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Record W4392580314 · doi:10.5194/egusphere-egu24-9530

Effect of a salinity gradient on methane emissions in paddy rice: a mesocosm experiment

2024· preprint· en· W4392580314 on OpenAlexaff
Maite Martínez‐Eixarch, Sruthi Padinhariyil, Yolanda Lucas, Míriam Guivernau, Carles Alcaráz, Lluís Jornet, Julie Garnier, A. Fernandez, Joan Noguerol, Marc Viñas

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsContinental (Canada)
Fundersnot available
KeywordsMesocosmSalinityMethaneEnvironmental scienceAgronomyMethane emissionsPaddy fieldBiologyEcologyEcosystem

Abstract

fetched live from OpenAlex

Rice is a crucial crop for food security, but it is also a significant source of anthropogenic greenhouse gas emissions, particularly methane (CH4). Projected sea level rise caused by climate change will impact on rice yield through increased salinity. On the other hand, increased salinity potentially mitigates CH4 emissions by inhibiting methanogenesis mediated by the dominance of sulphate-reducing bacteria. To investigate this dual effect, we conducted a mesocosm experiment creating a water salinity gradient with four levels: 2 ppm (control), 4 ppm, 6 ppm and 35 ppm (seawater). CH4 emissions, abundance and gene expression of microbial populations and grain yield were assessed.The experiment took place in year 2022 at IRTA facilities (Spain) using a variety of japonica rice (Oryza sativa L.). Rice was grown following the standard practices, notably permanent flooding, and crop residue incorporation into the soil after the harvest. CH4 emissions were weekly assessed throughout the rice growing season (May to September) and the post-harvest (October to December). Gas samples were collected using gas chambers and analysed through gas chromatography. Yield and aboveground biomass were measured at harvest. Thereafter, crop residues in each mesocosm, where present, were incorporated into the soil. Soil samples for microbial analyses were taken twice during post-harvest: 6 days before the harvest and 28 days after straw incorporation. The microbial community diversity was assessed based on 16S/ITS-metataxonomy of total (DNA) and metabolically active (cDNA) bacteria, archaea, and fungi, as well as the quantification of total bacteria (16S rRNA gene), methanogenic archaea (mcrA gene ), and sulphate-reducing bacteria (aprA gene) by qPCR. The activity of methanogenic archaea and sulphate reducing populations were assessed by quantifying gene transcripts of mcrA and aprA by RT-qPCR.Rice grain yield decreased by 30% with increasing salinity from 2 ppm to 4 ppm, while there was no yield above 6 ppm. The biomass of straw produced and then added into the soil declined along the salinity gradient: 72.9 ± 12.4 g and 49.8 ± 6.1 g at 2 ppm and 4 ppm treatments, respectively, and zero in the remainder. The results confirmed that salinity significantly reduces CH4 emissions, but the sensitivity of this response differed between the growing and post-harvest seasons. During the growing season, CH4 declined with increasing salinity, ranging from 8.0 ± 1.7 to 0.05 ± 0.02 mg CH4 m-2 h-1. However, in the post-harvest, no CH4 emissions were detected at water salinities above 4 ppm, in contrast to 14.8 ± 0.75 mg CH4 m-2 h-1 found at 2 ppm. In regard to the microbial processes, the abundance of methanogenic archaea declined with increased salinity and the gene expression was highly inhibited at salinities larger than 6 ppm. By contrast, the abundance of sulphate-reducing bacteria was preserved over the salinity gradient while gene expression remained active, though slightly reduced from 6 ppm, probably due to the lower availability of organic carbon at the highest salinities.Acknowledgments: The study has been carried out within the framework of the MIC-RICE project PID2019-111572RB-I00 funded by AEI/10.13039/501100011033

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.298
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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