Changes in soil organic carbon and phosphorus status under three different land use systems in a tropical Ultisol
Bibliographic record
Abstract
Anthropogenic land use systems and their management practices influence carbon (C) accumulation and storage and phosphorus (P) dynamics in soils. However, information on changes in soil organic C (SOC) reserves and P status in intensive annual cropping versus commercial perennial cropping systems is limited. This study examined the impact of long-term annual (vegetable) and perennial (tea) cultivation on the soil P and SOC status of a Tropical Ultisol compared to replanted forest land use. Surface (0–15 cm) soil samples obtained from forest- (25 ha), tea- (20 ha), and vegetable- (30 ha) lands within a micro-catchment were analyzed for available P (Mehlich 3-P), P fractions, SOC, permanganate oxidizable C (POxC, representing active SOC), and pH. Soils under long-term vegetable and tea with frequent applications of fertilizers had 78-fold and 7-fold greater available P (356.3 and 33.0 mg kg −1 , respectively) than forest (4.6 mg kg −1 ) soils. Moreover, vegetable-grown soils had greater P concentrations in labile, moderately labile, and recalcitrant fractions than tea-grown and forest soils. Active C fraction in tea-grown soils (899 mg kg-1) was 2-fold than that of vegetable-grown soils (484 mg kg −1 ), but similar to forest soils (804 mg kg −1 ). The SOC in tea-grown and forest soils were similar (6.05 % and 5.84 %, respectively), but significantly higher than in vegetable-grown soil (4.50 %). Thus, soils from intensive annual cropping systems showed substantial P accumulations and lower SOC quantity and quality than perennial cropping systems, warranting better nutrient and SOC management and soil conservation measures to prevent further soil deterioration with annual cropping.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".