Yield and Economic Assessments of Fertilizer Nitrogen and Phosphorus for Processing Tomato with Drip Fertigation
Bibliographic record
Abstract
Agronomic and economic assessments of response of processing tomato (Lycopersicon Esculentum Mill.) to nutrient application with drip fertigation are essential to optimize soil fertility management that maximizes farmers' profitability in a sustainable manner. A field study was conducted to evaluate the yield and economic responses of drip fertigated processing tomatoes to additions of fertilizer nitrogen (N) and phosphorus (P) from 2003 to 2005. The experiment was arranged in a factorial design with four levels of fertilizer N (0, 120, 240, and 360 kg N ha−1) and three levels of fertilizer P (0, 100, and 200 kg P2O5 ha−1). Fertilizer N application affected biomass yield of stems and leaves, total and marketable fruit yields, N use efficiency, and N agronomic efficiency. However, neither P application nor the interaction between fertilizer N and P influenced these variables. Nitrogen use efficiency and N agronomic efficiency decreased with increases in fertilizer N rate, with N use efficiency averaging 443 kg kg−1 and N agronomic efficiency averaging 237 kg kg−1 Both fruit yields and net economic returns responded quadratically to the fertilizer N rate, with a maximum marketable yield of 127 Mg ha−1 averaged across the 3 yr. The fertilizer N rates were 271 kg N ha−1 for the maximum marketable yield and 265 kg N ha−1 for the optimum economic yield. These values are considerably greater than the current recommendation, due to the largely increased yield with drip fertigation. Fertilizer N should be applied at an increased rate for processing tomatoes with drip fertigation to maximize the economic return.
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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 | 0.001 |
| 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 | 0.001 |
| 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".