Ivens Navarro Haponiuk Prus, Saulo Henrique Weber, Andre Ostrensky, Ruan R Daros, R Daniel Ollhoff, Cristina Santos Sotomaior
{"title":"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。","authors":"Ivens Navarro Haponiuk Prus, Saulo Henrique Weber, Andre Ostrensky, Ruan R Daros, R Daniel Ollhoff, Cristina Santos Sotomaior","doi":"10.1017/S0022029925101337","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":15615,"journal":{"name":"Journal of Dairy Research","volume":" ","pages":"1-7"},"PeriodicalIF":1.2000,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluation of sensor measurements for early identification of clinical mastitis in an automatic milking system.\",\"authors\":\"Ivens Navarro Haponiuk Prus, Saulo Henrique Weber, Andre Ostrensky, Ruan R Daros, R Daniel Ollhoff, Cristina Santos Sotomaior\",\"doi\":\"10.1017/S0022029925101337\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":15615,\"journal\":{\"name\":\"Journal of Dairy Research\",\"volume\":\" \",\"pages\":\"1-7\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2025-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Dairy Research\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1017/S0022029925101337\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AGRICULTURE, DAIRY & ANIMAL SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Dairy Research","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1017/S0022029925101337","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRICULTURE, DAIRY & ANIMAL SCIENCE","Score":null,"Total":0}
Evaluation of sensor measurements for early identification of clinical mastitis in an automatic milking system.
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.
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.