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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach
Clément Grelet, Amélie Vanlierde, Miel Hostens, et al.
animal (2018) Vol. 13, Iss. 3, pp. 649-658
Open Access | Times Cited: 60

Showing 1-25 of 60 citing articles:

Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods
Maria Frizzarin, Isobel Claire Gormley, D.P. Berry, et al.
Journal of Dairy Science (2021) Vol. 104, Iss. 7, pp. 7438-7447
Open Access | Times Cited: 63
Large-scale phenotyping in dairy sector using milk MIR spectra: Key factors affecting the quality of predictions
Clément Grelet, Pierre Dardenne, Hélène Soyeurt, et al.
Methods (2020) Vol. 186, pp. 97-111
Closed Access | Times Cited: 62
Prediction of detailed blood metabolic profile using milk infrared spectra and machine learning methods in dairy cattle
Diana Giannuzzi, Lúcio Flávio Macêdo Mota, Sara Pegolo, et al.
Journal of Dairy Science (2023) Vol. 106, Iss. 5, pp. 3321-3344
Open Access | Times Cited: 20
Phenotypic variation of dairy cows’ hematic metabolites and feasibility of non-invasive monitoring of the metabolic status in the transition period
Silvia Magro, Angela Costa, Damiano Cavallini, et al.
Frontiers in Veterinary Science (2024) Vol. 11
Open Access | Times Cited: 6
Prediction of metabolic clusters in early-lactation dairy cows using models based on milk biomarkers
Jenne De Koster, Mazdak Salavati, Clément Grelet, et al.
Journal of Dairy Science (2019) Vol. 102, Iss. 3, pp. 2631-2644
Open Access | Times Cited: 45
Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems
Tiago Bresolin, João Ricardo Rebouças Dórea
Frontiers in Genetics (2020) Vol. 11
Open Access | Times Cited: 42
In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle
Diana Giannuzzi, Lúcio Flávio Macêdo Mota, Sara Pegolo, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 24
A comparison of average daily gain, apparent digestibilities, energy balance, rumen fermentation parameters, and serum metabolites between yaks (Bos grunniens) and Qaidam cattle (Bos taurus) consuming diets differing in energy level
Hu Liu, Jianwei Zhou, A. Allan Degen, et al.
Animal nutrition (2022) Vol. 12, pp. 77-86
Open Access | Times Cited: 23
Heritability estimates of predicted blood β-hydroxybutyrate and nonesterified fatty acids and relationships with milk traits in early-lactation Holstein cows
A. Benedet, Angela Costa, Massimo De Marchi, et al.
Journal of Dairy Science (2020) Vol. 103, Iss. 7, pp. 6354-6363
Open Access | Times Cited: 39
Prediction of blood metabolites from milk mid-infrared spectra in early-lactation cows
A. Benedet, Marco Franzoi, Mauro Penasa, et al.
Journal of Dairy Science (2019) Vol. 102, Iss. 12, pp. 11298-11307
Open Access | Times Cited: 37
Validation of milk mid-infrared spectroscopy for predicting the metabolic status of lactating dairy cows in Australia
Phuong N. Ho, T.D.W. Luke, J.E. Pryce
Journal of Dairy Science (2021) Vol. 104, Iss. 4, pp. 4467-4477
Open Access | Times Cited: 27
Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data
Lúcio Flávio Macêdo Mota, Sara Pegolo, Toshimi Baba, et al.
Journal of Dairy Science (2021) Vol. 104, Iss. 7, pp. 8107-8121
Open Access | Times Cited: 27
Exploring the relationship between predicted negative energy balance and its biomarkers of Holstein cows in first-parity early lactation
H. Hu, Sébastien Franceschini, Pauline Lemal, et al.
Journal of Dairy Science (2025)
Open Access
Classifying the fertility of dairy cows using milk mid-infrared spectroscopy
Phuong N. Ho, V. Bonfatti, T.D.W. Luke, et al.
Journal of Dairy Science (2019) Vol. 102, Iss. 11, pp. 10460-10470
Open Access | Times Cited: 35
Diagnostic milk biomarkers for predicting the metabolic health status of dairy cattle during early lactation
S. Heirbaut, Xiaoping Jing, Barbara Stefańska, et al.
Journal of Dairy Science (2022) Vol. 106, Iss. 1, pp. 690-702
Open Access | Times Cited: 16
Quality characteristics and nutrient contents of Bactrian camel milk as determined by mid‐infrared spectroscopy
Yongqing Li, Li Liu, Yikai Fan, et al.
International Journal of Dairy Technology (2024) Vol. 77, Iss. 2, pp. 304-312
Closed Access | Times Cited: 3
Milk phenomics: leveraging biological bonds with blood and infrared technologies for evaluating animal nutritional and health status
Diana Giannuzzi, Chiara Evangelista, Angela Costa, et al.
Italian Journal of Animal Science (2024) Vol. 23, Iss. 1, pp. 780-801
Open Access | Times Cited: 3
Changes in milk lactose content as indicators for longevity and udder health in Holstein cows
Angela Costa, H. Bovenhuis, Mauro Penasa
Journal of Dairy Science (2020) Vol. 103, Iss. 12, pp. 11574-11584
Open Access | Times Cited: 25
Clustering and Characterization of the Lactation Curves of Dairy Cows Using K-Medoids Clustering Algorithm
Mingyung Lee, Seong‐Hun Lee, Jaehwa Park, et al.
Animals (2020) Vol. 10, Iss. 8, pp. 1348-1348
Open Access | Times Cited: 22
Integrating on-farm and genomic information improves the predictive ability of milk infrared prediction of blood indicators of metabolic disorders in dairy cows
Lúcio Flávio Macêdo Mota, Diana Giannuzzi, Sara Pegolo, et al.
Genetics Selection Evolution (2023) Vol. 55, Iss. 1
Open Access | Times Cited: 7
The use of milk mid-infrared spectroscopy to improve genomic prediction accuracy of serum biomarkers
Irene van den Berg, Phuong N. Ho, T.D.W. Luke, et al.
Journal of Dairy Science (2020) Vol. 104, Iss. 2, pp. 2008-2017
Open Access | Times Cited: 20
Time profiles of energy balance in dairy cows in association with metabolic status, inflammatory status, and disease
J. Ma, A. Kok, E.E.A. Burgers, et al.
Journal of Dairy Science (2024) Vol. 107, Iss. 11, pp. 9960-9977
Open Access | Times Cited: 2
Mid-infrared spectroscopic analysis of raw milk to predict the blood nonesterified fatty acid concentrations in dairy cows
Ben Aernouts, Ines Adriaens, Jose A. Diaz-Olivares, et al.
Journal of Dairy Science (2020) Vol. 103, Iss. 7, pp. 6422-6438
Open Access | Times Cited: 17
Variation of Blood Metabolites of Brown Swiss, Holstein-Friesian, and Simmental Cows
A. Benedet, Marco Franzoi, Carmen L. Manuelian, et al.
Animals (2020) Vol. 10, Iss. 2, pp. 271-271
Open Access | Times Cited: 16
Can unsupervised learning methods applied to milk recording big data provide new insights into dairy cow health?
Sébastien Franceschini, Clément Grelet, Julie Leblois, et al.
Journal of Dairy Science (2022) Vol. 105, Iss. 8, pp. 6760-6772
Open Access | Times Cited: 10
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