Branch Configuration Impacts on Production, Fruit Quality, and Leaf Minerals of ‘Aztec Fuji’ Apple Trees in an Upright Single Row High-Density Orchard System Over Five Years
Bibliographic record
Abstract
Tree architectures play a critical role in the productivity of high-density orchards, but limited information is available in this subject. We studied effects of three branch configurations on tree growth, yield components, fruit quality and leaf mineral nutrients in ‘Aztec Fuji’ apple (Malus domestica Bork.) in a single row upright high-density system under southwest Idaho, USA conditions over 2012-2016. This study revealed that trees trained into a Tall Spindle (TS) had larger trunk cross sectional area (TCSA) than those with an Overlapped Arm (OA) system. Trees trained into a TS had higher number of fruit and yield per tree, three years after planting in 2012, than those with a Tipping Arm (TA) or OA system. However, in 2013, trees with a TA system had higher yield than those with a TS or OA configuration due to trees’ biennial bearing habit and higher spur formation in trees with a TA system. Trees receiving a TA training had lower biennial bearing index between all consecutive years. Trees with an OA training had smaller fruit than those with either a TA or TS training in all years between 2012-2016. Training systems did not have any effect on fruit color, soluble solids concentration, or starch degradation pattern at harvest. However, fruit from trees with an OA training had higher firmness and lower water core than those from trees with a TS or TA training. Leaves from trees receiving a TA training had greater leaf area, fresh weight, and potassium (K) and magnesium (Mn) concentrations than those with other trainings. Leaves from trees receiving an OA training had higher leaf iron (Fe), zinc (Zn), and copper (Cu) than those with a TS training. In this study, we concluded that training trees into a TA configuration rather than an OA system is recommended if the management and operation of apple production mandate the use of an “upright wall” structure to facilitate mechanical harvesting.
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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".