Evaluation of dry matter, starch and beta-carotene content in orange-fleshed sweet potato (Ipomoea batatas L.) genotypes tested in three agro-ecological zones of Malawi
Bibliographic record
Abstract
Evaluation of dry matter, starch, beta-carotene content and stability of eight orange-fleshed sweet potato genotypes was conducted at Bunda College in Malawi. Genotypes LU06/0527, LU06/0252, LU06/0428, LU06/0299, LU06/0258, BV/009, Kenya and Zondeni were evaluated. The genotypes were grown in three agro-ecological zones of Malawi namely Maseya in Chikhwawa District representing low altitude areas with hot climate; Bunda in Lilongwe District representing medium altitude with warm climate and Bembeke in Dedza District representing high altitude areas with cool climate. Harvested tubers were evaluated for dry matter, starch and beta-carotene content using spectrophotometry. Analysis of variance on the main effects between genotypes and environments as well as Interaction Principle Component Analysis (IPCA) for the residual multiplication interaction between genotypes and environments for beta-carotene content in the eight genotypes were conducted. Results showed significant differences in dry matter, starch and beta-carotene content among genotypes and across sites. Zondeni produced highest dry matter (34.4%) while BV/009 was the least (26.8%). Genotype LU06/0252 produced highest beta-carotene (6793.2 μg/100 g) followed by Zondeni (5620.9 μg/100 g). Beta-carotene content increased significantly with decreasing altitude and was highest at Maseya (4258.5 μg/100 g) followed by Bunda (3556.2 μg/100 g). Stability analysis showed that Kenya (SPN/O) was the most stable genotype in beta-carotene content across the sites. Bembeke was the most stable site while Maseya recorded highest beta-carotene content but was unstable site. Key words: Orange-fleshed sweet potato, agro-ecological zones, beta-carotene content, starch content, dry matter content.
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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".