Anaerobic codigestion of organic residues is a proven strategy for enhancing methane recovery. However, the complexity of microbial interactions and variability in operational conditions make it difficult to estimate methane concentration in real time, particularly in rural contexts. This study developed a multiple linear regression model to predict methane concentration using operational data and microbial community profiles derived from 16S rRNA gene sequencing. The system involved the codigestion of cassava by-product and pig manure in a two-phase anaerobic reactor. Predictor variables were selected through a hybrid approach combining statistical correlation with microbial functional relevance. The final model, trained on 70% of the dataset, demonstrated satisfactory generalization capability on the other 30 test set, achieving a coefficient of determination (R²) of 0.92 and a mean relative error (MRE) of 6.50%. Requiring only a limited set of inputs and minimal computational resources, the model offers a practical and accessible solution for estimating methane levels in decentralized systems. The integration of microbial community data represents a meaningful innovation, improving prediction by capturing biological variation not reflected in operational parameters alone. This approach can support local decision making and contribute to Sustainable Development Goal 7 by promoting reliable and affordable technologies for clean energy generation in rural and resource-constrained settings.

Anaerobic codigestion of organic residues is a proven strategy for enhancing methane recovery. However, the complexity of microbial interactions and variability in operational conditions make it difficult to estimate methane concentration in real time, particularly in rural contexts. This study developed a multiple linear regression model to predict methane concentration using operational data and microbial community profiles derived from 16S rRNA gene sequencing. The system involved the codigestion of cassava by-product and pig manure in a two-phase anaerobic reactor. Predictor variables were selected through a hybrid approach combining statistical correlation with microbial functional relevance. The final model, trained on 70% of the dataset, demonstrated satisfactory generalization capability on the 30) test set, achieving a coefficient of determination (R²) of 0.92 and a mean absolute error (MRE) of 6.50%. Requiring only a limited set of inputs and minimal computational resources, the model offers a practical and accessible solution for estimating methane levels in decentralized systems. The integration of microbial community data represents a meaningful innovation, improving prediction by capturing biological variation not reflected in operational parameters alone. This approach can support local decision-making and contribute to Sustainable Development Goal 7 by promoting reliable and affordable technologies for clean energy generation in rural and resource-constrained settings.

The gut microbiome plays a crucial role in insect nutrition and performance, yet its targeted exploitation in cricket farming remains underexplored. Here, we combined gut microbiota profiling of wild and farmed Gryllus bimaculatus with probiotic testing of host-derived bacterial isolates to explore microbiome-informed strategies for sustainable cricket farming. Wild crickets exhibited higher Shannon diversity but lower phylogenetic diversity than farmed counterparts. Wild populations were enriched in Oscillospiraceae and Christensenellaceae families, while farmed crickets showed higher abundance of Parabacteroides. From 199 bacterial isolates, wild populations showed higher frequencies of uricolytic capabilities (44% vs. 31%), related to nitrogen recycling, while farmed crickets had more pectinolytic isolates (70% vs. 50%), linked to plant fiber degradation. Pantoea agglomerans I53BLB, which demonstrated broad enzymatic capabilities, was selected for probiotic evaluation; we further provide its genome sequence and analysis to contextualize its metabolic and probiotic potential. A feeding experiment with a 2 × 3 factorial design (two diets × three probiotic treatments, n = 10 replicates per group) compared control chicken feed versus a high-fiber diet formulated with agricultural by-products, each supplemented with water, live or heat-inactivated P. agglomerans. A significant diet × probiotic interaction was observed for weight gain (χ² = 18.8, p = 0.0021) and adult emergence (χ² = 17.7, p = 0.0033). Live P. agglomerans enhanced performance only when combined with the high-fiber diet, with individuals reaching a mean wet weight of 0.602 g compared to 0.451 g (heat-inactivated, p = 0.035) and 0.427 g (water control, p = 0.003), and a significantly higher adult emergence rate (37%) compared to all other treatment combinations (13%, p < 0.05), suggesting a symbiotic effect likely related with carbohydrate digestion. No effects were observed on survival or reproductive output. Notably, the high-fiber diet alone performed comparably to commercial feed, suggesting potential for sustainable cricket production using agricultural by-products. These findings demonstrate the feasibility of microbiome informed probiotic strategies to enhance cricket farming efficiency while reducing feed costs. Graphical Abstract The alternative text for this image may have been generated using AI.