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利用决策树技术预测自动挤奶系统中奶牛的挤奶效率

Forecasting Milking Efficiency of Dairy Cows Milked in an Automatic Milking System Using the Decision Tree Technique.

作者信息

Aerts Joanna, Kolenda Magdalena, Piwczyński Dariusz, Sitkowska Beata, Önder Hasan

机构信息

Lely Services B.V., Cornelis van der Lelylaan 1, 3147 PB Maassluis, The Netherlands.

Department of Animal Biotechnology and Genetics, Faculty of Animal Breeding and Biology, Bydgoszcz University of Science and Technology, 85-084 Bydgoszcz, Poland.

出版信息

Animals (Basel). 2022 Apr 16;12(8):1040. doi: 10.3390/ani12081040.

DOI:10.3390/ani12081040
PMID:35454286
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9024698/
Abstract

In barns equipped with an automatic milking system, the profitability of production depends primarily on the milking efficiency of a cow (ME; kg/min) defined as cow milk yield per minute of box time. This study was carried out on 1823 Polish Holstein−Friesian cows milked by the automatic milking system (AMS) in 20 herds. Selected milking parameters recorded by the AMS were analyzed in the research. The aim of the study was to forecast ME using two statistical techniques (analysis of variance and decision trees). The results of the analysis of variance showed that the average ME was 1.67 kg/min. ME was associated with: year of AMS operation (being the highest in the first year), number of cows per robot (the highest in robots with 61−75 cows), lactation number (highest for multiparas), season of calving (the highest in spring), age at first calving (>36 months), days in milk (151−250 days) and finally, rear quarter to total milk yield ratio (the highest between 51% and 55%). The decision tree predicted that the highest ME (2.01 kg/min) corresponded with cows that produced more than 45 kg of milk per day, were milked less than four times/day, had a short teatcup attachment time (<7.65 s) and were milked in robots that had an occupancy lower than 56 cows.

摘要

在配备自动挤奶系统的牛舍中,生产的盈利能力主要取决于奶牛的挤奶效率(ME;千克/分钟),其定义为每箱挤奶时间内的牛奶产量。本研究对20个牛群中由自动挤奶系统(AMS)挤奶的1823头波兰荷斯坦-弗里生奶牛进行。研究中分析了AMS记录的选定挤奶参数。本研究的目的是使用两种统计技术(方差分析和决策树)预测ME。方差分析结果表明,平均ME为1.67千克/分钟。ME与以下因素相关:AMS运行年份(第一年最高)、每个机器人的奶牛数量(61-75头奶牛的机器人中最高)、泌乳次数(经产牛最高)、产犊季节(春季最高)、初产年龄(>36个月)、产奶天数(151-250天),最后是后乳房产奶量与总产奶量之比(51%-55%之间最高)。决策树预测,最高的ME(2.01千克/分钟)对应于每天产奶超过45千克、每天挤奶少于4次、乳头杯附着时间短(<7.65秒)且在奶牛存栏量低于56头的机器人中挤奶的奶牛。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57cf/9024698/3deac3fe5cff/animals-12-01040-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57cf/9024698/0d71d7d81cb1/animals-12-01040-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57cf/9024698/4e396231618c/animals-12-01040-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57cf/9024698/71bdbd4f0919/animals-12-01040-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57cf/9024698/90524c496ba6/animals-12-01040-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57cf/9024698/3f57c6c92b61/animals-12-01040-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57cf/9024698/3deac3fe5cff/animals-12-01040-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57cf/9024698/0d71d7d81cb1/animals-12-01040-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57cf/9024698/4e396231618c/animals-12-01040-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57cf/9024698/71bdbd4f0919/animals-12-01040-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57cf/9024698/90524c496ba6/animals-12-01040-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57cf/9024698/3f57c6c92b61/animals-12-01040-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57cf/9024698/3deac3fe5cff/animals-12-01040-g006.jpg

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