Agronomic Performance of Kenyan Orange Fleshed Sweet Potato Varieties
Bibliographic record
Abstract
Sweet potato is one of the most important staple crops in Sub-Sahara Africa because of its supply of carbohydrates, vitamin A and C, fiber, iron, potassium and protein.The objective of this study was to determine phenotypic variation in diverse sweetpotato varieties for marketable roots, fresh root yield, fresh biomass weight, harvest index, beta carotene content and root dry matter content. Twenty five sweet potato genotypes were evaluated at two sites in two seasons in Kenya using a randomized complete block design of three replications. The results indicated that there were highly significantly variations for genotypes and sites for all the traits studied. There were no significant differences for genotype x site x season effects for the traits except for the number of marketable roots and fresh root yield. The mean fresh root was 32.19 t/ha,with the genotype Ininda expressing the highest fresh root yield of 54.79 t/ha. Genotypes, Naspot 13, Ejumula, Kabode, Vitaa and Tio-Joe gave the highest beta carotene content ranging between 11.830 and 10.040 mg/100g; with a mean of 5.384 mg/100g. The mean root dry matter content was 24.84 % with clones Amelia and Melinda showing the highest and lowest root dry matter content of 30.62 and 16.52 % respectively. Ten genotypes including Ininda, Erica, Jane, Naspot 13, Ejumula, Kabode, Vitaa, Tio-Joe, Amelia and Mayai were recommended as potential parents for sweet potato breeding program in Kenya.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".