An Agro‐Environmental Phosphorus Model for Potato in the Canadian Maritime Provinces
Bibliographic record
Abstract
Core Ideas Data from 42 field trials were used to develop an agro‐environmental P model for potato based on soil P saturation index. A critical agronomic PSI (P/Al)M3 of 10% was identified, which corresponded to 85% of relative yield. Seven agro‐environmental P risk classes were identified with rates ranging from 21 to 105 kg P ha−1. Phosphorus is essential for potato (Solanum tuberosum L.), an economically important crop for the Canadian Maritime provinces. Nutrient loss, including P, into waterways and estuaries has contributed to reduced water quality such as algae blooms. The objective of this study was to develop an agro‐environmental model for potato based on the phosphorus saturation indicator (PSI) expressed as the ratio between Mehlich‐III extractable P and Al (P/Al)M3. A total of 42 field trials (6 conducted from 2000 to 2002 and 36 from 2013 to 2015) were conducted in Prince Edward Island (PEI), New Brunswick (NB), and Nova Scotia (NS) with six increasing P rates. A critical agronomic PSI of 10% was identified, which corresponded to 85% of relative yield. The P recommendation model was subdivided into seven P fertility and environmental risk classes with P rates ranging from 21 to 105 kg P ha−1, i.e., on average from one to five times the level of P removal by the potato crop, corresponding to the highest ([P/Al]M3 ≥ 14) and lowest ([P/Al]M3 ≤ 2.5) PSI levels, respectively. Only one site was in the very low fertility class. Additional trials are needed within this class to build a more robust and representative model. This is the first agro‐environmental P model for sustainable potato production that reduces the risk of P loss to watercourses in the region. The new P rates were developed at plot scale and need to be validated at larger scale.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".