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Soil chemical properties and microbiome composition impact N2O emission potential, but the relative importance of these factors as determinants of N2O emission in denitrifying systems is rarely tested. In addition, previous linkages between microbiome composition and N2O emission potential rarely demonstrate causality. Here, we determined the relative impact of bacteriome composition (i.e., soil extracted bacterial cells) and soil water extract (i.e., water extractable chemicals and particles below 0.22 µm) on N2O emission potential utilizing an anoxic cell-based assay system. Cells and water extract for assays were sourced from soils with contrasting N2O/N2O + N2 ratios, combined in various combinations and denitrification gas production was measured in response to nitrate addition. Analysis of 16S amplicon sequencing data revealed similarity in composition between extracted and parent soil bacteriomes. Average directionless effects of cell and water extract on N2O/N2O + N2 (Cell: ∆0.17, soil water extract: ∆0.22) and total N2O hypothetically emitted (Cell: ∆2.62 µmol-N, soil water extract: ∆4.14 µmol-N) across two assays indicated water extract is the most important determinant of N2O emissions. Independent pH differences of just 0.6 points impacted N2O/N2O + N2 on par with independent water extract differences, supporting the dominance of this variable in previous studies. However, impacts on overall N2O hypothetically emitted were smaller, suggesting that soil pH manipulation may not necessarily be a successful approach to mitigate emissions. In addition, we observed increased N2O accumulation and emission potential at the end of incubations concomitant with predicted decreases in carbon (C) availability, suggesting that C limitation increases N2O emission transiently with the magnitude of emission dependent on both chemical and bacteriome controls.

Inoculating agricultural soils with nitrous oxide respiring bacteria (NRB) can reduce N2O-emission, but would be impractical as a standalone operation. Here we demonstrate that digestates obtained after biogas production are suitable substrates and vectors for NRB. We show that indigenous NRB in digestates grew to high abundance during anaerobic enrichment under N2O. Gas-kinetics and meta-omic analyses showed that these NRB’s, recovered as metagenome-assembled genomes (MAGs), grew by harvesting fermentation intermediates of the methanogenic consortium. Three NRB’s were isolated, one of which matched the recovered MAG of a Dechloromonas, deemed by proteomics to be the dominant producer of N2O-reductase in the enrichment. While the isolates harbored genes required for a full denitrification pathway and could thus both produce and sequester N2O, their regulatory traits predicted that they act as N2O sinks in soil, which was confirmed experimentally. The isolates were grown by aerobic respiration in digestates, and fertilization with these NRB-enriched digestates reduced N2O emissions from soil. Our use of digestates for low-cost and large-scale inoculation with NRB in soil can be taken as a blueprint for future applications of this powerful instrument to engineer the soil microbiome, be it for enhancing plant growth, bioremediation, or any other desirable function.

Modeling the C and N dynamics during decomposition of plant residues depends on robust estimation of the litter pool partitioning (LPP) of residues, i.e. the fraction of their C and N allocated to rapidly and slowly decomposing pools. We searched for ways to estimate LPP by analyzing data on C and N mineralization during decomposition of 60 widely different plant residues, using a simple model with two litter pools and one microbial pool. LPP and relevant global parameters were estimated by model optimization (Levenberg-Marquardt-least-squares algorithm) in one operation. These kinetically-defined LPP values were used in regression analyses against data from stepwise chemical digestion (SCD) and near-infrared reflectance (NIR) analysis of the plant residues. LPP predicted by these regression models resulted in better performance than LPP from measured neutral-detergent-soluble (NDS) C and N when validated on independent data (n = 15 plant residues). The results demonstrated the potential improvement by simultaneous estimation of residue specific LPP and global parameters, and that kinetically-defined LPP can be equally well predicted by NIR as by total N and NDS-C. Model failure for a minority of the plant-residues could partly be removed by altering the microbial C/N ratio (global optimum 7.1) within the range 5–15, possibly reflecting a variable dominance of bacteria or fungi.