Click to edit Master text styles
Second level
Third level
Fourth level
Fifth level
‹#›
1.Knowledge of feed and animal characteristics, intake, and digestion and passage rates into biological feed value
2.
2
3
The next question is how did we get to the current requirements?  The NRC provides recommendations most often used as a basis for ration formulation systems.  Having been involved with 6 NRC committees over the past 20 years, it is my observation that these committees do an excellent job of summarizing the accumulated data and making recommendations, based on published data.   The weight of the evidence indicates we have a lot of accumulated knowledge of cattle nutrient requirements.  The 1963 Beef NRC had 60 references; the 1996 beef NRC had about 1200 references and the 2001 Dairy NRC had about 1900 references. The 1966 Dairy NRC had a short paragraph about crude protein requirements and 10 references; the 2001 Dairy NRC had 42 pages and 770 references on protein requirements.
I’ll do a quick review of the major improvements that I have seen and used in the field that improved accuracy of predicting requirements.  1) The introduction of the NE system in 1970 and 1971 improved the accuracy in predicting energy requirements for maintenance, growth, and lactation and the useful energy in feeds to meet those requirements.  2) In the 80’s, improved methods to describe feed carbohydrate and protein fractions were developed. 3) In 1989 and 1996 the metabolizable protein system was introduced to improve the accuracy in meeting ruminal N requirements and undegraded diet protein to supplement microbial protein.  4)  Accumulated knowledge led to considerable refinement in requirements in 1996 and 2001 NRC: a) Description of feeds by carbohydrate and protein fractions, (b) Equations to predict feed energy values from feed analysis, (c) improved accuracy in predicting DMI, (D) rumen models that predict ruminal N, microbial protein production and escape of undegraded feed protein and amino acids, (D) systems to accurately predict growth requirements for cattle with different mature sizes, (e) amino acid requirements for growth and milk, and (f) computer models to integrate and apply the accumulated knowledge.
1.Variation in environment: Bell Ranch, New Mexico, US (October 23, 2003)
2.Variation in feed: Experimental Station in Pernambuco, PE, Brazil (December 2, 2003)
3.
4
17
4
18
May 2002
22
24
9-11th Rib section: talk about the color: more fat in the corn-fed animal than in the hay-fed animal.
24
25
25
30
34
31
35
32
3
Animals A and C have about 48% of mature weight whereas animals B and D have about 87% of mature weight, therefore, their composition of the gain is similar and require similar amount of energy to gain a 1 kg per day.
39
40
42
44
* = computed by difference
45
Fraction A and B1 are soluble in Borate Phosphate Buffer. Fraction B2 precipitates with Trichloroacetic Acid (TCA); usually 9 AA long.
47
48
The reason that each feed component must has its own Kd value is due to the fact that the rumen operates as a continuous culture system and there is a competition between degradation and passage rates from the rumen.
CLICK
If the Kd and Kp are the same, 50% of the feed is ruminally degraded and 50% passes, but the ratio of Kd to Kp can be significantly different.
CLICK
The graph at the right shows the relationship between Kd and ruminal degradation at 2 different passage rates. Please note that ruminal degradations can vary from virtually zero to nearly 100%.
53
59
The rumen is inhabited by an diverse microflora, but the CNCPS has only 2 pools of bacteria: The FC bacteria ferment fiber and the NFC bacteria utilize non-fiber carbohydrates.
These two types of bacteria also have fundamental difference in nitrogen metabolism.
The FC bacteria use ammonia as a N source, do not take up amino nitrogen and never produce ammonia.
The NFC bacteria can use ammonia, but they prefer amino nitrogen like peptide and amino acids.  If the rate of NFC carbohydrate degradation is insufficient to drive protein synthesis, incoming amino acids will be degraded  and  converted to ammonia.
The growth rate of both types of bacteria is dictated by the Kd values of each feed component. The  rumen submodel uses growth rate to predict the amount  of energy that is available for growth and the amount that is expended on maintenance.
Ruminal bacteria can also dissipate energy in futile reactions that are called energy spilling. NFC bacteria spill energy if they are deprived of amino N or ammonia N and the Kd is faster  than the growth rate.
73
Based on data provided by Spencer Swingle, Cactus Feeders.
Feeding time:
30% @ 800
30% @ 1300
40% @ 1800
10 Meals at:
5% @ 600, 15% @ 800, 10% @ 1000, 5% @ 1200, 15% @ 1300, 10% @ 1500, 5% @ 1700, 15% @ 1800, 10% @ 2000, 10% @ 2200
74
Feeding time:
30% @ 800
30% @ 1300
40% @ 1800
10 Meals at:
5% @ 600, 15% @ 800, 10% @ 1000, 5% @ 1200, 15% @ 1300, 10% @ 1500, 5% @ 1700, 15% @ 1800, 10% @ 2000, 10% @ 2200
78
We developed new empirical equations to predict fractional passage rate of forage, concentrate and liquid, and they were the best among a total of 8 tested equations, as assessed by the highest R-square and the lowest root mean square prediction error.
However, the predictabilities were still low.
When evaluated with an independent database,
Forage passage equation explained 39% of the variation in independent observations with a root mean square prediction of .011 1 over hour,
And for liquid passage, r-square was only 25% and root mean square prediction error was .033 one over hour
80
So we developed the whole liquid dynamic model
The model has two inflows including water consumption and salivary secretion, and two outflows including liquid flux through the rumen wall and liquid outflow through the ROO.
An empirical equation for each component was developed and complete equations will be presented on Tuesday as a poster. 
81
Model can explain 81% of variation
82
84
The point here is never give up!