Participatory selection of mung bean (Vigna radiata L.) varieties in Eastern Amhara region, Ethiopia
Bibliographic record
Abstract
It is one of lowland pulses crop primarily grow in the lowland areas of Ethiopia’s Amhara region. A major problem in mung bean production is the lack of improved varieties that are adaptable and resistant to key biotic and abiotic stresses. As a result, these farmers still produce local varieties, which are low-yielding and highly susceptible to both biotic and abiotic stresses. Two potential areas were selected for this study: South Wollo Zone and Oromia Special Zone in the Amhara region. To achieve sustainable high yield on their farms, farmers rely critically on selecting high-yielding genotypes. This study used participatory varietal selection (PVS) to evaluate and select the best adapted mung bean varieties that meet the criteria of farmers’ with the aim of accelerating their adoption. The materials have three improved and one local mung bean variety (NVL-01, Boreda, (Rasa), and local (Shewa robit) were evaluated at Dawa Cheffa and Ambasel woredas research site and on farmers’ fields. Varietal evaluation and selection were conducted using the participatory mother and baby trial approach. A randomized block design with four replications was employed for the mother trial at the research site. The material was planted in a 5- row plot of 3 m length with a spacing of 0.4 m between rows and 0.05 m between plants. The baby trial approach was conducted farmers as a replication, was planted in a 12- row plot of 5 m length with a spacing of 0.4 m between rows and 0.05 m between plants. The combined ANOVA revealed significant differences among varieties for all studied parameters. Farmers were involved in varietal selection during flowering and at maturity. The most chosen varieties by farmers were Rasa both location but Rasa already popularized farmers adoption scaling up was done. Based on yield potential and selection by both farmers and breeders, Rasa (1731 kg/ha) and Boreda (1690 kg/ha) varieties were ranked first and second, respectively. As their yields were statistically similar, both are recommended for wider dissemination and production in the study areas and similar agro ecologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.004 |
| 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 teacher head, 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".