Controls and Adaptive Management of Nitrification in Agricultural Soils

Agriculture is responsible for over half of the input of reactive nitrogen (N) to terrestrial systems; however improving N availability remains the primary management technique to increase crop yields in most regions. In the majority of agricultural soils, ammonium is rapidly converted to nitrate by nitrification, which increases the mobility of N through the soil matrix, strongly influencing N retention in the system. Decreasing nitrification through management is desirable to decrease N losses and increase N fertilizer use efficiency. We review the controlling factors on the rate and extent of nitrification in agricultural soils from temperate regions including substrate supply, environmental conditions, abundance and diversity of nitrifiers and plant and microbial interactions with nitrifiers. Approaches to the management of nitrification include those that control ammonium substrate availability and those that inhibit nitrifiers directly. Strategies for controlling ammonium substrate availability include timing of fertilization to coincide with rapid plant update, formulation of fertilizers for slow release or with inhibitors, keeping plant growing continuously to assimilate N, and intensify internal N cycling (immobilization). Another effective strategy is to inhibit nitrifiers directly with either synthetic or biological nitrification inhibitors. Commercial nitrification inhibitors are effective but their use is complicated by a changing climate and by organic management requirements. The interactions of the nitrifying organisms with plants or microbes producing biological nitrification inhibitors is a promising approach but just beginning to be critically examined. Climate smart agriculture will need to carefully consider optimized seasonal timing for these strategies to remain effective management tools

Short-Term Nitrogen Fertilization Affects Microbial Community Composition and Nitrogen Mineralization Function in an Agricultural Soil

Soil extracellular enzymes play a significant role in the N mineralization process. However, few studies have documented the linkage between enzyme activity and the microbial community that performs the function. This study examined the effects of inorganic and organic N fertilization on soil microbial communities and their N mineralization functions over 4 years. Soils were collected from silage corn field plots with four contrasting N treatments: control (no additional N), ammonium sulfate (AS; 100 and 200 kg of N ha−1), and compost (200 kg of N ha−1). Illumina amplicon sequencing was used to comprehensively assess the overall bacterial community (16S rRNA genes), bacterial ureolytic community (ureC), and bacterial chitinolytic community (chiA). Selected genes involved in N mineralization were also examined using quantitative real-time PCR and metagenomics. Enzymes (and marker genes) included protease (npr and sub), chitinase (chiA), urease (ureC), and arginase (rocF). Compost significantly increased diversity of overall bacterial communities even after one application, while ammonium fertilizers had no influence on the overall bacterial communities over four seasons. Bacterial ureolytic and chitinolytic communities were significantly changed by N fertilization. Compost treatment strongly elevated soil enzyme activities after 4 years of repeated application. Functional gene abundances were not significantly affected by N treatments, and they were not correlated with corresponding enzyme activities. N mineralization genes were recovered from soil metagenomes based on a gene-targeted assembly. Understanding how the structure and function of soil microbial communities involved with N mineralization change in response to fertilization practices may indicate suitable agricultural management practices that improve ecosystem services while reducing negative environmental consequences

Plant-Soil Feedbacks Help Explain Biodiversity-Productivity Relationships

Species-rich plant communities can produce twice as much aboveground biomass as monocultures, but the mechanisms remain unresolved. We tested whether plant-soil feedbacks (PSFs) can help explain these biodiversity-productivity relationships. Using a 16-species, factorial field experiment we found that plants created soils that changed subsequent plant growth by 27% and that this effect increased over time. When incorporated into simulation models, these PSFs improved predictions of plant community growth and explained 14% of overyielding. Here we show quantitative, field-based evidence that diversity maintains productivity by suppressing plant disease. Though this effect alone was modest, it helps constrain the role of factors, such as niche partitioning, that have been difficult to quantify. This improved understanding of biodiversity-productivity relationships has implications for agriculture, biofuel production and conservation

Moderate Plant–Soil Feedbacks Have Small Effects on the Biodiversity–Productivity Relationship: A Field Experiment

Plant–soil feedback (PSF) has gained attention as a mechanism promoting plant growth and coexistence. However, most PSF research has measured monoculture growth in greenhouse conditions. Translating PSFs into effects on plant growth in field communities remains an important frontier for PSF research. Using a 4-year, factorial field experiment in Jena, Germany, we measured the growth of nine grassland species on soils conditioned by each of the target species (i.e., 72 PSFs). Plant community models were parameterized with or without these PSF effects, and model predictions were compared to plant biomass production in diversity–productivity experiments. Plants created soils that changed subsequent plant biomass by 40%. However, because they were both positive and negative, the average PSF effect was 14% less growth on “home” than on “away” soils. Nine-species plant communities produced 29 to 37% more biomass for polycultures than for monocultures due primarily to selection effects. With or without PSF, plant community models predicted 28%–29% more biomass for polycultures than for monocultures, again due primarily to selection effects. Synthesis: Despite causing 40% changes in plant biomass, PSFs had little effect on model predictions of plant community biomass across a range of species richness. While somewhat surprising, a lack of a PSF effect was appropriate in this site because species richness effects in this study were caused by selection effects and not complementarity effects (PSFs are a complementarity mechanism). Our plant community models helped us describe several reasons that even large PSF may not affect plant productivity. Notably, we found that dominant species demonstrated small PSF, suggesting there may be selective pressure for plants to create neutral PSF. Broadly, testing PSFs in plant communities in field conditions provided a more realistic understanding of how PSFs affect plant growth in communities in the context of other species traits

Are Plant–Soil Feedbacks Caused by Many Weak Microbial Interactions?

We used high-throughput sequencing and multivariate analyses to describe soil microbial community composition in two four-year field plant–soil feedback (PSF) experiments in Minnesota, USA and Jena, Germany. In descending order of variation explained, microbial community composition differed between the two study sites, among years, between bulk and rhizosphere soils, and among rhizosphere soils cultivated by different plant species. To try to identify soil organisms or communities that may cause PSF, we correlated plant growth responses with the microbial community composition associated with different plants. We found that plant biomass was correlated with values on two multivariate axes. These multivariate axes weighted dozens of soil organisms, suggesting that PSF was not caused by individual pathogens or symbionts but instead was caused by \u27many weak\u27 plant–microbe interactions. Taken together, the results suggest that PSFs result from complex interactions that occur within the context of a much larger soil microbial community whose composition is determined by factors associated with \u27site\u27 or year, such as soil pH, soil type, and weather. The results suggest that PSFs may be highly variable and difficult to reproduce because they result from complex interactions that occur in the context of a larger soil microbial community

Patient-Centered Outcomes Measurement: Does It Require Information From Patients?

Purpose: Since collecting outcome measure data from patients can be expensive, time-consuming, and subject to memory and nonresponse bias, we sought to learn whether outcomes important to patients can be obtained from data in the electronic health record (EHR) or health insurance claims. Methods: We previously identified 21 outcomes rated important by patients who had advanced imaging tests for back or abdominal pain. Telephone surveys about experiencing those outcomes 1 year after their test from 321 people consenting to use of their medical record and claims data were compared with audits of the participants’ EHR progress notes over the time period between the imaging test and survey completion. We also compared survey data with algorithmically extracted data from claims files for outcomes for which data might be available from that source. Results: Of the 16 outcomes for which patients’ survey responses were considered to be the best information source, only 2 outcomes for back pain and 3 for abdominal pain had kappa scores above a very modest level of ≥ 0.2 for chart audit of EHR data and none for algorithmically obtained EHR/claims data. Of the other 5 outcomes for which claims data were considered to be the best information source, only 2 outcomes from patient surveys and 3 outcomes from chart audits had kappa scores ≥ 0.2. Conclusions: For the types of outcomes studied here, medical record or claims data do not provide an adequate source of information except for a few outcomes where patient reports may be less accurate

Genome Sequence of Nitrosomonas sp. Strain AL212, an Ammonia-Oxidizing Bacterium Sensitive to High Levels of Ammonia

Nitrosomonas sp. strain AL212 is an obligate chemolithotrophic ammonia-oxidizing bacterium (AOB) that was originally isolated in 1997 by Yuichi Suwa and colleagues. This organism belongs to Nitrosomonas cluster 6A, which is characterized by sensitivity to high ammonia concentrations, higher substrate affinity (lower K[subscript m]), and lower maximum growth rates than strains in Nitrosomonas cluster 7, which includes Nitrosomonas europaea and Nitrosomonas eutropha. Genome-informed studies of this ammonia-sensitive cohort of AOB are needed, as these bacteria are found in freshwater environments, drinking water supplies, wastewater treatment systems, and soils worldwide

Oral abstracts 3: RA Treatment and outcomesO13. Validation of jadas in all subtypes of juvenile idiopathic arthritis in a clinical setting

Background: Juvenile Arthritis Disease Activity Score (JADAS) is a 4 variable composite disease activity (DA) score for JIA (including active 10, 27 or 71 joint count (AJC), physician global (PGA), parent/child global (PGE) and ESR). The validity of JADAS for all ILAR subtypes in the routine clinical setting is unknown. We investigated the construct validity of JADAS in the clinical setting in all subtypes of JIA through application to a prospective inception cohort of UK children presenting with new onset inflammatory arthritis. Methods: JADAS 10, 27 and 71 were determined for all children in the Childhood Arthritis Prospective Study (CAPS) with complete data available at baseline. Correlation of JADAS 10, 27 and 71 with single DA markers was determined for all subtypes. All correlations were calculated using Spearman's rank statistic. Results: 262/1238 visits had sufficient data for calculation of JADAS (1028 (83%) AJC, 744 (60%) PGA, 843 (68%) PGE and 459 (37%) ESR). Median age at disease onset was 6.0 years (IQR 2.6-10.4) and 64% were female. Correlation between JADAS 10, 27 and 71 approached 1 for all subtypes. Median JADAS 71 was 5.3 (IQR 2.2-10.1) with a significant difference between median JADAS scores between subtypes (p < 0.01). Correlation of JADAS 71 with each single marker of DA was moderate to high in the total cohort (see Table 1). Overall, correlation with AJC, PGA and PGE was moderate to high and correlation with ESR, limited JC, parental pain and CHAQ was low to moderate in the individual subtypes. Correlation coefficients in the extended oligoarticular, rheumatoid factor negative and enthesitis related subtypes were interpreted with caution in view of low numbers. Conclusions: This study adds to the body of evidence supporting the construct validity of JADAS. JADAS correlates with other measures of DA in all ILAR subtypes in the routine clinical setting. Given the high frequency of missing ESR data, it would be useful to assess the validity of JADAS without inclusion of the ESR. Disclosure statement: All authors have declared no conflicts of interest. Table 1Spearman's correlation between JADAS 71 and single markers DA by ILAR subtype ILAR Subtype Systemic onset JIA Persistent oligo JIA Extended oligo JIA Rheumatoid factor neg JIA Rheumatoid factor pos JIA Enthesitis related JIA Psoriatic JIA Undifferentiated JIA Unknown subtype Total cohort Number of children 23 111 12 57 7 9 19 7 17 262 AJC 0.54 0.67 0.53 0.75 0.53 0.34 0.59 0.81 0.37 0.59 PGA 0.63 0.69 0.25 0.73 0.14 0.05 0.50 0.83 0.56 0.64 PGE 0.51 0.68 0.83 0.61 0.41 0.69 0.71 0.9 0.48 0.61 ESR 0.28 0.31 0.35 0.4 0.6 0.85 0.43 0.7 0.5 0.53 Limited 71 JC 0.29 0.51 0.23 0.37 0.14 -0.12 0.4 0.81 0.45 0.41 Parental pain 0.23 0.62 0.03 0.57 0.41 0.69 0.7 0.79 0.42 0.53 Childhood health assessment questionnaire 0.25 0.57 -0.07 0.36 -0.47 0.84 0.37 0.8 0.66 0.4