Response of Various Nitrogen Levels on the Growth and Yield Performance of Tomato (Lycopersicon Esculentum Mill.)
Bibliographic record
Abstract
Nitrogen (N) fertilization at optimum quantity to cultivated plants is essential for sustainable crop productivity. The experiment was conducted to evaluate the response of nitrogen fertilizers on the growth and yield traits of tomato during 2016, at the experimental field of Orchard, | department of Horticulture, Sindh Agriculture University Tandojam. Six nitrogen treatment doses were tested (T1 =control (untreated), T2 = 30 N kg ha-1, T3 = 60 N kg ha-1, T4 = 90 N kg ha-1, T5 = 120 N kg ha-1 and T6 = 150 N kg ha-1.). The data were recorded on the number of branches plant-1, days to flowering, days to fruiting, number of fruits plant-1, weight of single fruit (g), yield plot-1 and yield ha-1 (tons). The results demonstrated that N fertilization showed significant influence on both vegetative and reproductive traits of tomato. On the contrary, plants which were not fertilized with N displayed minimum values for all investigated traits. The highest N level (150 kg ha-1) showed maximum number of branches (5.51) plant-1, minimum day to flowering (42.43) less days to fruiting (46.00), more fruits plant-1 (31.00) maximum weight of single fruit (46.50 g) and highest fruit yield plot-1 (21.01 kg) and per hectare (14008 tons), respectively. It was concluded that the crop growth and productivity was significantly influenced by various levels of nitrogen. However, 150 N kg ha-1 as compared to 120 N and other concentrations considerably displayed better performance in terms of vegetative and reproductive traits of tomato. Hence for optimum growth and economically higher yield, the tomato crop may be fertilized with 150 N kg ha-1.
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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".