The timing of pruning affects flushing, flowering and yield of macadamia
Bibliographic record
Abstract
Macadamia (Macadamia integrifolia, M. integrifolia × M. tetraphylla) trees were pruned at different times at sites near Alstonville, northern New South Wales, Australia, to examine the effects on vegetative flushing, subsequent flower raceme production and yield. Pruning of cv. 849 and cv. A268 modified the cycle of flush development. Pruning times that resulted in immature flushes on the canopy in late autumn or early winter inhibited raceme production. In contrast, pruning in late May and early June did not generally reduce raceme production relative to production on unpruned trees. The times of pruning that reduced raceme production also reduced yield. The yields of trees pruned in late May were also reduced, presumably because of decreased light interception. In the season after treatment the trees pruned in early April had greater numbers of racemes per unit of tree canopy volume than the trees pruned in late May. The trees of the lighter flowering cv. 849 pruned in early April had higher yield efficiencies than the trees pruned in late May, whereas there was no effect on yield efficiency in the prolifically flowering cv. A268. The differences in raceme production in the season after pruning may have been due to a combination of an alternate bearing response, characteristics of the stems produced after pruning, or maturity of the flushes. In a separate experiment, uniconazole sprays immediately after pruning reduced the length of the new stems, slowed canopy expansion, and increased kernel recovery compared with untreated hedged trees, but did not affect flowering or yield. In another experiment, hedging in early June had no effect on raceme production in cv. 849 trees in consecutive seasons, and no effect on canopy volume or yield in the first season. In contrast, canopy volume and yield were reduced in the second season. Finally, pruning of young, yet-to-flower cv. 849 trees from late winter to spring staggered flush development, with the earliest pruned trees producing more racemes and setting more fruit than the later pruned trees.
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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".