Mid-infrared milk screening as a phenotyping tool for feed efficiency in dairy cattle

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027162%3A_____%2F25%3AN0000004" target="_blank" >RIV/00027162:_____/25:N0000004 - isvavai.cz</a>

Alternative codes found

RIV/62156489:43210/25:43926569 RIV/00027014:_____/25:10006212

Result on the web

<a href="https://cjas.agriculturejournals.cz/artkey/cjs-202501-0001_mid-infrared-milk-screening-as-a-phenotyping-tool-for-feed-efficiency-in-dairy-cattle.php" target="_blank" >https://cjas.agriculturejournals.cz/artkey/cjs-202501-0001_mid-infrared-milk-screening-as-a-phenotyping-tool-for-feed-efficiency-in-dairy-cattle.php</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.17221/165/2024-CJAS" target="_blank" >10.17221/165/2024-CJAS</a>

Result language

angličtina

Original language name

Mid-infrared milk screening as a phenotyping tool for feed efficiency in dairy cattle

Original language description

Feed efficiency (FE) is one of the most essential traits in dairy cattle. As could be assumed, the importance of FE lies in the price of feed, which accounts for a large part of the cost of dairy herds. Unfortunately, assessing FE in an individual cow involves measuring feed consumption, is laborious and financially expensive, and is not usable for group cows feeding on production farms. There are efforts to predict FE or, more precisely, dry matter intake (DMI) using predictors such as a cow's weight (BW), milk production (MY), and milk composition. However, it is proposed that FT-MIR data can be used to increase the accuracy of DMI prediction. The paper aims to review using the Fourier transform mid-infrared spectroscopy (FT-MIR) of milk to acquire FE phenotype in dairy cattle. FT-MIR of milk is an accurate method widely used to determine milk components' content routinely. In FE phenotyping, the predictive equations include FT-MIR as a predictor and/or other predictive traits such as BW, MY, milk composition, herd, breed, day in milk, and pregnancy. The most commonly used mathematical methods are partial least squares (PLS) and artificial neural networks (ANN). The prediction accuracy differs between experiments and depends on the mathematical method and model used. The predictions based on FT-MIR alone showed accuracy in the range of 0.19–0.40. However, we emphasise that combining all sources of information, including MY, milk composition, FT-MIR, and near-infrared reflectance spectroscopy NIR, is crucial and results in higher accuracy values, range of 0.03 to 0.81.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

40301 - Veterinary science

Project

<a href="/en/project/QL24010350" target="_blank" >QL24010350: Ensuring the sustainability of efficient dairy farming using genomic breeding using mining data from modern technologies including infrared spectroscopy</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Czech Journal of Animal Science

ISSN

1212-1819

e-ISSN

1805-9309

Volume of the periodical

70

Issue of the periodical within the volume

1

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

16

Pages from-to

1-16

UT code for WoS article

001408791300001

EID of the result in the Scopus database

2-s2.0-85216769891