EFFECT OF CROPPING AND LOW-CHEMICAL INPUT SYSTEMS ON SOIL PHOSPHORUS FRACTIONS
Bibliographic record
Abstract
The adoption of alternative management practices has been shown to increase soil organic matter. However, the effect of adopting these practices on soil phosphorus (P), especially organic P, is not clear. We evaluated the effect of such practices-mainly no-tillage, zero and low-chemical input, organic-based, row crop agricultural systems-on soil P and compared them with conventional agriculture and perennial farming systems. We also compared soil P under conventional agriculture to an adjacent forest site and a never-tilled native successional community site in southwest Michigan. Sequential fractionation analysis of soil inorganic and organic P fractions showed that long-term conventional row crop agriculture resulted in a 79% reduction of NaOH-extractable organic P compared with adjacent forested sites. The calcium phosphate pool and the residual P fraction, however, increased under conventional agriculture compared with the forest site, probably because of fertilizer inputs. Adoption of no-tillage and low-chemical input systems with a winter leguminous cover crop in the rotation for 7 years did not increase organic P significantly in any of the fractions extracted from the annual cropping systems. However, organic P extracted by NaOH increased to 22.1% after adoption of continuous alfalfa for the same period compared with 11.4% extracted under conventionally tilled annual cropping systems. We conclude that continuous alfalfa can help restore soils to their native P fertility levels by taking up P from the stable residual fraction and transforming it into moderately labile organic P through root death. We found no evidence that low chemical input organic based systems are sustainable with regard to P as there is no evidence that organic P is accumulating as a result of the use of cover crops. Further investigation is warranted after these soils become P limiting and more years have passed under the same treatments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".