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Inferring individual cow effects, dairy system effects and feeding effects on latent variables underlying milk protein composition and cheese-making traits in dairy cattle

Published online by Cambridge University Press:  10 November 2017

Christos Dadousis ,
Claudio Cipolat-Gotet ,
Stefano Schiavon ,
Giovanni Bittante  and
Alessio Cecchinato
Christos Dadousis
Affiliation:
Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
Claudio Cipolat-Gotet
Affiliation:
Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
Stefano Schiavon
Affiliation:
Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
Giovanni Bittante
Affiliation:
Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
Alessio Cecchinato*
Affiliation:
Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
*
*For correspondence; e-mail: alessio.cecchinato@unipd.it
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Abstract

We examined the latent structure of 26 cheese related phenotypes in dairy cattle. Traits related to milk yield and quality (8 traits), milk protein fractions (8 traits), coagulation and curd firmness indicators (CF, 5 traits) and cheese-making phenotypes (cheese yields (%CY) and nutrient recoveries in the curd (REC), 5 traits) were analysed through multivariate factor analysis (MFA) using a varimax rotation. All phenotypes were measured in 1264 Brown Swiss cows. Ten mutual orthogonal, latent variables (factors; Fs) were obtained explaining 74% of the original variability. These Fs captured basic concepts of the cheese-making process. More precisely, the first 4 Fs, sorted by variance explained, were able to capture the underlying structure of the CY percentage (F1: %CY), the CF process with time (F2: CFt), the milk and solids yield (F3: Yield) and the presence of nitrogen (N) in the cheese (F4: Cheese N). Moreover, 4 Fs (F5: as1-β-CN, F7: κ-β-CN, F8: as2-CN and F9: as1-CN-Ph) were related to the basic milk caseins and 1 factor was associated with the α-LA whey protein (F10: α-LA). A factor describing udder health status (F6: Udder health), mainly loaded on lactose, other nitrogen compounds in the milk and SCS, was also obtained. Further, we inferred the effects of some potential sources of variation (e.g. stage of lactation and parity) including feeding and management systems. Stage of lactation had a significant effect for 7 of the 10 Fs, followed by parity of the cow (3 Fs), dairy system and feeding (3 Fs). Our work demonstrates the usefulness of MFA in reducing a large number of variables to a few latent factors with biological meaning and representing groups of traits that describe a complex process like cheese-making. Such an approach would be a valuable tool for studying the influence of different production environments and individual animal factors on protein composition and cheese-making related traits.

Keywords

Multivariate factor analysismilk proteincoagulationcurd firmnessindividual cheese yield
Type
Research Article
Information
Journal of Dairy Research , Volume 85 , Issue 1 , February 2018 , pp. 87 - 97
Copyright
Copyright © Hannah Dairy Research Foundation 2017 

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