Pollinators and crops in Bhutan: insect abundance improves fruit quality in Himalayan apple orchards
Bibliographic record
Abstract
Apples are one of the most important global crops that relies heavily on insect pollination, which has been shown to increase apple production and value. However, recent reports indicate that apple production has been declining in certain regions, including in Bhutan. One of the potential causes of declining production are pollination deficits driven by a low abundance and diversity of pollinators, a phenomenon that has received little attention in Bhutan to date. Here, we present the first study examining the diversity of flying insects in Bhutanese apple orchards in relation to apple quality. During the apple flowering season, 1,006 insects comprising 44 unique (morpho-)species from the orders Hymenoptera, Diptera, and Lepidoptera were recorded using a standardized method of passive and active trapping within nine different orchards in Thimphu, Paro, and Haa districts, in the western part of Bhutan. During the harvest season, 495 apples were collected from these nine orchards, and we measured five different parameters; weight, size, sugar concentration, seed number, and malformation score. The most dominant flower visitors across all orchards were honey bees (mostly Apis mellifera, followed by A. cerana and A. dorsata). Orchards with a higher abundance of flying insects (both managed and wild) had better apple quality (weight, size and sugar concentration). Contrary to reports from other regions of the world, flower visitor diversity did not correlate with the quality of the apples. This represents the first study reporting on apple pollination in Bhutan and highlights the importance of pollinators and reinforces the need to develop pollinator friendly practices to ensure sustainable apple production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".