Phosphorus availability and corn ( <i>Zea mays</i> L.) response to application of P‐based commercial organic fertilizers to a calcareous soil
Bibliographic record
Abstract
Abstract Recycling of organic wastes for agricultural production has been recommended as a sustainable way to close the phosphorus (P) cycle. This study seeks to characterize the P forms in organic fertilizers derived from agricultural and nonagricultural source materials to understand how these organic fertilizers will affect corn growth and soil available P. Organic fertilizers including turkey litter compost (TLC), biosolid pellets (BSP), and source separated organic compost (SSO) were applied to a sandy loam soil and compared to controls that received no nutrients or chemical fertilizer. Results from sequential chemical extractions and X‐ray absorption near edge structure spectroscopy revealed that most of the P species are inorganic, with SSO and TLC containing primarily Ca‐P and Mg‐P forms, while the P in the BSP treatments was mostly associated with aluminium (Al). The two chemical fertilizer treatments had the greatest impact on corn height at the initial growth stage; however, after 42 days, corn height was similar for the organic fertilizers and chemical treatment. This initial slow response from the organic fertilizer treatments reduced the grain yield compared to chemical treatment. The results of this study suggest that priority should be given to sufficient P availability at the initial growth stage. Overall, organic fertilizer is a promising P source for grain crop production, but calibration and correlation studies are needed to ensure optimum supply of P for the initial growth stage.
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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 | 0.000 |
| 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 | 0.000 | 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".