Performance Evaluation of Apple Varieties at Wadla District, North Wollo, Ethiopia
Bibliographic record
Abstract
Apple is one of the most important fruit crops in the highland areas of Ethiopia. This fruit tree is the only producible fruit tree in the highlands of North Wollo. However, its productivity is very low compared to other countries particularly that of temperate regions. This is partly attributed to lack of adaptable, high yielding and better quality apple varieties to farmers. To solve this problem apple variety trial was carried out at Wadela District from 2012-2019. The trial was laid out in randomized complete block design with three replications. Low to medium chill grafted apple seedlings were planted at a spacing of 3.0 m between rows and 3.0m between plants. Each plot was planted with three seedlings. On average irrigation water was applied on 7 days interval. Necessary plant protection and agronomic practices like training and pruning were applied as required. Scion diameter, rootstock diameter, girth ratio, canopy diameter, plant height, mean fruit weight and fruit yield data were collected for two consecutive years. Yield data was collected two times within a year. The Anna variety gave significantly the highest fruit yield (9.52 t. ha-1) followed by Gransmith (8.92 t .ha-1). On the other hand, Crispin gave the lowest fruit yield (7.77 t .ha-1). Fruit yield obtained by Anna and Gransmith were higher by 23% and 15%, respectively, compared to the lowest yielding variety, Crispin. Similarly, Anna and Gransmith also gave significantly the highest mean fruit weights of 85.98 and 62.34 g, respectively, as compared to the variety Crispin, which gave a mean fruit weight of 41.01 g. Farmers also select Anna variety by their criteria setted. Therefore, Anna and Gransmith are recommended for producers at Gashena conditions and similar agro-ecologies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".