The Potential of Pig Slurry Application on Pasture Production: A Systematic Approach
Bibliographic record
Abstract
Abstract Livestock production is fundamental to agricultural systems, particularly in communal farming contexts where it directly influences rural livelihoods. This study investigates the application of pig slurry as a natural fertilizer on pasture production, focusing on its impact on both forage quantity and quality. Through a comprehensive bibliometric analysis using the Web of Science and Scopus databases, we identified key research trends, spatial distributions, and collaboration networks within the field from 1975 to 2024. Our findings reveal that Brazil leads in publications, followed by the USA, Canada, China, and Australia, with significant international collaboration primarily among developed nations. The average annual growth rate of publications was found to be 2.32%, demonstrating exponential growth (R² = 0.44) in the scientific output, aligning with Price’s Law of bibliometrics. Keywords such as “pig slurry,” “nitrous oxide,” and “soil” emerged as prominent themes, indicating a strong focus on nutrient management and environmental impacts. Notably, the top 10 cited documents emphasized greenhouse gas emissions and nitrogen dynamics, reflecting significant environmental concerns related to pig slurry’s application. Despite substantial advancements in research, a considerable gap persists in research activity from developing nations, particularly in Africa, where only Senegal has shown engagement in this area. This highlights a need for enhanced collaboration and investment in research to optimize the use of pig slurry in pasture systems, thereby promoting sustainable agricultural practices and improving livestock productivity. By addressing these research gaps, future studies could contribute to effective nutrient management strategies, fostering resilience in communal farming systems.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.048 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".