Evaluation of Agronomic and Economic Effects of Nitrogen and Phosphorus Additions to Green Pepper with Drip Fertigation
Bibliographic record
Abstract
Drip fertigation is an effective way in splitting soluble fertilizer application to simultaneously meet water and nutrient demands of multi‐harvested green pepper (Capsicum annuum L.). However, fruit yield and the profitability of green pepper can be constrained, if nutrients are either insufficiently or excessively supplied. A 3‐yr experiment was conducted to assess both agronomic and economic effects of fertilizer N and P addition for green pepper grown under drip fertigation. Both fruit yields, including total and marketable, and net economic return responded quadratically to fertilizer N rate. The 3‐yr average maximum marketable yield of 38 Mg ha−1 was achieved at the N rate of 227 kg N ha−1 The economic optimum N rate was identical to the one required for the production of maximum marketable yield, due to the large price ratio of green pepper to fertilizer N. Nitrogen use efficiency and N agronomic efficiency decreased as N rate increased. The amount of fertilizer N required for production of each megagram of marketable fruit yield increased with the level of yield, with an average of 6.0 kg N Mg−1 fruit across the 3 yr at the maximum marketable yield. Fertilizer P did not affect selected variables, except for both total and marketable fruit yields that increased linearly with increases in P rate in one of the 3 yr. The results suggested that an increase in the optimum N rate to 227 kg N ha−1 is needed to maximize the profitability of green pepper production with drip fertigation
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".