A study on the curve of far percentage, protein percentage and fat to protein ratio in test day milk records of Iranian dairy cows using quantile regression statistical technique

Document Type : Research Paper

Authors

1 MSc Graduated, university of birjand

2 professor, university of birjand

Abstract

A study on the curve of far percentage, protein percentage and fat to protein ratio in test day milk records of Iranian dairy cows using quantile regression statistical technique

Abstract
Introduction: While the physiological conditions of most animals are reflected in their body fluids, in dairy cows physiological conditions are reflected in milk combinations. When negative energy balance occur, production that caused body fat store destruction, goes in two major metabolic ways in liver and breast tissues. In breast tissues, free fatty acids increase fat to protein ratio and in liver, free fatty acid change metabolism leading to increase of ketone bodies. Lactation persistency in dairy cows is one of inheritable economic trait and important characteristic of the lactation curve. The change of this trait is under control of different factors such as animal genetics structure. The amount of cow production at the peak of milk production depends on the calving physical condition, genetics, the absence of infectious and metabolic diseases, and postpartum diet. Fat to protein ratio in early lactation is associated with performance during lactation. Fat to protein ratio is also correlated with some diseases such as retained placenta, displacement of abomasum, metritis, endometritis, mastitis and culling. Fat to protein ratio (FPR) in test day milk records is a good indicator for body fat metabolism and the level of negative energy balance, and that it could be used as a selection criterion to improve metabolic stability. Objective: Quantile regression has not been widely used for modeling of biologic characteristics of livestock and this research aimed to evaluate the shape of fat percentage, protein percentage and FPR in test day milk records of Iranian dairy cows using a polynomial quantile regression model. Material and methods: The data used in this study were provided by the Animal Breeding Centre, Iran. Initial data set were edited by Excel and Foxpro software. The traits under consideration were fat percentage, protein percentage and fat to protein ratio (calculated based on the magnitude of the first two traits). All cows had value for all traits. Final data comprised a total of 784,532 test day records collected from 93,259 first-parity dairy cows (progeny of 2741 sires and 79843 dams) distributed in 660 herds over the country and calved between 2003 and 2013. In the data, test day fat percentage and protein percentage had a minimum of 1 and 1.5 and a maximum of 7 and 7, respectively. By SPSS software, some statistical characteristics of the traits were calculated, and by SAS software a quadratic polynomial quantile regression model (in the range of quantiles 5 to 95) was fitted to the data. Based upon the fitted model, linear and quadratic regression coefficients were estimated for the effect of day of lactation on each trait in different quantiles. In the model, traits and day of lactation were response and independent variables, respectively. Quadratic effect of day of lactation is considered due to nonlinear variation of the traits over the course of the lactation curve. Results and discussion: Mean fat percentage, protein percentage and fat to protein ratio over the lactation course were found to be 3.35%, 3.08% and 1.1, respectively. As expected, for each trait a nonlinear change was observed over the lactation period. The highest content fat to protein ratio occurs in the early period of lactation and the lowest was observed in the sixth and seventh months of lactation. In terms of absolute values, minimum (953.564) and maximum (1797.661) linear regression coefficient of days in milk were obtained in quantiles 5 and 95, respectively. The same trend was also observed for quadratic regression coefficient of days in milk. Linear and quadratic regression coefficients of fat and protein percentages ranged over different quantiles so that minimum and maximum absolute values were observed in quantiles 5 and 95, respectively. Conclusion: Quantile regression model is an appropriated statistical technique for evaluate the effects of an independent factor which differently affects the shape of a response variable. In this research, the effect of days in milk on three traits (fat and protein percentage, as well as fat to protein ratio) was modeled by quantile regression. The findings of the present research indicate that the curve shape of fat percentage, protein percentage and fat to protein ratio are different for the quantiles suggesting that these differences are needed to be taken into account in nutrition management and cattle breeding scheme.
Keywords: Dairy cow, Modeling, Quantile regression, Ratio of milk composition
A study on the curve of far percentage, protein percentage and fat to protein ratio in test day milk records of Iranian dairy cows using quantile regression statistical technique
A study on the curve of far percentage, protein percentage and fat to protein ratio in test day milk records of Iranian dairy cows using quantile regression statistical technique
A study on the curve of far percentage, protein percentage and fat to protein ratio in test day milk records of Iranian dairy cows using quantile regression statistical technique
A study on the curve of far percentage, protein percentage and fat to protein ratio in test day milk records of Iranian dairy cows using quantile regression statistical technique
A study on the curve of far percentage, protein percentage and fat to protein ratio in test day milk records of Iranian dairy cows using quantile regression statistical technique
A study on the curve of far percentage, protein percentage and fat to protein ratio in test day milk records of Iranian dairy cows using quantile regression statistical technique

Keywords

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Animal Science Research
Volume 32, Issue 4
June 2023
Pages 15-29

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History

  • Receive Date: 17 September 2018
  • Revise Date: 21 July 2021
  • Accept Date: 09 February 2022

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