Xifeng NING1, Lina SONG2, Yaowu TIAN

1. Agricultural and Rural Bureau of Jianxi District of Luoyang City, Luoyang 471000, China; 2. Henan Lindeping Forestry Co., Ltd., Luoyang 471000, China; 3. Forestry College, Henan University of Science and Technology, Luoyang 471000, China

Abstract [Objectives]By testing applicability of SOC depth distribution model in geographical and climatic conditions of Funiu Mountain area, SOC depth distribution model in the region was established and applied. The constructed model was used to estimate SOC mass density in other regions, thereby obtaining SOC abundance distribution chart at different depths. [Methods]165 soil sampling sites were selected from Quercus variabilis forest, Pinus tabulaeformis forest, mixed forest, and shrub forest in Taowan basin of Funiu Mountain area, to determine SOC content at different depths, study SOC depth distribution pattern of forest in Taowan basin of Funiu Mountain area, and assess SOC reserve at different depths. [Results]Average SOC density of Q. variabilis forest, P. tabulaeformis forest, mixed forest, and shrub forest at the depth of 0-20 cm was 7.92, 8.42, 8.14 and 9.67 kg/m2, and there was significant difference in SOC density between shrub forest and Q. variabilis forest, P. tabulaeformis forest, mixed forest (P<0.05), and SOC density of four kinds of vegetation all abruptly declined with soil depth increased. At the depth of 0-20 cm, correlation between SOC density and vegetation type, canopy density, clay content and sand content was significant, and the correlation with altitude was insignificant. When carbon density at the depth of 0-100 cm was used to describe regional SOC reserve, the estimated value was lower. The established space model could predict SOC density of forest. [Conclusions]The estimation of deep-layer SOC by the established model needed further consideration, and estimation method for special areas needed to be further demonstrated.

Key wordsFuniu Mountain Area, Taowan basin, Forest, Soil organic carbon, Depth distribution

1 Introduction

Soil organic carbon (SOC) pool of forest is one of the most important reservoirs in carbon cycle of terrestrial ecosystem[1-2]. SOC dynamic affects ecological process of terrestrial biosphere, greenhouse gas composition and climate change rate[3-4]. Generally speaking, land use manner affects SOC reserve and depth distribution model. SOC density value is the highest on surface layer, and quickly declines in the form of exponential function with depth increase[2,5]. It is very important to understand terrestrial biosphere correctly by understanding and regulating SOC depth distribution model[6], and SOC reserve and depth distribution model affect vegetation productivity and is also affected by vegetation productivity. Radioisotope study of carbon shows that both turnover period and stability of carbon increase with depth increase[7]. Fontaineetal. think that SOC depth distribution pattern affects its stability and intensity of soil respiration[8]. When studying or estimating SOC abundance, SOC at 30 and 100 cm is generally considered, while SOC content and its change at deeper layer are considered less. But there are fewer studies based on SOC depth distribution. The research aimed to establish SOC depth distribution model under geographical and climatic conditions of Funiu Mountain area, and test applicability of the model in the region and its promotion and application. The constructed model was used to estimate SOC mass density in other regions, and obtain SOC abundance distribution map at different depths.

2 Materials and methods

2.1 General situation of research zoneTaowan basin is in Funiu Mountain area of west Henan, and geographical position is 111°20′0″-111°35′55″ E and 33°43′0″-33°55′0″ N. Headwatershed of the Yihe River contains Taowan Town, Shimiao Township and western half of Luanchuan Township in Luanchuan County of Henan Province. The basin belongs to warm temperate continental monsoon climate, and annual sunshine is 2 103 h. Frost-free period is 198 d, and annual average rainfall is 872.6 mm. Watershed area is 329.9 km2, and main soil type in the watershed is brown earth. Land use is divided into woodland, shrubland, grassland, orchard and farmland. In the research zone, altitude of woodland is higher, and dominant species isQuercusvariabilis. In shrubland, dominant species isLespedezabicolorand fern, while dominant species of grassland isMiscanthusandDendranthemaindicum.

(1)

whereSOCDkwas SOC density of thek-th layer (kg/m2);kwas soil layer;Ckwas SOC content of thek-th layer (g/kg);Dkwas soil density of thek-th layer (g/cm3);Ekwas soil thickness of thek-th layer (cm);Gkwas volume percentage of gravel with diameter >2 mm in thek-th soil layer (%).

2.3 SOC depth distribution model of forestGenerally speaking, SOC mass density was the highest on surface layer of forest soil, and quickly declined in the form of exponential function with depth increased[12]. To accurately estimate SOC density at any soil depth, depth distribution model of SOC mass density in four kinds of forest vegetation in Taowan basin was constructed according to the researches by Meersmansetal[12-15].

Sh=Sn+(S0-Sn)·exp(k·h)

(2)

Qh=h·Sn+B·(e-k·h-1)/k

(3)

whereShwas SOC mass density at the depth ofh(kg/m3);hwas soil depth (m);S0was SOC mass density of section surface (kg/m3);Snwas SOC mass density (kg/m3) of section bottom;kwas constant;Qhwas SOC reserve per area at the depth ofh(kg/m2).

SOC mass density (Sh) was product of SOC content and soil bulk density, and it was calculated by the formula (4). Soil bulk density was an important index of affecting reserve and density of SOC, and the lacked soil bulk density was estimated by the formula (5)[5,16].

(4)

(5)

whereρswas soil bulk density (kg/m3), and [SOC] was content of organic carbon (g/kg).

My dear sir, replied the old man, my stock in trade is not very large-I don t deal in laxatives and teething mixtures-but such as it is, it is varied2. I think nothing I sell has effects which could be precisely3 described as ordinary.

SPSS 11.5 was used for variance and correlation analysis, and nonlinear parameter estimation of the formula (2) was conducted by SPSS relevant modules. The constructed spatial distribution model (formula 3) was used to estimate SOC density at the depth of 0-30, 0-100 and 0-200 cm in Taowan basin.

3 Results and analyses

3.1 Depth distribution of SOC density in Taowan basinSeen from Table 1, average SOC density inQ.variabilisforest,P.tabulaeformisforest, mixed forest, and shrub forest at the depth of 0-20 cm was 7.92, 8.42, 8.14 and 9.67 kg/m2, and SOC density had insignificant difference amongQ.variabilisforest,P.tabulaeformisforest, and mixed forest (P>0.05), while there was significant difference between shrub forest and other vegetation types. SOC density had insignificant difference among four kinds of forest land at the depth ≥20-40, ≥40-60, ≥60-80 and ≥80-100 cm (P<0.05). It illustrated that vegetation type only affected distribution of SOC density on soil surface layer. SOC density inQ.variabilisforest,P.tabulaeformisforest, mixed forest, and shrub forest at the depth of 0-20 cm respectively occupied 72.5%, 74.4%, 75.6% and 76.2% of those at the depth of 0-100 cm. It illustrated that SOC was mainly distributed on soil surface layer.

At the depth of 0-100 cm, average SOC density inQ.variabilisforest,P.tabulaeformisforest, mixed forest, and shrub forest was 10.93, 11.32, 10.77 and 12.69 kg/m2. Similar with the depth layer of 0-20 cm, SOC density had insignificant difference amongQ.variabilisforest,P.tabulaeformisforest, and mixed forest (P>0.05), while there was significant difference between shrub forest and other vegetation types.

Table 1 displayed that SOC density in the four kinds of vegetation abruptly declined with soil depth increased. For example, SOC density ofQ.variabilisforest was 7.92 kg/m2at the depth of 0-20 cm, and has declined to 2.23 kg/m2at the depth ≥20-40 cm and 0.10 kg/m2at the depth ≥80-100 cm.

Table 1 Determined value of SOC density in Taowan basin of Funiu Mountain area

Soil depthcmQ. variabilis forestSOC densitykg/m2Proportion%P. tabulaeformis forestSOC densitykg/m2Proportion%Mixed forestSOC densitykg/m2Proportion%Shrub forestSOC densitykg/m2Proportion%0-207.92±2.9872.58.42±3.4574.48.14±4.4575.69.67±2.5676.2≥20-402.23±0.6820.41.70±0.7815.01.88±0.9917.52.04±0.4516.0≥40-600.49±0.564.50.92±0.658.10.52±0.784.80.70±0.345.5≥60-800.18±0.051.70.20±0.061.80.16±0.071.50.20±0.071.6≥80-1000.10±0.020.90.08±0.010.70.07±0.030.70.08±0.020.60-10010.93100.011.32100.010.77100.012.69100.0

Note: The proportion was ratio of SOC density at each soil layer to that at the depth of 0-100 cm.

3.2 Influence factors of depth distribution of SOC densitySeen from Table 2, SOC density had significant correlation with vegetation type, canopy density, clay content and sand content at the depth of 0-20 cm, and insignificant correlation with altitude. At 0-100 and 0-20 cm, there was similar rule. At the depth ≥20-40 cm, SOC density had significant correlation with canopy density, clay content and sand content, and insignificant correlation with vegetation type and altitude. At the depth ≥40-60 cm, SOC density only had significant correlation with clay content and sand content, and insignificant correlation with other environmental factors. It illustrated that attribute factors of plant such as vegetation type and canopy density only affected SOC density on the surface layer significantly, but had insignificant impact on deep layer. Clay content and sand content significantly affected SOC density at different depths.

Table 2 Correlation coefficient between SOC density and environmental factors

Soildepth∥ cmVegetationtypeCanopydensityAltitudeClaycontentSandcontent0-20-0.46?0.61??0.390.47?-0.54??≥20-40-0.410.52??0.350.49??-0.58??≥40-60-0.210.51??0.360.58??-0.63??≥60-80-0.390.42?0.360.49?-0.64??≥80-100-0.320.390.190.58??-0.71??0-100-0.44?0.58?0.220.52?-0.65??

Note:***showed correlation of double tail test was significant at the level of 0.001;**showed correlation of double tail test was significant at the level of 0.01;*showed correlation of double tail test was significant at the level of 0.05.

3.3 Depth distribution model of SOC density in different forest vegetation types of Taowan basinNonlinear parameter estimation of SOC mass density inQ.variabilisforest,P.tabulaeformisforest, mixed forest, and shrub forest was conducted, obtaining SOC depth distribution models of different tree species (formulas 6-9).

Q.variabilisforest:Sh=0.53+69.02×exp(-6.88×h)

(6)

P.tabulaeformisforest:Sh=0.43+65.20×exp(-6.59×h)

(7)

Mixed forest:Sh=0.41+68.54×exp(-6.78×h)

(8)

Shrub forest:Sh=0.92+77.88×exp(-6.22×h)

(9)

The above formulas showed that the maximum mass density (77.88 kg/m3) was on surface layer of shrub forest, followed byQ.variabilisforest (69.02 kg/m3), mixed forest (68.54 kg/m3) andP.tabulaeformisforest (65.20 kg/m3). Similarly, mass density of shrub forest at theSn-th layer was also the maximum (0.92 kg/m3), followed byQ.variabilisforest (0.53 kg/m3),P.tabulaeformisforest (0.43 kg/m3) and mixed forest (0.41 kg/m3).

SOC mass density of four kinds of trees decreased exponentially with soil depth increased (Fig.1). SOC decline curve was the closest inQ.variabilisforest,P.tabulaeformisforest, and mixed forest, and decline velocity in mixed forest was the quickest, while decline velocity in shrub forest was slower. Change in the index k was as below: shrub forest (-6.22) >P.tabulaeformisforest (-6.59) >Q.variabilisforest (-6.88)>mixed forest (-6.78).

Note: F1.Q.variabilisforest; F2.P.tabulaeformisforest; F3. Mixed forest; F4. Shrub forest.

Fig.1 Relationship between SOC mass density and depth distribution in four kinds of trees

3.4 Test of predicted results by modelThe simulated results of four kinds of trees by model were shown as Table 3. At the depth of 0-20 cm, SOC density inQ.variabilisforest,P.tabulaeformisforest, mixed forest, and shrub forest was lower than actual value in Table 2, while the simulated values at the depth ≥20-40 and ≥40-60 cm were more than actual values in Table 2. Simulated value ofQ.variabilisforest at the depth of 0-100 cm was 10.55 kg/m2, which was 3.44% lower than actual value (10.93 kg/m2). Simulated value of shrub forest was 14.42 kg/m2, which was 5.69% higher than actual value (12.69 kg/m2). It illustrated that the established spatial model could predict SOC density of forest. The established SOC spatial distribution model could simulate SOC density of above tree species at any depth. ForQ.variabilisforest,P.tabulaeformisforest, mixed forest, and shrub forest, SOC density at the depth of 0-30 cm was respectively 8.91, 8.65, 8.90 and 10.85 kg/m2, while SOC density at the depth of 0-200 cm was respectively 11.08, 10.76, 10.91 and 14.36 kg/m2(Table 3).

Table 3 Predicted results of SOC density in Taowan watershed of Funiu Mountain area

Soil depthcmQ. variabilis forestSOC densitykg/m2Proportion%P. tabulaeformis forestSOC densitykg/m2Proportion%Mixed forestSOC densitykg/m2Proportion%Shrub forestSOC densitykg/m2Proportion%0-207.6072.107.3471.107.5872.209.1067.80≥20-402.0019.002.0319.602.0119.202.7520.50≥40-600.585.500.615.900.585.500.926.90≥60-800.232.100.232.200.212.000.403.00≥80-1000.141.300.121.200.111.100.251.800-10010.55100.0010.32100.0010.50100.0013.42100.000-20011.08105.0210.76104.3010.911.0014.36107.03

Note: Proportion was the ratio of SOC density at each soil layer to that at the depth of 0-100 cm.

4 Discussions

4.1 Relationship between forest species and depth distribution pattern of SOCOverground vegetation pattern is the determinant of SOC depth distribution pattern, and climate and soil texture are the controlling factors of total SOC in a region[13-15,17-18]. Vegetation productivity, aboveground and underground biomass allocation, and soil microbial mechanism all affect depth distribution mode of SOC[19]. In this paper, tree species of forest affected distribution of SOC density at horizontal shallow soil layer, and had weaker impact on depth distribution mode. Jacksonetal. thought that distribution depth of plant root system affected distribution depth of SOC, and distribution differences between aboveground and underground parts of plants affected the relative amount of SOC, and litter amount on the surface affected SOC on surface layer[20]. In the investigation on root system of forest tree species in watershed, it was found that the distribution of woodland was relatively shallow, while root distribution of shrub forest was deeper, which corresponded with SOC depth distribution (shrub forest>woodland). SOC was the highest on surface layer (0-20 cm) of shrub forest, maybe there was higher litter fall amount in shrub forest of the watershed. Shrub forest had a lot of herbaceous vegetation, and carbon input was large, with higher decomposition, which greatly increased SOC density on surface layer.

4.2 Relationship between environmental factors and depth distribution of SOCResearch showed that altitude was also an important environmental factor affecting SOC distribution. Ding Xianqing thought that change trend of SOC with altitude was obvious in forest of Dawei Mountain, and SOC increased with altitude rose and significantly declined with depth of soil profile increased[21]. Cheng Haoetal. thought that SOC at different soil layers respectively showed extremely significant positive correlation and extremely significant negative correlation with altitude and bulk density[22]. In this paper, altitude was not related to SOC density, maybe altitude change was little in the research zone.

SOC distribution depth was also affected by clay and sand contents of soil. Donetal. thought when soil sand content was high, downward vertical transport velocity of SOC was quicker. Surface SOC decreased, while deep-layer SOC content increased, and it was more obvious in the soil with high permeability[17]. It was also found if sand content was high, soil capillary pore was large, and soil permeability was strong, vertical transportation characteristics were more obvious, and SOC declined slowly with the depth in the same vegetation type of the watershed. Generally, SOC is positively correlated with clay content and negatively correlated with sand content, and correlation of deep profile is larger than that at shallow layer. SOC of surface layer is affected by land use and temperature, while SOC of deep layer is not related to above factors, illustrating that mechanical composition of deep soil is an important factor affecting SOC.

4.3 Estimation error of SOCBatjesetal. reported that SOC distribution depth could reach more than 300 cm[8,17,23]. Due to the impacts of many factors, survey depth of SOC generally takes 0-100 cm and even lower, which may cause lower estimation value of SOC in forest. At the depth of 0-100 cm, global SOC reserve is about 1 500-1 600 Pg. If considering soil profile layer of 100-200 cm, SOC could increase by 60%, and SOC content at the depth of 100-200 cm may be 12% of that at 0-100 cm[23]. In Taowan basin, depth of most sampling sites did not exceed 150 cm, and sampling site lower than 80 cm and more than 200 cm accounted for 5%, and SOC content lower than 150 cm was lower.

Based on the established model, it was calculated that SOC density of four kinds of trees in research watershed would increase by 2.0%-3.6% than that at 0-100 cm if considering distribution of SOC at 0-150 cm. If considering 0-200 cm, SOC density would increase by 3.9%-7.0%. Fontaineetal. thought that SOC error was about 7% due to the limitation of soil depth distribution layer, and error may be larger in soil distribution zone with larger slope[8]. The estimation of deep-layer SOC needed to be further considered, and estimation methods for special areas also needed further demonstration.