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Record W2588513421 · doi:10.5539/jas.v9n3p193

Nitrogen Use and Protein Yield of Two Maize Cultivars in Cohesive Tropical Soil

2017· article· en· W2588513421 on OpenAlexvenueno aff
Georgiana Eurides Carvalho Marques, Alana das Chagas Ferreira Aguiar, Vinícius Ribamar Alencar Macedo, Ester de Paiva Alves, Emanoel Gomes de Moura

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsLeucaenaGliricidiaAgronomyGliricidia sepiumAcaciaSowingCultivarLoamBiologyBotanySoil water

Abstract

fetched live from OpenAlex

One major challenge to developing sustainable family farms in tropical regions is increasing nitrogen use efficiency. The aim of this study was to evaluate the combined effects of leguminous residues of low-and high-quality on nitrogen uptake, as well as on content of protein of a Quality Protein Maize (QPM) and of a hybrid maize in a tropical sandy loam soil. The experimental design consisted of randomized blocks with four replicates in a 6 × 2 factorial and six treatments: Gliricidia + Clitoria (GC); Gliricidia + Acacia (GA); Leucaena + Gliricidia (LG); Leucaena + Clitoria (LC); Leucaena + Acacia (LA) and a control without legumes (C). A sub-plot was constructed, sowing in each plot two maize cultivars, opened pollination QPM BR 473 and hybrid Ag 7088. We conclude that the combined use of leguminous residues applied on the soil surface might increase the uptake of nitrogen, the protein contents of maize and the grain yield. In bare soil prone to cohesion, the use of synthetic N is not feasible for both maize yield and for protein yield compared with use of covered soil. The results also showed that the effects of leguminous residue quality on N uptake may differ from year to year due to variation in water stress days.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.635
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.037
GPT teacher head0.256
Teacher spread0.218 · 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
Published2017
Admission routes1
Has abstractyes

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