High yield and efficiency: cultivar selection to improve potato nitrogen use efficiency
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
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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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 | 0.001 |
| 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 it