Effects of Enhanced-Efficiency Nitrogen Fertilizers on Soil Microbial Biomass and Respiration in Tropical Soil Under Upland Rice Cultivation
Bibliographic record
Abstract
While over-use of N fertilizers can suppress microbial biomass, application of urease inhibitors is known to be a potential way to rebuilt microbial diversity and improve soil functions. However, the hypothesis of this study is that the application of N fertilizers regardless of the source would increase soil microbial biomass and reduce soil respiration. A two-year field experiment was conducted to assess the effects of enhanced-efficiency N sources on soil microbial biomass, and soil respiration. The experiment was set up in a randomized block design in a 3 × 4 + 1 factorial scheme, with four replicates. Treatments comprised three sources (conventional uncoated urea, NBPT (N-(n-butyl) thiophosphoric triamide)-treated urea, and polymer-coated urea) and four rates (30, 60, 90 and 120 kg ha-1) of N, in addition to a control treatment (no fertilizer application). Microbial biomass C (MBC) and microbial biomass N (MBN), and soil respiration (C-CO2 and qCO2) were determined in upland rice rhizosphere in each crop season. Responses of soil microbial properties to N fertilization were dependent on the N rates, but no significant effect of the N sources was observed. All measured parameters, except MBC in the first season and C-CO2 in the second season, were increased with increasing N rates. However, the application of N higher than 60 kg ha-1 suppressed soil microbial biomass, as well as soil respiration. Therefore, the lack of response by added urease inhibitors to the N sources indicate that optimizing N rates for upland rice production is a far more effective option for improving soil microbial community than using enhanced-efficiency N sources.
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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".