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Using quantile regression for fitting lactation curve in dairy cows

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Title: Using quantile regression for fitting lactation curve in dairy cows
Authors: Naeemipour Younesi, Hossein, Shariati, Mohammad Mahdi, Zerehdaran, Saeed, Jabbari Nooghabi, Mehdi, Løvendahl, Peter
Source: Naeemipour Younesi, H, Shariati, M M, Zerehdaran, S, Jabbari Nooghabi, M & Løvendahl, P 2019, 'Using quantile regression for fitting lactation curve in dairy cows', Journal of Dairy Research, vol. 86, no. 1, pp. 19-24. https://doi.org/10.1017/S0022029919000013
Publisher Information: Cambridge University Press (CUP), 2019.
Publication Year: 2019
Subject Terms: TEST-DAY MILK, quantile regression, MODELS, CATTLE, Lactation curve, milk production traits, Fats/analysis, Cell Count, somatic cell score, Iran, Fats, Lactation/genetics, Quantitative Trait, Heritable, GENETIC-ANALYSIS, Milk/chemistry, Animals, Lactation, COVARIANCE, Cattle/genetics, Dairying/statistics & numerical data, 2. Zero hunger, 0402 animal and dairy science, SOMATIC-CELL SCORE, 04 agricultural and veterinary sciences, Models, Theoretical, Milk Proteins, Dairying, YIELD, Milk, Nonlinear Dynamics, PATTERNS, Regression Analysis, Cattle, Female, UDDER TYPE TRAITS, CLINICAL MASTITIS, Milk Proteins/analysis
Description: The main objective of this study was to compare the performance of different ‘nonlinear quantile regression’ models evaluated at theτth quantile (0·25, 0·50, and 0·75) of milk production traits and somatic cell score (SCS) in Iranian Holstein dairy cows. Data were collected by the Animal Breeding Center of Iran from 1991 to 2011, comprising 101 051 monthly milk production traits and SCS records of 13 977 cows in 183 herds. Incomplete gamma (Wood), exponential (Wilmink), Dijkstra and polynomial (Ali & Schaeffer) functions were implemented in the quantile regression. Residual mean square, Akaike information criterion and log-likelihood from different models and quantiles indicated that in the same quantile, the best models were Wilmink for milk yield, Dijkstra for fat percentage and Ali & Schaeffer for protein percentage. Over all models the best model fit occurred at quantile 0·50 for milk yield, fat and protein percentage, whereas, for SCS the 0·25th quantile was best. The best model to describe SCS was Dijkstra at quantiles 0·25 and 0·50, and Ali & Schaeffer at quantile 0·75. Wood function had the worst performance amongst all traits. Quantile regression is specifically appropriate for SCS which has a mixed multimodal distribution.
Document Type: Article
Language: English
ISSN: 1469-7629
0022-0299
DOI: 10.1017/s0022029919000013
Access URL: https://pubmed.ncbi.nlm.nih.gov/30729906
https://europepmc.org/article/MED/30729906
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022029919000013
https://www.ncbi.nlm.nih.gov/pubmed/30729906
https://www.cambridge.org/core/journals/journal-of-dairy-research/article/using-quantile-regression-for-fitting-lactation-curve-in-dairy-cows/08C1DE44E814A5C8628BA1A8DB55B174
https://pubmed.ncbi.nlm.nih.gov/30729906/
https://pure.au.dk/portal/en/publications/e56e3cc7-840c-4cdf-bf8d-445024de7cbe
https://doi.org/10.1017/S0022029919000013
Rights: Cambridge Core User Agreement
Accession Number: edsair.doi.dedup.....54c4556187a4b9fe7c3b45a237f11d16
Database: OpenAIRE
Description
Abstract:The main objective of this study was to compare the performance of different ‘nonlinear quantile regression’ models evaluated at theτth quantile (0·25, 0·50, and 0·75) of milk production traits and somatic cell score (SCS) in Iranian Holstein dairy cows. Data were collected by the Animal Breeding Center of Iran from 1991 to 2011, comprising 101 051 monthly milk production traits and SCS records of 13 977 cows in 183 herds. Incomplete gamma (Wood), exponential (Wilmink), Dijkstra and polynomial (Ali & Schaeffer) functions were implemented in the quantile regression. Residual mean square, Akaike information criterion and log-likelihood from different models and quantiles indicated that in the same quantile, the best models were Wilmink for milk yield, Dijkstra for fat percentage and Ali & Schaeffer for protein percentage. Over all models the best model fit occurred at quantile 0·50 for milk yield, fat and protein percentage, whereas, for SCS the 0·25th quantile was best. The best model to describe SCS was Dijkstra at quantiles 0·25 and 0·50, and Ali & Schaeffer at quantile 0·75. Wood function had the worst performance amongst all traits. Quantile regression is specifically appropriate for SCS which has a mixed multimodal distribution.
ISSN:14697629
00220299
DOI:10.1017/s0022029919000013