Associative effects may be tough to predict

Associative effects may be tough to predict

It is frustrating when feed analyses and model predictions suggest that a diet should support high production, only to find out the cows do not agree.

*Bill Mahanna, Ph.D., Dipl ACAN, is a collaborative faculty member at Iowa State University and a board-certified nutritionist for DuPont Pioneer based in Johnston, Iowa. To expedite answers to questions concerning this article or to submit ideas for future articles, please direct inquiries to Feedstuffs, Bottom Line of Nutrition, 7900 International Dr., Suite 650, Bloomington, Minn. 55425, or email [email protected]

IT is frustrating when feed analyses and model predictions suggest that a diet should support high production, only to find out that the cows do not agree.

One plus one may not always equal two — a reference to how associative effects (interactions) can occur among feedstuff nutrients in a total mixed ration that cannot be well predicted from the analysis of the individual ingredients.

 

Positive or negative

Associative effects can be either positive or negative in both an economic and biological sense and are extremely difficult to predict using model algorithms (J. Johnston, personal communication).

Both positive and negative associative effects are exemplified by the increase in cellulose digestion when small quantities of sugar or starch are added to an all-forage diet, whereas high levels of sugar or starch can cause a decrease in cellulose digestion (Hoover and Miller, 1991).

Other examples of positive dietary associative effects include: (1) increased fiber utilization from nitrogen supplementation in nitrogen-deficient forages, (2) increased intake when more than one forage is fed (physically effective effect) and (3) increased microbial protein production due to synchronized/balanced diets (Adesogan, 2014).

Examples of negative dietary associative effects include: (1) concentrate supplementation of forage diets depressing fiber utilization, (2) fat supplementation of diets depressing fiber digestion, (3) intake reduced due to chemostatic feedback from high-energy diets, (4) certain polyunsaturated fatty acids being toxic to ruminal microbes and (5) the presence of anti-nutritive factors hindering nutrient utilization (Adesogan, 2014).

Starch and neutral detergent fiber (NDF) are major contributors to dietary net energy of lactation (NEL). On average, starch is about 92% digestible and NDF is about 50% digestible.

If there were no interactions between starch and NDF, replacing five percentage units of NDF with five percentage units of starch in a typical lactating cow diet would increase the NEL concentration by about 6.5%. However, because of negative associative effects, the increase is typically about 5% (substantial variation in both of these values will occur).

Within a reasonable range in dietary starch, increasing the starch concentration by replacing NDF should increase the NEL concentration, but the increase is less than expected because of negative effects on NDF digestibility (Weiss, 2013).

 

Causative factors

Associative effects result from changes in rumen pH and microbial population dynamics when nutrients from various ingredients interact. It is well established that ruminal pH can be a significant factor in determining competition among bacteria (Russel et al., 1979).

Interestingly, microbial associative effects are not just a condition affecting the rumen. Associative effects have also been observed in the development of microbial silage inoculants where the combination of two strains individually shown to improve fiber digestion resulted in lowered silage fiber digestion when the strains were combined. This makes the development of silage inoculants very costly as a large number of strain combinations must be tested due to the lack of individual strain performance additivity (W. Rutherford, personal communication).

For more than 60 years, it has been known that increasing dietary non-structural carbohydrates such as starch often reduces fiber digestion by dairy cows. A recent meta-analysis concluded that, on average, for every one percentage unit increase in dietary starch, NDF digestibility decreases about 0.5 percentage units; however, the actual response in a specific situation may differ greatly from the average response.

It is also known that a changing dietary starch concentration often affects intake, which can also affect NDF digestibility (Weiss, 2013).

Factors influencing associative effects include: (1) the nutrient content of dietary ingredients, (2) ingredient palatability, (3) energy:protein ratios, e.g., non-structural carbohydrates versus rumen degradable protein, (4) physical form of the feed, (5) rumen pH and microbial populations, (6) level of feeding and (7) rumen solid and liquid turnover rates (Adesogan, 2014; F. Owens, personal communication).

Researchers (Grant and Mertens, 1992) have also identified lag time of nutrient digestion as an important parameter in explaining milk production responses from different feedstuffs.

Finally, diets that induce microbial energy spilling can also be linked to associative effects. When there is excess dietary energy, or when other key nutrients for cellular growth such as amino acids are lacking, some rumen bacteria deposit glycogen as an energy storage mechanism. Although stored energy can be later mobilized for growth, this storage is still wasteful because ATP is irreversibly spent to synthesize glycogen. This waste represents 20-50% of the available ATP in glucose (Hackmann, 2014; Feedstuffs, Dec. 10, 2012).

Even four decades ago (Tyrrel and Moe, 1975), it was proved that the digestibility of dairy cow diets is reduced with increasing intake; however, the discounts for the feeding level effects on digestibility are variable in commercial feed evaluation systems (Huhtanen et al., 2008).

The National Research Council's (NRC) 2001 "Nutrient Requirements of Dairy Cattle" made improvements in the handling of associative effects of feeds when estimating the digestibility of a diet compared to previous systems using fixed discounts for each feed. Discounts in the 2001 NRC cannot be calculated without knowing the total amount and the blend of feeds consumed. Similar to the 1989 NRC, a single digestibility discount is applied to each feed within a diet, but the discount is not fixed at 8%. Rather, the digestibility discount in the 2001 NRC is dependent upon feed intake and initial digestibility.

The discount is a total diet calculation, so it applies equally to all feeds within a diet but changes depending on the animal eating it. As intake increases, the energy value of feeds decreases. In addition, as the initial digestibility of the diet increases, the discount per multiple of maintenance increases, so the depression in digestibility is greatest for diets with the highest value of fat-free total digestible nutrients.

In other words, adding corn (starch) to a diet will increase the discount so that digestibility is depressed more for a high-corn grain diet than a high-forage diet. Adding fat will actually reduce the discount because it replaces feeds that contain fat-free total digestible nutrients (VandeHaar, 2002).

When the feeds are given to dairy cows at high intake, different extrinsic factors influencing both digestion and passage rates have a strong influence on digestibility. As a result of the increased passage rate, digestibility is depressed with increased intake, with the responses being linear or slightly increasing with intake.

When non-fiber carbohydrates replace fiber in the diet, microbial populations can shift away from those responsible for optimal fiber digestion. Similarly, increased concentrate fat intake is inhibitory to cellulolytic bacteria, whereas supplementary protein stimulates fiber digestion. The reasons for improved fiber digestion can be variable and dependent upon dietary circumstances.

Recent validation of the 2001 NRC and CNCPS models suggests that the depression in digestibility as intake increases may be overestimated. This may partly be because the majority of diets in the modeling data sets did not contain corn silage or corn grain, of which starch digestibility decreases as intake increases (Huhtanen et al., 2008).

 

Practical examples

Research with beef steers (Joanning, 1981) investigated the assumption that the nutritional value of corn silage and corn grain, when mixed, was simply the weighted average of the nutritional value of the feeds fed individually. What the research showed was that dry matter digestion coefficients for the silage/grain mixes averaged 112% less (P < 0.01) than those calculated from the weighted means. Depression in starch, fiber and protein digestibility accounted for 59%, 26% and 14%, respectively, of the depression in dry matter digestibility for the immature silage/grain mix and 53%, 36% and 10% for the mixture containing mature corn silage. Incomplete starch digestion was suggested as the major reason for the decreased efficiency observed when corn silage and grain were fed in combination.

There is fairly good agreement on the levels of non-structural carbohydrates needed in the diet to optimize both milk production and microbial protein when conventional sources of grains and forages are fed. However, when alternative feed sources enter the ration, the composition and rates of fermentation of both the non-structural carbohydrates and NDF fractions become less well-defined (Hoover and Miller, 1991).

Two types of associative effects have been reported (Hoover and Miller, 1991) when byproduct feeds are used as dietary sources of rapidly degradable NDF. The first effect is related to how various byproducts contribute to rumen pH reduction and to the extent at which reduced ruminal pH depresses both NDF and non-structural carbohydrate digestion. The second associative effect is related to reduced rumen turnover and microbial protein yield when byproduct sources of rapidly degraded NDF are combined with high levels of non-structural carbohydrates (Hoover and Miller, 1991).

Research with soy hulls (Grant, 1997) led to the theory that fine particle size, high specific gravity and increased ruminal rate of passage may be responsible for lower ruminal NDF digestibility of non-forage fiber sources fed at high levels. As the amount of soybean hulls increased from 50% to 95% of a pelleted mix for dairy cows, the passage rate increased by 8%. In five studies, digestion of soybean hull diets was improved by the addition of coarse forage.

Fiber digestibility was theorized to improve due to the coarse hay increasing the ruminal retention time of non-forage fiber sources and allowing for more complete digestion. Adding coarsely chopped alfalfa hay to diets based on silage containing 25% soybean hulls increased ruminal mat consistency by 49% and tended to slow the ruminal escape rate of soybean hulls by 16%. The amount and particle size of forage in the diet interacts with the substituted non-forage fiber source to determine the net impact on the rate of ruminal digestion and passage of non-forage fiber.

Another practical example of inducing positive associative effects is the relatively common practice of adding wheat or barley straw to lactation diets. Whereas high straw feeding rates will decrease intake, total tract digestibility and animal performance, feeding straw at low inclusion rates is often beneficial by causing positive associative effects in the development of a rumen mat matrix, especially in low-forage diets (Weiss, 2013; Eastridge, 2004).

Brown mid-rib (BMR) corn silage also appears to elicit interesting dietary associative effects. Research comparing conventional corn silage versus BMR-based diets that also contained very high-quality alfalfa hay failed to show the anticipated effect of higher intakes with the BMR treatment (Holt et al., 2010).

BMR research at Michigan State University (Oba and Allen, 2000) also showed that a BMR corn silage diet shifted the site of starch digestion by decreasing starch digestibility in the rumen (and total tract) but increased postruminal starch digestibility compared with the control diet.

 

Possible solution

If the ruminal microbial fermentation of individual and combined feedstuffs diverges, the velocity of differing fermentations may lead to an incorrect interpretation of the risk for subacute rumen acidosis. This is particularly true for high-producing dairy cows in early lactation because of their high intake levels of concentrate to meet energy demands.

A recent European study (Metzler-Zebeli et al., 2012) investigated the associative effects between observed and calculated gas production of grass silage and cereal concentrate-based diets at early incubation hours. Their findings supported the notion that evaluating rations on the basis of arithmetically adding the values for the separate total mixed ration components likely underestimates the fermentation intensity at early hours of incubation (two to eight hours), which, in turn, introduces error in predicting the fermentation products and their effect on rumen physiology.

Similar research by Robinson et al. (2008) demonstrated that the associative effects can reach 15-25% at early hours of incubation but can dissipate by 24 hours of incubation.

Several lactation studies support the link between rumen microbial protein yield and milk production, showing that optimum rumen fermentation, as indicated by high microbial protein yield, is a major factor affecting lactation performance (Hoover and Miller, 1991).

A recent study using gas production techniques for estimating ruminal microbial efficiencies clearly showed a strong relationship between increasing maturity of ensiled ryegrass and a measurable and significant decline in microbial biomass yields (Grings et al., 2005).

The commercially available gas production method, Fermentrics, allows for the direct measurement of microbial biomass production (MBP), which can prove extremely helpful in quantifying and managing the associative effects of various dietary ingredients.

In the Fermentrics methods, MBP is measured directly by analyzing the substrate that remains after a 48-hour incubation with an NDF analysis (without amylase or sodium sulfite). The difference between the weight of the substrate before and after NDF analysis is the microbial biomass.

Fermentrics can be used to anticipate dietary associative effects and allows producers to identify which combination of ingredients (or dietary supplements) produces the highest microbial biomass yield. This approach can be used to determine the inclusion rate of the various on-farm ingredients by mixing multiple diets and analyzing them via Fermentrics before settling on the final ration.

Researchers at Quality Liquid Feed (P. Dyk, personal communication) were generous in sharing unpublished data from a field trial intended to show how different diets react to the addition of molasses-based QLF Liquid Feed. The diets were designed to be as iso-caloric and iso-nitrogenous as practically possible (Table 1). Table 2 shows that the combination of ingredients in diet 4 — reduced levels of high-moisture shelled corn, along with whey and QLF Liquid Feed — produced the highest level of MBP, which theoretically should result in higher milk production.

 

The Bottom Line

The nutritional value of feedstuffs is typically evaluated individually despite the fact that modern dairy cattle diets consist of a mixture of ingredients blended in a total mixed ration. There is strong research evidence that the nutritional value of feedstuffs differs when they are combined together (associative effects) and that these interactions cannot be well predicted from the analysis of the individual ingredients.

Several studies support the link between rumen microbial protein yield and milk production. New analytical gas methods, like Fermentrics, that allow producers to proactively measure dietary microbial biomass yield help quantify and manage associative effects resulting from combining various ingredients in rations before the diet is ever offered to the lactating herd.

 

References

Adesogan, G. 2014. Associative effects of feeds. Accessed at: http://animal.ifas.ufl.edu/ans6452/documents/powerpoints/sinteractbeforfeeds.pdf.

Eastridge, M.L. 2004. Straw in rations for dairy cows. Proceedings of the Tri-State Nutrition Conference.

Grant, R.J. 1997. Interactions among forages and non-forage fiber sources. J. Dairy Sci. 80:1438-1446.

Grant, R.J., and D.R. Mertens. 1992. Influence of buffer pH and raw corn starch addition on in vitro fiber digestion. J. Dairy Sci. 75:1581-1587.

Grings, E.E., M. Blummel and K.-H. Sudekum. 2005. Methodological considerations in using gas production techniques for estimating ruminal microbial efficiencies for silage-based diets. Animal Feed Science & Technology 123-124:527-545.

Hackmann, T.J. 2014. Strategies to improve rumen microbial efficiency. Proceedings of the 2014 Florida Ruminant Nutrition Symposium.

Holt, M.S., C.M. Williams, C.M. Dschaak, J.-S. Eun and A.J. Young. 2010. Effects of corn silage hybrids and dietary non-forage fiber sources on feed intake, digestibility, ruminal fermentation and productive performance of lactating Holstein dairy cows. J. Dairy Sci. 93:5397-5407.

Hoover, W.H., and T.K. Miller 1991. Associative effects of alternative feeds. Symposium on Alternative Feeds for Dairy & Beef Cattle.

Huhtanen, P., M. Rinne and J. Nousiainen. 2008. Factors affecting diet digestibility in dairy cattle. Proceedings of the 2008 Cornell Nutrition Conference for Feed Manufacturers.

Joanning, S.W., D.E. Johnson and B.P. Barry. 1981. Nutrient digestibility depressions in corn silage-corn grain mixtures fed to steers. J. Anim. Sci. 53(4):1095-1103.

Metzler-Zebeli, B., C. Scherr, E. Sallaku, W. Drochnerd and Q. Zebeli. 2012. Evaluation of associative effects of total mixed ration for dairy cattle using in vitro gas production and different rumen inocula. J. Sci. Food Agric.

Oba, M., and M.S. Allen. 2000. Effects of brown midrib 3 mutation in corn silage on productivity of dairy cows fed two concentrations of dietary neutral detergent fiber: 3. Digestibility and microbial efficiency. J. Dairy Sci. 83:1350-1358.

Robinson, P.H., F. Getachew and J.W. Cone. 2008. Evaluation of the extent of associative effects of two groups of four feeds using an in vitro gas production procedure. Animal Feed Science & Technology.

Russell, J.B., W.M. Sharp and R.L. Baldwin. 1979. The effect of pH on maximum bacterial growth rate and its possible role as a determinant of bacterial competition in the rumen. 1. Anim. Sci. 48:251.

Tyrrel, H.F., and P.W. Moe. 1975. Effect of intake on digestive efficiency. J. Dairy Sci. 58:1151-1163.

VandeHaar, M.J. 2002. Energy and protein in the 2001 Dairy NRC: Challenges for a ration formulation program. 2002 Tri-State Dairy Nutrition Conference.

Weiss, W.P. 2013. Dietary starch inter-relationships with other nutrients: Interactions between starch and fiber. 28th Discover Conference Summaries.

 

1. Lactation diets with varying ingredient inclusion levels (%)

 

Diet 1

Diet 2

Diet 3

Diet 4

Dry matter

40.7

41.4

45.0

45.0

Crude protein

16.2

17.7

17.5

17.2

Soluble protein, % of CP

47.03

50.11

53.20

46.42

Lignin

3.05

3.05

3.16

3.52

Acid detergent fiber

21.17

20.80

21.14

21.40

NDF (analyzed)

30.7

29.6

29.7

30.1

Ether extract

4.04

4.17

4.26

4.42

Starch

21.70

19.86

21.06

22.74

Sugar

7.1

8.1

7.1

5.6

NEL, Mcal/lb.

0.73

0.73

0.74

0.73

Ash

8.09

8.89

8.34

8.15

 

2. Lactation diets with varying ingredient inclusion levels (%)

Ingredient

Diet 1

Diet 2

Diet 3

Diet 4

Protein mix

20.8

20.8

20.8

20.8

High-moisture shelled corn

10.07

14.63

10.50

5.75

Corn silage 1

18.4

18.4

18.4

18.4

Corn silage 2

15.3

15.3

15.3

15.3

Haylage

22.6

22.6

21.0

21.0

Hay

8.3

8.2

8.3

8.3

QLF Liquid Feed, molasses based

0

0

8.27

6.03

Whey

4.50

0

0

4.25

MBP, mg/g

132.0

117.0

102.5

142.5

 

Volume:87 Issue:06

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