Comparing the Early Stage Carbon Sequestration Rates and Effects on Soil Physico-Chemical Properties after Two Years of Planting Agroforestry Trees
Bibliographic record
Abstract
Farm friendly and fast growing trees are the sustainable, cheaper and efficient source of carbon sequestration and carbon stock, however, their carbon sequestration potential vary among tree species depending upon several factors. This study was conducted to determine the carbon sequestration potential and carbon storage difference among different tree species at early stage. Second objective of this study was to observe the effects of trees on the physico-chemical properties of soils. Seedlings of fifteen widely planted farm trees species were planted under same set of climatic and soil conditions. Employing tree biomass after two year of planting (2014-2016), carbon stocks and carbon sequestration rates were calculated. Soil samples were collected under each tree species at two depths: 0-15cm and 16-30 cm, to determine the physico-chemical properties of soils such as pH, EC, N, P, K, C and organic matter (O.M.). It was found that Populus deltoides contained the highest carbon stocks (7.21 ± 1.31 kg C) and sequestered the CO2 at the highest rate of 13.21 ± 0.84 kg C/year as compared to all other fourteen tree species. O.M. (%) and Carbon (mg/kg) were also the highest in the soils under P. deltoides (2.29 ± 0.42 and 3.8 ± 0.2 respectively) as compared to and all other tree species. Nitrogen contents (%) were found the maximum in the soils under D. sissoo (0.063 ± 0.04) > Acacia nilotica (0.058 ± 0.008) and Albizia lebbeck (similar to Acacia nilotica). Such information enhances our capacity to better predict the carbon sequestration potential and carbon stock in different trees.
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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".