Arid
DOI10.3168/jds.2017-14310
Development of a new clinical mastitis detection method for automatic milking systems
Khatun, M.1,2; Thomson, P. C.1; Kerrisk, K. L.1; Lyons, N. A.3; Clark, C. E. F.1; Molfino, J.1; Garcia, S. C.1
通讯作者Khatun, M.
来源期刊JOURNAL OF DAIRY SCIENCE
ISSN0022-0302
EISSN1525-3198
出版年2018
卷号101期号:10页码:9385-9395
英文摘要

This study investigated the potential for accurate detection of clinical mastitis (CM) in an automatic milking system (AMS) using electronic data from the support software. Data from cows were used to develop the model, which was then tested on 2 independent data sets, 1 with 311 cows (same farm but from a different year) and 1 with 568 cows (from a different farm). In addition, the model was used to test how well it could predict CM 1 to 3 d before actual clinical diagnosis. Logistic mixed models were used for the analysis. Twelve measurements were included in the initial model before a backward elimination, which resulted in the following 6 measurements being included in the final model: quarter-level milk yield (MY; kg), electrical conductivity (EC; mS/cm), average milk flow rate (MF; kg/min), occurrence of incompletely milked quarters in each milking session (IM; yes or no), MY per hour (IVIYII; kg/h), and EC per hour (ECH; mS/cm/h) between successive milking sessions. The other 6 measurements tested but not included in the final model were peak milk flow rate (kg/min), kick-offs (yes or no) in each milking session, lactation number, days in milk (d), blood in milk (yes or no), and a calculated mastitis detection index used by DeLaval (DelPro software; DeLaval International AB, Tumba, Sweden). All measurements were assessed to determine their ability to detect CM as both individual variables and combinations of the 12 above-mentioned variables. These were assessed by producing a receiver operating characteristic curve and calculating the area under the curve (AUG) for each model. Overall, 9 measurements (i.e., EC, ECH, MY, MYH, MF, IM, peak flow rate, lactation number, and mastitis detection index) had significant mastitis detection ability as separate predictors. The best mastitis prediction was possible by incorporating 6 measurements (i.e., EC, ECH, MY, MYH, MF, and IM) as well as the random cow and quarter effects in the model, resulting in 90% sensitivity and 91% specificity with excellent AUC (0.96). Assessment of the model was found to produce robust results (AUG >0.9) in different data sets and could detect CM with reductions in sensitivity and specificity with increasing days before actual diagnosis. This study demonstrated that improved mastitis status prediction can be achieved by using multiple measurements, arid new indexes based on that are expected to result in improved accuracy of mastitis alerts, thereby improving the detection ability and utility on farm.


英文关键词dairy cow clinical mastitis automatic milking system pasture-based
类型Article
语种英语
国家Australia ; Bangladesh
收录类别SCI-E
WOS记录号WOS:000445019000061
WOS关键词SOMATIC-CELL COUNT ; DAIRY-COWS ; ELECTRICAL-CONDUCTIVITY ; UDDER HEALTH ; BOVINE MASTITIS ; INDICATORS ; INTERVAL ; QUARTER ; MANAGEMENT ; DISEASE
WOS类目Agriculture, Dairy & Animal Science ; Food Science & Technology
WOS研究方向Agriculture ; Food Science & Technology
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/210809
作者单位1.Univ Sydney, Sydney Inst Agr, Sch Life & Environm Sci, Dairy Sci Grp, Camden, NSW 2570, Australia;
2.Bangladesh Agr Univ, Mymensingh 2202, Bangladesh;
3.Elizabeth Macarthur Agr Inst, Dept Primary Ind, Intens Livestock Ind, Menangle, NSW 2568, Australia
推荐引用方式
GB/T 7714
Khatun, M.,Thomson, P. C.,Kerrisk, K. L.,et al. Development of a new clinical mastitis detection method for automatic milking systems[J],2018,101(10):9385-9395.
APA Khatun, M..,Thomson, P. C..,Kerrisk, K. L..,Lyons, N. A..,Clark, C. E. F..,...&Garcia, S. C..(2018).Development of a new clinical mastitis detection method for automatic milking systems.JOURNAL OF DAIRY SCIENCE,101(10),9385-9395.
MLA Khatun, M.,et al."Development of a new clinical mastitis detection method for automatic milking systems".JOURNAL OF DAIRY SCIENCE 101.10(2018):9385-9395.
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