Effect of Water Deficit on Relationship between Yield and Physiological Attributes of Banana Cultivars and Hybrids
Bibliographic record
Abstract
This study examined the relationship between the yield reduction by CSI, MSI and RWC. The field experiment was conducted at national research centre for banana to screen the banana cultivars and hybrids for water deficit tolerance and to elucidate information on growth attribute mechanism of banana cultivars and hybrids. Stress was imposed at different critical stages viz., 3 rd , 5 th , 7 th and 9 th month after planting. The stress was given by scheduling irrigation at the 50 per cent available soil moisture (ASM) characteristic during critical stages. The soil moisture content was analyzed by using pressure plate membrane apparatus. In control plots, the irrigation was given at the ASM of 80% with the soil water potential of around -6 bars and in the case of stressed plots; the irrigation was given when an ASM reached 50 per cent with the soil water potential of -14 bars. In stressed plots, 50 per cent ASM was reached around 30 days. In this present study conducted with twelve cultivars and hybrids with three replications. The data were analyzed by using split plot design. The results revealed that the cultivars of Karpuravalli, Karpuravalli×Pisang Jajee, Saba, and Sannachenkathali recorded significantly higher yield (67.3 t/ha, 52.4 t/ha, 55.8 t/ha and 41.3 t/ha) and the magnitude of yield decrease was 12% than the cultivars and hybrids of Matti, Pisang Jajee×Matti, Matti×Anaikomban and Anaikomban×Pisang Jajee (14.9 t/ha, 11.1 t/ha, 10.3 t/ha and 10.6 t/ha). Similarly, Karpuravalli, Karpuravalli×Pisang Jajee, Saba, and Sannachenkathali recorded significantly relative water content, chlorophyll stability index and membrane stability index with lesser reduction percent were showed than the cultivars and hybrids of Matti, Pisang Jajee×Matti, Matti×Anaikomban and Anaikomban×Pisang Jajee.
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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".