Renewed interest in quantifying greenhouse gas emissions from soil has led to an increase in the application of chamber-based fl ux measurement techniques. Despite the apparent conceptual simplicity of chamber-based methods, nuances in chamber design, deployment, and data analyses can have marked eff ects on the quality of the fl ux data derived. In many cases, fl uxes are calculated from chamber headspace vs. time series consisting of three or four data points. Several mathematical techniques have been used to calculate a soil gas fl ux from time course data. Th is paper explores the infl uences of sampling and analytical variability associated with trace gas concentration quantifi cation on the fl ux estimated by linear and nonlinear models. We used Monte Carlo simulation to calculate the minimum detectable fl uxes (α = 0.05) of linear regression (LR), the Hutchinson/Mosier (H/M) method, the quadratic method (Quad), the revised H/M (HMR) model, and restricted versions of the Quad and H/M methods over a range of analytical precisions and chamber deployment times (DT) for data sets consisting of three or four time points. We found that LR had the smallest detection limit thresholds and was the least sensitive to analytical precision and chamber deployment time. Th e HMR model had the highest detection limits and was most sensitive to analytical precision and chamber deployment time. Equations were developed that enable the calculation of fl ux detection limits of any gas species if analytical precision, chamber deployment time, and ambient concentration of the gas species are known.

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The shoot apical meristem (SAM) maintains a pool of indeterminate cells within the SAM proper, while lateral organs are initiated from the SAM periphery. Laser microdissection–microarray technology was used to compare transcriptional profiles within these SAM domains to identify novel maize genes that function during leaf development. Nine hundred and sixty-two differentially expressed maize genes were detected; control genes known to be upregulated in the initiating leaf (P0/P1) or in the SAM proper verified the precision of the microdissections. Genes involved in cell division/growth, cell wall biosynthesis, chromatin remodeling, RNA binding, and translation are especially upregulated in initiating leaves, whereas genes functioning during protein fate and DNA repair are more abundant in the SAM proper. In situ hybridization analyses confirmed the expression patterns of six previously uncharacterized maize genes upregulated in the P0/P1. P0/P1-upregulated genes that were also shown to be downregulated in leaf-arrested shoots treated with an auxin transport inhibitor are especially implicated to function during early events in maize leaf initiation. Reverse genetic analyses of asceapen1 (asc1), a maize D4-cyclin gene upregulated in the P0/P1, revealed novel leaf phenotypes, less genetic redundancy, and expanded D4-CYCLIN function during maize shoot development as compared to Arabidopsis. These analyses generated a unique SAM domain-specific database that provides new insight into SAM function and a useful platform for reverse genetic analyses of shoot development in maize.

Perennial agroecosystems have the potential to promote plant-microbial linkages by increasing the quantity of root carbon entering the soil. However, an understanding of how perennial cropping systems affect microbial communities remains incomplete. The objective of this study was to determine the potential for a fertilized perennial bioenergy cropping system to impact microbial growth and enzyme activity. Three times throughout the growing season we examined the activity of four enzymes involved in decomposition (ß-glucosidase, ß-xylosidase, cellobiohydrolase, and N-acetyl glucosaminidase) in replicated plots of an annual (corn) and perennial-based (switchgrass) cropping system. We also took simultaneous measurements of microbial biomass and potential rates of microbial respiration and net N mineralization. Microbial biomass was unaffected by cropping system. Mid-summer, however, we observed increases in enzyme activity and potential microbial respiration in the perennial system that were independent of microbial biomass, likely in response to labile carbon inputs. Further, we observed lower net N mineralization, higher microbial biomass nitrogen and higher activity of nitrogen liberating enzymes, which are indicative of a community with high nitrogen demands. Overall, our research demonstrates that perennial agroecosystems can affect the physiological capacity of the microbial community, yielding communities with greater nitrogen retention and greater rates of decomposition as a result of allocation of resources towards enzyme production and nitrogen mining. These results can inform biogeochemical models with respect to the importance of temporally dynamic changes in carbon and nitrogen availability and microbial carbon use efficiency as drivers of enzyme production.

Plant and soil properties cooperatively structure soil microbial communities, with implications for ecosystem functioning. However, the extent to which each factor contributes to community structuring is not fully understood. To quantify the influence of plants and soil properties on microbial diversity and composition in an agricultural context, we conducted an experiment within a corn-based annual cropping system and a perennial switchgrass cropping system across three topographic positions. We sequenced barcoded 16S ribosomal RNA genes from whole soil three times throughout a single growing season and across two years in July. To target the belowground effects of plants, we also sampled rhizosphere soil in July. We hypothesized that microbial community α-diversity and composition (β-diversity) would be more sensitive to cropping system effects (annual vs. perennial inputs) than edaphic differences among topographic positions, with greater differences occurring in the rhizosphere compared to whole soil. We found that microbial community composition consistently varied with topographic position, and cropping system and the rhizosphere influenced α-diversity. In July, cropping system and rhizosphere structured a small but specific group of microbes implying a subset of microbial taxa, rather than broad shifts in community composition, may explain previously observed differences in resource cycling between treatments. Using rank abundance analysis, we detected enrichment of Saprospirales and Actinomycetales, including cellulose and chitin degraders, in the rhizosphere soil and enrichment of Nitrospirales, Syntrophobacterales, and MND1 in the whole soil. Overall, these findings support environmental filtering for the soil microbial community first by soil and second by the rhizosphere. Across cropping systems, plants selected for a general rhizosphere community with evidence for plant-specific effects related to time of sampling.