Accounting for Energy and Protein Reserve Changes in Predicting Diet-Allowable Milk Production in Cattle

Abstract

Current ration formulation systems used to formulate diets on farms and to evaluate experimental data estimate metabolizable energy (ME)-allowable and metabolizable protein (MP)-allowable milk production from the intake above animal requirements for maintenance, pregnancy, and growth. The changes in body reserves, measured via the body condition score (BCS), are not accounted for in predicting ME and MP balances. This paper presents 2 empirical models developed to adjust predicted diet-allowable milk production based on changes in BCS. Empirical reserves model 1 was based on the reserves model described by the 2001 National Research Council (NRC) Nutrient Requirements of Dairy Cattle, whereas empirical reserves model 2 was developed based on published data of body weight and composition changes in lactating dairy cows. A database containing 134 individually fed lactating dairy cows from 3 trials was used to evaluate these adjustments in milk prediction based on predicted first-limiting ME or MP by the 2001 Dairy NRC and Cornell Net Carbohydrate and Protein System models. The analysis of first-limiting ME or MP milk production without adjustments for BCS changes indicated that the predictions of both models were consistent (r(2) of the regression between observed and model-predicted values of 0.90 and 0.85), had mean biases different from zero (12.3 and 5.34%), and had moderate but different roots of mean square errors of prediction (5.42 and 4.77 kg/d) for the 2001 NRC model and the Cornell Net Carbohydrate and Protein System model, respectively. The adjustment of first-limiting ME- or MP-allowable milk to BCS changes improved the precision and accuracy of both models. We further investigated 2 methods of adjustment; the first method used only the first and last BCS values, whereas the second method used the mean of weekly BCS values to adjust ME- and MP-allowable milk production. The adjustment to BCS changes based on first and last BCS values was more accurate than the adjustment to BCS based on the mean of all BCS values, suggesting that adjusting milk production for mean weekly variations in BCS added more variability to model-predicted milk production. We concluded that both models adequately predicted the first-limiting ME- or MP-allowable milk after adjusting for changes in BCS.

A nutrition mathematical model to account for dietary supply and requirements of energy and nutrients for domesticated small ruminants: the development and evaluation of the Small Ruminant Nutrition System

“…The r 2 was 0.82 and mean bias was 0.174 Mcal/d. This finding supports the need to adjust for changes in BCS or BW to accurately predict ME or MP available for lactation as discussed by TEDESCHI et al (2006). To evaluate the MP calculations of the CNCPS-S for growing or finishing sheep, the NRC (2007) (2007) recommended the use of the CNCPS-S for non-pregnant and early-or mid-gestation ewes, lactating ewes, and late gestating ewes.…”

Invited review: Body condition score and its association with dairy cow productivity, health, and welfare

“…Several nutritional models calculate changes in BCS on the basis that body lipid is solely an energy buffer (e.g., Molly;McNamara and Baldwin, 2000;CPM model;Tedeschi et al, 2008). However, because in these models body lipid is simply a reservoir for excess energy intake, they are vulnerable to seemingly insignificant systematic biases in the model parameters that can rapidly accumulate across model time-steps into sizeable errors (Ellis et al, 2006) resulting in the model animal becoming excessively and unrealistically fat or thin (McNamara, 2004;Tedeschi et al, 2006). This can be corrected by adjusting for observed changes in BCS (Tedeschi et al, 2006) but then the model no longer predicts BCS change.…”

“…However, because in these models body lipid is simply a reservoir for excess energy intake, they are vulnerable to seemingly insignificant systematic biases in the model parameters that can rapidly accumulate across model time-steps into sizeable errors (Ellis et al, 2006) resulting in the model animal becoming excessively and unrealistically fat or thin (McNamara, 2004;Tedeschi et al, 2006). This can be corrected by adjusting for observed changes in BCS (Tedeschi et al, 2006) but then the model no longer predicts BCS change. These models do not include an innate (genetic) driving force for body reserve change.…”

“…Accepting the notion of an evolutionary derived, genetically regulated process, body reserve change has been reported to improve nutritional management in practice. Including a compulsory mobilization of energy in early lactation improves prediction of feed intake during this period (Petruzzi et al, 2004;Tedeschi et al, 2006). This has also been used to predict delays in return to estrus cycling (Friggens and Chagunda, 2005;Blanc and Agabriel, 2008) and to identify viable dry cow feeding strategies (Friggens et al, 2004a;Drackley, 2009).…”

“…Using statistical approaches, such as random regression models with a covariance structure fitted among terms of the polynomial, or differential smoothing using extended Kalman filters on individual cows, BCS profiles of cows into the future may be predicted, thereby alerting, at an early stage, the producer to potential "problem cows" or cows requiring preferential treatment. Further, such data would then become available for incorporation into decision support systems that, for example, predict the risk of the cow developing ketosis or of having reproductive problems (Friggens and Chagunda, 2005;Nielsen et al, 2005) or the need to adjust nutrition recommendations (Tedeschi et al, 2006).…”