BACKGROUND: Denitrification is a microbial process that has received considerable attention during the past decade since it can result in losses of added nitrogen fertilisers from agricultural soils. Paddy soil has been known to have strong denitrifying activity, but the denitrifying microorganisms responsible for fertilisers in paddy soil are not well known. The objective of this study was to explore the impacts of 17\uffe2\uff80\uff90year application of inorganic and organic fertiliser (rice straw) on the abundance and composition of a nosZ\uffe2\uff80\uff90denitrifier community in paddy soil. Soil samples were collected from CK plots (no fertiliser), N (nitrogen fertiliser), NPK (nitrogen, phosphorus and potassium fertilisers) and NPK + OM (NPK plus organic matter). The nitrous oxide reductase gene (nosZ) community composition was analysed using terminal restriction fragment length polymorphism, and the abundance was determined by quantitative PCR.
RESULTS: Both the largest abundance of nosZ\uffe2\uff80\uff90denitrifier and the highest potential denitrifying activity (PDA) occurred in the NPK + OM treatment with about four times higher than that in the CK and two times higher than that in the N and NPK treatments (no significant difference). Denitrifying community composition differed significantly among fertilisation treatments except for the comparison between CK and N treatments. Of the measured abiotic factors, total organic carbon was significantly correlated with the observed differences in community composition and abundance (P < 0.01 by Monte Carlo permutation).
CONCLUSION: This study shows that the addition of different fertilisers affects the size and composition of the nosZ\uffe2\uff80\uff90denitrifier community in paddy soil. Copyright \uffc2\uffa9 2011 Society of Chemical Industry
", "keywords": ["2. Zero hunger", "0301 basic medicine", "0303 health sciences", "Bacteria", "Nitrogen", "0402 animal and dairy science", "Agriculture", "Oryza", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water", "Carbon", "Soil", "03 medical and health sciences", "Genes", " Bacterial", "Denitrification", "0405 other agricultural sciences", "Fertilizers", "Oxidoreductases", "Monte Carlo Method", "Polymorphism", " Restriction Fragment Length", "Soil Microbiology"]}, "links": [{"href": "https://doi.org/10.1002/jsfa.4533"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20the%20Science%20of%20Food%20and%20Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/jsfa.4533", "name": "item", "description": "10.1002/jsfa.4533", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/jsfa.4533"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2011-07-27T00:00:00Z"}}, {"id": "10.3929/ethz-b-000278733", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:22:11Z", "type": "Journal Article", "created": "2018-07-06", "title": "Cost\u2013benefit optimization of structural health monitoring sensor networks", "description": "Structural health monitoring (SHM) allows the acquisition of information on the structural integrity of any mechanical system by processing data, measured through a set of sensors, in order to estimate relevant mechanical parameters and indicators of performance. Herein we present a method to perform the cost\uffe2\uff80\uff93benefit optimization of a sensor network by defining the density, type, and positioning of the sensors to be deployed. The effectiveness (benefit) of an SHM system may be quantified by means of information theory, namely through the expected Shannon information gain provided by the measured data, which allows the inherent uncertainties of the experimental process (i.e., those associated with the prediction error and the parameters to be estimated) to be accounted for. In order to evaluate the computationally expensive Monte Carlo estimator of the objective function, a framework comprising surrogate models (polynomial chaos expansion), model order reduction methods (principal component analysis), and stochastic optimization methods is introduced. Two optimization strategies are proposed: the maximization of the information provided by the measured data, given the technological, identifiability, and budgetary constraints; and the maximization of the information\uffe2\uff80\uff93cost ratio. The application of the framework to a large-scale structural problem, the Pirelli tower in Milan, is presented, and the two comprehensive optimization methods are compared.
", "keywords": ["Stochastic Processes", "structural health monitoring", "structural health monitoring; Bayesian inference; cost\u2013benefit analysis; stochastic optimization; information theory; Bayesian experimental design; surrogate modeling; model order reduction", "Chemical technology", "Cost-Benefit Analysis", "Bayesian inference", "Bayesian experimental design", "Uncertainty", "Bayes Theorem", "TP1-1185", "02 engineering and technology", "stochastic optimization", "Bayesian experimental design; Bayesian inference; Benefit analysis; Cost; Information theory; Model order reduction; Stochastic optimization; Structural health monitoring; Surrogate modeling; Algorithms; Monte Carlo Method; Nonlinear Dynamics; Stochastic Processes; Uncertainty; Bayes Theorem; Cost-Benefit Analysis; Analytical Chemistry; Atomic and Molecular Physics", " and Optics; Biochemistry; Instrumentation; Electrical and Electronic Engineering", "Article", "surrogate modeling", "0201 civil engineering", "Nonlinear Dynamics", "model order reduction", "cost\u2013benefit analysis", "Monte Carlo Method", "Algorithms", "information theory"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/18/7/2174/pdf"}, {"href": "https://re.public.polimi.it/bitstream/11311/1085132/1/Sensors_2018b.pdf"}, {"href": "https://doi.org/10.3929/ethz-b-000278733"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3929/ethz-b-000278733", "name": "item", "description": "10.3929/ethz-b-000278733", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3929/ethz-b-000278733"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-07-06T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Monte+Carlo+Method&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Monte+Carlo+Method&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Monte+Carlo+Method&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Monte+Carlo+Method&offset=2", "hreflang": "en-US"}], "numberMatched": 2, "numberReturned": 2, "distributedFeatures": [], "timeStamp": "2026-05-31T01:09:13.753667Z"}