Lablab Purpureus Influences Soil Fertility and Microbial Diversity in a Tropical Maize-Based No-Tillage System
Bibliographic record
Abstract
There are multiple mechanisms by which enhanced diversity of plant communities improves soil structure and function. One critical pathway mediating this relationship is through changes to soil prokaryotic communities. Here, nine different cropping systems were studied to evaluate how legume and grass cover crops influence soil fertility and microbial communities in a maize-based no tillage system. The soil’s bacterial and archaeal communities were sequenced (Illumina GAIIx, 12 replicates for treatment) and correlated with eight different soil features. The microbial community composition differed widely between planting treatments, with three primary “community types” emerging in multivariate space: (1) A community type associated with bare soil linked with low P, low pH, and high aluminum [Al]; (2) a community type associated with Lablab beans linked with high soil N, total organic carbon and other base cation concentrations, and high pH; and (3) a community type of all other non-lablab planting arrangements linked with higher soil P (relative to bare soil), but lower soil fertility (N and base cations). Lablab-based arrangements also expressed the highest microbial richness and alpha diversity. The inclusion of Lablab in maize-based cropping systems represents a potential alternative to reduce the use of chemical fertilizers and increase the chemical and biological quality in agricultural soils under the no-tillage system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".