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Record W2947799261 · doi:10.5539/jas.v11n8p100

Evaluation of the American Yam Bean (Pachyrhizus spp.) for Storage Root Yield Across Varying Eco-geographic Conditions in Uganda

2019· article· en· W2947799261 on OpenAlexvenueno aff
Charles Andiku, Phinehas Tukamuhabwa, James M. Ssebuliba, H. Talwana, Silver Tumwegamire, Wolfgang J. Grüneberg

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsnot available
Fundersnot available
KeywordsAmmiYield (engineering)LegumePruningBiologyCropHorticultureAgronomyGene–environment interactionGenotype

Abstract

fetched live from OpenAlex

The American yam bean (Pachyrhizus spp.) is a legume crop that is exclusively used for its storage roots. The seeds are inedible due to presence of toxic rotenone. It produces high storage root yields comparable of major root crops like cassava or sweetpotato. And flower pruning more than doubles its root yield performance. Using twenty five yam bean accessions, the current study aimed to determine root yield stability and adaptability, and presence of yam bean production mega environments in Uganda. Trials were planted at three stations, Namulonge, Serere, and Kachwekano during two consecutive seasons of 2011. Fresh storage root yields were significantly different (p < 0.05) across locations with the ideal location being Namulonge (fresh storage root yield of 10.1 t ha-1), followed by Serere (8.0 t ha-1), and Kachwekano (3.1 t ha-1). Results of AMMI analysis indicated the presence of genotype-by-environment interaction for fresh storage root yield. Through AMMI estimates and GGE visual assessment, genotype 209017 was the highest yielding with mean yield of 20.7 t ha-1. Genotype 209018 with mean yield of 15.5 t ha-1 was the most stable and adapted accession in the entire discriminating environment in Uganda. From the environmental focusing plot, the six environments were grouped into two putative mega environments for yam bean production.

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)0.003
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 score0.710
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.285
Teacher spread0.255 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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