Estimating in vitro ruminal ammonia-N using multiple linear models and artificial neural networks based on the CNCPS nitrogenous fractions of cattle rations with low concentrate/roughage ratios

Abstract

The objectives of this study were to investigate the relationship between the in vitro ruminal ammonia nitrogen (NH₃ -N) concentration and the Cornell Net Carbohydrate and Protein System (CNCPS) N-fractions of feeds for cattle and further compare the performance of developing multiple linear regression (MLR) and artificial neural network (ANN) models in estimating the NH₃ -N concentration in rumen fermentation. Two data sets were established, of which the training data set containing forty-five rations for cattle with concentrate/roughage ratios of 50:50, 40:60, 30:70, 20:80 and 10:90 used for developing models and the test data set containing ten other rations with the same concentrate/roughage ratios with the training data set were used for validating of models. The NH₃ -N concentrations of feed samples were measured using an in vitro incubation technique. The CNCPS N-fractions (g), for example PB₁ (rapidly degraded true protein), PB₂ (neutral detergent soluble nitrogen), PB₃ (acid detergent soluble nitrogen) of rations, were calculated based on chemical analysis. Statistical analysis indicated that the NH₃ -N concentration (mg) was significantly correlated with the CNCPS N-fractions (g) PB₁ , PB₂ and PB₃ in a multiple linear pattern: NH₃ -N = (130.70±33.80) PB₁ + (155.83±17.89) PB₂ - (85.44±37.69) PB₃ + (42.43±1.05), R² = 0.77, p < 0.0001, n = 45. The results indicated that both MLR and ANN models were suitable for predicting in vitro NH₃ -N concentration of rations using CNCPS N-fractions PB₁ , PB₂ , and PB₃ as independent variables while the neural network model showed better performance in terms of greater r² , CCC and lower RMSPE between the observed and predicted values.

Keywords: Cornell net carbohydrate and protein system; artificial neural networks; cattle; models; ruminal ammonia nitrogen.