Abstract Agricultural nitrous oxide (N2O) emissions comprise a majority of the global source of this powerful greenhouse gas. Mitigation approaches for reducing emissions are difficult to evaluate at appropriate field scales because of the substantial effort and expense associated with relatively new technology allowing eddy covariance measurements of N2O fluxes (FN2O). Here we present a new approach for gap filling eddy covariance FN2O and estimating annual uncertainties for a temperate grazed grassland. We tested the potential of using one flux tower to evaluate emissions mitigation options in one paddock relative to an adjacent, unchanged paddock by partitioning data by source footprint contribution. Because of the complexity of spatiotemporal controls on FN2O, we generated a large set of environmental variables and features as input for machine learning algorithms. Inputs were transformed using partial least squares (PLS) decomposition, isolating features with the greatest influence on FN2O. PLS scores were fed to both a neural network (NN) and a locally-weighted k-nearest neighbours (kNN) regression. While the NN and kNN preformed similarly well, kNN regression accounted for the largest proportion of variance (52-72%) and resulted in the lowest bias for each of the three source footprint areas (full footprint and two separated adjacent paddocks, P53 and P54). Annual uncertainty estimates included random measurement uncertainty, accuracy and precision of the gap filling approach, and uncertainty associated with choice of threshold for atmospheric turbulence filtering and footprint contributions. Total N2O emissions for the full footprint, P53, and P54 were 7.4 ±0.35, 7.7 ±0.80, and 6.4 ±0.63 kg N2O-N ha−1, respectively in Year 1, and 6.9 ±0.33, 7.3 ±0.63, and 6.7 ±0.63 kg N2O-N ha−1, respectively in Year 2. These 95% confidence intervals on the annual FN2O suggest that we could detect differences of 10-15% between paddocks at this site when testing mitigation options.

中文翻译：

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涡流协方差 N2O 通量的改进间隙填充方法和不确定性估计

摘要 农业一氧化二氮 (N2O) 排放占这种强大温室气体的全球来源的大部分。由于与允许对 N2O 通量 (FN2O) 进行涡流协方差测量的相对较新的技术相关的大量工作和费用，因此难以在适当的现场尺度上评估用于减少排放的缓解方法。在这里，我们提出了一种填充涡流协方差 FN2O 和估计温带放牧草地年度不确定性的新方法。我们通过按源足迹贡献对数据进行分区，测试了使用一个通量塔来评估一个围场相对于相邻的未改变围场的排放减缓方案的潜力。由于 FN2O 时空控制的复杂性，我们生成了大量环境变量和特征作为机器学习算法的输入。输入使用偏最小二乘 (PLS) 分解进行转换，隔离对 FN2O 影响最大的特征。PLS 分数被馈送到神经网络 (NN) 和局部加权的 k 最近邻 (kNN) 回归。虽然 NN 和 kNN 的表现相似，但 kNN 回归占方差的最大比例 (52-72%) 并导致三个源足迹区域（完整足迹和两个分离的相邻围场，P53 和 P54）中的每一个的偏差最低）。年度不确定性估计包括随机测量不确定性、间隙填充方法的准确性和精度，以及与大气湍流过滤和足迹贡献阈值选择相关的不确定性。