title: Bacterial diversity and composition in major fresh produce growing soils affected by physiochemical properties and geographic locations.
abstract: Microbial diversity of agricultural soils has been well documented, but information on leafy green producing soils is limited. In this study, we investigated microbial diversity and community structures in 32 (16 organic, 16 conventionally managed soils) from California (CA) and Arizona (AZ) using pyrosequencing, and identified factors affecting bacterial composition. Results of detrended correspondence analysis (DCA) and dissimilarity analysis showed that bacterial community structures of conventionally managed soils were similar to that of organically managed soils; while the bacterial community structures in soils from Salinas, California were different (P<0.05) from those in soils from Yuma, Arizona and Imperial Valley, California. Canonical correspondence analysis (CCA) and artificial neural network (ANN) analysis of bacterial community structures and soil variables showed that electrical conductivity (EC), clay content, water-holding capacity (WHC), pH, total nitrogen (TN), and organic carbon (OC) significantly (P<0.05) correlated with microbial communities. CCA based variation partitioning analysis (VPA) showed that soil physical properties (clay, EC, and WHC), soil chemical variables (pH, TN, and OC) and sampling location explained 16.3%, 12.5%, and 50.9%, respectively, of total variations in bacterial community structure, leaving 13% of the total variation unexplained. Our current study showed that bacterial community composition and diversity in major fresh produce growing soils from California and Arizona is a function of soil physiochemical characteristics and geographic distances of sampling sites.
authors.affiliation: Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China; USDA-ARS U. S. Salinity Laboratory, Riverside, CA 92507, United States.
authors.forename: Jincai
authors.initials: J
authors.lastname: Ma
authors.affiliation: USDA-ARS U. S. Salinity Laboratory, Riverside, CA 92507, United States. Electronic address: Mark.Ibekwe@ars.usda.gov.
authors.forename: A Mark
authors.initials: AM
authors.lastname: Ibekwe
authors.affiliation: Department of Biological Sciences, University of Wisconsin, Milwaukee, WI 53211, United States.
authors.forename: Ching-Hong
authors.initials: CH
authors.lastname: Yang
authors.affiliation: Department of Environmental Sciences, University of California, Riverside, CA 92521, United States.
authors.forename: David E
authors.initials: DE
authors.lastname: Crowley
mesh_terms: D001130:Arizona; D001419:Bacteria; D002140:California; D000072480:Farms; D064307:Microbiota; D016571:Neural Networks, Computer; D012987:Soil / Q000737:chemistry*; D012988:Soil Microbiology
chemical_list: D012987:Soil
keywords: Artificial neural network; Bacterial ecology; Pyrosequencing