Crop productivity, yield and seasonality of breadfruit ( <i>Artocarpus</i> spp., Moraceae)
Bibliographic record
Abstract
Introduction. Breadfruit, Artocarpus spp., is a staple crop with the potential to alleviate hunger and increase food security in tropical regions. Guidelines and recommenda- tions for cultivar selection and production practices are now required for establishment of breadfruit in new areas. Materials and methods. To respond to this need for spreading breadfruit, our study quantified the growth, development, yield and seasonality of 24 bread- fruit cultivars (26 trees) established in Kauai, Hawaii, over a 7-year period from 2006-2012. Individual production profiles were generated for each accessioned cultivar based on major agricultural factors. Results. Across all cultivars of breadfruit (A. altilis), an average of 269 fruits per year was produced by each tree with an average fruit weight of 1.2 kg. Based on the planting density of 50 trees⋅ha -1 , this translates to an average projected yield of 5.23 t⋅ha -1 after 7 years. Hybrids (A. altilis × A. mariannensis) had a higher yield than bread- fruit. The data of our article support the previously proposed hypothesis for predicting bread- fruit seasonality. On average, the peak season occurred from July to November. Conclusions. Ma'afala, the first widely available commercial cultivar, started to bear fruit within 22 to 23 months of planting. Other cultivars with potential for commercial production include Toneno, White, Rotuma and Meinpadahk. Hawaii / Artocarpus / fruits / breadfruit / variety trials / choice of species / crop yield / seasonality / adaptation
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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".