Abstract

Simple SummaryOur study evaluated seven DMI models for dairy heifers grouped by their genotypes (Bos taurus or crossbred Bos taurus × Bos indicus) raised under tropical climatic conditions. The HHJ and OFNLin DMI models performed better for Bos taurus heifers, whereas the STA model performed better for crossbred heifers. NRC, HH, QUI, and OFLin DMI models had significant significant slope bias, mean bias, or both.Several models for predicting dry matter intake (DMI) of replacement dairy heifers have been developed; however, only a few have been evaluated using data from heifers of different breeds raised under tropical conditions. Thus, the objective of this study was to evaluate the DMI equations for dairy heifers managed under tropical conditions. A total of 230 treatment means from 61 studies using dairy heifers (n = 1513 heifers, average body weight = 246 kg) were used. The animals were grouped into two groups based on their genetics: (1) Bos taurus (Holstein, Jersey, Brown Swiss, and Holstein × Jersey) and (2) crossbred (Bos taurus × Bos indicus). Seven previously published DMI equations (HH, HHJ, QUI, STA, 2001 NRC, OFLin, and OFNLin) for heifers were evaluated using mean bias, slope bias, mean squared prediction errors (MSPE) and its decomposition, and other model evaluation statistics. For Bos taurus heifers, our results indicated that OFNLin and HHJ had lower mean bias (0.13 and 0.16 kg/d, respectively) than other models. There was no significant slope or mean bias for HHJ and OFNLin (p > 0.05), indicating agreement between the observed and predicted DMI values. All other models had a significant mean bias (p < 0.05), whereas the QUI model also presented a significant slope bias (p < 0.02). For crossbred heifers, the STA equation was the only one that did not present mean and slope bias significance (p > 0.05). All other DMI models had significant mean bias when evaluated using crossbred data (p < 0.04), and QUI, OFLin, and OFNLin also presented significant slope bias (p < 0.01). Based on our results, predictions from OFNLin and HHJ best represented the observed DMI of Bos taurus heifers (MSPE ≤ 1.25 kg2/d2, mean bias ≤ 0.16 kg/d), whereas STA was the best model for crossbred heifers (MSPE = 1.25 kg2/d2, mean bias = 0.09 kg/d). These findings indicate that not all available models are adequate for estimating the DMI of dairy heifers managed under a tropical climate, with HHJ and OFNLin for Bos taurus and STA for crossbreds being the most suitable models for DMI prediction. There is evidence that models from Bos taurus heifers could be used to estimate the DMI of heifers under tropical conditions. For heifer ration formulation is necessary to consider that DMI is influenced by breed, diet, management, and climate. Future work should also include animal genetic and environmental variables for the prediction of DMI in dairy heifers.

Highlights

  • Dry matter intake (DMI) is one of the most important animal health and performance indicators of dairy cattle [1]

  • We evaluated seven equations used to predict the DMI of Bos taurus and crossbred (Bos taurus × Bos indicus) dairy heifers raised in tropical conditions

  • We focused on evaluating existing models instead of developing new equations because there are already numerous DMI equations developed for dairy heifers in the literature

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Summary

Introduction

Dry matter intake (DMI) is one of the most important animal health and performance indicators of dairy cattle [1]. Substantial advances have been made in this area, it is necessary to consider variation in the actual DMI within and between animals, which models and science still cannot explain. An important aspect of ration formulation is that DMI can be considered input (if measured at the farm) or output (if estimated by models). Dairy nutritionists usually use models to estimate the DMI of heifers because many dairy farmers do not have a feed measurement system because of the high implementation cost. Several intake models have been developed and used in feed formulations for dairy cattle, in which animal characteristics, dietary components, environmental conditions, and management factors are frequently used as inputs

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