ReviewVeterinary sciences2022
Predictive Models of Dairy Cow Thermal State: A Review from a Technological Perspective.
Review in Veterinary sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- A Supervised Deep Learning Model Was Developed to Classify Nelore Cattle (Animals : an open access journal from MDPI · 2026Article
- Polymeric rumen-stable delivery systems for delivering nutricines.Open veterinary journal · 2025Review
- Welfare indicators in cattle farming in the face of heat stress: a review in climate change scenarios.Frontiers in veterinary science · 2025Article
- Review of the Heat Stress-Induced Responses in Dairy Cattle.Animals : an open access journal from MDPI · 2023Review
- Health Status Classification for Cows Using Machine Learning and Data Management on AWS Cloud.Animals : an open access journal from MDPI · 2023Article
- Climate Spaces and Cliffs: A Novel Bovine Thermodynamic and Mass Balances Model.Animals : an open access journal from MDPI · 2023Article
- Climate Change Influences the Spread of African Swine Fever Virus.Veterinary sciences · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Dairy cattle are particularly sensitive to heat stress due to the higher metabolic rate needed for milk production. In recent decades, global warming and the increase in dairy production in warmer countries have stimulated the development of a wide range of environmental control systems for dairy farms. Despite their proven effectiveness, the associated energy and water consumption can compromise the viability of dairy farms in many regions, due to the cost and scarcity of these resources. To make these systems more efficient, they should be activated in time to prevent thermal stress and switched off when that risk no longer exists, which must consider environmental variables as well as the variables of the animals themselves. Nowadays, there is a wide range of sensors and equipment that support farm routine procedures, and it is possible to measure several variables that, with the aid of algorithms based on predictive models, would allow anticipating animals' thermal states. This review summarizes three types of approaches as predictive models: bioclimatic indexes, machine learning, and mechanistic models. It also focuses on the application of the current knowledge as algorithms to be used in the management of diverse types of environmental control systems.
Indexed as
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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.