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Record W2546901562 · doi:10.5539/jas.v8n12p86

Influence of Water Management and Silica Application on Rice Growth and Productivity in Central Java, Indonesia

2016· article· en· W2546901562 on OpenAlexvenueno aff
Adha Fatmah Siregar, Ibrahim Adamy Sipahutar, Husnain Husnain, Heri Budi Wibowo, Kuniaki Sato, Toshiyuki Wakatsuki, Tsugiyuki Masunaga

Bibliographic record

VenueJournal of Agricultural Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsJavaRandomized block designYield (engineering)Blast diseaseAgronomyProductivityPaddy fieldEnvironmental scienceSystem of Rice IntensificationBiologyAgricultureMaterials scienceEcologyComputer scienceOperating system

Abstract

fetched live from OpenAlex

Rice cultivation in our study site at Central Java, Indonesia, is constrained by water scarcity and blast disease problems. A field experiment was thus conducted to evaluate the effect of water management and silicon (Si) application (with 500 kg ha-1 of silica gel) on improving rice growth and productivity and blast disease infection in Jakenan, Central Java. Split plot in randomized complete block design with 4 replications was used. The results showed that two water saving methods, intermittent (IT) and aerobic rice (AR) increased rice yield compared to conventional flooding water management. Further, IT showed better root growth and hence lodging resistance and decreased blast disease infection. IT had higher yield potential compared to AR although the yield of IT and AR were not statistically different. Si application gave significant effect on reducing leaf and neck blast infection and also increased stomata density (p < 0.01) in all water treatments. Si application did not result in increased yield but exhibited potential towards improving rice plant growth and production. Since Si fertilizer was never used in rice cultivation in Indonesia, the study reveals that IT combine with Si application was a suitable management for rice production in dry season in water limited Central Java region.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.125

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.192
Teacher spread0.187 · 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 teacher head, 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".

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

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