Effects of Hydroponics Systems on Growth, Yield and Quality of Zucchini (Cucurbita pepo L.)
Bibliographic record
Abstract
There is dearth of information pertaining to hydroponics production of zucchini in the Kingdom of Eswatini. The objective of this study was to determine the effects of hydroponics systems on growth, yield and nutritional content of zucchini. The research was conducted in three greenhouses of the Horticulture Department, Faculty of Agriculture, Luyengo Campus at the University of Eswatini between July and October 2018. The experiment was laid out in a split-plot design replicated four times. Three hydroponics systems were used as the main plots, i.e. elevated tray, ground lay bed and Nutrient Film Technique (NFT) systems. The sub-plots were allocated to the three varieties, i.e., Amanda, Hygreen and Terminator. The zucchini grown in elevated tray hydroponics system had the highest yield in all the varieties compared to the other hydroponics systems. The results showed that there were significant differences in the growth, yield and nutritional content of zucchini cultivars grown in the different hydroponics systems. The tallest plants (26.1cm) were obtained in cultivar Terminator grown in the elevated tray system and the highest number of leaves (15) was obtained in cultivar Terminator grown in the elevated tray system. Cultivar Terminator grown in the elevated tray system had the highest total yield (15.8 tons/ha) while Hygreen plants produced in the NFT system recorded the lowest total yield (1.04 tons/ha). There were no significant differences in the iron content of zucchini among the cultivars grown in the different hydroponics systems. The results of this study revealed that different zucchini cultivars responded differently when grown in the different hydroponics systems. Therefore based on the results of this study it is recommended that Terminator zucchini cultivar can be produced in the elevated tray hydroponics 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".