Environmental Index for Estimating the Risk of Phosphorus Loss in Calcareous Soils of Manitoba
Bibliographic record
Abstract
The degree of phosphorus saturation (DPS) has been used in evaluating the risk of P loss from soil to runoff. While techniques are available for calculating DPS for acid soils, no widely used technique exists for neutral to calcareous soils that are typical of the Northern Great Plains, including Manitoba (Canada) soils. This study aimed to develop techniques of calculating the DPS of neutral to alkaline soils. Four measures of soil labile P and ten indices of P sorption capacity were used to calculate the DPS of 115 Manitoba soils. The various DPS calculated were evaluated using water-extractable ((H2O)) P as an index of P susceptibility to runoff loss. The DPS obtained using Olsen-extractable ((Ols)) P and the Langmuir adsorption maximum (ES(max)) ranged from 0.5 to 31.9% while those obtained from P(Ols) and the single-point adsorption index (P(150)) ranged from 0.9 to 73.9%. Of all the DPS evaluated, those that included P(Ols) and Mehlich 3-extractable ((M3)) P as the numerator with either P(150) or ES(max) as the denominator were fairly well correlated with P(H2O) (r values ranged between 0.45 and 0.63). Along with ES(max) and P(150), a new method of calculating DPS was formulated as the ratio of P(Ols) or P(M3) to Ca(M3) or (Ca + Mg)(M3). We found that the ratio of ammonium oxalate-extractable ((ox)) P to (Al + Fe)(ox), which has been widely used to calculate DPS in acid soils, was not suitable for neutral to alkaline soils of Manitoba. In these neutral to alkaline soils, Ca(M3) or (Ca + Mg)(M3) were better indices of P sorption capacity while P(Ols) and P(M3) provided better estimates of labile soil P. The DPS calculated using Ca(M3) or (Ca + Mg)(M3) were well correlated with P(H2O); however, they were numerically smaller than those obtained from the Langmuir adsorption maximum. As such, a saturation coefficient (alpha) with a value of 0.2 was generated to improve the numerical values of the newly estimated DPS. This new approach can be used to estimate the DPS in neutral and calcareous soils without the need to generate a P adsorption maximum.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".