Kliś Piotr, Piwczyński Dariusz, Sawa Anna, Sitkowska Beata
Lely Center Bydgoszcz, Lisi Ogon, 86-065 Łochowo, Poland.
Department of Animal Biotechnology and Genetics, Faculty of Animal Breeding and Biology, UTP University of Science and Technology, 85-084 Bydgoszcz, Poland.
Animals (Basel). 2021 Feb 3;11(2):383. doi: 10.3390/ani11020383.
Early prediction of lactation milk yield enables more efficient herd management. Therefore, this study attempted to predict lactation milk yield (LMY) in 524 Polish Holstein-Friesian cows, based on information recorded by the automatic milking system (AMS) in the periparturient period. The cows calved in 2016 and/or 2017 and were used in 3 herds equipped with milking robots. In the first stage of data analysis, calculations were made of the coefficients of simple correlation between rumination time (expressed as mean time per cow during the periparturient period: second (14-8 days) and first (7-1 days) week before calving, 1-4, 5-7, 8-14, 15-21 and 22-28 days of lactation), electrical conductivity and temperature of milk (expressed as means per cow on days 1-4, 5-7, 8-14, 15-21 and 22-28), amount of concentrate intake, number of milkings/day, milking time/visit, milk speed and lactation milk yield. In the next step of the statistical analysis, a decision tree technique was employed to determine factors responsible for LMY. The study showed that the correlation coefficients between LMY and AMS traits recorded during the periparturient period were low or moderate, ranging from 0.002 to 0.312. Prediction of LMY from the constructed decision tree model was found to be possible. The employed Classification and Regression Trees (CART) algorithm demonstrated that the highest lactation yield is to be expected for cows with completed lactations (survived until the next lactation), which were milked 4.07 times per day on average in the 4th week of lactation. We proved that the application of the decision tree method could allow breeders to select, already in the postparturient period, appropriate levels of AMS milking variables, which will ensure high milk yield per lactation.
早期预测泌乳期产奶量有助于更高效地管理牛群。因此，本研究试图基于自动挤奶系统（AMS）在围产期记录的信息，对524头波兰荷斯坦-弗里生奶牛的泌乳期产奶量（LMY）进行预测。这些奶牛于2016年和/或2017年产犊，并在3个配备挤奶机器人的牛群中使用。在数据分析的第一阶段，计算了反刍时间（表示为围产期每头奶牛的平均时间：产犊前第二周（14 - 8天）和第一周（7 - 1天）、泌乳第1 - 4天、5 - 7天、8 - 14天、15 - 21天和22 - 28天）、牛奶电导率和温度（表示为每头奶牛在第1 - 4天、5 - 7天、8 - 14天、15 - 21天和22 - 28天的平均值）、精饲料摄入量、每天挤奶次数、每次挤奶时间、挤奶速度和泌乳期产奶量之间的简单相关系数。在统计分析的下一步，采用决策树技术来确定影响LMY的因素。研究表明，围产期记录的LMY与AMS性状之间的相关系数较低或中等，范围为0.002至0.312。发现从构建的决策树模型预测LMY是可行的。所采用的分类与回归树（CART）算法表明，对于完成泌乳期（存活至下一泌乳期）的奶牛，预计其泌乳产量最高，这些奶牛在泌乳第4周平均每天挤奶4.07次。我们证明，决策树方法的应用可以使育种者在产后阶段就选择合适水平的AMS挤奶变量，这将确保每泌乳期有较高的产奶量。
