Curriculum Vitae - Miel Hostens

Robert and Anne Everett Associate Professor of Digital Dairy Management

Author

Miel Hostens

Published

March 26, 2026

Miel Hostens, PhD, DVM

Robert and Anne Everett Endowed Associate Professor
Digital Dairy Management and Data Analytics
Department of Animal Science

Cornell University
273 Morrison Hall
Ithaca, NY 14853
United States

📞 +1 607-663-0808
✉️ miel.hostens@cornell.edu

Current position

Robert and Anne Everett Associate Professor of Digital Dairy Management and Data Analytics at Department of Animal Science, College of Agriculture and Life Sciences, Cornell University (9 months position) focusing on the creation of methodologies using precision dairy farming to monitor sustainable food production systems from a global perspective.

Identifiers & Profiles

Previous Scientific and Professional Activities

Academic Appointments & Professional Experience

Role Institution From To
Robert and Anne Everett Associate Professor of Digital Dairy Management and Data Analytics focusing on the creation of methodologies using precision dairy farming to monitor sustainable food production systems from a global perspective. Department of Animal Science, Cornell University Jan 2024 Present
Adjunct Associate Professor Department of Laboratory for Animal Nutrition and Animal Product Quality, Ghent University Jan 2024 Present
Tenured Assistant Professor Department of Population Health Sciences, Utrecht University (0.9 FTE) Jan 2019 Dec 2023
Adjunct Assistant Professor Department of Laboratory for Animal Nutrition and Animal Product Quality, Ghent University (0.1 FTE) Jan 2021 Dec 2023
Post-doctoral fellow focusing on the optimisation of productiveand reproductive performances in small and large dairy herds usingdigital technologies. Department of Reproduction, Obstetrics and Herd Health, Ghent University Nov 2012 Dec 2018

Pre‑doctoral Fellow focusing on optimisation of productive and

reproductive performances of small and large herds with an emphasison nutrition using digital technologies, while finalizing PhD research.

 Department of Reproduction, Obstetrics and Herd Health, Ghent University Sep 2010 Oct 2012

PhD Candidate funded by the Institute for the Promotion of

Innovation by Science and Technology in Flanders called “Inductionof milk fat depression through specific fatty acids to reduce thenegative energy balance after parturition of high yielding dairy cattlein relation to fertility”

 Department of Reproduction, Obstetrics and Herd Health, Ghent University Sep 2007 Aug 2010
Veterinarian in a dairy cattle and veal calve practice Dierenkliniek Den Ham, The Netherlands Jan 2007 Aug 2007
PhD Pre‑applicant forthe Institute for the Promotion of Innovation by Science and Technology in Flanders on the topic of “Polyunsaturated fatty acids in dairy cattle nutrition and theconsequences for follicle, egg and embryo quality.” Department of Reproduction, Obstetrics and Herd Health, Ghent University Jul 2006 Dec 2006

Education

Post-academic training

Dairy Science Domain

Title of degree or diploma Grade obtained Institution Date
Cursus rundveevoeding: recente ontwikkelingen en nieuwe inzichten Certificate of attendance Wageningen Business School, The Netherlands 2007
BASF, Tagungsveranstaltung Certificate of attendance Haus Riswick, Germany 2007
WIAS Seminar: Strategies to improve health and fertility in dairy cows Certificate of attendance Wageningen University & Research, The Netherlands 2008
ITB Schulung Februar Certificate of attendance DSP Agrosoft, Verden, Germany 2008
14th DISCOVER Conference: Lipids for Dairy Cattle: Today’s Issues, Tomorrow’s Challenges Certificate of attendance ADSA, Nashville, Indiana, United States 2008
25th World Buiatrics Congress Certificate of attendance World Buiatrics Association, Budapest, Hungary 2008
International symposium: Nutritional strategies to manage the challenges of today’s dairy cows Certificate of attendance Wageningen University & Research, The Netherlands 2009
International Symposium on Ruminant Physiology Certificate of attendance ISRP, Clermont-Ferrand, France 2009
Rindergesundheitstag – Milch und gute Fruchtbarkeit: Die besten Strategien Certificate of attendance Leipzig, Germany 2009
ITB Schulung August Certificate of attendance DSP Agrosoft, Verden, Germany 2009
17th DISCOVER Conference: Dairy Herd Analytics Certificate of attendance ADSA, Nashville, United States 2009
20th Discover Conference: The Transition Cow: Biology and Management Certificate of attendance ADSA, Champaign, United States 2010
Dairy Solutions Symposium – Rumen Health: A 360° Analysis Certificate of attendance Utrecht University, The Netherlands 2010
Alta Value Services Konferenz Certificate of attendance Alta, Bremen, Germany 2010
14th International Conference on Production Diseases in Farm Animals Certificate of attendance ISRP, Ghent, Belgium 2010
International Reproduction Conference Certificate of attendance Anchorage, United States 2010
Bovine Professionals Meeting, Fertility and Rumen Health Certificate of attendance BFP, Hofheim, Germany 2010
21st Discover Conference: Improving Reproductive Efficiency of Lactating Dairy Cattle Certificate of attendance ADSA, Itasca, United States 2011
22nd Discover Conference: Milk Components: Opportunities for Maximizing Farm Gate Returns Certificate of attendance ADSA, Chicago, United States 2011
Rindergesundheitstag Certificate of attendance University of Giessen, Germany 2011
European Buiatrics Forum Certificate of attendance EBF, Marseille, France 2011
Meeting American Association for Bovine Practitioners Certificate of attendance AABP, Louisville, United States 2011
25th Discover Conference: New Developments in Immunity, Nutrition, and Management of the Preruminant Calf Certificate of attendance ADSA, Chicago, United States 2012
27th World Buiatrics Congress Certificate of attendance World Buiatrics Association, Lisbon, Portugal 2012
Meeting American Association for Bovine Practitioners Certificate of attendance AABP, Toronto, Canada 2012
14th International Congress of SIVAR Certificate of attendance SIVAR, Cremona, Italy 2012
Bovine Professionals Meeting, Claw Health and Transition Management Certificate of attendance BPM, Hofheim, Germany 2012
26th Discover Conference: Dairy Feed Efficiency Certificate of attendance ADSA, Chicago, United States 2013
Meeting of the European Society for Domestic Animal Reproduction Certificate of attendance ESDAR, Milano, Italy 2013
Dairyland Initiative Meeting on Transition Cow and Positive Pressure Tube Ventilation Certificate of attendance Dairyland Initiative, Madison, United States 2013
28th Discover Conference: Starch for Ruminants Certificate of attendance ADSA, Chicago, United States 2014
Annual National Mastitis Council Meeting Certificate of attendance NMC, Fort Worth, United States 2014
Joint ASA–ADSA Annual Meeting Certificate of attendance ADSA, Kansas City, United States 2014
65th Annual Conference of the European Federation of Animal Science Certificate of attendance EAAP, Copenhagen, Denmark 2014
Blanca Reproduction Workshop Certificate of attendance Blanca, Barcelona, Spain 2015
ICAR Technical Meeting Certificate of attendance ICAR, Cracow, Poland 2015
66th Annual Conference of the European Federation of Animal Science Certificate of attendance EAAP, Warsaw, Poland 2015
7th European Conference on Precision Livestock Farming Certificate of attendance EC‑PLF, Milan, Italy 2015
Large Dairy Herd Management Conference Certificate of attendance ADSA, Chicago, United States 2016
67th Annual Conference of the European Federation of Animal Science Certificate of attendance EAAP, Belfast, United Kingdom 2016
68th Annual Conference of the European Federation of Animal Science Certificate of attendance EAAP, Tallinn, Estonia 2017
70th Annual Conference of the European Federation of Animal Science Certificate of attendance EAAP, Ghent, Belgium 2019
ADSA Annual Meeting Certificate of attendance ADSA, Cincinnati, Ohio 2019
Annual ICAR Meeting Certificate of attendance ICAR, Prague, Czech Republic 2019
9th European Conference on Precision Livestock Farming Certificate of attendance EC‑PLF, Cork, Ireland 2019
39th Discover Conference: The Transition Period – From Physiology to Management (online) Certificate of attendance ADSA, Itasca, United States 2020
Annual ICAR Conference Certificate of attendance ICAR, Leeuwarden, The Netherlands 2021
Dairy InnovCongress Certificate of attendance ULiege–Gembloux, Namur, Belgium 2022
Annual ICAR Conference Certificate of attendance ICAR, Montreal, Canada 2022
10th European Conference on Precision Livestock Farming Certificate of attendance EC‑PLF, Vienna, Austria 2022
43rd Discover Conference: Dairy Cattle Reproduction – Lessons learned and future frontiers Certificate of attendance ADSA, Itasca, United States 2022
Joint Committee for Dairy Diagnostics Certificate of attendance Zoetis, Rome, Italy 2022
21st International Symposium & 13th International Conference on Lameness in Ruminants Certificate of attendance LIR, Bloomington, USA 2023
11th European Conference on Precision Livestock Farming Certificate of attendance EC‑PLF, Milan, Italy 2024
Annual Meeting American Dairy Science Association Certificate of attendance ADSA, West Palm Beach, USA 2024
Annual Meeting American Dairy Science Association Certificate of attendance ADSA, Louisville, USA 2025

Informatics, data science and statistical domain

Title of degree or diploma Grade obtained Institution Date
Post-graduate courses in applied informatics Certificate of attendance HoGent, Belgium 2007–2010
EPI on the Island 2009: An introduction to multilevel modeling Certificate Prince Edward Island, Canada 2009
Introduction to SAS Doctoral School Credit Institute for Continued Education, Ghent, Belgium 2009
Introductory Statistics: Basics of Statistical Inference Doctoral School Credit Institute for Continued Education, Ghent, Belgium 2009
Analysis of Variance Doctoral School Credit Institute for Continued Education, Ghent, Belgium 2009
Applied Linear Regression Doctoral School Credit Institute for Continued Education, Ghent, Belgium 2010
Applied Logistic Regression Doctoral School Credit Institute for Continued Education, Ghent, Belgium 2011
Multilevel Analysis for Grouped and Longitudinal Data Doctoral School Credit Institute for Continued Education, Ghent, Belgium 2011
Design and Analysis of Clinical Trials Doctoral School Credit Ghent University, Belgium 2011
Survival Analysis Doctoral School Credit Ghent University, Belgium 2012
Introduction to R Doctoral School Credit Institute for Continued Education, Ghent, Belgium 2013
Tech Transfer Skills Workshop Doctoral School Credit Institute for Continued Education, Ghent, Belgium 2013
Multivariate Data Analysis Doctoral School Credit Institute for Continued Education, Ghent, Belgium 2014
An Introduction to Big Data Doctoral School Credit Institute for Continued Education, Ghent, Belgium 2015
Professional Certificate in Bioinformatics Professional Certificate University College Dublin, Ireland 2016
Principles of Statistical Data Analysis ECTS Ghent University, Belgium 2016
Statistical Modelling ECTS Ghent University, Belgium 2016
Data + AI Summit Certificate of attendance Databricks, Brussels, Belgium 2016
Big Data Science ECTS Ghent University, Belgium 2017
Computer-Intensive Statistical Methods ECTS Ghent University, Belgium 2017
Databases ECTS Ghent University, Belgium 2017
Programming and Algorithms ECTS Ghent University, Belgium 2017
Statistical Computing ECTS Ghent University, Belgium 2017
Data + AI Summit Certificate of attendance Databricks, Dublin, Ireland 2017

Research focus

  • Sustainable dairy and veterinary systems

    Advancing resilient, efficient, and socially accepted dairy production systems that balance productivity with animal health, welfare, environmental impact, and food safety.

  • Precision livestock farming (PLF) and real‑world evidence

    Leveraging sensor‑based technologies (production, behavior, health, emissions) to generate continuous, real‑time data streams that support early detection, monitoring, and management decisions at animal and herd level.

  • Large‑scale dairy data integration and reuse

    Exploiting heterogeneous, real‑world datasets from platforms such as dairydatawarehouse and MmmooOgle to move beyond isolated experiments toward scalable, generalizable insights across farms, regions, and production systems.

  • Privacy‑preserving data science for agriculture

    Developing methodological frameworks that address data ownership, privacy, security, and governance, enabling collaboration without centralizing sensitive farm data.

  • Federated learning for dairy and veterinary science

    Pioneering the application of federated learning to train statistical, machine‑learning, and AI models across distributed farm, industry, and research data sources by “bringing the code to the data.”

  • Ontologies and semantic interoperability

    Creating and applying ontologies to harmonize heterogeneous data sources, support causal inference, and enable interoperability across technologies, institutions, and countries.

  • Digital twins of cows and farms

    Building data‑driven digital twins for simulation, hypothesis testing, training, and decision support, integrating historical experimental data with real‑time PLF data.

  • AI‑driven decision support and prediction

    Using advanced analytics, machine learning, and artificial intelligence to predict disease events, performance outcomes, and sustainability indicators at animal, herd, and system levels.

  • Human‑centered AI interfaces

    Integrating fine‑tuned large language models (LLMs) and retrieval‑augmented generation (RAG) to allow farmers, veterinarians, and policymakers to interact with complex models through natural language.

  • Translation from research to practice

    Ensuring that advanced analytics are deployable on commercial farms, including small and medium‑sized operations, and directly support decision‑making in daily management and policy contexts.

Significant output

In my research domain, first, second and last authors have made significant contributions. As my research group focuses on applied research, a large international network and participation in consortia or advisory committees are globally also acknowledged as important output. My current h-index is 35 (https://scholar.google.com/citations?user=fZ1xfdQAAAAJ&hl=nl). I consider the following papers/achievements as my personal best output, although I have other clinically important output due to active collaborations within the veterinary domain (Pardon et al.; Kemel et al.). I have ordered and grouped output together given common projects or background. A clear move from the veterinary and dairy domain towards the precision livestock farming and data science domain can be seen in my key output.

First peer-reviewed paper

Hostens, M., Ehrlich, J., Van Ranst, B., & Opsomer, G. (2012). On-farm evaluation of the effect of metabolic diseases on the shape of the lactation curve in dairy cows through the MilkBot lactation model. Journal of dairy science, 95(6), 2988–3007. https://doi.org/10.3168/jds.2011-4791

The first paper from my PhD work extended a data warehouse architecture I created during my PhD with a novel Bayesian lactation curve model applied to dairy cow transition disease. It was initiated through an international collaboration with Jim Ehrlich, a veterinarian from New York (USA). This collaboration eventually led to the co-organization of the 31st Discover Conference on Big Data Dairy Management in 2016 (Chicago, USA). Ultimately, the paper even contributed to me taking the lead organization of the 46th ADSA Discover Conference on Milking the Data: Value-Driven Dairy Farming in Chicago (USA).

The paper has initiated other researchers to use the methodology leading to several co-authorships (Charlier et al., 2012; Verschave et al., 2014). Through my current position as Chair of the Milk Recording Working Group within the International Committee of Animal Recording, the model is being evaluated as one of the newer models to be applied in the milk recording industry. The paper also had 2 follow-up papers (Probo et al., 2018; Pascottini et al., 2020) re-using the same dataset using novel machine learning techniques which illustrates me actively advocating and applying open-code, open-source and FAIR principles, motivating other researchers to follow the approach.

DairyDataWarehouse & MmmooOgle

The data warehouse architecture developed during the previous paper was eventually acquired by Delaval, a leading milking software and hardware provider, from the department of Reproduction, Obstetrics and Herd health and transformed into www.DairyDataWarehouse.com. The product is still actively used across the world. Ghent University was compensated for this acquisition in 2012. After this, I re-initiated a new software company with the original co-creator of the data warehouse, focusing on creating the next-generation data-science platform ready for the multitude in PLF technologies being installed on dairy farms called www.MmmooOgle.com. This illustrates my academic entrepreneurship, which:

  • accelerated my scientific output as it provided me with unlimited access to data from dairy farms around the world.

  • because of the combined expertise in data and dairy science attracted several successful project consortia (GPLUS, VEERKRACHT, DECIDE, GREENFEED, see appendix 3)

  • illustrates my academic drive to create decision support tools which can be implemented in the dairy industry at sufficient technology-readiness-level.

GplusE

In 2013, my PhD supervisor Prof. dr. Geert Opsomer and me were approached to join a large FP7 consortium called GplusE. This multi-institutional international project resulted in a large amount of peer reviewed articles (see Appendix 3). Our team was responsible for the data intensive work packages integrating research data from heterogeneous farms and creating best practices for data pipelines within the project. The project resulted in my first article as last author:

De Koster, J., Salavati, M., Grelet, C., Crowe, M. A., Matthews, E., O’Flaherty, R., Opsomer, G., Foldager, L., GplusE, & Hostens, M. (2019). Prediction of metabolic clusters in early-lactation dairy cows using models based on milk biomarkers. Journal of dairy science, 102(3), 2631–2644. https://doi.org/10.3168/jds.2018-15533

The article compared several biomarkers with a standardized prediction methods for novel indicators for dairy cow resilience. The methodology was built using 2 ‘Microsoft for Research awards’ mentioned in this application, implementing highly innovative techniques such as scalable machine learning and artificial intelligence in its early stages. The methodology of clustering cows according to their metabolic blood profile was adopted by multiple researchers around the world (e.g. Tremblay et al., 2018; Grelet et al., 2019; Xu et al., 2019; Girma et al., 2024). Subsequently, the method was translated, in collaboration with a visiting researcher from Iran, into a genome wide association study involving multiple industry partners from the Netherlands (Atashi et al., 2020). It illustrates my capability of working with multiple stakeholders, across several domains (phenotype and genotypes), and attracting visiting researchers.

SenseOfSensors

During my appointment as Assistant Professor at Utrecht University between 2018 and 2023, my scientific output was boosted due to the daily supervision of 5 PhD students (Liseune A., Hut P., Scheurwater J., Salamone M. and Chen Y.). All of them were applying some of my previous work (such as the Milkbot model) as well as novel data science methods (including artificial intelligence) and precision dairy farming techniques to monitor and predict dairy cow health and behavior. I consider the following paper as key output from that 5 year period.

Hut, P. R., Kuiper, S. E. M., Nielen, M., Hulsen, J. H. J. L., Stassen, E. N., & Hostens, M. M. (2022). Sensor based time budgets in commercial Dutch dairy herds vary over lactation cycles and within 24 hours. PloS one, 17(2), e0264392. https://doi.org/10.1371/journal.pone.0264392

It illustrates several important aspects of my research philosophy:

  • The article demonstrates the enormous potential of using and combining real-world farm data to test field assumptions made about cow behavior.

  • The article started as an MSc project in Veterinary Medicine. The student (listed as second author) was illiterate in data science at the beginning of the project, but through my daily supervision and training was successful in converting this project into a peer-reviewed paper. It illustrates my active approach to train the next generation student in data-driven Veterinary Medicine.

  • The methodology of the article was also made publicly available (open-source/open-code), demonstrating my current default strategy when publishing data-driven dairy science as it supports training and dissemination.

Resilience

Around the same time, I still had an active appointment at Ghent University, through which I successfully applied for a Flemish VLAIO with collaborators from Bio-science engineering at UGent and the KULeuven. The aim of the project was to create data-driven tools applying artificial intelligence to monitor the transition success of dairy cows at individual level and herd level. The project, using real-world farm data yielded several publications in the highly ranked Journal Computers and Electronics in Agriculture (impact factor 7.7), of which is I value the following as key output: 

Liseune, A., Salamone, M., Van den Poel, D., Van Ranst, B., & Hostens, M. (2020). Leveraging latent representations for milk yield prediction and interpolation using deep learning. Computers and Electronics in Agriculture, 175, 105600. https://doi.org/10.1016/j.compag.2020.105600

The article was foundational for my research group because

  • It served 5 other papers of my group using the same AI method to predict milk production (Liseune et al 2021, Salamone et al., 2022), metabolic health (Salamone et al., 2024; van Leerdam et al., 2024) and behavior (Liseune et al., 2021) in dairy cows.

  • It is currently used as base-model to create a digital twin of a dairy cow including milk components, body weight, feed intake and cow behavior in the current work of 2 of my current PhD students (Hayu S. and van Leerdam M.)

The model is also being integrated with Large Language Models (LLM) by my current post-doctoral fellow Liu E. to allow farmers to interact with the model using natural languages instead of complex computer interfaces.

Grants & Projects (Current, Pending, Finished)

Current

Project Description Role Year Budget
DECIDE, Horizon Europe H2020-SFS-2018-2020 / 101000494, is a five-year Horizon 2020 project running from 2021 to 2025. It will develop data-driven decision support tools that offer robust and early signals of disease emergence and options for diagnostic confirmation. Moreover, options will be provided for controlling the disease along with their implications in terms of disease spread, economic burden and animal welfare. CoPI 2021 €9,998,805
PLAY BEHAVIOR CALVES IN EUROPE – A project funded by the Dutch dairy organization ZuivelNL to detect play behaviour in relation to space allowance using accelerometer and video analytics in dairy calves. PI 2023 €100,000
PLAY BEHAVIOR CALVES IN THE US – A project funded by the Cornell Institute for Digital Agriculture to detect play behaviour in relation to space allowance using accelerometer and video analytics in dairy calves. PI 2023 €100,000
GLOBAL GREENFEED PROJECT – A project funded by the Global Methane Hub to accurately measure and collect gas emissions from cattle with the GreenFeed system. PI 2024 $3,279,652
AI-Driven Pandemic Preparedness in Dairy Farming: Enhancing Health Monitoring and Disease Management through Generative AI and Data Integration (Don Bennink Endowment). PI 2025 $249,175
PDASP Track 3: Privacy-Preserving Dairy-Digitalization with Federal Learning (NSF-PDASP) PI 2025 $1,000,000
Leveraging AI for Sustainable Livestock Farming: A RAG-Enhanced LLM Approach to Mitigate Carbon Emissions Around The World (Bezos Earth Fund) PI 2025 $50,000
Selective Antimicrobial Disease Treatments in Dairy cows – Lessons learned from real-world farm data. (Federal Capacity Funds) PI 2025 $90,000
A Multimodal Approach to Dairy Methane Reduction: Validation of Remote Satellite Images, Mid-InfraRed and Genomic Selection in Methane Mitigation (Cornell Atkinson Center for Sustainability) PI 2025 $150,000

Pending

Project Description Role Year Budget
HERD-AID:Connecting dairy herd health, environment and economics through applied and innovative decision modeling (FFAR) Co-PI 2025 $194,995
DSFAS: A Farm-Deployable, Privacy-Aware Computer Vision Decision for Dairy Cattle Health and Welfare Management (USDA-NIFA-AFRI-011134) PI 2025 $650,000
Modernizing Simulation Models to Enhance Computational Efficiency and User-Friendliness for Better Decision-Making on Small and Medium-Sized Dairy Farm (USDA-NIFA-AFRI-011134 PI 2025 $650,000
Enhancing CNCPS Accuracy and Adaptability through Ontology-Enabled Farm Data Integration and AI-Driven Model Modernization (USDA-NIFA-AFRI-011134) PI 2025 $650,000

Finished

Project Description Role Year Budget
GplusE was an FP7 project funded by the European Union. It was a five-year project from 2014 and 2018 executed by 15 research and industry partners. The project covered the interaction between genotype and environment contributing to the sustainability of dairy cow production systems. This was achieved through the optimal integration of genomic selection and novel management protocols based on the development and exploitation of genomic data and supporting novel phenotyping approaches. CoPI 2014 €9,000,000
In the SUMMERFAIR project (SUMmarizing transmission data to Enable data Reanalysis and predictions by FAIR data use) we tackle the issue of lack of a common terminology and need for repetition of costly experiments by developing a shared vocabulary (domain-ontology) and a workflow enabling reuse and combination of transmission data. The project running from 2021–2022 was granted by the dutch ZoNMW. CoPI 2021 €250,000
VEERKRACHT/Resilience – The transition period as a window and metabolic resilience to monitor of dairy cattle, granted by national Belgian VLAIO 2018 is a project that aims at creating tools to monitor the transition success of dairy cows at individual level and herd level. These tools allow the farmer to monitor individual animals at risk, in addition to allow individualized preventive measures. This will reduce the development of transition associated problems, which will increase productivity and animal welfare. CoPI 2018 €1,300,000
CLAWHEALTH.NL – A project sponsored by the Dutch Foundation WakkerDier to map the prevalence of leg and hoof problems in Dutch dairy cows, and to uncover the risk factors for leg and claw problems in the Netherlands using AI driven systematic reviews. PI 2023 €100,000

Graduate research experience

Finalized PhDs

Regular supervision

  • Osvaldo Bogado Pascottini (Ghent University, 2016), PhD title: “Subclinical endometritis in dairy cattle: a practical approach”.

  • Josje Scheurwater (Utrecht University, 2024), PhD title: “The Happy Healthy Cow”.

Ongoing PhD supervision

  • Kristof Hermans, a PhD focusing on data quality in dairy cows as a follow up on the development of the DairyDataWarehouse.

  • Yara Sleghers, a PhD focusing on the applying federated machine learning to disease data in poultry.

  • Saba Noor, a PhD focusing on the use of semantic web technologies and federated learning to disease data in livestock.

  • Thomas Vandeputte, a PhD on computer vision and sensor aided analysis of behavioural and feed intake patterns in pigs (Ghent University, expected 2025).

  • Sonam Hayu, a PhD focusing on the use of federated vision and learning technologies in dairy production (Cornell University, expected 2028).

  • Meike van Leerdam, a PhD focusing on the use of federated learning technologies to prediction production reproduction and animal welfare traits in dairy cows (Cornell University, expected 2029).

  • Sumit Sharma, a PhD focusing on the use of artificial intelligence and machine learning for Precision Livestock Farming: From data quality to digital twin–based decision support (Cornell University, expected 2029).

PhD examination committee

  • Dr. Lucia Trapanese, 2025 - High-tech farming: Study and application of machine learning techniques for improving production efficiency in livestock.

  • Dr. Ismalia Bouba, 2024 - A data driven apporach to understand factors influencing health, welfare and performance of laying hens and Pullets.

  • Dr. Mingqi Zhang, 2024 - Inter-animal variability in metabolic and oxidative status as well as in inflammatory response in Holstein cattle during the transition period.

  • Dr. Yujie Liu, 2022 - Cross-species combination of cohort and intervention studies to assess the fatty acid composition in various lipid fractions of the follicular fluid in relation to blood lipid composition and embryo quality

  • Dr. Zhaoju Deng, 2021 - Improving udder health management in dairy herds with automatic milking systems.

  • Dr. Marlene Tremblay, 2019 - Systematic pattern recognition and modeling with imperfect data: An integration of datascience, data mining, machine learning and epidemiology.

  • Dr. Wei Xu, 2019 - Energy balance and metabolic status of dairy cows: a study using metabolomics, proteomics and machine learning approaches.

  • Dr. Cyriel Ververs, 2018 - Breeding on the brink of extinction: what can we learn from game-ranched white rhinoceros (Ceratotherium simum simum)?´.

Teaching

Professionalization of education

  • University Teaching Qualification at Centre for Academic Teaching and Learning, Utrecht, The Netherlands, 2021

Teaching experience

Institution Programme Duration
Cornell University
  • Undergraduate in Animal Science
 2024–present
Utrecht University
  • Master of Veterinary Medicine
 2019–present
  • Bachelor of Clinical Sciences
 2022–2023
  • Bachelor of Veterinary Medicine
 2019–2023
  • Master of Bio‑informatics and Bio‑complexity
 2019–2023
  • Lifelong Learning Course
 2022–2023
  • Inter‑faculty Course
 2021–2023
Ghent University
  • Bachelor of Science in Bioscience Engineering Technology
 2020–present
  • Master of Science in Bioscience Engineering Technology
 2020–present
  • Bachelor of Science in Veterinary Medicine
 2020–present
  • Master of Science in Veterinary Medicine – Ruminants
 2015–2018
  • Institute for Continued Education (Faculty of Veterinary Medicine)
 2010–2019
  • Basic Summer Course on Veterinary Epidemiology
 2016
University College Dublin
  • Summer School – Professional Certificate in Bioinformatics
 2017

Cornell University

Undergraduate in Animal Science

  • Main lecturer in “Data Science Applications in Agriculture” (2024–present).
  • Co‑lecturer in Hackathon in Digital Agriculture (2024–present).
  • Main lecturer in “Digital Dairy Management and Data Analytics”.

Utrecht University

Master of Veterinary Medicine
https://www.uu.nl/masters/diergeneeskunde

  • Main developer and co‑lecturer of the “Dairy Health Management” course (2019–present).
  • Main developer and lecturer of the “Dier and Data” course (2022–2023).
  • Main lecturer in “International Dairy Study Trip” (2019–present).
  • Clinical rotations in “Bovine health management” (2019–2024).
  • Main lecturer in “Veal calf management” (2019–2023).

Bachelor of Clinical Sciences
https://www.uu.nl/bachelors/zorg-gezondheid-en-samenleving

  • Co‑developer of “Digitalization and technology in clinical sciences” (2022–2023).

Bachelor of Veterinary Medicine
https://www.uu.nl/bachelors/diergeneeskunde

  • Weekly lectures on bovine health management (2019–2023).

Master of Bio‑informatics and Bio‑complexity
https://www.uu.nl/en/masters/bioinformatics-and-biocomplexity/study-programme

  • Co‑lecturer in “Introductory course to bioinformatics” (2019–2023).

Lifelong Learning Course

Inter‑faculty Course

Ghent University

Bachelor of Science in Bioscience Engineering Technology

Master of Science in Bioscience Engineering Technology: Agriculture and Horticulture

Bachelor of Science in Veterinary Medicine

Master of Science in Veterinary Medicine – Ruminants

  • Theoretical courses for Ruminant and Porcine Herd Health Medicine with Clinical Training II and III (2015–2018).
  • Weekly practical training sessions on herd record interpretation (2014–2018).
  • Clinical Training III during nightly duties at the Veterinary Service of the Department of Reproduction, Obstetrics and Herd Health (2007–2018).
  • Clinical Training III during Bovine Herd Health service visits (2008–2018).

Institute for Continued Education – Faculty of Veterinary Medicine

  • A practical approach to risk factors for transition cows (2010).
  • Ration balancing for dairy cows, more than VEM and DVE (2012).
  • Key Performance Indicators on dairy herds (2013).
  • To synch or not to synch dairy cows (2013).
  • Factors influencing reproduction and production results in dairy cows (2014).
  • Data management in high‑yielding dairy cows (2015).
  • Transition cow disease (2016).
  • Short course Dairy Cow Nutrition (2018).
  • Short course Dairy Cow Nutrition (2019).

Basic Summer Course on Veterinary Epidemiology

  • Big Data in Dairy Analytics (2016).

University College Dublin

Summer School – Professional Certificate in Bioinformatics

  • An introduction to scalable data analytics in animal science (2017).

Intellectual property and knowledge transfer

In 2013, intellectual property created by my group on data processing and visualisation for dairy reproduction data, co-developed between Uniform-Agri (Assen, the Netherlands) and Ghent University, was transferred towards www.DairyDataWarehouse.com (2007-2013). Dairy Data Warehouse nowadays is a specialist dairy data company providing digital solutions for a sustainable, profitable future for dairy farmers and stakeholders throughout the dairy industry.

Contributions to Congresses, Symposia and Workshops

Presentations available online

Invited speaker

Title Year Presentation link
Fresh Cow — Risiken und chancen. Was Sie darüber unbedingt wissen sollten! 2011 —
Visualisation of fertility records in dairy herds 2012 —
The use of dairy data in herd health management 2014 —
The economics of breeding protocols in dairy cows 2015 —
The past, the present and the future of bovine herd health management 2015 —
Bovi‑Analytics: an e‑learning platform to educate veterinary students big data in dairy cows 2015 —
Challenges for data‑intensive projects 2016 —
From Big Data to Decisions in Dairy Cows 2016 —
Big Dairy in Dairy Cows 2016 —
Visualisation and analysis of reproductive performance 2017 —
Pitfalls in Dairy Analytics (advanced technical) 2017 —
Analysis of reproductive performance (farmer orientated) 2017 —
Transition management as a key to fertility success 2017 —
How to monitor productive performance on a small vs large dairy 2017 —
Common analytical data pitfalls every practitioner should know about 2017 —
Will semantics help disentangle the Gordian knot of Big Data in animal health 2017 —
Monitoring fertility control programs in small and large dairy herds 2017 —
Opening keynote: A novel approach to data mining and prediction modelling in dairy cows 2017 —
Opening Keynote: BigData Moscow — A Novel Approach to Data Mining and Prediction Modelling in Dairy Cows 2018 —
Transforming Big Data into Value: Put Data to Work for Your Dairy (Connect Summit) 2018 —
Transforming Big Data into Value: Put Data to Work for Your Dairy (CDCB – 10 years of genomics) 2018 —
Metabolic clustering of dairy cows at early and peak lactation 2018 —
Dairy Intelligence and Turning Data Into Information 2019 —
Predicting the Moment of Birth using Sensor Data in Dairy Cows 2019 —
What makes a biomarker a good one? 2019 —
Put Data to Work for the Dairy Industry 2020 —
Agricultural Genome to Phenome Initiative (AG2PI) 2020 —
Monitoring transition cows using novel techniques 2021 —
Using data to embrace excellence in hoof health — challenges and opportunities 2022 Link to presentation
How to Make Sense of 24×7 Sensor Data 2022 Link to presentation
Sustainable dairy production, EU within a global perspective 2023 Link to presentation
Sustainable dairy production from a global perspective 2023 Link to presentation
The Finnish Veterinary Congress (Helsinki, Finland) 2023

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Een toekomst voor de veehouderij in Nederland, Europa en ver daarbuiten? 2024 Link to presentation
European Perspective on Longevity — the Data and Technology 2024 Link to presentation
Advancing precision dairy farming through AI 2024 Link to presentation
Digital Dairy Farming (CIDA Symposium) 2024 Link to presentation
Cattle Camp (Triesdorf, Germany) 2024

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How can AI change the dairy industry? 2024 Link to presentation
CIDA Seminar (Ithaca, USA) 2025 Link to presentation
What can camera technology and artificial intelligence bring to a dairy in 2025? 2025 Link to presentation
How artificial intelligence can transform an entire agricultural industry, or NOT? (IEEE MeAVeAS) 2025 Link to presentation
Effectively Implementing AI at the Interface Between Veterinary and Dairy Science (Savy) 2025 Link to presentation
Effectively Implementing AI at the Interface Between Veterinary and Dairy Science (VEE) 2025 Link to presentation
How artificial intelligence can transform an entire agricultural industry, or NOT? (AABP) 2025 Link to presentation
How artificial intelligence can transform an entire agricultural industry, or NOT? (Georgia Dairy Conference) 2026 Link to presentation
Growing with AI — Input Optimization Webinar 2026 Link to presentation

Oral conference presentation

Title Year Presentation link
The effect of marine algae supplementation in the ration of high‑yielding dairy cows during transition and its effect on metabolic parameters in the serum and follicular fluid around parturition 2009 —
On‑farm evaluation of the effect of metabolic diseases on the shape of the lactation curve in dairy cows through the MilkBot lactation model 2012 —
Potential for novel glycan measurements in milk as biomarker phenotypes for dairy traits 2016 —
MmmooOgle: From Big Data to Decisions for Dairy Cows 2016 —
Data Mining and Prediction Modelling in the Dairy Industry Using Time Series and Sliding Windows with Apache Spark 2 2016 —
Exploratory classification of multiparous dairy cows based on fertility‑related phenotypes 2017 —
Investigating metabolic phenotypes in multiparous dairy cows by component analysis and clustering 2017 —
The use of technologies in dairy innovation 2018 —
Can artificial intelligence be used on historical cow data to improve data quality and standardization of disease records 2019 —
Predicting the next life event of cows by applying deep learning on sequential and pictorial data 2019 —
Big Data for Dairy and Monitoring Cow Health and Performance 2020 —
The veterinary toolbox for reproductive herd health management now and in the (near) future 2020 —
Modern tools for milk recording management 2021 —
The importance of sensor data in transition cow monitoring 2021 —
Detecting the subclinical diseased transition cow: how novel phenotyping strategies can help 2021 —
Prediction of persistency at day 305 in lactation at the moment of the insemination decision 2023 —
A predictive model for hypocalcemia in dairy cows utilizing behavioural sensor data combined with deep learning 2023 —
Generative AI in agriculture 2024 Link to presentation
A Foundational Framework for Animal Behavior Analysis Using Computer Vision (AI4AS, Zurich) 2024 Link to presentation
ICAR–IDF initiative on sensor data for functional traits: genetics and reference standards for rumination 2024 —
A Foundational Framework for Animal Behavior Analysis Using Computer Vision (Precision Dairy Farming Conference, Christchurch) 2024 Link to presentation

Extension workshops

Title Year Presentation link
How we practitioners should implement our nutrition knowledge to help our farmers? 2011 —
Technology in dairy 2012 —
HACCP approaches for fertility management in livestock 2013 —
Herd Health Management: The future for bovine practitioners: challenge or opportunity 2013 —
Visualisation of heat detection and conception rates in small and large dairy herds 2013 —
Transition management and its influence on fertility 2013 —
Pitfalls in the analysis of reproductive records in dairy herds 2013 —
Subclinical ketosis during the transition period 2013 —
Masterclasses reproductive management in dairy cows 2014 —
Visualisation and interpretation of reproductive herd records 2014 —
Masterclasses reproductive management in dairy cows 2015 —
Masterclasses Transition Management 2015 —
Transforming Big Data into real world evidence for SARA 2016 —
Bovine herd health management 2016 —
Dairy data management, where to start 2017 —
How to monitor reproductive performance 2017 —
Multiple sessions for the Ruminant Specialisation (General intro, pitfalls, monitoring production & reproduction, insemination & conception strategy, fertility) 2017 —
The truth about transition disease in dairy cows 2017 —
First results of the GplusE project 2017 —
Ruminant clinical cases 2017 —
Monitoring reproduction in high yielding dairy cows 2018 —
Feeding the dairy cow — Basics of high yielding dairy cows 2018 —
Feeding the dairy cow — Feeding high intake and milk composition 2018 —
Zoetis Advanced Fertility Consulting 2018 —
Act, think and work like a datascientist 2018 —
GplusE Training School 2018 —
Not only a success story: lessons learned moving from precision to smart dairy farming 2022 —
Sustainable Ruminant Health: How to manage it successfully 2023 Link to presentation
Sustainable Ruminant Health (DAP Oostland) 2023 Link to presentation
Duurzame melkveehouderij vanuit een internationaal perspectief 2023 Link to presentation
Meer melk uit (ruw)voer 2023 Link to presentation
NutriVice studie bijeenkomst — Een toekomst voor de Nederlandse melkveehouderij 2023 Link to presentation
Evolution or revolution 2023 Link to presentation
Toekomst van de melkveehouderij in Nederland, Europa en daarbuiten (DAMB) 2024 Link to presentation
Toekomst van de melkveehouderij in Nederland, Europa en daarbuiten (Boerensymposium) 2024 Link to presentation
How artificial intelligence can transform an entire agricultural industry, or NOT? (Dairy Center of Excellence) 2025 Link to presentation
Eine Zukunft für die Milchwirtschaft 2025 Link to presentation
Effectively Implementing AI at the Interface Between Veterinary and Dairy Science (Bovine Practitioners Meeting) 2025

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Effectively Implementing AI at the Interface Between Veterinary and Dairy Science (Ohio Veterinarians Meeting) 2026

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Toekomst van de melkveehouderij in Vlaanderen, Europa en daarbuiten 2025 Link to presentation

Academic services

Professional Service, Leadership & Editorial Activities

Professional membership

  • Member of the American Dairy Science Association.

  • Member of the Dutch Veterinary Association.

  • Member of the Flemish Veterinary Association.

Extension services

National Institute for Agricultural Training (NCBL)

  • Multiple extension training sessions for dairy and beef herd managers on production, transition and reproductive management of dairy cows (2006-2022).

Industry and Stakeholder Engagement

Active involvement in extension services for several Belgian, European and global agricultural businesses. Some examples but not limited to

Feed Industry

Milk Recording Organizations

Genetic Companies

Pharmaceuticals

Dairy Cooperatives

Precision Dairy Farming Technologies

Academic distinctions, awards, prizes

  1. In 2014 and 2018, I was twice awarded the Microsoft Azure Research award (2014 - €150,000 and 2018 - €25,000) to accelerate the adoption of scalable machine learning techniques to monitor sustainable agriculture practices.

  2. In 2022, I was awarded the ‘Applied data science award’ (€ 5,000) from the Utrecht University to create the first LoraWAN network at the faculty of Veterinary Medicine allowing real time monitoring of PLF data from the research farm ‘De Tolakker’.

  3. In 2024, I was awarded the ‘CIDA Summer Research Award’ ($25,000) to advance calve welfare monitoring using computer vision.

Natural Languages

Mother tongue : Dutch

Other:

Understanding Speaking Writing
English +++ +++ +++
French ++ + ±
German ± ± -
Italian ± ± -

Datascience skills

Computer languages

Datascience software

Statistical frameworks

[R]

Python

Scala

SQL

SAS code

Bash

C++

SQL Server/warehouse

Jupyter notebooks

Jupyter lab

Intellij

Google colab

Spark scala

Tableau

Docker

TensorFlow

Git & Github

SPSS

SAS-Project

R-Project

Cloud

Hardware

Other

Microsoft azure

Google cloud

Docker

TheThingsNetwork

IoT (eg LoraWAN)

Arduino framework

RaspberryPi

Publications

(a1) Articles published in journals listed in the ISI Web of Science

Title Year IF
Salamone, M., Hostens, M., Canniere, E., Goossens, T., van Beest, V. W. M., van Gasteren, T., Opsomer, G., Aernouts, B. & Adriaens, I. (2026). The association between transition management and modelled milk yield in multiparous dairy cows. Biosystems Engineering, 263, 104382. https://doi.org/10.1016/j.biosystemseng.2025.104382 2026 5.3
Sharma, S., Liu, E., van Leerdam, M., Hu, H., Villalobos-Barquero, R., Dorea, J. R., Cabrera, V., Iwersen, M., Bewley, J., Soyeurt, H. & Hostens, M. (2026). Invited review: Milking the data for value-driven dairy farming. Journal of Dairy Science. https://doi.org/10.3168/jds.2025-27011 2026 3.7
Sitko, E. M., Hostens, M., O’Connor, J., Heffernan, C., McCarthy, J., & Butler, S. T. (2025). Predictors of reproductive outcomes in seasonal-calving, pasture-based lactating dairy cows. Journal of Dairy Science. https://doi.org/10.3168/jds.2025-27537 2025 3.7
Hostens, M., Franceschini, S., van Leerdam, M., Yang, H., Pokharel, S., Liu, E., Sharma, S. (2025). The future of big data and artificial intelligence on dairy farms: A proposed dairy data ecosystem. JDS Communications. https://doi.org/10.3168/jdsc.2025-0843 2025 2.2
Slegers, Y., Matthijs, M., Stegeman, A., Hostens, M., & de Wit, S. (2025). Applied research note: exploring the relationship between first-week mortality and performance after the first week in broiler chickens. Journal of Applied Poultry Research, 100611. https://doi.org/10.1016/j.japr.2025.100611 2025 2.0
Brito, L. F., Heringstad, B., Klaas, I. C., Schodl, K., Cabrera, V. E., Stygar, A., Hostens, M., & Egger-Danner, C. (2025). Invited review: Using data from sensors and other precision farming technologies to enhance the sustainability of dairy cattle breeding programs. Journal of Dairy Science, 108(10), 10447–10474. https://doi.org/10.3168/jds.2025-26554 2025 3.7
Liu, E., Yang, H., Sharma, S., van Leerdam, M. B., Niu, P., VandeHaar, M. J., & Hostens, M. (2025). Agents are all you need: Pioneering the use of agentic artificial intelligence to embrace large language models into dairy science. Journal of Dairy Science. https://doi.org/10.3168/jds.2025-26775 2025 3.7
Kemel, C., Salamone, M., Aernouts, B., Adriaens, I., Opsomer, G., Hut, P., & Hostens, M. (2025). Association of artificial intelligence–predicted milk yield residuals to behavioral patterns and transition success in multiparous dairy cows. Journal of Dairy Science. https://doi.org/10.3168/jds.2024-26134 2025 3.7
Wessels, W., Bokkers, E. A., de Boer, I. J., Meijer, E., Rodenburg, T. B., Hostens, M., & Koop, G. (2025). A systematic review and meta-analysis of lameness scoring methods and the prevalence of lameness of dairy cows in Northwest Europe. Journal of Dairy Science. https://doi.org/10.3168/jds.2025-26585 2025 3.7
Slegers, Y., Hostens, M., de Wit, S., Stegeman, A., & Jensen, D. B. (2025). Predicting footpad lesion scores of Dutch broiler flocks using routinely collected data. Smart Agricultural Technology, 101080. https://doi.org/10.1016/j.atech.2025.101080 2025 5.7
Van De Putte, T., Van Kerschaver, C., Hostens, M., & Degroote, J. (2025). Investigating the development of piglet feeding behaviour during the immediate postweaning phase using computer vision. animal, 19(6), 101524. https://doi.org/10.1016/j.animal.2025.101524 2025 4.2
Pascottini, O. B., Crowe, A. D., Ramil, U. Y., Hostens, M., Opsomer, G., & Crowe, M. A. (2025). Perspectives in cattle reproduction for the next 20 years – A European context. Theriogenology, 233, 8–23. https://doi.org/10.1016/j.theriogenology.2024.11.007 2025 2.4
Slegers, Y., Hostens, M., Matthijs, M. G. R., Stegeman, J. A., & de Wit, J. J. (2024). Broiler flocks in production systems with slower-growing breeds and reduced stocking density receive fewer antibiotic treatments and have lower mortality. Poultry Science, 103(11), 104197. https://doi.org/10.1016/j.psj.2024.104197 2024 3.8
van Leerdam, M., Hut, P. R., Liseune, A., Slavco, E., Hulsen, J., & Hostens, M. (2024). A predictive model for hypocalcaemia in dairy cows utilizing behavioural sensor data combined with deep learning. Computers and Electronics in Agriculture, 220, 108877. https://doi.org/10.1016/j.compag.2024.108877 2024 7.7
Chen, Y., Steeneveld, W., Frankena, K., Leemans, I., Aardema, H., Vos, P. L. A. M., Nielen, M., & Hostens, M. (2024). Association between days post-conception and lactation persistency in dairy cattle. Journal of Dairy Science, 107(8), 5794–5804. https://doi.org/10.3168/jds.2023-24282 2024 3.7
Trapanese, L., Hostens, M., Salzano, A., & Pasquino, N. (2024). Short review of current limits and challenges of application of machine learning algorithms in the dairy sector. Acta IMEKO, 13(1), 1–7. https://doi.org/10.21014/actaimeko.v13i1.1725 2024 1.06
Probo, M., Atashi, H., & Hostens, M. (2024). Lactation performances in primiparous Holstein cows following short and normal gestation lengths. Frontiers in Veterinary Science, 11, 1289116. https://doi.org/10.3389/fvets.2024.1289116 2024 2.6
Salamone, M., Adriaens, I., Liseune, A., Heirbaut, S., Jing, X. P., Fievez, V., Vandaele, L., Opsomer, G., Hostens, M., & Aernouts, B. (2024). Milk yield residuals and their link with the metabolic status of dairy cows in the transition period. Journal of Dairy Science, 107(1), 317–330. https://doi.org/10.3168/jds.2023-23641 2024 3.7
Hostens, M., Lam, T., & Koop, G. (2023). Sustainable Ruminant Health at Utrecht University. American Journal of Veterinary Research, 84(7), ajvr.23.05.0104. https://doi.org/10.2460/ajvr.23.05.0104 2023 1.3
Zare, M., Atashi, H., & Hostens, M. (2022). Genome-Wide Association Study for Lactation Performance in the Early and Peak Stages of Lactation in Holstein Dairy Cows. Animals, 12(12), 1541. https://doi.org/10.3390/ani12121541 2022 2.7
Chen, Y., Steeneveld, W., Nielen, M., & Hostens, M. (2023). Prediction of persistency for day 305 of lactation at the moment of the insemination decision. Frontiers in Veterinary Science, 10, 1264048. https://doi.org/10.3389/fvets.2023.1264048 2023 2.6
Chen, Y., Atashi, H., Mota, R. R., Grelet, C., Vanderick, S., Hu, H., GplusE Consortium, & Gengler, N. (2023). Validating genomic prediction for nitrogen efficiency index and its composition traits of Holstein cows in early lactation. Journal of Animal Breeding and Genetics, 140(6), 695–706. https://doi.org/10.1111/jbg.12819 2023 1.9
Franceschini, S., Grelet, C., Leblois, J., Gengler, N., Soyeurt, H., & GplusE consortium. (2022). Can unsupervised learning methods applied to milk recording big data provide new insights into dairy cow health? Journal of Dairy Science, 105(8), 6760–6772. https://doi.org/10.3168/jds.2022-21975 2022 3.7
Chen, Y., Hostens, M., Nielen, M., Ehrlich, J., & Steeneveld, W. (2022). Herd level economic comparison between the shape of the lactation curve and 305 d milk production. Frontiers in Veterinary Science, 9, 997962. https://doi.org/10.3389/fvets.2022.997962 2022 2.6
Hut, P. R., Kuiper, S. E. M., Nielen, M., Hulsen, J. H. J. L., Stassen, E. N., & Hostens, M. (2022). Sensor based time budgets in commercial Dutch dairy herds vary over lactation cycles and within 24 hours. PLOS ONE, 17(2), e0264392. https://doi.org/10.1371/journal.pone.0264392 2022 2.9
Hut, P. R., Scheurwater, J., Nielen, M., van den Broek, J., & Hostens, M. (2022). Heat stress in a temperate climate leads to adapted sensor-based behavioral patterns of dairy cows. Journal of Dairy Science, 105(8), 6909–6922. https://doi.org/10.3168/jds.2021-21756 2022 3.7
Kemel, C., Salamone, M., Van Loo, H., Latour, C., Vandeputte, S., Callens, J., Hostens, M., & Opsomer, G. (2022). Unaffected semen quality parameters in Neospora caninum seropositive Belgian Blue bulls. Theriogenology, 191, 10–15. https://doi.org/10.1016/j.theriogenology.2022.07.013 2022 2.4
Salamone, M., Adriaens, I., Vervaet, A., Opsomer, G., Atashi, H., Fievez, V., Aernouts, B., & Hostens, M. (2022). Prediction of first test day milk yield using historical records in dairy cows. Animal, 16(11), 100658. https://doi.org/10.1016/j.animal.2022.100658 2022 4.0
Zare, M., Atashi, H., & Hostens, M. (2022). Genome-Wide Association Study for Lactation Performance in the Early and Peak Stages of Lactation in Holstein Dairy Cows. Animals, 12(12), 1541. https://doi.org/10.3390/ani12121541 2022 2.7
Atashi, H., Asaadi, A., & Hostens, M. (2021). Association between age at first calving and lactation performance, lactation curve, calving interval, calf birth weight, and dystocia in Holstein dairy cows. PLOS ONE, 16(1), e0244825. https://doi.org/10.1371/journal.pone.0244825 2021 2.9
Atashi, H., Hostens, M., & GplusE consortium. (2021). Genetic parameters for milk urea and its relationship with milk yield and compositions in Holstein dairy cows. PLOS ONE, 16(6), e0253191. https://doi.org/10.1371/journal.pone.0253191 2021 2.9
Atashi, H., & Hostens, M. (2021). Genetic Aspects of Somatic Cell Count in Holstein Dairy Cows in Iran. Animals, 11(6), 1637. https://doi.org/10.3390/ani11061637 2021 2.7
Hut, P. R., Hostens, M., Beijaard, M. J., van Eerdenburg, F. J. C. M., Hulsen, J. H. J. L., Hooijer, G. A., Stassen, E. N., & Nielen, M. (2021). Associations between body condition score, locomotion score, and sensor-based time budgets of dairy cattle during the dry period and early lactation. Journal of Dairy Science, 104(4), 4746–4763. https://doi.org/10.3168/jds.2020-19200 2021 3.7
Liseune, A., Van den Poel, D., Hut, P. R., van Eerdenburg, F. J., & Hostens, M. (2021). Leveraging sequential information from multivariate behavioral sensor data to predict the moment of calving in dairy cattle using deep learning. Computers and Electronics in Agriculture, 191, 106566. https://doi.org/10.1016/j.compag.2021.106566 2021 7.7
Liseune, A., Salamone, M., Van den Poel, D., Van Ranst, B., & Hostens, M. (2021). Predicting the milk yield curve of dairy cows in the subsequent lactation period using deep learning. Computers and Electronics in Agriculture, 180, 105904. https://doi.org/10.1016/j.compag.2020.105904 2021 7.7
Meyer, A., Faverjon, C., Hostens, M., Stegeman, A., & Cameron, A. (2021). Systematic review of the status of veterinary epidemiological research in two species regarding the FAIR guiding principles. BMC Veterinary Research, 17(1), 270. https://doi.org/10.1186/s12917-021-02971-1 2021 2.3
Scheurwater, J., Hostens, M., Nielen, M., Heesterbeek, H., Schot, A., van Hoeij, R., & Aardema, H. (2021). Pressure measurement in the reticulum to detect different behaviors of healthy cows. PLOS ONE, 16(7), e0254410. https://doi.org/10.1371/journal.pone.0264392 2021 2.9
Tobolski, D., Łukasik, K., Bacławska, A., Skarżyński, D. J., Hostens, M., & Barański, W. (2021). Prediction of Calving to Conception Interval Length Using Algorithmic Analysis of Endometrial mRNA Expression in Bovine. Animals, 11(1), 236. https://doi.org/10.3390/ani11010236 2021 2.7
Wathes, D. C., Cheng, Z., Salavati, M., Buggiotti, L., Takeda, H., Tang, L., Becker, F., Ingvartsen, K. I., Ferris, C., Hostens, M., Crowe, M. A., & GplusE Consortium. (2021). Relationships between metabolic profiles and gene expression in liver and leukocytes of dairy cows in early lactation. Journal of Dairy Science, 104(3), 3596–3616. https://doi.org/10.3168/jds.2020-19165 2021 3.7
Rousing, T., Holm, J. R., Krogh, M. A., Østergaard, S., & GplusE Consortium. (2020). Expert-based development of a generic HACCP-based risk management system to prevent critical negative energy balance in dairy herds. Preventive Veterinary Medicine, 175, 104849. https://doi.org/10.1016/j.prevetmed.2019.104849 2020 2.2
Atashi, H., Salavati, M., De Koster, J., Crowe, M. A., Opsomer, G., GplusE consortium, & Hostens, M. (2020). Genome-wide association for metabolic clusters in early-lactation Holstein dairy cows. Journal of Dairy Science, 103(7), 6392–6406. https://doi.org/10.3168/jds.2019-17369 2020 3.7
Atashi, H., Salavati, M., De Koster, J., Crowe, M. A., Opsomer, G., Hostens, M., & The GplusE Consortium. (2020). A Genome-Wide Association Study for Calving Interval in Holstein Dairy Cows Using Weighted Single-Step Genomic BLUP Approach. Animals, 10(3), 500. https://doi.org/10.3390/ani10030500 2020 2.7
Atashi, H., Salavati, M., De Koster, J., Ehrlich, J., Crowe, M., Opsomer, G., GplusE consortium, & Hostens, M. (2020). Genome-wide association for milk production and lactation curve parameters in Holstein dairy cows. Journal of Animal Breeding and Genetics, 137(3), 292–304. https://doi.org/10.1111/jbg.12442 2020 1.9
Atashi, H., Salavati, M., De Koster, J., Crowe, M. A., Opsomer, G., GplusE consortium, & Hostens, M. (2020). Genome-wide association for metabolic clusters in early-lactation Holstein dairy cows. Journal of Dairy Science, 103(7), 6392–6406. https://doi.org/10.3168/jds.2019-17369 2020 3.7
Bogado Pascottini, O., Probo, M., LeBlanc, S. J., Opsomer, G., & Hostens, M. (2020). Assessment of associations between transition diseases and reproductive performance of dairy cows using survival analysis and decision tree algorithms. Preventive Veterinary Medicine, 176, 104908. https://doi.org/10.1016/j.prevetmed.2020.104908 2020 2.2
Foldager, L., Gaillard, C., Sorensen, M. T., Larsen, T., Matthews, E., O’Flaherty, R., Carter, F., Crowe, M. A., Grelet, C., Salavati, M., Hostens, M., Ingvartsen, K. L., Krogh, M. A., & GplusE Consortium. (2020). Predicting physiological imbalance in Holstein dairy cows by three different sets of milk biomarkers. Preventive Veterinary Medicine, 179, 105006. https://doi.org/10.1016/j.prevetmed.2020.105006 2020 2.2
Grelet, C., Froidmont, E., Foldager, L., Salavati, M., Hostens, M., Ferris, C. P., Ingvartsen, K. L., Crowe, M. A., Sorensen, M. T., Fernandez Pierna, J. A., Vanlierde, A., Gengler, N., GplusE Consortium, & Dehareng, F. (2020). Potential of milk mid-infrared spectra to predict nitrogen use efficiency of individual dairy cows in early lactation. Journal of Dairy Science, 103(5), 4435–4445. https://doi.org/10.3168/jds.2019-17910 2020 3.7
Krogh, M. A., Hostens, M., Salavati, M., Grelet, C., Sorensen, M. T., Wathes, D. C., Ferris, C. P., Marchitelli, C., Signorelli, F., Napolitano, F., Becker, F., Larsen, T., Matthews, E., Carter, F., Vanlierde, A., Opsomer, G., Gengler, N., Dehareng, F., Crowe, M. A., Ingvartsen, K. L., … Foldager, L. (2020). Between- and within-herd variation in blood and milk biomarkers in Holstein cows in early lactation. Animal, 14(5), 1067–1075. https://doi.org/10.1017/S1751731119002659 2020 4.0
Østergaard, S., Krogh, M. A., Oliveira, V. H. S., Larsen, T., GplusE Consortium, & Otten, N. D. (2020). Only few benefits from propylene glycol drench in early lactation for cows identified as physiologically imbalanced based on milk spectra analyses. Journal of Dairy Science, 103(2), 1831–1842. https://doi.org/10.3168/jds.2019-17205 2020 3.7
Liseune, A., Salamone, M., Van den Poel, D., Van Ranst, B., & Hostens, M. (2020). Leveraging latent representations for milk yield prediction and interpolation using deep learning. Computers and Electronics in Agriculture, 175, 105600. https://doi.org/10.1016/j.compag.2020.105600 2020 7.7
Llamas-Luceño, N., Hostens, M., Mullaart, E., Broekhuijse, M., Lonergan, P., & Van Soom, A. (2020). High temperature-humidity index compromises sperm quality and fertility of Holstein bulls in temperate climates. Journal of Dairy Science, 103(10), 9502–9514. https://doi.org/10.3168/jds.2019-18089 2020 3.7
Bogado Pascottini, O., Probo, M., LeBlanc, S. J., Opsomer, G., & Hostens, M. (2020). Assessment of associations between transition diseases and reproductive performance of dairy cows using survival analysis and decision tree algorithms. Preventive Veterinary Medicine, 176, 104908. https://doi.org/10.1016/j.prevetmed.2020.104908 2020 2.2
Asaadi, A., Kafi, M., Atashi, H., Azari, M., & Hostens, M. (2019). Frozen-thawed ampullary cell monolayer improves bovine embryo in vitro development and quality. Zygote, 27(5), 337–346. https://doi.org/10.1017/S0967199419000388 2019 1.5
De Koster, J., Salavati, M., Grelet, C., Crowe, M. A., Matthews, E., O’Flaherty, R., Opsomer, G., Foldager, L., GplusE, & Hostens, M. (2019). Prediction of metabolic clusters in early-lactation dairy cows using models based on milk biomarkers. Journal of Dairy Science, 102(3), 2631–2644. https://doi.org/10.3168/jds.2018-15533 2019 3.7
Grelet, C., Vanlierde, A., Hostens, M., Foldager, L., Salavati, M., Ingvartsen, K. L., Crowe, M., Sorensen, M. T., Froidmont, E., Ferris, C. P., Marchitelli, C., Becker, F., Larsen, T., Carter, F., GplusE Consortium, & Dehareng, F. (2019). Potential of milk mid-IR spectra to predict metabolic status of cows through blood components and an innovative clustering approach. Animal, 13(3), 649–658. https://doi.org/10.1017/S1751731118001751 2019 4.0
Bogado Pascottini, O., Hostens, M., & Opsomer, G. (2018). Cytological endometritis diagnosed at artificial insemination in repeat breeder dairy cows. Reproduction in Domestic Animals, 53(2), 559–561. https://doi.org/10.1111/rda.13110 2018 1.6
Crowe, M. A., Hostens, M., & Opsomer, G. (2018). Reproductive management in dairy cows – the future. Irish Veterinary Journal, 71, 1. https://doi.org/10.1186/s13620-017-0112-y 2018 2.7
Depreester, E., De Koster, J., Van Poucke, M., Hostens, M., Van den Broeck, W., Peelman, L., Contreras, G. A., & Opsomer, G. (2018). Influence of adipocyte size and adipose depot on the number of adipose tissue macrophages and the expression of adipokines in dairy cows at the end of pregnancy. Journal of Dairy Science, 101(7), 6542–6555. https://doi.org/10.3168/jds.2017-13777 2018 3.7
Probo, M., Pascottini, O. B., LeBlanc, S., Opsomer, G., & Hostens, M. (2018). Association between metabolic diseases and the culling risk of high-yielding dairy cows in a transition management facility using survival and decision tree analysis. Journal of Dairy Science, 101(10), 9419–9429. https://doi.org/10.3168/jds.2018-14422 2018 3.7
Bogado Pascottini, O., Hostens, M., Sys, P., Vercauteren, P., & Opsomer, G. (2017). Cytological endometritis at artificial insemination in dairy cows: Prevalence and effect on pregnancy outcome. Journal of Dairy Science, 100(1), 588–597. https://doi.org/10.3168/jds.2016-11529 2017 3.7
De Koster, J., Urh, C., Hostens, M., Van den Broeck, W., Sauerwein, H., & Opsomer, G. (2017). Relationship between serum adiponectin concentration, body condition score, and peripheral tissue insulin response of dairy cows during the dry period. Domestic Animal Endocrinology, 59, 100–104. https://doi.org/10.1016/j.domaniend.2016.12.004 2017 1.9
De Koster, J., Van Eetvelde, M., Hermans, K., Van den Broeck, W., Hostens, M., & Opsomer, G. (2017). Short communication: Limitations of glucose tolerance tests in the assessment of peripheral tissue insulin sensitivity during pregnancy and lactation in dairy heifers. Journal of Dairy Science, 100(3), 2381–2387. https://doi.org/10.3168/jds.2016-11792 2017 3.7
Depreester, E., Meyer, E., Demeyere, K., Van Eetvelde, M., Hostens, M., & Opsomer, G. (2017). Flow cytometric assessment of myeloperoxidase in bovine blood neutrophils and monocytes. Journal of Dairy Science, 100(9), 7638–7647. https://doi.org/10.3168/jds.2016-12186 2017 3.7
Hermans, K., Waegeman, W., Opsomer, G., Van Ranst, B., De Koster, J., Van Eetvelde, M., & Hostens, M. (2017). Novel approaches to assess the quality of fertility data stored in dairy herd management software. Journal of Dairy Science, 100(5), 4078–4089. https://doi.org/10.3168/jds.2016-11896 2017 3.7
Pascottini, O. B., Hostens, M., Sys, P., Vercauteren, P., & Opsomer, G. (2017). Risk factors associated with cytological endometritis diagnosed at artificial insemination in dairy cows. Theriogenology, 92, 1–5. https://doi.org/10.1016/j.theriogenology.2017.01.004 2017 2.4
Ververs, C., van Zijll Langhout, M., Hostens, M., Otto, M., Govaere, J., Durrant, B., & Van Soom, A. (2017). Reproductive performance parameters in a large population of game-ranched white rhinoceroses (Ceratotherium simum simum). PLOS ONE, 12(12), e0187751. https://doi.org/10.1371/journal.pone.0187751 2017 2.9
Bogado Pascottini, O., Hostens, M., Dini, P., Vandepitte, J., Ducatelle, R., & Opsomer, G. (2016). Comparison between cytology and histopathology to evaluate subclinical endometritis in dairy cows. Theriogenology, 86(6), 1550–1556. https://doi.org/10.1016/j.theriogenology.2016.05.014 2016 2.4
Bogado Pascottini, O., Hostens, M., Dini, P., Vandepitte, J., Ducatelle, R., & Opsomer, G. (2016). Distribution of inflammation and association between active and chronic alterations within the endometrium of dairy cows. Reproduction in Domestic Animals, 51(5), 751–757. https://doi.org/10.1111/rda.12742 2016 1.6
De Koster, J., Hostens, M., Hermans, K., Van den Broeck, W., & Opsomer, G. (2016). Validation of different measures of insulin sensitivity of glucose metabolism in dairy cows using the hyperinsulinemic euglycemic clamp test as the gold standard. Domestic Animal Endocrinology, 57, 117–126. https://doi.org/10.1016/j.domaniend.2016.06.004 2016 1.9
De Koster, J., Van den Broeck, W., Hulpio, L., Claeys, E., Van Eetvelde, M., Hermans, K., Hostens, M., Fievez, V., & Opsomer, G. (2016). Influence of adipocyte size and adipose depot on the in vitro lipolytic activity and insulin sensitivity of adipose tissue in dairy cows at the end of the dry period. Journal of Dairy Science, 99(3), 2319–2328. https://doi.org/10.3168/jds.2015-10440 2016 3.7
Dini, P., Bogado Pascottini, O., Ducheyne, K., Hostens, M., & Daels, P. (2016). Holding equine oocytes in a commercial embryo-holding medium: New perspective on holding temperature and maturation time. Theriogenology, 86(5), 1361–1368. https://doi.org/10.1016/j.theriogenology.2016.04.079 2016 2.4
Atanasov, B., Hostens, M., Hajrulai-Musliu, Z., Uzunov, R., Adamov, N., Davkov, F., Velev, R., Opsomer, G., & Dovenski, T. (2016). Comparison of PUFA Profiles in the Blood and in Follicular Fluid and its Association with Follicular Dynamics after PGF2α Induced Luteolysis in Dairy Cows. Macedonian Veterinary Review, 39(2), 175–183. https://doi.org/10.1515/macvetrev-2016-0083 2016 0.4
Pascottini, O. B., Hostens, M., Dini, P., Van Eetvelde, M., Vercauteren, P., & Opsomer, G. (2016). Prevalence of cytological endometritis and effect on pregnancy outcomes at the time of insemination in nulliparous dairy heifers. Journal of Dairy Science, 99(11), 9051–9056. https://doi.org/10.3168/jds.2016-11348 2016 3.7
Van Eetvelde, M., Kamal, M. M., Hostens, M., Vandaele, L., Fiems, L. O., & Opsomer, G. (2016). Evidence for placental compensation in cattle. Animal, 10(8), 1342–1350. https://doi.org/10.1017/S1751731116000318 2016 4.0
De Koster, J., Hostens, M., Van Eetvelde, M., Hermans, K., Moerman, S., Bogaert, H., Depreester, E., Van den Broeck, W., & Opsomer, G. (2015). Insulin response of the glucose and fatty acid metabolism in dry dairy cows across a range of body condition scores. Journal of Dairy Science, 98(7), 4580–4592. https://doi.org/10.3168/jds.2015-9341 2015 3.7
Dini, P., Farhoodi, M., Hostens, M., Van Eetvelde, M., Pascottini, O. B., Fazeli, M. H., & Opsomer, G. (2015). Effect of uterine lavage on neutrophil counts in postpartum dairy cows. Animal Reproduction Science, 158, 25–30. https://doi.org/10.1016/j.anireprosci.2015.04.005 2015 2.2
Kamal, M. M., Van Eetvelde, M., Bogaert, H., Hostens, M., Vandaele, L., Shamsuddin, M., & Opsomer, G. (2015). Environmental factors and dam characteristics associated with insulin sensitivity and insulin secretion in newborn Holstein calves. Animal, 9(9), 1490–1499. https://doi.org/10.1017/S1751731115000701 2015 4.0
Meganck, V., Goddeeris, B. M., De Campeneere, S., Hostens, M., Van Eetvelde, M., Piepers, S., Cox, E., & Opsomer, G. (2015). Effect of β-hydroxybutyric acid, parity, and body condition score on phenotype and proliferative capacity of colostral mononuclear leukocytes of high-yielding dairy cows. Journal of Dairy Science, 98(10), 6782–6791. https://doi.org/10.3168/jds.2014-8780 2015 3.7
Pascottini, O. B., Dini, P., Hostens, M., Ducatelle, R., & Opsomer, G. (2015). A novel cytologic sampling technique to diagnose subclinical endometritis and comparison of staining methods for endometrial cytology samples in dairy cows. Theriogenology, 84(8), 1438–1446. https://doi.org/10.1016/j.theriogenology.2015.07.032 2015 2.4
Atanasov, B., Hostens, M., Celeska, I., Ilieska, K., Opsomer, G., & Dovenski, T. (2015). Follicular dynamics following induced luteolysis and transvaginal ultrasound-guided aspiration of the largest follicle in dairy cows. Vet arhiv, 85(3), 247–260. 2015 0.5
Cools, S., Van den Broeck, W., Bossaert, P., Hostens, M., & Opsomer, G. (2014). A field study to unravel factors that are significantly associated with the secretory activity of the corpus luteum during the first three postpartum cycles in high yielding dairy cows. Reproduction in Domestic Animals, 49(6), 881–893. https://doi.org/10.1111/rda.12348 2014 1.6
Cools, S., Van den Broeck, W., Vanhaecke, L., Heyerick, A., Bossaert, P., Hostens, M., & Opsomer, G. (2014). Feeding soybean meal increases the blood level of isoflavones and reduces the steroidogenic capacity in bovine corpora lutea. Animal Reproduction Science, 144(3–4), 79–89. https://doi.org/10.1016/j.anireprosci.2013.12.008 2014 2.2
Kamal, M. M., Van Eetvelde, M., Depreester, E., Hostens, M., Vandaele, L., & Opsomer, G. (2014). Age at calving in heifers and level of milk production during gestation in cows are associated with the birth size of Holstein calves. Journal of Dairy Science, 97(9), 5448–5458. https://doi.org/10.3168/jds.2014-7898 2014 3.7
Kamal, M. M., Van Eetvelde, M., Depreester, E., Hostens, M., Vandaele, L., & Opsomer, G. (2014). Age at calving in heifers and level of milk production during gestation in cows are associated with the birth size of Holstein calves. Journal of Dairy Science, 97(9), 5448–5458. https://doi.org/10.3168/jds.2014-7898 2014 3.7
Verschave, S. H., Vercruysse, J., Forbes, A., Opsomer, G., Hostens, M., Duchateau, L., & Charlier, J. (2014). Non-invasive indicators associated with the milk yield response after anthelmintic treatment at calving in dairy cows. BMC Veterinary Research, 10, 264. https://doi.org/10.1186/s12917-014-0264-x 2014 2.3
Cools, S., Van den Broeck, W., De Vliegher, S., Piepers, S., Hostens, M., & Opsomer, G. (2013). Topographic distribution of the different cell types, connective tissue and vascular tissue/lumina within a functional bovine corpus luteum. Reproduction in Domestic Animals, 48(4), 627–635. https://doi.org/10.1111/rda.12136 2013 1.6
Hostens, M., Fievez, V., Leroy, J. L., van de Burgwal, E. J., Van Ranst, B., Vlaeminck, B., & Opsomer, G. (2013). Milk fat saturation and reproductive performance in dairy cattle. Animal Reproduction Science, 141(3–4), 116–123. https://doi.org/10.1016/j.anireprosci.2013.08.001 2013 2.2
Pardon, B., Hostens, M., Duchateau, L., Dewulf, J., De Bleecker, K., & Deprez, P. (2013). Impact of respiratory disease, diarrhea, otitis and arthritis on mortality and carcass traits in white veal calves. BMC Veterinary Research, 9, 79. https://doi.org/10.1186/1746-6148-9-79 2013 2.3
Pardon, B., Hostens, M., Duchateau, L., Dewulf, J., De Bleecker, K., & Deprez, P. (2013). Impact of respiratory disease, diarrhea, otitis and arthritis on mortality and carcass traits in white veal calves. BMC Veterinary Research, 9, 79. https://doi.org/10.1186/1746-6148-9-79 2013 2.3
Charlier, J., Hostens, M., Jacobs, J., Van Ranst, B., Duchateau, L., & Vercruysse, J. (2012). Integrating fasciolosis control in the dry cow management: the effect of closantel treatment on milk production. PLOS ONE, 7(8), e43216. https://doi.org/10.1371/journal.pone.0043216 2012 2.9
Hostens, M., Ehrlich, J., Van Ranst, B., & Opsomer, G. (2012). On-farm evaluation of the effect of metabolic diseases on the shape of the lactation curve in dairy cows through the MilkBot lactation model. Journal of Dairy Science, 95(6), 2988–3007. https://doi.org/10.3168/jds.2011-4791 2012 3.7
Hostens, M., Fievez, V., Leroy, J. L., Van Ranst, J., Vlaeminck, B., & Opsomer, G. (2012). The fatty acid profile of subcutaneous and abdominal fat in dairy cows with left displacement of the abomasum. Journal of Dairy Science, 95(7), 3756–3765. https://doi.org/10.3168/jds.2011-5092 2012 3.7
Hostens, M., Fievez, V., Leroy, J. L., Van Ranst, J., Vlaeminck, B., & Opsomer, G. (2012). The fatty acid profile of subcutaneous and abdominal fat in dairy cows with left displacement of the abomasum. Journal of Dairy Science, 95(7), 3756–3765. https://doi.org/10.3168/jds.2011-5092 2012 3.7
Pardon, B., Catry, B., Dewulf, J., Persoons, D., Hostens, M., De Bleecker, K., & Deprez, P. (2012). Prospective study on quantitative and qualitative antimicrobial and anti-inflammatory drug use in white veal calves. Journal of Antimicrobial Chemotherapy, 67(4), 1027–1038. https://doi.org/10.1093/jac/dkr570 2012 3.9
Pardon, B., De Bleecker, K., Hostens, M., Callens, J., Dewulf, J., & Deprez, P. (2012). Longitudinal study on morbidity and mortality in white veal calves in Belgium. BMC Veterinary Research, 8, 26. https://doi.org/10.1186/1746-6148-8-26 2012 2.3
van Knegsel, A. T., Hostens, M., de Vries Reilingh, G., Lammers, A., Kemp, B., Opsomer, G., & Parmentier, H. K. (2012). Natural antibodies related to metabolic and mammary health in dairy cows. Preventive Veterinary Medicine, 103(4), 287–297. https://doi.org/10.1016/j.prevetmed.2011.09.006 2012 2.2
Wullepit, N., Hostens, M., Ginneberge, C., Fievez, V., Opsomer, G., Fremaut, D., & De Smet, S. (2012). Influence of a marine algae supplementation on the oxidative status of plasma in dairy cows during the periparturient period. Preventive Veterinary Medicine, 103(4), 298–303. https://doi.org/10.1016/j.prevetmed.2011.09.007 2012 2.2
Hostens, M., Fievez, V., Vlaeminck, B., Buyse, J., Leroy, J., Piepers, S., De Vliegher, S., & Opsomer, G. (2011). The effect of marine algae in the ration of high-yielding dairy cows during transition on metabolic parameters in serum and follicular fluid around parturition. Journal of Dairy Science, 94(9), 4603–4615. https://doi.org/10.3168/jds.2010-3899 2011 3.7
Pardon, B., Stuyven, E., Stuyvaert, S., Hostens, M., Dewulf, J., Goddeeris, B. M., Cox, E., & Deprez, P. (2011). Sera from dams of calves with bovine neonatal pancytopenia contain alloimmune antibodies directed against calf leukocytes. Veterinary Immunology and Immunopathology, 141(3–4), 293–300. https://doi.org/10.1016/j.vetimm.2011.02.017 2011 1.4
Hostens, M., Bossaert, P., Cools, S., de Kruif, A., & Opsomer, G. (2010). Het gebruik van glucogene precursoren in de voeding van hoogproductief melkvee. Vlaams Diergeneeskundig Tijdschrift, 79(4), 247–258. 2010 0.2
Ververs, C., Hostens, M., Caluwaerts, T., de Kruif, A., & Opsomer, G. (2010). Is er een verband tussen het verloop van de aanvangsfase van de lactatiecurve en het optreden van de eerste oestrus post partum bij hoogproductieve melkkoeien? Vlaams Diergeneeskundig Tijdschrift, 79(4), 381–388. 2010 0.2

(a2) Articles published in scholarly or scientific journals with a wide distribution, international peer review

Title Year
van Schaik, G., Hostens, M., Faverjon, C., Jensen, D. B., Kristensen, A. R., Ezanno, P., Frössling, J., Dórea, F., Jensen, B. B., Carmo, L. P., Steeneveld, W., Rushton, J., Gilbert, W., Bearth, A., Siegrist, M., Kaler, J., Ripperger, J., Siehler, J., de Wit, S., Garcia-Morante, B., … Nielen, M. (2023). The DECIDE project: from surveillance data to decision-support for farmers and veterinarians. Open Research Europe, 3, 82. https://doi.org/10.12688/openreseurope.15988.1 2023
Wathes, D. C., Becker, F., Buggiotti, L., Crowe, M. A., Ferris, C., Foldager, L., … GplusE Consortium. (2021). Associations between circulating IGF-1 concentrations, disease status and the leukocyte transcriptome in early lactation dairy cows. Ruminants, 1(2), 147–177. 2021
Hermans, K., Opsomer, G., Waegeman, W., Moerman, S., De Koster, J., Van Eetvelde, M., Van Ranst, B., Hostens, M. (2018). Interpretation and visualisation of data from dairy herds. In Practice, 40(5), 195–203. 2018
Dehkordi, S. K., Vlaeminck, B., Hostens, M., Opsomer, G., & Fievez, V. (2008). In vitro rumen biohydrogenation of trans‑10, cis‑12 conjugated linoleic acid in a lipid‑encapsulated (LE‑CLA) supplement incorporated or not in a processing pellet. Communications in Agricultural and Applied Biological Sciences, 73(1), 119–122. 2008

(b2) Chapters in books

Title Year
Noor, S., Bokma, J., Pardon, B., van Schaik, G., & Hostens, M. (2024). Agri Semantics: developments to improve data interoperability to support farm information management and decision support systems in agriculture. In Smart farms: Improving data‑driven decision making in agriculture (pp. 75–96). Burleigh Dodds Science Publishing Limited. 2024
Hermans, K., Van Ranst, B., Opsomer, G., & Hostens, M. (2018). Promises and challenges of big data associated with automated dairy cow welfare assessment. In A. Butterworth (Ed.), Animal Welfare in a Changing World (pp. 199–207). CAB International. 2018

(p1) Articles in proceedings listed in the ISI Web of Science

Title Year
Noor, S., Degroote, J., Van Schaik, G., Pardon, B., Bokma, J., Slegers, Y., Morante, B., & Hostens, M. (2024). Advancing precision livestock farming through ontology-driven interoperable health data management: Extending the Livestock Health Ontology (LHO) for enhanced disease surveillance. In 11th European Conference on Precision Livestock Farming, Bologna, Italy. 2024
Egger-Danner, C., Klaas, I., Brito, L., Schodl, K., Bewley, J. M., Cabrera, V., Haskell, M. J., Iwersen, M., Heringstad, B., Stock, K., Stygar, A., van der Linde, R., Hostens, M., Charfeddine, N., Gengler, N., & Vasseur, E. (2024). Improving animal health and welfare by using sensor data in herd management and dairy cattle breeding – a joint initiative of ICAR and IDF. In 11th European Conference on Precision Livestock Farming, Bologna, Italy. 2024
Kemel, C., Salamone, M., Hut, P., Adriaens, I., Aernouts, B., & Hostens, M. (2024). Association between AI‑generated milk yield residuals as measure of transition success and behavioural patterns in dairy cows. In 11th European Conference on Precision Livestock Farming, Bologna, Italy. 2024
Bokma, J., Santman‑Berends, I., Vidal, G., Hostens, M., Ribbens, S., Evrard, J., Theuns, S., Sánchez‑Miguel, C., van Schaik, G., & Pardon, B. (2023). European veterinary barometer for bovine respiratory diseases: a comprehensive tool for mapping diagnostic test results and geolocation for respiratory tract samples from cattle. In Proc. 5th International Conference of the European College of Veterinary Microbiology. 2023
Bokma, J., Santman‑Berends, I., Vidal, G., Hostens, M., Ribbens, S., Evrard, J., Theuns, S., van Schaik, G., & Pardon, B. (2023). European Veterinary Barometer for Bovine Respiratory Diseases: a tool showing diagnostic test results and geolocation of respiratory tract samples from cattle. In Proc. European Buiatrics Congress and ECBHM Jubilee Symposium. 2023
Slavco, E., De Vos, M., Hostens, M., Top, J., & Fischer, E. A. J. (2022). Infection Transmission Ontology: Standardization of Infection Transmission Data. In IEEE International Conference on e‑Science, pp. 65–73. https://doi.org/10.1109/eScience55777.2022.00021 2022
Chen, Y., Hostens, M., Nielen, M., Ehrlich, J., & Steeneveld, W. (2022). An empirical analysis of economic herd performance in relation to herd lactation curve characteristics. In European Conference on Precision Livestock Farming & International Conference on Precision Dairy Farming, Vienna, Austria, pp. 728–736. 2022
Chen, Y., Hostens, M., Nielen, M., Ehrlich, J., & Steeneveld, W. (2022). An empirical analysis of economic herd performance in relation to herd lactation curve characteristics. In Dair’Innov Congress, Namur, Belgium, p. 35. 2022
Chen, Y., Hostens, M., Nielen, M., Ehrlich, J., & Steeneveld, W. (2021). An empirical analysis on the association between persistency of dairy cows and economic herd performance. In ISESSAH, p. 72. 2021
Salamone, M., Adriaens, I., Hostens, M., & Aernouts, B. (2021). The Individual Transition Index: development of a new window inside the transition period. Presented at Precision Livestock Farming Workshop Seminar, Leuven, Belgium (virtual). 2021
Salamone, M., Adriaens, I., Opsomer, G., Aernouts, B., & Hostens, M. (2021). Machine‑learning‑based prediction of test day milk yield using historical data of the previous lactation. Presented at 44th ICAR Annual Conference (virtual). 2021
Lietaer, L., Hernandez Sanabria, E., Hostens, M., Vlaeminck, L., Van Soom, A., Van de Wiele, T., & Opsomer, G. (2019). Presented at 23rd Annual Conference of the European Society for Domestic Animal Reproduction (ESDAR). 2019
Salamone, M., Atashi, H., Salavati, M., De Koster, J., Crowe, M. A., Opsomer, G., & Hostens, M. (2019). Genome‑wide association for metabolic adaptation in early lactation dairy cows. Presented at 70th Annual Meeting of EAAP, Ghent, Belgium. 2019
Salamone, M., Atashi, H., De Koster, J., Opsomer, G., & Hostens, M. (2019). Genetic parameters for lactation curve traits in Holstein dairy cows. Presented at 70th Annual Meeting of EAAP, Ghent, Belgium. 2019
De Koster, J., Opsomer, G., & Hostens, M. (2018). Metabolic clustering of dairy cows at early and peak lactation. Presented at 22nd Annual Conference of ESDAR. 2018
Gengler, N., & Hostens, M. (2018). How fast can we change resilience and efficiency through breeding and management? In Book of Abstracts, 69th EAAP Annual Meeting, p. 264. 2018
Grelet, C., Froidmont, E., Hostens, M., Vanlierde, A., Foldager, L., Salavati, M., Ingvartsen, K., Sorensen, M., Crowe, M., Ferris, C., & Marchitelli, C. (2018). Predicting nitrogen efficiency of dairy cows through milk FT‑MIR spectra. In Book of Abstracts, 69th EAAP Annual Meeting. 2018
Dini, P., Pascottini, O. B., Hostens, M., & Daels, P. (2016). Holding equine oocytes in Syngro® embryo holding medium at 4 °C. International Symposium on Equine Embryo Transfer and Technology, Ghent, Belgium. 2016