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Record W2120221779 · doi:10.5539/jas.v7n9p220

Potato Production in the Hot Tropical Areas of Africa: Progress Made in Breeding for Heat Tolerance

2015· article· en· W2120221779 on OpenAlexvenueno aff
Jane Muthoni, Jackson N. Kabira

Bibliographic record

VenueJournal of Agricultural Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsnot available
Fundersnot available
KeywordsSowingTropicsCroppingAgronomyAgricultureCropPopulationGeographyAgroforestryClimate changeMulchEnvironmental scienceBiologyEcology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.142

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.065
GPT teacher head0.284
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2015
Admission routes1
Has abstractyes

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