The Influence of Cultivar and Orchard SystemonPruning Time Per Tree, Per Hectare, and Per Unit ofYield
Bibliographic record
Abstract
In 1990 an NC-140 Orchard Systems Trial was established near Blacksburg, VA. The trial had four replications often orchard systems, which were combinations of three training systems and several rootstocks. The training systems were Slender Spindle (SS) planted at 2460 trees/ha, Vertical Axe (VA) planted at 1502 trees/ha, and Central Leader (CL) planted at 1111 trees/ha. Rootstocks used with each system were: SS: Budagovsky 9 (B.9), Mailing 9EMLA (M.9EMLA), and Mark; VA: M.9EMLA, M.26EMLA, Ottawa 3 (O.3), Polish 1 (P.I), and Mark; CL: M.26EMLA and Mark. From 1996/1997 through 1998/1999, the time required to prune each plot (same two people each year) was recorded. Pruning times for the winters of 1996/1997 through 1998/1999 were related to yields from these plots from 1997-1999. Yields per tree and per hectare, pruning time per tree and per hectare, kg of fruit per min of pruning time, and estimated cost of pruning per box of fruit all varied with cultivar as well as system. Only estimated pruning costs per box of fruit and pruning time per hectare had a significant interaction between system and cultivar. Compared to ‘Empire’, ‘Delicious’ yielded less per tree and per hectare, required more time to prune, and yielded less fruit per minute of pruning with the result that estimated pruning costs per box were 76% higher. Pruning time per tree was lowest for SS/Mark, CL/Mark, and VA/Mark and highest for VA/P.1. Pruning costs per 19.05 kg box of ‘Empire’ and ‘Delicious’ ranged from $0.11 and $0.14 for CL/Mark to $0.31 and $0.66 for VA/P.1, respectively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".