Evaluating the impact of nitrogen-fixing microbial inoculants on nitrous oxide emissions and nitrogen dynamics in corn cropping systems

Abstract

Nitrogen (N) is a critical nutrient for plant growth and essential for synthesizing amino acids, proteins, and nucleic acids, which are fundamental for crop yield and quality. The introduction of N fertilizers in the early 20th century significantly boosted agricultural productivity, meeting the demands of a growing global population. However, the widespread use of N fertilizers has led to environmental concerns, particularly the emission of nitrous oxide (N2O), a potent greenhouse gas with a global warming potential approximately 300 times greater than carbon dioxide (CO2). Our comprehensive research study integrates three related investigations, each exploring different aspects of N management in corn cropping systems, including the efficacy of N-fixing microbial inoculants in mitigating N2O emissions, enhancing fertilizer N recovery, and modeling N dynamics using the Denitrification-Decomposition (DNDC) model. The first study assessed the effectiveness of N-fixing microbial inoculants in reducing N2O emissions and improving N recovery in corn production. Conducted from 2021 to 2023 at the Agronomy North Farm at Kansas State University, the experiment employed a randomized complete block design with N application rates of 0, 56, 112, and 168 kg N ha-1, both with and without bioinoculants. N2O fluxes were measured bi-weekly using the static chamber technique. N2O emissions increased significantly with higher N fertilizer rates, with the highest cumulative emissions reaching 1.13 kg N2O-N ha-1 in 2022. Most emissions occurred within the first 40 days after planting, particularly in 2022, when precipitation events were more frequent. In 2023, N2O emissions were lower and more evenly distributed throughout the growing season. Bioinoculant-treated plots consistently exhibited lower N2O emissions than untreated plots, although these reductions were not statistically significant. Emission factors (EF%) remained below the IPCC default emission factor value of 1% across all treatments in 2022 and 2023. N2O emission-yield curves had a significant linear trend with yield without bioinoculant application. However, there was no apparent trend with bioinoculant application. These findings highlight the significant role of N fertilizer rates in driving N2O emissions. The effect of bioinoculant on N2O flux was not clear and non-significant. The second study focused on N recovery in above-ground biomass and soil N dynamics using a stable isotope 15N technique, which explored the effects of N-fixing microbial inoculant across different growth stages and N application rates. Conducted over the same period and location as the previous study, this study used micro-plots to accommodate 15N fertilizer treatments. In 2022, fertilizer N recovery ranged from 6.7% to 11.62% at R6 corn growth stage, with the bioinoculant having minimal impact. However, in 2023, N recovery in biomass increased significantly, ranging from 17% to 30.9%, with the greatest recovery observed at the 168 kg N ha-1 rate with bioinoculant application. The lower N recovery in 2022 might be due to significant early-season precipitation, which can potentially increase N losses by way of leaching and denitrification. N2O emissions were higher in 2022, and more fertilizer N was recovered at the 15-30 cm soil layer. Well-distributed precipitation in 2023 facilitated better N recovery in above-ground biomass. Nitrogen immobilization was profoundly greater at 15-30 cm compared to 0-5cm and 5-15cm, especially at lower N rates. Fertilizer N recovery as organic N ranged from 8.7% to 28.3% at the harvest stage in 2023. Overall fertilizer N recoveries for both above-ground biomass and below-ground soil pools were lower than most other 15N research studies. The effect of N-fixing bioinoculant in N recovery was not clear. Long-term field trials need to be implemented to investigate the impact of N-fixing microbial inoculant on N recovery in corn biomass and soil profiles. The third study employed the Denitrification-Decomposition (DNDC) model to simulate cumulative N2O emissions under no-till, continuous corn management without bioinoculants. The objective was to evaluate the model's accuracy in predicting N₂O emissions and to assess its sensitivity to varying environmental conditions, particularly precipitation. The model was calibrated using 2022 data, incorporating daily precipitation and temperature inputs and fine-tuning soil parameters such as bulk density, pH, and soil organic carbon obtained from the Web Soil Survey database. The calibration process demonstrated strong accuracy, with a Mean Absolute Error (MAE) of 0.016, Root Mean Squared Error (RMSE) of 0.023, Mean Bias Error (MBE) of 0.016, and a Coefficient of Determination (R2) of 0.997. Validation using 2023 data showed a slight decline in accuracy, with MAE of 0.056, RMSE of 0.067, MBE of -0.051, and R2 of 0.934, indicating a minor underprediction bias. The model effectively captured the influence of precipitation on N2O emissions, accurately reflecting emission peaks during periods of high rainfall. These findings validate the DNDC model's reliability in predicting N2O emissions under specified agricultural practices and highlight the need for continuous evaluation and recalibration to maintain predictive accuracy across different growing seasons. In conclusion, these studies provide comprehensive insights into N management in corn cropping systems, emphasizing the critical role of N application rates, the potential benefits and limitations of bioinoculants, and the utility of modeling tools like the DNDC model in predicting greenhouse gas emissions. The findings underscore the importance of integrating N-fixing bioinoculants with precise N management strategies to enhance N use efficiency, improve crop yields, and mitigate environmental impacts. However, the stage-specific and N rate-dependent effectiveness of bioinoculants and the need for continuous model recalibration highlight the complexity of N dynamics in agricultural systems and the necessity for ongoing targeted research to optimize these practices for sustainable agriculture.