Influence of Hydroponics Nutrient Solution on Quality of Selected Varieties of Potato Minitubers
Bibliographic record
Abstract
Addressing poor seed quality is pivotal for increased potato yields in Kenya. For this to be realized there is a need for nutrient optimization in the hydroponic system. The objective of this study was to examine the effects of nutrient stock solution concentrations on the quality of minitubers produced under a hydroponic system. Two greenhouse experiments were set up at Egerton University, Kenya in 2022. The treatments included three nutrient solution concentrations: 75% (N75), 100% (N100) and 125% (N125) and four potato varieties (Wanjiku, Unica, Shangi and Nyota) grown in a cocopeat substrate hydroponic system. The results indicated that the application of N125 produced minitubers that had significantly higher specific gravity, dry matter, starch, ash and sugar content. Crude protein and phosphorus did not differ significantly with the application of varying nutrient concentrations. The varieties did not differ significantly in the quality parameters except for total sugars where Unica was significantly different from Nyota and Wanjiku while Shangi did not differ from all varieties. Therefore, it will be advisable to apply 125% of the ADC-Molo recommended nutrient stock formulation which should be considered as an effective method of increasing minitubers quality under a hydroponic system.
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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 | 0.001 |
| 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 | 0.001 | 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".