| 标题 |
 Modeling net energy partition patterns of growing–finishing pigs using nonlinear regression and artificial neural networks |
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Abstract / 摘要MT翻译 The objectives of this study were to evaluate the net energy （NE） partition patterns of growing–finishing pigs at different growing stages and to develop the corresponding prediction models using nonlinear regression （NLR） and artificial neural networks （ANN）. Twenty-four pigs with an initial body weight （BW） of <sup>3</sup>0 kg were kept in metabolic cages and fed ad libitum and were moved into six respiration chambers in turns until <sup>9</sup>0 kg. The NE partition patterns, i.e., NE for maintenance （NEm）, NE retained as protein （NE<sub>p</sub>）, and NE retained as lipid （NE<sub>l</sub>）, were calculated based on indirect calorimetry and nitrogen balance techniques. The energy balance data collected through the animal trial was then randomly split into a training data set containing 75% of the samples and a testing data set containing the remaining 25% of the samples. The NLR models and a series e, NEm, NE<sub>p</sub>, and NE<sub>l</sub> |
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(2025-6-4)
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