Grzesiak Wilhelm, Zaborski Daniel, Szatkowska Iwona, Królaczyk Katarzyna
Department of Ruminants Science, West Pomeranian University of Technology, 71-270 Szczecin, Poland.
Department of Animal Anatomy and Zoology, West Pomeranian University of Technology, 71-466 Szczecin, Poland.
Anim Biosci. 2021 Apr;34(4):770-782. doi: 10.5713/ajas.19.0939. Epub 2020 Apr 12.
The aim of the present study was to compare the effectiveness of three approaches (the seasonal auto-regressive integrated moving average [SARIMA] model, the nonlinear autoregressive exogenous [NARX] artificial neural networks and Wood's model) to the prediction of milk yield during lactation.
The dataset comprised monthly test-day records from 965 Polish Holstein-Friesian Black-and-White primiparous cows. The milk yields from cows in their first lactation (from 5 to 305 days in milk) were used. Each lactation was divided into ten lactation stages of approximately 30 days. Two age groups and four calving seasons were distinguished. The records collected between 2009 and 2015 were used for model fitting and those from 2016 for the verification of predictive performance.
No significant differences between the predicted and the real values were found. The predictions generated by SARIMA were slightly more accurate, although they did not differ significantly from those produced by the NARX and Wood's models. SARIMA had a slightly better performance, especially in the initial periods, whereas the NARX and Wood's models in the later ones.
The use of SARIMA was more time-consuming than that of NARX and Wood's model. The application of the SARIMA, NARX and Wood's models (after their implementation in a user-friendly software) may allow farmers to estimate milk yield of cows that begin production for the first time.
本研究旨在比较三种方法（季节性自回归积分滑动平均模型[SARIMA]、非线性自回归外生[NARX]人工神经网络和伍德模型）对泌乳期产奶量预测的有效性。
数据集包括965头波兰荷斯坦-弗里生黑白花初产奶牛的月度测定日记录。使用了奶牛头胎泌乳期（产奶5至305天）的产奶量。每个泌乳期分为大约30天的十个泌乳阶段。区分了两个年龄组和四个产犊季节。2009年至2015年收集的记录用于模型拟合，2016年的记录用于预测性能验证。
预测值与实际值之间未发现显著差异。SARIMA生成的预测略更准确，尽管与NARX和伍德模型生成的预测没有显著差异。SARIMA的性能略好，尤其是在初始阶段，而NARX和伍德模型在后期阶段表现较好。
使用SARIMA比使用NARX和伍德模型更耗时。SARIMA、NARX和伍德模型（在以用户友好软件实现后）的应用可能使农民能够估计首次开始产奶的奶牛的产奶量。
