Linking C and N Cycling to Microbial Community Function in Cover Crop Systems

2007 Annual Report for GW06-004

Project Type: Graduate Student
Funds awarded in 2006: $9,995.00
Projected End Date: 12/31/2008
Region: Western
State: California
Graduate Student:

Linking C and N Cycling to Microbial Community Function in Cover Crop Systems


The objective of this project, to elucidate how the interrelationships between cover crop-C and -N input, soil microenvironments, and microbial community structure and function foster C sequestration in cropping systems, is nearly achieved. Thus far, I found the organic rotation (cover crop and organic amendments) sequestered 5.70 Mg SOC ha-1 and 590 kg N ha-1, whereas the conventional system (synthetic fertilizer) maintained and the low-input system (alternating synthetic fertilizer and cover crop) lost SOC and soil N after 13 years of crop management. In the short term, the abundance of total bacteria and ammonia-oxidizing bacteria did not differ among cropping systems, but were different among soil microenvironments.

Objectives/Performance Targets

The global objective of this proposal is to elucidate the spatial and temporal dimensions of cover crop effects on soil carbon (C) and nitrogen (N) cycling. More specifically, I will investigate how the microbial community structure mediates the turnover and subsequent stabilization of below- and aboveground cover crop inputs across various soil microenvironments (operationally defined as soil microaggregates within the rhizosphere and the whole soil).

To achieve the global objective of this project, the following specific objectives were formulated:

Objective 1: Identify and quantify the microbial communities (e.g., nitrifying and denitrifying populations) associated with overall C stabilization and N cycling within soil microenvironments, during the cover crop and subsequent maize growing season.

Objective 2: Link microbial-mediated turnover and stabilization of C with soil microenvironment formation.

Objective 3: Evaluate the impact of long-term agroecosystem management on short- and long-term C and N flows through the microbial community and soil microenvironments.




Field Study

Since 1993, the Center for Integrated Farming Systems (CIFS) has conducted research on the sustainability of conventional and alternative cropping management practices. Within the CIFS, I conducted this field study in three cropping systems (Table 1), which vary in N levels and source: i) conventional (synthetic fertilizer only), ii) low-input (synthetic fertilizer and cover crop) and iii) organic (cover crop and composted manure). My experimental plots (4.6m x 9.1m) were established in each cropping system, which were replicated in three 0.4-ha field plots. In October of 2005, Time-Zero soil samples (4-cm dia.; 0-15 cm) were taken from each of the plots to measure baseline soil C and N concentrations. Two 1-m2 and four 0.13-m^2 sub-plots were established within the experimental plots. Subsequently, a winter legume cover crop (Vicia dasycarpa) was sown into each experimental plot. To address Objective1, only one of the two 1-m2 sub-plots was pulse-labeled with 13CO2 (99 atom%) across the cover crop growing season. At the time of cover crop incorporation, the aboveground biomass of the 13CO2 labeled 1-m^2 sub-plot was incorporated into the unlabeled 1-m^2 sub-plot, and the aboveground biomass of the unlabeled 1-m^2 sub-plot was incorporated into the 13C-labeled 1-m^2 sub-plot. To address Objective2 and Objective3, four soil samples (4-cm dia.; 0-15 cm) were taken from each of the 1-m^2 sub-plots throughout the maize growing season that followed the cover crop growing season. To address Objective1 and Objective2, the 0.13-m^2 sub-plots were individually 13CO2-labeled (99 atom%) across the cover crop season. Soil cores and above- and belowground cover crop samples (0.04 m3) were taken 24 hours after the 13CO2-labeling session.

Soil Processing

Whole soil samples collected from the two 1-m^2 sub-plots at Time-Zero and the 1st, 2nd, 3rd, and Final sampling periods as well as the samples taken from the 0.13-m^2 sub-plots were separated into three soil organic matter (SOM) fractions by wet sieving through a microaggregate isolator according to the methodology outlined in Six et al. (2000): i) coarse particulate organic matter (CPOM; >250 μm), ii) microaggregates (53-250 μm), and iii) silt-and-clay (

Phospholipid Fatty Acid Assay

13C-Phospholipid fatty acids (13C-PLFAs) were extracted and derivatized to fatty acid methyl esters from the microaggregate and silt-and-clay fractions of the 1st sampling event using modifications of the method according to Bossio and Scow (1995). Identification, quantification, and δ13C signature measurement of the PLFAs were determined using a Thermo gas chromatograph-combustion-isotope ratio mass spectrometer (GC-C-IRMS) system composed of a Trace GC Ultra gas chromatograph (Thermo Electron Corp., Milan, Italy) coupled to a Delta Plus Advantage isotope ratio mass spectrometer through a GC/C-III interface (Thermo Electron Corp., Bremen, Germany), with a J&W DB-5 column.

Real-time PCR Quantification of amo A and 16S rDNA genes

Humic acid, a PCR inhibitor, was removed from the whole soil as well as the SOM fractions as follows: 1g of soil was incubated in 2mL of 0.1% Na4P2O7 in 10mM Tris-HCl buffer (pH 8.0)-1mM ethylenediaminetetraacetic acid (EDTA), at room temperature for 30 min and then centrifuged at 8,500xg for 10 min at room temperature. The supernatant was discarded and total DNA was extracted from 0.5g humic-washed soil using a Bio 101 Fast DNA SPIN kit for soil (QBiogene, Irvine, CA). The yield of DNA extracted was quantified with a Qubit flourometer (Invitrogen, Carlsbad, CA). DNA concentrations are expressed on a dry soil basis.
To measure changes in total bacterial abundance within the soil microenvironments, I employed real-time TaqMan polymerase chain reaction (PCR) primers targeting the universal bacterial 16S rRNA gene, as described previously in Suzuki et al. 2000. Thermal cycling, fluorescent data collection, and data analysis were carried out with the ABI Prism 7300 sequence detection system according to the manufacturer’s instructions using Taqman based detection. Real-time PCR for 16S contained 5 uL of 1:100 and 1:200 dilutions of DNA extracts from whole soil and SOM fractions, respectively, as a template, 0.8 µL of 20 µM stock of both the forward and reverse primers, 10 µL of 2x Invitrogen Taqman probe master mix, 0.4 µL of 10 µM Invitrogen 1389 Taqman probe, and 4 µL of nano-pure water to give a total of a 20 µL reaction. The primers (5’-3’) used to detect the 16S gene were BACT1369F (CGG TGA ATA CGT TCY CGG) and PROK1492R (GGW TAC CTTG TTA CGA CTT) (Suzuki et al. 2000). The probe (5’-3’) used in the qPCR reaction was TM1389F (CTTGTACACACCGCCCGTC; Suzuki et al. 2000). The conditions for 16S quantification were as follows: 1 cycle of soak step consisting of 2 minutes at 50˚C, 1 cycle of 10 minutes enzyme activation at 95°C, and then 40 cycles of 15 s at 95˚C for denaturation, 1 minute at 56˚C for annealing.
To quantify the ammonia oxidizing bacteria (AOB) in the DNA extracted from whole soil and SOM fractions of the 1st sampling event, I used PCR primers to target the functional gene, ammonia monoxygenase (amoA), which encodes for an enzyme involved in NH4+ oxidation. Thermal cycling, fluorescent data collection, and data analysis are carried out with the ABI Prism 7300 sequence detection system according to the manufacturer’s instructions using SYBR-green based detection. Real-time PCR for amoA contained 5 uL of 1:100 and 1:200 dilutions of DNA extracts from whole soil and SOM fractions, respectively, as a template, 0.3 µL and 0.9µL of 5 µM stock of the forward and reverse primers, respectively, 10 µL of 2x ABI Power SYBR green master mix, 0.2 µL of nano-pure water to give a total of a 20 µL reaction. The primers (5’-3’) used to detect the amoA gene were A189 (GGH GAC TGG GAY TTC TGG) and amoA-2R’ (CCT CKG SAA AGC CTT CTTC) (Okano et al. 2004). The conditions for amoA quantification were as follows: 10 min at 95˚C for enzyme activation as recommended by the manufacturer (Applied Biosystems), then 40 cycles consisting of 15 s at 95˚C for denaturation, 30 s at 55˚C for annealing, 31 s at 72˚C for extension, which served as the data acquisition step, then finally, a one cycle of 95˚C for 15 s, 60˚C for 30 s and 95˚C for 15 s was added to obtain a specific denaturation curve. Purity of amplified products was checked by the observation of a single melting peak (~85˚C) and the presence of a unique band of the expected size in a 2% agarose gel, stained with ethidium bromide.
For both the 16S and amo A real-time PCRs, the cycle number (CT) that corresponds with fluorescence readings above a threshold is proportional to the starting amount of template DNA. Copy numbers of the 16S rDNA and amoA genes in whole soil samples and SOM fractions were determined with an external standard curve generated by a 10-fold dilution series of either cloned 16S or amoA genes into plasmids. The following equations were used to convert 16S and amo A real-time PCR results, respectively:

CT = -3.31 x Log10(16S copies/reaction) + 38.67 and R^2= 0.99

CT = -3.37 x Log10(amoA copies/reaction) + 41.47 and R^2= 0.98
The AOB and total bacterial population sizes presented here are corrected for efficiency of extraction from soil.

Direction for the Remaining Funding Period

At this juncture of the project, I have completed the field studies and have made inroads to completing the laboratory analyses of the samples collected from the field. During the final funding period, I will complete the molecular assays on the microenvironments and begin work on a manuscript detailing the results from this project.


Baseline Soil C and N Measurements

At the start of the experiment (Time-Zero), both the soil C and N concentrations of the organic system (20.2 Mg SOC/ha and 2.15 Mg N/ha) were greater than soil C and soil N levels in the conventional (15.6 Mg SOC/ha and 1.49 Mg N/ha) and low-input systems (15.3 Mg SOC/ha and 1.49 Mg N/ha) (p

Incorporation of 13C-labeled Cover Crop Biomass

Overall, belowground cover crop biomass decomposed faster and more belowground biomass was incorporated into the three cropping systems compared to the aboveground cover crop residue (Figure 3). More specifically, 13C-labeled belowground biomass measured in the whole soil, e.g., 494.1 kg Cnew/ha in the low-input rotation at the 3rd sampling period, nearly doubled the concentrations of 13C-labeled aboveground biomass measured in the three cropping systems, e.g., 235.7 kg Cnew/ha in the low-input rotation at the 3rd sampling period (Figure 3).
Incorporation of belowground biomass into the cropping systems did not differ at the beginning of the growing season; however, by the 3rd sampling period, incorporation of belowground biomass was greatest into the organic system (850.0 kg Cnew/ha), with the low-input rotation (494.1 kg Cnew/ha) showing nominally intermediate, but similar decomposition dynamics to the conventional system, where incorporation was the lowest (322.4 kg Cnew/ha). Trends in aboveground residue incorporation were the inverse of the belowground biomass in that decomposition in the organic system were lowest in the beginning and in the end of the season (117.5 kg Cnew/ha), while incorporation of the aboveground residues into the conventional system was greatest at the Final sampling period (348.9 kg Cnew/ha).

13C-PLFA in Microenvironments

Based on the PLFAs extracted from the microaggregate and silt-and-clay fractions of the 1st sampling period, microbial biomass in the cropping systems generally decreased according to the following: organic > conventional > low-input (Figure 4). Due to high variability, no differences were found in total biomass between the soils enriched with 13C-labeled aboveground cover crop residue (Figure 4). Nevertheless, total biomass of samples enriched with 13C-labeled belowground biomass was highest in the organic system (46.1 nmoles g-1 dry soil), intermediate in the conventional system (41.6 nmoles/g dry soil), and lowest in the low-input system (33.7 nmoles/g dry soil).
Correspondence analysis (CA) (CANOCO version 4.0, Microcomputer Power, Inc. Ithaca, NY) of PLFA-C in microenvironment samples explained a total of 61.1% of the variation between two axes and separated the samples based on soil sampling period (Figure 5). Meanwhile, a second CA of 13C-PLFA in the samples indicated that PLFA profiles of samples 13C-labeled with aboveground cover crop biomass are dissimilar to PLFA profiles from 13C-labeled belowground biomass samples (Figure 6). Axes 1 and 2 of the latter CA explained 29.4 and 15.2% of the variation, respectively, and separated the samples by both SOM fraction (microaggregate versus silt-and-clay) and the type of cover crop biomass incorporated (Figure 7). One reason for the overlap in the 13C-belowground biomass-silt-and-clay samples with its microaggregate counterpart is that the dataset is not yet complete and a third axis may create more distinct separations among the fractions.

Ammonia Oxidizing Bacteria (AOB) Abundance

The abundance of AOB in the whole soil neither differed (p > 0.05) among the cropping systems nor differed as a function of sampling time (data not shown). The mean amoA copy numbers across all whole soil samples was 9.14 x 106 (± 2.10 x 106)/g dry soil. Assuming an average amoA copy number of 2.5 AOB cell-1 (Okano et al. 2004), our results translate to a mean of 3.66 x 106 (± 8.38 x 105) AOB cells/g dry soil.
In contrast, measurements of the abundance of total bacteria (16S) and AOB among the SOM fractions showed slight differences among cropping systems. The CPOM fraction in both the conventional and organic systems had the greatest copy numbers of 16S rDNA compared to the microaggregate and silt-and-clay fractions (Figure 8). More specifically, total bacteria in the silt-and-clay fraction (~107 copies/g dry soil) in the three cropping systems were consistently one and two orders of magnitude lower than that of the microaggregate and CPOM fractions, respectively (Figure 8). Mean AOB copy numbers in the microaggregate and silt-and-clay fractions, across the three cropping systems were 1.95 x 107 (± 1.02 x 102)/g dry soil, however, AOB were not detected in the silt-and-clay fraction (Figure 9). Overall, the distribution of AOB among the SOM fractions as a percentage of the total bacteria was greatest in the microaggregates rather than in the CPOM fraction (Figure 10). Percentages of AOB in total bacteria were 13.1, 7.25 and 3.49% in the microaggregates of the conventional, low-input, and organic systems, respectively, whereas 1.31, 2.86, and 1.23% of the total bacteria in the CPOM consisted of AOB, among the conventional, low-input, and organic rotations, respectively (Figure 10).

Impacts and Contributions/Outcomes


Long-term Crop Management Effects on SOC and Soil N sequestration

Increased N fertilization has been correlated to increased SOC sequestration (Campbell et al. 1991; Dumanski et al. 1998). Despite receiving a relatively high N fertilization rate and producing the largest maize yields and vegetative biomass (i.e., greater maize stover returned to the system), the conventional system showed neither the greatest SOC nor soil N stocks of the three cropping systems after 13 years of cropping. This supports findings from recent studies showing that, while synthetic fertilizer-N may increase crop residue returns, N fertilization has a net negative effect on SOC sequestration (Omay et al. 1997; Halvorson et al. 2002; Russell et al. 2005). Moreover, this data imply that fertilizer (e.g., synthetic versus organic) plays a role in long-term SOC and soil N sequestration. The conventional and organic systems both received high rates of N additions (280 and 473 kg N/ha yr, respectively), yet the organic system, where solely organic amendments were applied, sequestered disproportionately more SOC and soil N, than the amount of C and N input it received compared to the other systems, after 13 years of crop management. The greater long-term protection and stabilization of C and N derived from the cover crop and composted manure within aggregate structures may have fostered the gradual accumulation of a large pool of soil organic matter in the organic system compared to the conventional and low-input cropping systems.
The combined application of organic amendments and synthetic fertilizers in low-input cropping systems has been shown to contribute N from both N sources in temporally distinct patterns (Palm et al. 2001; Kramer et al. 2002). The similarity between the conventional and low-input systems with regards to whole soil C and N concentrations, and the amount of SOC and N sequestered after 13 years of continuous cropping, suggests that the use of synthetic fertilizers may have negated the positive, long-term effects of organic amendments on soil C and N sequestration. The whole soil samples and the SOM fractions collected over the 2006 growing season were analyzed for possible explanations, observed in the short-term, of the discrepancies measured in the amount of SOC and N sequestered in the different cropping systems in the long-term.

Cover Crop Biomass Incorporation

The intermediate levels of 13C-labeled cover crop biomass (above- and belowground) incorporated into the low-input cropping system were expected since this system receives an intermediate level of N and receives a cover crop in alternate years (following the tomato rotation). This system likely houses a microbial community that is adapted to the processing and cycling of cover crop biomass, but is not as robust as the community in the organic system, in which a cover crop is grown annually. Because the 2006 incorporation of a cover crop into the conventional cropping system was the first in 13 years of continuous management, the consistent amount of 13C-labeled biomass incorporated into the conventional system across the season was also not surprising. The greater contribution to SOM from the belowground biomass versus the aboveground residue was likely due to the greater contact between root-derived material and the soil matrix, which can to lead to a stronger interaction and faster stabilization of root-derived C in soil. In addition, during cover crop growth, active crop roots are continuously releasing a range of organic compounds (i.e., carbohydrates, carboxylic acids, and amino acids) into the rhizosphere (Oades 1978) in the form of root exudates and sloughed off root material, which are more readily available C sources for microorganisms than incorporated residues.

Microbial Community Structure and Activity in Microenvironments

The structural organization of soil particles provides a spatially heterogeneous habitat for microorganisms characterized by different substrate, nutrient, and oxygen concentrations and water contents as well as variable pH values (Ladd et al. 1996). Several studies have shown that soil aggregates represent an ecological niche whose chemical and physical properties may contribute to the heterogeneous distribution of microorganisms and their activity among aggregates of different sizes. Different population numbers and structure of denitrifiers (Seech and Beauchamp 1988, Philippot et al. 1997), rhizobia (Mendes and Bottomley 1998), and diazotrophic communities (Poly et al. 2001) among aggregate size classes have been reported. Thus far, the 13C-PLFA results have started to link the processing of cover crop biomass to different microbial communities, corresponding to either above- or belowground biomass (Figure 6). This discrepancy in microbial communities associated with the processing of above- versus belowground biomass may be related to the differences observed in rate and amount of belowground versus aboveground biomass incorporation. Furthermore, the CA biplot of 13C-PLFA further support the notion that different soil microenvironments (e.g., microaggregates versus silt-and-clay fraction) support different microbial communities, which process C dissimilarly (Figure 7).

Ammonia-Oxidizing Populations – Whole soil versus Microenvironments

We expected that long-term (>10 years) cropping management would create a distinctive microbial community structure and population size that would lead to a differentiation in N cycling and storage. The annual cover cropping and manure amendments characteristic of organic cropping systems are expected to produce a more abundant, active, compositionally diverse, and resilient community of soil microorganisms (Gunapala et al. 1998). However, not only did the ammonium-oxidizing bacteria population size in the whole soil not increase over the course of this field study, but no differences were detected between the different cropping systems. Measurements at the whole soil level suggest that different types of N amendments (e.g., NH4+, organic N, or NO3-) do not have a long-term effect on AOB population size compared to the differences observed in annually fertilized versus unfertilized soil, even when AOB populations were quantified eight months after fertilization (Okano et al. 2004).
Sessitsch et al. (2001) found that bacterial community structure was affected to a greater extent by the particle size fraction than by the kind of fertilizer applied to the long-term cropping systems in their study. Although no differences were found in the sizes of the total bacteria populations in the SOM fractions across the cropping systems, the larger CPOM fraction of the conventional system showed a nominally greater abundance of total bacteria than the all the fractions of the other rotations. From a community function perspective, the significantly higher percentage of total bacteria comprised by AOB in the microaggregates of the conventional system compared to microaggregate and silt-and-clay fractions of the low-input and organic cropping systems (Figure 10) indicates that the potential for ammonification was higher in the microaggregates of the conventional system at the 1st sampling period than in the other microenvironments. Moreover, the finding of generally higher percentages of AOB (out of the total bacteria) in the microaggregate structures compared to the CPOM and silt-and-clay fractions (Figure 10) corroborates my hypothesis that microorganisms would preferentially colonize the microaggregates, which are characterized by low predation pressure, relatively stable water potential, low O2 availability and low accessibility for exogenous toxic elements (Poly et al. 2001, Ranjard et al. 2000, Ranjard and Richaume 2001, Postma et al. 1989).

Impacts and Contributions to Stakeholders in the Western Region

With mounting scientific evidence linking anthropogenic combustion of fossil fuels to the rise in greenhouse gas emissions, it has become more evident that cropping system management must be improved to mitigate global warming. Data (e.g., soil C) collected from my experiment has been incorporated into regional greenhouse gas modeling exercises as a part of a larger modeling effort by biophysical modelers and economists at the University of California, Davis. The models produced from these collaborations will help both policy-makers and land managers to develop best management practices to reduce greenhouse gas emissions from California croplands. At the local management level, I hope to elucidate a link between the selection processes of soil microorganisms, as dictated by cropping system practices, and C and N cycling in cover crop systems. Having a more complete understanding of the mechanisms and the pivotal components of cover crop systems will lead to more successful adoption of cover crops into cropping systems as a profitable and sustainable farming practice. Ultimately, results from this project will enable the development of crop management practices that improve environmental quality, optimize the C sequestration within agricultural lands, and achieve and N synchrony for maximum crop yields.