AHP-GRA based multi-criteria optimization of sorghum-sudangrass hybrid and alfalfa silage mixtures for enhanced quality and methane reduction

Abstract

The objective of this study was to evaluate the effects of ensiling sorghum-sudangrass hybrid (SS) and alfalfa at varying ratios on silage quality, ruminal fermentation characteristics, and anti-methanogenic potential. Silage mixtures containing SS and alfalfa at ratios of 100:0, 75:25, 50:50, 25:75, and 0:100 (wet weight basis) were ensiled for 45 days. As the proportion of SS decreased, there was a significant linear increase in crude protein, mineral content (Ca, Mg, P), and secondary metabolites (condensed tannins and phenolics) (P < 0.01), along with a decrease in dry matter (DM), Flieg score, acid detergent fiber (ADF), neutral detergent fiber (NDF), lactate, and butyrate concentrations (P < 0.01). Increasing alfalfa inclusion linearly reduced 24-h in vitro gas production (P < 0.01), but had no effect on cumulative gas production at 96 h. To address the complex interrelationships among fermentation and nutritional parameters, grey relational analysis (GRA) was performed. The Analytic Hierarchy Process (AHP) was integrated into GRA to assign expert-weighted significance levels to each parameter. Based on the AHP-GRA model, the 75:25 SS:alfalfa mixture was identified as the optimal silage combination, displaying the lowest rumen pH, acetate, propionate, CO2, and CH4 concentrations (P < 0.05), and reduced proteolysis. These findings suggest that co-ensiling SS and alfalfa at a 75:25 ratio improves silage quality and mitigates enteric CH4 emissions. However, further in vivo validation is necessary to confirm these effects under practical feeding conditions.