Use of Nitrification Inhibitor DMPP to Improve Nitrogen Uptake Efficiency in Citrus Trees
Bibliographic record
Abstract
In citrus orchards, nitrogen uptake efficiency (NUE) is between 40 to 60% where any excess of nitrate is subjected to leaching below the rooting zone. The compound, 3,4-dimethylpyrazole phosphate (DMPP) inhibits the nitrification process in soil thus reducing/delaying nitrate leaching. The objective of this study was to evaluate the performance of DMPP added to ammonium sulphate (AS+DMPP), compared to ammonium sulphate (AS) and calcium-potassium nitrate (CPN), in fertigation of bearing orange trees grown outdoors in drainage lysimeters. Fertilizers were 15N-labeled to trace N through soil-plant-drainage system. Soil was sampled monthly from April to December and trees were destructively harvested in December. In trees fertilized with AS+DMPP, increased biomass was observed with a more profuse development of root system and higher fruit yield. Fertilizer 15N uptake significantly increased with DMPP addition. In AS+DMPP amended soils, increased values of -15N and lower -N concentrations were recorded from April to June as evidence of the inhibitory effect of DMPP on the nitrification process during this period. In CPN and AS fertilized soils, 15N losses in drainage solutions represented 9-10% of the fertilizer supplied whereas less than 3% was leached when DMPP was added. At the end of the cycle, highest NUE was recorded in the AS+DMPP treatment (69%), while CPN and AS had lower values (61% and 54%, respectively). Therefore, the use of DMPP enables a more efficient utilization of the fertilizer-N in citrus trees, minimizing the risk of nitrate-N pollution in groundwater. However, DMPP supply should be considered during spring fertilization, since high temperatures of summer months significantly reduced its activity.
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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".