Nitrogen use efficiency characteristics of commercial potato cultivars
Bibliographic record
Abstract
One approach for reducing the contribution of potato (Solanum tuberosum L.) production to nitrate contamination of groundwater is to develop cultivars which utilize N more efficiently. In this study, variation in N use efficiency (NUE; dry matter production per unit crop N supply) characteristics of 20 commercial potato cultivars of North American and European origin were evaluated in 2 yr. Cultivars were grown with or without application of 100 kg N ha-1 as ammonium nitrate banded at planting. The recommended within-row spacing was used for each cultivar and no irrigation was applied. Plant dry matter and N accumulation were determined prior to significant leaf senescence. Crop N supply was estimated as fertilizer N applied plus soil inorganic N measured at planting plus apparent net soil N mineralization. Nitrogen use efficiency decreased curvilinearly with increasing crop N supply. Nitrogen use efficiency was lower for early-maturing cultivars compared to mid-season and late-maturing cultivars. A curvilinear relationship was obtained between plant dry matter accumulation and plant N accumulation using data for all cultivars. Deviations from this relationship were interpreted as variation in N utilization efficiency (NUtE; dry matter accumulation per unit N accumulation). Significant differences in NUtE were measured among cultivars of similar maturity. Nitrogen uptake efficiency (NUpE; plant N content per unit crop N supply) and soil nitrate concentration measured at plant harvest were uniformly low for all cultivars when crop N supply was limited, but varied among cultivars when N was more abundant. This suggests that potato cultivars vary more in terms of N uptake capacity (plant N accumulation in the presence of an abundant N supply) than in terms of NUpE. Key words: Solanum tuberosum, N mineralization, dry matter accumulation, N accumulation, N utilization efficiency
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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".