Effects of Environmental Temperature and Precipitation Pattern on Growth Stages of Magnifera indica cv. Harumanis Mango
Bibliographic record
Abstract
Magnifera indica cv. Harumanis is one of the most commercially grown mango cultivar in Malaysia due to market demand and price. However, the fruit supply never meets the demand as Harumanis is highly sensitive towards the climate and only grows in Perlis and part of Kedah. Crop productivity and development are mainly related to climatic variables where temperature and precipitation are the most importance. Temperature and precipitation distribution pattern affect flowering, fruit set, fruit growth and also fruit development. This study evaluates the relationship between temperature and precipittaion distribution pattern towards development of Harumanis growth stages including flowering and fruit development aspects in Zone 1 (area with a clear dry season up to three or four months) and Zone 2 (area with a clear dry season between one to two months). Thermal calendar based on daily and accumulated growing degree days (GDD) used to predict growth stages. Results indicate that Harumanis need a hot and dry environment (high temperature with less precipitation) during reproductive stage. However, there is no significant difference between Zone 1 and Zone 2 on the GDD required during every growth stages. On yield and fruit quality aspects, Zone 1 produced higher yield and better quality than Zone 2 due to the environmental factor even though there is no significant difference. Future study needs to be done as this information together with projection of future climate change scenarios are crucial in developing Decision Support Tool (DST) to guide the farmers in planning their crop management practices for the upcoming 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".