Comparison of soil Mehlich‐3 phosphorus quantified by colorimetry and inductively coupled plasma: A case study for temperate agroecosystems
Bibliographic record
Abstract
Abstract Phosphorus (P) is one of the most limiting essential nutrients for agricultural production and its availability to crops is assessed by various methods. Mehlich‐3, however, remains the most used method worldwide. For decades, the colorimetric method by blue ascorbic acid‐molybdate reaction has been used to determine soil P concentration following Mehlich‐3 extraction. Since early 1990s, the use of automated methods to quantify soil nutrients including P has expanded rapidly, and the inductively coupled plasma (ICP) emission spectroscopy is becoming one of the most popular instruments in routine soil testing. The main objective of this study was to compare ICP (where M3P is Mehlich‐3 P, M3P‐ICP) with colorimetric (M3P‐Col) methods to estimate soil P using data from soil samples (3020) collected between 2005 and 2021 from 16 experiments conducted under different agroecosystems in Canada and Europe. Five case studies were assessed: (1) laboratory incubation, (2) native lowbush blueberry, (3) soil depth, (4) soil tillage, and (5) annual field crops versus perennial forage. In each study, a regression equation was established between soil M3P‐ICP and M3P‐Col. Results indicated that the two methods were strongly related in all studies (0.82 < r 2 < 0.99; p < 0.001), where soil P measured by ICP (2.1–352 mg kg −1 ) was higher than that measured by colorimetry (0.6–339 mg kg −1 ) except for the incubation study. Most important P differences were observed with forage and blueberry. Further analysis revealed that large differences between M3P‐ICP and M3P‐Col occurred primarily due to soil total C content. Soil pH, clay and Fe content, and previous crops also affected the relationship.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".