Evaluation of different feed intake models for dairy cows

Abstract

The objective of the current study was to evaluate feed intake prediction models of varying complexity using individual observations of lactating cows subjected to experimental dietary treatments in periodic sequences (i.e., change-over trials). Observed or previous period animal data were combined with the current period feed data in the evaluations of the different feed intake prediction models. This would illustrate the situation and amount of available data when formulating rations for dairy cows in practice and test the robustness of the models when milk yield is used in feed intake predictions. The models to be evaluated in the current study were chosen based on the input data required in the models and the applicability to Nordic conditions. A data set comprising 2,161 total individual observations was constructed from 24 trials conducted at research barns in Denmark, Finland, Norway, and Sweden. Prediction models were evaluated by residual analysis using mixed and simple model regression. Great variation in animal and feed factors was observed in the data set, with ranges in total dry matter intake (DMI) from 10.4 to 30.8kg/d, forage DMI from 4.1 to 23.0kg/d, and milk yield from 8.4 to 51.1kg/d. The mean biases of DMI predictions for the National Research Council, the Cornell Net Carbohydrate and Protein System, the British, Finnish, and Scandinavian models were -1.71, 0.67, 2.80, 0.83, -0.60kg/d with prediction errors of 2.33, 1.71, 3.19, 1.62, and 2.03kg/d, respectively, when observed milk yield was used in the predictions. The performance of the models were ranked the same, using either mixed or simple model regression analysis, but generally the random contribution to the prediction error increased with simple rather than mixed model regression analysis. The prediction error of all models was generally greater when using previous period data compared with the observed milk yield. When the average milk yield over all periods was used in the predictions of feed intake, the increase in prediction error of all models was generally less than when compared with previous period animal data combined with current feed data. Milk yield as a model input in intake predictions can be substantially affected by current dietary factors. Milk yield can be used as model input when formulating rations aiming to sustain a given milk yield, but can generate large errors in estimates of future feed intake and milk production if the economically optimal diet deviates from the current diet.

Short communication: Added value of rumination time for the prediction of dry matter intake in lactating dairy cows

“…Milk yield is a function of the genetic merit of the cow and the quality of the diet. As a consequence, when current diet deviates from the economically optimal diet, using actual milk yield as an input variable can result in seriously biased intake predictions (Friggens et al, 1998), as discussed by Krizsan et al (2014). In the NRC RT model, the estimate for RT was low (0.031 kg/h; P < 0.01) but significantly different from zero (Table 2).…”

“…Simple models with few input variables lack accuracy, with mean biases of 1 or 2 kg of DMI/d. On the other hand, more complex models may be more accurate and have mean biases <1 kg of DMI/d (Huhtanen et al, 2011;Krizsan et al, 2014) but are less suitable for on-farm application as they require large amounts of input data concerning diet characteristics.…”

Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. II. Approaches to and implications of more mechanistic prediction

“…Several theories regarding regulation of DMI in ruminants suggest that intake is regulated by NDF digestibility and starch fermentation, among other factors (Allen et al, 2009). Although numerous equations predicting DMI have been derived (Krizsan et al, 2014), few have explicitly addressed the circular calculation issue surrounding intake and ruminal carbohydrate digestibility. Arguably, DMI equations should have an effect for degradability of NDF and starch.…”

Effects of CPM model software on diet energy and nitrogen diagnosis, and lactating performance of dairy cows

“…Therefore, the nitrogen excretion decreased by 16% and 25% in theory, respectively, for the modulated diets. These changes would decrease nitrogen excretion to environment (Krizsan et al, 2014). In addition, the costs of diets adjusted (Test diets I and II) were reduced by RMB 2.43 and RMB 2.12 day/cow compared with original diets.…”