Potential legacy effects of biofuel cropping systems on soil microbial communities in southern Wisconsin, USA

doi:10.4236/as.2011.22019

Paper Menu >>

Journal Menu >>

Vol.2, No.2, 131-137 (2011)

doi:10.4236/as.2011.22019

Agricultural Science s

Potential legacy effects of biofuel cropping systems on

soil microbial communities in southern Wisconsin, USA

Chao Liang1,2*, Gregg R. Sanford1,3, Randall D. Jackson1,3, Teri C. Balser2

1DOE Great Lakes Bioenergy Research Center, University of Wisconsin, Madison, USA;

*Corresponding Author: chaoliang@wisc.edu

2Department of Soil Science, University of Wisconsin, Madison, USA;

3Department of Agronomy, University of Wisconsin, Madison, USA.

Received 11 March 2011; revised 16 March 2011; accepted 31 March 2011.

ABSTRACT

Soil microbial community structure is clearly

linked to current plant species composition, but

less is known about the legacy effects of plant

species and agricultural management practices

on soil microbial communities. Using microbial

lipid biomarkers, we assessed patterns of com-

munity-level diversity and abundance at depths

of 0 - 10 and 10 - 25 cm from three hay (alfalfa/or-

chardgrass) and two corn plots in southern Wis-

consin. Principal components analysis of the

lipid biomarkers revealed differential composi-

tion of the soil microbial communities at the tw o

depths. Despite similar abundance of fungi,

bacteria, actinomycete, protozoa, and total mi-

crobial lipids in the hay and corn at 0 - 10 cm,

community structure differed with a significantly

higher absolute abundance of arbuscular my-

corrhizal fungi and gram-negative bacteria in the

hay plots. No significant microbial lipid mass

differences were detected between the two

management regimes at 10 - 25 cm, but the

proportional dominance of bacterial gram type

differed with depth. These results indicate the

potential for legacy effects of annual and peren-

nial cropping systems management on microbial

community composition and suggests the im-

portance of considering past land-use when ini-

tiating long-term agroecological trials.

Keywords: Lipid Biomarker; Alfalfa; Hay; Corn;

Agroecosystem

1. INTRODUCTION

Concern about ecological sustainability has stimulated

interest in describing the structure and function of soil

microbial communities, and in understanding how they

are affected by past and current land use [1-3]. Soil mi-

crobial community structure can be a sensitive indicator

of sustainable land use [4,5], and the variation in soil

microbial communities can further have significant im-

pacts on ecosystem processes that are central to key

ecosystem services, including plant production, carbon

mineralization, nutrient decomposition, and greenhouse

gas fluxes [6,7].

Land use management often alters plant species com-

position and soil properties, which exert selective pres-

sures on soil microbial taxa via differences in the quan-

tity and quality of organic inputs and altered microbial

competition for soil nutrients [8,9]. These selective pre-

ssures can play a key role in shaping the in situ composi-

tion of microbial communities [2,10-12]. Recent studies

have demonstrated how plant diversity can drive the

composition and function of the soil microbial commu-

nity and vice versa [13-15]. Alternative plant combina-

tions may result in unique microbial populations of bac-

teria and fungi with differing capabilities to utilize car-

bon substrates and withstand stresses. In agroecosystems,

this is manifested by the effect of crop rotation - in-

creasing microbial diversity, which can also alleviate

pathogen load and reduce weed and insect populations

compared to continuous monocultures [9,16].

Agroecosystems are increasingly scrutinized with re-

spect to their sustainability as the global priority of de-

velopment and production of alternatives to fossil fuels

as energy sources grows [17]. Biofuel feedstock produc-

tion in agricultural landscapes will in all likelihood alter

microbial diversity and drive changes in ecosystem fun-

ction through its impact on plant species choice. How-

ever, the direction and magnitude of these changes will

depend on the specific biofuel production systems im-

plemented. Alfalfa hay and corn have considerable po-

tential for use in the production of ethanol and other in-

dustrial materials in the United States [18,19]. In par-

ticular, alfalfa can contribute to making the United States

C. Liang et al. / Agricultural Sciences 2 (2011) 131-137

132

energy independent, improving the soil resource, reduc-

ing greenhouse gas emissions, and protecting ground-

water quality. Consequently, the use of alfalfa for biofuel

production is receiving significant attention, despite that

its refining remains underdeveloped relative to tradi-

tional corn-derived biofuel. However, because of con-

cerns over the environmental sustainability of different

crops and land management it is important to have a

better understanding of the potential legacy effects of

alfalfa and corn on soil microbial community abundance

and composition.

Using microbial lipid biomarkers, we assessed micro-

bial communities at two soil depths under crops with

contrasting histories, a 3-year alfalfa/orchardgrass hay-

field and a 3-year corn monoculture, in a southern Wis-

consin agroecosystem. Microbial community fingerprints,

total abundance and relative abundance of specific mi-

crobial groups, were determined to examine past man-

agement and soil depth influences on soil microbiota.

2. METHODS

2.1. Study Site and Soil Sampling

We sampled bulk soils from the 15-ha Great Lakes

Bioenergy Research Center (GLBRC) cropping system

experiment, located at the Arlington Agricultural Research

Station of the University of Wisconsin–Madison (Arling-

ton, WI, 43º18'10.86''N, 89º20'40.09'' W). Roughly 2/3 of

the 15-ha study area was maintained as a hayfield mix of

alfalfa and orchardgrass (Medicago sativa L. and Dactylis

glomerata L.) and 1/3 as a corn (Zea mays L) monocul-

ture from 2005 to 2008. Before 2005, the entire study area

was part of a long-term maize-soybean annual rotation

that received annual inputs of swine effluent and UW Ex-

tension-recommended rates of inorganic fertilizer. The

15-ha area contained sixty 43 × 27-m plots within 5

blocks meant to account for spatial variability. Within

each block, one plot (experiment unit) was randomly se-

lected for soil microbial sampling. The nearest distance

between foci plots is larger than 90-m. Based on the spa-

tial separation and random selection we assume insignifi-

cant relationship or independence between our experi-

mental units which are replicates of our interest. Our ex-

perimental design was pseudoreplicated [20,21] because

our intent here was to describe the microbial communities

and explore the potential for cropping system legacy ef-

fects in an inference space limited to our study site.

The sampled soils were classified as a Plano silt loam

(fine-silty, mixed, superactive, mesic Typical Argiul-

dolls), which formed under tallgrass prairie and charac-

terized by high organic matter (OM), high cation ex-

change capacity (CEC), and exceptional agricultural pro-

ductivity (Table 1). Five 37-mm diameter soil cores were

collected from a grid pattern in each plot in August of

2008 to ensure a representative sample. The cores were

sectioned into two depths (0 - 10 cm and 10 - 25 cm),

tran- sported back to the laboratory immediately, passed

through a 6.33-mm sieve to remove visible stones and

debris, freeze-dried, and subsequently homogenized for

storage at –20˚C.

2.2. Microbial Community Composition

Analysis

We used a hybrid procedure of phospholipid fatty acid

(PLFA) and fatty acid methyl ester (FAME) analysis to

assay microbial community composition [22]. The pro-

cedure was based on the extraction of signature lipid

biomarkers from the cell membrane of microorganisms.

Lipids were extracted, purified and identified using steps

from a modified lipid extraction technique first de-

scribed by Bligh and Dyer [23] for lipid extraction, com-

bined with FAME as described by Microbial ID Inc.

(Hayward, CA). Briefly, approximately 3 g lyophilized

soil was extracted with phosphate buffer-chloroform-

methanol (2.7 ml-3.0 ml-6.0 ml). We analyzed extracts

with a Hewlett-Packard Agilent 6890A gas chromato-

graph (Agilent Tech. Co., Santa Clara, CA) equipped with

a 25-m × 0.2-mm × 0.33-µm Agilent Ultra-2 (5%

phenyl)-methylpolysiloxane capillary column (Hewlett

Packard, Palo Alto, CA) and flame ionization detector.

MIDI’s EUKARY method database was used to identify

fatty acids. We added 19:0 (nonadecanoic methyl ester)

and 9:0 (nonanoic methyl ester) as internal standards and

used them to convert fatty acid peak areas to nmol/g soil

(absolute abundance) and mol% (proportional abun-

dance). We quantified the abundance of different mi-

crobial groups using the abundance of signature lipids.

Microbial biomass was represented by the sum of all

identifiable lipids (carbon number < 20).

2.3. Lipid Nomenclature

Fatty acids are named according to the convention

X:YωZ, where “X” is the number of carbon atoms, “Y”

is the number of double bonds, and “Z” is the number of

carbon atoms from the methyl end of the molecule to the

first double bond. Branched chain fatty acids are indi-

cated by the prefixes “i” and “a” for iso and anteiso

branching respectively. The prefix “cy” stands for cyclo-

propane ring, and “10Me” indicates methyl branching on

the 10th carbon from the carboxyl end.

2.4. Statistical Analysis

The mole percentage distribution of lipids was ana-

lyzed by JMP 5.0 software for principal component

analysis (PCA) to identify soil microbial community

C. Liang et al. / Agricultural Sciences 2 (2011) 131-137

133

Table 1. Selected physical and chemical characteristics of the soils from the hay and corn sites. The means and standard er-

rors (shown in parentheses) are calculated by t test (P < 0.01) based on all plots in the field (n = 36 and 24 for hay and corn

sites, respectively). Bold values indicate significant effects within depth, and underlined values indicate significant effects

between sites.

Soil Depth

(cm) PH Value CEC Total C

(%)

Total N

(%)

Bulk Density

(g/cm3)

0-10 6.49 (0.03) 9.70 (0.27) 2.17 (0.04) 0.18 (0.01) 3.72 (0.07) 1.29 (0.02)

Hay 10-25 6.44 (0.03) 9.57 (0.29) 1.83 (0.05) 0.15 (0.01) 3.47 (0.07) 1.52 (0.06)

0-10 6.55 (0.06) 9.44 (0.46) 2.22 (0.07) 0.22 (0.02) 3.75 (0.08) 1.28 (0.03)

Corn 10-25 6.44 (0.07) 9.54 (0.42) 2.26 (0.04) 0.22 (0.00) 3.80 (0.08) 1.38 (0.02)

structure. Analysis of variance on the principal compo-

nents (PC1 and PC2) was conducted to assess the dif-

ferences in cropping system and soil depth. PCA was

performed on all lipid data from the different systems

and different depths. The grouping of the samples was

visualized with a scatter diagram of the scores. The

loading score factor for the individual lipids were used

to assess the relative importance of each individual lipid

in the calculation of the principal component axes. We

used a general linear mixed model (GLMM) to test the

effects of management regime and depth layer on soil

lipid absolute and proportional abundance, including 5

subplot replicates as a random factor. GLMM was per-

formed separately for each soil layer in order to focus on

the effect of management history in this study. Sample

outliers were detected by Grubbs’ test (P < 0.05). For

comparisons of soil background indices at the field scale,

a paired t test was used to compare the means with depth,

and an unpaired t test to compare the means between

different cropping sites (P < 0.01).

3. RESULTS

Soil physical and chemical properties were generally

similar between the hayfield and corn monoculture areas

in 0-10 cm topsoil, but we did find significantly greater

total carbon (C), total nitrogen (N) and organic matter

(OM) contents in the 10 - 25 cm soil layer in the corn

versus the hay site (Ta bl e 1 ). In the corn site by depth,

we found no significant differences in pH value, CEC,

total C, total N or OM contents. The hay site, in contrast,

showed a significant decrease in soil total C, total N and

OM contents at the 10 - 25 cm sampling depth compared

to the surface layer. The bulk density at the hay site in-

creased from 1.29 g/cm3 in the 0 - 10 cm layer to 1.52

g/cm3 in 10 - 25 cm layer, but did not change that sig-

nificantly in the corn site (Table 1).

A total of 58 different fatty acids were identified. Of

these, 43 were consistently present in the samples and

used for calculating total lipid amount and PCA. These

43 fatty acids ranged in carbon chain length from C12 to

C20. From these, 35 fatty acids known to be of microbial

origin were used for determining proportional abundance

and loading scores (Appendix and Table 2). The hay and

corn sites contained similar total microbial lipid biomass

in both 0 - 10 cm and 10 - 25 cm soil layer. The total

amount of microbial lipids in 0 - 10 cm soils (mean =

201.1 nmol/g soil) was more than double (mean = 88.5

nmol/g soil) in the 10 - 25 cm layer.

Principal component analysis (PCA) of the lipid data

suggested a substantial degree of differentiation in mi-

crobial communities between 0 - 10 cm and 10 - 25 cm

(Figure 1). The first principal component axis (PC1)

explained 49.9% of the variance in the data while the

second principal component axis (PC2) explained 11.3%.

Overall, the differentiation of the lipid signatures be-

tween the two depths was greater than that between

samples collected at the same depth from the two dif-

ferent cropping systems. We also note that the variance

in the lipid signatures from replicate samples was higher

at the 10 - 25 cm layer comparing to that from the sur-

face 0 - 10 cm layers.

The differences in lipid signatures with soil depth

primarily resulted from differences in individual lipid

proportional abundances (mol%). Individual lipids with

the highest positive and the lowest negative loading

scores in both sites are shown in Ta ble 2 . Indicators for

protozoa and fungi had a large positive PCA loading

scores on PC1 and appeared to become proportionately

less abundant in 10 - 25 cm than 0 - 10 cm depth (Table

2 and Appendix). Lipids that indicate gram-positive

(Gm+) bacteria and actinomycetes had large negative

loading scores that appeared to increase in proportional

abundance with depth (Table 2 and Appendix).

We also used specific lipids as biomarkers to quantify

the abundances of specific microbial groups. For exam-

ple, the lipid indicative of protozoa (20:4ω6,9,12,15c)

was detected in the surface 0 - 10cm soil in both crop-

ping systems but not at the greater depth. For both crop-

ping systems, the absolute abundance (nmol/g soil) of

bacteria, fungi, actinomycete, protozoa and total micro-

bial lipid biomass in 0 - 10 cm were significantly greater

than at 10 - 25 cm depth. Despite similar absolute

amounts of fungal, bacterial, actinomycetic, protozoal

and total microbial lipids between hay and corn sites in

C. Liang et al. / Agricultural Sciences 2 (2011) 131-137

134

Figure 1. Principal component analysis (PCA) of lipid signatures (mol%) from soil sam-

ples collected at two different depths within the hay and corn sites. PC1 explains 49.9% of

the variance in the lipid data, PC2 explains 11.3%. The PCA analysis was carried out us-

ing 43 individual lipids. Two of the 50 scores were taken away as outliers. All the 0~10

cm samples were clustered in yellow shaded area, the 10 - 25 cm samples in dark shaded

area. Error bars indicate standard deviations (n = 24).

Table 2. Individual microbial lipids most responsible for the changes in lipid signatures with depth along the first

principal component (PC1). We chose the lipids with loading scores (correlations of PC1 with each lipid) that

were relatively high in magnitude and had similar scores in both sites. A positive loading score is driven by a de-

crease in proportional abundance with depth.

PLFA Loading score

in Hay site

Loading score

in Corn site

Specificity as a

biomarker

20:4ω6,9,12,15c 0.9362 0.9361 Protozoa

i14:0 0.9116 0.8059 Gram-positive bacteria

17:1ω8c 0.8814 0.9139 Gram-negative bacteria

18:2ω6c 0.8425 0.9274 Fungi

15:0 0.6322 0.5516 Uncertain

18:1ω9c 0.6163 0.8135 Fungi

18:1ω7c 0.5707 0.7139 Gram-negative bacteria

19:0cy –0.9348 –0.9420 Gram-negative bacteria

16:1 2OH –0.8551 –0.8411 Uncertain

i16:0 –0.8501 –0.6293 Gram-positive bacteria

a15:0 –0.8474 –0.7513 Gram-positive bacteria

20:0 –0.8455 –0.8642 Uncertain

a17:0 –0.8116 –0.7037 Gram-positive bacteria

i17:0 –0.8098 –0.5762 Gram-positive bacteria

10Me 16:0 –0.7224 –0.7834 Actinomycete

i15:0 –0.6759 –0.4260 Gram-positive bacteria

the depth of 0 - 10 cm soil, microbial community struc-

ture did differ in some ways. This was most evident in

the significantly higher absolute abundance of arbuscular

mycorrhizal fungi (AMF) and gram-negative (Gm–) bac-

teria in the hay site compared to the corn (Figure 2). In

addition, we found no significant system differences in

microbial properties as defined by lipid biomass in the

10 - 25 cm soils. When considering the relative propor-

tional abundances (mol%) of specific microbial groups,

we observed a significant increase in Gm+ bacteria and

actinomycetes with depth (Appendix). Besides the sig-

nificant differences in proportional abundances (%) of

AMF between hay and corn sites in 0 - 10 cm soils as

they were in the absolute abundance (nmol/g), we also

found significant difference in the relative proportional

abundance of Gm- bacteria in 10 - 25 cm soil between

sites. Finally, the calculated lipid ratios also confirmed

the community change in different microbial groups

C. Liang et al. / Agricultural Sciences 2 (2011) 131-137

135

Figure 2. Changes in select microbial indices (abundance at nmol/g dry soil or mol% by lipid) in two depths from hay and corn

sites. Error bars are standard deviations. Asterisk symbol denotes a significant difference based on GLMM analysis between two

sites at * p < 0.1, ** p < 0.05, *** p < 0.001, respectively. Note: we use the sum of 16:1ω5c, 18:1ω9c, 18:3ω6c and 18:2ω6c to

represent fungal lipids. Gm+ bacteria are represented by the sum of i14:0, i15:0, a15:0, i16:0, i17:0 and a17:0, while the sum of

16:1ω7c, 16:1ω9c, cy17:0, 17:1ω8c, cy19:0 and 18:1ω7c is used to indicate Gm– bacteria. The sum of 17:0 10M, 16:0 10Me

and 18:0 10Me represents actinomycete. Protozoa is represented by 20:4ω6,9,12,15c, Arbuscular mycorrhizal fungi (AMF) by

16:1ω5c and Saprotrophic fungi (SF) by the sum of 18:1ω9c and 18:2ω6c.

between two sites (data not shown). The lipid ratios of

Gm+ to Gm– bacteria, and saprotrohic fungi (SF) to AMF

were significantly higher (p < 0.05) in the corn site than

in the hay site in 0 - 10 cm soil layer; in the 10-25 cm

layer, we did not detect significant difference (p > 0.05)

in lipid ratios for the fungi to bacteria, Gm+ to Gm– bac-

teria, and SF/AMF ratios between two sites.

4. DISCUSSION

Aboveground crop residues function as the dominant

source of nutrients for microorganisms in the upper soil

horizons, while root degradants and exudates contribute

organic materials at lower soil depths. At our site, the

corn monoculture did not have significant chemical and

physical differences between the 0 - 10 cm and 10 - 25

cm soil layers compared with the relatively distinct lay-

ers in the alfalfa/orchardgrass hay field. We reason that

this stems from the fact that corn management includes

tillage while the hay systems do not. Typical tillage op-

erations for corn disturb and partially mix the upper 20

cm of a soil profile. It is plausible that this mixing action

resulted in the lack of difference between the 0 - 10 cm

and 10 - 25 cm horizons in the corn monoculture.

In our study, soil depth appeared to have a stronger

effect on soil microbial community composition than

crop system underscoring the importance of biological

habitat versus substrate quantity and quality on the

composition of soil microorganisms. Microbial commu-

nity change with depth appeared to be driven primarily

by decreasing soil protozoa and fungi, and by increasing

Gm+ bacteria and actinomycetes in proportion to the

total as indicated by the major lipid contributors. A

number of other studies have also found that the soil

microbial community differs with depth across varied

ecosystems [2,24,25]. However, the greater variance in

lipids we observed in the 10 - 25 cm layer compared to

the surface 0 - 10 cm layer does not align with a study

by Fierer et al. [25], who reported greater variance in

topsoils. We suggest that the higher spatial heterogeneity

in deeper layer at the corn site was caused by tillage

practices mixing surface microbes into the lower soil

layer; while at the hay site, heterogeneity was imparted

by deeper root growth and frequent occurrence of asso-

ciated N-fixing species.

There were distinct differences in the lipid patterns

between the two cropping systems. As might be ex-

pected, AMF abundance in the hay site was significantly

higher than in the corn site within the 0 - 10 cm sam-

10 cm ～ 25 cm 0 cm ～ 10 cm

C. Liang et al. / Agricultural Sciences 2 (2011) 131-137

136

pling depth. This was likely to the result of tillage in the

corn site, which destroys fungal hyphae [26] and dis-

rupts the signaling action initiated by rhizobial nodula-

tion that can also stimulate mycorrhizal colonization

[27]. Historic tillage may have affected Gm– bacteria as

well since higher absolute abundance of Gm– bacteria

and fungi were found in non-tilled than tilled 0 - 5 cm

soil [28]. Higher Gm– bacterial abundance at the hay site

in the 0 - 10 cm depth may be also explained by the en-

riched symbiotic Gm– N-fixing rhizobia with legume

alfalfa plants, as the rhizosphere usually harbors more

Gm– bacteria and fewer Gm+ bacteria [29].

In conclusion, we found that total microbial biomass

was constant between hay and corn sites, but markedly

decreased at the 10 - 25 cm depth in a southern Wiscon-

sin agroecosystem. Despite similar biomass between

sites, or distinct biomass between depths, there were

distinct microbial communities with a greater change in

composition and biomass between depths than between

sites. These results suggest habitat (depth in soils) may

be more important in controlling microbial community

composition than plant species and that pre-existing dif-

ferences in soil microbial communities under different

land use legacies may serve as an important covariate in

cropping systems analyses.

5. ACKNOWLEDGEMENTS

We would like to thank Laura Lipps, John Hall, Lawrence Oates,

and Stephan Miramontes for the assistance with field sampling, Harry

Read for assistance with analyzing lipid biomarkers, and Ting-Li Lin

(UW-Madison Department of Statistics, CALS statistical consultant)

and Masayuki Ushio for statistical expertise. This work was funded by

the DOE Great Lakes Bioenergy Research Center (DOE BER Office of

Science DE-FC02-07ER64494).

REFERENCES

[1] Lundquist, E.J., Scow, K.M., Jackson, L.E., Uesugi, S.L.

and Johnson, C.R. (1999) Rapid response of soil micro-

bial communities from conventional, low input, and or-

ganic farming systems to a wet/dry cycle. Soil Biology

and Biochemistry, 31, 1661-1675.

doi:10.1016/S0038-0717(99)00080-2

[2] Yao, H., Bowman, D. and Shi, W. (2006) Soil microbial

community structure and diversity in a turfgrass

chronosequence: Land-use change versus turfgrass man-

agement. Applied Soil Ecology, 34, 209-218.

doi:10.1016/j.apsoil.2006.01.009

[3] Zelles, L., Rackwitz, R., Bai, Q., Beck, T. and Beese, F.

(1995) Discrimination of microbial diversity by fatty acid

profiles of phospholipids and lipopolysaccharides in dif-

ferently cultivated soils. Plant and Soil, 170, 115-122.

doi:10.1007/BF02183059

[4] Anderson, T.-H. (2003) Microbial eco-physiological

indicators to asses soil quality. Agriculture, Ecosystems

& Environment, 98, 285-293.

doi:10.1016/S0167-8809(03)00088-4

[5] Visser, S. and Parkinson, D. (1992) Soil biological crite-

ria as indicators of soil quality: Soil microorganisms.

American Journal of Alternative Agriculture , 7, 33-37.

doi:10.1017/S0889189300004434

[6] Steenwerth, K.L., Jackson, L.E., Calderon, F.J., Strom-

berg, M.R., Scow, K.M. (2002) Soil microbial commu-

nity composition and land use history in cultivated and

grassland ecosystems of coastal California. Soil Biology

and Biochemistry, 34, 1599-1611.

doi:10.1016/S0038-0717(02)00144-X

[7] Balser, T.C. and Firestone, M.K. (2005) Linking micro-

bial community composition and soil processes in a

California annual grassland and mixed-conifer forest.

Biogeochemistry, 73, 395-415.

doi:10.1007/s10533-004-0372-y

[8] Nusslein, K. and Tiedje, J.M. (1999) Soil bacterial com-

munity shift correlated with change from forest to pas-

ture vegetation in a tropical soil. Applied and Environ-

mental Microbiology, 65, 3622-3626.

[9] Kennedy, A.C., Stubbs, T.L. and Schillinger, W.F. (2004)

Soil and crop management effects on soil microbiology.

CRC Press, Boca Raton, FL.

[10] Myers, R.T., Zak, D.R., White, D.C. and Peacock, A.

(2001) Landscape-Level Patterns of Microbial Commu-

nity Composition and Substrate Use in Upland Forest

Ecosystems. Soil Science Society of America Journal, 65,

359-367. doi:10.2136/sssaj2001.652359x

[11] Grayston, S.J., Griffith, G.S., Mawdsley, J.L., Campbell,

C.D. amd Bardgett, R.D. (2001) Accounting for variabil-

ity in soil microbial communities of temperate upland

grassland ecosystems. Soil Biology and Biochemistry, 33,

533-551. doi:10.1016/S0038-0717(00)00194-2

[12] Ushio, M., Wagai, R., Balser, T.C. and Kitayama, K.

(2008) Variations in the soil microbial community com-

position of a tropical montane forest ecosystem: Does

tree species matter? Soil Biology and Biochemistry, 40,

2699-2702. doi:10.1016/j.soilbio.2008.06.023

[13] Van der Heijden, M.G.A., Bardgett, R.D. and van

Straalen, N.M. (2008) The unseen majority: soil mi-

crobes as drivers of plant diversity and productivity in

terrestrial ecosystems. Ecology Letters, 11, 296-310.

doi:10.1111/j.1461-0248.2007.01139.x

[14] Zak, D.R., Holmes, W.E., White, D.C., Peacock, A.D.

and Tilman, D. (2003) Plant diversity, soil microbial

communities, and ecosystem function: are there any links?

Ecology, 84, 2042-2050. doi:10.1890/02-0433

[15] Bartelt-Ryser, J., Joshi, J., Schmid, B., Brandl, H. and

Balser, T. (2005) Soil feedbacks of plant diversity on soil

microbial communities and subsequent plant growth.

Perspectives in Plant Ecology, Evolution and Systematics,

7, 27-49. doi:10.1016/j.ppees.2004.11.002

[16] Pankhurst, C.E., Blair, B.L., Magarey, R.C., Stirling, G.R.,

Garside, A.L. (2005) Effects of biocides and rotation

breaks on soil organisms associated with the poor early

growth of sugarcane in continuous monoculture. Plant

and Soil, 268, 255-269.

doi:10.1007/s11104-004-0287-3

[17] Robertson, G.P., Dale, V.H., Doering, O.C., Hamburg,

S.P., Melillo, J.M., Wander, M.M., Parton, W.J., Adler,

P.R., Barney, J.N., Cruse, R.M., Duke, C.S., Fearnside,

C. Liang et al. / Agricultural Sciences 2 (2011) 131-137

137

P.M., Follett, R.F., Gibbs, H.K., Goldemberg, J.,

Mladenoff, D.J., Ojima, D., Palmer, M.W., Sharpley, A.,

Wallace, L., Weathers, K.C., Wiens, J.A. and Wilhelm,

W.W. (2008) Sustainable biofuels redux. Science, 322,

49-50. doi:10.1126/science.1161525

[18] Vadas, P., Barnett, K. and Undersander, D. (2008) Eco-

nomics and energy of ethanol production from alfalfa,

corn, and switchgrass in the Upper Midwest, USA. Bio-

Energy Research, 1, 44-55.

doi:10.1007/s12155-008-9002-1

[19] Samac, D.A., Jung, H.G. and Lamb, J.F. (2006) Devel-

opment of alfalfa (Medicago sativa L.) as a feedstock for

production of ethanol and other bioproducts. In: Alco-

holic, F. and Minteer, S. Eds., CRC Press, Boca Raton,

FL, 79-98.

[20] Hurlbert, S. (1984) Psudoreplication and the design of

ecological experiments. Ecological Monographs, 54,

187-211. doi:10.2307/1942661

[21] Simmons, B.L., Coleman, D.C. (2008) Microbial com-

munity response to transition from conventional to con-

servation tillage in cotton fields. Applied Soil Ecology,

40, 518-528. doi:10.1016/j.apsoil.2008.08.003

[22] Kao-Kniffin, J. and Balser, T.C. (2007) Elevated CO2

differentially alters belowground plant and soil microbial

community structure in reed canary grass-invaded ex-

perimental wetlands. Soil Biology and Biochemistry, 39,

517-525. doi:10.1016/j.soilbio.2006.08.024

[23] Bligh, E.G. and Dyer, W.J. (1959) A rapid method of total

lipid extraction and purification. Canadian Journal of

Physiology, 37, 911-917. doi:10.1139/y59-099

[24] Fritze, H., Pietikainen, J. and Pennanen, T. (2000) Dis-

tribution of microbial biomass and phospholipid fatty

acids in Podzol profiles under coniferous forest. Euro-

pean Journal of Soil Science, 51, 565-573.

[25] Fierer, N., Schimel, J.P. and Holden, P.A. (2003) Varia-

tions in microbial community composition through two

soil depth profiles. Soil Biology and Biochemistry, 35,

167-176. doi:10.1016/S0038-0717(02)00251-1

[26] Jansa, J., Mozafar, A., Kuhn, G., Anken, T., Ruh, R.,

Sanders, I.R. and Frossard, E. (2003) Soil tillage affects

the community structure of mycorrhizal fungi in maize

roots. Ecologica l Applic ations , 13, 1164-1176.

doi:10.1890/1051-0761(2003)13[1164:STATCS]2.0.CO;

[27] Xie, Z.P., Staehelin, C., Vierheilig, H., Wiemken, A.,

Jabbouri, S., Broughton, W.J., Vogeli-Lange, R. and

Boller, T. (1995) Rhizobial nodulation factors stimulate

mycorrhizal colonization of nodulating and nonnodulat-

ing soybeans. Plant Physiology, 108, 1519-1525.

[28] White, P.M., Rice, C.W. (2009) Tillage effects on micro-

bial and carbon dynamics during plant residue decompo-

sition. Soil Science Society of America Journal, 73,

138-145.doi:10.2136/sssaj2007.0384

[29] Paul, E.A. and Clark, F.E. (1996) Soil microbiology and

biochemistry. Academic Press, San Diego.

APPENDIX

Mol% of lipid in the different cropping sites shown as means of the measurements.

Mol%

Site

type

Depth

(cm) 14:0 i14:0 15:0 a15:0 i15:0 15:1

ISO G 16:0 16:0

2OH

16:0

ISO

16:1

2OH

16:1

ISO G1 6:1ω5c 16:1ω7c 16:1ω9c 17:0 a17:0 cy17:0 i17:0

0-10 1.63 0.56 0.67 3.15 4.74 0.66 12.76 0.47 1.91 0.59 0.64 2.58 4.33 0.52 0.49 1.10 1.45 1.51

Hay

10-25 1.50 0.10 0.46 3.93 5.35 0.26 11.93 0.00 2.30 1.05 0.00 2.45 3.88 0.27 0.17 1.31 0.99 1.85

0-10 1.40 0.55 0.78 3.18 5.08 0.72 11.18 0.57 1.92 0.72 0.63 2.19 4.03 0.50 0.53 1.15 1.42 1.68

Corn

10-25 1.46 0.10 0.56 3.89 5.35 0.08 11.70 0.07 2.17 1.12 0.00 2.75 4.13 0.49 0.37 1.40 1.53 1.98

Mol%

Site

type

Depth

(cm)

17:0

ISO

3OH

17:1

ω8c 18:0 18:0

2OH

18:1

ω9c

10Me

17:0

18:3

ω6c 19:0 cy19:0 20:020:1

ω9c

20:4ω

691215c

10Me

16:0

19:1

ω6c

18:2

ω6c

10Me

18:0

18:1

ω7c Total

0-10 0.40 0.47 4.38 0.33 7.78 0.92 1.65 0.16 1.88 1.74 0.48 0.53 3.58 0.92 5.58 1.10 4.19 75.85

Hay

10-25 0.00 0.00 5.65 0.05 6.61 0.59 1.76 0.24 2.98 2.75 0.21 0.00 4.71 1.89 3.29 1.28 3.62 73.43

0-10 0.47 0.55 4.04 0.42 8.89 0.68 0.93 0.16 1.91 1.98 0.52 0.51 3.81 1.10 6.15 1.19 3.92 75.48

Corn

10-25 0.00 0.00 5.31 0.23 6.32 0.70 0.98 0.31 2.86 2.70 0.00 0.00 5.24 1.68 3.10 1.39 3.49 73.41