Potato Production in the Hot Tropical Areas of Africa: Progress Made in Breeding for Heat Tolerance
Bibliographic record
Abstract
Potato is a cool season crop and grows best between 15 and 18 oC and soil pH of 5.5 to 6.0. Temperatures above 21 oC have adverse effects on growth. In tropical Africa, potato is grown in the highlands at altitudes between 1500 and 3500 meters above sea level. These areas are characterized by cool temperatures with high rainfall of at least 1000 mm per annum. With climate change, these highlands are rapidly warming up. In addition, the rapidly increasing population and consequent diminishing land sizes in these highlands have forced farmers to migrate to the lower, warmer and drier areas, where the migrants have moved with their cropping systems including the potato. Most of the tropical African countries are poor with an exploding population; there is need to come up with strategies to feed this population. Although a short duration crop such as potato can go a long way in solving the food crisis, most of the locally available potato varieties do not do well under high temperatures. Measures such as planting of heat-tolerant varieties and/or late maturing varieties as well as cultural practices such as mulching and changing the planting date may contribute to alleviating the situation. This review paper looks at breeding heat-tolerant potato varieties as a means for profitable production of potatoes in the hot tropical African region.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".