Exploring phosphorus use efficiency of different potato cultivars on the Canadian Prairies
Bibliographic record
Abstract
Potato ( Solanum tuberosum L.) is a staple crop for food security. Unfortunately, potatoes have low phosphorus use efficiency (PUE). To compensate for low phosphorus (P) uptake and maintain yields, high rates of P fertilizer are often repeatedly applied. Little research has been done to identify cultivars that yield well and take up P efficiently on the Canadian Prairies, especially on soils with high P levels. We conducted a 3-year study on high soil test P sites (66–110 kg ha −1 extractable P) in Saskatchewan to evaluate six potato cultivars (Clearwater Russet, Dark Red Norland, Milva, Poppy, Russet Burbank, and Sangre) for PUE under P fertilizer rates ranging from 0–30 kg P ha −1 . Total yield, tuber P concentration, tuber P content, P balance intensity (PBI), and tuber phosphorus uptake efficiency (PUpE) were quantified; and cultivar significantly influenced all metrics, whereas fertilizer and the two-way interaction did not. Depending on the year, total tuber yield averaged 14.8–38.6 Mg ha −1 and tuber P content averaged 10.7–19.1 kg P ha −1 . Based on the PUE indicators of PBI and PUpE, Dark Red Norland, Sangre, and Milva cultivars consistently ranked in the top half of cultivars. Our experiment suggests that when yield is not limited by soil P, PUE is largely driven by the ability of the plant to produce greater yield. Reducing fertilizer applications to better account for soil P levels is not the only strategy to improve PUE, because cultivar selection is also influential. Selecting high producing cultivars can provide both agronomic and environmental benefits, in the form of yield and PUE.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".