International Journal of Advanced Biological and Biomedical Research

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Estimate Residual Feed Intake (RFI) in Iranian Dairy Cows Using Multitrait Stochastic Regression Framework

Document Type : Original Article

Authors

Fatemeh Ala Noshahr 1
Reza Seyed Sharifi 1
Jamal Seifdavati 1
Nemat Hedayat 1
Abdolfattah Zeidan Mohammad Salem 2

1 Department of Animal Sciences, Faculty of Agriculture and Natural Resources University of Mohaghegh Ardabili, Ardabil, Iran

2 Department of Animal Nutrition School of Veterinary Medicine and Zootechnics Autonomous University of the State of Mexico Toluca, Edo de México, México

https://doi.org/10.48309/ijabbr.2026.2072349.1666
Abstract
In the dairy industry, nutritional efficiency is typically assessed using the residual feed intake (RFI) method. The traditional application of this method relies on linear regression, which inherently overlooks how the components of RFI change over time, leading to potential inaccuracies in the findings. By employing a multitrait stochastic regression framework, the relationships were explored between milk production, live weight, intake of dry matter (DMI), and body condition score (BCS) throughout the lactation period. Furthermore, at each measurement point, an animal effect was estimated for intake through a matrix regression analysis based on the variance covariance matrix and the animal effects of the three predictor traits. By comparing this predicted effect with the actual intake effect, an estimate was derived for the RFI. The model was evaluated using historical data collected from the Iranian National Breeding Centre from 2008 to 2023, encompassing 1,852 lactations from 870 cows. The analysis revealed strong positive correlations between various animal effects, particularly for milk production and intake of dry matter (DMI), as well as between body weight and DMI. These correlations peaked around mid-lactation and remained stable over time, averaging around 0.4 for body weight and BCS. Additionally, the correlations for milk and weight, DMI and BCS, as well as milk and BCS, showed a gradual decline as lactation progressed. On the Legendre polynomial coefficient scale, the correlations were measured with high precision, indicated by an average standard error of 0.04, with minimum and maximum values of 0.02 and 0.05, respectively. The estimated animal effect for feed intake consistently demonstrated a strong correlation with milk production and, for most of the lactation period, also with body weight. However, the correlation with BCS was only moderate and turned negative during the latter half of lactation. The relationship between the average RFI throughout lactation and RFI at individual time points was consistently positive, exceeding 0.5, with peak correlations observed mid-lactation, exceeding 0.9. This suggests a robust and reliable model for understanding the dynamics of nutritional efficiency and its relationship with milk production and other physiological parameters in dairy cows.

Graphical Abstract

Estimate Residual Feed Intake (RFI) in Iranian Dairy Cows Using Multitrait Stochastic Regression Framework

Keywords

Correlations
Feed Efficiency
Multitrait Random Regression
Residual Feed Intake (RFI)

Subjects

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©2026 The author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit: http://creativecommons.org/licenses/by/4.0/

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International Journal of Advanced Biological and Biomedical Research
Volume 14, Issue 2
March and April 2026
Pages 239-250

  • Receive Date 21 September 2025
  • Revise Date 11 October 2025
  • Accept Date 25 November 2025

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