Prediction of dry matter intake by feedlot beef cattle under tropical conditions

Abstract

Context Dry matter intake (DMI) is the variable that most affects beef cattle performance in feedlot conditions. Accurate prediction of DMI is essential because it is the basis for calculating nutritional requirements for maintenance and production. Aims A meta-analysis was conducted to develop DMI prediction models for feedlot beef cattle under tropical conditions, and to compare the models with those proposed by the National Research Council, USA, in 2000 and 2016, as well as those recommended by the Brazilian System of Nutritional Requirements (BR-Corte) and published by Azevêdo and colleagues in 2010 and 2016. Methods The dataset was created from 56 published studies conducted under tropical conditions. The dataset was randomly separated into two subsets for statistical analysis. The first subset was used to develop the models to predict DMI, and the second to evaluate the adequacy of the prediction models. The models were developed by using mixed linear and nonlinear analysis. Key results A nonlinear model and a linear model to predict DMI are proposed. These models were similar in terms of accuracy and were superior to the other evaluated models. The nonlinear and linear models explained, respectively, 59% and 62% of the DMI variation and had greater accuracy and precision than the other models. The 2016 model used by BR-Corte explained 55% of the DMI variation, and underestimated it at 0.20 kg/day. The remaining three models presented a systematic constant bias and were not adequate for predicting DMI. Conclusion The proposed nonlinear and linear prediction models of beef cattle in feedlot developed under tropical conditions are more precise and accurate than those recommended by the National Research Council and the 2010 model used by BR-Corte. They also present better prediction quality of DMI from beef cattle in feedlots under tropical conditions than the 2016 model used by BR-Corte. Implications The proposed models in the present study are the most suitable for use in predicting the DMI of beef cattle under tropical conditions.