Nonlinear Mixed Models Applied to Ruminal Degradability Studies

  •  Vanderly Janeiro    
  •  Robson Marcelo Rossi    
  •  Terezinha Aparecida Guedes    
  •  Ana Beatriz Tozzo Martins    
  •  Lucimary Afonso dos Santos    


This article presents an application of three classical models to studies of ruminal degradation kinetics, namely Ørskov and McDonald’s model (1979); Van Milgen, Murphy and Berger’s model (1991), and Richard’s model proposed in France, Dijkstra, and Dhanoa (1996). Our approach is focused on accounting for animal e ects given that measurements are repeated in the same animal. The models were studied under the perspective of nonlinear mixed-e ects (NLME) models. In this way, we intended to accommodate the problems of response variance heterogeneity and correlations between repeated measures. To apply the proposed method, we used data from an experiment conducted in a Latin square design to assess the dry matter degradability of the following three silages: Elephant grass (Pennisetum purpureum Schumach.) silage treated with bacterial inoculant, Elephant grass silage treated with enzyme-bacterial inoculant, and corn (Zea mays L.) silage. Samples were incubation for 0, 2, 6, 12 , 24, 48, 72 and 96 h. For these experimental data, the Van Milgen, Murphy, and Berger’s model showed better performance than the others. The proposed approach indicated that inclusion of animal e ects is important for obtaining more accurate information and can be considered in NLME modeling. Furthermore, it was also possible to perform an easy-to-interpret analysis of contrasts between treatments by using Tukey’s test.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1927-7032
  • ISSN(Online): 1927-7040
  • Started: 2012
  • Frequency: bimonthly

Journal Metrics

  • h-index (December 2021): 20
  • i10-index (December 2021): 51
  • h5-index (December 2021): N/A
  • h5-median(December 2021): N/A

( The data was calculated based on Google Scholar Citations. Click Here to Learn More. )