Agri-environmental models using Mehlich-III soil phospho rus saturation index for corn in Quebec
Bibliographic record
Abstract
Soil phosphorus (P), which is potentially a risk for environmental contamination, is currently interpreted using soil P saturation in North America. Our objective was to assess the ratio of P to aluminum (Al) in the Mehlich-III (M-III) soil test to build P requirement models for corn and soybean. We analyzed 129 corn and 19 soybean P fertilizer trials. For corn, the (P/Al)M-III ratio improved soil fertility classification compared with PM-III alone. The critical PM-III value as determined by the Cate-Nelson procedure was found to be 31.5 mg PM-III kg-1, close to published values. The critical (P/Al)M-III ratios of 0.025 for > 300 g clay kg-1 soils and 0.040 for ≤ 300 g clay kg-1 soils differed significantly between the two soil groups. For (P/Al)M-III ratios above 0.214, there was no positive response to added P for all soils regardless of texture. Using published critical environmental (P/Al)M-III ratios of 0.076 for > 300 g clay kg-1soils and 0.131 for ≤ 300 g clay kg-1 soils as benchmarks values, agri-environmental P requirement models were built using conditional expectations of 50 to 80% of computed optimum P values within a soil class. A validation study supported the low critical (P/Al)M-III ratios and the 50% conditional expectation model except for a high carbon soil which was outside the application range of the models. However, banded P decreased corn yield at four validation sites although the model predicted positive response to P. Soybean did not respond to P except at extremely low fertility levels ((P/Al)M-III ≤ 0.02) and behaved as a P-mining crop even in low-P soils. Corn-soybean rotations can reduce soil P to low (P/Al)M-III ratios with minimal agronomic risk. Key words: Soil phosphorus saturation, Mehlich-III soil extraction method, soil fertility classification, soil texture, fertilizer P requirement model, corn, soybean
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".