Enhancing Soil Resilience to Climate Change: Long-Term Effects of Organic Amendments on Soil Thermal and Physical Properties in Tea-Cultivated Ultisols
Bibliographic record
Abstract
This study examined the impact of the long-term application (25 years) of tea waste (TW), compost (COM), and neem oil cake (NOC) compared to conventional synthetic fertilizers (CONV) on soil thermal and physical properties of a tea-cultivated Ultisol. Soil samples were collected from 0–15 cm and 15–30 cm depths of an experimental site of the Tea Research Institute in Sri Lanka. These samples were analyzed for soil thermal conductivity (k), volumetric heat capacity (C), thermal diffusivity (D), bulk density (BD), aggregate stability, soil organic carbon (SOC), and volumetric water contents at 0 kPa (θ0) and 10 kPa (θ10). TW and COM significantly (p < 0.05) increased surface SOC, leading to better aggregation, lower BD, and, consequently, a substantial reduction in k and D compared to CONV plots. Further, TW and COM amendments slightly increased C compared to CONV plots due to elevated SOC and water content. However, NOC had no impact on soil thermal and physical properties compared to CONV. The reduced thermal conductivity and thermal diffusivity indicated an improved soil capacity to buffer extreme temperature fluctuations. Moreover, soils treated with TW and COM exhibited greater water retention and improved soil resistance to erosion. The findings suggest that the long-term application of tea waste and compost could be a sustainable soil management strategy for improving soil health and enhancing resilience to climate change in tea-cultivated Ultisols.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".