Biofertilizer Microgeo® on Rice Crop: Yield and Seed Quality
Bibliographic record
Abstract
Rice is the most important crop for the south and south-west region of the Rio Grande do Sul state (Brazil); as all commercial crops, irrigated rice is requiring the use of alternatives for increasing yield and quality of its product with less aggressive/toxic inputs in the environment. The aim of this study was to analyze the influence of the biofertilization in the rice crop yield and the parameter of its seed quality and grain quality. It was carried out a field experiment with two treatments (with and without biofertilization with 150 + 150 L CLC® ha-1) with 12 repetitions in an irrigated rice. Biofertilization did not influenced the percentages of whole grain, polished grain and no broken grain. There were no difference between the treatments for final seed germination, seed hectoliter weight and pH. The rice yield with biofertilization using continuous liquid composting was not statistically higher, an expected result for a first year of biofertilization. However, it provided 398 kg ha-1 grain yield increase, which is a very positive and a promising result considering being the first year of application. Therefore, a significant yield increase for the following years of application is expected.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".