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Stoyan, Helmut; De-Polli, Helvécio; Bohm, Susan; Robertson, G. Philip; Paul, Eldor A.

doi:10.1023/a:1004757405147

Cited by 206 publications

(35 citation statements)

References 30 publications

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“…Similarly, in our case, no significant correlation between soil respiration and controlling environmental factors were found in both DE and TE sites, identical to the results from Panosso's research [44]. Significant relationships were reported for spatial variation of SR and soil moisture [7] [45] [46], C/N ratio and topsoil bulk density jointly [43], plant root and plant residue patterns [25] in previous studies, but the correlation between spatial variation of SR with single soil temperature or soil moisture was barely reported, suggesting that when spatial variation is investigated, soil properties and/or vegetation information should be paid more attention to beyond soil temperature and soil moisture.…”

Section: Soil Respiration and Environmental Factorssupporting

confidence: 87%

“…Graphing the semivariance values across all separation distances provided the semivariogram [25]. A typical semivariogram has a nugget variance (C 0 ) and the variance will increase up to the sill variance (C 0 + C) with increasing separation distance.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, Wang, et al [45] found that the location of the high value positions of soil moisture, soil microbial biomass carbon and soil respiration were clearly around the positions of scrubs. Furthermore, Stoyan, et al [25] estimated that the main causes of soil heterogeneity around poplar trees were likely to be controlled in part by root and plant at micro scales.…”

Section: Spatial Variations Of Soil Respiration In Three Ecosystemsmentioning

confidence: 99%

“…As a central tool in geostatistics, the semivariance statistic is extensively applied to evaluate the spatial heterogeneity of soil respiration in forest ecosystems [25] [26] [48] or farmland ecosystems [7] [44], but there are few reports using geostatistical analysis to clarify the spatial structure of soil respiration in the arid desert ecosystems. Wang, et al [45] conducted an experiment in an area adjacent to the our TE site which contained 42 sampling points with 2 m grids and found a moderate spatial autocorrelation of SR with the nugget to sill value of 0.49 and a range of 4.78 m, compared with our more homogeneous distribution as deduced from larger nugget/sill value of 0.62 and range of 16.35 m. It is reported that the range of spatial variation models of SR changed temporally [25] [26] [51] and could also be affected by the plot size [52], which might account for the discrepancy.…”

Section: Spatial Variations Of Soil Respiration In Three Ecosystemsmentioning

confidence: 99%

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Spatial Variations of Soil Respiration in Arid Ecosystems

Liu¹,

Sonobe²,

Wang³

2016

OJE

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Soil respiration releases a major carbon flux back to atmosphere and thus plays an important role in global carbon cycling. Soil respiration is well known for its significant spatial variation in terrestrial ecosystems, especially in fragile ecosystems of arid land, where vegetation is distributed sparsely and the climate changes dramatically. In this study, soil respiration in three typical arid ecosystems: desert ecosystem (DE), desert-farmland transition ecosystem (TE) and farmland ecosystem (FE) in an arid area of northwestern China were studied for their spatial variations in 2012 and 2013. Along with soil respiration (SR), soil surface temperature (ST), soil moisture (SM) and soil electrical conductivity (ECb) were also recorded to investigate the spatial variations and the correlations among them. The results revealed that averaged soil respiration rate was much lower in DE than those in TE and FE. No single factor could adequately explain the variation of soil respiration, except a negative relationship between soil temperature and soil respiration in FE (P < 0.05). Geostatistical analysis showed that the spatial heterogeneity of soil respiration in DE was insignificant but notably in both TE and FE, especially in FE, which was mainly attributed to the different vegetation or soil moisture characteristics in the three ecosystems. The results obtained in this study will help to provide a better understanding on spatial variations of soil respiration and soil properties in arid ecosystems and also on macroscale carbon cycling evaluations.

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Section: Soil Respiration and Environmental Factorssupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Section: Spatial Variations Of Soil Respiration In Three Ecosystemsmentioning

confidence: 99%

Section: Spatial Variations Of Soil Respiration In Three Ecosystemsmentioning

confidence: 99%

See 2 more Smart Citations

Spatial Variations of Soil Respiration in Arid Ecosystems

Liu¹,

Sonobe²,

Wang³

2016

OJE

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“…de Blécourt et al, 2013;de Koning et al, 2003;Mekuria et al, 2009;Post and Kwon, 2000), and geomorphological characteristics such as slope and slope position (Chaplot et al, 2005;Corre et al, 2015;Pennock and Corre, 2001). Spatial patterns of SOC are also greatly influenced by small-scale variability in biophysical factors that influence plant productivity and decomposition of soil organic matter (Hook et al, 1991;Stoyan et al, 2000). A comprehensive understanding of the sources of spatial variability of SOC and its key drivers at multiple scales is an important prerequisite for upscaling SOC data to larger areas.…”

Section: Introductionmentioning

confidence: 99%

Spatial variability in soil organic carbon in a tropical montane landscape: associations between soil organic carbon and land use, soil properties, vegetation, and topography vary across plot to landscape scales

Blécourt¹,

Corre²,

Paudel

et al. 2017

SOIL

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Abstract. Presently, the lack of data on soil organic carbon (SOC) stocks in relation to land-use types and biophysical characteristics prevents reliable estimates of ecosystem carbon stocks in montane landscapes of mainland SE Asia. Our study, conducted in a 10 000 ha landscape in Xishuangbanna, SW China, aimed at assessing the spatial variability in SOC concentrations and stocks, as well as the relationships of SOC with land-use types, soil properties, vegetation characteristics and topographical attributes at three spatial scales: (1) land-use types within a landscape (10 000 ha), (2) sampling plots (1 ha) nested within land-use types (plot distances ranging between 0.5 and 12 km), and (3) subplots (10 m radius) nested within sampling plots. We sampled 27 one-hectare plots -10 plots in mature forests, 11 plots in regenerating or highly disturbed forests, and 6 plots in open land including tea plantations and grasslands. We used a sampling design with a hierarchical structure. The landscape was first classified according to land-use types. Within each land-use type, sampling plots were randomly selected, and within each plot we sampled within nine subplots. SOC concentrations and stocks did not differ significantly across the four land-use types. However, within the open-land category, SOC concentrations and stocks in grasslands were higher than in tea plantations (P < 0.01 for 0-0.15 m, P = 0.05 for 0.15-0.30 m, P = 0.06 for 0-0.9 m depth). The SOC stocks to a depth of 0.9 m were 177.6 ± 19.6 (SE) Mg C ha −1 in tea plantations, 199.5 ± 14.8 Mg C ha −1 in regenerating or highly disturbed forests, 228.6 ± 19.7 Mg C ha −1 in mature forests, and 236.2 ± 13.7 Mg C ha −1 in grasslands. In this montane landscape, variability within plots accounted for more than 50 % of the overall variance in SOC stocks to a depth of 0.9 m and the topsoil SOC concentrations. The relationships of SOC concentrations and stocks with land-use types, soil properties, vegetation characteristics, and topographical attributes varied across spatial scales. Variability in SOC within plots was determined by litter layer carbon stocks (P < 0.01 for 0-0.15 m and P = 0.03 for 0.15-0.30 and 0-0.9 m depth) and slope (P ≤ 0.01 for 0-0.15, 0.15-0.30, and 0-0.9 m depth) in open land, and by litter layer carbon stocks (P < 0.001 for 0-0.15, 0.15-0.30 and 0-0.9 m depth) and tree basal area (P < 0.001 for 0-0.15 m and P = 0.01 for 0-0.9 m depth) in forests. Variability in SOC among plots in open land was related to the differences in SOC concentrations and stocks between grasslands and tea plantations. In forests, the variability in SOC among plots was associated with elevation (P < 0.01 for 0-0.15 m and P = 0.09 for 0-0.9 m depth). The scale-dependent relationships between SOC and its controlling factors demonstrate that studies that aim to investigate the land-use effects on SOC Published by Copernicus Publications on behalf of the European Geosciences Union. 124 M. de Blécourt et al.: Spatial variability of soil organic carbon need an appropriate samp...

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A practical approach for uncertainty quantification of high‐frequency soil respiration using Forced Diffusion chambers

2015

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This paper examines the sources of uncertainty for the Forced Diffusion (FD) chamber soil respiration (R s ) measurement technique and demonstrates a protocol for uncertainty quantification that could be appropriate with any soil flux technique. Here we sought to quantify and compare the three primary sources of uncertainty in R s : (1) instrumentation error; (2) scaling error, which stems from the spatial variability of R s ; and (3) random error, which arises from stochastic or unpredictable variation in environmental drivers and was quantified from repeated observations under a narrow temperature, moisture, and time range. In laboratory studies, we found that FD instrumentation error remained constant as R s increased. In field studies from five North American ecosystems, we found that as R s increased from winter to peak growing season, random error increased linearly with average flux by about 40% of average R s . Random error not only scales with soil flux but scales in a consistent way (same slope) across ecosystems. Scaling error, measured at one site, similarly increased linearly with average R s , by about 50% of average R s . Our findings are consistent with previous findings for both soil fluxes and eddy covariance fluxes across other northern temperate ecosystems that showed random error scales linearly with flux magnitude with a slope of~0.2. Although the mechanistic basis for this scaling of random error is unknown, it is suggestive of a broadly applicable rule for predicting flux random error. Also consistent with previous studies, we found the random error of FD follows a Laplace (double-exponential) rather than a normal (Gaussian) distribution.

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Untitled

Cited by 206 publications

References 30 publications

Spatial Variations of Soil Respiration in Arid Ecosystems

Spatial Variations of Soil Respiration in Arid Ecosystems

Spatial variability in soil organic carbon in a tropical montane landscape: associations between soil organic carbon and land use, soil properties, vegetation, and topography vary across plot to landscape scales

A practical approach for uncertainty quantification of high‐frequency soil respiration using Forced Diffusion chambers

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