Evaluation of sensor measurements for early identification of clinical mastitis in an automatic milking system.

IF 1.2 3区 农林科学 Q2 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Ivens Navarro Haponiuk Prus, Saulo Henrique Weber, Andre Ostrensky, Ruan R Daros, R Daniel Ollhoff, Cristina Santos Sotomaior
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引用次数: 0

Abstract

This study aimed to identify the best automatic milking system (AMS) parameters and monitoring data for early detection of clinical mastitis in dairy cows and to determine the earliest possible detection within 30 days with the highest predictive accuracy. From August 2021 to February 2022, 55 Holstein cows were monitored for mastitis using physical examination, positive California mastitis test (CMT) and the AMS manufacturer's software (Delpro®) criteria: milk electrical conductivity ≥ 5.37 mS/cm, milk yield ≤ 80%, somatic cell count (SCC) > 200,000 cells/mL and Mastitis Detection Index (MDi) ≥ 2.0. For every cow suspected of mastitis, two other lactating cows were randomly chosen for evaluation to provide a comparison with healthy herd companions. In total, 129 inspections were evaluated: 39 with clinical mastitis and 90 without. Data on milking, milk composition and production from the AMS, and behavioural data from monitoring collars were summarized for the 30 days leading up to the mastitis diagnosis. Thirty measurement parameters were analysed using generalized linear models. Sensitivity, specificity, accuracy, positive predictive value and negative predictive value were calculated. In the final model, significant parameters included: milk production per day (kg), SCC (cells/mL), average flow mean (kg/min), average conductivity (mS/cm), average flow peak (kg/min), average production per milking (kg), milking duration (s), rumination (min/day), panting (min/day) and feeding activity (min/day). From -30 to -10 days, accuracy, sensitivity and specificity varied without a defined pattern. However, from day -9, there was stabilization of the evaluated parameters. Results showed an average accuracy of 79.2%, a sensitivity of 82.5%, a specificity of 78.7%, a positive predictive value of 41.5% and a negative predictive value of 92.2% in predicting mastitis occurrence. In conclusion, using AMS parameters and behavioural data from monitoring collars, it was possible to predict clinical mastitis in dairy cows in an AMS with a 9-day advance notice.

自动挤奶系统中传感器测量对临床乳腺炎早期识别的评价。
本研究旨在确定用于奶牛临床乳腺炎早期检测的最佳自动挤奶系统(AMS)参数和监测数据,并确定在30天内以最高的预测准确率尽早检测到乳腺炎。从2021年8月至2022年2月,采用体格检查、加州乳腺炎试验(CMT)阳性和AMS制造商软件(Delpro®)标准监测55头荷斯坦奶牛的乳腺炎:乳电导率≥5.37 mS/cm,产奶量≤80%,体细胞计数(SCC) bbb20万个细胞/mL,乳腺炎检测指数(MDi)≥2.0。对于每一头疑似乳腺炎的奶牛,随机选择另外两头泌乳奶牛进行评估,与健康的牛群同伴进行比较。总共评估了129次检查:39次有临床乳腺炎,90次没有。在乳腺炎诊断前的30天内,对AMS提供的挤奶、牛奶成分和产量数据以及监测项圈提供的行为数据进行总结。采用广义线性模型对30个测量参数进行了分析。计算敏感性、特异性、准确性、阳性预测值和阴性预测值。在最终模型中,显著参数包括:每天产奶量(kg)、SCC(细胞/mL)、平均流量平均值(kg/min)、平均电导率(mS/cm)、平均流量峰值(kg/min)、平均每次挤奶产量(kg)、挤奶时间(s)、反刍(min/day)、喘气(min/day)和采食活性(min/day)。从-30天到-10天,准确性、敏感性和特异性变化没有明确的模式。然而,从第-9天开始,评估参数趋于稳定。结果显示,预测乳腺炎发生的平均准确率为79.2%,敏感性为82.5%,特异性为78.7%,阳性预测值为41.5%,阴性预测值为92.2%。综上所述,利用AMS参数和监测项圈的行为数据,可以提前9天在AMS中预测奶牛的临床乳腺炎。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Dairy Research
Journal of Dairy Research 农林科学-奶制品与动物科学
CiteScore
3.80
自引率
4.80%
发文量
117
审稿时长
12-24 weeks
期刊介绍: The Journal of Dairy Research is an international Journal of high-standing that publishes original scientific research on all aspects of the biology, wellbeing and technology of lactating animals and the foods they produce. The Journal’s ability to cover the entire dairy foods chain is a major strength. Cross-disciplinary research is particularly welcomed, as is comparative lactation research in different dairy and non-dairy species and research dealing with consumer health aspects of dairy products. Journal of Dairy Research: an international Journal of the lactation sciences.
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