Variability and Driving Mechanisms of Cropland N₂O Emission Factors: A Review

Peiyi Zhang

doi:10.62051/ijnres.v8n6.06

Authors

Peiyi Zhang

DOI:

https://doi.org/10.62051/ijnres.v8n6.06

Keywords:

Cropland; Nitrous Oxide; Emission Factor; Driving Mechanism; Climate Sensitivity; Extreme Events; Machine Learning.

Abstract

Nitrous oxide (N₂O) is an important component of agricultural greenhouse gas emissions, and nitrogen inputs to croplands represent one of the major anthropogenic sources of N₂O. The emission factor (EF), which links nitrogen input to direct N₂O emissions, has long been used in national greenhouse gas inventories and agricultural mitigation assessments. The conventional fixed-EF approach is simple and suitable for large-scale accounting, but extensive field observations have shown that cropland EFs vary substantially across climate zones, soil backgrounds, cropping systems, and management practices. A single fixed coefficient cannot adequately capture the spatial heterogeneity, interannual variability, and event-driven pulse characteristics of cropland N₂O emissions. In recent years, with the development of global field observation databases, meta-analyses, machine learning, and interpretable modelling approaches, EF research has gradually shifted from estimating average coefficients to explaining the mechanisms underlying EF variability. Existing studies indicate that cropland N₂O emissions are jointly regulated by nitrification, denitrification, and N₂O reduction processes. Their variability is mainly controlled by nitrogen substrate availability, soil moisture and oxygen diffusion, soil pH, soil organic carbon, temperature, precipitation, and agricultural management practices. Acidic soils, high SOC, high nitrogen input, humid conditions, and heavy rainfall after fertilization are more likely to create high-emission risks. Meanwhile, drying–rewetting, freeze–thaw cycles, and extreme rainfall events can trigger short-term emission peaks, further challenging the fixed-EF approach. This review synthesizes current knowledge on the variability, driving mechanisms, multifactorial interactions, and methodological advances in cropland N₂O EF research. It further suggests that future EF systems should move from fixed empirical coefficients toward regionalized, dynamic, and mechanism-constrained parameter frameworks, thereby improving the scientific basis of cropland N₂O inventories and mitigation strategies.

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