Alvarenga Tatiane C, Lima Renato R, Bueno Filho Júlio S S, Simão Sérgio D, Mariano Flávia C Q, Alvarenga Renata R, Rodrigues Paulo B
Department of Statistics, Federal University of Lavras, Lavras, Minas Gerais, Brazil.
Department of Animal Science, Federal University of Lavras, Lavras, Minas Gerais, Brazil.
Transl Anim Sci. 2021 Jan 22;5(1):txaa215. doi: 10.1093/tas/txaa215. eCollection 2021 Jan.
Designing balanced rations for broilers depends on precise knowledge of nitrogen-corrected apparent metabolizable energy (AMEn) and the chemical composition of the feedstuffs. The equations that include the measurements of the chemical composition of the feedstuff can be used in the prediction of AMEn. In the literature, there are studies that obtained prediction equations through multiple regression, meta-analysis, and neural networks. However, other statistical methodologies with promising potential can be used to obtain better predictions of energy values. The objective of the present study was to propose and evaluate the use of Bayesian networks (BN) to the prediction of the AMEn values of energy and protein feedstuffs of vegetable origin used in the formulation of broiler rations. In addition, verify that the predictions of energy values using this methodology are the most accurate and, consequently, are recommended to Animal Science professionals area for the preparation of balanced feeds. BN are models that consist of graphical and probabilistic representations of conditional and joint distributions of the random variables. BN uses machine learning algorithms, being a methodology of artificial intelligence. The bnlearn package in R software was used to predict AMEn from the following covariates: crude protein, crude fiber, ethereal extract, mineral matter, as well as food category, i.e., energy (corn, corn by-products, and others) or protein (soybean, soy by-products, and others) and the type of animal (chick or cockerel). The data come from 568 feeding experiments carried out in Brazil. Additional data from metabolic experiments were obtained from the Federal University of Lavras (UFLA) - Lavras, Minas Gerais, Brazil. The model with the highest accuracy (mean squared error = 66529.8 and multiple coefficients of determination = 0.87) was fitted with the max-min hill climbing algorithm (MMHC) using 80% and 20% of the data for training and test sets, respectively. The accuracy of the models was evaluated based on their values of mean squared error, mean absolute deviation, and mean absolute percentage error. The equations proposed by a new methodology in avian nutrition can be used by the broiler industry in the determination of rations.
为肉鸡设计平衡日粮取决于对氮校正表观代谢能（AMEn）和饲料原料化学成分的精确了解。包含饲料原料化学成分测量值的方程可用于预测AMEn。在文献中，有通过多元回归、荟萃分析和神经网络获得预测方程的研究。然而，其他具有潜在前景的统计方法可用于获得更好的能量值预测。本研究的目的是提出并评估使用贝叶斯网络（BN）来预测用于肉鸡日粮配方的植物源性能量和蛋白质饲料原料的AMEn值。此外，验证使用该方法对能量值的预测是最准确的，因此推荐给动物科学专业领域用于配制平衡饲料。BN是由随机变量的条件分布和联合分布的图形和概率表示组成的模型。BN使用机器学习算法，是一种人工智能方法。使用R软件中的bnlearn包，根据以下协变量预测AMEn：粗蛋白、粗纤维、粗脂肪、矿物质，以及食物类别，即能量（玉米、玉米副产品等）或蛋白质（大豆、大豆副产品等）和动物类型（雏鸡或公鸡）。数据来自在巴西进行的568次饲养实验。代谢实验的其他数据来自巴西米纳斯吉拉斯州拉夫拉斯联邦大学（UFLA）。使用最大最小爬山算法（MMHC），分别使用80%和20%的数据作为训练集和测试集，拟合出精度最高的模型（均方误差 = 66529.8，多重决定系数 = 0.87）。基于均方误差、平均绝对偏差和平均绝对百分比误差值评估模型的准确性。家禽营养新方法提出的方程可被肉鸡行业用于日粮的确定。
