Development of Drip Flow Technique Hydroponic in Growing Cucumber
Bibliographic record
Abstract
Hydroponics is a new branch and aspect of food crop growing that in recent years made its mark in developing country such as Nigeria. Although, its adoption has not been too encouraging. This research work aimed at developing a drip technique system of hydroponics in determination of the agronomic parameters of cucumber by comparing the yield, water and nutrient efficiency, its consumptive use and proximate and mineral composition of cucumber. The experiment was carried out in a complete randomized design with three treatments; organic substrate (coconut coir), inorganic substrate (styrofoam) and soil. These treatments were replicated five times. The vegetative growth (agronomic parameters), yield, water and nutrient, proximate and mineral composition were measured. The result showed the consumptive use as 0.0044 m3 per day and 0.3212 m3 as the water and nutrient use efficiency. The result also showed that organic substrate gave the highest mean plant height of 736.66 mm, highest mean stem diameter of 5.79 mm and highest mean number of leaves of 9.75 while inorganic substrate gave highest mean plant height, mean stem diameter and mean number of leaves as 336.28 mm, 4.95 mm and 7.68 respectively. Also, the highest result of control (soil) gave 301.23 mm, 5.47 mm and 7.06 for the mean plant height, stem diameter and number of leaves respectively. The yield of cucumber as compared with the different growing media showed that there is no significant difference between the growing media (Fcrit> Fcal) unless for the plant height and number of flowers having Fcrit less than Fcal. From these results, it is advisable that drip technique system should be embraced by farmers whose primary aim of farming is for leafy vegetables and non-leafy vegetables as seen in the increase in stem diameter and plant height in the organic substrate.
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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 | 0.001 |
| 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".