SisAgriV1, Main, Merge, bibRecord, 000090

Using robust Bayesian network to estimate the residuals of fluoroquinolone antibiotic in soil.

Identifieur interne : 000090 ( Main/Merge ); précédent : 000089; suivant : 000091

Using robust Bayesian network to estimate the residuals of fluoroquinolone antibiotic in soil.

Auteurs : Xuewen Li [République populaire de Chine] ; Yunfeng Xie [République populaire de Chine] ; Lianfa Li [République populaire de Chine] ; Xunfeng Yang [République populaire de Chine] ; Ning Wang [République populaire de Chine] ; Jinfeng Wang [République populaire de Chine]

Source :

Environmental science and pollution research international [ 1614-7499 ] ; 2015.

RBID : pubmed:26141975

Descripteurs français

Wicri :
- geographic : République populaire de Chine.

English descriptors

KwdEn :
- Anti-Bacterial Agents (analysis), Bayes Theorem, China, Crop Production (methods), Drug Residues (analysis), Environmental Monitoring (methods), Environmental Monitoring (statistics & numerical data), Fluoroquinolones (analysis), Manure (analysis), Models, Theoretical, Organic Agriculture (methods), Probability, Soil (chemistry), Soil (standards), Soil Pollutants (analysis), Vegetables (growth & development).
MESH :
- chemical , analysis : Anti-Bacterial Agents, Fluoroquinolones, Manure, Soil Pollutants.
- chemical , chemistry : Soil.
- chemical , standards : Soil.
- geographic : China.
- analysis : Drug Residues.
- growth & development : Vegetables.
- methods : Crop Production, Environmental Monitoring, Organic Agriculture.
- statistics & numerical data : Environmental Monitoring.
- Bayes Theorem, Models, Theoretical, Probability.

Abstract

Prediction of antibiotic pollution and its consequences is difficult, due to the uncertainties and complexities associated with multiple related factors. This article employed domain knowledge and spatial data to construct a Bayesian network (BN) model to assess fluoroquinolone antibiotic (FQs) pollution in the soil of an intensive vegetable cultivation area. The results show: (1) The relationships between FQs pollution and contributory factors: Three factors (cultivation methods, crop rotations, and chicken manure types) were consistently identified as predictors in the topological structures of three FQs, indicating their importance in FQs pollution; deduced with domain knowledge, the cultivation methods are determined by the crop rotations, which require different nutrients (derived from the manure) according to different plant biomass. (2) The performance of BN model: The integrative robust Bayesian network model achieved the highest detection probability (pd) of high-risk and receiver operating characteristic (ROC) area, since it incorporates domain knowledge and model uncertainty. Our encouraging findings have implications for the use of BN as a robust approach to assessment of FQs pollution and for informing decisions on appropriate remedial measures.

DOI: 10.1007/s11356-015-4751-9
PubMed: 26141975

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pubmed:26141975

Le document en format XML

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<front><div type="abstract" xml:lang="en">Prediction of antibiotic pollution and its consequences is difficult, due to the uncertainties and complexities associated with multiple related factors. This article employed domain knowledge and spatial data to construct a Bayesian network (BN) model to assess fluoroquinolone antibiotic (FQs) pollution in the soil of an intensive vegetable cultivation area. The results show: (1) The relationships between FQs pollution and contributory factors: Three factors (cultivation methods, crop rotations, and chicken manure types) were consistently identified as predictors in the topological structures of three FQs, indicating their importance in FQs pollution; deduced with domain knowledge, the cultivation methods are determined by the crop rotations, which require different nutrients (derived from the manure) according to different plant biomass. (2) The performance of BN model: The integrative robust Bayesian network model achieved the highest detection probability (pd) of high-risk and receiver operating characteristic (ROC) area, since it incorporates domain knowledge and model uncertainty. Our encouraging findings have implications for the use of BN as a robust approach to assessment of FQs pollution and for informing decisions on appropriate remedial measures.</div>
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Système d'information stratégique et agriculture (serveur d'exploration)

Using robust Bayesian network to estimate the residuals of fluoroquinolone antibiotic in soil.

Using robust Bayesian network to estimate the residuals of fluoroquinolone antibiotic in soil.

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

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Pour générer des pages wiki