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Record W2011199803 · doi:10.5539/jas.v7n1p224

Cowpea Crude Protein as Affected by Cropping System, Site and Nitrogen Fertilization

2014· article· en· W2011199803 on OpenAlexvenueno aff
Erick Sebetha, Albert Thembinkosi Modi, L. G. Owoeye

Bibliographic record

VenueJournal of Agricultural Science · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsnot available
Fundersnot available
KeywordsHuman fertilizationCroppingNitrogen fertilizerAgronomyNitrogenBiologyEnvironmental scienceChemistryAgricultureEcologyFertilizer

Abstract

fetched live from OpenAlex

High protein content in cowpea (Vigna unguiculata (L.) Walp.) is considered as major advantage for its use in nutritional components. In this way, an experiment was conducted to investigate the effect of cropping system, site, and nitrogen fertilization on cowpea crude protein. The study comprised of three cropping systems (Maize-cowpea rotation, monocropping cowpea and intercropped cowpea), three sites (Potchefstroom, Taung, and Rustenburg, South Africa) and two rates of nitrogen fertilizers applied in kg ha-1 at each site (0 and 20 at Potchefstroom, 0 and 17 at Rustenburg, 0 and 23 at Taung). Moreover, a factorial experiment randomized in complete block design with three replications was conducted during 2011/12 and 2012/13 planting seasons. The protein content was determined from green leaves harvested before flowering, immature green pods and seeds during reproductive stage and maturity. Results showed that cropping system had significant effect on cowpea leaf protein content (P < 0.05). Intercropped cowpea significantly gave higher leaf protein (26.7% more) content than rotational cowpea. Cowpea planted at Taung had significantly higher leaf protein (30.1% more) content as compared to cowpea planted at other sites. Application of nitrogen fertilizer contributed to higher protein content of immature pods. Moreover, cowpea protein content differs among the different locations due to different soil types and climatic conditions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.961
Threshold uncertainty score0.680

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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

Citations24
Published2014
Admission routes1
Has abstractyes

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