Effects of implementing optimal sampling and diet reformulation practices in milk production and profitability of NYS dairy farms
- Date: May 23, 2022 - August 5, 2022
- Location: Mushkoday Dairy Farm (Delaware County) and Murcrest Farm (Watertown/Copenhagen)
- Intern: Chloe Chavez (Chloe's blog)
- Faculty sponsor: Kristan Reed, Dept. of Animal Science
- Campus-based mentor/supervisor: Jorge Barrientos, Dept. of Animal Science
- Field mentors/supervisors: Joe Lawrence
- Stipend: $5,000
Forage nutrient variability cause differences between the nutrient content of the formulated diet and the diet that is delivered to cows. This difference increases the uncertainty of the delivered dietary nutrients and therefore increases the risk of overfeeding or underfeeding cows. Both scenarios affect the economics of the farm and the environment. We have developed a protocol to manage forage variability on dairy farms. The protocol consists of optimizing forage sampling practices and monitoring forage and diet nutrient composition. To validate the developed management protocol, we will measure the production and economic impacts of implementing the our proposed protocol on 4 NYS commercial dairy farms. We will collect samples from feed ingredients and mixed diets and production records for up to 16 weeks during the spring and summer of 2022.
Roles and responsibilities
The student will be responsible for collecting feed and diet samples and diet formulation and milk production data records at 1 or 2 of the enrolled farms. Sampling frequency at farms will depend on the specific nutrient variability management protocol for each farm and will range from every 3 days to 1x per week. In addition to collecting farm data and samples, the student will assist with organizing, cleaning, and analyzing the collected data. Other responsibilities will include participating in bi-weekly lab meetings, journal article discussions, and presentation of their work to the group.
Qualifications and previous coursework
This opportunity is available to non-graduating students in Cornell University's College of Agriculture and Life Sciences.
The student needs to be responsible, independent, and self-motivated. The students need to be willing to participate actively in the fieldwork at dairy farms that will be enrolled in the study. The student must have experience in Microsoft Excel and preferred qualifications include experience with R statistical software, a basic knowledge of US dairy production, and have taken an introductory statistics course.
The student will learn about protocols for collecting feed samples at dairy farms and will be exposed to methods for managing the forage variability based on industrial quality control principals. The student will learn how to interpret feed laboratory analysis and the effects of farm management and environment on the quality of dairy dietary ingredients. The student will also learn about data management and cleaning, and statistical tools used to monitor time-series data at the farms.