Functional Dependencies of Soil CO2 Emissions on Soil Biological Properties in Northern German Agricultural Soils Derived from A

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Functional dependencies of soil CO2 emissions on soil biological properties in northern German agricultural soils derived from a glacial till Yang Wangabc, Manfred Bölterd, Qingrui Changa, Rainer Duttmannb, Kirstin Marxb, James F. Petersene & Zhanli Wangcf a College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, Shaanxi, PR China b Division of Physical Geography: Landscape Ecology and Geoinformation Science (LGI), Department of Geography, Christian-Albrechts-University Kiel, Ludewig-Meyn-Str. 14, Click for updates 24098 Kiel, Germany c State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, Shaanxi, PR China d Institute for Ecosystem Research, Christian-Albrechts-University Kiel, Olshausenstr. 75, 24118 Kiel, Germany e Department of Geography, Texas State University, San Marcos, TX 78666, USA f Institute of Soil and Water Conservation, Chinese Academy of Science and Ministry of Water Resources, Yangling 712100, Shaanxi, PR China Published online: 21 Jan 2015.

To cite this article: Yang Wang, Manfred Bölter, Qingrui Chang, Rainer Duttmann, Kirstin Marx, James F. Petersen & Zhanli Wang (2015) Functional dependencies of soil CO2 emissions on soil biological properties in northern German agricultural soils derived from a glacial till, Acta Agriculturae Scandinavica, Section B — Soil & Plant Science, 65:3, 233-245, DOI: 10.1080/09064710.2014.1000369 To link to this article: http://dx.doi.org/10.1080/09064710.2014.1000369

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ORIGINAL ARTICLE

Functional dependencies of soil CO2 emissions on soil biological properties in northern German agricultural soils derived from a glacial till

Yang Wanga,b,c, Manfred Bölterd, Qingrui Changa, Rainer Duttmannb, Kirstin Marxb, James F. Petersene and Zhanli Wangc,f*

aCollege of Natural Resources and Environment, Northwest A&F University, Yangling 712100, Shaanxi, PR China; bDivision of Physical Geography: Landscape Ecology and Geoinformation Science (LGI), Department of Geography, Christian-Albrechts-University Kiel, Ludewig-Meyn-Str. 14, 24098 Kiel, Germany; cState Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, Shaanxi, PR China; dInstitute for Ecosystem Research, Christian-Albrechts-University Kiel, Olshausenstr. 75, 24118 Kiel, Germany; eDepartment of Geography, Texas State University, San Marcos, TX 78666, USA; fInstitute of Soil and Water Conservation, Chinese Academy of Science and Ministry of Water Resources, Yangling 712100, Shaanxi, PR China (Received 8 November 2014; accepted 16 December 2014)

Agricultural soil CO2 emissions and their controlling factors have recently received increased attention because of the high potential of carbon sequestration and their importance in soil fertility. Several parameters of soil structure, chemistry, and microbiology were monitored along with soil CO2 emissions in research conducted in soils derived from a glacial till. The investigation was carried out during the 2012 growing season in Northern −1 −1 Germany. Higher potentials of soil CO2 emissions were found in grassland (20.40 µg g dry weight h ) compared to arable land (5.59 µg g−1 dry weight h−1) within the incubating temperature from 5°C to 40°C and incubating moisture from 30% to 70% water holding capacity (WHC) of soils taken during the growing season. For agricultural soils regardless of pasture and arable management, we suggested nine key factors that influence changes in soil CO2 emissions including soil temperature, metabolic quotient, bulk density, WHC, percentage of silt, bacterial biomass, pH, soil organic carbon, and hot water soluble carbon (glucose equivalent) based on Downloaded by [Zhanli Wang] at 03:03 27 July 2015 principal component analysis and hierarchical cluster analysis. Slightly different key factors were proposed concerning individual land use types, however, the most important factors for soil CO2 emissions of agricultural soils in Northern Germany were proved to be metabolic quotient and soil temperature. Our results are valuable in providing key influencing factors for soil CO2 emission changes in grassland and arable land with respect to soil respiration, physical status, nutrition supply, and microbe-related parameters. Keywords: bacterial biomass; hierarchical cluster analysis; principal component analysis; soil organic carbon; soil respiration

Introduction 1995). Soil CO2 emissions varied significantly with Soil CO emission (soil respiration) plays a major role different vegetation types and climate conditions 2 −2 −1 in the increase of atmospheric greenhouse gases and ranging from 0.23 to 5.20 g C m d ,thatpresent the potential for global warming (Schlesinger & difficulties in predicting local soil CO2 emissions Andrews 2000; Hassan et al. 2014). Annual global (Raich & Tufekciogul 2000). In Northern German

CO2 emissions from soil to atmosphere are estimated agricultural soils formed and altered by glacial till, the at approximately 76.5 Pg C y−1,butlocalclimate various topographical attributes and soil characteris-

conditions are important to consider (Raich & Potter tics play crucial roles in determining the soil CO2

*Corresponding author. Email: [email protected]

emissions. Various biotic and abiotic factors affect the soil CO2 emissions and soil properties based on PCA process of CO2 release from soils, and investigations and HCA in Northern Germany and comparable on key factors for soil CO2 emissions assist the regions. monitoring of annual CO2 budgets and carbon balances (Han et al. 2007; Giacometti et al. 2013). To accurately predict temporal and spatial variation Materials and methods in soil CO2 emissions, many researchers have focused Study sites on the key driving factors during the recent decades (Davidson et al. 1998; Mäkiranta et al. 2008;Zhou Sampling sites are located within a 4.1 thousand et al. 2013). However, better understanding of prac- hectare watershed in the state of Schleswig-Holstein, Germany between 54° 24ʹ22ʺ – 54° 27ʹ48ʺ N and tical factors, which are easily generated and mon- 9° 55ʹ36ʺ– 10° 5ʹ55ʺ E(Figure 1). The mean annual itored among holistic factors is needed to provide air temperature and precipitation is 8.9°C and 778.0 effective and less cost-consuming improvements in mm, respectively (Climatological data of the weather modeling soil CO emissions. 2 station Kiel-Holtenau for the period 1981–2010, In addition to soil organic matter quality, soil obtained from the German Weather Service, www. temperature is widely regarded as one of the most dwd.de). The soil type according to WRB mainly important factors controlling biochemical reactions, consists of Luvisol, Stagnic Luvisol, Gleysols, and basically for the activity of enzymes (Fang & Moncrieff 2001). Topsoil bulk density (BD) reflects the porosity Cambisols (Duphorn 1995, State Agency for Agri- and thus determines soil atmosphere, and temperature culture, Environment and Rural Areas [LLUR]). The grassland sites were under management includ- also interacts strongly by O2/CO2 diffusioninsoil depending on moisture content (Paul & Clark 1989). ing grazing of cattle or one to two times mowing Further, soil temperature and moisture determine the during the growing season. Winter wheat was the substrate supply for microbial processes by affecting vegetation type in arable sites during the sampling the quantity of dissolved carbon and other minerals period, while winter rapeseed – winter wheat – (Giacometti et al. 2013). Hot water soluble carbon winter barley rotation dominated the study area (HWSC) represents the concentration of labile soil (Dilly et al. 2003). carbon, which is accepted as the most biologically available fraction of total soil organic matter (SOM) and of fast turnover (Ros et al. 2011). Soil carbon Sampling balance and nutrition including inorganic nitrogen During the growing season from April to July 2012, and phosphorus provide energy and elements for soil samples were retrieved once a month between 9 microbial anabolism and catabolism (Thomas & am and 6 pm mainly on around 24th as well as once Madigan 1991). Because microbes are generally in the middle of the previous (2011) November. regarded as the main driving force or catalyst in soil Originally, 18 randomly chosen sampling sites organic carbon (SOC) decomposition, total bacterial Downloaded by [Zhanli Wang] at 03:03 27 July 2015 included nine for grassland and nine for arable number (TBN), bacterial biomass (BBM), and their land. The free software Geospatial Modelling Envir- derived parameters can be considered as important onment (Version GME 0.5.3 Beta [GUI]) depends factors affecting soil CO2 emissions. Additionally, the metabolic quotient (q CO ,theratioofrespiredCto on R (Version 2.15.0) was used for generating 2 ‘ BBM C) is accepted as another indicator of the random points by the command of genstratran- ’ efficiency of energy use by microbes and demonstrates dompnts . Numbers of samples were reduced in the substrate limitation (Moscatelli et al. 2007). later sampling months, however, there were at least Our study was designed to better understand the three replicates for each sampling month in each effect of pasture and arable managements on agri- land use type (Appendix). For one soil sample taken cultural soils derived from a glacial till and to in one sampling month, only topsoil (0–5 cm) were taken from five subsamples within one square meter evaluate key influencing factors of soil CO2 emissions from a set of practical factors. Principal and homogenized to constitute a mixed sample. All component analysis (PCA) and hierarchical cluster samples were transported in cooling boxes and analysis (HCA) were both applied to identify the stored unsieved with maintenance of field moisture important factors that explain variations in soil CO2 in zipper plastic bags at 4°C until being measured emissions and their driving force. The major objec- within the following six weeks after sampling. Soil tives are: (1) to identify the main soil physical, samples taken in November 2011 were stored frozen chemical, and microbial properties in grassland and at –18°C for 12 weeks before soil CO2 emission arable land; (2) to identify essential dependencies of analysis (Stenberg et al. 1998). Acta Agriculturae Scandinavica, Section B — Soil & Plant Science 235

Figure 1. Location of sampling sites distributed in grasslands and arable lands within the study area. The inset is a map showing the study area’s location in Northern Germany.

Measurement of soil CO2 emission and [MCV]), based on epifluorescence microscopy and soil properties image analysis (Leica Co.) and length and width measurements of cocci and rods following the method Soil CO emission was analyzed in open flow 2 described by Bölter et al. (2002). chambers using an infrared-gas analyser (Rosemount From the originally collected fresh soils, the meas- Co.; Bölter et al. 2003). In this study, soil subsam- ured physical and chemical soil properties included ples which equivalent to 20 g wet weight were BD (by core method), WHC (determined on a mass incubated in temperatures ranging from 5°C to 40° Downloaded by [Zhanli Wang] at 03:03 27 July 2015 biases by saturating and draining soil subsamples until C with an interval of 5°C and with the moisture equilibrium), particle size distribution (sieved wet and controlled at 30%, 50%, and 70% water holding Köhn pipette method), SOC (loss on ignition), capacity (WHC). Incubating temperature in cham- HWSC (glucose equivalent), available nitrogen bers was controlled by the water bath and lasted for À (NO3 , Nitrate Cell Test), available phosphorous two hours each. In every five minutes, gas in one 3–, (AP, PO4 Phosphate Cell Test), pH (water 1:2.5), chamber was sampled and analyzed by the infrared- pH (CaCl2), and total carbon to nitrogen (C/N) ratio gasanalyser for one record during one minute. And (by elemental analyzer, EuroVector Co.). These at least 10 records were used for calculating the methods follow standard procedures in soil science mean CO2 emitting rate. Pre-incubation was carried (Neumann 1954; Elprince 1986;Carter1993). out with tinfoil cover on the soil subsamples for one hour after water addition until the preset soil moisture. That aimed to allow the sterile water permeate Data analyses the whole subsample as well as to avoid the flush of Medians and ranges were used to describe the rewetting. Before incubation in the experiments, the dataset, as a normal distribution cannot be assumed samples recovered to starting temperature during for time series. Box-Whisker plots indicated medians one night. (lines within the box), 25th and 75th percentiles Descriptors of the microbes are BBM, total bac- (lower and upper boarder of the box), 10th and 90th terial surface (TBS), mean values of surface and percentiles (Whiskers), 5th and 95th percentiles volume (mean bacterial surface and mean cell volume (circles). Kruskal-Wallis Rank Sum Test was used 236 Y. Wang et al.

to determine effects of land use on the physical, a single cluster at the end (He 1999). Single linkage, chemical, and microbial properties of soils. Data complete linkage and average linkage clustering were used for regression analysis were rank-transformed used. The cutoff value of clusters is determined in advance (Conover & Iman 1981). All statistical according to thresholds given by the critical values of analyses were conducted by the free software envir- the Spearman rank correlation coefficient (Bölter & onment R, Version 2.15.0. Meyer 1986). Two-tailed significance levels of 99%, Spearman rank correlation was computed to select 95%, and 90% were separately applied for a thresh- suitable descriptors without redundancy and for old (Ebdon 1977). Best linkage criteria and most building up the basic correlation matrix. Multivariate suitable threshold for those datasets were chosen statistical approaches including PCA and HCA were concerning the stability of clusters adopting all three applied in order to evaluate key influencing factors algorithms. Finally, to investigate cluster validity, with regard to soil respiration. PCA extracts and two criteria concerning compactness and separation weights functional groups from complex multidi- were chosen in this study (Halkidi et al. 2001). To mensional variables (Bautista-Cruz et al. 2012). select the most appropriate clustering schemes, we Thus it is possible to reduce redundant data and adopted a general-purpose measure by using the experience easier interpretation (Waswa et al. 2013). similarity obtained from Spearman rank correlation PCA is used to select a smaller group of important analysis for determining the clusters (Berry & Linoff factors that contain a significant proportion of total 2004). With the respect to correlation coefficient, a variance. In contrast, HCA is used as an objective strong categorization following zero (0), weak classification technique for grouping similar descrip- (<0.1), modest (0.1–0.3), moderate (0.3–0.5), tors or objects (Smith & Mather 2012). Based on the strong (0.5–0.8), very strong (0.8–0.9), and perfect distance or correlation of two variables, HCA use (1) are defined (Correlations: Direction and Strength). algorithms to fuse variables into different clusters, Clusters are accepted when at least a moderate with grouped members having minimum dissimil- correlation exists between each pair of members arity within their cluster while they have maximum within one cluster. dissimilarity among clusters (Yemefack et al. 2005). Both statistical methods are successfully used in ecological research to evaluate important factors (e.g., Bölter & Meyer 1983; 1986; Yao et al. 2013). Results These statistical methods were applied separately to Soil properties and CO2 emissions in grassland the data of grassland and arable land as well as the and arable land whole dataset. A brief description of soil properties from May to July PCA was applied based on the correlation matrix, 2012 follows. Detailed results are presented in Table 1 for it standardized data and made soil properties in and Figure 2. Both soil total organic carbon and soil different units comparable. To decide the number of HWSC are significantly higher in grassland than in principal components, eigenvalue was used as a NOÀ

Downloaded by [Zhanli Wang] at 03:03 27 July 2015 arable land (Table 1)Soil concentration is criterion. Only when eigenvalue was greater than 1, 3 significantly higher in grassland than in arable land. the component was retained. That is because when NOÀ eigenvalue was less than 1, the component explains The maximum value of arable soil 3 concentration (533.9 µg g−1), however, greatly exceeded the max- less variation than an individual variable (Waswa −1 et al. 2013). In order to interpret the principal imum value of grassland (342.3 µg g ) in May 2012. components meaningfully, a varimax rotation was According to the soil microbial properties shown in performed to maximize the correlations between Table 1, significantly higher values of TBN, BBM, principal components and the measured variables and TBS occur in grassland. The highest BBM values (Sharma 1995). Important factors were selected in in grassland and arable land both occurred in Novem- each component by the high-weighted loadings, ber 2011, while the median BBM values are 29.41 −1 −1 which exceeded the level of 0.5. µg g d.wt. and 9.22 µg g d.wt., respectively. Soils HCA is based on agglomerative hierarchical tech- in grassland tend to have higher median values of C/N À niques to group the selected descriptors. First, a ratio, SOC/AP ratio, and NO3 –N/AP ratio in com- distance matrix of all descriptors was computed parison with arable soils. Only SOC/HWSC ratio using the equation of Distance = 1 – |ρ|, where appears lower in grassland (34.8) than in arable land ρ represents the Spearman rank correlation coeffi- (48.0). No statistically significant difference of soil cient of soil property pairs which include only metabolic quotient (q CO2) can be found between symmetric data (Liu et al. 2012). Specifically, the grassland and arable land, though median values are, −1 closest individuals or clusters are fused into one respectively, 0.61 and 1.00 µg C-CO2 µg BBM larger cluster at any level until all descriptors formed Ch−1 (Table 1). Acta Agriculturae Scandinavica, Section B — Soil & Plant Science 237

Table 1. Soil physical, chemical, and microbial properties as well as ratios of selected chemical and microbial properties in grassland and arable land.

Grassland Arable land

Soil properties Median Range Median Range

BD (g cm−3) 1.15 0.83–1.39 1.46 1.33–1.63 WHC (% d.wt.) 64.68 48.35–103.14 35.05 33.94–40.87 Clay (<2 µm) (% d.wt.) 16.26 7.81–23.66 16.00 11.38–19.39 Silt (2–63 µm) (% d.wt.) 27.28 15.31–32.94 28.33 22.50–31.72 Sand (63–2000 µm) (% d.wt.) 58.13 49.55–76.88 57.13 50.43–64.78 SOC (mg g−1) 36.0 21.6–86.4 17.6 14.4–20.7 HWSC (µg glucose g−1) 1079.3 15.7–2690.5 375.2 7.2–729.7 pH-H2O 5.66 4.91–7.39 6.41 5.82–7.15 À −1 NO3 (µg g ) 80.3 29.2–342.3 59.4 26.5–533.9 3− −1 AP (µg PO4 g ) 2.64 0.59–11.70 3.81 0.77–7.59 TBN (106 g−1) 570 159–1551 175 106–345 BBM (µg g−1) 29.41 9.14–122.89 9.22 5.09–29.50 TBS (mm2 g−1) 407 127–1566 125 77–369 MCV (µm3) 0.058 0.035–0.079 0.056 0.037–0.088 MCV/MBS (µm) 0.074 0.065–0.079 0.073 0.066–0.080 SOC/AP 44.81 14.47–184.52 13.58 7.63–73.43 NO3-N/AP 18.56 6.23–166.82 10.38 4.64–175.92 C/N 13.0 11.5–17.0 11.4 10.4–13.1 SOC/HWSC 79.4 48.0–4287.7 109.8 59.2–5406.1 −1 −1 qCO2 (µg C-CO2 µg BBM C h ) 0.61 0.00–15.44 1.00 0.00–8.26

Note: Numbers of measured results range from 8 to 35 in each land use type. WHC, water holding capacity; SOC, soil organic carbon; HWSC, hot water soluble carbon; AP, available phosphate; TBN, total bacterial number; BBM, bacterial biomass; TBS, total bacterial surface; MCV, mean cell volume; MBS, mean bacterial surface; C/N, soil total carbon to soil total nitrogen ratio; q CO2, metabolic quotient; d.wt., dry weight. Downloaded by [Zhanli Wang] at 03:03 27 July 2015

−1 −1 Figure 2. The soil CO2 emission rate (µg CO2 g h ) responds to temperature increases with soil moisture ranging from 30% to 70% water holding capacity (WHC) in grassland (a) and arable land (b). Box-Whisker plots indicate medians (lines within the box), 25th and 75th percentiles (lower and upper border of the box), 10th and 90th percentiles (Whiskers), 5th and 95th percentiles (points).

Soil CO2 emission rates were significantly higher Selection of key influencing factors in grassland than they are in arable land (Figure 2). Results of PCA Soil moisture ranged between 30% and 70% WHC, soil CO2 emission rates increased when incubated at PCA revealed the presence of five components in 5°C to 40°C both in grassland (median values grassland (Table 2) and in arable land (Table 3); −1 −1 ranging from 0 to 20.40 µg CO2 g h ) and arable while six components were found for the entire land (median values ranging from 0 to 5.59 µg dataset of soils under both land use types (Table 4). −1 −1 CO2 g d.wt. h ). According to Figure 3, the first two PCs accounted 238 Y. Wang et al.

Table 2. Rotated factor loadings for the five principal components (PC) in grassland.

Soil property PC1 PC2 PC3 PC4 PC5

Soil moisture −0.030 0.034 −0.044 0.029 0.085 Soil CO2 emission −0.046 0.965 0.051 0.036 0.059 MCV 0.053 −0.029 0.127 −0.089 0.943 BBM −0.642 −0.184 0.170 0.444 0.469 HWSC −0.156 0.042 0.145 0.194 0.156 NO3-N −0.278 −0.066 0.645 0.597 −0.198 AP 0.203 0.006 0.165 0.141 0.092 pH-H2O 0.888 0.099 0.051 −0.266 0.169 Soil temperature −0.032 0.573 −0.011 0.040 0.021 Metabolic quotient 0.220 0.937 −0.049 −0.198 −0.133 SOC −0.101 −0.088 0.147 0.924 −0.038 BD −0.017 −0.026 −0.955 −0.070 −0.211 Water holding capacity −0.318 0.005 0.734 0.177 0.077 Clay 0.203 −0.005 −0.126 −0.033 −0.015 Silt 0.963 0.022 −0.186 0.046 0.003 Proportion of variance explained 0.17 0.15 0.14 0.11 0.09 Cumulative proportion 0.17 0.32 0.45 0.56 0.65

Note: Numbers in bold indicate soil properties with relatively high loadings within the same PC. Extraction method: PCA; rotation method: varimax rotation. MCV, mean cell volume; BBM, bacterial biomass; HWSC, hot water soluble carbon; AP, available phosphate; SOC, soil organic carbon.

Table 3. Rotated factor loadings for the five principal components (PC) in arable land.

Soil property PC1 PC2 PC3 PC4 PC5

Soil moisture 0.049 0.062 0.088 0.035 0.050 Soil CO2 emission 0.045 0.969 0.070 0.055 0.022 MCV −0.058 −0.045 0.906 −0.013 0.053 BBM 0.050 −0.112 0.929 0.111 0.073 HWSC 0.097 0.045 0.150 0.146 −0.029 NO3-N 0.043 0.090 −0.122 0.048 −0.058 AP 0.190 −0.006 0.173 0.071 0.932 pH-H2O −0.048 −0.040 0.157 0.079 −0.058 Soil temperature −0.001 0.232 0.015 −0.008 0.001 Metabolic quotient 0.001 0.945 −0.258 0.009 −0.014 SOC 0.192 0.055 0.063 0.940 0.081 BD 0.610 0.058 −0.208 −0.072 0.561 Downloaded by [Zhanli Wang] at 03:03 27 July 2015 Water holding capacity −0.867 0.005 −0.087 −0.116 −0.281 Clay 0.933 0.035 −0.015 0.117 0.008 Silt −0.061 0.017 0.071 0.657 −0.069 Proportion of variance explained 0.15 0.14 0.13 0.10 0.09 Cumulative proportion 0.15 0.28 0.42 0.52 0.61

for, respectively, 14.0–22.0% and 12.4–14.7% of represented by high loadings on BD, WHC, and

total variance. Following the varimax rotation, NO3-N. The fourth component (PC4) gave high cumulatively 65%, 61%, and 69% of total variance loadings on SOC and NO3-N. The fifth component of grassland, arable land and the whole dataset, (PC5) only gave high loadings on MCV. respectively, were explained by those components. For arable land, the first component (PC1) repre- For grassland, the factors with high loadings in the sented high loadings on clay, WHC, and BD. The first component (PC1) were silt, pH, and BBM. The second component (PC2) was represented by high

second component (PC2) represented high loadings loadings on soil CO2 emission and metabolic quo- on soil CO2 emission, metabolic quotient, and soil tient. The third component (PC3) gave high load- temperature. The third component (PC3) was ings on BBM and MCV. The fourth component Acta Agriculturae Scandinavica, Section B — Soil & Plant Science 239

Table 4. Rotated factor loadings for the six principal components (PC) for agricultural soils under both pasture and arable management.

Soil property PC1 PC2 PC3 PC4 PC5 PC6

Soil moisture −0.023 0.041 0.003 0.070 0.022 −0.003 Soil CO2 emission 0.280 0.881 −0.108 0.061 0.022 0.131 MCV 0.030 −0.075 0.137 0.967 −0.073 0.084 BBM 0.720 −0.158 −0.328 0.382 0.120 0.297 HWSC 0.449 0.122 0.001 0.161 −0.088 0.807 NO3-N 0.255 0.016 −0.176 −0.060 0.087 0.026 AP −0.085 −0.010 0.053 0.085 −0.007 −0.044 pH-H2O −0.167 0.076 0.847 0.192 0.127 −0.337 Soil temperature −0.009 0.323 0.002 0.017 −0.013 −0.032 Metabolic quotient −0.127 0.955 0.113 −0.147 0.003 −0.021 SOC 0.804 0.009 −0.100 −0.120 0.169 0.399 BD −0.924 −0.094 0.069 −0.048 0.167 −0.042 Water holding capacity 0.902 0.086 −0.215 −0.037 −0.304 0.041 Clay −0.150 0.023 0.137 −0.065 0.959 −0.047 Silt −0.218 −0.035 0.917 0.010 0.079 0.198 Proportion of variance explained 0.22 0.12 0.12 0.08 0.08 0.070 Cumulative proportion 0.22 0.34 0.47 0.55 0.62 0.69

(PC4) was represented by high loadings on SOC and Results of cluster analysis silt. The fifth component (PC5) gave high loadings Cluster analysis was based on the Spearman rank cor- on AP and BD. relation matrix and finally a complete linkage algo- For agricultural soils under both pasture and rithm was determined for clusters (see Figures 4a–c). arable management, the factors with high loadings Cutoff line was accepted at the two-tailed 90% (for in the first component (PC1) were BD, WHC, SOC, dataset of grassland and arable land) and 95% (for and BBM. The second component (PC2) was the whole dataset) significance level according to represented by high loadings on metabolic quotient thresholds given by critical Spearman rank correla- and soil CO2 emission. The third component (PC3) tion values. gave high loadings on silt and pH values. The fourth According to the results of soil property clusters, to the sixth component (PC4–PC6) only gave high all factors formed four and five statistically signific- Downloaded by [Zhanli Wang] at 03:03 27 July 2015 loadings on MCV, Clay, and HWSC, respectively. ant clusters in grassland (Figure 4a) and arable land

Figure 3. PCA ordination diagram for grassland (a), arable land (b), and agricultural soils under both managements (c). T, soil temperature; Re, soil CO2 emission rate; q CO2, metabolic quotient; SOC, soil organic carbon; BBM, bacterial biomass; MCV, mean cell volume; AP, available phosphate; HWSC, hot water soluble carbon; BD, bulk density; WHC, water holding capacity. 240 Y. Wang et al.

Figure 4. Cluster dendrograms showing soil property groups for grassland (a), arable land (b), and agricultural soils under both managements (c). The rectangles highlighted the corresponding clusters determined by the threshold shown as the dotted cutoff line. T, soil temperature; Re, soil CO2 emission rate; q CO2, metabolic quotient; SOC, soil organic carbon; BBM, bacterial biomass; MCV, mean cell volume; AP, available phosphate; HWSC, hot water soluble carbon; BD, bulk density; WHC, water holding capacity.

(Figure 4b); while the entire dataset was repre- microbial community, as shown for TBN, BBM, sented by only three clusters regardless of land use and TBS, and it supports stronger microbial activit- (Figure 4c). Similar results were obtained by cluster ies in grassland than arable land. analysis and PCA for grassland. Four clusters were Specifically, a higher SOC content in grassland formed with the same constituents as the first four than in arable land was observed within the ranges of principal components, except that NO3-N was only comparable soils, as a result of less disturbance by grouped with SOC. For arable land, cluster H and tillage and a greater input of plant biomass (Abdalla cluster G were, respectively, the same as arable PC3 et al. 2013). In line with earlier studies (Chantigny and PC4. Arable cluster A included one more factor 2003; Embacher et al. 2007), a higher concentration (soil temperature) compared to arable PC2. The key of HWSC in grassland compared to arable land and factors selected by arable PC1 mainly comprised the significant variations of HWSC are reported in this combination of cluster E and cluster F. For the research. The variations of water extractable organic whole dataset of agricultural soils under both man- carbon are mainly due to temporal changes of plant agements, cluster A also formed with one extra Downloaded by [Zhanli Wang] at 03:03 27 July 2015 exudes and microbial metabolites (Murata et al. factor (soil temperature) compared with its PC2. 1999). Higher soil nitrate concentrations in grass- Cluster I consists of the combination of PC1 and land than in arable land is a finding consistent with PC6, while cluster J is the same as PC3 according to other researchers (Xue et al. 2013), which may be the results of the overall dataset. due to a stronger soil nitrogen mineralization related to greater amounts of SOC and soil microbial Discussion activity (Yang et al. 2010). However, the application of chemical nitrogen fertilizer (e.g., urea) in arable Comparison of soil properties and CO2 land in May 2012 contributed to the highest soil emissions between grassland and arable land nitrate content in this study, as higher nitrate Concerning ranges in parameters during the growing concentration is typically found with labile N season, the results show in this study prove for application due to its rapid mineralization (Bowles significant but different effects on soil physical, et al. 2014). chemical, and microbial properties under treatment The much higher amounts of BBM in grassland in grassland and arable land. Stronger soil CO2 than in arable land are generally consistent with emissions are found in grassland compared to arable results from Wallenius et al. (2011). More BBM, land in Northern Germany. A lower BD and higher mainly derived from soils with higher SOM content, WHC, in grassland soils provide more substrate as demonstrated by the most important factor influ- including SOC, labile carbon, and mineral nitrogen encing soil enzyme activity (Štursová & Baldrian than the arable soils. This further stimulates a larger 2011). Limitations of substrate C in arable soil Acta Agriculturae Scandinavica, Section B — Soil & Plant Science 241

(supported by the low concentration of HWSC) may between BD and WHC in both grassland (ρ = −0.78) be responsible for the restricted fluctuation observed and arable land (ρ = −0.74) as well as the entire in the content of BBM, TBN, and TBS during the dataset (ρ = −0.93). Topsoil BD also reflects the soil whole research period comparable to results of Patra water and solute movement as well as aeration, which et al. (1990). are important environmental factors controlling aer- The lower SOC/HWSC ratio in grassland indi- obic microbial decomposition of soil organic matter cates a lower availability of high-quality carbon for (Kwon et al. 2013). Though not revealed in physical microbial metabolic activities (Spohn & Giani 2011). soil property dominated principal components and Although not at statistically significant level, the clusters, silt plays important role in affecting soil CO2 trend observed by the median values shows a lower emissions in grassland and arable land as particle size soil q CO2 in grassland compared to arable land. distribution affects soil aggregations. This result confirms the negative relation between soil microbial biomass and metabolic quotient. The Soil nutrition. The key factors that reflect soil nutri- possible reasons are the inhibition of bacterial activ- tion supply to microbial and plant root respirations À ity by high amount of elevated CO2 and limitation by in grassland are SOC and NO3 -N. They are com- nutrient shortage (Šantrůčková & SiraŠicraba 1991; bined in PC4/cluster C with a positive Spearman Xu et al. 2006). rank correlation coefficient of 0.64. In the research Soil CO2 emissions consist of both microbe of Huang et al. (2014), total soil organic carbon and À respiration and plant root respiration. As soil tem- NO3 -N were also revealed to be key factors in perature rises from 5°C to 40°C in the incubating affecting soil microbial community. Ros et al. chambers while moisture ranges between 30% and (2011) supported the strong relationship between 70% WHC, an increasing soil CO2 emission rate is total organic matter and mineralizable nitrogen in observed for both grassland and arable land. This agricultural soils. Further, it is claimed that SOC has finding corresponds to previous research that commonly received more attention than other soil explains the gain through a regression analysis of properties (e.g., WHC) in predicting potential soil CO2 emission rate and temperature (Fang & mineralizable nitrogen and stimulates stronger Moncrieff 2001). When the soil temperature is not a microbial activity (Yang et al. 2010; Ros et al. À limiting factor in grassland, higher soil labile carbon 2011). Soil organic carbon and NO3 -N are import- contents stimulate a stronger mineralization and a ant factors for soil CO2 emissions because organic higher microbial metabolic activity (Ng et al. 2014). and inorganic soil compounds C and N are the main In contrast, the low available C as indicated by low sources providing chemical energy for most of the HWSC in arable soil results in nutrition or energy microorganisms as well as sources for biosynthesis. shortage. Low available C even fortifies this wearing Bacterial populations grow larger with addition of down effect on soil CO2 emission rate (Yoshitake fresh substrate, while depletion of nutrients drives it et al. 2007). However, it should be considered that back to the original size (Kauri 1982). Moreover, it these relationships can be validated only through has been found that soil nitrate concentration

Downloaded by [Zhanli Wang] at 03:03 27 July 2015 actual experimental design. is correlated with soil microbiological attributes (Vasconcellos et al. 2013). In addition, soil microbial community structure regulates nitrate accumulation Factors generated from PCA and cluster by denitrification (Rachid et al. 2013). analyses However, for arable soils, the application of Key influencing factors were selected from four main chemical nitrogen fertilizer on the study sites can aspects concerning the output of soil respiration, soil be interpreted as a disturbance of the natural microbes, soil physical restriction, and soil nutrition concentration of mineral nitrogen. Arable soil CO2 À supply. emissions are then not regulated by NO3 -N when no shortage of available mineral nitrogen limits the Soil physical status. Comparing the results of grassland process of soil respiration. In the long term, fertil- from the principal component and cluster analyses, ization also results in elevated SOC and is thus not cluster D (Figure 4a) consisting of BD and WHC is a in the same group or within related clusters. The key relevant group of important physical factors that affect influencing factors for arable soil CO2 emissions are soil CO2 emissions. For arable results, constituents of revealed in arable PC4/cluster G with SOC and silt. cluster E and cluster F were kept in each group at Available phosphate is another important nutrition |ρ| > 0.52 level, however, the arable PC1 is more in arable soil CO2 emissions as shown in PC5 and reasonable representing soil physical status for soil Cluster F. According to Cleveland et al. (2002), one CO2 emissions. BD affects key soil functions such as possible reason is that AP becomes the limiting WHC, our results show a strong negative correlation nutrient on carbon decomposition when adequate 242 Y. Wang et al.

nitrogen exists in arable soils after the application of emission rate and metabolic quotient) exist for fertilizer. arable data and the whole dataset. The low availab- For agricultural soils under both pasture and ility of substrate in arable land can be regarded as a arable managements, HWSC also performs as a key limiting factor for stimulating respiration via higher factor in explaining variations in soil CO2 emissions. temperatures (Teklay et al. 2006). However, using It may be due to the combination of different ranges soil temperature and metabolic quotient together of data from grassland, also arable land enlarges the gave a better fit than using metabolic quotient alone effect of HWSC in stimulating soil respiration. in predicting soil CO2 emissions for arable land, According to Gershenson et al. (2009), availability based on the determination of coefficients (0.41 and of a labile carbon source plays the dominant role on 0.34, respectively). Fang and Moncrieff (2001)also CO2 production in short-term incubation. indicated that soil temperature plays a critical role in affecting soil CO2 emission rate among ecosystems, Soil microbes. According to the statistical results, the e.g., farmland and forest soils. Hence, soil temper- important microbiological properties are assigned ature is strongly related to soil CO2 emissions and into different principal components/clusters in grass- probably the major controlling factor (see Figure 2) – land (PC1 and PC5, Table 2/cluster B, Figure 4a) at least within the ranges of our dataset. and arable land (PC3, Table 3/cluster H, Figure 4b). Soil moisture conditions were not shown to be a However, BBM is one of the main indicators in both key factor in soil CO2 emissions. The main reason land use types, as it is most intensively studied by may be that the soil water content determined here researchers and has been proved to account for the ranged between 30% and 70% WHC, and shows no temporal variations in microbial activity (Iqbal et al. significant effect on soil CO2 emission rate. The 2010; Yao et al. 2011). For grassland, BBM, silt, community does not face thresholds of soil moisture, and pH form a group at |ρ| > 0.63 level. This which corresponds to the data and findings of concurs with the research of Dilly et al. (2003) that Williamson and Wardle (2007) for microbes not pH value is negatively correlated with BBM, and imposed to water stress. they proposed that high H+ concentrations (low pH Metabolic quotients serve as a valid indicator values) accompany high amounts of microbial bio- affecting soil CO2 emissions in our study, they mass and strong microbial activities. However, why describe substrate limitations and energy utility the cluster in arable land excludes pH value may be efficiency (Moscatelli et al. 2007). Mariani et al. due to low potential of microbial metabolites and (2006), also found metabolic quotient to be closely growth, and no significant correlations were found. correlated to soil respiration in redundant analysis. For arable land, BBM and MCV are positively Although soil CO2 emission rates and metabolic correlated (ρ = 0.77) and thus are reasonably kept quotient are correlated partly due to a self-correla- in the same group. MCV constitutes an important tion, they are declared to act in similar way facing factor for soil CO2 emission changes as it reflects the disturbance (e.g., increasing temperature; Xu et al. microbial growth and the nutritional status of soil 2006). Both elevation of respiration and reduction of

Downloaded by [Zhanli Wang] at 03:03 27 July 2015 (Thomas & Madigan 1991). Cells tend to have low microbial biomass were shown as paired with volume adopting coccal morphology when starved; increasing temperature from 5°C to 35°C in the in contrast, rod-shaped morphology with high vol- research of Xu et al. (2006), which is in accordance ume is preferred when enough nutrition is provided with the rise of metabolic quotient. by soil (West et al. 1987). In conclusion, similar groups were established based on PCA and HCA, in which parameters can Soil respiration. Key influencing factors concerning serve as key influencing factors for soil CO2 emis- the soil respiration output are agglomerated by PC2 sions. Cluster analysis based on agglomerative hier- (see Tables 2–4)/clusters A (see Figure 4a–c)in archical techniques is applied to group all the grassland, arable land, and agricultural soils under selected parameters that influence soil CO2 emis- both managements. The members of PC2/cluster A sions. In this research, complete linkage algorithm in grassland have proved to be well correlated with was finally determined for clusters. Complete linkage each other at ρ > 0.5 level according to positive approach makes fusion of individuals or clusters Spearman rank correlation coefficients. Key factors when the maximum distance of two objects (each generated from arable data and the overall dataset individual pair within clusters is concerned) is just included soil CO2 emission rate and metabolic within a certain partition (Sokal & Sneath 1963). quotient according to results of PCA; while soil According to the principle of HCA, the group temperature is also included in cluster A. Compared members share the strongest similarity within each to grassland, weaker correlations between soil tem- cluster while they share the weakest similarity among perature and the other two factors (soil CO2 clusters (Sokal & Sneath 1963). Thus, according to Acta Agriculturae Scandinavica, Section B — Soil & Plant Science 243

the results from HCA, metabolic quotient and Simulating the impacts of land use in Northwest temperature are suggested to be the best factors Europe on Net Ecosystem Exchange (NEE): the role of arable ecosystems, grasslands and forest plantations describe soil CO2 emissions. Soil moisture in the – in climate change mitigation. Sci Total Environ. range of our experiments (30 70% WHC) was not a 465:325–336. key influence on soil CO2 emissions. Hence, soil Bautista-Cruz A, del Castillo RF, Etchevers-Barra JD, microbes may not be facing a threshold in this range. Gutiérrez-Castorena M del C, Baez A. 2012. Selec- However, many researchers regard soil moisture as a tion and interpretation of soil quality indicators for crucial factor for forest ecosystems. The results from forest recovery after clearing of a tropical montane cloud forest in Mexico. For Ecol Manag. 277:74–80. PCA mainly suggested all important factors to Berry MJA, Linoff GS. 2004. Data mining techniques: for variations of soil CO2 emissions. Beside metabolic marketing, sales, and customer relationship manage- quotient and soil temperature, the key physical ment. 2nd ed. Wiley. Bölter M, Bloem J, Meiners K, Möller R. 2002. Enu- factors that affect soil CO2 emissions in grassland were BD and silt; while an additional important meration and biovolume determination of microbial cells – a methodological review and recommendations factor, clay, is relevant for arable land. Soil organic for applications in ecological research. Biol Fertil carbon is the only common chemical indicator for Soils. 36:249–259. the two land use types. Other key chemical factors Bölter M, Meyer M. 1983. The sandy beach area of Kiel À Fjord and Kiel Bight (Western Baltic Sea) – a affecting soil CO2 emissions are NO3 -N and pH values for grassland; but in contrast, AP for arable structural analysis of a shallow water ecosystem. In: McLachlan A, Erasmus T, editors. Sandy beaches land. The key microbial factors are BBM and MCV, ecosyst [Internet]. Dordrecht: Springer Netherlands; which relate to soil CO2 emission changes in both [cited 2013 Jun 21]; p. 263–270. Available from: grassland and arable land. Although our findings are http://link.springer.com/content/pdf/10.1007/978-94- preliminary with respect to models, these approaches 017-2938-3_19.pdf offer opportunities to develop key influencing factors Bölter M, Meyer M. 1986. Structuring of ecological data sets by methods of correlation and cluster analysis. from a practical standpoint for investigating agricul- Ecol Model. 32:1–13. tural CO2 emissions. Bölter M, Müller-Lupp W, Takata K, Yabuki H, Moller R. 2003. Potential CO2-production in aerobic condi- Acknowledgments tions from a Siberian tundra environment. Polar Biosci. 16:70–85. We are grateful to Institute of Geography (Division of Bowles TM, Acosta-Martínez V, Calderón F, Jackson LE. Physical Geography: Landscape Ecology and Geoinforma- 2014. Soil enzyme activities, microbial communities, tion Science, LGI) and Institute of Polar Ecology of the and carbon and nitrogen availability in organic agroe- Christian-Albrechts-University Kiel (CAU) for financial cosystems across an intensively-managed agricultural and logistical support. We especially thank the farmers of landscape. Soil Biol Biochem. 68:252–262. the Danish Wahld peninsula to authorize us taking soil Carter MR. 1993. Soil sampling and methods of analysis. samples from their farmland. We acknowledge Mrs Ann- CRC Press. ette Scheltz who contributed a lot with laboratory mea- Chantigny MH. 2003. Dissolved and water-extractable surements. The LGI student assistants Katrin organic matter in soils: a review on the influence of Schünemann, Nicole Wilder, and Wolfgang Hamer kindly land use and management practices. Geoderma. – Downloaded by [Zhanli Wang] at 03:03 27 July 2015 helped with collecting soil samples and lab work. The first 113:357 380. author is very grateful to an overseas visiting PhD schol- Cleveland CC, Townsend AR, Schmidt SK. 2002. Phos- arship from the China Scholarship Council, Ministry of phorus limitation of microbial processes in Moist Education, PR China to study and develop research Tropical Forests: evidence from short-term laboratory collaborations at CAU, Germany. incubations and field studies. Ecosystems. 5:680–691. Conover WJ, Iman RL. 1981. Rank transformations as a bridge between parametric and nonparametric statist- Disclosure statement ics. Am Stat. 35:124–129. Correlations: Direction and Strength [Internet]. [date No potential conflict of interest was reported by the unknown]. Glasgow: University of Strathclyde; authors. [cited 2014 Feb 24]. Available from: http://www. strath.ac.uk/aer/materials/4dataanalysisineducation alresearch/unit4/correlationsdirectionand strength/ Funding Davidson EA, Belk E, Boone RD. 1998. Soil water content and temperature as independent or confoun- We are also grateful to partially financial support for this ded factors controlling soil respiration in a temper- research provided by the National Natural Science ate mixed hardwood forest. Glob Change Biol. Foundation of China funded project [grant number 4:217–227. 41471230], [grant number 41171227]. Dilly O, Blume H-P, Munch JC. 2003. Soil microbial activities in Luvisols and Anthrosols during 9 years of References region-typical tillage and fertilisation practices in northern Germany. Biogeochemistry. 65:319–339. Abdalla M, Saunders M, Hastings A, Williams M, Smith Duphorn K. 1995. Die deutsche Ostseeküste. Berlin: P, Osborne B, Lanigan G, Jones MB. 2013. Borntraeger. 244 Y. Wang et al.

Ebdon D. 1977. Statistics in geography: a practical Moscatelli MC, Di Tizio A, Marinari S, Grego S. 2007. approach. Oxford: Blackwell. Microbial indicators related to soil carbon in Medi- Elprince AM. 1986. Chemistry of soil solutions. New terranean land use systems. Soil Tillage Res. York: Van Nostrand Reinhold. 97:51–59. Embacher A, Zsolnay A, Gattinger A, Munch JC. 2007. Murata T, Tanaka H, Yasue S, Hamada R, Sakagami K, The dynamics of water extractable organic matter Kurokawa Y. 1999. Seasonal variations in soil micro- (WEOM) in common arable topsoils: I. Quantity, bial biomass content and soil neutral sugar composi- quality and function over a three year period. Geo- tion in grassland in the Japanese Temperate Zone. derma. 139:11–22. Appl Soil Ecol. 11:253–259. Fang C, Moncrieff JB. 2001. The dependence of soil CO2 Neumann F. 1954. Farbreaktion von Kohlenhydraten efflux on temperature. Soil Biol Biochem. 33:155–165. mit Anthron [Color reaction of carbohydrates with Gershenson A, Bader NE, Cheng W. 2009. Effects of anthrone]. Fresenius Z Für Anal Chem. 141:157–158. substrate availability on the temperature sensitivity of Ng E-L, Patti AF, Rose MT, Schefe CR, Wilkinson K, soil organic matter decomposition. Glob Change Biol. Smernik RJ, Cavagnaro TR. 2014. Does the chemical 15:176–183. nature of soil carbon drive the structure and function- Giacometti C, Demyan MS, Cavani L, Marzadori C, ing of soil microbial communities? Soil Biol Biochem. Ciavatta C, Kandeler E. 2013. Chemical and micro- 70:54–61. biological soil quality indicators and their potential to Patra DD, Brookes PC, Coleman K, Jenkinson DS. 1990. differentiate fertilization regimes in temperate agroe- Seasonal changes of soil microbial biomass in an cosystems. Appl Soil Ecol. 64:32–48. arable and a grassland soil which have been under Halkidi M, Batistakis Y, Vazirgiannis M. 2001. On uniform management for many years. Soil Biol clustering validation techniques. J Intell Inf Syst. Biochem. 22:739–742. 17:107–145. Paul EA, Clark FE. 1989. Soil microbiology and biochem- Han G, Zhou G, Xu Z, Yang Y, Liu J, Shi K. 2007. Biotic istry. San Diego, CA: Academic Press. and abiotic factors controlling the spatial and tem- Rachid CTCC, Balieiro FC, Peixoto RS, Pinheiro YAS, poral variation of soil respiration in an agricultural Piccolo MC, Chaer GM, Rosado AS. 2013. Mixed ecosystem. Soil Biol Biochem. 39:418–425. plantations can promote microbial integration and soil Hassan W, David J, Abbas F. 2014. Effect of type and quality nitrate increases with changes in the N cycling genes. of two contrasting plant residues on CO2 emission Soil Biol Biochem. 66:146–153. potential of Ultisol soil: implications for indirect influ- Raich JW, Potter CS. 1995. Global patterns of carbon ence of temperature and moisture. Catena. 114:90–96. dioxide emissions from soils. Glob Biogeochem Cycles. He Q. 1999. A review of clustering algorithms as applied 9:23–36. in IR. Urbana-Champaign (US): University of Illinois Raich JW, Tufekciogul A. 2000. Vegetation and soil at Urbana-Champaign, Graduate School of Library respiration: correlations and controls. Biogeochemis- and Information Science. try. 48:71–90. Huang X, Liu S, Wang H, Hu Z, Li Z, You Y. 2014. Ros GH, Hanegraaf MC, Hoffland E, van Riemsdijk WH. Changes of soil microbial biomass carbon and com- 2011. Predicting soil N mineralization: relevance of munity composition through mixing nitrogen-fixing organic matter fractions and soil properties. Soil Biol species with Eucalyptus urophylla in subtropical Biochem. 43:1714–1722. China. Soil Biol Biochem. 73:42–48. Šantrůčková H, SiraŠicraba M. 1991. On the relationship Iqbal J, Hu R, Feng M, Lin S, Malghani S, Ali IM. 2010. between specific respiration activity and microbial Microbial biomass, and dissolved organic carbon and biomass in soils. Soil Biol Biochem. 23:525–532. nitrogen strongly affect soil respiration in different land Schlesinger WH, Andrews JA. 2000. Soil respiration and –

Downloaded by [Zhanli Wang] at 03:03 27 July 2015 uses: a case study at Three Gorges Reservoir Area, the global carbon cycle. Biogeochemistry. 48:7 20. South China. Agric Ecosyst Environ. 137:294–307. Sharma S. 1995. Applied multivariate techniques. New Kauri T. 1982. Seasonal fluctuations in numbers of York: Wiley. aerobic bacteria and their spores in four horizons of Smith JM, Mather ME. 2012. Using assemblage data in a beech forest soil. Soil Biol Biochem. 14:185–190. ecological indicators: a comparison and evaluation of Kwon MJ, Haraguchi A, Kang H. 2013. Long-term water commonly available statistical tools. Ecol Indic. regime differentiates changes in decomposition and 13:253–262. microbial properties in tropical peat soils exposed to Sokal RR, Sneath PHA. 1963. Principles of numerical the short-term drought. Soil Biol Biochem. 60:33–44. taxonomy. San Francisco, CA: Freeman. Liu X, Zhu X-H, Qiu P, Chen W. 2012. A correlation- Spohn M, Giani L. 2011. Total, hot water extractable, and matrix-based hierarchical clustering method for func- oxidation-resistant carbon in sandy hydromorphic tional connectivity analysis. J Neurosci Methods. soils-analysis of a 220-year chronosequence. Plant 211:94–102. Soil. 338:183–192. Mäkiranta P, Minkkinen K, Hytönen J, Laine J. 2008. Stenberg B, Johansson M, Pell M, Sjödahl-Svensson K, Factors causing temporal and spatial variation in Stenström J, Torstensson L. 1998. Microbial biomass heterotrophic and rhizospheric components of soil and activities in soil as affected by frozen and cold respiration in afforested organic soil croplands in storage. Soil Biol Biochem. 30:393–402. Finland. Soil Biol Biochem. 40:1592–1600. Štursová M, Baldrian P. 2011. Effects of soil properties Mariani L, Chang SX, Kabzems R. 2006. Effects of tree and management on the activity of soil organic matter harvesting, forest floor removal, and compaction on transforming enzymes and the quantification of soil- soil microbial biomass, microbial respiration, and N bound and free activity. Plant Soil. 338:99–110. availability in a boreal aspen forest in British Colum- Teklay T, Nordgren A, Malmer A. 2006. Soil respiration bia. Soil Biol Biochem. 38:1734–1744. characteristics of tropical soils from agricultural and Acta Agriculturae Scandinavica, Section B — Soil & Plant Science 245

forestry land-uses at Wondo Genet (Ethiopia) in Xue Z, Cheng M, An S. 2013. Soil nitrogen distributions response to C, N and P amendments. Soil Biol for different land uses and landscape positions in a Biochem. 38:125–133. small watershed on Loess Plateau, China. Ecol Eng. Thomas DB, Madigan MT. 1991. Biology of microorgan- 60:204–213. isms. 6th ed. Upper Saddle River, NJ: Prentice Hall. Yang L, Zhang F, Gao Q, Mao R, Liu X. 2010. Impact of Vasconcellos RLF, Bonfim JA, Andreote FD, Mendes land-use types on soil nitrogen net mineralization in LW, Baretta D, Cardoso EJBN. 2013. Microbiologi- the sandstorm and water source area of Beijing, cal indicators of soil quality in a riparian forest China. Catena. 82:15–22. recovery gradient. Ecol Eng. 53:313–320. Yao H, Bowman D, Shi W. 2011. Seasonal variations of Wallenius K, Rita H, Mikkonen A, Lappi K, Lindström K, soil microbial biomass and activity in warm- and cool- Hartikainen H, Raateland A, Niemi RM. 2011. season turfgrass systems. Soil Biol Biochem. Effects of land use on the level, variation and spatial 43:1536–1543. structure of soil enzyme activities and bacterial com- Yao R, Yang J, Gao P, Zhang J, Jin W. 2013. Determining munities. Soil Biol Biochem. 43:1464–1473. minimum data set for soil quality assessment of typical Waswa BS, Vlek PLG, Tamene LD, Okoth P, Mbakaya D, salt-affected farmland in the coastal reclamation area. Zingore S. 2013. Evaluating indicators of land Soil Tillage Res. 128:137–148. degradation in smallholder farming systems of west- Yemefack M, Rossiter DG, Njomgang R. 2005. Multi- ern Kenya. Geoderma. 195–196:192–200. scale characterization of soil variability within an West AW, Sparling GP, Grant WD. 1987. Relationships agricultural landscape mosaic system in southern between mycelial and bacterial populations in stored, Cameroon. Geoderma. 125:117–143. air-dried and glucose-amended arable and grassland Yoshitake S, Sasaki A, Uchida M, Funatsu Y, Nakatsubo soils. Soil Biol Biochem. 19:599–605. T. 2007. Carbon and nitrogen limitation to microbial Williamson WM, Wardle DA. 2007. The soil microbial respiration and biomass in an acidic solfatara field. community response when plants are subjected to Eur J Soil Biol. 43:1–13. water stress and defoliation disturbance. Appl Soil Zhou Z, Zhang Z, Zha T, Luo Z, Zheng J, Sun OJ. 2013. Ecol. 37:139–149. Predicting soil respiration using carbon stock in roots, Xu X, Inubushi K, Sakamoto K. 2006. Effect of vegeta- litter and soil organic matter in forests of Loess tions and temperature on microbial biomass carbon Plateau in China. Soil Biol Biochem. 57:135–143. and metabolic quotients of temperate volcanic forest soils. Geoderma. 136:310–319.

Appendix

Table A1. Measured numbers of soil samples (replicates) in grassland and arable land during the research period.

11-Nov 12-Apr 12-May 12-Jun 12-Jul

Measured parameters G A G A G A G A G A

Soil CO2 emissions At 30% WHC 8 8 8 7 6 7 6 7 6 7 Downloaded by [Zhanli Wang] at 03:03 27 July 2015 At 50% WHC 9 8 9 8 6 7 6 7 6 7 At 70% WHC 9 9 9 8 6 7 6 7 6 7 Soil microbial properties TBN, BBM, TBS, MCV, MBS 9 9 8 7 3 5 3 4 3 4 Soil chemical properties SOC 9 9 8 7 6 7 6 7 6 7 C/N, pH-H2O6666676767 À HWSC, NO3 ,AP 9 9 8 7 3 4 3 4 3 4 Soil physical properties BD, Clay, Silt, Sand 9 9 ––– –––––

Note: Soil CO2 emissions were measured at increasing incubating temperatures from 5°C, 10°C, 15°C, 20°C, 25°C, 30°C, 35°C to 40°C. WHC, water holding capacity; G, grassland; A, arable land; TBN, total bacterial number; BBM, bacterial biomass; TBS, total bacterial surface; MCV, mean cell volume; MBS, mean bacterial surface; SOC, soil organic carbon; C/N, soil total carbon to soil total nitrogen ratio; HWSC, hot water soluble carbon; AP, available phosphate; BD, bulk density; –, no data.