Carbon Sequestration Implementation through Sustainable Agricultural Land Management (SALM) Methodology in Nigeria
Bibliographic record
Abstract
Climate-Smart Agriculture (CSA) as an adaptation strategy that helps rural farmers adapt to climate change by making them resilient to its effects. SALM methodology is a CSA practice that promotes carbon sequestration, which in the long run increase farmers’ productivity. This study assessed SALM methodology using RothC model to calculate the effi- cacy of CSA on Umar Lere farm. Activity Baseline and Monitoring Survey was used to acquire data for a period of 3 years of practicing SALM methodology. Results showed that after 3 years of SALM adoption, the farm produced maize (2.6), soybeans (0.7), guinea corn (1.1), and tomatoes (1.7) tons/hectare/year respectively in 2015 compared to maize (1.2), soybeans (0.3), guinea corn (1.6), and tomatoes (0.7) tons/hectare/year respectively produced in 2012. The farm also recorded 56 trees sequestrating 10.2 tons of carbon dioxide per hectare in 2015 compared to 15 trees sequestrating 2.6 tons of carbon dioxide per year in 2012. In 3 years, Umar Lere farm significantly increased its crop yields from the project; RothC model shows that the modelled soil carbon stock changes increased significantly as a result of the adoption of SALM practices from around 0:5 tCO2 ha-1yr-1 in 2012 to 3:5 ha-1 yr-1 in 2015.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".