Farmers’ Preferences for Varietal Traits, Their Knowledge and Perceptions in Traditional Management of Drought Constraints in Rice Cropping in Benin: Implications for Rice Breeding
Bibliographic record
Abstract
Rice (Oryza spp.) is one of the most important crops that significantly contribute to food security in Benin. In the current context of climate change, drought is known to be the main abiotic stress in crops and a major yield-limiting factor for agricultural production worldwide. To assess farmers’ knowledge, the preference traits of the rice cultivars in use, their perceptions and management of drought stress in rice production in Benin, an ethnobotanical investigation was conducted in 50 villages throughout the major zones. The results showed that High yield combined with good grain quality (including good taste, softness after cooking, less starch, white pericarp, long grain length and swelling when cooked), medium maturing and tolerance to drought and flood were the most desired traits motivating farmers for growing rice cultivars. Taste and high yield were the paramount traits of IR841, the most popular rice variety currently cropped in Benin followed by its fragrance. Drought constraints was reported as the most damaging abiotic stress across the villages surveyed with field lost estimated up to 100% at the flowering stage. Changing sowing date (80%), the use of irrigation systems (10%) and the cropping of early maturing cultivars (7%) were the most traditional strategies to reduce drought impacts. Needs for tolerant varieties were clearly expressed by farmers to mitigate drought effects on rice production in Benin. The results of this survey emphasize the need for rice breeders to focus more on improving grain quality in addition to high yield potential and tolerance to abiotic stresses mainly drought.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".