Soil Microbial Dynamics in Alternative Cropping Systems to Monoculture Cotton in the Southern High Plains.

In the semi-arid Texas High Plains region, agricultural systems which integrate cattle and utilize perennial-based systems have been shown to conserve water, maintain productivity and enhance soil quality. Soil microbial communities and their importance in biogeochemical cycling are critical components of soil health and agricultural sustainability. The purpose of this study was to assess microbial community composition and functioning as impacted by three long-term (> 5 years) integrated cropping and livestock (ICL) systems and two cotton monocultures. A total of six different vegetation types were represented within the ICLs: perennial native grasses (PNG), bermudagrass, W.W.Bdahl-Old world bluestem (OWB), corn, and a millet-cotton rotation.  Soil samples were collected at 0-5 and 5-20 cm depths in July 2010. Areas excluded from grazing in two of the ICLs were also evaluated. Pyrosequencing (0-5 cm samples only), microbial biomass carbon (MBC), fatty acid profiles (EL-FAME) and six enzyme assays were used to assess soil microbial community composition and their activities. Pyrosequencing and EL-FAME results differentiated systems based on dominant taxa and showed unique bacterial communities associated with vegetation type. Bermuda was associated with relatively high abundance of β-Proteobacteria. Relative abundance of Rubrobacteriales was higher under PNG, cotton-millet rotation, and OWB compared to cotton and corn.  In general Proteobacteria and Actinobacteria were negatively correlated across different system. This observation, most evident in PNG, agrees with all vegetation types except with bermudagrass. Most samples under OWB contained the greatest MBC and total FAME content and had the greatest enzymatic potentials. Using EL-FAMEs, a significant effect of grazing was only observed at 5-20 cm depth, however, differences due to vegetation types were more prevalent. Regardless, grazing samples contained greater relative abundance of Gram-positive and fungal FAME biomarkers. Our results showed that each agroecosystem supported a unique microbial community driven by the interactions of management and vegetation types.