Effects of Different Bioactivator Rates on Growth, Yield and Soil Properties of Two Rice Varieties in Indonesia
Bibliographic record
Abstract
The progressive decrease in rice production in Indonesia is associated with soil infertility, resulting in inhibited plant growth.This study investigated the impact of bioactivators on soil properties and the growth and yield of two rice (Oryza sativa L.) varieties, namely Inpari 32 and IR 64.A factorial nesting design was implemented, taking into account two primary factors: the dose of bioactivator (ranging from 10-30 ml/l, applied pre-or postplowing) and the rice variety.Two control conditions, namely inorganic and organic fertilizers, were also included.The results demonstrated that both the rice variety and bioactivator application significantly influenced various plant and soil parameters, such as plant height, clump diameter, number of tillers per clump, root length, plant fresh and dry weight, yield, and soil properties.While the control treatments of inorganic and organic fertilizers yielded superior results in terms of clump diameter and number of tillers, bioactivator application resulted in longer roots and higher biomass across both rice varieties.Notably, the application of a 10 ml dose of bioactivator prior to plowing, specifically on the Inpari 32 variety, was found to improve plant dry weight and fresh weight more effectively than the control and other treatments.Moreover, post-plowing application of a 20 ml/l bioactivator concentration increased soil water content, the potential of C-organic, and P2O5.These findings suggest that bioactivator application could be an effective strategy for enhancing soil fertility and rice crop productivity, thereby addressing the ongoing decrease in rice production in Indonesia.
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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 | 0.000 | 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 | 0.001 |
| 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".