Do French beans (<i>Phaseolus vulgaris</i> L.) grown in proximity to Mt Kenya forest- Kenya- experience pollination deficit?
Bibliographic record
Abstract
Yields of commercially important crops in Kenya are often far below their potential. Amongst the possible reasons for such low yields may be the ecosystem degradation that can be expected to have negative impacts on pollinator presence in cropland, and the consequent food security issue for smallholder farmers who depend on these crops for their livelihood. Our study was carried out to assess the potential pollination deficit of French beans (Phaseolus vulgaris L.), a major export vegetable crop in Kenya grown by small-scale farmers. Sufficient pollination of French beans likely results in high seed set and uniform heavier green pods. Such pods get the highest grade while malformed pods are unmarketable, reducing family income. We hypothesized that pollination success was linked to the abundance and diversity of large pollinators, itself associated with the proximity to natural habitats. Flower visitors to French beans were sampled in 2011 and 2012 in ten farmer-managed plots, five within 200 m from the edge of Mt. Kenya forest and five farther away, more than 1000 m. Each plot measured 760 m2 and was planted at the same time, with the “Julia” variety. Flowers were observed for 2 h in each plot once weekly for three weeks at peak flowering from 0900-1100 h in the morning and 1200 – 1400 h in the afternoon on alternate days. Honey bees (Apis mellifera) were the most abundant visitors of French bean flowers followed by carpenter bees (Xylocopa spp.) and leafcutter bees (Megachile spp.). Significantly higher numbers of leafcutter bees were recorded on farms far to the forest. There was no significant difference in honeybee abundance among the study sites, probably because apiaries and wild colonies are located across the landscape. French bean yield was significantly correlated with the mean abundance of carpenter bees in 2011. This suggests the possible occurrence of pollination deficit in French beans where the density of carpenter bees is insufficient, which was reflected by the high variability of yields in the farmland. We advocate that area-wide management and conservation of carpenter bees should be initiated to support French bean farming in the area.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".