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

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 (R2 > 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 (R2 > 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.

Effect of hydroxypropyl methylcellulose (HPMC) hard capsules on in vitro rumen fermentation kinetics of commonly used feeds in ruminant feeding

“…The net gas volumes at each time point of incubation were then adjusted to the substrate DM. Net in vitro gas production data were then fitted using the Gompertz equation (Palangi, 2023):…”

“…The kinetic parameters of gas production were calculated according to Palangi ( 2023 ). Briefly, after subtracting the average gas volume of the blank sample from the gas production of the substrate at each incubation time, the net gas production of the substrate was determined.…”

“…The net gas volumes at each time point of incubation were then adjusted to the substrate DM. Net in vitro gas production data were then fitted using the Gompertz equation (Palangi, 2023 ): Y t = B × e ( –C × e ( –D × t )), where Y t is the gas produced at time ‘ t ’ (mL/g DM), ‘ B ’ is the asymptotic amount of gas produced (total potential gas production (mL/g DM)), ‘ C ’ is the specific gas production rate affected by ‘ t ’, which is governed by a constant ‘ D ’ describing the decrease in the specific gas production rate (caused by the decreasing growth rate of the microorganisms and the increasing substrate limitation reflected in the gas production), and ‘ t ’ is the time in hours. The Marquardt compromise for a nonlinear regression method (PROC NLIN; Statistical Analysis System Institute (SAS), 2015 ) was used to estimate the kinetic parameters and fit the curve for each syringe within a substrate.…”