Appropriate Ammonium-Nitrate Ratio Improves Nutrient Accumulation and Fruit Quality in Pepper (Capsicum annuum L.)
Bibliographic record
Abstract
Ammonium (NH4+) and nitrate (NO3−) are the two forms of inorganic nitrogen essential for physiological and biochemical processes in higher plants, but little is known about how the NH4+:NO3− ratio may affect nitrogen metabolism. This study determined the effect of NH4+:NO3− ratios on plant growth, accumulation, and distribution of nutrient elements, fruit quality, enzyme activity, and relative expression of genes involved in nitrogen (N) metabolism in pepper (Capsicum annuum L.). In a pod experiment, the NH4+:NO3− ratios of 0:100, 12.5:87.5, 25:75, 37.5:62.5, and 50:50 were arranged in a complete randomized design with three replicates. The application of NH4+:NO3− at 25:75 resulted in highest dry matter and N, phosphorus (P), and potassium (K) accumulation. Pepper treated with 25:75 ratio increased root length, surface areas, and root volume and tips. The contents of vitamin C, soluble sugar, soluble protein, total phenols, flavonoids, and capsaicinoids in the fruits were significantly higher with the NH4+:NO3− ratio of 25:75 compared with 0:100 treatment, while lowering nitrate content was found in NH4+:NO3− ratios of 25:75, 37.5:62.5, and 50:50 treatments. Activity of glutamine synthetase (GS), glutamate synthases (GOGAT) enzyme and the levels of relative expression of genes coding these enzymes were superior when the NH4+:NO3− ratio of 25:75 were applied. Therefore, an appropriate ratio of NH4+:NO3− (25:75) in nitrogen application can stimulate root development, promote enzyme activities, and enhance the productivity and fruit quality in pepper.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".