Identifying gram-negative and gram-positive clinical mastitis using daily milk component and behavioral sensor data.

Autor:	Steele NM; Department of Dairy Science, Virginia Tech, Blacksburg 24061; DairyNZ Ltd., Private Bag 3221, Hamilton 3240, New Zealand. Electronic address: Nicole.Steele@dairynz.co.nz., Dicke A; Farm Credit, Bellefontaine, OH 43311., De Vries A; Department of Animal Sciences, University of Florida, Gainesville 32611., Lacy-Hulbert SJ; DairyNZ Ltd., Private Bag 3221, Hamilton 3240, New Zealand., Liebe D; Department of Animal and Poultry Science, Virginia Tech, Blacksburg 24061., White RR; Department of Animal and Poultry Science, Virginia Tech, Blacksburg 24061., Petersson-Wolfe CS; Department of Dairy Science, Virginia Tech, Blacksburg 24061.
Jazyk:	angličtina
Zdroj:	Journal of dairy science [J Dairy Sci] 2020 Mar; Vol. 103 (3), pp. 2602-2614. Date of Electronic Publication: 2019 Dec 25.
DOI:	10.3168/jds.2019-16742
Abstrakt:	Opportunities exist for automated animal health monitoring and early detection of diseases such as mastitis with greater on-farm adoption of precision technologies. Our objective was to evaluate time series changes in individual milk component or behavioral variables for all clinical mastitis (CM) cases (ACM), for CM caused by gram-negative (GN) or gram-positive (GP) pathogens, or CM cases in which no pathogen was isolated (NPI). We developed algorithms using a combination of milk and activity parameters for predicting each of these infection types. Milk and activity data were collated for the 14 d preceding a CM event (n = 170) and for controls (n = 166) matched for breed, parity, and days in milk. Explanatory variables in the univariate and multiple regression models were the slope change in milk (milk yield, conductivity, somatic cell count, lactose percentage, protein percentage, and fat percentage) and activity parameters (steps, lying time, lying bout duration, and number of lying bouts) over 7 d. Slopes were estimated using linear regression between d -7 and -5, d -7 and -4, d -7 and -3, d -7 and -2, and d -7 and -1 relative to CM detection for all parameters. Univariate analyses determined significant slope ranges for explanatory variables against the 4 responses: ACM, GN, GP, and NPI. Next, all slope ranges were offered into the multivariate models for the same 4 responses using 3 baselines: d -10, -7, and -3 relative to CM detection. In the univariate analysis, no explanatory variables were significant indicators of ACM, whereas at least 1 parameter was significant for each of GN, GP, and NPI models. Superior sensitivity (Se) and specificity (Sp) estimates were observed for the best GP (Se = 82%, Sp = 87%) and NPI (Se = 80%, Sp = 94%) multiple regression models compared with the best ACM (Se = 73%, Sp = 75%) and GN (Se = 71%, Sp = 74%) models. Sensitivity for the GN model was greater at the baseline closest to the day of CM detection (d -3), whereas the opposite was observed for the GP and NPI model as Se was maximized at the d -10 baseline. Based on this screening of relationships, milk and activity sensor data could be used in CM detection systems. (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu