Multi-environmental traits selection and farmer's participatory assessment of mean performance and stability of orange-fleshed sweet potato genotypes in Benin
Bibliographic record
Abstract
Regularly consuming orange-fleshed sweet potatoes (OFSP) is widely recognised as an effective way to treat vitamin A deficiency (VAD), particularly in low-income countries. Unfortunately, cultivars of OFSP are poorly disseminated in most countries in sub-Saharan Africa, where VAD is a major cause of blindness. This study was conducted to evaluate the effect of the genotype-environment interaction (GEI) on the performance and stability of the yield components of OFSP cultivars to trigger their adoption by farmers. Nine OFSP genotypes were evaluated through a multi-environment trial (MET) carried out in 14 environments and in a complete randomised block (RCB) design to select the best genotypes based on the multi-trait mean performance and stability index (MTMPS) and participatory variety selection (PVS). The across-environment likelihood ratio test (LRT) showed significant differences across environments, genotypes, and GEIs for all traits studied, except for the number of marketable roots (NMR). Our findings revealed that genotypes ACAB, Apomuden, and BF59xCIP had satisfactory mean performances and stabilities across all evaluation environments. The PVS revealed that farmers assigned high importance to yield performance for field evaluation and root dry matter content. In addition, they prioritized many different traits, such as the attractiveness of orange flesh colour and the lower fibrousness of boiled roots. The genotypes preferred by farmers were ACAB and Apomuden, generally followed by BF59xCIP, indicating excellent concordance between the genotypes selected with the MTPMPS and those selected by farmers during the PVS. We concluded that farmers' participation in the OFSP genotype evaluation process for future dissemination is necessary to select the most suitable genotypes for production and increase the chances of adopting of these genotypes in rural areas. ACAB and Apomuden will be submitted for registration in the national catalogue.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".