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TECHNICAL REPORTS

Atmospheric Pollutants and Trace Gases

Spatial Variability of Nitrous Oxide Emissions and Their Soil-Related Determining Factors in an Agricultural Field

^a Graduate School of Agriculture, Kyoto University, Kitashirakawa Oiwake, Sakyo, Kyoto 606-8502, Japan
^b Graduate School of Agriculture, Hokkaido University, Sapporo, Hokkaido 060-8589, Japan
^c Faculty of Horticulture, Chiba University, Matsudo, Chiba 271-8510, Japan
^d Faculty of Life and Environmental Science, Shimane University, Matsue Shimane 690-8504, Japan

^* Corresponding author (yanai{at}kais.kyoto-u.ac.jp).

Received for publication July 14, 2002.

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
To evaluate spatial variability of nitrous oxide (N₂O) emissions and to elucidate their determining factors on a field-scale basis, N₂O fluxes and various soil properties were evaluated in a 100- x 100-m onion (Allium cepa L.) field. Nitrous oxide fluxes were determined by a closed chamber method from the one-hundred 10- x 10-m plots. Physical (e.g., bulk density and water content), chemical (e.g., total N and pH), and biological (e.g., microbial biomass C and N) properties were determined from surface soil samples (0–0.1 m) of each plot. Geostatistical analysis was performed to examine spatial variability of both N₂O fluxes and soil properties. Multivariate analysis was also conducted to elucidate relationships between soil properties and observed fluxes. Nitrous oxide fluxes were highly variable (average 331 µg N m^-2 h^-1, CV 217%) and were log–normally distributed. Log-transformed N₂O fluxes had moderate spatial dependence with a range of >75 m. High N₂O fluxes were observed at sites with relatively low elevation. Multivariate analysis indicated that an organic matter factor and a pH factor of the principal component analysis were the main soil-related determining factors of log-transformed N₂O fluxes. By combining multivariate analysis with geostatistics, a map of predicted N₂O fluxes closely matched the spatial pattern of measured fluxes. The regression equation based on the soil properties explained 56% of the spatially structured variation of the log-transformed N₂O fluxes. Site-specific management to regulate organic matter content and water status of a soil could be a promising means of reducing N₂O emissions from agricultural fields.

Abbreviations: MBF, microbial biomass factor • OMF, organic matter factor • PHF, pH factor • SOF, soluble salts factor • SSF, soil structure factor • WFPS, water-filled pore space

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
NITROUS OXIDE is a trace gas that is involved in both the greenhouse effect (Yung et al., 1976) and destruction of stratospheric ozone (Johnson, 1971). Total annual emissions are estimated to be 17.7 Tg N (6.7–36.6 Tg N), about 60% of which is derived from the soil (Ehhalt et al., 2001). Estimates of N₂O emissions from agricultural soils vary widely, for example, 2.29 to 3.65 Tg N (Bouwman, 1990), 0.2 to 2.1 Tg N (Eichner, 1990), 0.03 to 3 Tg N (Watson et al., 1992), or 2.4 Tg N (Bouwman, 1996). Accurate evaluation of the N₂O emissions from agricultural land on a field scale is urgently required to quantify the amount of total emissions more precisely and to establish practical methods of reducing N₂O emissions.

Evaluation of N₂O emissions in agricultural fields must take account of spatial variability because it is generally accepted that emissions from within even a small area may vary by orders of magnitude (Bouwman, 1996). This heterogeneity makes predictions highly uncertain because in most cases only weak or no spatial dependencies have been demonstrated with considerable variation in the field (Folorunso and Rolston, 1984, 1985; Ball et al., 1997; Clemens et al., 1999; Roever et al., 1999). It is not certain, however, whether this tendency for weak spatial dependency is universal. Elucidation of the determining factors of N₂O emissions is also important because N₂O emissions have been reported to be highly variable depending on soil conditions such as organic matter, water and oxygen status, pH, substrate concentration, carbon supply, and temperature as well as fertilization and land management (Sahrawat and Keeney, 1986). In particular, overall effects of soil properties on N₂O emission rates in the field should be fully evaluated because in many studies only individual factors have been investigated. Furthermore, understanding of spatial relationships is also required to mitigate N₂O emissions in the field through soil management.

The objectives of the present study were to (i) evaluate the spatial variability of N₂O fluxes and soil properties in an agricultural field, (ii) determine the possible cause–effect relationship between soil properties and N₂O fluxes by multivariate analysis, and (iii) interpret the relationships spatially by combining multivariate analysis with geostatistics.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Experimental Field
The experiment was performed in an agricultural field located in Mikasa City, Hokkaido prefecture, Japan (43°14' N, 141°50' E) with a mean annual temperature and precipitation of 7.2°C and 1204 mm, respectively (Fig. 1) . The field examined had an area of 2.1 ha (147 x 146 m) with a slight gradient (0.2%) across the northwest (relatively low) and southeast (relatively high) corners. It has been used to cultivate onion for more than 20 yr after long-term paddy rice (Oryza sativa L.) cultivation. The soil is of alluvial origin (clayey aquic) and classified as a fine, mesic, Mollic Fluvaquent (Soil Survey Staff, 1998). The soil in the plow layer has a texture class of silty clay (12% sand, 51% silt, and 37% clay) and a cation exchange capacity (CEC) of 25.5 cmol_c kg^-1. Further information on this site can be obtained from Hayashi and Hatano (1999). Fertilization and crop management were conducted homogeneously across the field, and followed standard procedures for this area. On 27 Apr. 1999 chemical fertilizer was applied as ammonium sulfate at rates equivalent to 0.032 kg m^-2 of N; onion seedlings were transplanted on May 1; onion tops were cut off by agricultural machinery on 23 August to dry out the aboveground material, and onions were harvested on 6 September.

View larger version (35K): [in this window] [in a new window]   Fig. 1. Location of the study site: Mikasa, Hokkaido, Japan (•).

The N₂O concentration of each gas sample was measured using gas chromatography (GC-14B; Shimadzu, Kyoto, Japan) with an electron capture detector. Nitrous oxide flux rate from the soil surface was then calculated based on the differences in N₂O concentration between ambient air and the headspace gas sample taken after 15 min.

Each of the soil core samples was subdivided into three subsamples to evaluate physical, chemical, and biological properties of the soil. The first set of subsamples (undisturbed) was used to measure physical properties: bulk density; solid-, liquid-, and gas-phase volumetric percentage; the percentage of water-filled pore space (WFPS) in total pore space; and water content. Three variables, liquid phase percentage, WFPS, and water content, were measured in relation to water status of the soil because water was expected to influence the level of N₂O fluxes to a considerable degree. The second set of subsamples was air-dried, sieved to 2 mm, and analyzed for the following chemical properties: total N content; total C content; C to N ratio; inorganic C content; water-soluble NO^-₃; NH⁺₄ and inorganic N (as NO^-₃ + NH⁺₄); available P (Bray 2 method); exchangeable Ca, Mg, K, and Na; pH; and electrical conductivity (EC). Total N content and total C content were determined by the dry combustion method (NC-800 analyzer; Sumika Chemical Analysis Service, Osaka, Japan) with C to N ratio calculated. Inorganic C was determined after extracting the soil with 0.5 M K₂SO₄. The contents of water-soluble NO^-₃ and NH⁺₄ were determined by the Griess–Ilosvay method (after reduction of NO^-₃ to NO^-₂) and indophenol method, respectively, after shaking 1 h at the soil to water ratio of 1:5 (Mulvaney, 1996). All other analytical procedures were as described by Yanai et al. (2000). Total and readily available fractions of N and C were measured because soil N was assumed to supply substrates, and soil C the energy, for the N₂O fluxes. The third set of subsamples (only sieved, not dried) was used to measure biological properties: microbial biomass N and C with the fumigation extraction method (Brookes et al., 1985; Vance et al., 1987). In total, 23 properties (7 physical, 14 chemical, and 2 biological properties) were measured as the soil properties of the field.

Statistical Analysis
Normality and log–normality tests were conducted on the data distributions (chi-square test). In addition to descriptive statistics, to evaluate the determining factors of N₂O fluxes, multivariate analysis was performed. Principal component analysis (PCA) of the soil properties was conducted to summarize data and investigate the relationships among the properties (Kosaki and Juo, 1989). Stepwise multiple regression analysis was subsequently performed, using the scores of the extracted principal components as independent variables and N₂O flux as a dependent variable. The statistical software SYSTAT 8.0 (SPSS, 1998) was used in the analysis.

Geostatistical Analysis
Geostatistical analysis was performed not only on the original data (i.e., measured N₂O fluxes and soil properties), but also on calculated data (i.e., extracted PCA factors and predicted N₂O fluxes). In this analysis, a semivariogram was first used to evaluate the spatial variability of the properties (i.e., to describe the average variances of pairs of points at a given distance apart) (Oliver, 1987; Webster and Oliver, 2001). This mirrors the similarity of pairs separated by equal distance. Often, it is found that the semivariance increases with increasing distance between sampling points to a maximum (the sill) at a moderate distance (the range). Points closer together than the range are autocorrelated whereas points further apart are not related to one another. The variation below the scale of investigation and/or due to experimental errors, the nugget variance, is determined as the ordinate intercept. In the analysis, two indices of spatial dependence were employed. One is the Q value [calculated as (sill - nugget)/sill], which indicates the spatial structure at the sampling scale (Goerres et al., 1997), and the other is the range, which indicates the limit of spatial dependence. In the analysis, the semivariogram model with the smallest residual sum of squares was used for the estimation of the semivariogram parameters. Details of the fitting models are available from Webster and Oliver (2001). Maps were computed subsequently using block kriging to evaluate regional patterns of variation rather than local details. The geostatistical software, GS⁺ Version 5.3 for Windows (Gamma Design Software), was used in the analysis (Robertson, 1998).

	RESULTS AND DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Descriptive Statistics of Nitrous Oxide Fluxes and Soil Properties
The average value of the N₂O fluxes was 331 µg N m^-2 h^-1 (Table 1), which was comparable with previously reported values at this site (i.e., 871 µg N m^-2 h^-1) obtained in the middle of August as the maximum value of the growing period (Hatano and Sawamoto, 1997; Sawamoto and Hatano, 2000; Kusa et al., 2002). This value was relatively high compared with reported values for arable soils, for example, 5.8 to 139 (Goodroad et al., 1984), 41.2 (Ambus and Christensen, 1995), 8.6 (Ball et al., 1997), 0.8 to 11.2 (Roever et al., 1999), and 53.6 µg N m^-2 h^-1 (Clemens et al., 1999), as reviewed by Mosier (1994) and Bouwman (1996). It was comparable with data after N fertilization (Bremner et al., 1981; Thornton et al., 1996) or after plowing (van der Weerden et al., 2000). This could be due to the high amounts of N fertilizer application (Sahrawat and Keeney, 1986) and relatively moist soil conditions reflecting clayey texture and topography of lowland areas (Sawamoto and Hatano, 2000). Nitrous oxide fluxes displayed high variability with the minimum, maximum, and coefficient of variation (CV) of 6.83 and 5980 µg N m^-2 h^-1, and 217%, respectively (Table 1). Nitrous oxide data were not normally distributed (i.e., fluxes from several plots appeared to be much higher than the rest). They were, therefore, log-transformed and the descriptive statistics calculated. The log-transformed fluxes had a normal distribution (P < 0.01), as has been reported in previous studies (Ambus and Christensen, 1994; Ball et al., 1997; Roever et al., 1999). This result may be because N₂O emissions are related to microbial processes, which are often log–normally distributed (Parkin, 1993).

Spatial Variability of Nitrous Oxide Fluxes and Soil Properties
The variability of N₂O fluxes described in the previous section can be interpreted as the outcome of both spatial and temporal effects, as it took 5 h to perform gas sampling in the 100- x 100-m area. However, measurement of diurnal variation in N₂O emission, which was performed at the northwest corner of the same field on another date (one month earlier), suggested that the temporal fluctuation of N₂O fluxes during daytime was relatively small (less than two times) and even the diurnal difference was within several times (data not shown) as reported by other researchers (e.g., Blackmer et al., 1982; Thomson et al., 1997). Judging from the fact that the variability of N₂O fluxes of this experiment was as much as two orders of magnitude, it was concluded that most of the variation, if not all, was derived from a spatial effect.

Based on this assumption, spatial analysis of N₂O fluxes was performed. Figure 2 shows the semivariogram and isarithmic map of the log-transformed N₂O fluxes. Table 2 shows the geostatistical parameters, which were estimated from the semivariogram. The semivariogam shows that the semivariance increases with increasing distance between sampling points. The degree of spatial dependence was moderate with the Q value of 0.50. The range was estimated to be more than 75 m, suggesting a considerably long range of spatial dependence. This result was in contrast to almost all previously published studies, which indicated no spatial dependence (Folorunso and Rolston, 1984; Ball et al., 1997; Roever et al., 1999) or weak spatial dependence with ranges of several meters only (Folorunso and Rolston, 1985; Clemens et al., 1999), with one exception reporting strong spatial dependence with a range of up to 48 m in a flat grassland field (Ambus and Christensen, 1994). This discrepancy may be due to the fact that the landscape of this field was relatively flat and thus N₂O fluxes were more likely to reflect the gradual and continuous changes of other field conditions, which was rarely the case for ordinary undulating upland fields involved in previous studies. The existence of moderate spatial dependency suggests the possibility for control or regulation of N₂O fluxes with site-specific soil and land management. The map also shows that relatively high N₂O fluxes were observed at sites with a relatively lower elevation (i.e., in the northwest corner of the field).

View larger version (20K): [in this window] [in a new window]   Fig. 2. Semivariogram and isarithmic map of log-transformed N2O fluxes (measured).

View larger version (48K): [in this window] [in a new window]   Fig. 3. Semivariograms and isarithmic maps of the soil properties. (Units for the isarithmic maps can be seen in Table 1.)

View larger version (60K): [in this window] [in a new window]   Fig. 6. Semivariograms and isarithmic maps of the soil properties. (Units for the isarithmic maps can be seen in Table 1.)

View larger version (50K): [in this window] [in a new window]   Fig. 4. Semivariograms and isarithmic maps of the soil properties. (Units for the isarithmic maps can be seen in Table 1.)

View larger version (52K): [in this window] [in a new window]   Fig. 5. Semivariograms and isarithmic maps of the soil properties. (Units for the isarithmic maps can be seen in Table 1.)

View larger version (59K): [in this window] [in a new window]   Fig. 7. Semivariograms and isarithmic maps of five principal components of the soil properties.

[1]

Spatial analysis of the residual errors showed that the errors were spatially independent, suggesting that the application of this regression model was statistically appropriate.

The fact that R² equals 0.28 indicates that the model explains 28% of the total variance of the log-transformed measured N₂O fluxes. It should be noted that N₂O fluxes showed a slightly weak but significant relationship with soil properties on a field basis, in spite of the fact that N₂O fluxes were highly variable (as much as two orders of magnitude), as is often the case for those variables derived from biological activities (Parkin, 1993). As the regression coefficients in the equation generally indicate the magnitude of each factor's contribution to the N₂O fluxes, OMF can be identified as the most important factor in this relationship. High N₂O fluxes were thus related to high amounts of organic matter, high water content, and low temperatures. The contribution of PHF was of secondary importance; a low pH encouraged higher N₂O fluxes in this pH range. The components related to soil structure, soluble salts, and microbial biomass (SSF, SOF, and MBF) were not included in the model, although these factors were generally considered to affect the N₂O fluxes. Judging from the fact that N₂O fluxes were positively related to organic matter content and wetness, it was assumed that denitrification was the main mechanism of N₂O emissions. This was consistent with the results of Christensen et al. (1990) where the spatial distribution of denitrification activity in the soil depended on the soil water content and on the amount and distribution of available organic matter. The fact that the ratio of N₂O flux to NO flux was more than 100 also suggested active denitrification (Kusa et al., 2002) although nitrification may have occurred simultaneously based on the data of WFPS (Linn and Doran, 1984). From a spatial perspective, it was reasonable that PCs with relatively strong spatial dependency were extracted to explain the measured N₂O fluxes with moderate spatial dependency. As a practical interpretation, it is concluded that OMF variables can be good indicators of the spatial variation of N₂O fluxes, and site-specific management to regulate organic matter content as well as water status of soil may become an effective means of reducing N₂O fluxes in the field.

Application of Geostatistics to the Interpretation of Determining Factors of Nitrous Oxide Fluxes with Multivariate Analysis
Equation [1] was used to predict N₂O fluxes and to prepare a semivariogram and an isarithmic map (Fig. 8) . The isarithmic map of predicted N₂O fluxes was remarkably similar to that of measured N₂O fluxes (Fig. 2), although the variation was somewhat underestimated in the predicted map. Nevertheless, the prediction of N₂O fluxes from soil properties might be an effective way of estimating N₂O fluxes for comparative purposes (e.g., identifying N₂O emission "hot spots" in a field).

View larger version (21K): [in this window] [in a new window]   Fig. 8. Semivariograms and isarithmic map of log-transformed N2O fluxes (predicted).

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Spatial variability of the N₂O fluxes and soil properties was evaluated in an agricultural field, and the important role of soil properties in determining N₂O emissions was demonstrated by combining geostatistics with multivariate analysis. These results suggest that site-specific soil management such as the control of organic matter content or water status could be a promising strategy for reducing N₂O emissions in the field. Based on the fact that more than half of all N₂O emissions for this field were released in August and September (Hatano and Sawamoto, 1997; Sawamoto and Hatano, 2000; Kusa et al., 2002), site and time-specific management could be further established for the efficient reduction of N₂O emissions. In this context, investigations of the spatiotemporal variation of the N₂O fluxes would lead to a better understanding of N₂O emissions. Further studies are to be conducted to investigate the effectiveness of site-specific management (such as the control of organic matter content, water status, or inorganic fertilizer application) on the mitigation of N₂O fluxes from agricultural fields.

	ACKNOWLEDGMENTS

 
The authors are very grateful to Mathew Turner, Kyoto University, and three anonymous reviewers for their critical reading of the manuscript. This study was partly supported by a Grant-in-Aid for Scientific Research from the Ministry of Education, Science, Sports, and Culture of Japan (no. 10460030).

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TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

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