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• W3048480628 abstract "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." @default.
• W3048480628 created "2020-08-18" @default.
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• W3048480628 date "2020-09-01" @default.
• W3048480628 modified "2023-10-03" @default.
• W3048480628 title "Global Research Alliance N ₂ O chamber methodology guidelines: Guidelines for gap‐filling missing measurements" @default.
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• W3048480628 doi "https://doi.org/10.1002/jeq2.20138" @default.
• W3048480628 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33016449" @default.
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