A new PhD study shows how better measurement, smarter harvest timing, and technical processing can improve forage utilization.
Should grass be harvested early to achieve high quality, or later to maximize yield? The PhD research of Bernd Ollertz-Mertens addresses precisely this dilemma. Through three studies, the researcher investigated how forage digestibility can be measured in real-time, managed, and improved throughout the production chain. The goal was to provide a foundation for more data-driven decision-making in forage production.
Can We Measure Feed Quality Directly in the Field?
Organic matter digestibility (OMD) is a key indicator of forage quality. High digestibility has positive effects on milk production, dry matter intake, and methane intensity. However, practical solutions for real-time measurements and the use of such data in forage production have been limited.
In the first study, the researcher examined whether near-infrared spectroscopy (NIRS) combined with machine learning could be used to estimate digestibility directly in the field and in real-time. The results were promising under controlled conditions, with several methods showing good predictive accuracy. However, when the models were tested on independent datasets, their accuracy declined. Larger datasets, or datasets with even greater variability, may improve prediction performance and make the models more robust and better able to handle differences across regions, years, and growing conditions.
There Is No Perfect Harvest Time
The second part of the PhD focused on a question that many farmers continually face: When is the best time to harvest?
The research was based on field measurements of both forage quality and yield. Production models were then used to simulate how harvest timing affects outcomes under different conditions.
The results showed that there is no single optimal harvest date that fits all situations. It became clear that harvest timing cannot be based solely on the phenological development stage of the plants. Factors such as the harvest year, the number of cuts, and the type of grassland also influenced when the ideal harvest should take place.
These findings highlight that modern forage production is more complex than previously assumed and that decision-support tools may become valuable aids in the future.
Can Mechanical Processing Make Forage Easier to Digest?
While the first two studies focused on measurement and planning, the third study explored another possibility: Can the processing of forage itself improve digestibility?
In two feeding trials with lactating dairy cows, the researcher investigated the effects of physically processing alfalfa-based forage. The study identified a processing index, called the Processing Level Index (PLI), as a rapid and robust indicator of structural changes in the feed.
The index showed strong relationships with all measured digestibility parameters. However, the researchers found no corresponding relationship with the cows’ eating and rumination behavior.
What Does This Tell Us About the Future of Forage Production?
The results confirm that digestibility is not influenced only in the field through harvest timing. Technical processing can also affect how forage is digested and utilized.
The PhD research demonstrates how forage digestibility can be influenced through measurement, planning, and processing. The work provides new insights into how modern technology and data analysis can be used to support better decision-making in forage production.
The research lays a foundation for data-driven forage management. However, further studies are needed to improve and to validate the methods for other forage types and production systems.
Bernd Ollertz-Mertens is from Heinsberg, Germany. He holds a Master of Science in Animal Science from the University of Hohenheim. Ollertz-Mertens is an industrial PhD candidate, with John Deere serving as both employer and funding source.
He will defend his doctoral thesis at Norwegian University of Life Sciences (NMBU) on 13 August 2026. The title of the dissertation is: “Opportunities and limitations to improve sustainable dairy cow productivity at the farm level.”
His principal supervisor has been Professor Harald Volden (NMBU). Co-supervisors are Professor Egil Prestløkken (NMBU), Professor Matthew F. Digman (University of Wisconsin-Madison), and Professor Kenneth F. Kalscheur (United States Department of Agriculture).