Document Type : Research Paper

Authors

• عبدالرضا Salehi ¹
• gholamreza Peykani ²
• ali assadi-alamouti ³
• Mohammad Jajik khari ⁴

¹ Corresponding Author, Department of Animal and poultry Science, Faculty of Agricultural Technology, University of Tehran, Pakdasht, Iran. E-mail: arsalehi@ut.ac.ir

² Department of Economic Science, Faculty of Agriculture, University of Tehran, Karaj, Iran. E-mail: rpeykani@ut.ac.ir

³ Department of Animal and poultry Science, Faculty of Agricultural Technology, University of Tehran, Pakdasht, Iran. E-mail: a.alamuti@ut.ac.ir

⁴ Department of Animal and poultry Science, Faculty of Agricultural Technology, University of Tehran, Pakdasht, Iran. E-mail: m.Tajik@stu.ac.ir

Abstract

Introduction Determining the production function is one of the most effective ways to monitor the continuity of milk production, as it indicates the relationship between the intake of feedstuff and milk production. To explain the production function in breeding programs and estimate regression coefficients, third-degree nonlinear regression is used. The milk production curve follows a third-degree rule, and therefore, it can be divided into three basic parts. Second-degree production functions cannot accurately represent a milk production curve from the beginning of lactation to the time of dryness, because they only depict the second-degree performance of milk production from the beginning of lactation to the peak of lactation. The aim of this research was to investigate the effects of the production function in breeding programs and their potential use in selecting superior animals.
Materials and Methods In order to recognize the opportunities for profitability in a herd, we first need to create the right production function. To achieve these goals, data on milk production and feed intake from one of the industrial farms in Tehran province were used. Various methods were examined in this study: 1- using OLS approach in R environment to estimate the milk production function, 2- using the Peykani extended ordinary least square (POLS) method, 3- estimating the breeding value of milk production using POLS function and physical milk production (field data) using Wombat software, 4- comparing OLS production functions obtained in R with the POLS production function, 5- Conducting genetic evaluation of animals and ranking dairy cows. When the production functions were obtained according to the POLS program, the optimal amounts of feed consumption and milk production were calculated. The breeding value of milk production was estimated using a repeatability model with permanent environmental effects that consider covariance between records of an animal and this was done using the Wombat program. Finally, cows were ranked based on their genetic rank.
Results and Discussion The estimated functions based on the ordinary least squares method were incorrect in terms of signs and coefficients, and did not fit the milk production curve well. Based on the findings of this study, the non-linear regression model POLS is the best in the curve fitting and economical production of milk. The results show that with milk yield corrected using the optimal feed intake by the POLS model the ranking of the animals has changed and the breeding value of the animals is more accurately estimated. By using the breeding values estimated in this method, one can select the best animals as the parents of future generations.
Conclusion The ability to estimate the production function based on the POLS method, which is used to create a standard curve of dairy cows is very high. Our results contribute significantly to the field of animal breeding by shedding light on the role of production functions in enhancing breeding programs and facilitating the identification of high-performing animals. The insights gained from our study could drive improvements in animal selection processes and ultimately enhance milk production efficiency.

Keywords

• Breeding Value
• Dairy cattle production function
• Economic efficiency
• The law of diminishing return