The combination of 3,4-dimethylpyrazole phosphate and alternate drip irrigation with low irrigation quotas resulted in the lowest NH3 and N2O emissions
Bibliographic record
Abstract
Mitigating reactive nitrogen loss from soil is critical for sustainable agricultural intensification. However, the combined effects of the nitrification inhibitor DMPP (3,4-dimethylpyrazole phosphate) and irrigation strategies on soil nitrogen dynamics remain unclear. A two-year experiment in the North China Plain evaluated how DMPP application, irrigation method (alternate vs. conventional drip irrigation), and irrigation quota (27 mm vs. 36 mm) affected soil NH₃ and N₂O emissions, physicochemical properties, enzyme activities, and microbial communities in summer maize systems. DMPP application, irrigation method and irrigation quota significantly affected soil urease and catalase activities, while alkaline phosphatase was mainly influenced by irrigation method ( P < 0.05). Actinobacteriota and Proteobacteria dominated the microbial phyla, accounting for over 40 % of total relative abundance. Compared with treatments without DMPP, DMPP reduced N₂O emissions by 37.4–70.4 % but increased NH₃ volatilization by 13.5–18.7 % due to higher NH₄⁺-N concentrations and enhanced urease activity. Alternate drip irrigation (ADI) consistently lowered both NH₃ and N₂O emissions by 9.6–23.9 % and 17.8–37.6 %, respectively, compared with conventional drip irrigation, and when combined with DMPP under a 27 mm irrigation quota, achieved the lowest global warming potential and greenhouse gas intensity. Random forest regression analysis revealed soil water-filled pore space as the main driver of N₂O emission, while catalase and urease activities primarily controlled NH₃ volatilization. Integrating DMPP with ADI under 27 mm irrigation quota is recommended to mitigate gaseous nitrogen losses. Future research should examine microbial functional genes and long-term soil responses under integrated water-nitrogen management.
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 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".