Morpho-physiological response of water leaf (Talinum triangulare Jacq) to urea and Azomite
Bibliographic record
Abstract
The cultivation of waterleaf ( Talinum triangulare ) is crucial to mitigate its overexploitation from the wild; however, optimal fertilizer strategies remain poorly explored. Azomite, a naturally occurring mineral complex, is gaining interest in sustainable agriculture for its ability to improve plant growth and stress resilience without the environmental footprint of synthetic fertilizers. This study evaluated the novel combination of urea nitrogen (N) and Azomite supplementation on the growth and physiology of waterleaf plants to determine a sustainable fertilization strategy. Using a 2 × 6 factorial completely randomized design, plants were subjected to six N rates (0, 50, 100, 150, 200, and 250 kg/ha) with or without Azomite (12.45 g/pot). The results revealed a critical threshold for urea application with rates ≥ 150 kg/ha inducing severe ammonium toxicity, culminating in complete plant mortality at 250 kg/ha. In contrast, 50 kg/ha N rate was identified as optimal, significantly (P < 0.05) enhancing morphological traits such as increasing plant height, stem diameter, leaf number and branch number by 21–300 %, and boosting fresh and dry biomass by 281 % and 154 %, respectively compared to the control. The synergistic application of 50 kg/ha N with Azomite further amplified these benefits driving the most robust improvements in plant architecture and photosynthetic capacity. This combination resulted in a photosynthetic rate of 6.41 μmol m⁻² s⁻¹ and a transpiration rate of 4.50 mol m⁻² s⁻¹ , significantly higher than all other treatments. Principal Component Analysis (PCA) confirmed that the 50 kg/ha N and 50 kg/ha N with Azomite treatments were strongly associated with superior growth and physiological performance. In conclusion, 50 kg/ha is the optimal urea-N rate for waterleaf, with Azomite supplementation providing significant synergistic growth benefits. This strategy offers a practical efficient solution for enhancing waterleaf productivity while minimizing the risks of nitrogen toxicity and environmental pollution. • First study on the combined effects of urea and azomite on Talinum triangulare growth. • Optimal nitrogen rate identified as 50 kg/ha for growth and physiological performance. • Azomite supplementation enhanced plant height, leaf size, and photosynthetic rate. • Excess nitrogen (≥100 kg/ha) suppressed growth parameters in waterleaf. • Findings provide sustainable fertilization strategy for smallholder and urban farmers.
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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 | 0.001 |
| 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".