Morphological, physiological and biochemical performance of <em>Tectona grandis</em> and <em>Gmelina arborea</em> under drought stress conditions
Bibliographic record
Abstract
Drought is one of the most widespread global environmental problems leading to low water availability for plants, which causes a significant loss in growth, productivity and finally their yields. In the present study, the effect of drought stress on growth characteristics, physiological and biochemical parameters of Tectona grandis and Gmelina arborea at seedling stage under nursery conditions have been discussed. Pot culture experiments were conducted in RBD design to observe the effect of moderate drought (MD) and severe drought (SD) stress on the selected seedling sunder nursery conditions for one year. Moderate and severe drought conditions were artificially created with the help of CPE (Cumulative Pan Evaporation) values and PWP (Permanent Wilting Point). The amount of water equal to the calculated field capacity was provided to each polybag at the interval of calculated CPE. Physiological parameters viz. photosynthetic rate, stomatal conductance and transpiration rate of the seedlings were measured. Total Chlorophyll and Proline content were estimated for biochemical analysis.The outcome of the experiment showed that with the increasing age of the seedling, the effect of drought become more pronounced till the end of the experiment in terms of growth characteristics. Also, the severe drought condition was more lethal to the selected species seedlings. Further, the decreasing biomass, physiological parameters and chlorophyll content were found along with increased proline content with the severity of drought stress confirm the result. However, G. arborea found to be more affected than T. grandis. Hence, it can be concluded that the T. grandis species is better for plantation in an area with the moderate drought and can be maintained in severe drought climatic conditions. The plantations of suitable tree species in drought-prone areas will be helpful in sustainable forest management and resilient the forest ecosystem to climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".