Spatial structures of N2O, CO2, and CH4 fluxes from Acacia mangium plantation soils during a relatively dry season in Indonesia

Soil Biology & Biochemistry 40 (2008) 3021–3030

Contents lists available at ScienceDirect

Soil Biology & Biochemistry journal homepage: www.elsevier.com/locate/soilbio

Spatial structures of N2O, CO2, and CH4 fluxes from Acacia mangium plantation soils during a relatively dry season in Indonesia Ryota Konda a, *, Seiichi Ohta a, Shigehiro Ishizuka b, Seiko Arai a,1, Saifuddin Ansori c, Nagaharu Tanaka b, Arisman Hardjono c a b c

Graduate School of Agriculture, Kyoto University, Kyoto, Kyoto, Japan Department of Forest Site Environment Division, Forestry and Forest Products Research Institute, Tsukuba, Ibaraki, Japan PT.Musi Hutan Persada, Muara Enim, South Sumatra, Indonesia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 4 June 2008 Received in revised form 11 August 2008 Accepted 23 August 2008 Available online 9 October 2008

We investigated spatial structures of N2O, CO2, and CH4 fluxes during a relatively dry season in an Acacia mangium plantation stand in Sumatra, Indonesia. The fluxes and soil properties were measured at 1-m intervals in a 1  30-m plot (62 grid points) and at 10-m intervals in a 40  100-m plot (55 grid points) at different topographical positions of the upper plateau, slope, and valley bottom in the plantation. Spatial structures of each gas flux and soil property were identified using geostatistical analysis. The means (SD) of N2O, CO2, and CH4 fluxes in the 10-m grids were 0.54 (0.33) mg N m2 d1, 2.81 (0.71) g C m 2 1 d , and 0.84 (0.33) mg C m2 d1, respectively. This suggests that A. mangium soils function as a larger source of N2O than natural forest soils in the adjacent province on Sumatra during the relatively dry season, while CO2 and CH4 emissions from the A. mangium soils were less than or consistent with those in the natural forest soils. Multiple spatial dependence of N2O fluxes within 3.2 m (1-m grids) and 35.0 m (10-m grids), and CO2 fluxes within 1.8 m (1-m grids) and over 65 m (10-m grids) was detected. From the relationship among N2O and CO2 gas fluxes, soil properties, and topographic elements, we suggest that the multiple spatial structures of N2O and CO2 fluxes are mainly associated with soil resources such as readily mineralizable carbon and nitrogen in a relatively dry season. The soil resource distributions were probably controlled by the meso- and microtopography. Meanwhile, CH4 fluxes were spatially independent in the A. mangium soils, and the water-filled pore space appeared to mainly control the spatial distribution of these fluxes. Ó 2008 Elsevier Ltd. All rights reserved.

Keywords: Acacia mangium Carbon dioxide Fast-growing wood plantation Geostatistics Humid tropics Large area Methane Nitrous oxide Spatial heterogeneity Variogram

1. Introduction Nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4) are major greenhouse gases in the atmosphere and are significant contributors to global warming according to the latest data showing rapid increases in their atmospheric concentration (IPCC, 2007). Tropical rain forest soils have been identified as an important source of N2O and CO2 while acting as a significant sink for CH4 (Keller et al., 1986; Potter et al., 1996; Mosier et al., 1998; Raich et al., 2002). N2O is mainly produced by nitrification under aerobic conditions and denitrification under anaerobic conditions (Davidson et al., 2000). CO2 emissions are primarily determined by the

* Corresponding author at present address: Laboratory of Tropical Forest Resources and Environments, Forestry and Biomaterials Science, Graduate School of Agriculture, Kyoto University, Kitashirakawa, Oiwake-cho, Sakyo-ku, Kyoto 6068502, Japan. Tel.: þ81 75 753 6361; fax: þ81 75 753 6372. E-mail address: [email protected] (R. Konda). 1 Present address: Kokusai Kogyo Co. Ltd., Chiyoda-ku, Tokyo, Japan. 0038-0717/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.soilbio.2008.08.022

microbial decomposition of organic matter and respiration of living plant roots (Boone et al., 1998; Hanson et al., 2000). CH4 is produced by methanogens under anaerobic conditions and is consumed by methanotrophs under aerobic conditions (Le Mer and Roger, 2001). In tropical areas, industrial plantations of fast-growing tree species have recently been expanded, both to supply wood and to fix atmospheric CO2. In particular, fast-growing leguminous tree plantations have been widely introduced into tropical Asia (FAO, 2001). Leguminous trees may lead to high N2O fluxes because N cycling in the soils is accelerated by the high N content in their litter (leaves, branches, dead roots) due to their symbiotic N fixation (Binkley et al., 1992; Garcia-Montiel and Binkley, 1998; Erickson et al., 2001; Dick et al., 2006). Therefore, afforestation with N-fixing tree species may increase N2O emissions for much of the lifetime of the forest (IPCC, 2003). Arai et al. (2008) suggested that Acacia mangium plantations in tropical Asia boost N2O emissions from the soil surface and that the importance of N2O emissions from leguminous tree stands will increase over the next several decades because of the increase in the area of Acacia and other leguminous

3022

R. Konda et al. / Soil Biology & Biochemistry 40 (2008) 3021–3030

tree plantations in Asia. However, since there are few measurements of N2O emissions from leguminous tree plantations, it is difficult to conduct accurate, quantitative temporal and spatial evaluations of N2O emissions. It is necessary to understand the spatial and temporal patterns of N2O emissions in fast-growing leguminous tree plantations and the related mechanism(s) in order to accurately estimate N2O emissions from soils and the net function of plantations for mitigating global warming, as well as to prepare appropriate management options to mitigate these N2O emissions. Soil surface N2O fluxes show large spatial variability (Folorunso and Rolston, 1984; Rover et al., 1999) and this is a serious problem in precisely estimating N2O fluxes at a large scale. CO2 and CH4 fluxes also have high spatial heterogeneities (Raich et al., 1990; van den Pol-van Dasselaar et al., 1998; Stoyan et al., 2000). Since these greenhouse gases are mainly emitted through microbiological processes in soils, their flux variations are regulated by soil conditions that control microbial activities, such as temperature and water, and C and N availabilities (Firestone and Davidson, 1989; Raich and Tufekcioglu, 2000; Le Mer and Roger, 2001; Saiz et al., 2006). However, compared to data available on the spatial structures of greenhouse gas fluxes in agricultural areas, grasslands, and temperate forests (Folorunso and Rolston, 1985; Raich et al., 1990; Ambus and Christensen, 1994; Prieme et al., 1996; Velthof et al., 1996; van den Pol-van Dasselaar et al., 1998; Clemens et al., 1999; Rover et al., 1999; Stoyan et al., 2000; Yanai et al., 2003; Yates et al., 2006), few studies have been conducted in tropical forests (Veldkamp and Keller, 1997; Weitz et al., 1999; Ishizuka et al., 2005b). In particular, spatial structures of greenhouse gas fluxes in leguminous tree plantations are entirely unknown. Leguminous tree plantations supply nitrogen rich litter to the overall soil surface, and this excess N input not only increases total N2O emissions from the plantation soils (Arai et al., 2008) but also might influence the spatial structure of the fluxes (Veldkamp and Keller, 1997). To understand the structure of the spatially distributed data, geostatistical analysis can be useful (Webster, 1985). Geostatistics provides the basis for describing quantitative spatial variations in soils that can be used for estimating soil properties and for accurate mapping, and planning rational sampling schemes that make the best use of available labor (Webster, 1985). However, no study has used this method for analysis of the spatial structure of greenhouse gas fluxes in tropical fast-growing leguminous tree plantation soils. Our objectives were to elucidate the spatial structures of N2O as well as CO2, and CH4 fluxes under the uniform supply of N rich leaf litter in leguminous tree plantation soils consisting of different topographical elements using geostatistics, and to determine the possible factors controlling these spatial structures from the relationship between the gas fluxes and soil properties.

intervals in 1997, and 85 g phosphate (SP-36) and 35 g urea per tree were applied once during planting. The mean tree height, mean diameter at breast height (DBH), and basal area were 23.6 m, 22.5 cm, and 24.2 m2 ha1, respectively (Kaneko et al., unpublished observations). 2.2. Plot positions We measured the spatial structure of greenhouse gas emissions from A. mangium plantation soils at two sampling distances. A 40  100-m plot was established in an A. mangium plantation that included different topographical elements of the upper plateau, slope, and valley bottom (Fig. 1). The slope was relatively steep and convex with average and maximum inclinations of 21.8 and 31.4 , respectively. The 40  100-m plot was divided into 10  10-m grids (10-m grids; Fig. 1a). Two 30 m long transects were established in parallel, 1 m apart, along the maximum slope line from the upper plateau down to the valley bottom within the 40  100-m plot. Each transect plot was divided into 30 grids of 1  1-m (1-m grids; Fig. 1b). 2.3. Gas and soil sampling and analyses Gas and soil samples were collected at each grid point on 22 (10-m grids) and 23 (1-m grids) September. We measured N2O, CO2, and CH4 fluxes using the static chamber method (Ishizuka et al., 2002; Arai et al., 2008). Polypropylene chambers (22.2 cm upper diameter, 18.7 cm lower diameter, 12.0 cm high) were inserted into the soil to a depth of 2 cm 1 day (in the 10-m grids) or 2 h (in the 1-m grids) before sampling. The chamber diameter at the soil surface was 19.4 cm. After sealing the chambers with lids containing a sampling port and an air bag to equilibrate the inside pressure to atmospheric pressure, we took 40-ml gas samples with a syringe after 0, 15, and 30 min. The gas samples were ejected into previously evacuated 30-ml glass vials with butyl rubber stoppers. These glass vials were analyzed in the laboratory for the concentrations of CO2 and CH4 using a gas chromatograph (GC-14B, Shimadzu Co. Ltd., Kyoto, Japan) equipped with a thermal conductivity detector for CO2 and a flame ionization detector for CH4, while the concentrations of N2O were measured with another gas chromatograph (GC-14B, Shimadzu Co. Ltd., Kyoto, Japan) equipped with an electron capture detector. The increase in gas concentration in the chamber during this sampling period appeared linear, therefore we calculated the gas flux by linear regression.

2. Materials and methods 2.1. Site description The field measurements were done in an A. mangium plantation area (3 520 S, 103 580 E) in South Sumatra, Indonesia, in September 2004. This area contains about 1930 km2 of A. mangium plantations that are on undulating land. The mean annual temperature and precipitation of the area from 1991 to 2002 were 27.3  C and 2750 mm, respectively (Hardjono et al., 2005). Although there are no distinct dry and wet seasons, the period from June to September is relatively drier, as the average monthly precipitation is below 150 mm (Hardjono et al., 2005). Therefore, this study was conducted during the relatively dry season. The soils in the area are Acrisols (ISSS Working Group RB, 1998), with Tertiary sedimentary rock as the parent material. We selected a 7year-old A. mangium stand, in which trees were planted at 2  4-m

Fig. 1. Land form of (a) 10-m grids and (b) 1-m grids. The white circles are sampling points. The research plot was established in a 7-year old Acacia mangium plantation in South Sumatra, Indonesia.

R. Konda et al. / Soil Biology & Biochemistry 40 (2008) 3021–3030

After gas sampling, all litter inside each chamber was collected and separated into fresh (L layer) and decayed litter (FH layer). The dry weights of the L and FH layer were determined on an oven-dry basis (75  C, 24 h). After litter sampling, soil samples were collected from the top 5 cm of the mineral soil inside each chamber using five 100-ml (5.1 cm diameter, 5 cm high) sampling cylinders. We combined the contents of two cylinders (200 ml) for analyses of bulk density, moisture content, and total C and N contents. This subsample (200 ml) was first air-dried, and gravimetric moisture and bulk density were then calculated on an oven-dry basis (105  C, 24 h). The water-filled pore space (WFPS) was calculated as follows,

WFPS ð%Þ ¼ u  WS =ð1  WS =SS Þ

(1)

where u (kg kg1) is soil water content, WS (Mg m3) is bulk density, and SS (2.58 mg m3) is particle density, as determined using a 50-ml pycnometer. Total C and N contents were determined using an N/C analyzer (NC-900, Sumitomo Chemical Co. Ltd., Osaka, Japan). The remaining three cylinder samples (300 ml) were mixed and used for analyses of inorganic ammonium (NH4-N) and nitrate (NO3-N) content, and pH (H2O). The composite subsample (300 ml) was homogenized and stored in a refrigerator at 4  C. NH4-N and NO3-N were extracted by shaking a mixed solution of 10 g of fresh soil from the subsample and 100 ml 1 M KCl for 1 h within 2 days of sampling. After shaking, the sample was filtered through quantitative filter paper (Advantec No. 5B, Toyo Roshi Co. Ltd., Tokyo, Japan), and the filtrate was stored in a refrigerator. We determined the NH4-N and NO3-N concentrations in the extract using a flowinjection analyzer (Auto Analyzer 3, BranþLuebbe Co. Ltd., Hamburg, Germany). Soil pH (H2O) was measured using a glass electrode (HM-30G, Toa DDK Co. Ltd., Tokyo, Japan) with a solution of 10 g soil and 25 ml deionized water. Relative elevation was determined using a laser range-finder (Laser Ace-300, Measurement Device Ltd., Aberdeen, UK) for the 10-m grids and a portable compass (LS25, Ushikata Mfg. Co. Ltd., Tokyo, Japan) for the 1-m grids. The 1-m grids were divided into 50cm grids, and the relative elevation measured at every 50-cm grid intersection. We also measured relative elevation at 10 m and 50 cm around the 10-m and 1-m grids, respectively, except for points outside the ends of the transects (Fig. 1b). Slope inclination (SI) was calculated as follows:

SI ð Þ ¼ tan1 ðjZiþ1  Zi1 jÞ  360=2p

(2)

where Zi1 (m) and Ziþ1 (m) are the relative elevation of the upward and downward points of a certain point on the transects. Surface relief of the grid points was expressed by ‘convexity,’ which was calculated as follows:

Convexity ðmÞ ¼ Zi  ðZ1 þ Z2 þ Z3 þ Z4 þ Z5 þ Z6 þ Z7 þ Z8 Þ=8 (3) where Zi (m) is the relative elevation of a certain grid point (i), and Z1 to Z8 (m) are the relative elevations of eight 50-cm grid points surrounding Zi. 2.4. Statistical analyses For flux estimation, we calculated the minimum significant flux (a ¼ 0.10; Hutchinson and Livingston, 1993; Ishizuka et al., 2005b) from each chamber. The minimum fluxes of N2O and CO2 considered to be significantly higher than zero were 0.09 mg N m2 d1 and 1.48 g C m2 d1, respectively. The absolute value of the minimum CH4 flux considered significantly higher than zero was 0.08 mg C m2 d1. We defined the data in which the absolute flux value was below these minimum fluxes as trace values. All fluxes were significantly higher than zero for N2O and CO2. For calculation

3023

of arithmetic mean, standard deviation, and geostatistics, the trace data of the CH4 fluxes were replaced with minus one-half of the detection limit because all flux data for CH4 were below 0. When calculating the correlation coefficient among these gas fluxes and other soil properties, we excluded the trace data. Normality and log-normality tests were conducted on the data distributions of each flux and the soil properties before spatial structure analysis. To determine the spatial structure of each flux and the soil properties, we used GSþ Version 5.3b for Windows (Gamma Design Software, Michigan, USA). Log-normally distributed data were log-transformed for the calculations. A semivariogram model with the smallest residual sum of squares was used for estimation of related parameters. Using the parameters determined in the semivariogram analysis, we produced isarithmic maps of the properties by block kriging, if the data were spatially dependent, and otherwise by the inverse distance weighting method, if the data were spatially independent. Log-transformed data were back-transformed to original units prior to mapping. We used two indices of spatial dependence, a Q value and a range, in the analysis. The Q value, calculated as (sill  nugget)/sill, indicates the degree of spatial dependence at the sampling scale (Robertson et al., 1997; Gorres et al., 1998; Yanai et al., 2003), and the range indicates the limit of spatial dependence. To analyze the relationship among each gas flux and soil property, nonparametric statistics with the Spearman rank correlation were performed using SPSS version 10.0 (SPSS Inc., Chicago, USA). 3. Results 3.1. Soil properties General soil properties are shown in Table 1. Most of the soil properties displayed a normal distribution, while bulk density, L and FH amounts in the 10-m grids and NH4-N content, L and FH amounts in the 1-m grids were log-normally distributed (Table 1). The semivariograms of bulk density and WFPS showed no change in semivariance with distance, indicating that they had no spatial dependence (Table 2, Fig. 2a,b). In the maps, the bulk density and WFPS were high at some points in the upper plateau, slope, and valley bottom (Fig. 3a,b). The pH (H2O) had strong spatial dependency within 18.7 m (Table 2) and was relatively high on the slope and valley bottom. Although total C and N contents and the C:N ratio had moderate spatial dependency, the ranges and sills observed were not precisely determined because the ranges were more than the effective range of 64.6 m, which is equal to 60% of the maximum lag in the 10-m grids (Table 2, Fig. 2c–e). The total C and N contents and the C:N ratio decreased gently with topography from the upper plateau to the valley bottom (Fig. 3c–e). NH4N content had moderate spatial dependency within 30.7 m, while NO3-N content had no spatial dependency (Table 2, Fig. 2f,g). There were relatively high areas of NH4-N and NO3-N contents in the upper plateau (Fig. 3f,g). L amounts had no spatial dependency, while FH amounts had moderate spatial dependency within 30.2 m (Table 2, Fig. 2h,i). The L and FH amounts were large in the upper plateau and small in the slope (Fig. 3h,i). In the 1-m grids, the bulk density, WFPS, total C and N contents, and C:N ratio were spatially dependent within 2.2 m, 3.7 m, 4.1 m, 2.4 m, and 5.8 m, respectively, while pH, NH4-N and NO3-N contents, and FH amounts were spatially dependent within 18 m, which is the effective range of the 1-m grids (Table 2), while L amounts were spatially independent. FH amounts in the slope and valley bottom were negatively correlated with slope inclination in the 1-m grids, indicating that less litter accumulated on the steeper slope (Fig. 4). Most soil properties were not correlated with the ‘convexity’, except for a negative correlation with the C:N ratio (P < 0.05, data not shown).

3024

R. Konda et al. / Soil Biology & Biochemistry 40 (2008) 3021–3030

Table 1 The statistical data of gas fluxes and soil parameters Property 10 3 10-m grids N2O flux CO2 flux CH4 flux Bulk density WFPS pH (H2O) Total C Total N C:N ratio NH4-N NO3-N L amount FH amount 1 3 1-m grids N2O flux CO2 flux CH4 flux Bulk density WFPS pH(H2O) Total C Total N C:N ratio NH4-N NO3-N L amount FH amount a b

Unit

Mean

SD

Median

Max.

Min.

CV (%)

mg N m2 d1 g C m2 d1 mg C m2 d1 Mg m3 %

0.54 2.81 0.84a 0.73 44.4 4.56 1.78 0.14 12.9 1.45 0.96 0.34 0.46

0.33 0.71 0.33a 0.10 7.3 0.29 0.38 0.03 0.8 0.31 0.38 0.18 0.29

0.48 2.72 0.86a 0.72 44.5 4.54 1.74 0.14 12.9 1.39 1.02 0.29 0.43

1.77 4.87 Tr.b 0.98 62.8 5.15 2.76 0.21 14.7 2.14 1.91 0.93 1.39

0.09 1.60 1.67 0.46 25.5 3.69 0.80 0.06 11.2 0.94 0.15 0.07 0.03

60.3 25.4 38.9a 13.9 16.5 6.4 21.2 19.3 6.2 21.6 39.5 53.7 64.4

0.51 3.68 1.00a 0.73 43.7 4.70 1.73 0.14 12.4 1.45 1.05 0.44 0.52

0.21 1.78 0.25a 0.12 7.2 0.41 0.31 0.02 0.8 0.42 0.51 0.25 0.33

0.47 3.30 0.99a 0.73 42.7 4.68 1.76 0.14 12.4 1.39 0.99 0.38 0.47

1.05 9.67 Trb 1.06 68.4 5.60 2.47 0.20 14.8 2.88 2.62 1.22 1.59

0.20 1.48 1.56 0.41 26.3 4.01 0.80 0.07 10.5 0.73 0.29 0.06 0.04

42.2 48.2 24.6a 15.7 16.4 8.7 18.0 15.1 6.4 28.9 48.5 56.3 63.4

kg m2 kg m2 g m2 g m2 kg m2 kg m2

mg N m2 d1 g C m2 d1 mg C m2 d1 Mg m3 % kg m2 kg m2 g m2 g m2 kg m2 kg m2

The trace data ware replaced by minus one-half of the detection limit. Trace data.

3.2. N2O fluxes N2O fluxes were log-normally distributed both in the 10-m and 1-m grids. N2O fluxes had a moderate spatial dependence, with a Q value of 0.51 and a range of 35.0 m in the 10-m grids (Table 2,

Fig. 5a). At the same time, N2O fluxes were spatially dependent within 3.2 m in the 1-m grids (Table 2). The means (SD) of the fluxes were 0.54 (0.33) mg N m2 d1 and 0.51 (0.21) mg N m2 d1, and the coefficients of variation (CV) were 60.3% and 42.2% in the 10-m and 1-m grids, respectively (Table 1). There were

Table 2 Geostatistical parameters of the gas fluxes and soil properties Property 10 3 10-m grids N2O flux CO2 flux CH4 flux Bulk density WFPS pH(H2O) Total C Total N C:N ratio NH4-N NO3-N L amount FH amount 1 3 1-m grids N2O flux CO2 flux CH4 flux Bulk density WFPS pH(H2O) Total C Total N C:N ratio NH4-N NO3-N L amount FH amount a b

Unit

Nugget

Sill

Range

Q value

Modela

mg N m2 d1 g C m2 d1 mg C m2 d1 Mg m3 %

0.168 0.0449 –b –b –b 0.001 0.0659 0.000258 0.38 0.0363 –b –b 0.303

0.342 0.0899 –b –b –b 0.083 0.1658 0.000746 1.13 0.0966 –b –b 0.624

35.0 65þ –b –b –b 18.7 65þ 65þ 65þ 30.7 –b –b 30.2

0.51 0.50 –b –b –b 1.00 0.60 0.65 0.66 0.62 –b –b 0.51

S S –b –b –b S S E E S –b –b S

0.0214 0.0129 –b 0.00001 3.8 0.009 0.0149 0.000072 0.115 0.047 0.1611 –b 0.289

0.1798 0.1888 –b 0.01262 48.26 0.413 0.0978 0.000459 0.571 0.112 0.4452 –b 1.633

3.2 1.8 –b 2.2 3.7 18þ 4.1 2.4 5.8 18þ 18þ –b 18þ

0.88 0.93 –b 1.00 0.92 0.98 0.85 0.84 0.80 0.58 0.64 –b 0.82

E S –b S E S E E S S S –b E

kg m2 kg m2 g m2 g m2 kg m2 kg m2

mg N m2 d1 g C m2 d1 mg C m2 d1 Mg m3 % kg m2 kg m2 g m2 g m2 kg m2 kg m2

S, spherical; E, exponential. Spatial structures were not apparent.

R. Konda et al. / Soil Biology & Biochemistry 40 (2008) 3021–3030

a

0.011 0.006

Semivariance

32

48

65

Semivariance

c

0.12 0.08 0.04 Total C 0

16

32

48

0.17 C:N ratio 16

32

48

0.12 0.08 0.04 0.00

NO3-N 16

0

32

48

65

32

48

65

d

0.0002 Total N 0

16

32

48

65

f

0.10 0.08 0.05 0.03 0.00

NH4-N 16

0

32

48

65

h

0.29 0.22 0.14 0.07 0.00

L amounts 16

0

32

48

65

Separation Distance (m)

i

0.65

16

0

0.0004

65

g

0.16

WFPS

0.0006

65

0.35

0

13

0.0000

0.52

0.00

26

0.0007

e

0.69

Semivariance

16

0

39

0

Semivariance

Semivariance

BD

0.16

0.00

Semivariance

Semivariance

0.017

0.000

b

52

Semivariance

Semivariance

0.022

3025

0.49 0.33 0.16 0.00

FH amounts 0

16

32

48

65

Separation Distance (m) Fig. 2. Semivariograms of (a) bulk density (BD), (b) water-filled pore space (WFPS), (c) total C, (d) total N, (e) C:N ratio, (f) NH4-N, (g) NO3-N, (h) L amounts, and (i) FH amounts in 10-m grids.

two high flux areas in the upper plateau in the 10-m grids, whereas there were no such high flux areas in the slope and valley bottom (Fig. 5a). N2O fluxes were positively correlated with CO2 fluxes, total C content, NH4 content, and FH amounts (Table 3). In addition, N2O fluxes were negatively correlated with ‘convexity’ in 1-m grids.

48.2% in the 10-m and 1-m grids, respectively (Table 1). CO2 fluxes were positively correlated with L and FH amounts in addition to the N2O fluxes (Table 3). Moreover, in the 1-m grids, CO2 fluxes were negatively correlated with ‘convexity’.

3.3. CO2 fluxes

CH4 fluxes were normally distributed in both the 10-m and 1-m grids. The fluxes were below the detection limit at two points in the 10-m grids and at one point in the 1-m grids. CH4 fluxes had no spatial dependence and were randomly distributed in both the 10m and 1-m grids (Table 2, Fig. 5c). The means (SD) of the fluxes were 0.84 (0.33) mg C m2 d1 and 1.00 (0.25) mg C m2 d1, and the CV was 38.9% and 24.6% in the 10-m and 1-m grids, respectively (Table 1). In the relatively dry season, the soils in the study site were generally functioning as CH4 sinks. Low CH4 uptake areas existed in every topographic element. CH4 fluxes were positively correlated with WFPS but not with other properties (Table 3, Fig. 6).

CO2 fluxes were log-normally distributed both in the 10-m and 1-m grids. CO2 fluxes in the 10-m grids had moderate spatial dependence, with a Q value of 0.50 and a range of 98.4 m. However, the range and sill observed were not precisely determined because the range was more than the effective range of 64.6 m (Table 2, Fig. 5b). In the 1-m grids, the CO2 fluxes were also spatially dependent, within 1.8 m (Table 2). CO2 fluxes in the 10-m grids decreased gently from the upper plateau to the valley bottom (Fig. 5b). The means (SD) of the fluxes were 2.81 (0.71) g C m2 d1 and 3.68 (1.78) g C m2 d1, and the CV was 25.4% and

3.4. CH4 fluxes

R. Konda et al. / Soil Biology & Biochemistry 40 (2008) 3021–3030

m

40

20

0 0

25

50

75

100

m

c

100

2.20 2.10 2.00 1.90 1.80 1.70 1.60 1.50 1.40 1.30 1.20

100

14.0 13.8 13.6 13.4 13.2 13.0 12.8 12.6 12.4 12.2 12.0

Total C

m

40

20

0 0

25

50

75

m

e

C:N ratio 40

m

20

0 0

25

50

75

m

g

100

1.70 1.55 1.40 1.25 1.10 0.95 0.80 0.65 0.50 0.35 0.20

100

1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00

NO3-N

m

40

20

0 0

25

50

75

m

i

FH amounts

m

40

20

0 0

25

50

75

m

1.00 0.95 0.90 0.85 0.80 0.75 0.70 0.65 0.60 0.55 0.50

b

WFPS upper plateau

slope and valley bottom

100

60.0 57.0 54.0 51.0 48.0 45.0 42.0 39.0 36.0 33.0 30.0

100

0.18 0.17 0.16 0.15 0.14 0.13 0.12 0.11 0.10 0.09 0.08

100

2.00 1.90 1.80 1.70 1.60 1.50 1.40 1.30 1.20 1.10 1.00

100

1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00

40

m

slope and valley bottom

20

0 0

25

50

75

m Total N

d 40

m

upper plateau

20

0 0

25

50

75

m

f 40

m

BD

a

NH4-N

20

0 0

25

50

75

m

h

L amounts 40

m

3026

20

0 0

25

50

75

m

Fig. 3. Isarithmic maps of (a) bulk density (BD), (b) water-filled pore space (WFPS), (c) total C, (d) total N, (e) C:N ratio, (f) NH4-N, (g) NO3-N, (h) L amounts, and (i) FH amounts in 10-m grids. The maps of BD, WFPS, NO3-N, L amounts were created by the inverse distance weighting method. The maps of total C, total N, C:N ratio, NH4-N, FH amounts were created by block kriging with a block size of 0.4 m2 having a 2  2 grid size in the block. Units for the isarithmic maps can be seen in Table 1.

4. Discussion 4.1. N2O, CO2, and CH4 emissions in A. mangium soils The means (SD) of N2O, CO2, and CH4 fluxes in the 10-m grids were 0.54 (0.33) mg N m2 d1, 2.81 (0.71) g C m2 d1, and 0.84 (0.33) mg C m2 d1, respectively (Table 1). Ishizuka et al. (2005a) reported N2O, CO2, and CH4 fluxes of 0.22 (0.18) mg N m2 d1, 3.88 (0.50) g C m2 d1, and 0.70 (0.35) mg C m2 d1, respectively, in the soil of a natural forest in Jambi, Sumatra, Indonesia, in September 2001, a relatively dry month. Our data suggest that A. mangium soils function as a larger source of N2O, while CO2 and CH4 emissions were less

than or consistent with those in natural forest soils. This conclusion is also supported by the results of Arai et al. (2008), who investigated N2O fluxes for 1 year from A. mangium soils in the same region and found that the soils were a non-negligible source of N2O. The variability of greenhouse gas fluxes in A. mangium soils was consistent with that in other tropical forest soils. The CV of N2O, CO2, and CH4 in this study was comparable to those in other tropical forest soils: 14–132% for N2O (Breuer et al., 2000; Kiese and Butterbach-Bahl, 2002; Kiese et al., 2003; Ishizuka et al., 2005a), 12– 37% for CO2 (Kiese and Butterbach-Bahl, 2002; Ishizuka et al., 2005a), and 16–50% for CH4 (Kiese et al., 2003; Ishizuka et al., 2005a).

R. Konda et al. / Soil Biology & Biochemistry 40 (2008) 3021–3030

In many previous studies, the spatial structures of CO2 fluxes had no spatial dependence or ranged from a few centimeters up to 100 m (Robertson et al., 1988, 1997; van den Pol-van Dasselaar et al., 1998; Rayment and Jarvis, 2000; Stoyan et al., 2000; Ishizuka et al., 2005b). However, many studies performed in agricultural, grassland, and forest soils indicated that N2O fluxes had no or weak spatial dependence (Folorunso and Rolston, 1984; Clemens et al., 1999; Rover et al., 1999; Weitz et al., 1999; Ishizuka et al., 2005b). Agricultural fields are generally on flat land and regularly undergo human-induced influences, such as tillage and fertilization, which homogenize the variations in soil materials and even the topography of the fields. Thus, in agricultural fields, microscale variability in soil moisture and soil resources influencing microbial activity in soil microsites seem to be mainly responsible for the spatial structure of N2O fluxes, rather than topographical variability. In contrast, in forest and grassland soils, heterogeneous distribution of microtopography generally exists within changes in large topographic elements. Some studies have reported a topographic contribution to the spatial dependence of N2O fluxes in grassland soils (Ambus and Christensen, 1994; Velthof et al., 1996, 2000). Thus, spatial structures beyond ranges of a few meters, as reported in this study, can be detected by performing flux measurements at scales that include different topographic elements. Soil properties have multiple scaling of spatial structure (Robertson and Gross, 1994). Ambus and Christensen (1994) showed different spatial structures of N2O fluxes at two scales in fertilized grassland soils with topographic change. They suggested that spatial structures of N2O fluxes at a scale beyond 7 m were

FH amounts (kg m-2)

1.8 1.5 1.2 0.9 0.6 0.3 0.0

0

10

20

30

Slope inclination (°) slope and valley bottom

upper plateau

Fig. 4. Relationship between slope inclination and FH amounts in 1-m grids. FH amounts in slope and valley bottom are negatively correlated with slope inclination (R ¼ 0.493, P < 0.01).

4.2. Spatial structures of N2O, CO2, and CH4 fluxes Multiple spatial structures for N2O and CO2 fluxes were detected at the two sampling intervals, although CH4 fluxes were spatially independent both in the 10-m and 1-m grids, which is consistent with the results of Ishizuka et al. (2005b) (Table 2). N2O fluxes were spatially dependent at both scales, within 3.2 m (1-m grids) and 35.0 m (10-m grids). CO2 fluxes also had two scales of spatial dependency, within 1.8 m (1-m grids) and over 65 m (10-m grids).

a

N 2O

upper plateau

0.38

slope and valley bottom

100

1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00

100

3.30 3.18 3.06 2.94 2.82 2.70 2.58 2.46 2.34 2.22 2.10

100

-0.20 -0.35 -0.50 -0.65 -0.80 -0.95 -1.10 -1.25 -1.40 -1.55 -1.70

40

0.28

m

Semivariance

3027

0.19

20

0.09 0 0.00

b

16

32

48

0

65

25

50

75

m

CO2 40 0.06 0.04

0 0

16

32

48

0

65

25

50

75

m

CH4 0.11

40

0.08

m

Semivariance

20

0.02 0.00

c

m

Semivariance

0.08

0.05

20

0.03 0 0.00 0

16

32

48

Separation Distance (m)

65

0

25

50

m

75

Fig. 5. Semivariograms and isarithmic maps of the gas fluxes in the 10-m grids. The maps of N2O and CO2 fluxes were created by block kriging. The map of CH4 fluxes was created by the inverse distance weighting method. Units for the isarithmic maps can be seen in Table 1.

3028

R. Konda et al. / Soil Biology & Biochemistry 40 (2008) 3021–3030

Table 3 Correlation coefficients between the gas fluxes and soil properties

N2O CO2 CH4

CO2

CH4

WFPS

Total C

Total N

NH4-N

NO3-N

FH amount

L amount

Convexity

0.343**

NS NS

NS NS 0.203*a

0.213* NS NS

NS NS NS

0.301** NS NS

NS NS NS

0.508** 0.219* NS

NS 0.479** NS

0.283*b 0.292*b NS

Significant at *P < 0.05, **P < 0.01 respectively. NS, not significant. Trace data were excluded. b Convexity was measured in 1-m grids, except for both ends of the transects. a

controlled by soil moisture variability, governed by ground topography, whereas a patchy distribution of denitrifying microsites governed N2O emissions at a scale below 1 m. Thus, the wide ranges of N2O and CO2 fluxes in the 10-m grids in this study seemed to be caused by soil factors that are controlled by mesoscale topography. Moreover, in the 1-m grids, the relationship between N2O and CO2 fluxes and ‘convexity’ suggest that small-scale spatial dependences of N2O and CO2 fluxes were also associated with soil factors governed by the microtopography. We hypothesize that the multiple spatial structures of N2O and CO2 fluxes in the A. mangium soils were determined by multiple topographic scales, which control spatial patterns in various soil factors. The spatial distributions of N2O and CO2 fluxes were not uniform irrespective of uniform supply of N rich leaf litter to the soil. This is probably because topography has a greater influence on the spatial structures of soil properties including accumulated A. mangium litter amounts and these greenhouse gas fluxes, as discussed below. 4.3. Topographic influence on soil properties in controlling spatial patterns of gas fluxes N2O fluxes were positively correlated with CO2 fluxes, total C content (0–5 cm), NH4-N content (0–5 cm), and FH amounts (Table 3). CO2 fluxes were positively correlated with L and FH amounts (Table 3). Given that these soil resources related to the gas fluxes appeared to be governed by topography (Fig. 3), we consider that the spatial patterns of N2O and CO2 fluxes were simultaneously related to the topographic elements. In the 10-m grids, high N2O flux areas were found in the upper plateau but not in the slope and valley bottom (Fig. 5a). CO2 fluxes in the 10-m grids decreased from the upper plateau to the valley bottom (Fig. 5b). Our results contrast with previous studies conducted in temperate soils in Canada (Pennock et al., 1992; Van Kessel et al., 1993; Corre et al., 1996). Results from Canada indicated that denitrification rates and following N2O fluxes were highest at lower topographic positions and lowest at upper topographic

WFPS (%)

CH4 fluxes (mg C m-2 d-1)

20 0.0

30

40

50

60

70

80

-0.5

-1.0

-1.5

-2.0 Fig. 6. Relationship between water-filled pore space (WFPS) and CH4 fluxes (R ¼ 0.203, P < 0.05).

positions because of the topographical influence on hydrologic and pedologic processes in the soils. Ishizuka et al. (2000) also suggested that N2O production was higher in the lower part of a slope in a Japanese forest. Many previous studies found that N2O fluxes were spatially and temporally related to soil moisture content, often expressed by WFPS (Velthof et al., 2000; Kiese and Butterbach-Bahl, 2002; Kiese et al., 2003; Werner et al., 2006) because soil moisture content is the dominant environmental controller of O2 availability, the supply of which into the soil microsites significantly regulates both biological nitrification and denitrification (Davidson et al., 1993). According to Arai et al. (2008), there was a pronounced seasonal fluctuation in WFPS ranged from 40% in the drier season to >80% in the wetter season also in A. mangium soils, and the N2O fluxes were affected by the seasonal change in WFPS: low N2O fluxes in the drier season while high in the wetter season. However, in this study differences in WFPS were not a dominant factor for N2O flux differences among the sampling points because this study was conducted only once in a relatively dry season with mostly aerobic soil moisture conditions. We assumed that lower soil resources on the slope and valley bottom caused lower N2O and CO2 fluxes in those positions even though their WFPS was higher. Total C and N contents decreased slowly from the upper plateau to the valley bottom in the 10-m grids (Fig. 3c,d). The C:N ratio also decreased gently from the upper plateau to the valley bottom (Fig. 3e). Therefore, in the slope and valley bottom, the C and N resources seemed to be poorer than in the upper plateau. In addition, because of the relatively steep convex slope in the plot, wet tropics-specific heavy rain can easily accelerate the erosion of surface soils on the slope and valley bottom. As a result, resource-constrained subsoils with a high bulk density and low C:N ratio were probably exposed at the soil surface. Moreover, the decrease in FH amounts in relation to the increase in slope inclination (Fig. 4) indicated that instability of litter on the slope could accelerate the erosion of surface soils and result in decreases in the resource supply, which may be one reason for the low level of resources on the slope and valley bottom. In addition to the mesotopographic effects on gas emissions, N2O and CO2 fluxes were negatively correlated with microtopographic elements, expressed as the ‘convexity’ in the 1-m grids (Table 3). Mohn et al. (2000) showed that the spatial structure of denitrification activity was driven by the microtopography in forest soils. In our studies, it might be reasonable to assume that the higher fluxes at concave positions possibly resulted in soil resources accumulating at concave positions, although most soil properties were not correlated with the ‘convexity,’ except for a higher C:N ratio in such positions. As a result, during the relatively dry season, the spatial structures of N2O and CO2 fluxes seemed to be mainly controlled by soil resources such as the readily mineralizable C and N in terms of a topographical influence on them rather than by soil moisture. We speculate that the L and FH layers were direct sources of N2O and CO2 fluxes. In a laboratory study, Breuer et al. (2000) showed that the most important N2O production sites were the litter layer and the uppermost (0–5 cm) mineral soil in tropical forest soils of Australia. Many studies have shown that CO2 fluxes are reduced by

R. Konda et al. / Soil Biology & Biochemistry 40 (2008) 3021–3030

litter removal from the forest floor (Rey et al., 2002; Li et al., 2004; Tang et al., 2006), indicating that litter layers contribute to CO2 production. Therefore, N2O and CO2 could also be emitted from litter layers in our A. mangium forest. The spatial distribution of CH4 fluxes was consistent with that of WFPS, as shown in the correlation between CH4 fluxes and WFPS (Fig. 6). CH4 consumption and production are largely related to O2 availability that controls anaerobic–aerobic conditions within the soil (Le Mer and Roger, 2001). Because of the large effect of O2 availability, soil moisture was shown to be the dominant factor controlling CH4 fluxes in many previous studies (Verchot et al., 2000; Kiese et al., 2003; Werner et al., 2006). 4.4. Conclusion A. mangium soils function as a larger source of N2O than the natural forest soils in the adjacent province on Sumatra during the relatively dry season, while CO2 and CH4 emissions from A. mangium soils were less than or consistent with those in the natural forest soils. Multiple spatial dependences of N2O and CO2 fluxes were detected under the uniform supply of N rich leaf litter in leguminous tree plantation. Though the N rich litter was supplied to the overall soil surface in the plantation, the spatial distributions of soil properties including accumulated A. mangium litter amounts and these gas fluxes were not uniform. This is probably because topography has a greater influence on the spatial structures of soil properties and emissions of these greenhouse gases. The multiple spatial structures of N2O and CO2 fluxes are considered to be mainly associated with available soil resources, such as the readily mineralizable C and N in a relatively dry season, though soil moisture had no relation with the spatial structures of these gas fluxes. The soil resource distributions were probably controlled by the meso- and microtopography. Meanwhile, CH4 fluxes were spatially independent and WFPS appeared to control the spatial structure of these fluxes. Acknowledgments We thank Mr. Shigeru Shimoda and Ms. Maya Liony Lioe for their helpful cooperation, and all staff members who assisted with fieldwork at the MHP Company. We also thank Dr. Junta Yanai for technical assistance on geostatistics, Dr. Mamoru Kanzaki for valuable suggestions, and Mr. Takayuki Kaneko for a topographic survey of the research plot. This study was supported financially by the Ministry of Education, Culture, Sports, Science, and Technology, Japan (number 19255011). References Ambus, P., Christensen, S., 1994. Measurement of N2O emission from a fertilized grasslanddan analysis of spatial variability. Journal of Geophysical Research Atmospheres 99, 16549–16555. Arai, S., Ishizuka, S., Ohta, S., Ansori, S., Tokuchi, N., Tanaka, N., Hardjono, A., 2008. Potential N2O emissions from leguminous tree plantation soils in the humid tropics. Global Biogeochemical Cycles 22, GB2028. Binkley, D., Sollins, P., Bell, R., Don, S., Myrold, D., 1992. Biogeochemistry of adjacent conifer and alder–conifer stands. Ecology 73, 2022–2033. Boone, R.D., Nadelhoffer, K.J., Canary, J.D., Kaye, J.P., 1998. Roots exert a strong influence on the temperature sensitivity of soil respiration. Nature 396, 570–572. Breuer, L., Papen, H., Butterbach-Bahl, K., 2000. N2O emission from tropical forest soils of Australia. Journal of Geophysical Research - Atmospheres 105, 26353–26367. Clemens, J., Schillinger, M.P., Goldbach, H., Huwe, B., 1999. Spatial variability of N2O emissions and soil parameters of an arable silt loamda field study. Biology and Fertility of Soils 28, 403–406. Corre, M.D., van Kessel, C., Pennock, D.J., 1996. Landscape and seasonal patterns of nitrous oxide emissions in a semiarid region. Soil Science Society of America Journal 60, 1806–1815.

3029

Davidson, E.A., Matson, P.A., Vitousek, P.M., Riley, R., Dunkin, K., Garcı´a-Me´ndez, G., Maass, J.M., 1993. Processes regulating soil emissions of NO and N2O in a seasonally dry tropical forest. Ecology 74, 130–139. Davidson, E.A., Keller, M., Erickson, H.E., Verchot, L.V., Veldkamp, E., 2000. Testing a conceptual model of soil emissions of nitrous and nitric oxides. Bioscience 50, 667–680. Dick, J., Skiba, U., Munro, R., Deans, D., 2006. Effect of N-fixing and non-N-fixing trees and crops on NO and N2O emissions from Senegalese soils. Journal of Biogeography 33, 416–423. Erickson, H., Keller, M., Davidson, E.A., 2001. Nitrogen oxide fluxes and nitrogen cycling during postagricultural succession and forest fertilization in the humid tropics. Ecosystems 4, 67–84. FAO, 2001. Global Forest Resources Assessment 2000. Food and Agriculture Organization of the United Nations, Rome, Italy, 479 pp. Firestone, M.K., Davidson, E.A., 1989. Microbiological basis of NO and N2O production and consumption in soil. In: Andreae, M.O., Schimel, D.S. (Eds.), Exchange of trace gases between terrestrial ecosystems and the atmosphere. John Wiley & Sons, Chichester, UK, pp. 7–21. Folorunso, O.A., Rolston, D.E., 1984. Spatial variability of field-measured denitrification gas fluxes. Soil Science Society of America Journal 48, 1214–1219. Folorunso, O.A., Rolston, D.E., 1985. Spatial and spectral relationships between fieldmeasured denitrification gas fluxes and soil properties. Soil Science Society of America Journal 49, 1087–1093. Garcia-Montiel, D.C., Binkley, D., 1998. Effect of Eucalyptus saligna and Albizia falcataria on soil processes and nitrogen supply in Hawaii. Oecologia 113, 547–556. Gorres, J.H., Dichiaro, M.J., Lyons, J.B., Amador, J.A., 1998. Spatial and temporal patterns of soil biological activity in a forest and an old field. Soil Biology & Biochemistry 30, 219–230. Hanson, P.J., Edwards, N.T., Garten, C.T., Andrews, J.A., 2000. Separating root and soil microbial contributions to soil respiration: A review of methods and observations. Biogeochemistry 48, 115–146. Hardjono, A., Ishibashi, N., Matsumura, N., Taniguchi, I., Heriyanto, N.M., Ando, K., 2005. The management aspects of industrial plantation in south SumatradA case of PT. MUSI HUTAN PERSADA. Carbon Fixing Forest Management Project (FORDA & JICA), Bogor, Indonesia, 25 pp. Hutchinson, G.L., Livingston, G.P., 1993. Use of chamber systems to measure trace gas fluxes. In: Harper, L.A. (Ed.), Agricultural Ecosystem Effects on Trace Gases and Global Climate. American Society of Agronomy, Madison, Wisconsin, USA, pp. 63–78. IPCC, 2003. Good practice guidance for land use, land-use change and forestry. In: Penman, J., Gytarsky, M., Hiraishi, T., Krug, T., Kruger, D., Pipatti, R., Buendia, L., Miwa, K., Ngara, T., Tanabe, K., Wagner, F. (Eds.), Institute for Global Environmental Strategies for the IPCC. Kanagawa, Japan, p. 599. IPCC, 2007. Climate Change 2007: The physical science basis. Contribution of Working Group I. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (Eds.), Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, New York, p. 996. Ishizuka, S., Sakata, T., Tanikawa, T., Ishizuka, K., 2000. N2O emission and spatial distribution in a Japanese deciduous forest (in Japanese, with English abstract). Journal of Japanese Forest Research 82, 62–71. Ishizuka, S., Tsuruta, H., Murdiyarso, D., 2002. An intensive field study on CO2, CH4, and N2O emission from soils at four land-use types in Sumatra, Indonesia. Global Biogeochemical Cycles 16, 1049. Ishizuka, S., Iswandi, A., Nakajima, Y., Yonemura, S., Sudo, S., Tsuruta, H., Murdiyarso, D., 2005a. The variation of greenhouse gas emissions from soils of various land-use/cover types in Jambi province, Indonesia. Nutrient Cycling in Agroecosystems 71, 17–32. Ishizuka, S., Iswandi, A., Nakajima, Y., Yonemura, L., Sudo, S., Tsuruta, H., Muriyarso, D., 2005b. Spatial patterns of greenhouse gas emissions in a tropical rainforest in Indonesia. Nutrient Cycling in Agroecosystems 71, 55–62. ISSS Working Group RB, 1998. World reference base for soil resources: Introduction. In: Deckers, J.A., Nachtergaele, F.O., Spaargaren, O.C. (Eds.), International Society of Soil Science, International Soil Reference and Information Centre, and Food and Agriculture Organization of the United Nations, Acco, Leuven, Belgium, 165 pp. Keller, M., Kaplan, W.A., Wofsy, S.C., 1986. Emissions of N2O, CH4 and CO2 from tropical forest soils. Journal of Geophysical Research - Atmospheres 91, 1791–1802. Kiese, R., Butterbach-Bahl, K., 2002. N2O and CO2 emissions from three different tropical forest sites in the wet tropics of Queensland, Australia. Soil Biology & Biochemistry 34, 975–987. Kiese, R., Hewett, B., Graham, A., Butterbach-Bahl, K., 2003. Seasonal variability of N2O emissions and CH4 uptake by tropical rainforest soils of Queensland, Australia. Global Biogeochemical Cycles 17, 1043. Le Mer, J., Roger, P., 2001. Production, oxidation, emission and consumption of methane by soils: A review. European Journal of Soil Biology 37, 25–50. Li, Y.Q., Xu, M., Sun, O.J., Cui, W.C., 2004. Effects of root and litter exclusion on soil CO2 efflux and microbial biomass in wet tropical forests. Soil Biology & Biochemistry 36, 2111–2114. Mohn, J., Schurmann, A., Hagedorn, F., Schleppi, P., Bachofen, R., 2000. Increased rates of denitrification in nitrogen-treated forest soils. Forest Ecology and Management 137, 113–119. Mosier, A., Kroeze, C., Nevison, C., Oenema, O., Seitzinger, S., van Cleemput, O., 1998. Closing the global N2O budget: nitrous oxide emissions through the agricultural

3030

R. Konda et al. / Soil Biology & Biochemistry 40 (2008) 3021–3030

nitrogen cycledOECD/IPCC/IEA phase II development of IPCC guidelines for national greenhouse gas inventory methodology. Nutrient Cycling in Agroecosystems 52, 225–248. Pennock, D.J., van Kessel, C., Farrell, R.E., Sutherland, R.A., 1992. Landscape-scale variations in denitrification. Soil Science Society of America Journal 56, 770–776. Potter, C.S., Davidson, E.A., Verchot, L.V., 1996. Estimation of global biogeochemical controls and seasonality in soil methane consumption. Chemosphere 32, 2219–2246. Prieme, A., Christensen, S., Galle, B., Klemedtsson, L., Griffith, D.W.T., 1996. Spatial variability of CH4 uptake in a Danish forest soil and its relation to different measurement techniques. Atmospheric Environment 30, 1375–1379. Raich, J.W., Tufekcioglu, A., 2000. Vegetation and soil respiration: Correlations and controls. Biogeochemistry 48, 71–90. Raich, J.W., Bowden, R.D., Steudler, P.A., 1990. Comparison of two static chamber techniques for determining carbon dioxide efflux from forest soils. Soil Science Society of America Journal 54, 1754–1757. Raich, J.W., Potter, C.S., Bhagawati, D., 2002. Interannual variability in global soil respiration, 1980–94. Global Change Biology 8, 800–812. Rayment, M.B., Jarvis, P.G., 2000. Temporal and spatial variation of soil CO2 efflux in a Canadian boreal forest. Soil Biology & Biochemistry 32, 35–45. Rey, A., Pegoraro, E., Tedeschi, V., De Parri, I., Jarvis, P.G., Valentini, R., 2002. Annual variation in soil respiration and its components in a coppice oak forest in central Italy. Global Change Biology 8, 851–866. Robertson, G.P., Gross, K.L., 1994. Assessing the heterogeneity of belowground resources: quantifying pattern and scale. In: Caldwell, M.M., Pearcy, R.W. (Eds.), Exploitation of Environmental Heterogeneity by Plants: Ecophysiological Processes Above- and Belowground. Academic Press, San Diego, USA, pp. 237–253. Robertson, G.P., Huston, M.A., Evans, F.C., Tiedje, J.M., 1988. Spatial variability in a successional plant communitydPatterns of nitrogen availability. Ecology 69, 1517–1524. Robertson, G.P., Klingensmith, K.M., Klug, M.J., Paul, E.A., Crum, J.R., Ellis, B.G., 1997. Soil resources, microbial activity, and primary production across an agricultural ecosystem. Ecological Applications 7, 158–170. Rover, M., Heinemeyer, O., Munch, J.C., Kaiser, E.A., 1999. Spatial heterogeneity within the plough layer: high variability of N2O emission rates. Soil Biology & Biochemistry 31, 167–173. Saiz, G., Green, C., Butterbach-Bahl, K., Kiese, R., Avitabile, V., Farrell, E., 2006. Seasonal and spatial variability of soil respiration in four Sitka spruce stands. Plant and Soil 287, 161–176.

Stoyan, H., De-Polli, H., Bohm, S., Robertson, G.P., Paul, E.A., 2000. Spatial heterogeneity of soil respiration and related properties at the plant scale. Plant and Soil 222, 203–214. Tang, X.L., Liu, S.G., Zhou, G.Y., Zhang, D.Q., Zhou, C.Y., 2006. Soil–atmospheric exchange of CO2, CH4, and N2O in three subtropical forest ecosystems in southern China. Global Change Biology 12, 546–560. van den Pol-van Dasselaar, A., Corre, W.J., Prieme, A., Klemedtsson, A.K., Weslien, P., Stein, A., Klemedtsson, L., Oenema, O., 1998. Spatial variability of methane, nitrous oxide, and carbon dioxide emissions from drained grasslands. Soil Science Society of America Journal 62, 810–817. Van Kessel, C., Pennock, D.J., Farrell, R.E., 1993. Seasonal variations in denitrification and nitrous oxide evolution at the landscape scale. Soil Science Society of America Journal 57, 988–995. Veldkamp, E., Keller, M., 1997. Nitrogen oxide emissions from a banana plantation in the humid tropics. Journal of Geophysical Research-Atmospheres 102, 15889–15898. Velthof, G.L., Jarvis, S.C., Stein, A., Allen, A.G., Oenema, O., 1996. Spatial variability of nitrous oxide fluxes in mown and grazed grasslands on a poorly drained clay soil. Soil Biology & Biochemistry 28, 1215–1225. Velthof, G.L., van Groenigen, J.W., Gebauer, G., Pietrzak, S., Jarvis, S.C., Pinto, M., Corre, W., Oenema, O., 2000. Temporal stability of spatial patterns of nitrous oxide fluxes from sloping grassland. Journal of Environmental Quality 29, 1397–1407. Verchot, L.V., Davidson, E.A., Cattanio, J.H., Ackerman, I.L., 2000. Land-use change and biogeochemical controls of methane fluxes in soils of eastern Amazonia. Ecosystems 3, 41–56. Webster, R., 1985. Quantitative spatial analysis of soil in the field. In: Stewart, B.A. (Ed.), Advances in Soil Science. Springer, New York, pp. 1–70. Weitz, A.M., Keller, M., Linder, E., Crill, P.M., 1999. Spatial and temporal variability of nitrogen oxide and methane fluxes from a fertilized tree plantation in Costa Rica. Journal of Geophysical Research - Atmospheres 104, 30097–30107. Werner, C., Zheng, X.H., Tang, J.W., Xie, B.H., Liu, C.Y., Kiese, R., Butterbach-Bahl, K., 2006. N2O, CH4 and CO2 emissions from seasonal tropical rainforests and a rubber plantation in southwest China. Plant and Soil 289, 335–353. Yanai, J., Sawamoto, T., Oe, T., Kusa, K., Yamakawa, K., Sakamoto, K., Naganawa, T., Inubushi, K., Hatano, R., Kosaki, T., 2003. Spatial variability of nitrous oxide emissions and their soil-related determining factors in an agricultural field. Journal of Environmental Quality 32, 1965–1977. Yates, T.T., Si, B.C., Farrell, R.E., Pennock, D.J., 2006. Probability distribution and spatial dependence of nitrous oxide emission: Temporal change in hummocky terrain. Soil Science Society of America Journal 70, 753–762.