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

[摘要] Tea fields emit large amounts of nitrous oxide (N₂O) to the atmosphere.Obtaining accurate estimations of N₂O emissions from tea-planted soilsis challenging due to strong spatial variability. We examined the spatialvariability in N₂O emissions from a red-soil tea field in HunanProvince, China, on 22 April 2012 (in a wet season) using 147 static minichambers approximately regular gridded in a 4.0 ha tea field. The N₂Ofluxes for a 30 min snapshot (10:00–10:30 a.m.) ranged from −1.73 to1659.11 g N ha⁻¹ d⁻¹ and were positively skewed with an average flux of102.24 g N ha⁻¹ d⁻¹. The N₂O flux data were transformed toa normal distribution by using a logit function. The geostatistical analysesof our data indicated that the logit-transformed N₂O fluxes (FLUX30t)exhibited strong spatial autocorrelation, which was characterized by anexponential semivariogram model with an effective range of 25.2 m. Asobserved in the wet season, the logit-transformed soil ammonium-N (NH4Nt),soil nitrate-N (NO3Nt), soil organic carbon (SOCt) and total soil nitrogen(TSNt) were all found to be significantly correlated with FLUX30t (r = 0.57–0.71,p< 0.001). Three spatial interpolation methods (ordinarykriging, regression kriging and cokriging) were applied to estimate thespatial distribution of N₂O emissions over the study area. Cokrigingwith NH4Nt and NO3Nt as covariables (r = 0.74 and RMSE = 1.18)outperformed ordinary kriging (r = 0.18 and RMSE = 1.74), regressionkriging with the sample position as a predictor (r = 0.49 and RMSE = 1.55)and cokriging with SOCt as a covariable (r = 0.58 and RMSE = 1.44). Thepredictions of the three kriging interpolation methods for the totalN₂O emissions of 4.0 ha tea field ranged from 148.2 to 208.1 g N d⁻¹,based on the 30 min snapshots obtained during the wet season. Ourfindings suggested that to accurately estimate the total N₂O emissionsover a region, the environmental variables (e.g., soil properties) and thecurrent land use pattern (e.g., tea row transects in the present study) mustbe included in spatial interpolation. Additionally, compared with otherkriging approaches, the cokriging prediction approach showed greatadvantages in being easily deployed and, more importantly, providing accurateregional estimation of N₂O emissions from tea-planted soils.

[学科分类] 地球化学与岩石