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Record W4376140450 · doi:10.5539/jas.v15n6p19

Yield Responses of Upland Rice Varieties to Low N Conditions in Central Kenya

2023· article· en· W4376140450 on OpenAlexvenueno aff
Sammy Kagito, Mayumi Kikuta, Hiroaki Samejima, Joseph P. Gweyi‐Onyango, E. W. Gikonyo, Emily Gichuhi, Daniel Makori Menge, John Kimani, Akira Yamauchi, Daigo Makihara

Bibliographic record

VenueJournal of Agricultural Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsnot available
FundersScience and Technology Research Partnership for Sustainable DevelopmentJapan Science and Technology AgencyJapan International Cooperation Agency
KeywordsAdaptabilityPanicleAgronomyDry matterSoil fertilityNutrientUpland riceYield (engineering)Sink (geography)Grain yieldBiologyOryza sativaSoil waterGeographyEcology

Abstract

fetched live from OpenAlex

Growth, yield, and yield components of five upland rice varieties (MWUR 1, MWUR 4, NERICA 1, NERICA 4, and IRAT109) were evaluated under four different soil N conditions (0, 26, 52, and 78 kg N/ha) to identify the factors contributing to their adaptability to low soil-fertility. The results showed that MWUR 1, MWUR 4, and NERICA 4 had greater adaptability to low N conditions. Specifically, MWUR 1 showed the highest adaptability to low soil fertility. The greater low soil-fertility adaptability of these varieties was attributed to their ability to maintain dry matter production. Furthermore, their greater dry matter production under low N conditions could be attributed to the increased root length, which allowed improved soil nutrient absorption. Our findings suggest that rice grain yield was mainly restricted by sink size, particularly panicle number per plant under low N conditions. The higher grain yield of MWUR 1 under low N conditions could be attributed to greater tillering ability. Thus, MWUR 1 could be a good candidate for cultivation under nutrient-poor soil conditions.

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)0.001
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 score0.945
Threshold uncertainty score0.114

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.028
GPT teacher head0.257
Teacher spread0.229 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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