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Record W2883446994 · doi:10.15575/1675

Aplikasi pupuk organik limbah rumah potong hewan untuk meningkatkan kesuburan tanah dan produktivitas padi

2018· article· id· W2883446994 on OpenAlexaff
Suhardjadinata Suhardjadinata, Dwi Pangesti, Tenten Tedjaningsih

Bibliographic record

VenueJurnal Agro · 2018
Typearticle
Languageid
FieldAgricultural and Biological Sciences
TopicPlant Growth and Agriculture Techniques
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsPhysicsAnimal scienceHorticultureBiology

Abstract

fetched live from OpenAlex

Limbah Rumah Potong Hewan (RPH) berpotensi digunakan sebagai pupuk organik. Teknologi yang dapat direkomendasikan untuk pemanfaatan limbah Rumah Potong Hewan adalah pengomposan. Penelitian bertujuan untuk mengkaji pengaruh pupuk organik limbah RPH terhadap kesuburan tanah dan produktivitas padi. Penelitian dilaksanakan di kebun percobaan Fakultas Pertanian Universitas Siliwangi Tasikmalaya dari Mei sampai September 2017. Percobaan menggunakan Rancangan acak kelompok 10 taraf perlakuan diulang 3 kali. Perlakuan kombinasi dosis N,P, K dan pupuk organik (PO), yaitu: A= Dosis N,P,K; B= dosis N,P,K + 2,5 t ha-1 PO; C=Dosis N,P,K + 5 t ha-1 PO; D= Dosis N,P,K + 7,5 t ha-1 PO; E= ¾ dosis N,P,K + 2,5 t ha-1 PO; F= ¾ dosis N,P,K + 5,0 t ha-1 PO; G= ¾ dosis N,P,K + 7,5 t ha-1 PO; H= ½ dosis N,P,K + 2,5 t ha-1 PO; I= ½ dosis N,P,K + 5,0 t ha-1 PO; J= ½ dosis N,P,K + 7,5 t ha-1 PO. Dosis rekomendasi N, P dan K: urea 300 kg ha-1, SP 36 150 kg ha-1, dan KCl 100 kg ha-1. Hasil penelitian menunjukkan bahwa aplikasi pupuk organik 2,5 t ha-1 dan ¾ dosis N, P dan K menghasilkan gabah lebih tinggi dan meningkatkan kesuburan tanah. Slaughterhouse waste is potential to be used as organic fertilizer. The technology recommended for the usage of slaughterhouse waste is composting. This research was aimed to study effect of organic fertilizer slaughterhouse waste on soil fertility and rice productivity. The research was conducted in the experiment site of Agricultural Faculty, Universitas Siliwangi Tasikmalaya from May to September 2017. Randomized block design was used with ten level treatments and replicated three times. The treatment was combination dosage N,P, K and organic fertilizer (OF), namely : A= (N,P,K recommendation dose), B= (N,P,K dose + 2.5 t ha-1 OF), C= (N,P,K dose + 5 t ha-1 OF), D= (N,P,K dose + 7.5 t ha-1 OF), E= (3/4 dose of N,P,K + 2.5 t ha-1 OF), F= (3/4 dose of N,P,K + 5 t ha-1 OF), G= (3/4 dose of N,P,K + 7.5 t ha-1 OF), H=(1/2 dose of N,P,K + 2.5 t ha-1 OF), I= (1/2 dose of N,P,K + 5 t ha-1 OF), J= ( ½ dose of N,P,K + 7.5 t ha-1 OF). The recommendation dose of N, P and K, respectively: urea 300 kg ha-1, SP 36 150 kg ha-1, and KCl 100 kg ha-1. The results showed application organic fertilizer 2,5 t ha-1 dan ¾ dose of N, P, K fertilizer increased yields grain higher and improve soil fertility.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.004

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.

Opus teacher head0.012
GPT teacher head0.211
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations1
Published2018
Admission routes1
Has abstractyes

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