Pengaruh jenis dan waktu aplikasi pupuk organik terhadap pertumbuhan dan hasil tanaman jagung manis (Zea Mays L. Saccarata Strurt)
Bibliographic record
Abstract
Jagung manis termasuk komoditi yang dapat diusahakan secara intensif karena banyak digemari sehingga terbuka peluang pasar yang baik. Kebutuhan pasar akan jagung manis yang terus meningkat dan harga jagung manis yang tinggi merupakan faktor yang dapat merangsang petani untuk mengembangkan usahatani jagung manis (Hayati, 2006). Aplikasi pupuk organik dalam budidaya jagung manis dapat menjaga kesehatan dan keberlanjutan lahan, melalui penambahan pupuk organik ke lahan pertanian dapat memperbaiki kesuburan tanah karena pupuk organik dapat memperbaiki sifat fisik, kimia dan biologi tanah. P enelitian ini bertujuan untuk mengetahui interaksi jenis pupuk organik dan saat pemberian terhadap produktivitas tanaman Jagung Manis (Zea mays L. Saccharata Sturt). Penelitian ini bertempat di Kelurahan Manisrenggo Kecamatan Manisrenggo Kota Kediri . Waktu penelitian di mulai pada bulan September 2019 – November 20 19 . Jenis tanah yang ada di Kelurahan Manisrenggo yaitu Lempung berpasir, pH 5,5 dan ketinggian tempat 68 m dpl. Metode Penelitian yang digunakan adalah rancangan faktorial dengan menggunakan Rancangan Acak Kelompok Faktorial (RAKF) terdiri dari 2 faktor, faktor pertama 2 level dan faktor yang kedua 3 level dan di ulang sebanyak empat kali. Hasil penelitian menunjukkan terdapat interaksi akibat pengaruh jenis pupuk organik dan saat pemberian terhadap produktivitas tanaman Jagung Manis (Zea mays L. Saccharata Sturt) pada parameter tinggi tanaman, jumlah daun (35, 45 hst), diameter batang (28,35,42 hst) .
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 0.001 |
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".