Statistical Analysis of the Impact of Weather Parameters on Wild Blueberry Yield (A Case Study; Nova Scotia)
Bibliographic record
Abstract
Abstract. Wild blueberries (Vaccinium angustifolium) are an important agricultural and ecological resource in Eastern Canada. Known for their economic value and contribution to biodiversity, wild blueberries thrive in regions with cold winters, essential for dormancy and bud development, and moderate summer temperatures between 20–27°C. They require acidic, well-drained sandy or loamy soils with a pH of 4.2–5.2, enriched with organic matter to retain moisture and provide nutrients. However, the impacts of climate change pose significant challenges to their growth and sustainability. This paper explores the effects of climate parameters on the yield of wild blueberries in Nova Scotia. The data from 1996-2016, the Pearson correlation coefficient, and the principal component analysis (PCA) have been used to evaluate the effects of climate parameters on the yield of wild blueberries. This paper determined the percentage impact of weather parameters on wild blueberry yield. The results indicated that temperature-related variables, particularly days with valid maximum temperature, days with valid mean temperature, and mean temperature, have the most significant impact on blueberry yield, with high temperatures negatively affecting crop productivity. Moderate precipitation levels positively correlated with yield, although excessive rainfall tended to have a detrimental effect. Factor analysis revealed that temperature-related variables accounted for more yield variability than precipitation factors. Despite some weak and marginally significant correlations, the study emphasizes the complex nature of climate-yield interactions. It suggests that other environmental factors may also play a role in determining yield. The findings contribute to a deeper understanding of the climatic influences on wild blueberry production. They can inform crop management and predictive models in the context of crop and climate change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".