Effect of plant growth regulators on seed tuber yield in potatoes
Bibliographic record
Abstract
Seed potato growers seek to maximize the number of desirable sized tubers. This study examined how foliar application of plant growth regulators (PGRs) influence total tuber number (TTN) and drop (25 -50 mm) seed tuber number (STN) in Norland (NOR), Russet Burbank (RB) and Shepody (SH) potatoes under field conditions in 1993 and 1994. In 1993, PGRs, paclobutrazol (PTZ; 300, 450, 600 mg/L), kinetin (KIN; 10 and 20 mg/L), all possible combinations of the above rates of PTZ and KIN and Methyl jasmonate (MJ; 10-7, 10-6, 10-5 and 10-4 M) were applied to NOR and RB potatoes. In 1994, PTZ (300 mg/L), both KIN rates, and the two lowest rates of MJ were eliminated and KIN 20 mg/L or GA3 250 mg/L were applied to some of the PTZ treatments. The potato cultivar, SH was also included. Plants were treated with the PGRs at two growth stages; NOR (1993), RB (1993 and 1994) SH (1994) were treated when tubers were <10 mm or <20 mm in diameter). NOR potatoes (1994) were treated at stolon initiation (no tubers) or early tuber initiation (<8 mm in diameter). PTZ increased STN in RB by 29 to 40% and in SH by 57 to 70 % over the controls. However, PTZ had no effect on TTN and STN in NOR in either year. MJ had no effect on STN in NOR (1993), in RB in either year or in SH in 1994. In 1994, the highest rate of MJ increased STN in NOR by 40% over the control. Application of KIN alone, in combination with PTZ or following PTZ treatment and GA3 to PTZ treated plants had no beneficial effect on either TTN or STN of all three cultivars, compare to the PTZ treatments applied alone. This study suggests that under field conditions PTZ can be used to increase seed tuber production in RB and SH while MJ appears to be effective in NOR potatoes.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".