Agronomic, culinary, and genetic characterization of selected cowpea elite lines using farmers' and breeder's knowledge: a case study from Malawi.
Bibliographic record
Abstract
Cowpea (Vigna unguiculata L.) is an important crop in Malawi. It provides dietary nutrients and income to poor-resource farmers. However production and productivity are below the potential level due to lack of suitable varieties. The objective of the study was to select the productive and diverse cowpea varieties that are acceptable to farmers and consumers using a participatory variety selection (PVS) strategy. Farmers’ perceptions based on focused group discussions, and interviews varied little among the villages. Yield was the most frequently used selection criteria by farmers, regardless of gender profile. There was great variability for seed production among entries. Other agronomic traits such as days to maturity, seed size, pod shape, disease resistance, growth habit, culinary traits including taste, cooking time, broth color and thickness were used at different stages of the selection process. Initially, farmers were invited at the research stations to select the best 20 lines out of 127 entries. These selected lines were subjected to further evaluation in community plots managed by male and female farmers. The genetic analysis revealed a high level of genetic variation among accessions and confirms the absence of redundancy within the genetic materials used. At the end, farmers selected six entries that were released in the two agricultural development divisions (ADDs). The present study is the first documented case of multidisciplinary approach for the selection of elite accessions while maintaining biodiversity. Key words: Participatory variety selection, Vigna unguiculata, Malawi, agrobiodiversity, indigenous knowledge.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".