Evidence map›Paper›PMID 40564324›Full record

ArticleAnimals : an open access journal from MDPI2025

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.

Xidi Yang, Liangyu Zhu, Wenyu Jiang, Yiting Yang, Mailin Gan, Linyuan Shen, Li Zhu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Animals : an open access journal from MDPI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Xidi YangState Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China.
Liangyu ZhuState Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China.
Wenyu JiangState Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China.
Yiting YangState Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China.
Mailin GanState Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China.ORCID 0000-0001-9900-3559
Linyuan ShenState Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China.
Li ZhuState Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China.

Funding

China Agriculture Research System CARS-35he Program for Pig Industry Technology System Innovation Team of Sichuan Province SCCXTD-2024-8National Center of Technology Innovation for Pigs NCTIP-XD/C13Sichuan Science and Technology Program 2021ZDZX0008, 2021YFYZ0030STI 2030-Major Projects 2023ZD04045
6 · The paper itself

Abstract

Feed conversion ratio (FCR) is a critical indicator of production efficiency in livestock husbandry. Improving FCR is essential for optimizing resource utilization and enhancing productivity. Traditional methods for FCR optimization rely on experience and long-term data collection, which are time-consuming and inefficient. This study explores the feasibility of predicting long-term FCR using short-term FCR data based on machine learning techniques. We employed nineteen machine learning algorithms, including Linear Regression, support vector machines (SVMs), and Gradient Boosting, using historical datasets to train and validate the models. The results show that the Gradient Boosting model demonstrated superior performance, achieving a coefficient of determination (R

Indexed as

data predictiondeep learningfeed conversion ratio (FCR)machine learningprecision livestock farming

Identifiers

PMID40564324
PMCPMC12189232

What OpenQuestion holds

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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.