Long‐term legacy of land‐use change in soils from a subtropical rainforest: Relating microbiological and physicochemical parameters
Bibliographic record
Abstract
Abstract Tropical and subtropical ecosystems are widely affected by the expansion of agriculture over pristine lands. Despite research efforts, knowledge of the impact of land‐use change on soil is still limited by intrinsic variability, inconsistent results and inadequate replication. This study aimed to better understand the consequences of land‐use change by focusing on long‐term effects on both soil biotic and abiotic parameters. For this purpose, we selected three productive farms under similar management, each of them with pristine forest sites and agricultural sites that had been deforested for ~15 and ~30 years. In each site, we analysed soil microbiological (phospholipid fatty acids [PLFAs], biomass and activity) and physicochemical parameters. Long‐term land‐use change caused a detriment in soil microbial biomass, activity and fungal abundance, but only small changes in PLFA composition. In fact, PLFA composition was more affected by soil physicochemical properties such as carbon‐to‐nutrient ratios and labile carbon than by land use. Some physicochemical parameters (e.g., organic carbon and nutrients) were also negatively affected by land‐use change and were more sensitive to time under agricultural use than microbiological parameters. The lower sensitivity of microbiological parameters could be the result of severe drought conditions at sampling, which may have affected soil microbial communities in both land uses. We were also able to detect associations between specific microbiological and physicochemical parameters. Among these, we identified some that seemed to result from their co‐variation in response to land‐use change and others that seemed to be independent of land use. Overall, our results show that soils can suffer further deterioration several years after deforestation. In order to restore soil health in these degraded lands, we need to keep on investigating the physical, chemical and biological mechanisms responsible for this deterioration. Highlights Land‐use change affected soil microbiological and physicochemical parameters. Microbiological parameters seemed to stabilize after continuous agriculture. Soil organic C, total N and fine particles were still reduced after long‐term cultivation. Microbiological parameters were mostly associated with C‐to‐nutrient ratios and labile C. Drought conditions may have affected microbial response to land‐use change.
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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 | 0.000 |
| 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".