Vine desiccation characteristics and influence of time and method of top kill on yields and quality of four cultivars of potato (<i>Solanum tuberosum</i> L.)
Bibliographic record
Abstract
Potato crops are typically flailed or chemically desiccated several weeks prior to harvest to promote tuber maturity and facilitate harvest operations. This study evaluated how yields and processing quality of several potato cultivars responded to mechanical top kill versus desiccation with diquat at four different treatment dates. The influences of year and cultivar on the rate and extent of vine desiccation were also evaluated in the chemically desiccated crop. The cultivar Ranger Russet was slower to desiccate than Russet Burbank, Shepody or Russet Norkotah, likely because of its larger canopy. Averaged over 3 yr and four treatment dates, flailing reduced yields of the four cultivars tested by an average of 4% relative to chemical desiccation of the tops. The yield difference between flailed and chemically desiccated crops increased if conditions after application of the desiccant favored a gradual die down of the canopy. Specific gravities of the chemically desiccated treatments were equal to or higher than treatments killed by flailing. Fry colors were not influenced by either the method or timing of top kill. Although chemical desiccation enhanced yields relative to a crop flailed at the same time, the dry down period required for the chemically treated crop was at least 3 wk in this study. By contrast, crops killed by flailing are ready for harvest immediately, as long as skin set is not critical. Early in the season, if flailing allowed top kill of Russet Burbank to be delayed by as little as a week, the result was a 9% yield gain. As growing conditions became less favorable later in the season, there was little potential for yield gain by opting to flail instead of using the chemical desiccant. Key words: Diquat, flailing, Russet Burbank, Shepody, Ranger Russet, Russet Norkotah
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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 | 0.003 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".