Weaning performance prediction in lactating sows using machine learning, for precision nutrition and intelligent feeding.

Jiayi Su, Xiangfeng Kong, Wenliang Wang, Qian Xie, Chengming Wang, Bie Tan, Jing Wang

Abstract read

In one paragraph

Article in Animal nutrition (Zhongguo xu mu shou yi xue hui), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it

0cells of the map it votes in

6citing 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

6 citing papers in PubMed.

Big data and artificial intelligence in animal nutrition: a new era of precision feeding.Tropical animal health and production · 2026
Review
Sensor-Based Precision Feeding Systems in Animal Production: Technologies and Applications.Animals : an open access journal from MDPI · 2026
Review
Cheminformatics and Machine Learning-Driven QSAR Analysis of SPHK2 Inhibitors for Anticancer Drug Design.Current medicinal chemistry · 2026
Article
From machine learning to digital twin integration for livestock production and research.Frontiers in veterinary science · 2026
Review
The gut-liver axis in animal diseases: roles, mechanisms, and veterinary clinical applications.Frontiers in veterinary science · 2026
Review
DietaryAnimals : an open access journal from MDPI · 2025
Article

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.

Jiayi SuKey Laboratory of Hunan Province for the Products Quality Regulation of Livestock and Poultry, College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China.

Xiangfeng KongLaboratory of Animal Nutritional Physiology and Metabolic Process, Key Laboratory of Agro-Ecological Processes in Subtropical Region, National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China.

Wenliang WangKey Laboratory of Hunan Province for the Products Quality Regulation of Livestock and Poultry, College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China.

Qian XieKey Laboratory of Hunan Province for the Products Quality Regulation of Livestock and Poultry, College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China.

Chengming WangKey Laboratory of Hunan Province for the Products Quality Regulation of Livestock and Poultry, College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China.

Bie TanKey Laboratory of Hunan Province for the Products Quality Regulation of Livestock and Poultry, College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China.

Jing WangKey Laboratory of Hunan Province for the Products Quality Regulation of Livestock and Poultry, College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional feeding strategy during lactation can result in nutrient deficiencies and negatively impact long-term productivity, compromising both the sustainability and profitability of the swine industry. Precision feeding, supported by decision-making systems built on advanced predictive models, offers a promising solution to address these challenges. This study aimed to develop prediction models for weaning performance, focusing on key indicators such as weaned litter weight (WLW), weaned litter size (WLS), dry matter in milk (DMm), and nitrogen in milk (Nm). The models integrate farm management practices and feed nutrient composition, providing a data-driven framework for optimizing performance. A total of 10,089 observations were collected from 17 trial pig farms across eight provinces in China. Eleven statistical and machine learning (ML) regression algorithms were employed, incorporating stratified sampling and the recursive feature elimination method for feature selection. The findings demonstrated that the ensemble learning models, specifically random forest and gradient boosting decision tree regression, delivered the best overall performance, with a coefficient of determination (

Indexed as

Feature selectionLactating sowMachine learningPrecision feedingWeaning performance prediction

Identifiers

PMID40487109

DOI10.1016/j.aninu.2025.01.007

PMCPMC12145688

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.