Off-Season Flower Induction in Mango Fruits Using Ethephon and Potassium Nitrate
Bibliographic record
Abstract
Seasonality in mango production is a major factor contributing to high postharvest losses reported in the value chain. Oversupply during the high season is one of the factors that contribute to the high postharvest losses (≥ 50%) in the supply chain in Kenya. Effective strategies to address seasonality can contribute significantly to postharvest loss reduction. Efficacy of two flower induction chemicals, potassium nitrate (KNO3) and ethephon on reproductive growth parameters and yield components were evaluated on two mango varieties: ‘Apple’ and ‘Ngowe’. KNO3 was applied at two concentrations (2 and 4%), and ethephon (600 and 1000 ppm) then compared to control (water). They were applied to trees which failed to flower/set fruit in 2014 season. There effect was established from reproductive growth parameters: days to flowering, number of panicles per tree, fruit set per 20 panicles, fruit fall and hormonal effect. KNO3 (4%) and ethephon increased percentage flowering in both ‘Ngowe’ and ‘Apple’ and AEZs (Agro-Ecological Zones), significantly (p < 0.05) shortened time to flowering and increased fruit set. The findings show that KNO3 and ethephon can be used to induce flowering/fruiting in mango fruits. These technologies can therefore be applied to induce off-season mango production to address seasonality and reduce postharvest losses during the peak season.
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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 | 0.000 |
| 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".