Where nitrogen input from fertilizer application exceeds plant demands, hotspots of microbially produced nitrous oxide (N ₂ O) can exhibit disproportionately high rates of emissions relative to longer periods of time, known as hot moments. Hotspots and hot moments of N₂O are sensitive to changes in agricultural management and weather, making it difficult to accurately quantify N ₂ O emissions. This study investigates the spatial and temporal variability of N₂O emissions using both static chambers (CH) and eddy covariance (EC) techniques, measured at a grassland site subject to four fertilizer applications of calcium ammonium nitrate (CAN) in 2019. Daily mean CH emissions were calculated using the arithmetic method and Bayesian statistics to explicitly account for the log-normal distribution of the dataset. N₂O fluxes measured by CH and EC were most comparable when flux measurements were > 115 N ₂ ON µg m ^− 2 hr ⁻¹, and EC and CH measurements showed spatial and temporal alignment when CH n ≥ 15. Where n ≤ 5, the Bayesian method produced large uncertainties due to the difficulty of fitting an arithmetic mean from a log-normally distributed data set with few flux measurements. Annual EC fluxes, gap-filled using a multi-variate linear model, showed a strong correlation with measured flux values (R ² = 0.92). Annual cumulative fluxes by EC were higher (3.35 [± 0.5] kg N ha⁻¹) than CH using the arithmetic (2.98 [± 0.17] kg N ha⁻¹ ) and Bayesian method (3.13 [± 0.24] kg N ha⁻¹), which quantified emission factors of 1.46%, 1.30% and 1.36%, respectively. This study implies that a large sample size and frequent CH flux measurements are necessary for comparison with EC fluxes and that Bayesian statistics are an appropriate method for estimating realistic means and ranges of uncertainty for CH flux data sets.

当施肥产生的氮输入超过植物需求时，微生物产生的一氧化二氮 (N ₂  O) 的热点 相对于较长的时间段（称为热时刻）会表现出不成比例的高排放率。N ₂ O 的热点和热点时刻对农业管理和天气的变化很敏感，因此很难准确量化 N  ₂  O 的排放量。本研究调查了 N _{2 的}时空变异性使用静态室 (CH) 和涡流协方差 (EC) 技术的 O 排放量，在 2019 年在受四次硝酸铵钙 (CAN) 施肥的草地场地测量。使用算术方法和贝叶斯统计计算每日平均 CH 排放量明确说明数据集的对数正态分布。当通量测量值 > 115 N ₂  O N µg m  ^{− 2}  hr  ^{-1 时}，CH 和 EC 测量的N ₂ O 通量最具可比性 ，并且当 CH n  ≥ 15时，EC 和 CH 测量值显示空间和时间对齐。其中n  ≤ 5，贝叶斯方法产生很大的不确定性，因为很难从具有少量通量测量值的对数正态分布数据集中拟合算术平均值。使用多变量线性模型填充间隙的年度 EC 通量显示出与测量的通量值 (R ²  = 0.92)的强相关性 。使用算术 (2.98 [± 0.17] kg N ha ^-1  ) 和贝叶斯方法 (3.13 [± 0.24] kg N ha ^-1 )，EC 的年累积通量 (3.35 [± 0.5] kg N ha ^-1 ) 高于 CH)，分别量化了 1.46%、1.30% 和 1.36% 的排放因子。这项研究意味着需要大样本量和频繁的 CH 通量测量才能与 EC 通量进行比较，并且贝叶斯统计是估计 CH 通量数据集的实际均值和不确定性范围的合适方法。