Grazing versus mowing: productivity and forage quality of mixtures and cultivars of perennial ryegrass

In the rolling hills of Western Europe, where the land is often too steep or rocky for growing crops, a different kind of agriculture thrives. Here, vast stretches of green grasslands serve as the foundation for dairy and beef production, turning plant matter that humans cannot eat into milk and meat. This system relies heavily on a single grass species, perennial ryegrass, which is prized for its ability to regrow quickly after being eaten or cut. For farmers and breeders, the goal is simple: find the grass that grows the most and offers the best nutrition for livestock. To do this, scientists test new varieties of grass in controlled fields, usually cutting them with machines to simulate how animals eat. However, real life on a farm is messier. Cows do not eat like machines; they walk on the grass, trample it, choose their favorite bites, and leave behind manure that feeds the soil unevenly. This raises a critical question for anyone managing these pastures: does the grass that looks best when cut by a machine also perform best when grazed by a herd of cows?

A team of researchers in Switzerland set out to answer this by putting six different varieties of perennial ryegrass to the test under three very different conditions. They planted these grasses in fields across two locations and managed them over three years. One group of plots was cut regularly with a machine at a height of eight centimeters, mimicking a standard harvest. A second group was cut much more frequently and lower, down to four centimeters, to simulate the pressure of grazing without using animals. The third group was the most realistic: actual dairy cows were allowed to graze the plots, walking over the grass and eating selectively. The researchers also tested mixtures, planting two different grass varieties together in the same plot to see if they worked better as a team than they did alone. They measured how much dry grass was produced and analyzed the nutritional quality of the leaves, looking for differences between two main types of grass: diploid varieties, which have smaller cells and form dense, tight mats, and tetraploid varieties, which have larger cells and are often considered more nutritious.

The results revealed that the way the grass was managed mattered far more than the specific type of grass planted. The year and the defoliation method were the dominant forces shaping the outcome. The plots cut regularly with a machine produced the highest amount of grass, followed closely by the plots that were mechanically cut low to simulate grazing. The plots actually grazed by cows produced the least amount of grass. This difference suggests that the physical stress of cows walking on the ground and the uneven way they eat and return nutrients creates a harsher environment than a machine can replicate. Because the ranking of the grass varieties changed depending on whether they were cut or grazed, the researchers found that using a machine to simulate grazing is not a reliable way to predict which grass will produce the most food for a herd. A variety that looks like a champion in a mechanical test might not be the best choice for a pasture.

However, the story was different when it came to the quality of the food. When looking at the nutritional value, such as the energy content and protein levels, the mechanical simulation of grazing worked very well. The grass grown under simulated grazing matched the nutritional profile of the grass eaten by cows, and the ranking of the varieties was consistent between the two. This means that farmers and breeders can still use machine-cutting tests to judge the quality of the forage, even if they cannot use those same tests to judge the total amount of grass a herd will produce. The type of grass also played a role, but it depended on the method. Under regular cutting and simulated grazing, the tetraploid varieties produced slightly more grass and had higher energy content. But under actual grazing, the diploid varieties tended to produce slightly more grass, likely because their dense growth habit helps them survive the trampling and selective eating of cows better than the looser tetraploid types.

The study also looked at what happens when two different grass varieties are mixed together. In most cases, the mixture performed somewhere in between the two individual varieties, neither beating them nor falling far behind. There was one notable exception: when a diploid and a tetraploid variety were mixed and then grazed by cows, the mixture produced slightly more grass than either variety grown alone. This suggests that the two types of grass might complement each other, with the dense diploid grass holding the ground against the cows' hooves while the nutritious tetraploid grass adds bulk. Over the three years, the researchers observed that the mixtures slowly changed. The grass that was originally planted as half of the mixture did not always stay at half; one variety often began to dominate the other, shifting the balance of the sward over time. This shift indicates that the environment, whether it is a machine or a herd of cows, acts as a filter, favoring the variety best suited to that specific pressure.

Ultimately, the research highlights that there is no single "best" grass for every situation. The choice of variety depends heavily on how the land will be used. If the grass is destined for a machine harvest, tetraploid varieties might offer a slight edge in yield and quality. If the land is for grazing, diploid varieties may be more resilient and productive. While mixing varieties can offer a small boost in yield under grazing, the most significant factor remains the management style itself. The study concludes that to truly know how a grass variety will perform on a farm, it must be tested under the conditions it will face. Relying solely on mechanical tests to predict grazing performance can lead to misleading conclusions about yield, even if those tests remain useful for judging nutritional quality. For the future of sustainable grassland management, this means breeders and farmers need to look beyond the machine and consider the complex reality of the grazing animal.