Nitrogen uptake and utilization in advanced fresh‐market red potato breeding lines
Bibliographic record
Abstract
Abstract Potato (Solanum tuberosum L.) production on sandy soils requires added N. Only 40–60% of the applied N is acquired by the crop. Increased N use efficiency (NUE) and its components, N utilization efficiency (NUtE) and N uptake efficiency (NUpE), could reduce fertilizer rates and environmental losses. We compared N efficiency in fresh‐market red potato varieties, in terms of yield and quality traits, and examined potential mechanisms for that efficiency including uptake, utilization, and increased root growth. We grew selections from a red potato breeding population and commercial varieties under two N rates: 101 and 202 kg N ha−1. We compared NUE, NUpE, and NUtE in low and high N. We compared root phenotypes at tuber initiation and yield and skin quality metrics at harvest. Values for NUtE correlated with NUE and yield in low N and NUpE correlated with NUE and yield in high N. Low‐N conditions produced smaller tubers, while high N resulted primarily in medium tubers. Nitrogen did not affect skinning and redness but low N did result in slightly lighter skin color. Total root mass 45 d after planting (DAP) correlated with final yield and NUE but did not correlate with measures of N uptake across treatments. Larger roots correlated with NUpE only in the high‐N treatment. Selection under low N may reveal NUE and expose more stable representations of the genetic components of skin quality phenotypes. While skinning and skin color were more variable among varieties in low N, within variety they exhibited year‐to‐year consistency.
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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".