Effect of a salinity gradient on methane emissions in paddy rice: a mesocosm experiment
Notice bibliographique
Résumé
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
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».