Responses of Upland Rice cv Inpago LIPI Go4 to Microbial Inoculant and Nitrogen Fertilization Dosage Treatments
Bibliographic record
Abstract
There is a huge potential of upland for developing food crops to shortage the increase in rice production in Indonesia. Upland rice that adaptable to dry land could support national rice production. Among the limit factors of upland rice productivity in Indonesia are infertile land and cultivation practices. The purpose of the study was to find out the effect of microbial inoculant application combined with nitrogen (N) fertilizer dosage to the cultivation of upland rice Inpago LIPI Go4. The factorial experimental design with two factors was applied, namely the supply of microbial inoculant and the dosage of N fertilizer and, i.e., 0%, 50%, 100% N (200 kg ha-1 Urea). The inoculant comprises of Aspergillus niger, Trichoderma viride, and Azotobacter. Each treatment combination was repeated four times. The microbial inoculant treatment solely effects significantly plant height, tiller number, and panicle weight of the upland rice, whereas N dosage treatment solely influences significantly plant height and tiller number. There was no interaction significant effect of microbial inoculant and N fertilizer dosage to all growth, production, and content of leaf N parameters. The maximum production of the upland rice was 4499 kg ha-1, whereas the average production was 3816 kg ha-1 grain weight. The highest yield was obtained from the plant with the supply of microbial inoculant and the treatment of 50% N fertilizer (100 kg ha-1 Urea).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".