Adjusting animal and dietary factors to predict actual performance

The previous discussion indicates accurate prediction of energy and amino acid supply depended on prediction of NDF, starch, CP and protein solubility pool sizes and their digestion and passage rates, and microbial amino acid composition. Prediction of absorbed energy and amino acid requirements depended on accurate prediction of protein retained, amino acid tissue composition, and efficiency of use of absorbed energy and protein. We believe that with adequate feed com position values and knowledge of how to use input values, mechanistic models such as the CNCPS that have an appropriate structure for accounting for these variables can be used as a beginning point to predict ME, NE, and amino acid requirements and supply. First, the animal, environmental and feed compositional factors must be described as accurately and completely as possible. However, because many of the factors (body size, environmental conditions, feed digestion rates, particle size, etc.) depend on field observation, the input factors must be adjusted in a logical way until the model predicts the performance that is being observed before alternatives can accurately be evaluated. This approach allow¬ę requirements to be computed for the specific animal, environmental, DMI and feed compositional conditions.

The following sequence of steps in using the CNCPS have been developed to determine the first limiting nutrient (energy, absorbed protein, amino acids) for specific conditions, based on our use of the CNCPS in designing experiments and in conducting field case studies (Roseler, 1991; Fox et al., 1992, 1995, 2000a; Stone et al., 1992). This hierarchy is necessary, because of the 'ripple effect' of all of the interactions in the model. When one factor is altered, several others are likely to be affected. The hierarchy inherent in the CNCPS assumes energy is first limiting, and amino acid requirements are supplied to meet the energy requirement for maintenance, pregnancy, or daily gain or milk production from the energy intake over that needed for maintenance and pregnancy.

  1. Dry matter intake comparison. Accurately determine DMI, and compare it to that expected. The actual dry matter intake must be accurately determined, taking into account feed wastage, moisture content of feeds and scale accuracy. The accuracy of any model prediction is highly dependent on the DMI used. Intake of each feed must be as uniform as possible over the day, because as far as we know all field application models assume a total mixed ration with steady state conditions.
  2. Energy allowable evaluation. Compare energy allowable to observed milk production or daily gain. The daily gain or milk production being obtained should agree with those predicted from the diet considering animal type, environmental conditions, feed intake and diet feedstuff carbohydrate composition. If not, the user should evaluate the following.
  3. Predicted change in body condition score, based on diet energy excess or deficiency. In the case of lactating and dry cows, the predicted energy balance compared to observed days over which animal condition will change one score are excellent indicators of the diet energy balance being achieved. However, predicted and observed body condition scores should agree.
  4. Mistakes or incorrect judgements about inputs such as body size, milk production and its composition, environmental conditions or feed additives are often made.
  5. Feed factors that may be influencing energy derived from the diet as the result of feed compositional changes, and possible effects on digestion and passage rates. The ME derived from forages are most sensitive to NDF amount and percentage of the NDF that is lignin, available NDF digestion rate, and peNDF value. After making sure the feed composition values are appropriate, the digestion rate is considered. Adjustments are made, using the ranges and descriptions in Table 6 of Sniff en et al. (1992). If the rumen pH is below 6.2, the digestion rate of the cell wall is reduced, based on pH predicted from peNDF. We next check the assignment of peNDF; it is used in computing passage rate. If too low, passage rate may be too high, reducing predicted ME value. The major factors influencing energy derived from feeds high in NFC are ruminal and intestinal starch digestion rate. This is mainly a concern when feeding maize and/or maize silage. We adjust this value based on appearance of maize in the manure, using the values in Table 7 of Sniffen et al. (1992) as a guide.
  6. Effective fibre adjustments. Make adjustments to insure effective fibre requirements are being met. In high-producing cows or high energy fed feedlot cattle, it is difficult to balance fibre requirements because of the increase in energy density needed to meet energy requirements for maximum production.

Based on Pitt et al. (1996), we make adjustments to insure that diet peNDF is a minimum of 20% in lactating cows, or growing cattle where forage utilization is important. As much as 25% peNDF may be required to maintain an adequate pH, depending on feeding management. In beef cattle fed high concentrate diets, a minimum of 8% is required to keep cattle on feed under typical feedlot conditions; under these low pH conditions (pH < 6) microbial yield will be reduced at least a third by the CNCPS model and very little energy will be derived from the fibre in forages fed. The peNDF is that required to keep rumen pH averaging above 5.6-5.7, the threshold below which cattle stop eating (R. Britton, University of Nebraska, personal communication).

  1. Rumen nitrogen balance. Feeds such as soybean meal that are high in degradable true protein are added until ruminal peptide needs are met if amino acids are expected to be deficient; they are required for optimal fermentation of non-structural carbohydrates. Then adjust remaining ruminal nitrogen requirements with feeds high in NPN or soluble protein until ammonia needs are met. In addition to maximizing microbial amino acids supplied, the total tract digestion of both fibre (Sniffen et al., 1992) and starch (Sniffen et al., 1992; Theurer et al., 1996) are dependent on the extent of ruminal fermentation.
  2. Metaholizahle protein balance. This component represents an aggregate of nonessential amino acids and essential amino acids. The MP requirement is determined by the animal type and the energy allowable gain or milk production. The adequacy of the diet to meet these requirements will depend on microbial protein produced from structural and non-structural carbohydrate fermentation and feed protein escaping fermentation. If MP balance appears to be unreasonable, we check first the starch digestion rates, using the ranges and descriptions in Sniffen et al. (1992). Altering the amount of degradable starch will also alter the peptide and total rumen N balance, because of altered microbial growth. Often the most economical way to increase MP supply is to increase microbial protein production by adding highly degradable sources of starch, such as processed grains. Further adjustments are made with feeds high in slowly degraded or rumen escape (bypass) protein (low Protein B2 digestion rates).
  3. Compare essential amino acids supplied to meet requirements. This is the last step to be adjusted because the amino acid balance is affected by changes made in all of the above. Essential amino acid balances can be estimated within the structure of the CNCPS because the effects of the interactions of intake, digestion and passage rates on microbial yield, available undegraded feed protein and estimates of their amino acid composition can be predicted along with microbial, body tissue and milk amino acid composition. However, the development of more accurate feed composition and digestion rates and more mechanistic approaches to predict utilization of absorbed amino acids will result in improved predictability of diet amino acid adequacy for cattle. Sources of first-limiting essential amino acids are adjusted where practical to improve the amino acid profile. In preliminary studies, energetic efficiency appeared to improve as essential amino acid profiles approached that of requirements (Fox et al., 1995).
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