Potential of 4R Nutrient Stewardship to reduce phosphorus losses from cultivated organic soils
Bibliographic record
Abstract
Reducing phosphorus (P) losses from agriculture is a global priority. Although best management practices (BMPs) have been recommended to reduce P losses, their applicability to specific environments or situations varies. Information on appropriate BMPs, including 4R nutrient stewardship practices, is especially lacking for North American organic soils (also commonly described as muck soils or peat soils) under agricultural production. This study reviews literature conducted in regions with cultivated organic soils to make recommendations on the application of 4R Nutrient Stewardship principles (Right fertilizer source, Right rate, Right placement, and Right timing) in these soils, with the goal of reducing environmental P losses without compromising yields and/or quality in vegetable crops. Improvement to the Right placement and Right timing may be a strategy for organic soils with low to moderate soil test P concentrations. Fertilizers that provide readily available P are often used in organic soils because crops have a high demand for P during early growth stages; thus, the suitability of organic P sources and slow-release P fertilizers in these soils requires further scrutiny and research. Additionally, research has clearly demonstrated that applying P in excess of crop requirements does not provide yield advantages in organic soils. Instead, it increases the potential for elevated edge-of-field P losses from cultivated organic soils. However, it can be challenging to accurately determine the plant available P in organic soils due to low bulk density and high organic matter content. Findings from this literature review identify potential challenges in managing soil P in cultivated North American organic soils and highlight opportunities to reduce edge-of-field P losses through the adoption of BMPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".