Effects of Supplementary Pollination on Macadamia Nut Set, Retention and Yield in Murang’a County, Kenya
Bibliographic record
Abstract
Macadamia is a promising prime dessert nut with the potential of alleviating poverty and enhancing food security in Kenya. Nut set and subsequent development of nuts to maturity is dependent on pollination which is mediated by animals, and honey bees are the dominant macadamia flower visitors. However, macadamia is pollen deficit as not all flowers set develop to mature nuts, thus supplemental pollination results to better nut yields. There is limited information in Kenya among the smallholder macadamia growers on the importance of supplementing pollination to enhance nut yields. This study was conducted at Kandara Macadamia Research Centre and in a smallholder macadamia farm located 15 km from the research centre, which was purposively selected from July 2021 to May 2023. Assessment of the influence of pollinators and supplementing pollination to nut set, retention and final yields was done. Honey bee, (Apis mellifera L.) colonies were also introduced at the Macadamia Research Centre to enhance pollination and mitigate deficits. Racemes were bagged to exclude pollinators, others left open to unlimited pollinator access while others received supplemental hand pollination. The results demonstrate that macadamia is pollen limited and pollination is highly reliant on insect pollinators. There were significant differences (p < 0.001) among the bagged (pollinator exclusion), open and hand pollinated racemes in both farms. The initial nut set and retention was increased significantly (21.54%) in hand pollinated racemes compared to those that were left to open pollination (unlimited pollinator visits) depicting pollination deficit in farm B. Introduction of supplementary honey bee colonies at the Kandara Macadamia Research Centre, resulted to higher nut set, retention, nut-in-shell and kernels. Macadamia growers are encouraged to introduce honey bee colonies in their farms to improve yields and also address pollination deficits.
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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 | 0.000 | 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".