Skip to main content

An On-Line Approach to Teaching On-Farm Mastitis Culture Techniques and Creation of a Practical Assessment Tool for Complicated Procedures on Dairies

Principal Investigator: Daryl Nydam

Co-PI: Wolfgang Heuwieser; Michael Zurakowski; Paul Virkler

Public & Ecosystem Health
Sponsor: New York Farm Viability Institute
Grant Number: FVI '22 011
Title: An On-Line Approach to Teaching On-Farm Mastitis Culture Techniques and Creation of a Practical Assessment Tool for Complicated Procedures on Dairies
Project Amount: $123,460
Project Period: August 2024 to July 2026

DESCRIPTION (provided by applicant):

In the dairy industry, a successful future involves improving management practices at the cow level. With consumer and legislator pressure to reduce antimicrobial use in the dairy industry, pathogen-based treatment of clinical mastitis (CM) will lead the way to judicious use of antimicrobials. CM cases are cultured to identify the cause of the infection and this information is used to manage the case appropriately. Previous studies have proven that pathogen-based treatment of CM will reduce antimicrobial use by 30-68%. Approximately 20-25% of New York's dairy cattle population have access to a routine service to provide culture data for cases of CM. The majority of the cattle population, though, has limited access due to not being near a milk lab or veterinary clinic proficient in milk culturing. Our project will develop an on-line education module to teach or improve on-farm and in-clinic milk culturing techniques. This module will allow 12 farms and 3 veterinary clinics in underserved areas of New York to either start performing milk cultures or improve their current system of culturing. In addition, we will further evaluate the effectiveness of on-line training modules through monitoring measurable farm level outcomes pre- and post-training. Also we will evaluate how objective structured clinical examinations (OSCEs) before and after training can measure behavior change and be used to successfully evaluate complicated tasks to avoid missing critical elements.