PERBANDINGAN PRODUKSI UBIKAYU (Manihot esculenta Crantz) AKIBAT PENAMBAHAN PUPUK KCl DAN PEMBERIAN PUPUK MIKRO SAAT PANEN 7 BULAN
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengevaluasi produksi ubikayu akibat penambahan unsur hara kalium dan unsur hara mikro saat panen 7 bulan. Lokasi penelitian di lahan bertipe tanah lempung berpasir, Desa Sukanegara, Tanjung Bintang, Lampung Selatan dari April sampai Februari 2018. Klon yang digunakan yaitu Klon BW-1 dengan jarak tanam 1m x 1m. Perlakuan disusun secara faktorial (2x2) dalam Rancangan Kelompok Teracak Lengkap (RKTL). Setiap perlakuan diulang tiga kali yang digunakan sebagai blok. Faktor pertama 2 taraf dosis pupuk KCl yaitu 200 kg ha-1 dan 300 kg ha-1. Faktor kedua 2 taraf pupuk mikro yaitu 0 kg ha-1 dan 20 kg ha-1. Variabel yang diamati yaitu tinggi tanaman, jumlah daun, bobot kering batang, bobot kering petiol, bobot kering daun, jumlah ubi, panjang ubi, diameter ubi, bobot ubi per tanaman dan kadar pati. Data dianalisis menggunakan aplikasi SAS 9.0. dengan taraf nyata 5%. Hasil penelitian menunjukan bahwa penambahan 300 kg KCl ha-1 menghasilkan rata-rata bobot ubi per tanaman yang lebih besar (1983,3 g) dibandingkan 200 kg KCl ha-1 rata-rata bobot ubi per tanaman (1450,0 g), begitu juga dengan panjang ubi yang diberi 300 kg KCl ha-1 menghasilkan rata-rata panjang ubi 25,15 ± 10,78 cm yang lebih panjang dibandingkan 200 kg KCl ha-1 rata-rata panjang ubi 21,27 ± 7,38 cm. Selajutnya pemberian 20 kg pupuk mikro ha-1 mampu menghasilkan menghasilkan rata-rata bobot ubi yang lebih besar (1.966,70 g) dibandingkan tanpa aplikasi mikro (1.466,70 g) begitu juga dengan diameter ubi 7 BST yang diberi 20 kg pupuk mikro ha-1 menghasilkan rata-rata diameter 30,73 ± 6,69 mm yang lebih besar dibandingkan tanpa aplikasi pupuk mikro rata-rata diameter ubi 35,16 ±8,45 mm. Namun aplikasi kedua dosis pupuk KCl dan aplikasi kedua dosis pupuk pupuk mikro tidak berpengaruh pada komponen ubi.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".