianzas para los efectos genético animal, fenotípico, de ambiente permanente y residual disminuyeron con el aumento de los días. La heredabilidad de la producción de leche al inicio de la lactancia fue de 0,36 y fue aumentando hasta los 95 días (0,41), con posterior disminución, llegando a 0,10 a los 245 días. Para la trayectoria de la proporción de ambiente permanente los valores descendieron hasta los 125 días (con 0,13), luego aumentaron hasta los 215 días (con 0,21), para finalizar en la última etapa de la lactancia con valores de 0,05. Las correlaciones genéticas y fenotípicas entre producciones de leche en los diferentes días de lactancia disminuyeron con el aumento del intervalo de los días. Conclusión: los resultados encontrados en este estudio sugieren que en los primeros 150 días de lactancia los animales expresan mejor su potencial, y que despues de 180 días hay mayor impacto ambiental

Milk quality: milking personnel associated factors

Objective. To identify factors associated with high and low Somatic Cell Counts (SCC) levels in bulk tanks of dairy farms in Southeast Brazil. Materials and methods. A total of 68 dairy herds with high and low bulk tank SCC levels were analyzed. Surveys and checklists were applied to the personnel regarding milking routines and equipment. Results. Milkers and management personnel explained up to 40.28% of the variability among herds, while the milker’s well-being and stability explained up to 28%. Planning and organization were relevant for SCC, as well as the state of the equipment and the milking routine. According to separate analyzes of employees and owners, employees have greater variability in terms of knowledge on milk production, mastitis, milking routine, and experience. Conclusion. There are qualifying factors in milking systems in southeastern Brazil associated with milking personnel, equipment and milking routine. Understanding these factors will enable the implementation of strategies to produce better quality milk.Objetivo. Identificar factores asociados a altos y bajos niveles de recuentos de células somáticas (RCS) en tanques de hatos lecheros del Sudeste de Brasil. Materiales y métodos. Se analizaron 68 hatos lecheros con niveles altos y bajos de RCS en tanque. Para identificar factores asociados al personal vinculado al ordeño y relacionarlos con RCS se aplicaron encuestas y listas de chequeo para la rutina y el equipo de ordeño. Resultados. El personal vinculado al ordeño, administración y gestión del productor explicaron hasta el 40.28% de la variabilidad entre rebaños, mientras que el bienestar y la estabilidad del ordeñador explicaron hasta el 28%. La planeación y organización del productor fueron relevantes en el RCS, al igual que el estado del equipo y la rutina de ordeño. Análisis separados de empleado y propietario permitieron concluir que existe mayor variabilidad para los primeros, diferenciándose por conocimientos en la producción de leche y el manejo de la mastitis, la rutina y la experiencia. Conclusión . Existen factores clasificatorios en los sistemas de ordeño del sudeste de Brasil asociados al personal, el equipo y la rutina de ordeño. El entendimiento de estos factores posibilitará la implementación de estrategias que permitan producir leche de mejor calidad

Calidad de la leche: factores asociados al personal vinculado al ordeño

Objetivo. Identificar factores asociados a altos y bajos niveles de recuentos de células somáticas (RCS) en tanques de hatos lecheros del Sudeste de Brasil. Materiales y métodos. Se analizaron 68 hatos lecheros con niveles altos y bajos de RCS en tanque. Para identificar factores asociados al personal vinculado al ordeño y relacionarlos con RCS se aplicaron encuestas y listas de chequeo para la rutina y el equipo de ordeño. Resultados. El personal vinculado al ordeño, administración y gestión del productor explicaron hasta el 40.28% de la variabilidad entre rebaños, mientras que el bienestar y la estabilidad del ordeñador explicaron hasta el 28%. La planeación y organización del productor fueron relevantes en el RCS, al igual que el estado del equipo y la rutina de ordeño. Análisis separados de empleado y propietario permitiero concluir que existe mayor variabilidad para los primeros, diferenciándose por conocimientos en la producción de leche y el manejo de la mastitis, la rutina y la experiencia. Conclusión. Existen factores clasificatorios en los sistemas de ordeño del sudeste de Brasil asociados al personal, el equipo y la rutina de ordeño. El entendimiento de estos factores posibilitará la implementación de estrategias que permitan producir leche de mejor calidad

Influence of attitudes and behavior of milkers on the hygienic and sanitary quality of milk

<div><p>Recognizing how human behaviors affect the milk process can be useful to understand variations in hygienic and sanitary parameters in bulk tank milk. Furthermore, this knowledge could be used to design management programs that guarantee milk quality, favoring the optimization of such processes. Forty-six milkers from the same number of dairy farms in Antioquia province (Colombia) were interviewed to establish the main factors associated to milk quality. Technical knowledge, motivations, and behavior of the personnel and its effect on hygienic and sanitary quality of milk were evaluated. Quality was assessed in terms of colony-forming units (CFU) and somatic cell count (SCC) in bulk tank milk. Two factors from a multivariate mixed data analysis were evaluated. One of those factors explained 9.51% of the total variability, related with in-farm availability and use of tools and the relationships between milker and manager. The other factor, associated with work environment and recognition, explained 6.97% of the total variability. The variables that best explained CFU levels were <i>Knowledge of the udder condition at milking</i>, and <i>Milking type (parlor or pasture)</i>. The SCC was associated to <i>knowledge of animal handling</i>, <i>schooling of milkers</i>, <i>milking site</i>, and the groups derived from the cluster analysis by farm. In conclusion, milker attitudes and behaviors can affect CFU and SCC in bulk tank milk.</p></div

Fuzzy system to predict physiological responses of Holstein cows in southeastern Brazil

Background:thermal environment exerts a direct influence on animal performance. Environmental factors, in different circumstances, may affect milk production and fertility of animals, compromising the profitability of the activity. Under heat stress conditions dairy cows reduce feed intake and, consequently, milk production. Sweating and panting are some of the mechanisms these animals use to relieve thermal stress. In addition, animals often suffer physiological and behavioral changes caused by heat stress.Objective: the goal of the present study was to develop and evaluate a model based on fuzzy set theory to predict rectal temperature (°C), and respiratory rate (breaths per minute) responses of Holstein cows exposed to different environmental thermal conditions. Methods: the proposed fuzzy model was based on data obtained experimentally (5,884 records) as well as from the literature (792 records) referring to the effect of environmental variables on both physiological responses. Input variables of each record were dry bulb air temperature and relative humidity. Output variables were rectal temperature and respiratory rate. Results: the adjusted model was evaluated for its ability to predict response variables as a function of input variables. The model was able to predict respiration rate with an average standard error of 7.73 and rectal temperature with an average standard error of 0.27. Conclusion: a fuzzy model was developed to predict physiological responses. The error (%) of model prediction for respiration rate and rectal temperature was +/- 12 and 0.5%, respectively

Fuzzy system to predict physiological responses of Holstein cows in southeastern Brazil

Antecedentes: o ambiente térmico exerce uma influencia direta no desempenho animal. Fatores ambientais, em diferentes circunstancias, podem afetar a produção de leite e a fertilidade dos animais, comprometendo assim a rentabilidade da atividade. Sobcondições de estresse por calor, as vacas leiteiras reduzem o seu consumo de alimento e, consequentemente, a sua produção de leite. Sudorese e respiração ofegante são alguns dos mecanismos que estes animais usam para aliviar o estresse térmico. Além destas consequências, os animais com frequência sofrem mudanças fisiológicas e comportamentais causados pelo estresse calórico, causando uma redução na produção de leite. Objetivo: o objetivo do presente estudo foi desenvolver e avaliar um modelo baseado na teoria dos conjuntos fuzzy para predizer respostas fisiológicas, temperatura retal e frequência respiratória, de vacas leiteiras de raça holandesa branca e preta, expostas a diferentes condições térmicas ambientais. Métodos: o modelo fuzzy proposto foi baseado em dados obtidos experimentalmente (5,884 registros) bem como da literatura (792 registros), referindo-se à influência das variáveis ambientais sobre essas respostas fisiológicas. Cada registro inclui valores de temperatura de bulbo seco do ar, umidade relativa (variáveis de entrada), temperatura retal e frequência respiratória (variáveis de saída). Resultados: o modelo ajustado foi avaliado para cada variável resposta e prediz estas em função das variáveis de entrada. Este modelo foi capaz de predizer a frequência respiratória com um erro padrão médio de 7,73 e a temperatura retal com um erro padrão médio de 0,27. Conclusão: o modelo fuzzy foi desenvolvido com sucesso para predizer respostas fisiológicas. O modelo foi capaz de predizer frequência respiratória e temperatura retal com erros percentuais de +/- 12 y 0,5%, respectivamente.Background:thermal environment exerts a direct influence on animal performance. Environmental factors, in different circumstances, may affect milk production and fertility of animals, compromising the profitability of the activity. Under heat stress conditions dairy cows reduce feed intake and, consequently, milk production. Sweating and panting are some of the mechanisms these animals use to relieve thermal stress. In addition, animals often suffer physiological and behavioral changes caused by heat stress.Objective: the goal of the present study was to develop and evaluate a model based on fuzzy set theory to predict rectal temperature (°C), and respiratory rate (breaths per minute) responses of Holstein cows exposed to different environmental thermal conditions. Methods: the proposed fuzzy model was based on data obtained experimentally (5,884 records) as well as from the literature (792 records) referring to the effect of environmental variables on both physiological responses. Input variables of each record were dry bulb air temperature and relative humidity. Output variables were rectal temperature and respiratory rate. Results: the adjusted model was evaluated for its ability to predict response variables as a function of input variables. The model was able to predict respiration rate with an average standard error of 7.73 and rectal temperature with an average standard error of 0.27. Conclusion: a fuzzy model was developed to predict physiological responses. The error (%) of model prediction for respiration rate and rectal temperature was +/- 12 and 0.5%, respectively.Antecedentes: el ambiente termal ejerce una influencia directa en el desempeño animal. Factores ambientales, en diferentes circunstancias, pueden afectar la producción de leche y la fertilidad de los animales, comprometiendo la rentabilidad de la actividad. Bajo condiciones de estrés por calor, las vacas lecheras reducen su consumo de alimento y, consecuentemente su producción de leche. Sudar y jadear son algunos de los mecanismos que estos animales usan para aliviar el estrés térmico. Además de estas consecuencias, los animales a menudo sufren cambios fisiológicos y comportamentales causados por el estrés calórico, causando una reducción en la producción de leche. Objetivo: el objetivo del presente estudio fue desarrollar y evaluar un modelo basado en la teoría de los conjuntos fuzzy para predecir respuestas fisiológicas, temperatura rectal y frecuencia respiratoria, de vacas lecheras de raza holandesa blanco y negro expuestas a diferentes condiciones ambientales. Métodos: el modelo fuzzy propuesto fue basado en datos obtenidos experimentalmente (5.884 registros), también como de la literatura (792 registros), refiriéndose a la influencia de las variables ambientales sobre esas respuestas fisiológicas. Cada registro incluye valores de temperatura de bulbo seco del aire, humedad relativa (variables de entrada), temperatura rectal y frecuencia respiratoria (variables de salida). Resultados: el modelo ajustado fue evaluado para cada variable respuesta y predice estas en función de las variables de entrada. Este modelo fue capaz de predecir la frecuencia respiratoria con un error estándar medio de 7,73 y la temperatura rectal con un error estándar medio de 0,27. Conclusión: un modelo fuzzy fue exitosamente desarrollado para predecir respuestas fisiológicas. El modelo fue capaz de predecir frecuencia respiratoria y temperatura rectal con errores porcentuales de +/- 12 y 0,5%, respectivamente

Variance and covariance components and genetic parameters for fat and protein yield of first-lactation Holstein cows using random regression models

Background: the genetic parameters of the lactation curve in dairy cattle can be analyzed as longitudinal data using Random Regression Models (RRM). Objective: to estimate the (co) variance components and genetic parameters for fat (F) and protein (P) yield in first lactation Holstein cows of Antioquia (Colombia) by RRM based on Legendre polynomials. Methods: monthly F and P records (9,479) from 1,210 first-lactation Holstein cows were used. Twenty-two and 24 RRM were used for F and P, respectively, with different orthogonal Legendre-polynomial orders to estimate the fixed-curve population coefficients and predict direct-genetic additive and permanent environment effects. The models considered homogeneous and heterogeneous residual variances of 5, 7, and 10 classes. Results: the best fit for F was the fourth order model for the population fixed-curve and the additive genetic effect, and the third order for the permanent environment with seven heterogeneous variances. The best fit for P was the fifth order model for the population fixed-curve and the additive genetic and permanent environmental effects with five heterogeneous variances. The variance for the animals' genetic, phenotypic, permanent environment, and residual effects for both F and P decreased as lactation progressed. F and P heritabilities were between 0.13 and 0.38, and 0.12 and 0.32, respectively. Conclusion: first-birth animals can be selected in Antioquia for F and P characteristics. Selection should be done preferably at the beginning of lactation since they reach the highest heritability values at this time

Behavior variables of milkers in Northern Antioquia.

<p>Behavior variables of milkers in Northern Antioquia.</p