Effects of Water Stress and Goat Manure Fertilizer on the Growth and Yield of Upland Rice in Indonesia
Bibliographic record
Abstract
High rice consumption, while low rice production, is a challenge for Indonesia, which is exacerbated by decreasing land availability and the impact of climate change.Upland rice cultivation increases rice production by utilizing dry land.The research aims to examine the effect of water treatment and organic fertilizer doses on the growth and yield of upland rice.The study used a Randomized Complete Block Design with two factors.The first factor was water stress with four levels: control, ¾, ½, and ¼ Field Capacity.The second factor was the dose of organic goat manure fertilizer with four levels: 0, 10, 20, and 30 tons.ha -1 .Water treatment did not affect the growth and yield of upland rice.The efficiency of water use in the field capacity treatment was 8.35 kg.l -1 and increased in the water stress treatment.The dose of cow manure fertilizer of 10 tons.ha-1 has encouraged plant height growth, number of leaves, and productive tillers, while the dose of 30 tons.ha -1 increased biomass and number of panicles.The combination of field capacity water treatment with 10 tons.ha-1 of goat manure fertilizer produced the highest number of grains per panicle.Organic fertilizer can optimize yields in limited water conditions.
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) | 0.001 | 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".