Reconsidering Integrated Crop–Livestock Systems in North America
Bibliographic record
Abstract
Although integrated crop–livestock systems have been employed globally for millennia, in the past century, farmers in North America have tended toward increased specialization. There is renewed interest in reintegrating crops and livestock because of concerns about natural resource degradation, the profitability and stability of farm income, long‐term sustainability, and increasing regulation of concentrated animal feeding operations. Integrated crop–livestock systems could foster diverse cropping systems, including the use of perennial and legume forages, which could be grown in selected areas of the landscape to achieve multiple environmental benefits. Integrated systems inherently would utilize animal manure, which enhances soil tilth, fertility, and C sequestration. Integration of crops and livestock could occur within a farm or among farms. Both scales of integration rely on farmers' knowledge, motivation, and resources. Despite the numerous benefits that could accrue if farms moved toward on‐farm or among‐farm integration of crops and livestock, the complexity of such systems could constrain adoption. However, farmers should expect that adoption of integrated crop–livestock systems would enhance both profitability and environmental sustainability of their farms and communities. The combination of system complexity and potential for public benefit justify the establishment of a new national or international research initiative to overcome constraints and move North American agriculture toward greater profitability and sustainability.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".