UNDERSTANDING FORAGE AND FIBER DIGESTION IS A DAUNTING TASK

by: Stephen B. Blezinger
Ph.D., PAS

Part 4

Over the last few weeks we have been taking an in-depth look at forage quality and fiber digestion in beef cattle. It becomes obvious that this is not a simple subject and that developing a comprehensive understanding of forage and fiber digestion and utilization in cattle (ruminants) is a daunting task.

One of the greatest hurdles to getting a firm grasp is dealing with the variability that is inherent to forages and how to account for and deal with this variation. In the current economy, perhaps an even great issue is determining how to place a value on a given sample of hay. This is certainly relevant as the producer attempts to compare different forages or forage types in an effort to make buying and feeding decisions.

So as we finish this series let's discuss a couple of systems that can be used to evaluate and compare forage quality and value.

A Quick Review

Recall that the critical nutrients we need to extract from forages for cattle nutrient provision include protein, energy, minerals and vitamins. Since our primary focus here will be on harvested/stored forages, much of the vitamin activity is lost over time so the focus becomes protein, energy and minerals. A major factor in determining the value of forages is the fiber content as this directly affects how digestible the forage sample is and subsequently how available the other nutrients are. With increased digestibility becomes increased access to protein and mineral content that is found in combination with the fiber components. Also with this increased digestibility comes the fact that this means there is a higher content of more digestible fiber components that are broken down to provide energy to the animal.

Also recall that lab analyses of forage samples typically do not provide a direct indication of digestibility. Using the lab assays, however, one can predict, reasonably well, the digestibility and subsequent feeding value using neutral detergent fiber (NDF) and acid detergent fiber (ADF) values. The NDF values represent the total fiber fraction (cellulose, hemicellulose, and lignin) that make up cell walls within the forage tissue. NDF values can vary significantly by plant species. Values of NDF for grasses will be higher (60-65) than for legumes (45-45). A high NDF content indicates high overall fiber in forage. Generally, the lower the NDF value the better. The ADF values represent cellulose, lignin and silica (if present). The ADF fraction of forages is moderately indigestible. Forages range from three percent in corn grain to 50 percent ADF in warm-season grass straw. Animals and laboratory testing have shown that high ADF values are associated with decreased digestibility; therefore, again a low ADF is desired.

Neutral detergent fiber has traditionally been used as a predictor of forage intake while ADF has been used as a predictor of forage digestibility. While these relationships often hold true for mixed diets, they can be misleading when forage is fed alone.

Relative Feed Value

The Relative Feed Value (RFV) is an index used to represent forage quality. This system, among others, has been used by forage testing laboratories for many years. The RFV index uses NDF and ADF as predictors of forage quality. The NDF content is correlated with intake and ADF with digestibility of the forage in the context of temperate forages, particularly, alfalfa. More specifically, the index ranks forages based on a calculation based on intake potential (predicted from NDF) and digestible DM (predicted from ADF) of alfalfa at full bloom. Relative Feed Values are commonly used as a sales and marketing tool (valuation) in the alfalfa market. While alfalfa is the standard forage with which RFV is used, it can be applied to other forages (hays, silages) as well although the values provided are not quite as closely correlated. Also, since the RFV system was initially developed for lactating dairy cows and their related energy requirements the system is not completely applicable to beef cows. It does, however provide an indicator of quality that can be used for comparative purposes when evaluating forages for beef cattle.

So as a frame of reference, the calculated value of RFV = 100 is an indicator of a forage quality that can be equated to alfalfa at full bloom. This said the index provides a number that can be associated to different quality alfalfa hays. If, for example, the alfalfa hay was cut and baled is at pre-bloom (less mature), the forage would have higher nutritive value and the subsequent RFV for the alfalfa hay would be higher (RFV= 164) as compared to the same hay cut and baled at full bloom. This index has been used by hay buyers and sellers as a mean of estimating hay quality. Based on this, the higher the quality the higher the RFV and consequently the higher pricing that may be obtained for a particular load of hay.

Relative Forage Quality

The Relative Forage Quality (RFQ) index is a system that was developed to have the same mean and range as RFV. RFQ calculations are different from RFV calculations. The RFQ is based on the values of CP, ADF, fat, ash, and NDF thus taking into consideration more nutrients. The advantage of RFQ over RFV is that RFQ considers the digestible fiber, which becomes relevant when testing warm season grasses that are high in fiber that is highly digestible. The values of RFQ can be applied to all forages (cool-season and warm-season or tropical), except for corn silage, making RFQ a much more versatile forage quality index.

RFQ uses the same concept and format for Relative Forage Value (RFV) except that for RFQ, TDN will be used rather than Digestible Dry Matter (DDM). Thus RFQ is calculated as follows:

RFQ = (DMI, % of BW) * (TDN, % of DM)/1.23

DMI = Dry Matter Intake as a percentage of body weight.

The divisor, 1.23, is used to adjust the equation to have a mean and range similar to RFV. The following two equations are recommended depending on whether or not the primary forage is a legume or a grass:

1)      For alfalfa, clovers, and legume/grass mixtures the equations will be:

RFQ = (DMIlegume, % of BW) * (TDNlegume, % of DM) / 1.23

2)      For warm and cool season grasses the equations will be:

RFQ= (DMIgrass, % of BW) * (TDNgrass, % of DM)/1.23

Relative Forage Quality should provide a better estimate than RFV of how the forages will perform when fed. This index can permit producers to categorize, group and use hay lots by quality. For example, hay with an RFQ of 115-130 can be fed to maintain beef cow-calf pairs, hay with an RFQ of 125-150 is adequate for stocker cattle or young growing replacement heifers.

Some Closing Thoughts:

1) The best single measure of forage quality is animal productivity.

2) Forage quality analyses are no better than the procedures used to obtain the samples.

3) When forages are fed to animals in limited quantities, such as supplements for cow-calf herds, the importance of RFV changes. Digestibility (estimated by ADF) and CP are the main quality factors to consider in these situations thus making the use of RFQ more appropriate.

Conclusions

Understanding forage quality and the factors that affect its constituents will help improve livestock production by making decisions that optimize forage nutritive value and intake. The decision to use hay or not (grazing vs. haying) or how to select the best hay available should be based on forage quality. Forage analyses are important because they reflect the quality of the forage and they are a relatively inexpensive tool to evaluate the nutritive value of the forage to be grazed or hay to be purchased or marketed. Knowing what affects forage quality will also help making appropriate selection of forages and supplements that will match animal requirements and result in economically optimum livestock performance.

Dr. Steve Blezinger is a management and nutritional consultant with an office in Sulphur Springs, TX. He can be reached at sblez@verizon.net or at (903) 352-3475. For more information please visit us on at www.facebook/reveillelivestockconcepts.







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