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Record W2896676411 · doi:10.5539/jas.v10n11p185

Profitability of Cowpea Intercropped With Maize in West Africa Guinea Savanna

2018· article· en· W2896676411 on OpenAlexvenueno aff
Abdulai Haruna, James Matent Kombiok, Askia Musah Mohamed, Joseph Sarkodie‐Addo, Asamoah Larbi, Nurudeen Abdul Rahman

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsnot available
FundersUnited States Agency for International Development
KeywordsIntercroppingAgronomyProductivityBiologyCroppingRandomized block designAgricultureEconomicsEcology

Abstract

fetched live from OpenAlex

An on-farm trial was conducted over a 2-year period in Tibali in the Savelugu district of Northern region of Ghana to evaluate the productivity and economic returns of hybrid and open pollinated maize (OPV) either in pure stands or intercropped with erect and trailing cowpea. The maize varieties used were medium maturing (110 days) whie the cowpea varieties were early maturiing (70 days). The experiment was conducted in a randomized complete block design with 14 treatments (sole pan53, sole Etubi, sole mamaba, sole obatampa, sole erect cowpea, sole trailing cowpea, erect cowpea + pan53, erect cowpea+etubi, erect cowpea+mamaba, erect cowpea+obatampa, traing cowpea + pan53, traling cowpea+etubi, trailing cowpea+mamaba and trailing cowpea + obatampa) replicated on 10 farms. Intercropping had better productivity and economic returns than sole cropping. Intercropping maize with trailing cowpea type had better productivity and economic return than intercropping with erect type of cowpea. Intercropping the OPV maize with cowpea had better productivity and economic return than intercropping hybrid maize with cowpea. Farmers may either intercrop OPV maize with trailing cowpea type or hybrid maize variety Pan 53 maize with trailing cowpea type for better productivity and economic return.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.025
GPT teacher head0.242
Teacher spread0.217 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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