Tea fields emit large amounts of nitrous oxide (N

According to the latest data, which show rapid increases in their
atmospheric concentrations (IPCC, 2013), nitrous oxide (N

Among the different agricultural soils, tea-planted soils are important
sources of N

The N

To understand the structure of the spatially distributed data and to predict
the N

In contrast with the dry season, the spatial variability in the N

The field experiment was conducted in a small catchment (4.0 ha) in Jinjing,
Changsha, in Hunan Province, China (28

In the 4.0 ha tea-planted catchment, 1964 evenly distributed points with
plane coordinates and elevation values and 456 centerlines of tea tree row
were recorded by a locally calibrated differential geographic positioning
system (DGPS) receiver (Sanding Southern Survey Co., China), and then were
used to develop the local DEM and land use data (at a spatial resolution of
0.1 m, respectively, as shown in Fig. 1c and d). The land use data showed
the four positions where the chambers were placed, including the inter-row,
fertilization point, under tea tree and in tea tree row, as described in Li
et al. (2013). The spatial positions of the gas sampling points in a 15 m

Daily

Gas and soil samples were collected at each grid point on 22 April 2012
using a closed mini chamber technique. A mini chamber set was composed of
PVC and had two parts (base and chamber). The base was 0.15 m in diameter
and 0.05 m high. The chamber was 0.15 m in diameter and 0.15 m high, and was
equipped with rubber septa on the top for gas sampling. In the field
operation, the base was gently inserted vertically into the soil on 20 April 2012,
and the chamber was clipped on the base with the sponge seals in
between to stop gas leaking before gas sampling on 22 April 2012. Therefore,
the effective static chambers volume was equal to the chamber volume of
0.002651 m

The descriptive statistical and geostatistical analyses were performed using R (R Development Core Team, 2014) with the gstat package (DGUU, 2010).

Descriptive statistical analyses were used to determine the mean, median, minimum and maximum values, SD, coefficient of variation (CV) and skewness of the original and logit-transformed data. These analyses were based on the four chamber placement positions. Because the FLUX30, NH4N, NO3N, SOC, TSN and SWC data were highly skewed, these values were transformed by using a logit function (Hengl et al., 2004). The transformed variables were named FLUX30t, NH4Nt, NO3Nt, SOCt, TSNt and SWCt. Using a Pearson's correlation, the relationships between FLUX30t, NH4Nt, NO3Nt, SOCt, TSNt, SWCt, DOC, BD, SAND, SILT and CLAY were tested. The significance of the differences in the FLUX30t and environmental factors (NH4Nt, NO3Nt, SOCt, TSNt and DOC) between any two of the different chamber positions along the entire tea tree row transect were evaluated using the Tukey's honest significant difference method.

Histograms of

In the geostatistical analyses, an experimental semivariogram of FLUX30t was calculated, and the theoretical semivariogram models were fit. The ratio of the partial sill to the total sill was used as an index of spatial dependence. Armstrong (1998) stated that a variable with a higher ratio of partial sill to sill and a longer semivariogram range were more structured. The spatial distribution of FLUX30t across the catchment was predicted using three kriging interpolation methods (OK, RK and CK). These data were transformed back to the original scale of FLUX30 for mapping. The leave-one-out cross-validation method was used to evaluate the accuracy of interpolating FLUX30t using the three different kriging methods.

In the 4.0 ha tea-planted catchment, the N

Descriptive statistics of the N

The Tukey's honest significant difference analysis for FLUX30t, NH4Nt, NO3Nt, SOCt, TSNt and SWCt based on the four-chamber placement positions (R, inter-row; F, fertilization point; U, under tea tree; and I, in the tea tree row).

The ELEVATION, BD, DOC, SWC, SAND, SILT and CLAY were approximately normally distributed, with skewness values of less than 1 (Table 1). Additionally, DOC displayed a moderate CV of 34.6 %, and the other variables had lower CVs (4.1–23.8 %). The NH4N, NO3N, SOC and TSN were positively skewed, and the logit transformations (NH4Nt, NO3Nt, SOCt and TSNt) had approximately normal distributions (Table1). The NH4N and NO3N had very high CVs (190.8 and 141.6 %, respectively), and the SOC and TSN had moderate CVs (50.1 and 38.3 %, respectively).

Correlation matrix with the Pearson's correlation coefficients (

Semivariograms (open circles) and best-fitted models (solid lines)
of the normal logit-transformed N

Semivariogram models for N

ND, not determined.

The NH4Nt, NO3Nt, SOCt, TSNt and SWC were significantly correlated with the
N

Because most of the soil properties were significantly correlated with the
chamber placement positions, two types of semivariogram models were
calculated for the N

Statistics for N

The numbers in the parentheses represent the sample numbers for each chamber placement position.

Three spatial interpolation methods were used in this study to predict the
spatial distribution of N

Direct and cross-semivariograms (open circles, detrending the
influence of chamber placement position for cokriging) and the best-fitted
linear model of the co-regionalization (solid lines) of the normal
logit-transformed N

Direct and cross-semivariograms (open circles, detrending the
influence of chamber placement position for cokriging) and the best-fit
linear model of co-regionalization (solid lines) for the normal
logit-transformed N

As shown in Fig. 9, the surface map for the spatial distribution of N

Cross-validations of the three different kriging interpolations for
N

OK, RK and CK correspond to ordinary kriging, regression kriging
and cokriging, respectively. For the dry-season campaign, ELEVATION,
SOCt and ELEV are the normalized elevation, the normalized soil
organic carbon content and the inverse of the normalized elevation,
respectively. For the wet-season campaign, SOCt, NH4Nt and NO3Nt are the
logit transformations of soil organic carbon, soil ammonium and soil nitrate
concentrations, respectively. “POSITION” (in the parentheses) indicates the
process of detrending the influence of chamber placement position. The ME,
RMSE and

The N

Spatial distributions of the N

Soil type, topography and land management (fertilization, tillage and
irrigation) are the primary factors that affect the spatial structures of
N

During the dry season, the topography (elevation) had a significant effect
on the spatial pattern of N

Spatial distributions of kriging standard deviations of the
predicted N

In view of the analysis of the primary factors that affected the spatial
pattern of N

The three interpolation methods (OK, RK and CK) were used to predict the
spatial distributions of N

Secondly, by comparing the performances of the three interpolation methods, the RK and CK methods, which are more sophisticated kriging technologies, performed better than the OK method for the dry and wet seasons. Similar results were obtained by previous researchers (Stein et al., 1988; Odeh et al., 1995; Goovaerts, 1997; Hengl et al., 2004). When comparing the performances of RK and CK, no differences were observed for the dry season. However, during the wet season, the CK significantly outperformed the RK (Table 4). Overall, few attempts have been made to provide a good method for selecting interpolation methods between RK and CK (Knotters et al., 1995; Odeh et al. 1995). Li et al. (2013) suggested that RK was a good choice because of the performance of the two interpolation methods and the difficulties encountered when applying CK. However, in this study, the CK method was better than the RK method because of its high predictive performance (Table 4), its readily available required covariables (e.g., NH4N, NO3N and SOC) at co-locations, and because expensive surface data were not needed (e.g., DEM and land use data, which are required by RK) (Goovaerts, 1997; Webster and Oliver, 2001). Our conclusions were similar to those of many previous studies that found that CK was the most versatile and rigorous statistical technique for estimating spatial points (Stein et al., 1988; Odeh et al., 1995; Webster and Oliver, 2001). For the application of CK, the covariables must show a correlation with the target variable and present a similar spatial structure as the target variable (Odeh et al., 1995; Goovaerts, 1997; Webster and Oliver, 2001). Therefore, we further compared the effects of the two groups of covariables for CK in this study. We found that the CK method with NH4Nt and NO3Nt (which showed significant correlations with FLUX30t) as covariables outperformed the CK method with SOCt (which presented a similar spatial structure to FLUX30t) as a covariable, indicating that the feature correlation was more important than the similarity of the spatial structure when selecting CK covariables. This finding can be regarded as a prerequisite for selecting covariables for CK application.

The three spatial interpolation methods predicted similar total N

During the wet season of 2012, the 30 min one-time measurements of N

To effectively mitigate high N

The National Basic Research Program of China (2012CB417105) and the National Natural Science Foundation of China (41171200) financially supported this research. Edited by: I. Trebs