Improved gap filling approach for N2O fluxes allows determination of separate annual budgets and uncertainties for two adjacent grazed pastures from one flux tower

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 (F_N2O). Here we present a new approach for gap filling eddy covariance F_N2O 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 F_N2O, 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 F_N2O. PLS loadings 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 F_N2O suggest that we could detect differences of 10-15% between paddocks at this site when testing mitigation options.