Animal manure application and soil organic carbon stocks: a meta‐analysis
Bibliographic record
Abstract
The impact of animal manure application on soil organic carbon (SOC) stock changes is of interest for both agronomic and environmental purposes. There is a specific need to quantify SOC change for use in national greenhouse gas (GHG) emission inventories. We quantified the response of SOC stocks to manure application from a large worldwide pool of individual studies and determined the impact of explanatory factors such as climate, soil properties, land use and manure characteristics. Our study is based on a meta-analysis of 42 research articles totaling 49 sites and 130 observations in the world. A dominant effect of cumulative manure-C input on SOC response was observed as this factor explained at least 53% of the variability in SOC stock differences compared to mineral fertilized or unfertilized reference treatments. However, the effects of other determining factors were not evident from our data set. From the linear regression relating cumulative C inputs and SOC stock difference, a global manure-C retention coefficient of 12% ± 4 (95% Confidence Interval, CI) could be estimated for an average study duration of 18 years. Following an approach comparable to the Intergovernmental Panel on Climate Change, we estimated a relative SOC change factor of 1.26 ± 0.14 (95% CI) which was also related to cumulative manure-C input. Our results offer some scope for the refinement of manure retention coefficients used in crop management guidelines and for the improvement of SOC change factors for national GHG inventories by taking into account manure-C input. Finally, this study emphasizes the need to further document the long-term impact of manure characteristics such as animal species, especially pig and poultry, and manure management systems, in particular liquid vs. solid storage.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.043 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".