Evaluation of the American Yam Bean (Pachyrhizus spp.) for Storage Root Yield Across Varying Eco-geographic Conditions in Uganda
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".