The improvement of efficiency traits, such as protein efficiency (PE), digestible energy efficiency (EnE) and lipid gain (LipG), are relevant given their associations with environmental pollution, cost of productions, and the quality of meat. However, these traits are difficult traits to measure and usually require slaughtering of pigs. Efficiency traits are complex, and several factors, such as genetic predisposition, feed composition, but also individual feeding behaviour may contribute to efficiency. The objective of this study was therefore to evaluate the potential of using feeding behaviour traits to predict efficiency traits under dietary protein restriction. A total of 587 Swiss Large White pigs, consisting of 312 females and 275 castrated males, had ad libitum access to feed and water, and were fed a protein-reduced diet (80% of recommended digestible protein and essential amino acids) from 22.5 ± 1.6 to 106.6 ± 4.6 kg BW. Individual feed intake was monitored and carcass composition (lean and fat mass) at slaughter was determined by dual-energy X-ray absorptiometry (DXA). The PE and EnE were calculated as the ratio of protein or energy in the carcass (estimated by DXA) to the total protein or energy consumed. Feeding behaviour traits monitored were daily feed intake (DFI; g/day), feed intake per meal (FIM; g/meal), number of daily meals (NDM; meals/day), duration of meal (DUM; min/meal), feeding rate (FR; g/min), and feeder occupation (FO; min/day). A partial least square (PLS) regression was used to predict PE, EnE and LipG from feeding behaviour traits, while including farrowing series (for PE only), age at slaughter and body weight at slaughter. Accuracy of PLS regression was assessed based on RMSE and R² for calibration and validation sets, and on concordance correlation coefficient (CCC), which were estimated over 100 replicates of calibration and validation sets. Models with a number of latent variables of 5, 2 and 3 were identified as optimal for PE, EnE, and LipG, which explained 34.64%, 55.42% and 82.68% of the total variation in PE, EnE, and LipG, respectively. Significant CCC were found between predicted and observed values for PE (0.50), EnE (0.70), and LipG (0.90). In conclusion, individual feeding behaviour traits can better predict EnE and LipG than for PE under dietary protein restriction when fed ad libitum.

中文翻译：

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使用偏最小二乘回归评估饲养行为特征以预测猪的效率特征

鉴于蛋白质效率 (PE)、可消化能量效率 (EnE) 和脂质增加 (LipG) 等效率性状的改进，它们与环境污染、生产成本和肉类质量有关。然而，这些性状是难以衡量的性状，通常需要屠宰猪。效率性状是复杂的，遗传易感性、饲料成分以及个体喂养行为等几个因素可能会影响效率。因此，本研究的目的是评估使用喂养行为特征来预测饮食蛋白质限制下的效率特征的潜力。共有 587 头瑞士大白猪，包括 312 头母猪和 275 头阉割公猪，随意采食获得饲料和水，并饲喂低蛋白质饮食（推荐的可消化蛋白质和必需氨基酸的 80%），体重为 22.5 ± 1.6 至 106.6 ± 4.6 kg。监测个体采食量，屠宰时的胴体成分（瘦肉和脂肪量）通过双能 X 射线吸收测定法 (DXA) 确定。PE 和 EnE 计算为屠体中的蛋白质或能量（由 DXA 估计）与消耗的总蛋白质或能量的比率。监测的摄食行为特征是每日采食量（DFI；g/天）、每餐的采食量（FIM；g/餐）、每日进餐次数（NDM；餐/天）、进餐持续时间（DUM；分钟/餐） 、喂食率（FR；克/分钟）和喂食器占用（FO；分钟/天）。偏最小二乘 (PLS) 回归用于从喂养行为特征预测 PE、EnE 和 LipG，同时包括分娩系列（仅适用于 PE），屠宰年龄和屠宰体重。基于RMSE和R评估PLS回归的准确性²用于校准和验证集，以及一致性相关系数 (CCC)，估计校准和验证集的 100 次重复。潜变量数为 5、2 和 3 的模型被确定为 PE、EnE 和 LipG 的最佳模型，分别解释了 PE、EnE 和 LipG 总变异的 34.64%、55.42% 和 82.68%。在 PE (0.50)、EnE (0.70) 和 LipG (0.90) 的预测值和观察值之间发现了显着的 CCC。总之，当喂给个体摄食行为的特征可以更好地预测烯和LIPG比PE下的膳食蛋白质限制自由采食。