Identification of Ruminal Fermentation Curves of Some Legume Forages Using Particle Swarm Optimization

SIMPLE SUMMARY: Forage plants are important for ruminant nutrition, so identifying their quality and nutritional value is effective in describing animal nutrition. A ruminal microbe attaches itself or is within close proximity to the surfaces of particulate substrates (primarily the inner surfaces) to digest them. However, incubation in the rumen can result in significant changes in the number of attached microbes. Nonlinear models may provide more accurate and comprehensive descriptions of feed fermentability. Improving the model suitability and validating the model are enhanced with low iterations. However, particle swarm optimization is the novelty of this study since it has never been used to study the digestive process with the above models. ABSTRACT: The modeling process has a wide range of applications in animal nutrition. The purpose of this work is to determine whether particle swarm optimization (PSO) could be used to explain the fermentation curves of some legume forages. The model suited the fermentation data with minor statistical differences (R(2) > 0.98). In addition, reducing the number of iterations enhanced this method’s benefits. Only Models I and II could successfully fit the fermentability data (R(2) > 0.98) in the vetch and white clover fermentation curve because the negative parameters (calculated in Models III and IV) were not biologically acceptable. Model IV could only fit the alfalfa fermentation curve, which had higher R values and demonstrated the model’s dependability. In conclusion, it is advised to use PSO to match the fermentation curves. By examining the fermentation curves of feed materials, animal nutritionists can obtain a broader view of what ruminants require in terms of nutrition.