Comparison between tropical legumes and natural grasses in improving tropical rainforest soil health: a case study in guava (Psidium Guajava L.) orchards
Bibliographic record
Abstract
Tropical rainforest soils, or latosols, are distinguished by their low pH and low fertility. In orchards, co-cultivating grass has become popular as a way to improve soil quality and boost fruit production. Nevertheless, insufficient information is currently available about the response of soil microbial communities in tropical rainforest orchards to grass co-cultivation. Therefore, the present research investigates the effect of grass cultivation on the soil properties and microbial diversity of guava (Psidium guajava L. cv Pearl) latosol orchards. Two varieties of the tropical legume grass Stylosanthes guianensis, i.e., Reyan No. 2 and Ubon, were studied, besides the control (CK), which is without any grass, and the natural grasses treatment (N). The study contained four treatments, i.e., S. guianensis cv. Reyan No. 2, S. guianensis cv. Ubon, CK, and N. Soil samples from the top layer (0-20 cm) and subsoil layer (20-40 cm) were collected to follow the changes in soil microbial biodiversity based on 16 S rDNA analysis. A total of 17,231 kinds of OTUs (Operational Taxonomic Units) were obtained, including 17,165 kinds of bacteria and 66 kinds of Archaea. S. guianensis cv. The Ubon variety, natural grasses, and CK treatments significantly increased the soil microbial richness and evenness in the topsoil layer compared to Reyan No. 2 variety. The β-diversity of soil microbial community was significantly reduced in the natural grasses and Ubon variety treatments at the topsoil layer compared to CK treatment. In the subsoil layer, natural grasses, Reyan No. 2, and Ubon treatments significantly increased the soil microbial community based on β-diversity. The presence of natural grasses caused 49% and 42% increases in the SOC in the top and subsoil layers, respectively, as well as remarkable increases in the available and total soil nitrogen. The grass intercropping enhanced the levels of soil carbon and nitrogen and altered the nature of the soil's microbial community. The diversity of soil microorganisms in the subsoil layer is significantly altered by the shallow root systems of tropical legume and natural grasses, which have most of their roots concentrated in the top soil layer. Overall, growing grass in tropical orchards benefits the latosolic soil microorganisms, which has enhanced the theoretical underpinnings for using grass to improve the soil quality in latosols orchards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".