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.

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.