Morphological and Ecophysiological Indicators for Coriander Under Irrigation Depths and Nitrogen Levels
Bibliographic record
Abstract
Nitrogen fertilization and supplying of water are crucial factors for quality and quantity produces of coriander. The objective of this study was to evaluate morphological and ecophysiological characteristics for coriander under five irrigation depths and two doses of nitrogen (N). Experimental layout was completely randomized design in a split plot scheme with five replications. The irrigation depths (plot) was 25, 50, 75, 100 and 125% of crop evapotranspiration (ETc). The doses of N (subplot) corresponded to 35 and 70 kg ha-1. The cultivation of coriander (“Vedete”) was in a protected environment. The ratio between the aerial part and roots linearly increased with the increment of the irrigation depths and was highest under 70 kg ha-1 of nitrogen. The leaf area index linearly increased with the increment of the irrigation depths at both doses of N. The leaf index of chlorophyll “a” was highest under irrigation depths of 87 and 75% of ETc for 35 and 70 kg ha-1 of N, respectively. The leaf index of chlorophyll “b” decrease linearly with the increase of irrigation depths in both doses of N. The nitrogen use efficiency was maximized with high soil moisture conditions. The water use efficiency decreases linearly with increasing of irrigation depth. The best irrigation depth and nitrogen dose obtained in this study was 125% of ETc and 70 kg/ha. The leaf index of chlorophyll “a” and “b” are important indicators of hydric stress. The leaf index of chlorophyll “b” are negatively correlated with leaf area index. N supply increase the water use efficiency.
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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 | 0.001 | 0.001 |
| 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".