The Cornell University Ruminant Center in the College of Agricultural and Life Sciences will conduct a series of studies to evaluate the technology for its potential to improve the nutritional, health, and reproductive management of lactating dairy cattle.
The studies will supplement an on-farm pilot program SomaDetect began in June 2017 in association with Milk2020 and the New Brunswick Crop and Livestock Health and Quality Program in Canada.
SomaDetect said the system showed promising results detecting progesterone levels, trace antibiotics residues, protein, fat, and other milk quality and cow health indicators in real time on working dairy farms.
The research study with Cornell, led by Drs Julio Giordano, Thomas Overton, and Daryl Nydam, and funded with a grant from the New York Farm Viability Institute (NYFVI), will give SomaDetect the additional data necessary to further develop and refine the models used to assess these dairy quality indicators.
Giordano said, “Unlocking the biological information present in cow milk through a non-invasive, fully automated technology will help us develop novel management strategies to improve the health and performance of lactating dairy cows.”
The technology will enable dairy farmers to know the health, reproductive status and quality of milk produced from each cow, resulting in better data for farmers.
NYFVI’s executive director David Grusenmeyer said the organization has a long history of identifying and funding work that advances the use of data analytics to improve the profitability of New York’s farms.
“As farmers, Farm Viability’s board of directors understands the potential of this work and the importance of this partnership,” Grusenmeyer said.
Analysis of milk
Founded in 2016, SomaDetect provides real-time, automated analysis of milk quality without any addition of chemicals or consumables.
The company measures every compound of interest in raw milk, enabling farmers to identify issues early, manage reproduction, and reduce unnecessary antibiotic usage.