Influence of Nitrogen Sources and Soil pH on Soil Microbial Communities in a Long-term Crop Rotation System Reji Mathew, Yucheng Feng, and Charles Mitchell Department of Agronomy and Soils, Auburn University, Auburn, AL Abstract Agricultural management practices such as crop rotation, nitrogen fertilization, and lime application may cause changes in soil microbial community structure. The objective of this study was to examine effects of nitrogen sources and soil pH on soil microbial communities in a long-term crop rotation system. The field experiment, Cullars Rotation, consisting of a three- year rotation of cotton, corn, wheat, soybean and clover, was established in 1911 on a Marvyn loamy sand soil. Soil samples were collected in June and October of 2008 and February of 2009 at two depths (0-5 and 5-15 cm). Soil pH values for treatments without fertilization or lime were much lower than other treatments. Analysis of variance of soil microbial biomass carbon and basal respiration showed that nitrogen sources, soil depth, and the interaction effects were significant (P=0.05). Multivariate analyses of phospholipid fatty acid and automated ribosomal intergenic spacer profiles showed that changes in soil microbial communities were associated with nitrogen sources and soil pH. Fungal biomarker (18:2ω6,9) was higher in the surface soil and the stress indicator ratio was higher in the subsurface soil for all treatments. Fungal biomarker (16:1ω5) and fungi/bacteria ratio were positively correlated to soil organic carbon content. These results indicate that changes in soil microbial community structure were influenced by changes in soil properties due to management practices, i.e., nitrogen fertilization, crop rotation with winter legumes and lime application. Objective To examine the effects of nitrogen sources and soil pH on soil microbial communities in a long- term crop rotation system Materials and Methods Field Experiment: Study site: Cullars Rotation, Auburn, AL. Soil type: Marvyn loamy sand (fine-loamy siliceous, thermic Typic Kanhapludults). Crop rotation: Cotton followed by crimson clover, corn followed by wheat, and wheat followed by soybean in a three year rotation. Soil samples were collected at depths of 0-5 and 5-15 cm in June (2008), October (2008) and February (2009). Fig. 1. Crops present in the field at the sampling time. Laboratory Analyses: Microbial biomass C: Determined using chloroform fumigation incubation method (Horwath and Paul, 1994). Basal respiration: Measured using static incubation-titrimetric method (Alef and Nannipieri, 1995). Phospholipid fatty acid (PLFA) analysis: Moist soil samples were used for PLFA analysis according the method described by Feng et al. (2003). Automated Ribosomal Intergenic Spacer Analysis (ARISA): DNA extraction: PowerMax™ Soil DNA Isolation kit (MO BIO Lab, Inc., Carlsbad, CA). Bacterial ARISA primers: ITS-F and ITS-Reub (Cardinale et al., 2004). Fungal ARISA primers: ITS1-F and 3126T (Nicolardot et al., 2007). Data Analyses: Data were analyzed with SAS software (Ver ) using PROC STEPDISC using 57 phospholipid fatty acids and PROC CANDISC using the selected PLFAs from STEPDISC procedure. ARISA images were processed with BIONUMERICS Ver. 5.0 (Applied Maths, Belgium). Fig. 2. Canonical discriminant analysis of PLFA profiles for three sampling periods. Table 2. PLFA having scores > |±0.72| for canonical components for three sampling periods. Table 1. Selected soil microbiological and chemical properties averaged. Fig. 3. Principal component analysis of ARISA profiles: No N + legume ( ), no N + no legume (■), no soil amendment ( ), NPK + no legume ( ● ), NPK + legume ( ) and no lime (  ). Summary Microbial biomass, basal respiration, total PLFA and soil pH were significantly lower in no soil amendment and no lime treatments compared to other treatments. Soil organic carbon was significantly lower in no soil amendment compared to other treatments in the surface soil. Canonical discriminant analysis of PLFAs showed that soil microbial community structure in no lime and no nitrogen treatments was different from that in other treatments. The no amendment treatment was separated from other treatments for June and October only. Only PLFA profiles showed differences in microbial communities between the surface and subsurface soils. Bacterial and fungal ARISA profiles showed that data points for no lime and no soil amendment treatments formed their own individual clusters. Bacterial ARISA also revealed the impact of legume on microbial community structure for treatments with NPK application. Fungal biomarker (18:2ω6,9) was higher in the surface soil and the stress indicator ratio was higher in the subsurface soil for all treatments. Arbuscular mycorrhizal biomarker (16:1ω5) and fungi/bacteria ratio were positively correlated with soil organic carbon. Table 4. Correlations between PLFA biomarkers and soil properties. Results June 2008 October 2008 Can 1 (46 %) Can 1 ( 48%) Can 2 (21%) February 2009 A No N + legume B No N + no legume C No soil amend 1 NPK + no legume 3 NPK + legume 8No lime d1 0-5 cm d cm BacteriaFungi Can 1 (41 %) Can 2 (32%) Can 2 (28%) June 2008 October 2008 February 2009 Axis 1 (34 %) Axis 2 (30%) Axis 1 (47 %) Axis 2 (20%) Axis 1 (48 %) Axis 2 (18%) Axis 2 (22%) Axis 1 (39 %) Axis 1 (42 %) Axis 1 (35 %) Axis 2 (27%)Axis 2 (26%) Table 3. PLFA biomarkers and PLFA ratios averaged by treatment.