Adamczyk Krzysztof, Grzesiak Wilhelm, Zaborski Daniel
Department of Animal Genetics, Breeding and Ethology, University of Agriculture in Krakow, al. Mickiewicza 24/28, 30-059 Kraków, Poland.
Department of Ruminants Science, West Pomeranian University of Technology, Klemensa Janickiego 29, 71-270 Szczecin, Poland.
Animals (Basel). 2021 Mar 6;11(3):721. doi: 10.3390/ani11030721.
The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76-99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24-99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00-97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood's model parameters.
本研究的目的是验证人工神经网络（ANN）是否可能成为一种基于常规收集的头胎记录来预测奶牛淘汰原因的有效工具。本研究使用了2017年至2018年在波兰淘汰的荷斯坦-弗里生奶牛的数据。将广义判别分析（GDA）用作ANN的参考方法。考虑所有预测性能指标，ANN在预测因年老导致的奶牛淘汰方面最为有效（正确分类案例的比例为99.76 - 99.88%）。此外，因繁殖问题淘汰动物的正确分类率也非常高（99.24 - 99.98%）。这很重要，因为不孕症是奶牛群中最难消除的状况之一。用GDA获得的个别淘汰原因的正确分类率（0.00 - 97.63%）总体上低于多层感知器（MLP）。所得结果表明，为了有效预测上述淘汰原因，应使用以下头胎参数：产犊年龄、产犊难度以及基于伍德模型参数的泌乳曲线特征。
