División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, km 25, Carretera Villahermosa-Teapa, R/A La Huasteca, 86280, Villahermosa, Tabasco, Mexico.
Benemérita Universidad Autónoma de Puebla, Complejo Regional Norte, Tetela de Ocampo, Puebla, Mexico.
Trop Anim Health Prod. 2023 Sep 21;55(5):307. doi: 10.1007/s11250-023-03717-x.
Determination of live weight, which is one of the most important features that determine meat production, is a very important issue for herd management and sustainable livestock. In this context, the necessity of finding alternative methods has emerged, especially in rural conditions, due to the difficulties to be experienced in finding the weighing tool. Especially for conditions with no weighing tool, it has been tried to establish relations between the information obtained from body measurements and live weight. Since these studies will differ from species to species and breed to breed, the need for new studies is extremely high. For this aim, it is to evaluate the body measurement information obtained with the present study using several statistical approaches. To implement this aim, several data mining and machine learning algorithms such as multivariate adaptive regression splines (MARS), classification and regression tree (CART), and support vector machine regression (SVR) algorithms were used for training (70%) and test (30%) sets. To predict final body weight, 280 hair sheep breeds (162 female and 118 male) ranging from 2 months to 3 years were used with different data mining and machine learning approaches. Various goodness-of-fit criteria were used to evaluate the performances of the aforementioned algorithms. Although the MARS and SVR algorithms gave the same and highest results in terms of R and r values for both the train and the test sets, the SVR algorithm is one of the methods to be recommended as a result of this study, especially when other goodness-of-fit criteria are evaluated. In conclusion, the usage of SVR algorithms may be a useful tool of machine learning approaches for detecting the hair sheep breed standards and may contribute to increasing the sheep meat quality in Mexico.
活体体重的测定是决定肉产量的最重要特征之一，对于畜牧业管理和可持续发展非常重要。在这方面，由于在农村地区找到称重工具存在困难，因此需要寻找替代方法。特别是对于没有称重工具的情况，已经尝试建立了从身体测量中获得的信息与活体体重之间的关系。由于这些研究将因物种和品种而异，因此需要进行新的研究。为此，本研究旨在使用多种统计方法评估通过本研究获得的身体测量信息。为了实现这一目标，使用了多种数据挖掘和机器学习算法，如多元自适应回归样条（MARS）、分类和回归树（CART）以及支持向量机回归（SVR）算法，用于训练（70%）和测试（30%）集。为了预测最终体重，使用了 280 只毛发绵羊品种（162 只雌性和 118 只雄性），年龄从 2 个月到 3 岁不等，使用了不同的数据挖掘和机器学习方法。使用了各种拟合优度标准来评估上述算法的性能。尽管 MARS 和 SVR 算法在训练和测试集的 R 和 r 值方面都给出了相同且最高的结果，但由于本研究的结果，SVR 算法是推荐的方法之一，特别是在评估其他拟合优度标准时。总之，SVR 算法的使用可能是一种有用的机器学习方法，可以用于检测毛发绵羊品种标准，并有助于提高墨西哥的羊肉质量。
