Response of Potato Varieties to Potassium Levels in Hamelmalo Area, Eritrea
Bibliographic record
Abstract
Poor soil fertility and lack of high yielding certified varieties are of the major potato production tribulations in Eritrea. Top soils are continually removed due to water run-off and thus soil fertility and productivity has declined as a result. An experiment was designed to assess the response of potato varieties to different levels of potassium application at Hamelmalo Agricultural College, Eritrea. Three varieties (Ajiba, Zafira and Picasso) and five potassium levels (0, 75, 150, 225 and 300 kg K2O/ha) along with all possible interactions were used. Experimental design following factorial Randomized Complete Block Design (RCBD) in three replications was employed. Data was collected on yield and tuber quality parameters. The result of the study indicated that there were significant variations in the performances of varieties in terms of yield and quality parameters in which Ajiba was found to be more responsive and high yielding. Tuber number, tuber diameter, tuber weight per plant, total yield, total soluble solids, specific gravity and tuber moisture content showed significant differences due to the application of potassium. As a result, the highest tuber weight (1.14 kg/plant) and yield (49.38 tones/ha) were recorded from Ajiba treated with 300 kg K2O/ha. The result further revealed that there is a promising profit return by investing more on potassium application upto 300 kg K2O/ha. It is, thus, recommended that potassium fertilizers should be introduced to optimize productivity in Hamelmalo area, Eritrea.
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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".