High yield and efficiency: cultivar selection to improve potato nitrogen use efficiency
Bibliographic record
Abstract
Optimizing nitrogen use efficiency (NUE) of crops is critical to maintain yields and profits while minimizing environmental damage from excessive fertilization and nitrogen (N) losses. Potato (Solanum tuberosum L.) typically requires high N rates to support yield, but low NUE risks N losses with cascading environmental and financial consequences. Identifying potato cultivars with improved NUE may reduce fertilizer needs and lower the risk of N loss. However, little research has focused on identifying such cultivars, especially on the Canadian Prairies. We conducted a field study encompassing five site-years in Saskatchewan to compare six seed potato cultivars (Clearwater Russet, Dark Red Norland, Milva, Poppy, Russet Burbank, and Sangre) for NUE traits, under N fertilizer rates ranging from 0 to 200 kg N ha-1. Total yield, tuber N content, N balance intensity (NBI) and tuber N uptake efficiency (NUpE) were quantified as measures of NUE. Cultivar significantly influenced all metrics (p < 0.05), whereas fertilizer or the two-way interaction did not. Cultivar yield varied by more than 45%, highlighting substantial productivity differences among cultivars. Dark Red Norland, Sangre and Poppy also showed 22.5-33.2% higher NUE than other cultivars. Our findings support the need for improved predictions of soil mineralizable N supply, as reducing or forgoing N fertilization improves potato NUE when indigenous soil N meets crop demand. Our results suggest that when yield is not limited by soil N, NUE is largely driven by the ability of the plant to produce greater yield. This research demonstrates specific cultivars deliver high yields and improved NUE, allowing for improved N balance in potato production systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".