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Record W1762601300 · doi:10.5539/jas.v7n9p154

Selection for High Yield and Stability among Early Maturing Greengram Genotypes

2015· article· en· W1762601300 on OpenAlexvenueno aff
Emmanuel K. Mbeyagala, Robert Amayo, J.E. Obuo

Bibliographic record

VenueJournal of Agricultural Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetics and Plant Breeding
Canadian institutionsnot available
FundersWorld Bank Group
KeywordsRadiataYield (engineering)VignaBiologySelection (genetic algorithm)GenotypeGene–environment interactionStability (learning theory)BiotechnologyAgronomyStatisticsHorticultureMathematicsComputer science

Abstract

fetched live from OpenAlex

Greengram, Vigna radiata (L.) Wilczek is an important source of proteins and micronutrients to small holder farmers in eastern and northern regions of Uganda. Most of the landraces currently grown by small scale farmers are characterized by low yields and late maturity. In order to identify high yielding and stable varieties, an experiment was conducted to evaluate the yield performance of introduced early maturing genotypes from the World Vegetable Centre (AVDRC) in seven locations of Uganda. Yield performance data for the genotypes was subjected to analysis of variance (ANOVA) to test the significance of genotype × environmental interactions as well as stability analysis using the yield – stability statistic (YSi) as an aid for simultaneous selection for high yield and stability. Analyses of variance showed that genotype × environmental interactions (G×E) were significant and therefore could not be ignored. Following the detection of significant genotype × environmental interactions, yield – stability statistics (YSi) were generated and used for simultaneous selection for high yield and stability among the introduced genotypes. Yield-stability statistics (YSi), indicated that three introduced genotypes (Filsan, Sunshine, and Blackgram) in addition to the local check were stable and high yielding. These genotypes need to be assessed for farmer preferences/tastes and other quality traits in on-farm participatory trials before they can be recommended for release.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score0.197

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.042
GPT teacher head0.208
Teacher spread0.165 · 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 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
Published2015
Admission routes1
Has abstractyes

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