ArticlePorcine health management2022
Machine learning based personalized promotion strategy of piglets weaned per sow per year in large-scale pig farms.
Article in Porcine health management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- KNLR: A heterogeneous ensemble learner for predicting Foie gras weight grade in mule ducks (Anas platyrhynchos × Cairina moschata).Poultry science · 2026Article
- Assessment of the relationship between lactation feeding patterns, litter performance, and sow characteristics on sow efficiency metrics.Translational animal science · 2026Article
- DietaryVeterinary sciences · 2025Article
- Evolution of Sow Productivity and Evaluation Parameters: Spanish Farms as a Benchmark.Veterinary sciences · 2024Article
- Comparative analysis of productive performance and fattening efficiency of commercial pigs in China for two consecutive years.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe purpose of this study was to analyze the relationship between different productive factors and piglets weaned per sow per year (PSY) in 291 large-scale pig farms and analyze the impact of the changes in different factors on PSY. We chose nine different algorithm models based on machine learning to calculate the influence of each variable on every farm according to its current situation, leading to personalize the improvement of the impact in the specific circumstances of each farm, proposing a production guidance plan of PSY improvement for every farm. According to the comparison of mean absolute error (MAE), 95% confidence interval (CI) and R
results17 production factors were non-linearly related to PSY. The top five production factors with the highest correlation with PSY were the number of weaned piglets per litter (WPL) (0.6694), mating rate within 7 days after weaning (MR7DW) (0.6606), number of piglets born alive per litter (PBAL) (0.6517), the total number of piglets per litter (TPL) (0.5706) and non-productive days (NPD) (- 0.5308). Among nine algorithm models, the gradient boosting regressor model had the highest R
conclusionsThe main productive factors related to PSY included WPL, MR7DW, PBAL, TPL and NPD. The gradient boosting regressor model was the optimal method to individually analyze productive factors that are non-linearly related to PSY.
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