A simple management adage in agriculture states, “Producers like what they know and know what they like.” Researchers and nutritionists can work to establish new measures for quality, digestibility or productivity, but it doesn’t mean producers will embrace the changes as expected.

Ralph Pearce

Freelance Writer

Ralph Pearce has worked as a freelance journalist and editor in agriculture for more than 30 year...

That may be true for relative feed value (RFV), first developed in 1972 as a measure of nutrients, primarily for those buying or selling hay. It was the industry standard until 2002 when relative feed quality (RFQ) was introduced with the goal of creating a newer metric that included digestibility. Twenty-four years later, considerable confusion remains about which is a more accurate assessment.

The equations – three for two

The answer, according to various sources, is “It depends.” Despite research papers that contend that the numbers for both indexes are often close to the same, it’s harder to compare the two because of their respective equations used in calculating specific numbers. For RFV, there are two variables: dry matter intake (DMI) – predicted by neutral detergent fiber (NDF) – and digestible dry matter (DM) – predicted by acid detergent fiber (ADF).

The RFQ uses the same concept except digestible DM doesn’t use ADF but uses total digestible nutrients (TDN), and TDN uses up to six measurements and an equation for legumes and one for grass blends. Both incorporate crude protein (CP), nonfiber carbohydrates (NFC), fatty acids (FA), nitrogen-free NDF (NDFn) and 48-hour in vitro NDF digestibility (NDFD). DMI in RFQ also varies based on whether it’s a legume or grass.

The equation for RFQ was first developed by John E. Moore, an animal nutritionist at the University of Florida. He teamed with Dan Undersander from the University of Wisconsin – Madison, who continued promoting RFQ after Moore retired.

“The RFV quality index was useful for alfalfa but did not work as well for forages with varied fiber digestibility, like warm-season grasses,” says Yoana Newman, once a student of Moore’s and now professor of crop science at the University of Wisconsin – River Falls. “RFQ and RFV are quality indexes – a number – for marketing or to have an indication of quality at harvest.”

She adds that rations cannot be balanced using a single number.

Newman acknowledges that RFV was the standard for many years but that with forages other than alfalfa, results didn’t account for the fact that fiber is not the same in terms of digestibility. Moore developed RFQ at Florida because there were more tropical forages with varying levels of digestible fiber. With Undersander’s work at Wisconsin – within a “dairy state” – Newman contends that RFQ has been well adopted among dairy producers.

“If they’re looking for milk production, they would be using RFQ,” she says. “For a long time, RFV was a step up from using crude protein as a sole predictor. Then [RFV] used ADF as a predictor of the nutritive value but fell short with dairy producers because it ignored fiber digestibility, thus not predicting energy well. Then RFQ came into play.”

Faster decision-making

Understanding how RFQ differs – and why – is fundamental, says Tyler Kappers, laboratory manager at Dairyland Laboratories in St. Cloud, Minnesota, especially in the less forgiving environment in which agriculture is currently operating. A lot of producers want to make a quick decision based on their knowledge level. The RFQ and RFV numbers are quick reference tools to help “pull the trigger,” whether in balancing rations, harvest timing or assessing forages when buying or selling.

“There is still a personal preference out there,” says Kappers. “I would say RFQ is more common in the dairy industry because it includes fiber digestibility, which is a huge focus in dairy nutrition.”

Any commercial laboratory, he notes, will perform RFV or RFQ calculations, depending on the product or test package purchased. As long as the necessary variables are available, either calculation is possible. It’s not a matter of which index is more accurate but about the comprehensive measure of forage quality.

“[With RFQ], it’s coming back to that fiber digestibility,” says Kappers. “If you take an alfalfa sample, they can have the same RFV value, but depending on that digestibility, the RFQ can be 20 to 30 points higher on one versus the other. RFQ took a step in the right direction of accounting for that variability.”

That’s why Kappers believes the adoption rate for RFQ has been higher among dairy producers; it’s the nature of the sector combined with quality differences producers can see almost instantaneously.

Consider all variables

From farm equipment to an index based on an equation calculating for feed quality and digestibility, knowing how something works is especially important. From his position as director of technical services at Cumberland Valley Analytical Services in Waynesboro, Pennsylvania, Matt Michonski emphasizes the need for producers to understand how the equations function.

“The math for the RFQ equation is fairly complicated,” he says, adding that his experience indicates a fairly even split between those who use RFV and RFQ. “You need to input different nutrients to see what impacts the RFQ the most. Some buyers and sellers like RFV because it’s a simple number. Others like RFQ, which is more dynamic.”

Complicating matters from his vantage point is how some of the numbers can be blurred. The NDF test with a forage, notes Michonski, is a filtration method, so in theory, whatever’s left in the filter is fiber. But it’s fiber and anything unfiltered, which in many cases is minerals (soil). Many hay samples have some level of soil contamination that affects RFV’s index. When ADF and NDF are reported on an organic basis, the RFV would be higher.

“The RFQ accounts for ash, so that equation helps negate some of that,” says Michonski. “It also takes into account NDF digestibility, and that’s an important component. The challenge is there are two RFQ equations [grass and legume-grass mix]. In our lab, if it’s a 15-point difference between ADF and NDF, we use the grass equation. These two equations give very different RFQ results.”

Understanding how these equations work helps to interpret the information.

References omitted but are available upon request by sending an email to the editor.