On-Farm Grain Yield Stability and Farmer Perceptions on Pre-release Pearl Millet Lines in Zimbabwe
Bibliographic record
Abstract
Farmer participation in on-farm research does not only accelerate information gathering but also results in adoption of new research products. Here, we report on-farm trials conducted across five districts of Matabeleland province in Zimbabwe to explicate grain yield stability and farmer perceptions on eight pre-release pearl millet (Pennisetum glaucum L.) lines. The results indicated that genotypic effects on grain yield were significant in both individual and across-site analysis of variance and that farmers prioritize earliness and grain yield as must-have traits in millet varieties. The five districts were grouped into two distinct environments, with four districts (i.e., Bulilima, Gwanda, Matopos and Tsholotsho) in one group (i.e., DGrp1) and Mangwe district forming the second group (DGrp2). Pearl millet pre-release lines PM1 (1.425 kg ha-1), PM9 (1.043 kg ha-1) and PM6 (761.8 kg ha-1) showed high yield, stability and were the most preferred by farmers. In conclusion, on-farm trials may offer the quickest possible solution to boost low-pearl millet production resulting from the continuous use of unproductive landraces and old varieties by farmers.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".