Optimizing soil and plant responses to land-applied manure nutrients in the Great Plains of North America
Bibliographic record
Abstract
Animal manures are recognized as valuable sources of plant nutrients in cropping systems and also play a role in soil improvement through the input of organic matter. Using recent research examples from Saskatchewan and Colorado, this paper covers beneficial management practices for effective recycling of manure nutrients applicable to the Great Plains region of North America. Challenges in using animal manures as fertilizers include low nutrient content per unit weight, variability and availability of nutrient content, and a balance of available nutrients that often does not meet the relative nutrient requirements of the crop. Examples of imbalances that may arise requiring special management considerations include low available N content relative to available P for many solid manures, and low available S relative to N for some liquid manures. Application decisions are best supported by manure and soil analyses, with nutrient balance issues addressed by rate adjustments and the addition of supplemental commercial fertilizer to avoid deficiency or loading of specific nutrients. Placement of manure into the soil by injection or incorporation is desirable in that nutrient losses by volatilization and runoff are reduced and crop recovery is increased. Balancing the rate of nutrient application with crop requirement and removal over time is key to avoiding nutrient loading on soils receiving repeated applications of manure. Key words: Manure management, nutrient cycling, beneficial management practices, Great Plains
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 | 0.000 | 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".