A meta-analysis of dietary metabolizable amino acids and energy supply on nitrogen retention and nitrogen utilization efficiency in beef cattle

Abstract

Increasing nitrogen utilization efficiency (NUE) can reduce environmental impact and increase economic benefits to maintain a sustainable development of the beef cattle industry. The objectives of this study were to systemically and quantitatively analyze the effects of dietary metabolizable amino acids and energy supply on nitrogen retention and NUE in beef cattle and to develop prediction models using a meta-regression and a machine learning approach. After literature screening and data collection, a dataset was assembled, which finally included 63 peer-reviewed studies published between 1980 and 2022 with 248 treatments. Following bivariate correlation analyses, univariate and multiple linear regression analyses were conducted to analyze effects of body weight (BW) and intake of metabolizable energy (MEI), crude protein (CPI), metabolizable protein (MPI), and 10 metabolizable essential amino acids (MAAI) on retained protein and NUE. The mixed model procedure with study as random effect was used to remove variations across studies. The random forest regression model was used to identify the importance ranking of predictor variables and to compare prediction adequacy with the multiple linear regression models. The stability of parameter estimates and the validity of the obtained model were evaluated using the Monte Carlo cross-evaluation approach. Both retained protein and NUE were negatively quadratically correlated with BW, MEI, CPI, MPI, and MAAI, suggesting that the marginal return of retained protein and NUE are decreased with increased dietary energy and protein supply. The multiple linear regression analyses indicated that retained protein and NUE were negatively correlated with BW, while positively correlated with MEI and metabolizable Arg intake. However, there was a negative correlation between NUE and metabolizable tryptophan intake. Compared with the multiple regression models, the random forest models had greater RMSPE and lesser CCC, suggesting that the prediction accuracy and precision of the random forest models were less than the multiple regression models. In conclusion, the conversion efficiency of MAAI to net protein is not constant, and the developed response surface models can be a reliable approach to estimate AA requirements and optimize NUE in beef cattle.