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Record W4229454602 · doi:10.5539/jas.v14n6p55

Effects of Foliar and Soil Application of Gibberellic Acid (GA3) at Different Growth Stages on Agronomic Traits and Yield of Rice (Oryza sativa L.)

2022· article· en· W4229454602 on OpenAlexvenueno aff
M. D. Iffah Haifaa, Christopher Moses

Bibliographic record

VenueJournal of Agricultural Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsnot available
FundersUniversiti Putra Malaysia
KeywordsPanicleGibberellic acidAgronomyBiologyOryza sativaYield (engineering)Growth regulatorSowingMathematicsHorticultureGerminationMaterials science

Abstract

fetched live from OpenAlex

Use of Gibberellic acid (GA3) application in rice cultivation for increasing the grain yield is well documented. However, improper and untimely use of GA3 could result in poor response to GA3 application. This study was aimed at to investigate the timing of application during different growth stages and mode of application of GA3 on the growth and yield of MR219, a popular Indica rice variety, released by Malaysian Agricultural Research and Development Institute (MARDI). Two commercial GA3 formulations, namely ProGibb SG containing 40% GA3 and ProGibb silica Granule containing 0.1% GA3 were used for foliar and soil application, respectively. GA3 was applied at late tillering stage and at 10-30% panicle heading stage. Interestingly, GA3 application as foliar spray during the early reproductive stage, ie at 10-30% panicle heading stage enhanced the grain yield significantly, recording over 27% grain yield increase over the untreated control. Moreover, two applications of GA3 at 7 days’ interval has consistently given higher grain yield than single application. However, there is no significant difference in flag leaf characteristics, one thousand grain weight and milling qualities among different treatments. Our study has clearly illustrated that foliar application of GA3 at weekly interval at 10-30% panicle heading stage, can increase rice grain yield significantly.

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)0.000
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 score0.580
Threshold uncertainty score0.151

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.010
GPT teacher head0.200
Teacher spread0.190 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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