ArticleSensors (Basel, Switzerland)2023
Feed Conversion Ratio (FCR) and Performance Group Estimation Based on Predicted Feed Intake for the Optimisation of Beef Production.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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.
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Who cites it
14 citing papers in PubMed, 23 citations in OpenAlex.
- Prediction model for daily feed intake during the growing period in floor-reared Wanxi White Geese based on machine learning and feeding behavior features.Poultry science · 2026Article
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- Intact pulse cotyledon cell diet improves systemic growth via the somatotropic axis in malnourished mice.iMetaOmics · 2026Article
- Cost-Effective Fish Volume Estimation in Aquaculture Using Infrared Imaging and Multi-Modal Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Nutritional and economic evaluation of fermented durian peel on laying duck performance and yolk omega profile.Journal of advanced veterinary and animal research · 2026Article
- Effects of breed and test duration on feed efficiency and feeding behavior in growing goats using GrowSafe feeding technology.Translational animal science · 2026Article
- Article
- DietaryAnimal nutrition (Zhongguo xu mu shou yi xue hui) · 2025Article
- Entomophagy: Nutritional Value, Benefits, Regulation and Food Safety.Foods (Basel, Switzerland) · 2025Review
- Multi-Stage Data Processing for Enhancing Korean Cattle (Hanwoo) Weight Estimations by Automated Weighing Systems.Animals : an open access journal from MDPI · 2025Article
- Machine Learning-Based Prediction of Feed Conversion Ratio: A Feasibility Study of Using Short-Term FCR Data for Long-Term Feed Conversion Ratio (FCR) Prediction.Animals : an open access journal from MDPI · 2025Article
- Detection of Remaining Feed in the Feed Troughs of Flat-Fed Meat Ducks Based on the RGB-D Sensor and YOLO V8.Animals : an open access journal from MDPI · 2025Article
- Green Processes for Chitin and Chitosan Production from Insects: Current State, Challenges, and Opportunities.Polymers · 2025Review
- Development of a Feed Conversion Ratio Prediction Model for Yorkshire Boars Using Cumulative Feed Intake.Animals : an open access journal from MDPI · 2025Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
This paper reports on the use of estimates of individual animal feed intake (made using time spent feeding measurements) to predict the Feed Conversion Ratio (FCR), a measure of the amount of feed consumed to produce 1 kg of body mass, for an individual animal. Reported research to date has evaluated the ability of statistical methods to predict daily feed intake based on measurements of time spent feeding measured using electronic feeding systems. The study collated data of the time spent eating for 80 beef animals over a 56-day period as the basis for the prediction of feed intake. A Support Vector Regression (SVR) model was trained to predict feed intake and the performance of the approach was quantified. Here, feed intake predictions are used to estimate individual FCR and use this information to categorise animals into three groups based on the estimated Feed Conversion Ratio value. Results provide evidence of the feasibility of utilising the 'time spent eating' data to estimate feed intake and in turn Feed Conversion Ratio (FCR), the latter providing insights that guide farmer decisions on the optimisation of production costs.
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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.