Yield, Nitrogen Dynamics, and Fertilizer Use Efficiency in Machine-harvested Cucumber
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it