Recipient Organization
CORNELL UNIVERSITY
(N/A)
ITHACA,NY 14853
Performing Department
Vet Population Medicine & Diagnostic Science
Non Technical Summary
Our project aims to improve milk quality and udder health with automated milking technology by identifying udder health alerts sooner, which will allow for a quicker udder healthintervention. Udder health should be improved by quicker decision making when udder alerts occur with the intention of increasing cure rates from intramammary infections.In addition to opportunities to improve milk quality, our project will open opportunities for monitoring reproduction, health, lameness, and transition cow diseases with AMS technology. Further, there is opportunity to implement our models in any milking system with milking time data. The collected data could help future scientists link SCM to other diseases that it may be influencing. For example, is there a negative effect of SCM on reproduction? Conversely, the retrospective milking characteristic data on a CM or SCM quarter could be used to determine possible causes of mastitis. In this way our grant becomes a way to proactively prevent CM and SCM events instead of simply reacting to them.
Animal Health Component
0%
Research Effort Categories
Basic
(N/A)
Applied
0%
Developmental
100%
Goals / Objectives
1. Identify quarter level milking data points from automated milking systems (AMS) that are associated with the onset of clinical mastitis (CM) and subclinical mastitis (SCM).2. Develop a predictive model that will allow AMS managers to identify at risk cows with subclinical and clinical mastitis.a. The development of a model, with quarter level milking time data points associated with CM and SCM will allow AMS managers to identify cows at risk for CM and SCM accurately.3. Determine if the additional quarters of an individual cow that did not flag with the model for CM or SCM are considered healthy by having a SCC < 200,000 cells/ml by documenting the SCC of all quarters when a cow has CM or SCM.4. Report results to producers that have AMS and companies that make the systems.
Project Methods
Aim 1: Collect individual quarter milk samples from all cows and submit these samples for SCC testing. Record and analyze all milking time data from the AMS software to determine which variables are associated with an increase in quarter level SCC.Study design: Individual quarter milk samples from all cows will be analyzed for SCC and aerobic culture analysis on milk samples from cows with CM. Quarter level milking time records will be collected from the AMS software. After analysis of milk samples for SCC, cows will be assigned to one of two cohorts: healthy or subclinical. Milk samples with a SCC < 200,000 cells/mL will be classified as healthy whereas milk samples with a SCC ≥ 200,000 cells/mL will be classified as subclinical. Clinical mastitis will be identified visually by technicians for clinical signs of abnormal milk or quarter inflammation. We will collect the following AMS data using DelPro (DeLaval, Tumba, Sweden): teatcup on time, milk yield/milking, milk deviation, milking frequency, milking interval, average milk flow rate, electrical conductivity, kick-offs, attachment failures, incomplete milkings, parity and more variables not listed here.Sample collection: We will collect quarter milk samples from 180 cows after milking with a DeLaval AMS. We will enroll cows from a New York State commercial dairy operation using AMS. Samples will be collected on Mondays, Wednesdays, and Fridays for 50 sampling periods (~120 days). Given that cows show signs of clinical mastitis for 3 - 5 days, sampling on Mondays, Wednesdays, and Fridays is intended to identify most if not all cows with CM. Individual quarter milk samples will be submitted to Dairy One, Ithaca, NY for SCC analysis by Fossomatic Cell Counter (FOSS, Hilleroed, Denmark). Individual aseptic quarter milk samples will be collected from cows identified with CM on one or more quarters and submitted to QMPS, Warsaw, NY for aerobic culture.Milking time data collection: AMS milking time variables will be exported daily from DelPro to Excel for analysis.Identification of influential variables: Data from the AMS and quarter milk samples will be merged and cleaned to ensure proper integration of data and to control for outliers. Simple Pearson's correlations between potential independent variables and the incidence of CM and SCM will be used. Further, correlations between variables will be assessed to identify a pool of potential variables for inclusion in a predictive model. In addition to the raw data, quadratic transformation of continuous variables like flow rate and conductivity will also be included to check for non-linear relationships. Further, the change in observed continuous variables between days will also be included as an additional transformed variable.Aim 2: Predictive models of CM and SCM will be developed in R statistical software. An autoregressive multivariate logistic regression mixed model to accommodate binary outcome variables, multiple records from one individual over time, and the random effects of the individual cow will be developed using the glmer() function in the lme4 package. A forward stepwise selection process will be used including variables identified in Aim 1 in the order of their correlation with the outcome variables. Model goodness of fit will be assessed with McFaddens R2 and the Hosmer-Lemeshow test. Finally, the model's predictive ability will be assessed with an ROC curve and Leave-One-Out Cross Validation.Revisit farm after model development to validate AMS model. Validation of model will require collecting milk samples from cows that are identified to have CM and SCM and analyzing these samples for SCC and aerobic culture.Aim 3: Analyze the individual quarter SCC data from the samples gathered for Aims 1 and 2 to evaluate the risk of quarters with visually normal milk having an elevated SCC if the cow has a SCC ≥ 200,000 cells/ml on one or more quarters.Aim 4: Upon completion, we will host a meeting to share our results.