Agricultural management practices can produce changes in soil microbial populations whose functions are crucial to crop production and may be detectable using high-throughput sequencing of bacterial 16S rRNA. To apply sequencing-derived bacterial community structure data to on-farm decision-making will require a better understanding of the complex associations between soil microbial community structure and soil function. Here 16S rRNA sequencing was used to profile soil bacterial communities following application of cover crops and organic fertilizer treatments in certified organic field cropping systems. Amendment treatments were hairy vetch (Vicia villosa), winter rye (Secale cereale), oilseed radish (Raphanus sativus), buckwheat (Fagopyrum esculentum), beef manure, pelleted poultry manure, Sustane® 8-2-4, and a no-amendment control. Enzyme activities, net N mineralization, soil respiration, and soil physicochemical properties including nutrient levels, organic matter (OM) and pH were measured. Relationships between these functional and physicochemical parameters and soil bacterial community structure were assessed using multivariate methods including redundancy analysis, discriminant analysis, and Bayesian inference. Several cover crops and fertilizers affected soil functions including N-acetyl-β-d-glucosaminidase and β-glucosidase activity. Effects, however, were not consistent across locations and sampling timepoints. Correlations were observed among functional parameters and relative abundances of individual bacterial families and phyla. Bayesian analysis inferred no directional relationships between functional activities, bacterial families, and physicochemical parameters. Soil functional profiles were more strongly predicted by location than by treatment, and differences were largely explained by soil physicochemical parameters. Composition of soil bacterial communities was predictive of soil functional profiles. Differences in soil function were better explained using both soil physicochemical test values and bacterial community structure data than using soil tests alone. Pursuing a better understanding of bacterial community composition and how it is affected by farming practices is a promising avenue for increasing our ability to predict the impact of management practices on important soil functions.
Bibliographical noteFunding Information:
This project was funded, in part, by grant 27144 from the CERES Trust (to CS) , by grant 13718 from the USDA National Needs Program (to NJ) and from a fellowship (to AF) from the University of Minnesota MnDRIVE Initiative. This work was completed, in part, using the resources of the Minnesota Supercomputing Institute