Effects of Planting Methods and Seed Density on Vegetable Yield and Nutrient Composition of <i>Solanum macrocarpon</i> and <i>Solanum scabrum</i> in Southwest Nigeria
Bibliographic record
Abstract
Two field studies were carried out using Solanum macrocarpon and Solanum scabrum to investigate the effects of planting methods and seed density required for optimum biomass yield and nutrient content in southwest Nigeria. Two planting methods (drilling and broadcasting), and two seed densities (4 and 8 table spoon full) were investigated as a 2×2×3 factorial experiment in randomized complete block design and replicated three times. Four seed spoons gave 25.78 kg seeds ha-1 for S. macrocarpon and 20.67 kg·seeds·ha-1 for S. scabrum. Significant highest S. macrocarpon shoots (2.75 kg·m-2) and Ca, K, Fe and Zn contents and S. scabrum (2.95 kg·m-2) and Zn content, were obtained with drilling and four spoons of seeds compared with broadcasting and 8 spoons of seeds. This treatment gave average values for N, P, K, Mg, Ca, Na, Fe, Zn, Mn, Cu, Ca/P and Na/K ratio were 2.79%, 0.03%, 3.37%, 1.64%, 0.02%, 300 mg·kg-1, 100 mg·kg-1, 300 mg·kg-1, 8 mg·kg-1, 1.22 and 0.008 for S. macrocarpon and 2.05%, 0.27%, 5.81%, 3.27%, 2.91%, 0.09%, 280 mg·kg-1, 52.99 mg·kg-1, 359.35 mg·kg-1 and 22.42 mg·kg-1, 11.63 and 0.016 for S. scabrum. It was concluded that planting in drilling made weeding, fertilizer application, irrigation and harvesting more effective rather than by broadcasting, and four spoons of seeds per 9 m-2 produced deep green and broader leaves and balanced nutrient contents than eight spoons of seeds.
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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".