Strategies to Reduce Inorganic Fertilizer Inputs in Crop Production through Integrated Crop-livestock Systems
Bibliographic record
Abstract
Adequate nutrition is essential for crop growth, production, and profit potential for farmers, but chemical fertilizer costs alone can constitute a greater portion of the total variable costs for wheat and canola. The present study evaluated seven cropping treatments (CT) in a 3-year crop rotation under two different soil types. Five of the CTs consisted of a one-time application (year 1) of beef cattle manure, and growing of cover crop cocktails (CCC) for annual pasture, swath grazing, green manure, and green feed. Canola and wheat were respectively grown in years 2 and 3 of the 3-year crop rotation. In year 2, CTs impacted canola seed yield and seed protein (only at site 2). Wheat had similar protein content in year 3 at both sites. At both sites, the application of beef cattle manure in year 1 seemed to encourage higher plant tissue P at the expense of plant tissue Zn. Overall, beef cattle manure and CCCs based CTs improved soil N, P, and K, but beef cattle manure application consistently improved crop yield and significantly reduced the need for additional in-organic fertilizer application to canola and wheat in subsequent years.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".