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Record W2114445540 · doi:10.5539/jas.v6n3p84

Field Performance of Quatity Protein Maize With Zinc and Magnesium Fertilizers in the Sub-Humid Savanna of Nigeria

2014· article· en· W2114445540 on OpenAlexvenueno aff
U. F. Chiezey

Bibliographic record

VenueJournal of Agricultural Science · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsHectareZincRandomized block designYield (engineering)Dry matterAnimal scienceMagnesiumAgronomyGrain yieldField experimentChemistryMathematicsBiologyMetallurgyMaterials scienceEcology

Abstract

fetched live from OpenAlex

Field experiments were conducted for three years (2006 – 2008) in Samaru (11o11’N, 7o38’E) 686m above sea level in Nigeria. The objective was to test the response of two Quality Protein Maize (QPM) varieties (SAMMAZ-14 and SAMMAZ-11) to four levels each of Zinc and Magnesium (0, 1.25, 2.5 and 5.0 kg) using their carbonates. The experiments were arranged in all possible factorial combinations and laid out as randomised complete block design (RCBD) and replicated three times. The two varieties tested did not differ significantly in all the parameters evaluated except for the number of days to 50% tasselling. Grain yield ranged between 1.9 – 2.0 t/ha when averaged over both years which was quite below the 5.0 t/ha potential. Zinc application had no significant influence on most characters evaluated except total dry matter per hectare in 2006 when the application of 1.25 kg 2n/ha produced the highest TDM. Grain yield per hectare remained unchanged with changes in Zinc rate. Magnesium application influenced grain yield on 2008 only when 1.25 kg Mg/ha increased yield compared with plots with 5.0kg mg/ha. When averaged over the three years, Mg application did not significantly influence grain yield, but increased protein yield. Grain yield correlated positively and significantly with leaf area index (r = 0.13**), plant height (r = 0.26*), TDM (r = 0.21**) and protein yield (r = 0.97**). Protein content of grain remained unchanged with changes in Zinc and Magnesium rates at 8%

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 score0.808
Threshold uncertainty score0.122

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 studies0.0000.000
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 head0.009
GPT teacher head0.196
Teacher spread0.187 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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