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 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 | 0.001 | 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) | 0.000 | 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 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".