Bahri Dagdas International Agricultural Research Institute, Konya, Türkiye.
Faculty of Agriculture, Department of Animal Science, Selcuk University, Konya, Türkiye.
Trop Anim Health Prod. 2024 Sep 19;56(8):262. doi: 10.1007/s11250-024-04145-1.
The purpose of this study was to evaluate the performance of various prediction models in estimating the growth and morphological traits of pure Hair, Alpine × Hair F (AHF), and Saanen × Hair F (SHF) hybrid offspring at yearling age by employing early body measurement records from birth till 9th month combined with meteorological data, in an extensive natural pasture-based system. The study also included other factors such as sex, farm, doe and buck IDs, birth type, gestation length, age of the doe at birth etc. For this purpose, seven different machine learning algorithms-linear regression, artificial neural network (ANN), support vector machines (SVM), decision tree, random forest, extra gradient boosting (XGB) and ExtraTree - were applied to the data coming from 1530 goat offspring in Türkiye. Early predictions of growth and morphological traits at yearling age; such as live weight (LW), body length (BL), wither height (WH), rump height (RH), rump width (RW), leg circumference (LC), shinbone girth (SG), chest width (CW), chest girth (CG) and chest depth (CD) were performed by using birth date measurements only, up to month-3, month-6 and month-9 records. Satisfactory predictive performances were achieved once the records after 6th month were used. In extensive natural pasture-based systems, this approach may serve as an effective indirect selection method for breeders. Using month-9 records, the predictions were improved, where LW and BL were found with the highest performance in terms of coefficient of determination (R score of 0.81 ± 0.00) by ExtraTree. As one of the rarely applied machine learning models in animal studies, we have shown the capacity of this algorithm. Overall, the current study offers utilization of the meteorological data combined with animal records by machine learning models as an alternative decision-making tool for goat farming.
本研究旨在评估各种预测模型在估计纯 Hair、Alpine×Hair F (AHF) 和 Saanen×Hair F (SHF) 杂种后代在周岁时生长和形态特征方面的性能，方法是利用从出生到第 9 个月的早期体尺记录，结合气象数据，在广泛的基于天然牧场的系统中进行。该研究还包括其他因素，如性别、农场、母羊和公羊 ID、出生类型、妊娠期、母羊出生时的年龄等。为此，七种不同的机器学习算法-线性回归、人工神经网络 (ANN)、支持向量机 (SVM)、决策树、随机森林、极端梯度提升 (XGB) 和 ExtraTree-被应用于来自土耳其的 1530 只山羊后代的数据。通过仅使用出生日期测量值、前 3 个月、6 个月和 9 个月的记录，对周岁时的生长和形态特征（如活重 (LW)、体长 (BL)、肩高 (WH)、臀高 (RH)、臀宽 (RW)、腿围 (LC)、胫骨周长 (SG)、胸围 (CW)、胸围 (CG) 和胸深 (CD) 进行早期预测。一旦使用 6 个月后的记录，就可以获得令人满意的预测性能。在广泛的基于天然牧场的系统中，这种方法可以作为一种有效的间接选择方法供饲养员使用。在使用 9 个月的记录时，通过 ExtraTree 发现，LW 和 BL 的预测性能最高，决定系数 (R 得分为 0.81±0.00)。作为动物研究中应用较少的机器学习模型之一，我们展示了该算法的能力。总的来说，本研究提供了一种利用气象数据结合机器学习模型进行动物记录的方法，作为养羊的一种替代决策工具。
