Denitrification in arable soils in relation to their physico-chemical properties and fertilization practice

Šimek, Miloslav; Cooper, J. E.; Picek, Tomáš; Šantrůčková, Hana

doi:10.1016/s0038-0717(99)00137-6

Cited by 55 publications

(41 citation statements)

References 36 publications

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“…Our results and other works (Cosandey et al, 2003;Simek et al, 2000 indicate that the relationship between DEA and Da is unclear. We successfully used OM instead of DEA without performance loss.…”

Section: Discussioncontrasting

confidence: 42%

“…The short term denitrification potential metrics (DEA) are most commonly used. Although successfully used as a denitrification indicator (Heinen, 2006a), DEA techniques are varied and have an imprecise relationship to Da (Oehler et al, 2007;Simek et al, 2000).…”

Section: Database and Input Factorsmentioning

confidence: 99%

“…-organic matter % (OM): OM could be a useful surrogate for soil LDP which is correlated to soil physical characteristics more than DEA is (Simek et al, 2000). Some models use OM to compute a LDP (Hansen et al, 1991;Johnsson et al, 1987) which has been suggested to be more appropriate than DEA for modelling purposes (Henault and Germon, 2000).…”

Section: Database and Input Factorsmentioning

confidence: 99%

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The use of machine learning algorithms to design a generalized simplified denitrification model

2010

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Abstract. We propose to use machine learning (ML) algorithms to design a simplified denitrification model. Boosted regression trees (BRT) and artificial neural networks (ANN) were used to analyse the relationships and the relative influences of different input variables towards total denitrification, and an ANN was designed as a simplified model to simulate total nitrogen emissions from the denitrification process. To calibrate the BRT and ANN models and test this method, we used a database obtained collating datasets from the literature. We used bootstrapping to compute confidence intervals for the calibration and validation process. Both ML algorithms clearly outperformed a commonly used simplified model of nitrogen emissions, NEMIS, which is based on denitrification potential, temperature, soil water content and nitrate concentration. The ML models used soil organic matter % in place of a denitrification potential and pH as a fifth input variable. The BRT analysis reaffirms the importance of temperature, soil water content and nitrate concentration. Generalization, although limited to the data space of the database used to build the ML models, could be improved if pH is used to differentiate between soil types. Further improvements in model performance and generalization could be achieved by adding more data.

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Section: Discussioncontrasting

confidence: 42%

Section: Database and Input Factorsmentioning

confidence: 99%

Section: Database and Input Factorsmentioning

confidence: 99%

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The use of machine learning algorithms to design a generalized simplified denitrification model

2010

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“…The identification of key factors controlling denitrification rates and their products is crucial to quantify the effects of human activity on the N cycle in terrestrial ecosystems and for managing and mitigating the severe environmental consequences associated with N pollution (Boyer et al, 2006). these processes in N loss at the ecosystem level, and the factors influencing denitrification capacity and environmental impacts (Wijler and Delwiche, 1954;Kralova, 1991;Simek et al, 2000;Simek and Cooper, 2002;Hofstra and Bouwman, 2005;Dannenmann et al, 2008). However, denitrification has been primarily measured in soils from temperate zones in industrialized countries (Hofstra and Bouwman 2005) and few measurements quantifying denitrification rates in soils from tropical and subtropical zones have been reported (Pu et al, 2001;Pu et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…Reduction of NO into nitrousoxide is also catalyzed by two types of enzymes: one NO reductase receives the electrons from cytochrome c orpseudoazurin (cNor) and the other from aquinol pool (qNor). The last step of the denitrification cascade, reduction of N 2 O into dinitrogen gas, is performed by the multicopper homodimeric N 2 O reductase (NosZ), which is located in the periplasm in Gram-negative bacteria (Simek et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Denitrification Process as an Indicator of Soil Health

Solanki¹,

Meena²,

Narayan³

et al. 2017

Int.J.Curr.Microbiol.App.Sci

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Influence of Vegetation Characteristics on Soil Denitrification in Shoreline Wetlands of the Danjiangkou Reservoir in China

Liu

Zhang

2011

CLEAN Soil Air Water

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Soil denitrification in reservoir shoreline wetlands is an important process for removing excess inorganic nitrogen from upland runoff and controlling eutrophication in aquatic ecosystems. As yet, little is known about the influence of vegetation characteristics on the soil denitrification potential in reservoir shoreline wetlands, although vegetation can affect both denitrifying bacteria and soil properties. In this study, we measured the spatial variability of denitrification enzyme activity (DEA) using acetylene block method in shoreline wetlands of the Danjiangkou Reservoir, a water source of the South-to-North Water Transfer Project in China. Results indicated that DEA ranged from 0.001 to 2.449 mg N (N 2 O) g À1 h À1 , with a mean of 0.384DEA varied significantly among five representative plant communities and the highest DEA (0.248-2.449 mg N (N 2 O) g À1 h À1 ) was observed in the Polygonum hydropiper community. Plant biomass and vegetation cover were significantly and positively related to DEA and together explained 44.2% of the total variance. These results suggest that vegetation characteristics should also be considered in assessing soil denitrification capacity and restoring shoreline wetlands for nitrogen pollution removal in the Danjiangkou Reservoir after dam heightening.

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Denitrification in arable soils in relation to their physico-chemical properties and fertilization practice

Cited by 55 publications

References 36 publications

The use of machine learning algorithms to design a generalized simplified denitrification model

The use of machine learning algorithms to design a generalized simplified denitrification model

Denitrification Process as an Indicator of Soil Health

Influence of Vegetation Characteristics on Soil Denitrification in Shoreline Wetlands of the Danjiangkou Reservoir in China

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