Wet-season spatial variability of N2O emissions from a tea field in subtropical central China

18 Tea fields emit large amounts of nitrous oxide (N2O) to the atmosphere. Obtaining accurate 19 estimations of N2O emissions from tea-planted soils is challenging due to strong spatial 20 variability. We examined the spatial variability of N2O emissions from a red-soil tea field in 21 Hunan province, China, on 22 April 2012 (in a wet season) using 147 static mini chambers 22 approximately regular gridded in a 4.0 ha tea field. The N2O fluxes for a 30-min snapshot 23 (10:00-10:30 am) ranged from -1.73 to 1,659.11 g N ha d and were positively skewed with 24 an average flux of 102.24 g N had. The N2O flux data were transformed to a normal 25 distribution by using a logit function. The geostatistical analyses of our data indicated that the 26 logit-transformed N2O fluxes (FLUX30t) exhibited strong spatial autocorrelation, which was 27 characterized by an exponential semivariogram model with an effective range of 25.2 m. As 28 observed in the wet season, the logit-transformed soil ammonium-N (NH4Nt), soil nitrate-N 29 (NO3Nt), soil organic carbon (SOCt), total soil nitrogen (TSNt) were all found to be 30 significantly correlated with FLUX30t (r=0.57-0.71, p< 0.001). Three spatial interpolation 31 methods (ordinary kriging, regression kriging and cokriging) were applied to estimate the 32 spatial distribution of N2O emissions over the study area. Cokriging with NH4Nt and NO3Nt 33 as covariables (r=0.74 and RMSE=1.18) outperformed ordinary kriging (r=0.18 and 34 RMSE=1.74), regression kriging with the sample position as a predictor (r=0.49 and 35 RMSE=1.55) and cokriging with SOCt as a covariable (r=0.58 and RMSE=1.44). The 36 predictions of the three kriging interpolation methods for the total N2O emissions of 4.0 ha 37 tea field ranged from 148.2 to 208.1 g N d, based on the 30 min snapshots obtained during 38