The effects of different water and nitrogen levels on yield, water and nitrogen utilization efficiencies of spinach (<i>Spinacia oleracea</i> L.)
Bibliographic record
Abstract
Zhang, J., Sha, Z., Zhang, Y., Bei, Z. and Cao, L. 2015. The effects of different water and nitrogen levels on yield, water and nitrogen utilization efficiencies of spinach (Spinacia oleracea L.). Can. J. Plant Sci. 95: 671−679. Water and nitrogen (N) are important factors that affect crop yield. The objective of this study was to explore the interactive effect of water and nitrogen on biomass production, yield and growth responses, water and nitrogen use efficiency of winter-grown spinach. A field experiment was grown with treatments of varying water (W) and nitrogen (N) levels near Shanghai, China. Leaf area, shoot biomass and height of spinach increased with the application of N in the well-watered treatment. The highest chlorophyll content was found in spinach treated with N 2 (170 kg ha −1 nitrogen). A response surface analysis was done on plant height, leaf number, leaf weight, and plant yield of each spinach plant at different water and nitrogen levels. The equation for each of the response surfaces was taken and solved for the mathematical optimum of the curves. Abundant water supply resulted in the highest spinach yield. Yield of spinach increased with N application rates but decreased when the N was excessive. Compared with the low water treatment (W 3 ), a higher N leaching ratio was observed in the high water treatment (W 1 ), regardless of N treatment. With the increase of N application, N use efficiency of spinach significantly decreased, while water use efficiency of spinach increased. In conclusion, water levels between 36.15 cm and 42 cm, and nitrogen applications between 86 and 152.74 kg ha −1 could be recommended as the optimal treatment for spinach growth.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".