Acidification in corn monocultures favor fungi, ammonia oxidizing bacteria, and nirK-denitrifier groups.

Agricultural practices of no-till and crop rotations are critical to counteract the detrimental effects of monocultures and tillage operations on ecosystem services related to soil health such as microbial N cycling. The present study explored the main steps of the microbial N cycle, using targeted gene abundance as a proxy, and concerning soil properties, following 19 and 20 years of crop monocultures and rotations of corn (Zea mays L.), and soybean [Glycine max (L.) Merr.], either under no-till or chisel tillage. Real-time quantitative polymerase chain reaction (qPCR) was implemented to estimate phylogenetic groups and functional genes related to the microbial N cycle: nifH (N2 fixation), amoA (nitrification) and nirK, nirS, and nosZ (denitrification). Our results indicate that long-term crop rotation and tillage decisions affect soil health as it relates to soil properties and microbial parameters. No-till management increased soil organic matter (SOM), decreased soil pH, and increased copy numbers of AOB (ammonia oxidizing bacteria). Crop rotations with more corn increased SOM, reduced soil pH, reduced AOA (ammonia oxidizing archaea) copy numbers, and increased AOB and fungal ITS copy numbers. NirK denitrifier groups were also enhanced under continuous corn. Altogether, the more corn years included in a crop rotation multiplies the amount of N needed to sustain yield levels, thereby intensifying the N cycle in these systems, potentially leading to acidification, enhanced bacterial nitrification, and creating an environment primed for N losses and increased N2O emissions.

Primer design to assess bacterial degradation of glyphosate and other phosphonates.

Phosphonates are organic phosphorous (P) compounds frequently detected in the environment due to a very stable CP bond that render them relatively recalcitrant. Glyphosate [N-phosphonomethyl glycine] is the most widely used and best-known synthetic phosphonate, and one of the most concerning herbicides in the world today. Microbial degradation of glyphosate and organophosphonates in general, is the main dissipation mechanism operating in most environments. One microbial metabolic pathway in this process is the CP lyase pathway, entailing an enzymatic complex encoded by about 14 genes (the Phn operon). Our goal was to develop a quantitative polymerase chain reaction (qPCR) assay for a key enzyme, the CP lyase that breaks down the CP bond, via quantification of the codifying phnJ gene. The primers designed in this study fulfill the requirements for a successful qPCR assay, with high efficiency and sensitivity, as well as specific detection of the target sequence in a wide range of taxonomic groups. This is, to our knowledge, the first report of primers designed to target phnJ in both pure cultures and metagenomic DNA from different environmental sources. Direct quantification of phnJ may be a cost-effective proxy to determine glyphosate degradation potential in different matrixes.

Long-term N fertilization imbalances potential N acquisition and transformations by soil microbes.

Nitrogen (N) fertilization in agricultural soils has been receiving worldwide attention due to its detrimental effects on ecosystem services, particularly on microbial N transformation. However, few studies provide a complete picture of N-fertilization effects on the N transformation cycle within a single agricultural ecosystem. Here, we explored the main steps of the microbial N cycle, using targeted gene abundances as proxies, in relation to soil properties, following 35 years of N-fertilization at increasing rates (0, 202 and 269 kg N/ha) in continuous corn (Zea mays L.) and corn-soybean [Glycine max (L.) Merr.] rotations. We used real-time quantitative polymerase chain reaction (qPCR) for the quantification of phylogenetic groups and functional gene screening of the soil microbial communities, including genes encoding critical enzymes of the microbial N cycle: nifH (N2 fixation), amoA (first step of nitrification), nirK and nirS (first step of denitrification), and nosZ (last step of denitrification). Our results showed that long term N-fertilization increased the abundance of fungal communities likely related to decreases in pH, and an enrichment of Al3+ and Fe3+ in exchange sites at the expense of critical macro and micronutrients. At the same time, long term N-fertilization damaged potential biological N2 fixation by significantly reducing the abundance of nifH genes in both continuous and rotated corn systems, while accelerating potential nitrification activities under continuous corn by increasing the abundance of bacterial amoA. Fertilization did not affect the abundance of denitrifying groups. Altogether, these results suggest that N fertilization in corn crops potentially decreases N2 acquisition by free-living soil microbes and stimulates nitrification activities, thus creating a vicious loop that makes the overall agricultural system even more dependent on external N inputs.

Multivariate analysis and visualization of soil quality data for no-till systems.

To evidence the multidimensionality of the soil quality concept, we propose the use of data visualization as a tool for exploratory data analyses, model building, and diagnostics. Our objective was to establish the best edaphic indicators for assessing soil quality in four no-till systems with regard to functioning as a medium for crop production and nutrient cycling across two Illinois locations. The compared situations were no-till corn-soybean rotations including either winter fallowing (C/S) or cover crops of rye (Secale cereale; C-R/S-R), hairy vetch (Vicia villosa; C-R/S-V), or their mixture (C-R/S-VR). The dataset included the variables bulk density (BD), penetration resistance (PR), water aggregate stability (WAS), soil reaction (pH), and the contents of soil organic matter (SOM), total nitrogen (TN), soil nitrates (NO(3)-N), and available phosphorus (P). Interactive data visualization along with canonical discriminant analysis (CDA) allowed us to show that WAS, BD, and the contents of P, TN, and SOM have the greatest potential as soil quality indicators in no-till systems in Illinois. It was more difficult to discriminate among WCC rotations than to separate these from C/S, considerably inflating the error rate associated with CDA. We predict that observations of no-till C/S will be classified correctly 51% of the time, while observations of no-till WCC rotations will be classified correctly 74% of the time. High error rates in CDA underscore the complexity of no-till systems and the need in this area for more long-term studies with larger datasets to increase accuracy to acceptable levels.