果冻传媒麻豆社

Researchers from 果冻传媒麻豆社鈥檚 College of Agricultural and Life Sciences are aiding in a multi-institutional project to develop and test a precision feeding system that optimizes rations to meet the nutritional needs of individual dairy cows.

The research team鈥檚 Self-learning Dairy (SLDairy) technology works with existing equipment used by automatic milking systems, where cows may enter a stall with a robotic milker under their own volition and feed is automatically dispensed as an incentive to lure them inside. The team鈥檚 cloud-based system monitors each cow鈥檚 performance in real time to adjust delivery of food pellets from the automated stations as needed, based on nutritional models, herd management software data, feeding software data, and records from the robotic milking system about milk production and milk components.

One of the expected outcomes is that the technology will help dairies select animals to breed that most efficiently convert feed into milk and nitrogen into milk protein.

The project, which launched in October, is funded with a four-year, $1.15 million grant from the U.S. Department of Agriculture鈥檚 National Institute of Food and Agriculture. U聽of聽I will receive about $370,000 of the funding. Most of U聽of聽I鈥檚 share is supporting nutrition and modeling work by Izabelle Teixeira, a UI Extension dairy specialist. Teixeira will recruit a doctoral student and a postdoctoral researcher to aid in the project. UI Extension Agricultural Economist Hernan Tejeda is helping with an economic analysis of the savings achieved through implementation of the system.

The team also includes Virginia Tech University as the lead institution and researchers from University of Nebraska, Colorado State University, University of Tennessee and Emory University.

Though cows within a dairy herd have varying nutritional requirements, most dairies feed a ration designed for an average cow. Teixeira uses the analogy of giving medium-sized shirts to a large group of people. The shirts will fit about a third of the group members but will be either too small or too large for about two-thirds of them. Optimizing rations should help dairies avoid both underfeeding at the expense of milk production and overfeeding, which results in unnecessary feed costs while increasing nitrogen levels in dairy waste that may pollute the environment.

鈥淥ur expectation is feeding each cow in a proper way is going to save money, help the environment and boost production,鈥� Teixeira said.

SLDairy provides a partial-mixed ration in a feed bunk. Individual cows supplement what they receive from the feed bunk at the automated milking stalls, which each have two feed lines filled with different grain blends in pellet form. Pellets are dispensed based on each cow鈥檚 specific protein and amino acid needs, as determined by modeling of data.

In small-scale testing at a Tennessee research dairy, the team documented considerable savings made possible by the system. The system will be tested at two commercial dairies in participating states next fall, followed by testing at two more commercial dairies in the spring of 2026.

In addition to assisting with nutritional modeling, Teixeira will also help with an Extension component of the project starting next year, initially sharing general information pertaining to dairy cow nutrition and eventually sharing results of the project, as well as instructions in how to implement the SLDairy system.

鈥淒airy farming has always required a careful balance between maximizing production and reducing costs, all while managing environmental impact,鈥� Teixeira said. 鈥淪LDairy is a self-learning feeding system that aims to make this balance easier by using available technology to refine how we feed dairy cows.鈥�

The project is funded by a four-year, $1.15 million USDA-NIFA-IDEAS grant under award No. 2024-68016-42391, of which 100% is the federal share.

Published in Catching Up with CALS