Contrasting Soil Organic Carbon Concentrations and Mass Storage Between Conventional Farming and Organic Farming: A Meta-Analysis
Bibliographic record
Abstract
This meta-analysis studied the impact of conventional versus organic farming practices on soil organic carbon (SOC) concentrations and mass storage. We aimed to understand the carbon dynamics associated with adopting organic agricultural practices by reviewing and synthesizing data from 1950 to 2023. We analyzed data from 64 experimental field comparisons that involved SOC concentration and mass storage measurements, covering a wide range of studies selected for soil depth analyses, carbon concentration measurements, and comparative methods between conventional and organic systems. Our results indicated a significant increase in SOC under organic agriculture. An analysis of the response ratios (LnRR) for SOC concentration showed variability, with a 95% confidence interval of 0.089–0.149. Similarly, the analysis of carbon storage data indicated a 95% confidence interval of LnRR of 0.053–0.205. These increases in SOC concentration and mass storage reflect the variable but statistically positive impact of organic farming on SOC. Fine soil textures demonstrated the highest mean LnRR for both SOC concentration (0.163) and mass storage (0.173), suggesting the role of soil texture in mediating the effects of organic agriculture on SOC dynamics. Furthermore, there was a significant negative correlation between soil pH and SOC mass storage, with a regression coefficient of −0.174 (p < 0.039). Although a slight positive correlation was observed between temperature and SOC mass storage as LnRR, other environmental factors likely play a critical role in SOC dynamics. These findings emphasize the complexity of SOC dynamics and the significant impact of organic agriculture on increasing SOC concentrations and mass storage. This study broadly contributes to the debate in sustainable agriculture by providing quantitative evidence of the role and benefits of organic agriculture on climate change mitigation. The results also emphasize the importance of adopting organic farming practices for broadly enhancing ecosystems’ resilience and long-term food security.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.046 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".