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 (N 2 O), accounting for approximately 60% of global emissions, highlighting the urgent need for innovative strategies to mitigate N 2 O emissions. Microbes conserve nearly as much energy with nitrate (NO 3 − ) as oxygen (O 2 ) respiration under limited O 2 availability. Thus, microorganisms prioritize NO 3 − , limiting exploration of alternative electron acceptors (EAs) to inhibit N 2 O emissions through NO 3 − respiration in upland arable soils. Current approaches remain insufficient, and the interactions between alternative EA reduction and pathways for N 2 O emissions remain poorly understood. This study evaluated oxidized iron, manganese, and sulfate as alternative EAs to reduce N 2 O emissions, along with the effects of zero‐valent metals (ZVMs). Metal sulfates (MSs) significantly minimized N 2 O emissions by inhibiting denitrification rather than altering nitrification in microcosms, as supported by isotope mapping and inorganic nitrogen concentrations. Among others, putative complete denitrifiers, N 2 O reducers, and sulfate reducers were stimulated, whereas ZVMs stimulated N 2 O 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 (SO 4 2− ) 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 SO 4 2− reduction, increased under MS treatment. These shifts likely redirected electron flow toward SO 4 2− respiration, reducing NO 3 − utilization and contributing to N 2 O mitigation. Field‐based manipulation experiments over 2 years demonstrated the feasibility of MSs in upland arable soils, reducing yield‐scaled N 2 O emissions by 21.5% without compromising crop yields. A systematic literature review and meta‐analysis revealed that SO 4 2− application mitigated N 2 O 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".