Nitrous oxide (N2 O) is a potent greenhouse gas that is primarily emitted from agriculture. Sampling limitations have generally resulted in discontinuous N2 O observations over the course of any given year. The status quo for interpolating between sampling points has been to use a simple linear interpolation. This can be problematic with N2 O emissions, since they are highly variable and sampling bias around these peak emission periods can have dramatic impacts on cumulative emissions. Here, we outline five gap-filling practices: linear interpolation, generalized additive models (GAMs), autoregressive integrated moving average (ARIMA), random forest (RF), and neural networks (NNs) that have been used for gap-filling soil N2 O emissions. To facilitate the use of improved gap-filling methods, we describe the five methods and then provide strengths and challenges or weaknesses of each method so that model selection can be improved. We then outline a protocol that details data organization and selection, splitting of data into training and testing datasets, building and testing models, and reporting results. Use of advanced gap-filling methods within a standardized protocol is likely to increase transparency, improve emission estimates, reduce uncertainty, and increase capacity to quantify the impact of mitigation practices.

中文翻译：

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全球研究联盟 N 2 O 室方法指南：常见间隙填充方法摘要及其应用建议

一氧化二氮 (N2 O) 是一种主要从农业排放的强效温室气体。抽样限制通常导致在任何给定年份的过程中 N2 O 观测不连续。在采样点之间进行插值的现状是使用简单的线性插值。这对于 N2 O 排放来说可能是有问题的，因为它们是高度可变的，并且在这些峰值排放期附近的采样偏差会对累积排放产生巨大影响。在这里，我们概述了五种填隙实践：线性插值、广义加性模型 (GAM)、自回归积分移动平均 (ARIMA)、随机森林 (RF) 和已用于填隙土壤 N2 的神经网络 (NN) O排放。为便于使用改进的填隙方法，我们描述了五种方法，然后提供每种方法的优点和挑战或缺点，以便可以改进模型选择。然后，我们概述了一个协议，其中详细说明了数据组织和选择、将数据拆分为训练和测试数据集、构建和测试模型以及报告结果。在标准化协议中使用先进的填补空白的方法可能会增加透明度、改进排放估计、减少不确定性并提高量化缓解做法影响的能力。