Effectiveness of mastitis detection index for cow monitoring and abnormal milk detection in milking robots

Lusis, I.; Antane, V.; Laurs, Armins

doi:10.22616/erdev2017.16.n314

Cited by 5 publications

(8 citation statements)

References 6 publications

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“…With a higher MDi threshold, no better SN was detected in our study. This contradicts in part the approach of Lusis et al [ 56 ], which suggested an MDi threshold of ≥2 for abnormal milk detection to keep the SCC of bulk tank milk at a low level. While the higher threshold markedly increased the SP, the SN decreased drastically.…”

Section: Discussionmentioning

confidence: 82%

Sensitivity and Specificity for the Detection of Clinical Mastitis by Automatic Milking Systems in Bavarian Dairy Herds

Bausewein

Mansfeld

Doherr

et al. 2022

Animals

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In automatic milking systems (AMSs), the detection of clinical mastitis (CM) and the subsequent separation of abnormal milk should be reliably performed by commercial AMSs. Therefore, the objectives of this cross-sectional study were (1) to determine the sensitivity (SN) and specificity (SP) of CM detection of AMS by the four most common manufacturers in Bavarian dairy farms, and (2) to identify routinely collected cow data (AMS and monthly test day data of the regional Dairy Herd Improvement Association (DHIA)) that could improve the SN and SP of clinical mastitis detection. Bavarian dairy farms with AMS from the manufacturers DeLaval, GEA Farm Technologies, Lely, and Lemmer-Fullwood were recruited with the aim of sampling at least 40 cows with clinical mastitis per AMS manufacturer in addition to clinically healthy ones. During a single farm visit, cow-level milking information was first electronically extracted from each AMS and then all lactating cows examined for their udder health status in the barn. Clinical mastitis was defined as at least the presence of visibly abnormal milk. In addition, available DHIA test results from the previous six months were collected. None of the manufacturers provided a definition for clinical mastitis (i.e., visually abnormal milk), therefore, the SN and SP of AMS warning lists for udder health were assessed for each manufacturer individually, based on the clinical evaluation results. Generalized linear mixed models (GLMMs) with herd as random effect were used to determine the potential influence of routinely recorded parameters on SN and SP. A total of 7411 cows on 114 farms were assessed; of these, 7096 cows could be matched to AMS data and were included in the analysis. The prevalence of clinical mastitis was 3.4% (239 cows). When considering the 95% confidence interval (95% CI), all but one manufacturer achieved the minimum SN limit of >80%: DeLaval (SN: 61.4% (95% CI: 49.0%–72.8%)), GEA (75.9% (62.4%–86.5%)), Lely (78.2% (67.4%–86.8%)), and Lemmer-Fullwood (67.6% (50.2%–82.0%)). However, none of the evaluated AMSs achieved the minimum SP limit of 99%: DeLaval (SP: 89.3% (95% CI: 87.7%–90.7%)), GEA (79.2% (77.1%–81.2%)), Lely (86.2% (84.6%–87.7%)), and Lemmer-Fullwood (92.2% (90.8%–93.5%)). All AMS manufacturers’ robots showed an association of SP with cow classification based on somatic cell count (SCC) measurement from the last two DHIA test results: cows that were above the threshold of 100,000 cells/mL for subclinical mastitis on both test days had lower chances of being classified as healthy by the AMS compared to cows that were below the threshold. In conclusion, the detection of clinical mastitis cases was satisfactory across AMS manufacturers. However, the low SP will lead to unnecessarily discarded milk and increased workload to assess potentially false-positive mastitis cases. Based on the results of our study, farmers must evaluate all available data (test day data, AMS data, and daily assessment of their cows in the barn) to make decisions about individual cows and to ultimately ensure animal welfare, food quality, and the economic viability of their farm.

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Section: Discussionmentioning

confidence: 82%

Sensitivity and Specificity for the Detection of Clinical Mastitis by Automatic Milking Systems in Bavarian Dairy Herds

Bausewein

Mansfeld

Doherr

et al. 2022

Animals

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“…The differences in the results might be due to the nature of the different outcomes studied (SCC vs. clinical mastitis defined as veterinary treatment), model choice (nonlinear vs. linear model), difference in milking interval, or available data (one vs. two herds). Furthermore, Lusis et al (2017) found the correlation between SCC and MDi to be poor; however, due to the small sample size and few observations in their study, the authors suggested that individual cows might have affected the results. When MDi and UC were excluded (model 2), variance in conductivity between quarters had the strongest statistical association with CMSCC.…”

Section: Discussionmentioning

confidence: 85%

“…Furthermore, Lusis et al . (2017) found the correlation between SCC and MDi to be poor; however, due to the small sample size and few observations in their study, the authors suggested that individual cows might have affected the results.…”

Section: Discussionmentioning

confidence: 85%

Modeling cow somatic cell count using sensor data as input to generalized additive models

Anglart

Hallén-Sandgren

Waldmann

et al. 2020

Journal of Dairy Research

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This research paper presents a study investigating if sensor data from an automatic milking rotary could be used to model cow somatic cell count (composite milk SCC: CMSCC). CMSCC is valuable for udder health monitoring and individual cow udder health surveillance could be improved by predicting CMSCC between routine samplings. Data regularly recorded in the automatic milking rotary, in one German dairy herd, were collected for analysis. The cows (Holstein-Friesian, n = 372) were milked twice daily and sampled once weekly in afternoon milkings for 8 weeks for CMSCC. From the potential independent variables, including quarter conductivity, milk flow, blood in milk, kick-offs, not milked quarters and incomplete milkings, new variables that combined quarter data were created. Past period records, i.e. lags, of up to seven days before the actual CMSCC sampling event were added in the dataset to investigate if they were of use in modeling the cell count. Univariable generalized additive models (GAM) were used to screen the data to select potential independent variables. Furthermore, several multivariable GAM were fitted in order to compare the importance of the potential independent variables and to explore how the model performance would be affected by using data from various number of days before the CMSCC sampling event. The result of the model selection showed that the best explanation of CMSCC was provided by the model incorporating all significant variables from the variable screening for the seven preceding days, including the day of the CMSCC sampling event. However, using data from only three days before the CMSCC sampling event is suggested to be sufficient to model CMSCC. Variables combining conductivity quarter data, together with quarter conductivity, are suggested to be important in describing CMSCC. We conclude that CMSCC can be modeled with a high degree of explanation using the information routinely recorded by the milking robot.

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“…For example, "Afimilk" company explains the methods chrestomatic basics in its website (Anonymous, 2010), and numerous scientific publications explain it in even more detail, with practical aspects (Anonymous, 2020a) or in depth chemical basics (Romero et al, 2016;Muchetti et al, 1993). The benefits of regular conductometric tests in veterianary aspects are discussed in (Lusis et al, 2017). New at our job was the hardware realisation method by means of iNA-170 giving more simple, accurate and effective implementation of measurement physically-chemical principle in comparison with other concurrent electronics, for example, instrumental amplifier in quadrature envelope detector.…”

Section: Results: Measure-system Efficiencymentioning

confidence: 99%

Old Principle of Liquid Ion-Impedance Spectrometry (Particularly in Milk) Realised by New Components

Blahins

Bžiškjans

Kalniņš³

2020

Proceedings of the Latvian Academy of Sciences. Section B. Natural, Exact, and Applied Sciences.

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Ion impedance spectrometry enables detection of the concentrations of multiple chemical compositions in a weak solution, usually if only part of a complex signal is used and the frequency is not swept. Prior to the method described in this paper, ion impedance required rather sophisticated electronics to collect the results from an instrument, as well as the electrode polarisation minimising. We elaborated and tested circuitry that uses the newest electronics components for a chemical analysis instrument designed according to a principle patented in Latvia (No. SU-1021440A), year 1981. The newly improved ion impedance spectrometry apparatus is suited for accurate measurement of ion concentrations for commercial use by various trade professionals. The primary target was an in-flow milk tester for dairy farm milking devices (yet this is not the exclusive use).

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Effectiveness of mastitis detection index for cow monitoring and abnormal milk detection in milking robots

Cited by 5 publications

References 6 publications

Sensitivity and Specificity for the Detection of Clinical Mastitis by Automatic Milking Systems in Bavarian Dairy Herds

Sensitivity and Specificity for the Detection of Clinical Mastitis by Automatic Milking Systems in Bavarian Dairy Herds

Modeling cow somatic cell count using sensor data as input to generalized additive models

Old Principle of Liquid Ion-Impedance Spectrometry (Particularly in Milk) Realised by New Components

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