A synthetic analysis of livestock manure substitution effects on organic carbon changes in China's arable topsoil
Bibliographic record
Abstract
Application of livestock manure has been recognized as an important strategy for enhancing soil organic carbon (SOC) stocks. However, the magnitude of manure application impacts on SOC changes as compared with conventional mineral fertilizers still needs to be better assessed. In this study, we conducted a meta-analysis to address the effects of various fertilization practices on SOC changes in China's arable topsoil. The analysis was based on 148 peer reviewed articles reporting 69 sites and 729 observations with an average experimental duration of 18 years across China. We compared the response of SOC content to application of manure alone (M) or manure plus mineral fertilizers (NPKM) with the SOC response to NPK or no fertilizers (Control). We systematically analyzed how SOC responses were influenced by different environmental and management factors (land use, climate and soil properties, etc.). First, the retention coefficient of manure was used to present the percentage of C input from manure “converted” to SOC. Compared to Control, on average, manure had C-retention coefficients of 31% ± 12% (95% Confidence Interval, CI) and 18% ± 2% for the experimental duration of ≤10 years and >10 years, respectively. When compared to NPK, manure had C-retention coefficients of 29% ± 13% and 9% ± 3% for the experimental duration of ≤10 years and >10 years, respectively. In addition, compared to Control and NPK, application of manure increased SOC content by 0.23–0.26 and 0.18–0.19 g kg−1 yr−1, respectively. The results also indicated that the mean differences of SOC change between the treatments with or without manure application were mainly controlled by soil properties (28–43%) and anthropogenic farming practices (21–27%). Our results highlight the significance of improving or maintaining SOC stocks by manure applications, and provide insights for making manure application recommendations or guidelines to improve SOC stocks of China's cropland based on soil properties, climatic conditions and management practices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".