Keeping score with RFV

Woody Lane, Ph.D.
Certified Forage and Grassland
Professional 
American Forage and

Grassland Council

RFV has some inherent problems, and the concern is not just academic. Any shortcomings in RFV would translate into bias or inaccuracy, which could mislead folks about the economic values of forages and distort their judgments about the market prices.

We all like scores: golf scores, bowling scores, limestone scores – single numbers that tell us volumes
about complex situations. We use scores all the time. A bowling score of 300 is not just good; it’s a
perfect game. A golf score of 300 is, well, not. Sometimes scores go by different names. An earthquake score is the Richter Scale, where 3.2 is hardly noticeable but 7.2 will topple buildings. Hurricane scores are called categories, where a Category 1 storm is relatively small but a Category 5 storm is a monster.

So wouldn’t it be useful to have a score for forages? A single number that unambiguously represents the nutritional value of a forage. We could use it to rank different forages from low to high and even use it to evaluate market prices when we buy and sell hay.

Well, we do have such a score:  It’s called relative feed value (RFV), which was designed to meet those exact needs. RFV has been around for years, and many analytical labs routinely list RFV in their forage reports. But RFV has some limitations that are actually quite severe. It’s now being replaced by a new and improved score called relative forage quality (RFQ). Two scores – RFV and RFQ. Let’s review them both.

First, RFV – first introduced in the late 1970s. The basic concept was very simple: Calculate an
RFV score for a forage and then compare that score to a standard reference forage assigned the RFV
value of 100. The reference forage was defi ned as a full-bloom alfalfa hay containing 53 percent neutral detergent fiber and 41 percent acid detergent fi ber. (These fiber values are listed in all modern forage quality reports.)

Armed with RFV scores, you could go to a hay sale and easily rank the various forages and appraise
their market prices. For example, if Forage X had an RFV of 110, you would know it had a slightly higher nutritional value than the reference standard alfalfa hay. If full-bloom alfalfa hay and Forage X were both selling for $130 per ton, then Forage X would be a good buy. But if Forage X were selling at $150 per ton, then it was probably overpriced. (Of course, you would need to convince the hay seller of your logic, but that is a different story.)

How is RFV derived? By an equation. In this article, however, I won’t bore you with the decimal-
laden details of this equation – or any other equation. Instead, I’ll concentrate on the main concepts
and give you enough details so you can follow the equation logic. But if you still hunger for mathematical coefficients, you can enjoy many hours of them by looking them up on Google or Wikipedia.

Back to RFV. The RFV equation includes just two terms: one for digestibility and one for
intake. The concept is: Animal production from a forage depends on both the nutritional quality of
the forage and also how much of that forage the animal consumes. In the RFV equation, dry matter
intake is calculated from the percent neutral detergent fiber of the forage. Digestibility (defi ned here as digestible dry matter) is calculated from the percent acid detergent fiber of the forage. Researchers
decided to use these two assays because neutral detergent fiber is best correlated with intake, while
acid detergent fiber is best correlated with digestibility.

In the world of alfalfa, these relationships are fairly strong, and RFV can be a reasonable score for
ranking and pricing alfalfa hay. Especially when you are comparing alfalfa hay to alfalfa hay.

RFV problems
But RFV has some inherent problems, and the concern is not just academic. Any shortcomings in RFV would translate into bias or inaccuracy, which could mislead folks about the economic values of
forages and distort their judgments about the market prices. Essentially, an RFV problem is a bottom-line pricing problem. The most glaring drawback with RFV is: The equation does not contain a term for protein. Which means if you have two forages (A and B) with the same levels of fi ber, but forage A contains 15 percent protein and forage B contains 12 percent protein, they would both have the same RFV. But clearly these forages are not equal nutritionally, and their economic values are probably different. By not including a protein term in the equation, the RFV score simply isn’t sensitive to different levels of protein. Actually, the developers of RFV were not overly concerned
about this problem because in alfalfa, protein levels and fiber levels are highly correlated. Well,
unfortunately, this correlation is not perfect, and the relationship between these two variables may be
different in different forage species.

Also, RFV was originally designed for alfalfa hay, which is the main, high-value forage crop bought and sold across state lines. (You don’t see much market demand for turnip silage, do you?) Working within one well-defined legume species, scientists thought the protein value really wouldn’t make that much difference, and besides, crude protein is really a complex concept. The crude protein
number is actually composed of many fractions. These fractions have different solubilities, different
bypass characteristics, different amounts of fiber-bound nitrogen (which will show up on good forage
tests as indigestible protein), etc.

Trying to parse a crude protein value into its important component parts, given the technology of that
time, would probably introduce more artifacts and errors than the effort was worth. Scientists knew omitting crude protein from the RFV equation would raise some eyebrows, but they judged that omitting it would cause fewer problems than including it, considering their goals and technology. This was not a unanimous decision, I’m sure – but there it is.

But RFV has another major problem. The RFV score is poor for comparing forages of different
species, particularly comparing legumes with grasses. RFV tends to underestimate the true value of
grasses. The reason is rather subtle: Grasses contain more fiber than legumes, but the fiber in grasses is generally more digestible than in legumes. The RFV equation does not account for fiber digestibility. When fiber digestibility is ignored, grasses don’t get the nutritional credit they deserve, which tends to skew the RFV score in favor of legumes. Which means that, as an example, an RFV for a grass hay would be calculated at 106 when its true value could be, say, 118. This drawback also affects forage improvement. Many commercial seed companies are currently trying to breed forage varieties with higher fiber digestibilities, particularly in various grass species. The RFV formula cannot distinguish between these varieties because fiber digestibility is not in the equation. So even within the same forage species, we cannot use RFV as an accurate tool for identifying genetic lines with improved fiber fermentation characteristics. In this situation, using the RFV score is like using a chainsaw when we really need a scalpel. So what to do? Well, we can develop something new. And there is a new kid on the block: relative forage quality. But we’re out of space. In a future article we’ll
discuss relative forage quality. We’ll have quality time then.