ReviewAnimals : an open access journal from MDPI2020
Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms.
Review in Animals : an open access journal from MDPI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 115 citations in OpenAlex.
- Inflammation-Associated Changes in Bioactive Proteins and Peptides of Bovine Milk: Evidence from Mastitis, Lameness, and Metabolic Disorders-A Review.Molecules (Basel, Switzerland) · 2026Review
- Modeling the Adaptation of Dairy Cows to Automatic Milking Systems Using Statistical Methods and Machine Learning: Development of the Robotic Adaptability Index.Animals : an open access journal from MDPI · 2026Article
- Artificial Neural Networks for Predicting Emissions from the Livestock Sector: A Review.Animals : an open access journal from MDPI · 2025Review
- Milk Biomarkers and Herd Welfare Status in Dairy Cattle: A Machine Learning Approach.Veterinary sciences · 2025Article
- The future of big data and artificial intelligence on dairy farms: A proposed dairy data ecosystem.JDS communications · 2025Review
- Article
- Development of Machine Learning-Based Sub-Models for Predicting Net Protein Requirements in Lactating Dairy Cows.Animals : an open access journal from MDPI · 2025Article
- Application of Machine Learning Models for the Early Detection of Metritis in Dairy Cows Based on Physiological, Behavioural and Milk Quality Indicators.Animals : an open access journal from MDPI · 2025Article
- AI and Data Analytics in the Dairy Farms: A Scoping Review.Animals : an open access journal from MDPI · 2025Review
- Development of Machine Learning Models for Estimating Metabolizable Protein Supply from Feed in Lactating Dairy Cows.Animals : an open access journal from MDPI · 2025Article
- Roughage Sources During Late Gestation and Lactation Alter Metabolism, Immune Function and Rumen Microbiota in Ewes and Their Offsprings.Microorganisms · 2025Article
- Linking Animal Feed Formulation to Milk Quantity, Quality, and Animal Health Through Data-Driven Decision-Making.Animals : an open access journal from MDPI · 2025Review
- Article
- Dairy DigiD: a keypoint-based deep learning system for classifying dairy cattle by physiological and reproductive status.Frontiers in artificial intelligence · 2025Article
- Predicting age at first calving of dairy breed calves using whale optimization-based ensemble learning framework.Scientific reports · 2024Article
- Applications of Artificial Intelligence for Heat Stress Management in Ruminant Livestock.Sensors (Basel, Switzerland) · 2024Review
- Discrete Homogeneous and Non-Homogeneous Markov Chains Enhance Predictive Modelling for Dairy Cow Diseases.Animals : an open access journal from MDPI · 2024Article
- Machine Learning to Predict Pregnancy in Dairy Cows: An Approach Integrating Automated Activity Monitoring and On-Farm Data.Animals : an open access journal from MDPI · 2024Article
- Human-computer interactions with farm animals-enhancing welfare through precision livestock farming and artificial intelligence.Frontiers in veterinary science · 2024Article
- Investigating the use of machine learning algorithms to support risk-based animal welfare inspections of cattle and pig farms.Frontiers in veterinary science · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dairy farmers use herd management systems, behavioral sensors, feeding lists, breeding schedules, and health records to document herd characteristics. Consequently, large amounts of dairy data are becoming available. However, a lack of data integration makes it difficult for farmers to analyze the data on their dairy farm, which indicates that these data are currently not being used to their full potential. Hence, multiple issues in dairy farming such as low longevity, poor performance, and health issues remain. We aimed to evaluate whether machine learning (ML) methods can solve some of these existing issues in dairy farming. This review summarizes peer-reviewed ML papers published in the dairy sector between 2015 and 2020. Ultimately, 97 papers from the subdomains of management, physiology, reproduction, behavior analysis, and feeding were considered in this review. The results confirm that ML algorithms have become common tools in most areas of dairy research, particularly to predict data. Despite the quantity of research available, most tested algorithms have not performed sufficiently for a reliable implementation in practice. This may be due to poor training data. The availability of data resources from multiple farms covering longer periods would be useful to improve prediction accuracies. In conclusion, ML is a promising tool in dairy research, which could be used to develop and improve decision support for farmers. As the cow is a multifactorial system, ML algorithms could analyze integrated data sources that describe and ultimately allow managing cows according to all relevant influencing factors. However, both the integration of multiple data sources and the obtainability of public data currently remain challenging.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.