MANURE MANAGEMENT STRATEGIES TO MITIGATE EMISSIONS IN PIG AND POULTRY PRODUCTION: INSIGHTS FROM LIFE CYCLE ASSESSMENT STUDIES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The intensification of pig and poultry production has raised growing concerns over their environmental footprint, particularly related to ammonia and greenhouse gas emissions. In this context, improved manure management practices play an important role in mitigating these emissions to enhance the sustainability of livestock systems. This review synthesizes current knowledge on manure management strategies for reducing such emissions and their integration into Life Cycle Assessment (LCA) studies in pig and poultry production. It explores the interactions of several practices, highlighting mitigation strategies such as anaerobic digestion, composting, and manure incorporation. Furthermore, the integration of these strategies into LCA frameworks is discussed, emphasizing how methodological choices may affect results. Although anaerobic digestion and improved application techniques consistently show potential for emission reduction, some trade-offs remain critical. By identifying effective mitigation strategies and emphasizing the importance of holistic LCA approaches, this review provides insights to guide the development of more sustainable practices. This highlights the need for science-based manure management strategies to support sustainable pig and poultry production. Future research should focus on standardizing LCA approaches, considering underexplored impacts such as odor, antimicrobial residues, and biodiversity, while advancing cost-effective mitigation strategies and regionally adapted solutions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it