Development of a Feed Conversion Ratio Prediction Model for Yorkshire Boars Using Cumulative Feed Intake.

Hao Zhou, Haoshi Cheng, Yuyang Wang, Dongdong Duan, Jinyi Han, Shenping Zhou, Wenshui Xin, Xinjian Li

Abstract read

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. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Hao ZhouCollege of Animal Sciences and Technology, Henan Agricultural University, Zhengzhou 450046, China.ORCID 0009-0004-3944-4052

Haoshi ChengCollege of Animal Sciences and Technology, Henan Agricultural University, Zhengzhou 450046, China.ORCID 0009-0009-4747-7061

Yuyang WangCollege of Animal Sciences and Technology, Henan Agricultural University, Zhengzhou 450046, China.ORCID 0009-0006-9499-4174

Dongdong DuanSanya Institute, Hainan Academy of Agricultural, Sanya 572025, China.ORCID 0009-0005-6688-1693

Jinyi HanSanya Institute, Hainan Academy of Agricultural, Sanya 572025, China.

Shenping ZhouSanya Institute, Hainan Academy of Agricultural, Sanya 572025, China.

Wenshui XinSanya Institute, Hainan Academy of Agricultural, Sanya 572025, China.ORCID 0000-0003-2713-9080

Xinjian LiCollege of Animal Sciences and Technology, Henan Agricultural University, Zhengzhou 450046, China.ORCID 0000-0003-2546-0145

Funding

National Science and Technology Major Project NK2201801-03

6 · The paper itself

Abstract

Feed conversion ratio (FCR) is a key indicator of pig productivity, but its measurement is labor-intensive and time-consuming. This study aimed to construct a predictive model for cumulative feeding intake (CFI), which could help estimate FCR more efficiently and reduce the time and effort needed for measurements. This study included a total of 987 Yorkshire boars raised in specific pathogen-free environments, with feeding and growth data collected using automatic feeders. The segmented R package and Bayesian ridge regression (BRR) were used to build a predictive model for CFI. The results showed that the optimal body weight range for predicting FCR was 80-110 kg. The BRR model achieved 80% accuracy for CFI prediction, and FCR calculated from predicted CFI showed 81.4% similarity to the corrected FCR. The results clearly demonstrate that even with a limited training dataset, the BRR model has good predictive potential for FCR. The findings of this study could reduce the selection pressure on FCR traits, decrease production costs, and shorten measurement periods, ultimately benefiting the swine industry significantly.

Indexed as

Bayesian ridge regressioncumulative feed intakefeed conversion ratioYorkshire boars

Identifiers

PMID40002989

DOI10.3390/ani15040507

PMCPMC11851840

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LicenceCC BY

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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 Socratic graph.