Total Carbon Sequestration on Soil and Plant Biomass Under Different Farming Systems of Organic, Semi-Organic and Conventional Rice Fields
Bibliographic record
Abstract
Rice fields have the potential for carbon sequestration, but on the other hand, also as a source of carbon transfer to the atmosphere depending on land management practices.The condition of flooded paddy fields causes agricultural activities to contribute large amounts of emission gases such as methane (CH4).It is important to adopt rice field management that increases carbon sequestration as a mitigation effort against global warming.This research is survey research with a descriptive exploratory approach that is carried out through direct field observations and laboratory analysis.The observed variables used were soil organic C, microbial C biomass, bulk density, pH, clay content, C rice biomass and rice biomass weight.Sampling method by purposive sampling.Data were processed by calculating total carbon sequestration and statistical tests with One Way ANOVA and Pearson's correlation.The results showed that different rice field management affect the total carbon sequestration on rice fields.The highest total sequestration was found in organic rice fields at 45.89 tons/ha followed by semi organic rice fields at 38.03 tons/ha and conventional rice fields being the lowest at 34.36 tons/ha.Factors determining the amount of carbon sequestration are soil organic carbon and microbial biomass carbon.The suggested land management recommendations are to increase organic fertilizers in semi-organic and conventional rice field management systems, maintain the soil tillage and application of fertilizers in organic systems and expand organic rice fields.
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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".