Chitosans of different molecular weight enhance potato (Solanum tuberosum L.) yield in a field trial
Bibliographic record
Abstract
Physico-chemical features of chitosan affect its biological activity on plants. In this work, the influence of chitosan molecular mass in potato (Solanum tuberosum L.) yields was investigated. By using chitosan polymers of high (CH-1) and low (CH-2) molecular weight and a hydrolysed chitosan derivative (CHH), two experiments were performed under field conditions to determine the effect of these polymers on yields of two potato varieties, ‘Call White’ and ‘Santana’. For this purpose, the foliar spray of low doses of the derivatives at three cultivation moments was performed and several yield variables were determined at crop harvest. All three chitosan compounds increased the performance variables determined respect to the control, depending on the variable, the dose employed and the mass of the derivative evaluated. In most variables determined, the two lowest doses (200 and 325 mg/ha) provoked the highest increments above control. Chitosans also affected distribution of mass per tuber size, particularly; in ‘Santana’ variety the two lowest doses enhanced the commercial tuber sizes. Among the polymers, CH-1 caused the greatest increases in performance, while, compared to the polymer, CHH provoked higher yields. In conclusion, foliar application at low doses of high molecular weight and hydrolysed chitosan enhanced potato yield between 15-30%.
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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".