Statistical Identification of Nitrous Oxide Hot Moments and Their Significance Across Global Agroecosystems

Abstract

Nitrous oxide (N₂O) emissions from agricultural soils contribute ∼4% of total anthropogenic greenhouse gas emissions globally. Events known as “hot moments” can occur following environmental changes that favor N₂O production, which contribute disproportionately to annual cumulative emissions. Despite their significance, hot moments and their impact have not been statistically well defined, particularly on a global scale. We collected 13,787 soil N₂O flux measurements from 42 publications and evaluated 14 methods of statistical anomaly detection for their ability to identify hot moments within data sets. Two methods achieved the highest overall performance by Matthews correlation coefficient (MCC): median absolute deviation (MCC: 0.80) and minimum covariance determinant (MCC: 0.80), the latter of which also performed evenly across highly dissimilar data sets and identified more contextually important minor hot moments (39%) that other methodologies may misidentify. Interquartile range, which has previously been used and recommended, performed poorly when hot moments were either very rare or very common within a data set and identified few local hot moments (14%). Overall, hot moments comprised ∼19% of measurements while contributing ∼75% of cumulative emissions. The median background N₂O emission reported in all data sets was 2.2 g N ha⁻¹ day⁻¹, whereas the median hot moment emission was 10-fold higher, ranging from 23 to 25 g N ha⁻¹ day⁻¹. These findings advance knowledge of how to accurately define and identify hot moments globally—a crucial task to investigating and mitigating these critical biogeochemical events.