ANALISIS NILAI EKONOMI LAHAN PADA POLA TANAM TUMPANGSARI JAGUNG UBIKAYU
Bibliographic record
Abstract
Lahan merupakan faktor produksi terpenting dalam menunjang keberhasilan usahatani. Luas lahan pertanian terus berkurang menyebabkan produktivitas tanaman pangan menurun sehingga pentingnya berusahatani dengan pola tanam tumpangsari. Pola tanam tumpangsari dapat meningkatkan pendapatan petani dibanding pola tanam monokultur. Pola tumpangsari menyebabkan pemanfaatan sumber daya lebih efisien terutama cahaya, air, dan unsur hara. Tujuan penelitian: menganalisis nilai ekonomi lahan pada pola tanam tumpangsari jagung ubikayu dan faktorr-faktor yang mempengaruhi nilai ekonomi lahan pada lahan pertanian pola tanam tumpangsari.Metode penelitian berupa metode deskriptif dan metode inferensia. Lokasi dipilih secara purposive sampling dengan pertimbangan sentra produksi jagung-ubikayu dengan pola tanam tumpangsari. Jenis data berupa primer dan sekunder. Teknik pengambilan sampel: Populasi ada 2 yaitu (1) petani pola tumpangsari jagung-ubikayu sebanyak 447 petani dan didapatkan 66 sampel yang diambil dengan simple random sampling, (2) petani jagung sebanyak 45 petani dan diambil semua untuk dijadikan sampel sebanyak 45 sampel dengan metode sensus. Analisis data (1) nilai ekonomi lahan: analisis Land Rent, dan (2) faktor yang mempengaruhi nilai ekonomi lahan pada lahan pertanian pola tanam tumpangsari. Hasil penelitian yaitu (1) Nilai ekonomi lahan dilihat dari indek perbandingan land rent untuk tanaman jagung dengan tanaman tumpang sari ubikayu masih lebih baik bila dibandingkan dengan tanaman jagung yang monokultur, (2) Faktor yang mempengaruhi nilai ekonomi lahan pada lahan pertanian pola tanam tumpangsari yaitu penerimaan dan jarak ke lokasi pasar.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".