Mitigating gaseous nitrogen emissions in cotton fields through green manure and reduced nitrogen fertilization

Abstract

Integrating green manure with reduced nitrogen (N) fertilization is a promising strategy to mitigate N emissions in intensive cotton cultivation, however, the underlying mechanisms remain poorly understood. This study investigated the effects of three green manure incorporation patterns-no green manure (NG), Orychophragmus violaceus (OVG), and Vicia villosa (VVG)-combined with four N reduction levels (100, 50, 25%, and conventional) on gaseous N emissions (NH₃ and N₂O), soil physicochemical properties, and bacterial community characteristics using a cotton field experiment in the Yellow River Basin. Results showed that OVG incorporation with 25% N reduction (N2 treatment) significantly reduced total gaseous N emissions by 36.07% on average during the cotton growth period, reducing NH₃ and N₂O emissions by 13.31-54.11% and 32.25-68.77%, respectively, compared with N2 application without OVG. OVG application also increased the relative abundance of Proteobacteria (28.10%), enhanced heterogeneous selection in bacterial community assembly (200%), and increased the complexity of co-occurrence networks, compared with NG. Compared with conventional N fertilization (N3 treatment), ≥50% N reduction significantly lowered NH₃ (>25.51%) and N₂O (>32.76%) emissions, reduced the relative abundance of Acidobacteria (-20.23%), simplified co-occurrence networks, and increased homogeneous selection in bacterial assembly (50.00%). Integrating green manure with 25% N reduction substantially reduced gaseous N emissions, which was associated with the enhanced microbial biomass carbon (MBC) and facilitated recruitment of key bacterial taxa (e.g., Sphingosinicella, Azohydromonas, Phototrophicus) within the microbial co-occurrence network. These findings provide insight into how green manure application coupled with N reduction can mitigate gaseous N losses and reshape soil microbial ecology, offering a theoretical basis for sustainable nutrient management during cotton production.

Keywords: N fertilizer reduction; N2O emissions; NH3 volatilization; bacterial keystone taxa; green manure.

Figure 1

Effects of green manure incorporation and N fertilizer reduction on NH₃ (A,C) and N₂O (B,D) emissions in cotton fields. Error bars represent the standard error of the mean. Different letters indicate significant differences (p < 0.05) among N treatments. NG, no incorporation (control); OVG, incorporation of Orychophragmus violaceus; VVG, incorporation of Vicia villosa; N3, maximum economic N rate; N2, 25% N reduction compared to N3; N1, 50% N reduction compared to N3; N0, no N application.

Figure 2

Effects of green manure incorporation and N fertilizer reduction on bacterial community composition and diversity. (A) Relative abundance of soil bacterial communities at the phylum level. (B) Nonmetric multidimensional scaling (NMDS) analysis showing the beta diversity of the soil bacterial communities. The ellipses represent 50% confidence intervals. (C) Ternary plot illustrating the distribution of the top 500 amplicon sequence variants (ASVs) responding to green manure patterns, with the most enriched genera displayed (p < 0.05, n = 12). (D) Bar charts showing significant phyla responding to varying N fertilization levels (p < 0.05, n = 9). NG, no incorporation (control); OVG, incorporation of Orychophragmus violaceus; VVG, incorporation of Vicia villosa; N3, maximum economic N rate; N2, 25% N reduction compared to N3; N1, 50% N reduction compared to N3; N0, no N application.

Figure 3

Bacterial community assembly processes and niche breadths in response to green manure incorporation and N fertilizer reduction. Fit of the neutral community models of bacterial taxa under patterns of (A) NG, (B) OVG, (C) VVG, (D) N0, (E) N1, (F) N2, and (G) N3. Niche breadth differences and relative contributions of deterministic and stochastic processes under patterns of green manure incorporation (H) and nitrogen fertilizer reduction (I). ASVs represented by green dots showed a higher frequency of occurrence than the model predicts, and those represented by orange dots were the opposite. ASVs predicted by the 95% confidence interval of the model are shown as red dots. m is the migration rate between populations, and R² indicates the fit to this model. NG, no incorporation (control); OVG, incorporation of Orychophragmus violaceus; VVG, incorporation of Vicia villosa; N3, maximum economic N rate; N2, 25% N reduction compared to N3; N1, 50% N reduction compared to N3; N0, no N application.

Figure 4

Co-occurrence networks analysis of bacterial communities in response to green manure incorporation and N fertilizer reduction. Co-occurrence networks and distributions of keystone taxa under patterns of (A) NG, (B) OVG, (C) VVG, (D) N0, (E) N1, (F) N2, and (G) N3. The size of each node in networks is proportional to the number of degrees. Keystone taxa in each network identified by within-module connectivity (Zi) and among-module connectivity (Pi). The nodes were colored at the phylum level. NG, no incorporation (control); OVG, incorporation of Orychophragmus violaceus; VVG, incorporation of Vicia villosa; N3, maximum economic N rate; N2, 25% N reduction compared to N3; N1, 50% N reduction compared to N3; N0, no N application.

Figure 5

Relationships between soil physicochemical properties and bacterial communities, and their potential contributions to gases N emissions. (A) Random forest model (RFM) revealing importance of soil properties influencing bacterial community diversity, assembly, and keystone taxa. (B) Pearson correlation matrix showing relationships among soil biotic and abiotic parameters. (C) Partial least squares-path modeling (PLS-PM) illustrating the direct and indirect effects of significant soil properties and bacterial communities on NH₃ and N₂O emissions. Path coefficients are indicated next to the arrows; line thickness is proportional to coefficient values. Red and blue arrows indicate positive and negative effects, respectively. Asterisks denote statistical significance at * p < 0.05, **p < 0.01, and ***p < 0.001. The cross-loading values for all parameters displayed by the bar charts; factor loadings above 0.6 are represented by dashed lines.