ArticleJournal of animal science and biotechnology2022
Predicting the growth performance of growing-finishing pigs based on net energy and digestible lysine intake using multiple regression and artificial neural networks models.
Article in Journal of animal science and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 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.
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.
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Who cites it
8 citing papers in PubMed.
- Prediction of Digestible and Metabolizable Energy in Swine Feed Using Machine Learning.ACS omega · 2026Article
- Effects of Different Protein Feeds on Nutrient Digestion, Energy Metabolism, Methane Emissions, and Rumen Microbiota in Mutton Sheep.Animals : an open access journal from MDPI · 2025Article
- Impacts of heat stress on growth performance and its mitigation in small ruminants.Animal frontiers : the review magazine of animal agriculture · 2025Article
- Effects of increasing dietary lysine and energy levels on growth efficiency, nutrient absorption, and meat carcass traits in growing-finishing pigs.Journal of animal science and technology · 2025Article
- Dietary fat and carbohydrate-balancing the lactation performance and methane emissions in the dairy cow industry: A meta-analysis.Animal nutrition (Zhongguo xu mu shou yi xue hui) · 2024Article
- Modeling energy partition patterns of growing pigs fed diets with different net energy levels based on machine learning.Journal of animal science · 2024Article
- Modeling net energy partition patterns of growing-finishing pigs using nonlinear regression and artificial neural networks.Journal of animal science · 2023Article
- Correction: Predicting the growth performance of growing-finishing pigs based on net energy and digestible lysine intake using multiple regression and artificial neural networks models.Journal of animal science and biotechnology · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
backgroundsEvaluating the growth performance of pigs in real-time is laborious and expensive, thus mathematical models based on easily accessible variables are developed. Multiple regression (MR) is the most widely used tool to build prediction models in swine nutrition, while the artificial neural networks (ANN) model is reported to be more accurate than MR model in prediction performance. Therefore, the potential of ANN models in predicting the growth performance of pigs was evaluated and compared with MR models in this study.
resultsBody weight (BW), net energy (NE) intake, standardized ileal digestible lysine (SID Lys) intake, and their quadratic terms were selected as input variables to predict ADG and F/G among 10 candidate variables. In the training phase, MR models showed high accuracy in both ADG and F/G prediction (R
conclusionBody weight, NE intake and SID Lys intake can be used as input variables to predict the growth performance of growing-finishing pigs, with trained ANN models are more flexible and accurate than MR models. Therefore, it is promising to use ANN models in related swine nutrition studies in the future.
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Registered trials
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