An Appraisal of Farmer Variety Selection in Drought Prone Areas and Its Implication to Breeding for Drought Tolerance
Bibliographic record
Abstract
Maize production and productivity among small scale farmers of southern Africa is limited mainly by drought and low soil fertility. This study aimed at assessing how farmers prioritize selection of varieties for planting under drought stress and how this could help improve the breeding approaches for varieties for resource constrained farmers in marginal environments. A survey was conducted in two drought prone districts of Zimbabwe. Data collection was done using a structured questionnaire, key informant interviews and focus group discussions. The study revealed that farmers have limited options for drought tolerant varieties available on the market. Contrary to breeders, farmers in drought prone areas do not consider disease resistance as an important trait. The farmer preferred traits include, high yield potential, drought tolerance, early maturity, and good performance even under poor soil conditions. Drought tolerance associated traits such as resistance to leaf rolling, tassel blast, general plant recovery to stress and stay green characteristics were identified as the most important traits but most of the varieties currently available on the market do not have these traits. The farmers were willing to make trade-offs among traits like taste or disease resistance for increased yield potential when selecting varieties to grow. Traits preferences or ranking and possible trade-offs were specific to specific areas and groups of farmers. In this study farmers still planted the traditional varieties or landraces because they are drought tolerant, taste better and can be propagated from farm saved seed. These findings show that farmers have limited options on drought tolerant varieties on the market and that scientists need to tap into farmer knowledge, especially on possible trade offs, trait ranking and germplasm for use in developing better adapted varieties which are specific to target farmers. Policies and seed systems analysis on variety availability, distribution and marketing channels also need to be strengthened.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".