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Rice (Oryza sativa L.) yield and potassium use efficiency as affected by potassium fertilizer sources

2024· article· en· W4391479504 on OpenAlexaboutno aff
Pallabi Rani Das, Mohammad Zaber Hossain, Falguni Akter

Bibliographic record

VenueSouth Asian Journal of Agricultural Sciences · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsOryza sativaPotassiumYield (engineering)AgronomyPotashFertilizerEnvironmental scienceHorticultureChemistryBiologyMaterials science

Abstract

fetched live from OpenAlex

Enhancing potassium use efficiency (KUE) is vital for sustainable rice production. In a pot experiment, T1 (Canada origin) significantly outperformed T2 (Morocco origin) in grain yield for BRRI dhan 61 (7205 kg ha-1), BRRI dhan 68 (6050 kg ha-1), and BRRI dhan 28 (4227.3 kg ha-1). BRRI 61 exhibited superior filled grain and 1000 grain weight under T1. In T1-treated soil, BRRI 61 recorded the highest grain K uptake (63.56 kg ha-1), surpassing BRRI 68 and BRRI 28. With T1, BRRI 61 demonstrated 1.24 and 2.70 times greater agronomic efficiency than BRRI 68 and BRRI 28, respectively, along with a crop recovery efficiency of 0.42. T1 treatment improved agronomic traits across varieties. Recommending T1 fertilizer for BRRI 61 is suggested to optimize yield, K uptake, and use efficiency, emphasizing its potential for sustainable rice cultivation.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.018
GPT teacher head0.223
Teacher spread0.206 · 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.

Study designObservational
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

Citations1
Published2024
Admission routes1
Has abstractyes

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