ArticleTranslational animal science2023
Statistical and machine learning approaches to describe factors affecting preweaning mortality of piglets.
Article in Translational animal science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- Maternal bis-glycinate bound zinc supplementation alters sow performance and milk metabolomic-lipidomic profiles and mitigates piglet diarrhea.Veterinary and animal science · 2026Article
- Accelerating sow nursing behavior monitoring with modified YOLO11n architecture and TensorRT integration.Porcine health management · 2026Article
- Dietary supplementation of Galactooligosaccharides improves growth performance and intestinal barrier function of offspring.Porcine health management · 2025Article
- Impacts of Farrowing Pen Design, Season, and Sow Parity on Litter Performance and Piglet Mortality.Animals : an open access journal from MDPI · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High preweaning mortality (PWM) rates for piglets are a significant concern for the worldwide pork industries, causing economic loss and well-being issues. This study focused on identifying the factors affecting PWM, overlays, and predicting PWM using historical production data with statistical and machine learning models. Data were collected from 1,982 litters from the U.S. Meat Animal Research Center, Nebraska, over the years 2016 to 2021. Sows were housed in a farrowing building with three rooms, each with 20 farrowing crates, and taken care of by well-trained animal caretakers. A generalized linear model was used to analyze the various sow, litter, environment, and piglet parameters on PWM. Then, different models (beta-regression and machine learning model: a random forest [RF]) were evaluated. Finally, the RF model was used to predict PWM and overlays for all listed contributing factors. On average, the mean birth weight was 1.44 kg, and the mean mortality was 16.1% where 5.55% was for stillbirths and 6.20% was contributed by overlays. No significant effect was found for seasonal and location variations on PWM. Significant differences were observed in the effects of litter lines on PWM (
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