Exploring Sulfate as an Alternative Electron Acceptor: A Potential Strategy to Mitigate <scp>N<sub>2</sub>O</scp> Emissions in Upland Arable Soils
Bibliographic record
Abstract
ABSTRACT Agricultural activities are a significant source of nitrous oxide (N2O), accounting for approximately 60% of global emissions, highlighting the urgent need for innovative strategies to mitigate N2O emissions. Microbes conserve nearly as much energy with nitrate (NO3−) as oxygen (O2) respiration under limited O2 availability. Thus, microorganisms prioritize NO3−, limiting exploration of alternative electron acceptors (EAs) to inhibit N2O emissions through NO3− respiration in upland arable soils. Current approaches remain insufficient, and the interactions between alternative EA reduction and pathways for N2O emissions remain poorly understood. This study evaluated oxidized iron, manganese, and sulfate as alternative EAs to reduce N2O emissions, along with the effects of zero‐valent metals (ZVMs). Metal sulfates (MSs) significantly minimized N2O emissions by inhibiting denitrification rather than altering nitrification in microcosms, as supported by isotope mapping and inorganic nitrogen concentrations. Among others, putative complete denitrifiers, N2O reducers, and sulfate reducers were stimulated, whereas ZVMs stimulated N2O emissions and 16S rRNA gene abundance. Moreover, the abundance of denitrifier‐related genes (nirK, nirS, norB, and nosZ) consistently decreased under MS treatments, while dsrA mRNA abundance significantly increased. Sulfate (SO42−) addition reshaped the soil microbial community by enriching sulfur‐cycling taxa—including sulfate‐reducing and sulfur‐oxidizing bacteria—while suppressing nitrifiers such as Nitrospira, potentially disrupting nitrification–denitrification coupling. Ureibacillus thermosphaerius, harboring genes for denitrification and SO42− reduction, increased under MS treatment. These shifts likely redirected electron flow toward SO42− respiration, reducing NO3− utilization and contributing to N2O mitigation. Field‐based manipulation experiments over 2 years demonstrated the feasibility of MSs in upland arable soils, reducing yield‐scaled N2O emissions by 21.5% without compromising crop yields. A systematic literature review and meta‐analysis revealed that SO42− application mitigated N2O emissions by an average of 9%, with over 70% of observations showing a decreasing trend, underscoring its potential as an effective soil amendment for sustainable agriculture.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".