Interannual variations in methane emission from an irrigated rice paddy caused by rainfalls during the aeration period

Kim, Y.; Talucder, Mohammad Samiul Ahsan; Kang, Minseok; Shim, Kang-Bo; Kang, Namgoo; Kim, Joon

doi:10.1016/j.agee.2016.02.032

Cited by 29 publications

(12 citation statements)

References 28 publications

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“…The eddy covariance technique has been widely used to measure CH 4 fluxes from ecosystems around the world including subtropical pastures and wetlands (Dengel et al 2011, Baldocchi et al 2012, Chamberlain et al 2015, Shoemaker et al 2015, Kim et al 2016. In a subtropical grazed pasture near our study site, Chamberlain et al (2015) showed that the eddy covariance and soil chamber methods yielded similar annual CH 4 flux estimates.…”

Section: Eddy Covariance Measurementsmentioning

confidence: 64%

Grazing alters net ecosystem C fluxes and the global warming potential of a subtropical pasture

Gomez‐Casanovas

DeLucia

Bernacchi

et al. 2018

Ecological Applications

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The impact of grazing on C fluxes from pastures in subtropical and tropical regions and on the environment is uncertain, although these systems account for a substantial portion of global C storage. We investigated how cattle grazing influences net ecosystem CO and CH exchange in subtropical pastures using the eddy covariance technique. Measurements were made over several wet-dry seasonal cycles in a grazed pasture, and in an adjacent pasture during the first three years of grazer exclusion. Grazing increased soil wetness but did not affect soil temperature. By removing aboveground biomass, grazing decreased ecosystem respiration (R ) and gross primary productivity (GPP). As the decrease in R was larger than the reduction in GPP, grazing consistently increased the net CO sink strength of subtropical pastures (55, 219 and 187 more C/m in 2013, 2014, and 2015). Enteric ruminant fermentation and increased soil wetness due to grazers, increased total net ecosystem CH emissions in grazed relative to ungrazed pasture (27-80%). Unlike temperate, arid, and semiarid pastures, where differences in CH emissions between grazed and ungrazed pastures are mainly driven by enteric ruminant fermentation, our results showed that the effect of grazing on soil CH emissions can be greater than CH produced by cattle. Thus, our results suggest that the interactions between grazers and soil hydrology affecting soil CH emissions play an important role in determining the environmental impacts of this management practice in a subtropical pasture. Although grazing increased total net ecosystem CH emissions and removed aboveground biomass, it increased the net storage of C and decreased the global warming potential associated with C fluxes of pasture by increasing its net CO sink strength.

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Section: Eddy Covariance Measurementsmentioning

confidence: 64%

Grazing alters net ecosystem C fluxes and the global warming potential of a subtropical pasture

Gomez‐Casanovas

DeLucia

Bernacchi

et al. 2018

Ecological Applications

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“…that impact the seasonal dynamics and flux of CH 4 emission from rice paddies through production control, oxidation and transport (Guo and Zhou, 2007;Bhattacharyya et al, 2013;Ahmad et al, 2009;Tyagi et al, 2010;Xu and Hosen, 2010;Gupta et al, 2015;Kim et al, 2016;Hu et al, 2016). Under continuous flooding of paddy soil, an anaerobic environment is created which favors anaerobic methanogens to produce CH 4 during the last step of anaerobic decomposition of organic substrate (Le Mer and Roger, 2001).…”

Section: Effect Of Water Management On Seasonal Ch 4 Emissionsmentioning

confidence: 97%

Greenhouse gas emission from direct seeded paddy fields under different soil water potentials in Eastern India

Kumar

Nayak

Mohanty

et al. 2016

Agriculture, Ecosystems & Environment

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“…Drainage facilitates methane oxidation, and thus FCH 4 in the remainder of the growing season is reduced after the week-long drainage period. Thus, drainage affects not only FCH 4 during the drainage period but also has a lagged effect persisting for a few weeks after the drainage period or even until the end of the growing season (Fumoto, Yanagihara, Saito, & Yagi, 2010;Kim et al, 2016). It is unclear that current gap-filling approaches can capture such a lagged effect (e.g., Reichstein et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Gap‐filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis

Kim

Johnson

Knox

et al. 2019

Global Change Biology

Self Cite

139

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Methane flux (FCH4) measurements using the eddy covariance technique have increased over the past decade. FCH4 measurements commonly include data gaps, as is the case with CO2 and energy fluxes. However, gap‐filling FCH4 data are more challenging than other fluxes due to its unique characteristics including multidriver dependency, variabilities across multiple timescales, nonstationarity, spatial heterogeneity of flux footprints, and lagged influence of biophysical drivers. Some researchers have applied a marginal distribution sampling (MDS) algorithm, a standard gap‐filling method for other fluxes, to FCH4 datasets, and others have applied artificial neural networks (ANN) to resolve the challenging characteristics of FCH4. However, there is still no consensus regarding FCH4 gap‐filling methods due to limited comparative research. We are not aware of the applications of machine learning (ML) algorithms beyond ANN to FCH4 datasets. Here, we compare the performance of MDS and three ML algorithms (ANN, random forest [RF], and support vector machine [SVM]) using multiple combinations of ancillary variables. In addition, we applied principal component analysis (PCA) as an input to the algorithms to address multidriver dependency of FCH4 and reduce the internal complexity of the algorithmic structures. We applied this approach to five benchmark FCH4 datasets from both natural and managed systems located in temperate and tropical wetlands and rice paddies. Results indicate that PCA improved the performance of MDS compared to traditional inputs. ML algorithms performed better when using all available biophysical variables compared to using PCA‐derived inputs. Overall, RF was found to outperform other techniques for all sites. We found gap‐filling uncertainty is much larger than measurement uncertainty in accumulated CH4 budget. Therefore, the approach used for FCH4 gap filling can have important implications for characterizing annual ecosystem‐scale methane budgets, the accuracy of which is important for evaluating natural and managed systems and their interactions with global change processes.

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Interannual variations in methane emission from an irrigated rice paddy caused by rainfalls during the aeration period

Cited by 29 publications

References 28 publications

Grazing alters net ecosystem C fluxes and the global warming potential of a subtropical pasture

Grazing alters net ecosystem C fluxes and the global warming potential of a subtropical pasture

Greenhouse gas emission from direct seeded paddy fields under different soil water potentials in Eastern India

Gap‐filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis

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