Applications of the CNCPS model

One of the current concerns about academic research is that often it is not used by those who might benefit most from it. In part, this is because our usual products, journal articles, are not usually read by practitioners. When research data are incorporated into the CNCPS model, the information reaches the end-user, producers and nutrition consultants, quickly and in a form that is immediately useful. When the CNCPS model predictions do not agree with field observations, we usually get immediate and direct feedback. Thus, the product and the process of model development create a beneficial linkage between scientists and those in the field. The CNCPS is being used as a teaching tool for students and consultants to design and interpret experiments, to apply experimental results, to develop tables of nutrient requirements for any cattle type and production level, and to evaluate and improve feeding programmes.

Many feed consultants have been trained to use the CNCPS, and they now use the model to improve feeding programmes on farms.

The CNCPS model is used for all classes of beef and dairy cattle. Data from an actual dairy herd in central New York where the CNCPS has been used for 10 years and from evaluations in a feedlot near Calgary, Alberta, will be used as an example because they demonstrate the principles and procedures described previously.

The dairy case study

This case study has been described in detail by Stone et al. (1992) and Klausner et al. (1998). When the CNCPS model was first used in this herd in June 1992, the average annual per cow milk production was about 11,000 kg of milk. The changes made by using the CNCPS were estimated to save $42,600 in the first year. The herd average is now over 13,000 kg of milk, and manure analysis indicate that nitrogen excretion has been reduced by about a third (Klausner et al., 1998). In a study in progress to evaluate plasma urea nitrogen levels relative to conception rates in 20 New York herds, PUN levels are about 20% lower in this herd than in nearly all of the other herds.

Initial data from the high-producing mature cows will be used to demonstrate how the model was used to predict animal requirements and feed utilization initially to formulate more efficient and cost-effective rations, and to adjust for various conditions encountered on the farm during the past 10 years. The base evaluations are summarized in Table 21.4. The base ration and evaluation is the diet that was being fed to this group before applying the CNCPS model. Predicted and measured DMI were within good agreement, and the CNCPS model predicted that the cows were in positive energy balance. The MP balance was within a reasonable range, considering our recommendation of 5% safety factor, and the requirement for the first limiting amino acid (methionine) was just being met. The effective NDF requirement was being met, giving a predicted rumen pH of 6.25, which would allow maximum microbial growth for the energy and protein ruminally available. Thus when inputs for the cows, environment and feeds were accurately described, the CNCPS model predicted the observed response (43.4 kg of milk). However, the excess of ammonia (bacterial balance) and MP in the diet resulted in predicted plasma urea nitrogen of 16.4 mg%.

The CNCPS balanced ration is for a period after the ration adjustments had stabilized and milk production had increased steadily to 46.5 kg. The diet protein had been decreased by two percentage units, with an improved overall balance of all four nitrogen pools (rumen ammonia and peptides, MP, and essential amino acids). Primary ration changes were to increase microbial yield with more fermentable carbohydrates and to replace some of the soybean meal with less-degradable soybean meal (treated SBM). With the new ration, less energy was being used to excrete excess N (urea cost). Actual milk production now exceeded predicted ME allowable milk. Based on our data, we assume this to be due to improved EAA balance.

A number of changes in the environment and feed composition occurred over subsequent years that show how they affect performance and these balances. When intake dropped 2 kg during hot weather, ME allowable milk production declined 7.5 kg. When input temperature was changed to actual, the decline in intake was accurately predicted. One of the results of instituting the CNCPS was setting up a DMI monitoring programme, and plotting predicted vs. actual. This provided a tool for diagnosing problems, avoiding the usual trial and error approach to solving the decline in milk production. In a crop year when forage NDF declined, ME balance was increased 12.6 MJ and energy allowable milk 2.7 kg and increased MP and methionine balance because of increased microbial growth. When diet peNDF declined, the MP from bacteria declined 186 g day-1 because peNDF requirement was not met and rumen pH dropped to 6.04 and methionine became deficient. When soluble CP declined, MP from feed increased by 93 g day-1. When the maize silage had a high percentage of small, hard, whole kernels and a lot of maize kernels were observed in the manure, ruminal starch digestion rate was lowered to 5% h1 and intestinal starch

Table 21.4. Results of the use of the CNCPS model in a field application3.

Diet ingredients

BSS6

Reformulated

Maize silage (kg DM day-1)

5.76

5.22

Lucerne silage (kg DM day-1)

2.40

4.54

HMEM (kg DM day 1)

4.94

5.76

Treated SBM (kg DM day-1)

1.13

SBM (kg DM day 1)

4.72

1.13

WCS (kg DM day 1)

2.54

2.68

Protein mix (kg DM day 1)

0.45

0.95

Maize grain (kg DM day 1)

2.09

2.40

Tallow (kg DM day-1)

0.23

Minerals (kg DM day 1)

0.32

0.86

Total DMI (kg)

23.4

24.7

Predicted DMI (kg)

23.4

24.8

Diet CP (% DM)

20.2

18.3

Plasma urea N (mg%)

16.4

13.0

Actual milk (kg day 1)

43.4

46.5

ME allowable milk (kg day 1)

43.4

45.2

ME balance (MJ)

4.2

—6,3

MP balance (g)

95

95

MP from bacteria (g)

1256

*1355

MP from feed (g)

1551

1614

Bacteria N balance (g N)

209

136

Peptide balance (g N)

122

45

Urea cost (MJ)

9.53

5.33

Days to BCS change

380

272

peNDF supplied (kg)

4.85

4.90

peNDF required (kg)

4.67

4.94

Predicted ruminal pH

6.25

6.44

Limiting AA

Met

Met

Limiting AA (% required)

94

96

aDiets and animal parameters specified as described by Klausner et al. (1998).

BCS, body condition score; HMEM, high moisture ear maize; SBM, soybean meal; WCS, whole cotton seed.

aDiets and animal parameters specified as described by Klausner et al. (1998).

BCS, body condition score; HMEM, high moisture ear maize; SBM, soybean meal; WCS, whole cotton seed.

digestibility was lowered to 50% to adjust for this condition. The lower ruminal starch digestibility reduced microbial CP (MCP) yield 448 g day-1, resulting in a deficiency of MP and methionine. The effect of the combination of escaping more starch and a lowered intestinal digestibility resulted in 8.4 MJ less ME day-1, and lowered energy allowable milk 1.8 kg.

The feedlot case study

The data from a 4000 head study with steers fed approximately 80% rolled barley and 20% coarse chopped barley silage based diets in a 20,000 head feedlot near Calgary, Alberta, were evaluated with the CNCPS model. These data were provided by Dr P.T. Guichon, Feedlot Health Management Services, Okotoks, Alberta. The cattle were fed in open dirt lots with mounds surrounded by windbreaks. The objective was to use the procedures described earlier to accurately predict observed performance, then to use the CNCPS to evaluate the effect of different animal, feed and environmental factors on nutrient requirements, feed utilization and performance in this feedlot. The average initial body weight was 380 kg, the final body weight was 584 kg, DMI was 11.05 kg day-1, ADG was 1.58 kg day-1, and feed conversion was 7.00 kg DMI kg-1 ADG. The cattle were fed in the autumn so environmental stress was minimal.

The results of this evaluation are shown in Tables 21.5, 21.6 and 21.7. Table 21.5 shows that the CNCPS model predicted the same ADG and feed conversion compared to the observed performance. Subsequently, a 10% decrease in DMI below actual, which was 4% more than predicted, will reduce ADG by 15% and feed conversion by 3.6%.

Table 21.5. Effect of animal and environmental factors on performance.

Scenario

ADG (kg day-1)

Feed conversion

Observed performance

1.58

7.00

CNCPS model predicted

No adjustments3

1.58

7.00

Effect of DMI changeb

1.35

7.25

Effect of mature size change0

1.75

6.23

Effect of body CS

Very thin

1.76

6.19

Very fleshy

1.39

7.84

Effect of environment

Winter, same DMI

1.45

7.52

Winter, increased DMI

1.54

7.40

Winter, wind at 24 km It1

1.42

7.67

Matted hair

1.27

8.57

Thin hide

1.16

9.41

Short hair

0.85

12.80

Thin flesh

-0.45

-

aMature weight of 582 kg at 28% empty body fat. bDecrease of 10% than observed.

"Current weight of 482 kg and mature weight of 682 kg at 28% empty body fat.

Table 21.6. Effect of effecti vsnsss of the feed fibre in Controlling rumen pH.

ADG

Rumen

Silage3

Grain3

MCPb

(kg day-1)

PH

NE9

NE9

(g dar1)

Current predicted

1.58

5.9

4.22

5.36

820

Silage processed fine

1.51

5.8

3.60

5.23

710

Silage and grain both processed fine

1.13

5.7

1.09

0.96

bMCP is microbial crude protein produced (g day-1).

bMCP is microbial crude protein produced (g day-1).

Table 21.7. Effect of body weight on protein requirements3.

ME allowed

Rumen

MP allowed

AA allowed

ADG (kg day-1)

balance (%)

ADG (kg day-1)

ADG (kg day-1)

at 482 kg

1.58

-2.5

2.37

2.37

at 364 kg

1.58

-2.5

1.96

1.96

at 273 kg

1.57

-2.5

1.57

1.57

at 273 kgb

1.75

-2.5

1.57

1.57

at 182 kg

1.45

-2.5

1.10

1.12

aThe base ration is 125 g kg-1 crude protein, which is 77% degraded. bThe large size evaluation reaches finished weight at 100 kg heavier (682 kg).

An increase in the finished weight by 100 kg would result in a predicted gain 11% faster at the same weight. However, since they must be fed to a heavier weight to be finished, their overall feed efficiency would be similar (data not shown). Thin cattle would be expected to make compensatory growth whereas fleshy cattle would be expected to gain more slowly, due to differences in maintenance requirements and feed intake. The effects of winter feeding conditions on performance at an average winter temperature of -32°C indicated that ADG decreases in the winter at the same DMI, due to an increase in metabolic rate. This can largely be offset by the higher predicted DMI expected in the winter conditions, although feed requirements are higher because of increased maintenance requirement. If the cattle were exposed to wind of 24 km h 1 instead of the current 8 km h_1, ADG will be reduced slightly. However, if the insulation is reduced by matted hair, thin hide, short hair or thin flesh, performance will be dramatically reduced.

Table 21.6 shows the effect of fine processing the silage or grain, or both. The rumen pH is predicted to drop, reducing cell wall digestion and therefore net energy derived from the fibre. Also microbial protein production will decline at lower pH.

Table 21.7 shows the weight at which the dietary undegraded and microbial protein will not meet the energy allowable ADG requirement for protein or essential amino acids. The ruminal requirement for degrad-able protein is essentially met (-2.5%), and does not change with cattle weight because the requirement for degradable protein is proportional to the fermentable carbohydrates in the diet. The breakpoint for the cattle size in this study is 273 kg, below which supplemental amino acids will be needed. However, the absorbed protein and first-limiting amino acid requirements of cattle with a 100 kg larger finished weight would not be met because their energy allowable ADG is higher. The last line shows that the cattle being evaluated would have a protein allowable ADG 0.35 kg day-1 below the energy allowable ADG.

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