Effect of Different Mixed Media (Merapi Volcanic Ash, Cow Manure and Mineral Soil) on Chemical Properties of Soil and Growth of Maize (Zea mays L.)
Bibliographic record
Abstract
The volcanic ash can not be used as crop growth media directly due to lack of physical characteristics and low rate of plant’s nutrient. With addition of other components (elements) such as organic matter and mineral soil, this mixed media expected to empower the volcanic ash used as growth media for crops. To realize this idea, a research was done with maize as a crop indicator. This study was conducted to find out the combination effects of Merapi volcanic ash, cow manure and mineral soil on organic carbon, total N, organic matter and growth of maize (Zea mays L.). During April to July 2011, in the pot under the glass house at Faculty of Agricultural Padjadjaran University, Jatinangor-West Java at about 740 m above sea level. The experiment used a randomized block design which arranged in one factor, nine treatments and three replications. The nine treatments consisted of: 0% volcanic ash + 50% cow manure + 50% mineral soil (I), 40% volcanic ash + 10% cow manure + 50% mineral soil (II), 30% volcanic ash + 20% cow manure + 50 mineral soil (III), 20% volcanic ash + 30% cow manure + 50% mineral soil (IV), 10% volcanic ash + 40% cow manure + 50% mineral soil (V), 40% volcanic ash + 50% cow manure + 10% mineral soil (VI), 30% volcanic ash + 50% cow manure + 20% mineral soil (VII), 20% volcanic ash + 50% cow manure + 30% mineral soil (VIII), and 10% volcanic ash + 50% cow manure + 40% mineral soil (IX). The data were analyzed by using Anova Test and Duncan's Multiple Range Test. The result showed that there were significant effects of mixed media (volcanic ash, cow manure and mineral soil) on organic carbon, total N, organic matter and plant growth of maize. The highest organic carbon (4.6%) and organic matter (8.08%) was found in combination (treatment VII): 30% volcanic ash, 50% cow manure and 20% mineral soils. Combination in treatment IX: combination of 10% ash volcano, 40% cow manure and 50% mineral soil gave a significant scored on total N.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".