Yield, Nitrogen Dynamics, and Fertilizer Use Efficiency in Machine-harvested Cucumber
Bibliographic record
Abstract
The increase in fertilizer costs as well as environmental concerns has stimulated growers to re-evaluate their fertilizer applications to optimize nitrogen use efficiency (NUE) while maintaining crop yields and minimizing N losses. With these objectives, field trials were conducted at seven sites with five N rates (0 to 220 kg N/ha) of ammonium-nitrate applied preplant broadcast and incorporated as well as a split application treatment of 65 + 45 kg N/ha. In three contrasting years (i.e., cool/wet versus warm/dry versus average), N treatment had no observable effect on grade size distribution or brine quality. Based on the zero N control treatment, the limited yield response to fertilizer N was the result of sufficient plant-available N over the growing season. In the N budget, there was no difference between N treatments in crop N removal, but there was a positive linear relationship between N applied and the quantity of N in crop residue as well as in the soil after harvest. As expected, apparent fertilizer N recovery and N uptake efficiency were lower at 220 versus 110 kg N/ha applied preplant or split. The preplant and split applications of 110 kg N/ha were not different in yield, overall N budget, or NUE. Considering the short growing season, planting into warm soils, and the generally productive, nonresponsive soils in the region, growers should consider reducing or eliminating fertilizer N applications in machine-harvested cucumber.
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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".