Dampak Penggunaan dan Pengolahan Tanah dalam Usahatani Sayuran Terhadap Infiltrasi Tanah Andisol di Kabupaten Kerinci
Bibliographic record
Abstract
Andisol is a volcanic soil with a medium texture and a high organic matter content, so it is generally loose. However, the majority of vegetable farmers in Kayu Aro Barat District, Kerinci Regency carry out intensive soil cultivation in intensive land use (planting throughout the year, 2-3 times/year). Unfortunately, the role of tillage in soil and water conservation (SWC) is almost non-existent and even detrimental. One approach in SWC is the application of conservation tillage to control soil compaction so that soil infiltration is maintained. The research aims to determine the rate and capacity of Andisol soil infiltration and several factors that influence it, due to differences in the intensity of land use and cultivation in vegetable farming in Kebun Baru Village, Kayu Aro Barat District. The research used an exploratory-descriptive survey method. Observation and soil sampling points were determined using purposive stratified random sampling covering flat to sloping land, intensive land use and cultivation (planting 2-3 times/year + land cultivation 2 times/year), semi-intensive (planting 1-2 times/year + land cultivation once/year), and non-intensive (planting 1-2 times /year + land cultivation once/2 years), each land is planted with potatoes every year with various planting patterns. Soil infiltration rate was measured using a Double Ring Infiltrometer, and infiltration capacity was estimated using the Horton Equation. Soil infiltration rate and capacity are respectively 26.5 cm/hour (very fast) and 8.12 cm/hour (as is the infiltration capacity of loam-textured soil in general) on flat land with intensive land use and cultivation; much larger than other fields. On the other hand, the infiltration rate and capacity were 6.68 cm/hour (medium) and 2.27 cm/hour respectively (smaller than the infiltration capacity of silty loam soil in general), and the smallest, on sloping land that was used and cultivated not intensively. It is necessary to educate farmers to cultivate the land as necessary according to the characteristics of the soil and plant needs, to maintain the sustainability of soil function and building sustainable vegetable farming in West Kayu Aro District, Kerinci Regency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".