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Record W2792906043 · doi:10.5539/eer.v8n1p10

Cost of Energy Input in the Production of Cassava (Manihot Esculenta)

2018· article· en· W2792906043 on OpenAlexvenueno aff
Babajide S. Kosemani, A. Isaac Bamgboye

Bibliographic record

VenueEnergy and Environment Research · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCassava research and cyanide
Canadian institutionsnot available
Fundersnot available
KeywordsHectareProduction (economics)Manihot esculentaFertilizerProfit (economics)Production costAgricultural scienceOgun stateEconomic analysisTotal costCost analysisUnit costMathematicsBusinessEnvironmental scienceAgricultural engineeringAgricultural economicsEconomicsAgronomyAgricultureGeographyOperations researchBiologyEngineering

Abstract

fetched live from OpenAlex

The economic analysis of input energy in cassava production was considered in this study. Farms were surveyed to collect data on fuel, natural gas, fertilizer, pesticides and chemicals used on the farm for cassava production. The areas of study were Oyo, Ogun, Osun and Kwara States of Nigeria. The data for cost input resources in all the selected farms during cassava production from land preparation to transportation to market or house was obtained using structured questionnaire and oral interviews. Mathematical expressions were developed to evaluate cost analysis for each of the defined unit operations and the cost incurred were then determined. The total cost of production of one hectare of cassava was N82,055 and cost analysis revealed that profit of production of one hectare of cassava was N123,745. Benefit cost ratio was 2.50, which was greater than 1.0, indicating that cassava production is feasible from the economic stand point.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.076
GPT teacher head0.291
Teacher spread0.215 · 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 designBench or experimental
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

Citations1
Published2018
Admission routes1
Has abstractyes

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