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Record W2736020490 · doi:10.5539/sar.v6n3p106

Growing Seed Yams in the Air: the Agronomic Performance of Two Aeroponics Systems Developed in Ghana

2017· article· en· W2736020490 on OpenAlexvenueno aff
Patricia Oteng‐Darko, Nicholas Kyei‐Baffour, Emmanuel Otoo, Wilson Agyei Agyare

Bibliographic record

VenueSustainable Agriculture Research · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsnot available
FundersBill and Melinda Gates Foundation
KeywordsVineCuttingEnvironmental scienceAgricultural engineeringMathematicsHorticultureAgronomyEngineeringBiology

Abstract

fetched live from OpenAlex

Aeroponics has been perceived as a technology crammed innovation, far out of reach of the ordinary farmer. Apart from its continuous dependency on electrical power, the technology comes with very sophisticated inputs such as solenoid valves, timers, misters, CO2 tanks, and air and water pumps. The main objective of this study was to evaluate the option of using gravity-fed and pressurised aeroponics system for propagating seed yams from vine cuttings. The study was setup at the CSIR–Crops Research Institute in collaboration with the Agricultural Engineering Department of the Kwame Nkrumah University of Science and Technology. The basic advantage of the gravity-fed system is its non-dependency on electrical power, pumps or timers and its ability for continuous production. The two systems were set-up using conventional materials and equipment available on the local market. The treatments were arranged in a split-split-plot design with the two aeroponic units as the main plot, four nutrient concentration levels (C1 - , C2 - , C3 - and C4 -) as the sub plots and vine cuttings from three Dioscorea rotundata varieties (Dente, Pona and Mankrong Pona) as sub-sub plots respectively. Results showed there were significant differences (P<0.05) in minituber weight and days to emergence of planted miniubers. The agronomic response of the two systems in producing mini-tubers was suggestive of the fact that both systems could be used to effectively produce mini-tubers.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.001
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 head0.049
GPT teacher head0.318
Teacher spread0.269 · 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.

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

Citations5
Published2017
Admission routes1
Has abstractyes

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