Response of Growth and Yield Components of Sweet Pepper to Tow Different Kinds of Fertilizers under Green House Conditions in Jordan
Bibliographic record
Abstract
Intensive agriculture is a farming system characterized by a lot use of input, causing a harm stress on the environment, as well as high price of inorganic fertilizers discouraged some farmers in Jordan to apply fertilizers to their crops. The objective of this study was to investigate the impact of chicken manure and NPK on growth and yield of sweet pepper (Caspicum annum L.), A greenhouse experiment was conducted at Albalqa applied university research station in Jordan using randomized complete block design (RCBD) replicated four times. Three treatments were used using Randomized Complete Block Design (RCBD) with four replications: control (without fertilizer), chicken manure at the rate of 15 t/ha, and NPK (15:15:30) with trace elements at 100 Kg/ha. We evaluated plant height (cm), leaves number per plant, number of days to 50% flowering, fruit number per plant, fruit length, yield of fruit per plant (kg), and yield of fruit per hectare (t/ha). Treatments showed significant differences between. The NPK treatment gave the highest plant height (cm), leaves number per plant, fruits number per plant, yield of fruits per plant (kg), and yield of fruits per hectare (t/ha).
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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".