4R Management of Phosphorus Fertilizer in the Northern Great Plains
Bibliographic record
Abstract
Phosphorus (P) fertilizer has played a vital role in increasing the productivity of crop production in the northern Great Plains for approximately 100 years. Throughout this period, agricultural production practices have changed dramatically, while our knowledge of P behavior and beneficial management practices has improved. Some of the more recent and substantial changes in farming practices on the northern Great Plains include widespread adoption of reduced tillage systems, introduction of new crops and high‐yielding cultivars, intensification and extension of crop rotations, development of new fertilizer products, increased appreciation of the role of microbial interactions in P dynamics, and growing concern for the effects of P on water quality. As cropping systems, technology, and societal demands evolve over time, nutrient management practices must also evolve to address concerns and take advantage of emerging opportunities. Classic principles and new P fertilizer technologies and management practices must be integrated into packages of 4R practices that optimize crop yield and agronomic efficiency while minimizing negative environmental impact and conserving P resources. Although a wide range of products and practices can be combined for this approach, placing ammonium phosphate fertilizer in a band, in or near the seed‐row, at the time of seeding and at a rate that matches P removal by the crop generally provides the greatest P efficiency, long‐term sustainability, and environmental protection for small grain, oilseed, and pulse crop production in the northern Great Plains. Core Ideas 4R stewardship for P fertilization is vital for sustainable crop production. The most efficient sources of P fertilizer for this region are ammonium phosphates. Long‐term sustainable crop production requires P fertilizer rates that match crop removal. Banding P fertilizer in or near the seed‐row is agronomically and environmentally beneficial.
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 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".