Productivity of Rhizomes and Starch Quantification in Cultures of Different Vegetative Propagules of Arrowroot
Bibliographic record
Abstract
Arrowroot rhizomes are rich in carbohydrate and are commonly grown by family farmers who have an important source of income in this activity and play a prominent role in the conservation of the species. There are few studies on the phytotechnical aspects of culture. The objective of this work was to evaluate the productive capacity of the ‘common’ arrowroot using different sizes and forms of propagation, aiming at the production of rhizomes and arrowroot starch, in different agricultural crops. The experimental design was the randomized block in a 4 × 2 factorial scheme, with 6 replicates. Four types of rhizome propagation (rhizomes-seeds of small size with a weight of 20 to 30 g; rhizomes-seeds of average weight between 30.01 to 45 g; rhizomes-seeds of large size weighing between 45.01 a 60 g, and seedlings produced in tissue culture), in two agricultural years (2015/2016 and 2016/2017). The variables total production, number of rhizomes, extraction yield and total starch production were evaluated. The type of propagule used interfered in the yield of rhizomes (9.85 to 34.75 t ha-1) and in the production of arrowroot starch (1.76 to 7.68 t ha-1). The vegetative propagation with pieces of rhizomes-seeds between 20 and 60 g was more viable than the micropropagation technique. Although the soil and climate conditions showed differences between the agricultural crops studied, they did not significantly influence the yield of rhizomes and arrowroot starch, by the type of propagule used.
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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) | 0.001 | 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".