ArticleTranslational animal science2026
Application of unsupervised learning to cluster swine breeding herds based on key performance indicators in Southern Brazil.
Article in Translational animal science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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
1 citing paper in PubMed.
- Batch-Level Average Market Weight Estimation and Interpretability Analysis for Pigs with Regional K-Means Clustering and AMFormer.Animals : an open access journal from MDPI · 2026Article
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11 authors.
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Abstract
Reproductive performance in sow farms has improved over time in most production systems. However, variability among farms remains substantial. This study employed unsupervised analysis to cluster farms based on reproductive indicators and assess the farm-level characteristics associated with performance differences. A total of 22 breeding herds in the Southern Brazil region were surveyed to gather demographic, labor, infrastructure, environmental, management, and production data. Monthly reproductive performance indicators from 2022 and 2023 were collected, including farrowing rate (FR), total piglets born (TPB), piglets born alive (TBA), stillborn (SB), and pre-weaning mortality (PWM). A K-means clustering analysis grouped farms based on five key reproductive performance indicators (KPIs). Cluster identification was used as an independent variable to identify differences in survey responses. The unsupervised model suggested two clusters based on the consensus of 26 indexes. The high-performance cluster (Cluster 2,
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